Merge "selinux: update prebuilt tools"
diff --git a/lib/python2.7/site-packages/selinux/__init__.py b/lib/python2.7/site-packages/selinux/__init__.py
index b81b031..7a14d18 100644
--- a/lib/python2.7/site-packages/selinux/__init__.py
+++ b/lib/python2.7/site-packages/selinux/__init__.py
@@ -69,7 +69,7 @@
_newclass = 0
-import shutil, os, stat
+import shutil, os, errno, stat
DISABLED = -1
PERMISSIVE = 0
@@ -87,7 +87,12 @@
status, context = matchpathcon(path, mode)
if status == 0:
- status, oldcontext = lgetfilecon(path)
+ try:
+ status, oldcontext = lgetfilecon(path)
+ except OSError as e:
+ if e.errno != errno.ENODATA:
+ raise
+ oldcontext = None
if context != oldcontext:
lsetfilecon(path, context)
@@ -405,1031 +410,6 @@
def selinux_status_deny_unknown():
return _selinux.selinux_status_deny_unknown()
selinux_status_deny_unknown = _selinux.selinux_status_deny_unknown
-COMMON_FILE__IOCTL = _selinux.COMMON_FILE__IOCTL
-COMMON_FILE__READ = _selinux.COMMON_FILE__READ
-COMMON_FILE__WRITE = _selinux.COMMON_FILE__WRITE
-COMMON_FILE__CREATE = _selinux.COMMON_FILE__CREATE
-COMMON_FILE__GETATTR = _selinux.COMMON_FILE__GETATTR
-COMMON_FILE__SETATTR = _selinux.COMMON_FILE__SETATTR
-COMMON_FILE__LOCK = _selinux.COMMON_FILE__LOCK
-COMMON_FILE__RELABELFROM = _selinux.COMMON_FILE__RELABELFROM
-COMMON_FILE__RELABELTO = _selinux.COMMON_FILE__RELABELTO
-COMMON_FILE__APPEND = _selinux.COMMON_FILE__APPEND
-COMMON_FILE__UNLINK = _selinux.COMMON_FILE__UNLINK
-COMMON_FILE__LINK = _selinux.COMMON_FILE__LINK
-COMMON_FILE__RENAME = _selinux.COMMON_FILE__RENAME
-COMMON_FILE__EXECUTE = _selinux.COMMON_FILE__EXECUTE
-COMMON_FILE__SWAPON = _selinux.COMMON_FILE__SWAPON
-COMMON_FILE__QUOTAON = _selinux.COMMON_FILE__QUOTAON
-COMMON_FILE__MOUNTON = _selinux.COMMON_FILE__MOUNTON
-COMMON_SOCKET__IOCTL = _selinux.COMMON_SOCKET__IOCTL
-COMMON_SOCKET__READ = _selinux.COMMON_SOCKET__READ
-COMMON_SOCKET__WRITE = _selinux.COMMON_SOCKET__WRITE
-COMMON_SOCKET__CREATE = _selinux.COMMON_SOCKET__CREATE
-COMMON_SOCKET__GETATTR = _selinux.COMMON_SOCKET__GETATTR
-COMMON_SOCKET__SETATTR = _selinux.COMMON_SOCKET__SETATTR
-COMMON_SOCKET__LOCK = _selinux.COMMON_SOCKET__LOCK
-COMMON_SOCKET__RELABELFROM = _selinux.COMMON_SOCKET__RELABELFROM
-COMMON_SOCKET__RELABELTO = _selinux.COMMON_SOCKET__RELABELTO
-COMMON_SOCKET__APPEND = _selinux.COMMON_SOCKET__APPEND
-COMMON_SOCKET__BIND = _selinux.COMMON_SOCKET__BIND
-COMMON_SOCKET__CONNECT = _selinux.COMMON_SOCKET__CONNECT
-COMMON_SOCKET__LISTEN = _selinux.COMMON_SOCKET__LISTEN
-COMMON_SOCKET__ACCEPT = _selinux.COMMON_SOCKET__ACCEPT
-COMMON_SOCKET__GETOPT = _selinux.COMMON_SOCKET__GETOPT
-COMMON_SOCKET__SETOPT = _selinux.COMMON_SOCKET__SETOPT
-COMMON_SOCKET__SHUTDOWN = _selinux.COMMON_SOCKET__SHUTDOWN
-COMMON_SOCKET__RECVFROM = _selinux.COMMON_SOCKET__RECVFROM
-COMMON_SOCKET__SENDTO = _selinux.COMMON_SOCKET__SENDTO
-COMMON_SOCKET__RECV_MSG = _selinux.COMMON_SOCKET__RECV_MSG
-COMMON_SOCKET__SEND_MSG = _selinux.COMMON_SOCKET__SEND_MSG
-COMMON_SOCKET__NAME_BIND = _selinux.COMMON_SOCKET__NAME_BIND
-COMMON_IPC__CREATE = _selinux.COMMON_IPC__CREATE
-COMMON_IPC__DESTROY = _selinux.COMMON_IPC__DESTROY
-COMMON_IPC__GETATTR = _selinux.COMMON_IPC__GETATTR
-COMMON_IPC__SETATTR = _selinux.COMMON_IPC__SETATTR
-COMMON_IPC__READ = _selinux.COMMON_IPC__READ
-COMMON_IPC__WRITE = _selinux.COMMON_IPC__WRITE
-COMMON_IPC__ASSOCIATE = _selinux.COMMON_IPC__ASSOCIATE
-COMMON_IPC__UNIX_READ = _selinux.COMMON_IPC__UNIX_READ
-COMMON_IPC__UNIX_WRITE = _selinux.COMMON_IPC__UNIX_WRITE
-COMMON_DATABASE__CREATE = _selinux.COMMON_DATABASE__CREATE
-COMMON_DATABASE__DROP = _selinux.COMMON_DATABASE__DROP
-COMMON_DATABASE__GETATTR = _selinux.COMMON_DATABASE__GETATTR
-COMMON_DATABASE__SETATTR = _selinux.COMMON_DATABASE__SETATTR
-COMMON_DATABASE__RELABELFROM = _selinux.COMMON_DATABASE__RELABELFROM
-COMMON_DATABASE__RELABELTO = _selinux.COMMON_DATABASE__RELABELTO
-FILESYSTEM__MOUNT = _selinux.FILESYSTEM__MOUNT
-FILESYSTEM__REMOUNT = _selinux.FILESYSTEM__REMOUNT
-FILESYSTEM__UNMOUNT = _selinux.FILESYSTEM__UNMOUNT
-FILESYSTEM__GETATTR = _selinux.FILESYSTEM__GETATTR
-FILESYSTEM__RELABELFROM = _selinux.FILESYSTEM__RELABELFROM
-FILESYSTEM__RELABELTO = _selinux.FILESYSTEM__RELABELTO
-FILESYSTEM__TRANSITION = _selinux.FILESYSTEM__TRANSITION
-FILESYSTEM__ASSOCIATE = _selinux.FILESYSTEM__ASSOCIATE
-FILESYSTEM__QUOTAMOD = _selinux.FILESYSTEM__QUOTAMOD
-FILESYSTEM__QUOTAGET = _selinux.FILESYSTEM__QUOTAGET
-DIR__IOCTL = _selinux.DIR__IOCTL
-DIR__READ = _selinux.DIR__READ
-DIR__WRITE = _selinux.DIR__WRITE
-DIR__CREATE = _selinux.DIR__CREATE
-DIR__GETATTR = _selinux.DIR__GETATTR
-DIR__SETATTR = _selinux.DIR__SETATTR
-DIR__LOCK = _selinux.DIR__LOCK
-DIR__RELABELFROM = _selinux.DIR__RELABELFROM
-DIR__RELABELTO = _selinux.DIR__RELABELTO
-DIR__APPEND = _selinux.DIR__APPEND
-DIR__UNLINK = _selinux.DIR__UNLINK
-DIR__LINK = _selinux.DIR__LINK
-DIR__RENAME = _selinux.DIR__RENAME
-DIR__EXECUTE = _selinux.DIR__EXECUTE
-DIR__SWAPON = _selinux.DIR__SWAPON
-DIR__QUOTAON = _selinux.DIR__QUOTAON
-DIR__MOUNTON = _selinux.DIR__MOUNTON
-DIR__ADD_NAME = _selinux.DIR__ADD_NAME
-DIR__REMOVE_NAME = _selinux.DIR__REMOVE_NAME
-DIR__REPARENT = _selinux.DIR__REPARENT
-DIR__SEARCH = _selinux.DIR__SEARCH
-DIR__RMDIR = _selinux.DIR__RMDIR
-DIR__OPEN = _selinux.DIR__OPEN
-FILE__IOCTL = _selinux.FILE__IOCTL
-FILE__READ = _selinux.FILE__READ
-FILE__WRITE = _selinux.FILE__WRITE
-FILE__CREATE = _selinux.FILE__CREATE
-FILE__GETATTR = _selinux.FILE__GETATTR
-FILE__SETATTR = _selinux.FILE__SETATTR
-FILE__LOCK = _selinux.FILE__LOCK
-FILE__RELABELFROM = _selinux.FILE__RELABELFROM
-FILE__RELABELTO = _selinux.FILE__RELABELTO
-FILE__APPEND = _selinux.FILE__APPEND
-FILE__UNLINK = _selinux.FILE__UNLINK
-FILE__LINK = _selinux.FILE__LINK
-FILE__RENAME = _selinux.FILE__RENAME
-FILE__EXECUTE = _selinux.FILE__EXECUTE
-FILE__SWAPON = _selinux.FILE__SWAPON
-FILE__QUOTAON = _selinux.FILE__QUOTAON
-FILE__MOUNTON = _selinux.FILE__MOUNTON
-FILE__EXECUTE_NO_TRANS = _selinux.FILE__EXECUTE_NO_TRANS
-FILE__ENTRYPOINT = _selinux.FILE__ENTRYPOINT
-FILE__EXECMOD = _selinux.FILE__EXECMOD
-FILE__OPEN = _selinux.FILE__OPEN
-LNK_FILE__IOCTL = _selinux.LNK_FILE__IOCTL
-LNK_FILE__READ = _selinux.LNK_FILE__READ
-LNK_FILE__WRITE = _selinux.LNK_FILE__WRITE
-LNK_FILE__CREATE = _selinux.LNK_FILE__CREATE
-LNK_FILE__GETATTR = _selinux.LNK_FILE__GETATTR
-LNK_FILE__SETATTR = _selinux.LNK_FILE__SETATTR
-LNK_FILE__LOCK = _selinux.LNK_FILE__LOCK
-LNK_FILE__RELABELFROM = _selinux.LNK_FILE__RELABELFROM
-LNK_FILE__RELABELTO = _selinux.LNK_FILE__RELABELTO
-LNK_FILE__APPEND = _selinux.LNK_FILE__APPEND
-LNK_FILE__UNLINK = _selinux.LNK_FILE__UNLINK
-LNK_FILE__LINK = _selinux.LNK_FILE__LINK
-LNK_FILE__RENAME = _selinux.LNK_FILE__RENAME
-LNK_FILE__EXECUTE = _selinux.LNK_FILE__EXECUTE
-LNK_FILE__SWAPON = _selinux.LNK_FILE__SWAPON
-LNK_FILE__QUOTAON = _selinux.LNK_FILE__QUOTAON
-LNK_FILE__MOUNTON = _selinux.LNK_FILE__MOUNTON
-CHR_FILE__IOCTL = _selinux.CHR_FILE__IOCTL
-CHR_FILE__READ = _selinux.CHR_FILE__READ
-CHR_FILE__WRITE = _selinux.CHR_FILE__WRITE
-CHR_FILE__CREATE = _selinux.CHR_FILE__CREATE
-CHR_FILE__GETATTR = _selinux.CHR_FILE__GETATTR
-CHR_FILE__SETATTR = _selinux.CHR_FILE__SETATTR
-CHR_FILE__LOCK = _selinux.CHR_FILE__LOCK
-CHR_FILE__RELABELFROM = _selinux.CHR_FILE__RELABELFROM
-CHR_FILE__RELABELTO = _selinux.CHR_FILE__RELABELTO
-CHR_FILE__APPEND = _selinux.CHR_FILE__APPEND
-CHR_FILE__UNLINK = _selinux.CHR_FILE__UNLINK
-CHR_FILE__LINK = _selinux.CHR_FILE__LINK
-CHR_FILE__RENAME = _selinux.CHR_FILE__RENAME
-CHR_FILE__EXECUTE = _selinux.CHR_FILE__EXECUTE
-CHR_FILE__SWAPON = _selinux.CHR_FILE__SWAPON
-CHR_FILE__QUOTAON = _selinux.CHR_FILE__QUOTAON
-CHR_FILE__MOUNTON = _selinux.CHR_FILE__MOUNTON
-CHR_FILE__EXECUTE_NO_TRANS = _selinux.CHR_FILE__EXECUTE_NO_TRANS
-CHR_FILE__ENTRYPOINT = _selinux.CHR_FILE__ENTRYPOINT
-CHR_FILE__EXECMOD = _selinux.CHR_FILE__EXECMOD
-CHR_FILE__OPEN = _selinux.CHR_FILE__OPEN
-BLK_FILE__IOCTL = _selinux.BLK_FILE__IOCTL
-BLK_FILE__READ = _selinux.BLK_FILE__READ
-BLK_FILE__WRITE = _selinux.BLK_FILE__WRITE
-BLK_FILE__CREATE = _selinux.BLK_FILE__CREATE
-BLK_FILE__GETATTR = _selinux.BLK_FILE__GETATTR
-BLK_FILE__SETATTR = _selinux.BLK_FILE__SETATTR
-BLK_FILE__LOCK = _selinux.BLK_FILE__LOCK
-BLK_FILE__RELABELFROM = _selinux.BLK_FILE__RELABELFROM
-BLK_FILE__RELABELTO = _selinux.BLK_FILE__RELABELTO
-BLK_FILE__APPEND = _selinux.BLK_FILE__APPEND
-BLK_FILE__UNLINK = _selinux.BLK_FILE__UNLINK
-BLK_FILE__LINK = _selinux.BLK_FILE__LINK
-BLK_FILE__RENAME = _selinux.BLK_FILE__RENAME
-BLK_FILE__EXECUTE = _selinux.BLK_FILE__EXECUTE
-BLK_FILE__SWAPON = _selinux.BLK_FILE__SWAPON
-BLK_FILE__QUOTAON = _selinux.BLK_FILE__QUOTAON
-BLK_FILE__MOUNTON = _selinux.BLK_FILE__MOUNTON
-BLK_FILE__OPEN = _selinux.BLK_FILE__OPEN
-SOCK_FILE__IOCTL = _selinux.SOCK_FILE__IOCTL
-SOCK_FILE__READ = _selinux.SOCK_FILE__READ
-SOCK_FILE__WRITE = _selinux.SOCK_FILE__WRITE
-SOCK_FILE__CREATE = _selinux.SOCK_FILE__CREATE
-SOCK_FILE__GETATTR = _selinux.SOCK_FILE__GETATTR
-SOCK_FILE__SETATTR = _selinux.SOCK_FILE__SETATTR
-SOCK_FILE__LOCK = _selinux.SOCK_FILE__LOCK
-SOCK_FILE__RELABELFROM = _selinux.SOCK_FILE__RELABELFROM
-SOCK_FILE__RELABELTO = _selinux.SOCK_FILE__RELABELTO
-SOCK_FILE__APPEND = _selinux.SOCK_FILE__APPEND
-SOCK_FILE__UNLINK = _selinux.SOCK_FILE__UNLINK
-SOCK_FILE__LINK = _selinux.SOCK_FILE__LINK
-SOCK_FILE__RENAME = _selinux.SOCK_FILE__RENAME
-SOCK_FILE__EXECUTE = _selinux.SOCK_FILE__EXECUTE
-SOCK_FILE__SWAPON = _selinux.SOCK_FILE__SWAPON
-SOCK_FILE__QUOTAON = _selinux.SOCK_FILE__QUOTAON
-SOCK_FILE__MOUNTON = _selinux.SOCK_FILE__MOUNTON
-FIFO_FILE__IOCTL = _selinux.FIFO_FILE__IOCTL
-FIFO_FILE__READ = _selinux.FIFO_FILE__READ
-FIFO_FILE__WRITE = _selinux.FIFO_FILE__WRITE
-FIFO_FILE__CREATE = _selinux.FIFO_FILE__CREATE
-FIFO_FILE__GETATTR = _selinux.FIFO_FILE__GETATTR
-FIFO_FILE__SETATTR = _selinux.FIFO_FILE__SETATTR
-FIFO_FILE__LOCK = _selinux.FIFO_FILE__LOCK
-FIFO_FILE__RELABELFROM = _selinux.FIFO_FILE__RELABELFROM
-FIFO_FILE__RELABELTO = _selinux.FIFO_FILE__RELABELTO
-FIFO_FILE__APPEND = _selinux.FIFO_FILE__APPEND
-FIFO_FILE__UNLINK = _selinux.FIFO_FILE__UNLINK
-FIFO_FILE__LINK = _selinux.FIFO_FILE__LINK
-FIFO_FILE__RENAME = _selinux.FIFO_FILE__RENAME
-FIFO_FILE__EXECUTE = _selinux.FIFO_FILE__EXECUTE
-FIFO_FILE__SWAPON = _selinux.FIFO_FILE__SWAPON
-FIFO_FILE__QUOTAON = _selinux.FIFO_FILE__QUOTAON
-FIFO_FILE__MOUNTON = _selinux.FIFO_FILE__MOUNTON
-FIFO_FILE__OPEN = _selinux.FIFO_FILE__OPEN
-FD__USE = _selinux.FD__USE
-SOCKET__IOCTL = _selinux.SOCKET__IOCTL
-SOCKET__READ = _selinux.SOCKET__READ
-SOCKET__WRITE = _selinux.SOCKET__WRITE
-SOCKET__CREATE = _selinux.SOCKET__CREATE
-SOCKET__GETATTR = _selinux.SOCKET__GETATTR
-SOCKET__SETATTR = _selinux.SOCKET__SETATTR
-SOCKET__LOCK = _selinux.SOCKET__LOCK
-SOCKET__RELABELFROM = _selinux.SOCKET__RELABELFROM
-SOCKET__RELABELTO = _selinux.SOCKET__RELABELTO
-SOCKET__APPEND = _selinux.SOCKET__APPEND
-SOCKET__BIND = _selinux.SOCKET__BIND
-SOCKET__CONNECT = _selinux.SOCKET__CONNECT
-SOCKET__LISTEN = _selinux.SOCKET__LISTEN
-SOCKET__ACCEPT = _selinux.SOCKET__ACCEPT
-SOCKET__GETOPT = _selinux.SOCKET__GETOPT
-SOCKET__SETOPT = _selinux.SOCKET__SETOPT
-SOCKET__SHUTDOWN = _selinux.SOCKET__SHUTDOWN
-SOCKET__RECVFROM = _selinux.SOCKET__RECVFROM
-SOCKET__SENDTO = _selinux.SOCKET__SENDTO
-SOCKET__RECV_MSG = _selinux.SOCKET__RECV_MSG
-SOCKET__SEND_MSG = _selinux.SOCKET__SEND_MSG
-SOCKET__NAME_BIND = _selinux.SOCKET__NAME_BIND
-TCP_SOCKET__IOCTL = _selinux.TCP_SOCKET__IOCTL
-TCP_SOCKET__READ = _selinux.TCP_SOCKET__READ
-TCP_SOCKET__WRITE = _selinux.TCP_SOCKET__WRITE
-TCP_SOCKET__CREATE = _selinux.TCP_SOCKET__CREATE
-TCP_SOCKET__GETATTR = _selinux.TCP_SOCKET__GETATTR
-TCP_SOCKET__SETATTR = _selinux.TCP_SOCKET__SETATTR
-TCP_SOCKET__LOCK = _selinux.TCP_SOCKET__LOCK
-TCP_SOCKET__RELABELFROM = _selinux.TCP_SOCKET__RELABELFROM
-TCP_SOCKET__RELABELTO = _selinux.TCP_SOCKET__RELABELTO
-TCP_SOCKET__APPEND = _selinux.TCP_SOCKET__APPEND
-TCP_SOCKET__BIND = _selinux.TCP_SOCKET__BIND
-TCP_SOCKET__CONNECT = _selinux.TCP_SOCKET__CONNECT
-TCP_SOCKET__LISTEN = _selinux.TCP_SOCKET__LISTEN
-TCP_SOCKET__ACCEPT = _selinux.TCP_SOCKET__ACCEPT
-TCP_SOCKET__GETOPT = _selinux.TCP_SOCKET__GETOPT
-TCP_SOCKET__SETOPT = _selinux.TCP_SOCKET__SETOPT
-TCP_SOCKET__SHUTDOWN = _selinux.TCP_SOCKET__SHUTDOWN
-TCP_SOCKET__RECVFROM = _selinux.TCP_SOCKET__RECVFROM
-TCP_SOCKET__SENDTO = _selinux.TCP_SOCKET__SENDTO
-TCP_SOCKET__RECV_MSG = _selinux.TCP_SOCKET__RECV_MSG
-TCP_SOCKET__SEND_MSG = _selinux.TCP_SOCKET__SEND_MSG
-TCP_SOCKET__NAME_BIND = _selinux.TCP_SOCKET__NAME_BIND
-TCP_SOCKET__CONNECTTO = _selinux.TCP_SOCKET__CONNECTTO
-TCP_SOCKET__NEWCONN = _selinux.TCP_SOCKET__NEWCONN
-TCP_SOCKET__ACCEPTFROM = _selinux.TCP_SOCKET__ACCEPTFROM
-TCP_SOCKET__NODE_BIND = _selinux.TCP_SOCKET__NODE_BIND
-TCP_SOCKET__NAME_CONNECT = _selinux.TCP_SOCKET__NAME_CONNECT
-UDP_SOCKET__IOCTL = _selinux.UDP_SOCKET__IOCTL
-UDP_SOCKET__READ = _selinux.UDP_SOCKET__READ
-UDP_SOCKET__WRITE = _selinux.UDP_SOCKET__WRITE
-UDP_SOCKET__CREATE = _selinux.UDP_SOCKET__CREATE
-UDP_SOCKET__GETATTR = _selinux.UDP_SOCKET__GETATTR
-UDP_SOCKET__SETATTR = _selinux.UDP_SOCKET__SETATTR
-UDP_SOCKET__LOCK = _selinux.UDP_SOCKET__LOCK
-UDP_SOCKET__RELABELFROM = _selinux.UDP_SOCKET__RELABELFROM
-UDP_SOCKET__RELABELTO = _selinux.UDP_SOCKET__RELABELTO
-UDP_SOCKET__APPEND = _selinux.UDP_SOCKET__APPEND
-UDP_SOCKET__BIND = _selinux.UDP_SOCKET__BIND
-UDP_SOCKET__CONNECT = _selinux.UDP_SOCKET__CONNECT
-UDP_SOCKET__LISTEN = _selinux.UDP_SOCKET__LISTEN
-UDP_SOCKET__ACCEPT = _selinux.UDP_SOCKET__ACCEPT
-UDP_SOCKET__GETOPT = _selinux.UDP_SOCKET__GETOPT
-UDP_SOCKET__SETOPT = _selinux.UDP_SOCKET__SETOPT
-UDP_SOCKET__SHUTDOWN = _selinux.UDP_SOCKET__SHUTDOWN
-UDP_SOCKET__RECVFROM = _selinux.UDP_SOCKET__RECVFROM
-UDP_SOCKET__SENDTO = _selinux.UDP_SOCKET__SENDTO
-UDP_SOCKET__RECV_MSG = _selinux.UDP_SOCKET__RECV_MSG
-UDP_SOCKET__SEND_MSG = _selinux.UDP_SOCKET__SEND_MSG
-UDP_SOCKET__NAME_BIND = _selinux.UDP_SOCKET__NAME_BIND
-UDP_SOCKET__NODE_BIND = _selinux.UDP_SOCKET__NODE_BIND
-RAWIP_SOCKET__IOCTL = _selinux.RAWIP_SOCKET__IOCTL
-RAWIP_SOCKET__READ = _selinux.RAWIP_SOCKET__READ
-RAWIP_SOCKET__WRITE = _selinux.RAWIP_SOCKET__WRITE
-RAWIP_SOCKET__CREATE = _selinux.RAWIP_SOCKET__CREATE
-RAWIP_SOCKET__GETATTR = _selinux.RAWIP_SOCKET__GETATTR
-RAWIP_SOCKET__SETATTR = _selinux.RAWIP_SOCKET__SETATTR
-RAWIP_SOCKET__LOCK = _selinux.RAWIP_SOCKET__LOCK
-RAWIP_SOCKET__RELABELFROM = _selinux.RAWIP_SOCKET__RELABELFROM
-RAWIP_SOCKET__RELABELTO = _selinux.RAWIP_SOCKET__RELABELTO
-RAWIP_SOCKET__APPEND = _selinux.RAWIP_SOCKET__APPEND
-RAWIP_SOCKET__BIND = _selinux.RAWIP_SOCKET__BIND
-RAWIP_SOCKET__CONNECT = _selinux.RAWIP_SOCKET__CONNECT
-RAWIP_SOCKET__LISTEN = _selinux.RAWIP_SOCKET__LISTEN
-RAWIP_SOCKET__ACCEPT = _selinux.RAWIP_SOCKET__ACCEPT
-RAWIP_SOCKET__GETOPT = _selinux.RAWIP_SOCKET__GETOPT
-RAWIP_SOCKET__SETOPT = _selinux.RAWIP_SOCKET__SETOPT
-RAWIP_SOCKET__SHUTDOWN = _selinux.RAWIP_SOCKET__SHUTDOWN
-RAWIP_SOCKET__RECVFROM = _selinux.RAWIP_SOCKET__RECVFROM
-RAWIP_SOCKET__SENDTO = _selinux.RAWIP_SOCKET__SENDTO
-RAWIP_SOCKET__RECV_MSG = _selinux.RAWIP_SOCKET__RECV_MSG
-RAWIP_SOCKET__SEND_MSG = _selinux.RAWIP_SOCKET__SEND_MSG
-RAWIP_SOCKET__NAME_BIND = _selinux.RAWIP_SOCKET__NAME_BIND
-RAWIP_SOCKET__NODE_BIND = _selinux.RAWIP_SOCKET__NODE_BIND
-NODE__TCP_RECV = _selinux.NODE__TCP_RECV
-NODE__TCP_SEND = _selinux.NODE__TCP_SEND
-NODE__UDP_RECV = _selinux.NODE__UDP_RECV
-NODE__UDP_SEND = _selinux.NODE__UDP_SEND
-NODE__RAWIP_RECV = _selinux.NODE__RAWIP_RECV
-NODE__RAWIP_SEND = _selinux.NODE__RAWIP_SEND
-NODE__ENFORCE_DEST = _selinux.NODE__ENFORCE_DEST
-NODE__DCCP_RECV = _selinux.NODE__DCCP_RECV
-NODE__DCCP_SEND = _selinux.NODE__DCCP_SEND
-NODE__RECVFROM = _selinux.NODE__RECVFROM
-NODE__SENDTO = _selinux.NODE__SENDTO
-NETIF__TCP_RECV = _selinux.NETIF__TCP_RECV
-NETIF__TCP_SEND = _selinux.NETIF__TCP_SEND
-NETIF__UDP_RECV = _selinux.NETIF__UDP_RECV
-NETIF__UDP_SEND = _selinux.NETIF__UDP_SEND
-NETIF__RAWIP_RECV = _selinux.NETIF__RAWIP_RECV
-NETIF__RAWIP_SEND = _selinux.NETIF__RAWIP_SEND
-NETIF__DCCP_RECV = _selinux.NETIF__DCCP_RECV
-NETIF__DCCP_SEND = _selinux.NETIF__DCCP_SEND
-NETIF__INGRESS = _selinux.NETIF__INGRESS
-NETIF__EGRESS = _selinux.NETIF__EGRESS
-NETLINK_SOCKET__IOCTL = _selinux.NETLINK_SOCKET__IOCTL
-NETLINK_SOCKET__READ = _selinux.NETLINK_SOCKET__READ
-NETLINK_SOCKET__WRITE = _selinux.NETLINK_SOCKET__WRITE
-NETLINK_SOCKET__CREATE = _selinux.NETLINK_SOCKET__CREATE
-NETLINK_SOCKET__GETATTR = _selinux.NETLINK_SOCKET__GETATTR
-NETLINK_SOCKET__SETATTR = _selinux.NETLINK_SOCKET__SETATTR
-NETLINK_SOCKET__LOCK = _selinux.NETLINK_SOCKET__LOCK
-NETLINK_SOCKET__RELABELFROM = _selinux.NETLINK_SOCKET__RELABELFROM
-NETLINK_SOCKET__RELABELTO = _selinux.NETLINK_SOCKET__RELABELTO
-NETLINK_SOCKET__APPEND = _selinux.NETLINK_SOCKET__APPEND
-NETLINK_SOCKET__BIND = _selinux.NETLINK_SOCKET__BIND
-NETLINK_SOCKET__CONNECT = _selinux.NETLINK_SOCKET__CONNECT
-NETLINK_SOCKET__LISTEN = _selinux.NETLINK_SOCKET__LISTEN
-NETLINK_SOCKET__ACCEPT = _selinux.NETLINK_SOCKET__ACCEPT
-NETLINK_SOCKET__GETOPT = _selinux.NETLINK_SOCKET__GETOPT
-NETLINK_SOCKET__SETOPT = _selinux.NETLINK_SOCKET__SETOPT
-NETLINK_SOCKET__SHUTDOWN = _selinux.NETLINK_SOCKET__SHUTDOWN
-NETLINK_SOCKET__RECVFROM = _selinux.NETLINK_SOCKET__RECVFROM
-NETLINK_SOCKET__SENDTO = _selinux.NETLINK_SOCKET__SENDTO
-NETLINK_SOCKET__RECV_MSG = _selinux.NETLINK_SOCKET__RECV_MSG
-NETLINK_SOCKET__SEND_MSG = _selinux.NETLINK_SOCKET__SEND_MSG
-NETLINK_SOCKET__NAME_BIND = _selinux.NETLINK_SOCKET__NAME_BIND
-PACKET_SOCKET__IOCTL = _selinux.PACKET_SOCKET__IOCTL
-PACKET_SOCKET__READ = _selinux.PACKET_SOCKET__READ
-PACKET_SOCKET__WRITE = _selinux.PACKET_SOCKET__WRITE
-PACKET_SOCKET__CREATE = _selinux.PACKET_SOCKET__CREATE
-PACKET_SOCKET__GETATTR = _selinux.PACKET_SOCKET__GETATTR
-PACKET_SOCKET__SETATTR = _selinux.PACKET_SOCKET__SETATTR
-PACKET_SOCKET__LOCK = _selinux.PACKET_SOCKET__LOCK
-PACKET_SOCKET__RELABELFROM = _selinux.PACKET_SOCKET__RELABELFROM
-PACKET_SOCKET__RELABELTO = _selinux.PACKET_SOCKET__RELABELTO
-PACKET_SOCKET__APPEND = _selinux.PACKET_SOCKET__APPEND
-PACKET_SOCKET__BIND = _selinux.PACKET_SOCKET__BIND
-PACKET_SOCKET__CONNECT = _selinux.PACKET_SOCKET__CONNECT
-PACKET_SOCKET__LISTEN = _selinux.PACKET_SOCKET__LISTEN
-PACKET_SOCKET__ACCEPT = _selinux.PACKET_SOCKET__ACCEPT
-PACKET_SOCKET__GETOPT = _selinux.PACKET_SOCKET__GETOPT
-PACKET_SOCKET__SETOPT = _selinux.PACKET_SOCKET__SETOPT
-PACKET_SOCKET__SHUTDOWN = _selinux.PACKET_SOCKET__SHUTDOWN
-PACKET_SOCKET__RECVFROM = _selinux.PACKET_SOCKET__RECVFROM
-PACKET_SOCKET__SENDTO = _selinux.PACKET_SOCKET__SENDTO
-PACKET_SOCKET__RECV_MSG = _selinux.PACKET_SOCKET__RECV_MSG
-PACKET_SOCKET__SEND_MSG = _selinux.PACKET_SOCKET__SEND_MSG
-PACKET_SOCKET__NAME_BIND = _selinux.PACKET_SOCKET__NAME_BIND
-KEY_SOCKET__IOCTL = _selinux.KEY_SOCKET__IOCTL
-KEY_SOCKET__READ = _selinux.KEY_SOCKET__READ
-KEY_SOCKET__WRITE = _selinux.KEY_SOCKET__WRITE
-KEY_SOCKET__CREATE = _selinux.KEY_SOCKET__CREATE
-KEY_SOCKET__GETATTR = _selinux.KEY_SOCKET__GETATTR
-KEY_SOCKET__SETATTR = _selinux.KEY_SOCKET__SETATTR
-KEY_SOCKET__LOCK = _selinux.KEY_SOCKET__LOCK
-KEY_SOCKET__RELABELFROM = _selinux.KEY_SOCKET__RELABELFROM
-KEY_SOCKET__RELABELTO = _selinux.KEY_SOCKET__RELABELTO
-KEY_SOCKET__APPEND = _selinux.KEY_SOCKET__APPEND
-KEY_SOCKET__BIND = _selinux.KEY_SOCKET__BIND
-KEY_SOCKET__CONNECT = _selinux.KEY_SOCKET__CONNECT
-KEY_SOCKET__LISTEN = _selinux.KEY_SOCKET__LISTEN
-KEY_SOCKET__ACCEPT = _selinux.KEY_SOCKET__ACCEPT
-KEY_SOCKET__GETOPT = _selinux.KEY_SOCKET__GETOPT
-KEY_SOCKET__SETOPT = _selinux.KEY_SOCKET__SETOPT
-KEY_SOCKET__SHUTDOWN = _selinux.KEY_SOCKET__SHUTDOWN
-KEY_SOCKET__RECVFROM = _selinux.KEY_SOCKET__RECVFROM
-KEY_SOCKET__SENDTO = _selinux.KEY_SOCKET__SENDTO
-KEY_SOCKET__RECV_MSG = _selinux.KEY_SOCKET__RECV_MSG
-KEY_SOCKET__SEND_MSG = _selinux.KEY_SOCKET__SEND_MSG
-KEY_SOCKET__NAME_BIND = _selinux.KEY_SOCKET__NAME_BIND
-UNIX_STREAM_SOCKET__IOCTL = _selinux.UNIX_STREAM_SOCKET__IOCTL
-UNIX_STREAM_SOCKET__READ = _selinux.UNIX_STREAM_SOCKET__READ
-UNIX_STREAM_SOCKET__WRITE = _selinux.UNIX_STREAM_SOCKET__WRITE
-UNIX_STREAM_SOCKET__CREATE = _selinux.UNIX_STREAM_SOCKET__CREATE
-UNIX_STREAM_SOCKET__GETATTR = _selinux.UNIX_STREAM_SOCKET__GETATTR
-UNIX_STREAM_SOCKET__SETATTR = _selinux.UNIX_STREAM_SOCKET__SETATTR
-UNIX_STREAM_SOCKET__LOCK = _selinux.UNIX_STREAM_SOCKET__LOCK
-UNIX_STREAM_SOCKET__RELABELFROM = _selinux.UNIX_STREAM_SOCKET__RELABELFROM
-UNIX_STREAM_SOCKET__RELABELTO = _selinux.UNIX_STREAM_SOCKET__RELABELTO
-UNIX_STREAM_SOCKET__APPEND = _selinux.UNIX_STREAM_SOCKET__APPEND
-UNIX_STREAM_SOCKET__BIND = _selinux.UNIX_STREAM_SOCKET__BIND
-UNIX_STREAM_SOCKET__CONNECT = _selinux.UNIX_STREAM_SOCKET__CONNECT
-UNIX_STREAM_SOCKET__LISTEN = _selinux.UNIX_STREAM_SOCKET__LISTEN
-UNIX_STREAM_SOCKET__ACCEPT = _selinux.UNIX_STREAM_SOCKET__ACCEPT
-UNIX_STREAM_SOCKET__GETOPT = _selinux.UNIX_STREAM_SOCKET__GETOPT
-UNIX_STREAM_SOCKET__SETOPT = _selinux.UNIX_STREAM_SOCKET__SETOPT
-UNIX_STREAM_SOCKET__SHUTDOWN = _selinux.UNIX_STREAM_SOCKET__SHUTDOWN
-UNIX_STREAM_SOCKET__RECVFROM = _selinux.UNIX_STREAM_SOCKET__RECVFROM
-UNIX_STREAM_SOCKET__SENDTO = _selinux.UNIX_STREAM_SOCKET__SENDTO
-UNIX_STREAM_SOCKET__RECV_MSG = _selinux.UNIX_STREAM_SOCKET__RECV_MSG
-UNIX_STREAM_SOCKET__SEND_MSG = _selinux.UNIX_STREAM_SOCKET__SEND_MSG
-UNIX_STREAM_SOCKET__NAME_BIND = _selinux.UNIX_STREAM_SOCKET__NAME_BIND
-UNIX_STREAM_SOCKET__CONNECTTO = _selinux.UNIX_STREAM_SOCKET__CONNECTTO
-UNIX_STREAM_SOCKET__NEWCONN = _selinux.UNIX_STREAM_SOCKET__NEWCONN
-UNIX_STREAM_SOCKET__ACCEPTFROM = _selinux.UNIX_STREAM_SOCKET__ACCEPTFROM
-UNIX_DGRAM_SOCKET__IOCTL = _selinux.UNIX_DGRAM_SOCKET__IOCTL
-UNIX_DGRAM_SOCKET__READ = _selinux.UNIX_DGRAM_SOCKET__READ
-UNIX_DGRAM_SOCKET__WRITE = _selinux.UNIX_DGRAM_SOCKET__WRITE
-UNIX_DGRAM_SOCKET__CREATE = _selinux.UNIX_DGRAM_SOCKET__CREATE
-UNIX_DGRAM_SOCKET__GETATTR = _selinux.UNIX_DGRAM_SOCKET__GETATTR
-UNIX_DGRAM_SOCKET__SETATTR = _selinux.UNIX_DGRAM_SOCKET__SETATTR
-UNIX_DGRAM_SOCKET__LOCK = _selinux.UNIX_DGRAM_SOCKET__LOCK
-UNIX_DGRAM_SOCKET__RELABELFROM = _selinux.UNIX_DGRAM_SOCKET__RELABELFROM
-UNIX_DGRAM_SOCKET__RELABELTO = _selinux.UNIX_DGRAM_SOCKET__RELABELTO
-UNIX_DGRAM_SOCKET__APPEND = _selinux.UNIX_DGRAM_SOCKET__APPEND
-UNIX_DGRAM_SOCKET__BIND = _selinux.UNIX_DGRAM_SOCKET__BIND
-UNIX_DGRAM_SOCKET__CONNECT = _selinux.UNIX_DGRAM_SOCKET__CONNECT
-UNIX_DGRAM_SOCKET__LISTEN = _selinux.UNIX_DGRAM_SOCKET__LISTEN
-UNIX_DGRAM_SOCKET__ACCEPT = _selinux.UNIX_DGRAM_SOCKET__ACCEPT
-UNIX_DGRAM_SOCKET__GETOPT = _selinux.UNIX_DGRAM_SOCKET__GETOPT
-UNIX_DGRAM_SOCKET__SETOPT = _selinux.UNIX_DGRAM_SOCKET__SETOPT
-UNIX_DGRAM_SOCKET__SHUTDOWN = _selinux.UNIX_DGRAM_SOCKET__SHUTDOWN
-UNIX_DGRAM_SOCKET__RECVFROM = _selinux.UNIX_DGRAM_SOCKET__RECVFROM
-UNIX_DGRAM_SOCKET__SENDTO = _selinux.UNIX_DGRAM_SOCKET__SENDTO
-UNIX_DGRAM_SOCKET__RECV_MSG = _selinux.UNIX_DGRAM_SOCKET__RECV_MSG
-UNIX_DGRAM_SOCKET__SEND_MSG = _selinux.UNIX_DGRAM_SOCKET__SEND_MSG
-UNIX_DGRAM_SOCKET__NAME_BIND = _selinux.UNIX_DGRAM_SOCKET__NAME_BIND
-PROCESS__FORK = _selinux.PROCESS__FORK
-PROCESS__TRANSITION = _selinux.PROCESS__TRANSITION
-PROCESS__SIGCHLD = _selinux.PROCESS__SIGCHLD
-PROCESS__SIGKILL = _selinux.PROCESS__SIGKILL
-PROCESS__SIGSTOP = _selinux.PROCESS__SIGSTOP
-PROCESS__SIGNULL = _selinux.PROCESS__SIGNULL
-PROCESS__SIGNAL = _selinux.PROCESS__SIGNAL
-PROCESS__PTRACE = _selinux.PROCESS__PTRACE
-PROCESS__GETSCHED = _selinux.PROCESS__GETSCHED
-PROCESS__SETSCHED = _selinux.PROCESS__SETSCHED
-PROCESS__GETSESSION = _selinux.PROCESS__GETSESSION
-PROCESS__GETPGID = _selinux.PROCESS__GETPGID
-PROCESS__SETPGID = _selinux.PROCESS__SETPGID
-PROCESS__GETCAP = _selinux.PROCESS__GETCAP
-PROCESS__SETCAP = _selinux.PROCESS__SETCAP
-PROCESS__SHARE = _selinux.PROCESS__SHARE
-PROCESS__GETATTR = _selinux.PROCESS__GETATTR
-PROCESS__SETEXEC = _selinux.PROCESS__SETEXEC
-PROCESS__SETFSCREATE = _selinux.PROCESS__SETFSCREATE
-PROCESS__NOATSECURE = _selinux.PROCESS__NOATSECURE
-PROCESS__SIGINH = _selinux.PROCESS__SIGINH
-PROCESS__SETRLIMIT = _selinux.PROCESS__SETRLIMIT
-PROCESS__RLIMITINH = _selinux.PROCESS__RLIMITINH
-PROCESS__DYNTRANSITION = _selinux.PROCESS__DYNTRANSITION
-PROCESS__SETCURRENT = _selinux.PROCESS__SETCURRENT
-PROCESS__EXECMEM = _selinux.PROCESS__EXECMEM
-PROCESS__EXECSTACK = _selinux.PROCESS__EXECSTACK
-PROCESS__EXECHEAP = _selinux.PROCESS__EXECHEAP
-PROCESS__SETKEYCREATE = _selinux.PROCESS__SETKEYCREATE
-PROCESS__SETSOCKCREATE = _selinux.PROCESS__SETSOCKCREATE
-IPC__CREATE = _selinux.IPC__CREATE
-IPC__DESTROY = _selinux.IPC__DESTROY
-IPC__GETATTR = _selinux.IPC__GETATTR
-IPC__SETATTR = _selinux.IPC__SETATTR
-IPC__READ = _selinux.IPC__READ
-IPC__WRITE = _selinux.IPC__WRITE
-IPC__ASSOCIATE = _selinux.IPC__ASSOCIATE
-IPC__UNIX_READ = _selinux.IPC__UNIX_READ
-IPC__UNIX_WRITE = _selinux.IPC__UNIX_WRITE
-SEM__CREATE = _selinux.SEM__CREATE
-SEM__DESTROY = _selinux.SEM__DESTROY
-SEM__GETATTR = _selinux.SEM__GETATTR
-SEM__SETATTR = _selinux.SEM__SETATTR
-SEM__READ = _selinux.SEM__READ
-SEM__WRITE = _selinux.SEM__WRITE
-SEM__ASSOCIATE = _selinux.SEM__ASSOCIATE
-SEM__UNIX_READ = _selinux.SEM__UNIX_READ
-SEM__UNIX_WRITE = _selinux.SEM__UNIX_WRITE
-MSGQ__CREATE = _selinux.MSGQ__CREATE
-MSGQ__DESTROY = _selinux.MSGQ__DESTROY
-MSGQ__GETATTR = _selinux.MSGQ__GETATTR
-MSGQ__SETATTR = _selinux.MSGQ__SETATTR
-MSGQ__READ = _selinux.MSGQ__READ
-MSGQ__WRITE = _selinux.MSGQ__WRITE
-MSGQ__ASSOCIATE = _selinux.MSGQ__ASSOCIATE
-MSGQ__UNIX_READ = _selinux.MSGQ__UNIX_READ
-MSGQ__UNIX_WRITE = _selinux.MSGQ__UNIX_WRITE
-MSGQ__ENQUEUE = _selinux.MSGQ__ENQUEUE
-MSG__SEND = _selinux.MSG__SEND
-MSG__RECEIVE = _selinux.MSG__RECEIVE
-SHM__CREATE = _selinux.SHM__CREATE
-SHM__DESTROY = _selinux.SHM__DESTROY
-SHM__GETATTR = _selinux.SHM__GETATTR
-SHM__SETATTR = _selinux.SHM__SETATTR
-SHM__READ = _selinux.SHM__READ
-SHM__WRITE = _selinux.SHM__WRITE
-SHM__ASSOCIATE = _selinux.SHM__ASSOCIATE
-SHM__UNIX_READ = _selinux.SHM__UNIX_READ
-SHM__UNIX_WRITE = _selinux.SHM__UNIX_WRITE
-SHM__LOCK = _selinux.SHM__LOCK
-SECURITY__COMPUTE_AV = _selinux.SECURITY__COMPUTE_AV
-SECURITY__COMPUTE_CREATE = _selinux.SECURITY__COMPUTE_CREATE
-SECURITY__COMPUTE_MEMBER = _selinux.SECURITY__COMPUTE_MEMBER
-SECURITY__CHECK_CONTEXT = _selinux.SECURITY__CHECK_CONTEXT
-SECURITY__LOAD_POLICY = _selinux.SECURITY__LOAD_POLICY
-SECURITY__COMPUTE_RELABEL = _selinux.SECURITY__COMPUTE_RELABEL
-SECURITY__COMPUTE_USER = _selinux.SECURITY__COMPUTE_USER
-SECURITY__SETENFORCE = _selinux.SECURITY__SETENFORCE
-SECURITY__SETBOOL = _selinux.SECURITY__SETBOOL
-SECURITY__SETSECPARAM = _selinux.SECURITY__SETSECPARAM
-SECURITY__SETCHECKREQPROT = _selinux.SECURITY__SETCHECKREQPROT
-SYSTEM__IPC_INFO = _selinux.SYSTEM__IPC_INFO
-SYSTEM__SYSLOG_READ = _selinux.SYSTEM__SYSLOG_READ
-SYSTEM__SYSLOG_MOD = _selinux.SYSTEM__SYSLOG_MOD
-SYSTEM__SYSLOG_CONSOLE = _selinux.SYSTEM__SYSLOG_CONSOLE
-CAPABILITY__CHOWN = _selinux.CAPABILITY__CHOWN
-CAPABILITY__DAC_OVERRIDE = _selinux.CAPABILITY__DAC_OVERRIDE
-CAPABILITY__DAC_READ_SEARCH = _selinux.CAPABILITY__DAC_READ_SEARCH
-CAPABILITY__FOWNER = _selinux.CAPABILITY__FOWNER
-CAPABILITY__FSETID = _selinux.CAPABILITY__FSETID
-CAPABILITY__KILL = _selinux.CAPABILITY__KILL
-CAPABILITY__SETGID = _selinux.CAPABILITY__SETGID
-CAPABILITY__SETUID = _selinux.CAPABILITY__SETUID
-CAPABILITY__SETPCAP = _selinux.CAPABILITY__SETPCAP
-CAPABILITY__LINUX_IMMUTABLE = _selinux.CAPABILITY__LINUX_IMMUTABLE
-CAPABILITY__NET_BIND_SERVICE = _selinux.CAPABILITY__NET_BIND_SERVICE
-CAPABILITY__NET_BROADCAST = _selinux.CAPABILITY__NET_BROADCAST
-CAPABILITY__NET_ADMIN = _selinux.CAPABILITY__NET_ADMIN
-CAPABILITY__NET_RAW = _selinux.CAPABILITY__NET_RAW
-CAPABILITY__IPC_LOCK = _selinux.CAPABILITY__IPC_LOCK
-CAPABILITY__IPC_OWNER = _selinux.CAPABILITY__IPC_OWNER
-CAPABILITY__SYS_MODULE = _selinux.CAPABILITY__SYS_MODULE
-CAPABILITY__SYS_RAWIO = _selinux.CAPABILITY__SYS_RAWIO
-CAPABILITY__SYS_CHROOT = _selinux.CAPABILITY__SYS_CHROOT
-CAPABILITY__SYS_PTRACE = _selinux.CAPABILITY__SYS_PTRACE
-CAPABILITY__SYS_PACCT = _selinux.CAPABILITY__SYS_PACCT
-CAPABILITY__SYS_ADMIN = _selinux.CAPABILITY__SYS_ADMIN
-CAPABILITY__SYS_BOOT = _selinux.CAPABILITY__SYS_BOOT
-CAPABILITY__SYS_NICE = _selinux.CAPABILITY__SYS_NICE
-CAPABILITY__SYS_RESOURCE = _selinux.CAPABILITY__SYS_RESOURCE
-CAPABILITY__SYS_TIME = _selinux.CAPABILITY__SYS_TIME
-CAPABILITY__SYS_TTY_CONFIG = _selinux.CAPABILITY__SYS_TTY_CONFIG
-CAPABILITY__MKNOD = _selinux.CAPABILITY__MKNOD
-CAPABILITY__LEASE = _selinux.CAPABILITY__LEASE
-CAPABILITY__AUDIT_WRITE = _selinux.CAPABILITY__AUDIT_WRITE
-CAPABILITY__AUDIT_CONTROL = _selinux.CAPABILITY__AUDIT_CONTROL
-CAPABILITY__SETFCAP = _selinux.CAPABILITY__SETFCAP
-CAPABILITY2__MAC_OVERRIDE = _selinux.CAPABILITY2__MAC_OVERRIDE
-CAPABILITY2__MAC_ADMIN = _selinux.CAPABILITY2__MAC_ADMIN
-PASSWD__PASSWD = _selinux.PASSWD__PASSWD
-PASSWD__CHFN = _selinux.PASSWD__CHFN
-PASSWD__CHSH = _selinux.PASSWD__CHSH
-PASSWD__ROOTOK = _selinux.PASSWD__ROOTOK
-PASSWD__CRONTAB = _selinux.PASSWD__CRONTAB
-X_DRAWABLE__CREATE = _selinux.X_DRAWABLE__CREATE
-X_DRAWABLE__DESTROY = _selinux.X_DRAWABLE__DESTROY
-X_DRAWABLE__READ = _selinux.X_DRAWABLE__READ
-X_DRAWABLE__WRITE = _selinux.X_DRAWABLE__WRITE
-X_DRAWABLE__BLEND = _selinux.X_DRAWABLE__BLEND
-X_DRAWABLE__GETATTR = _selinux.X_DRAWABLE__GETATTR
-X_DRAWABLE__SETATTR = _selinux.X_DRAWABLE__SETATTR
-X_DRAWABLE__LIST_CHILD = _selinux.X_DRAWABLE__LIST_CHILD
-X_DRAWABLE__ADD_CHILD = _selinux.X_DRAWABLE__ADD_CHILD
-X_DRAWABLE__REMOVE_CHILD = _selinux.X_DRAWABLE__REMOVE_CHILD
-X_DRAWABLE__LIST_PROPERTY = _selinux.X_DRAWABLE__LIST_PROPERTY
-X_DRAWABLE__GET_PROPERTY = _selinux.X_DRAWABLE__GET_PROPERTY
-X_DRAWABLE__SET_PROPERTY = _selinux.X_DRAWABLE__SET_PROPERTY
-X_DRAWABLE__MANAGE = _selinux.X_DRAWABLE__MANAGE
-X_DRAWABLE__OVERRIDE = _selinux.X_DRAWABLE__OVERRIDE
-X_DRAWABLE__SHOW = _selinux.X_DRAWABLE__SHOW
-X_DRAWABLE__HIDE = _selinux.X_DRAWABLE__HIDE
-X_DRAWABLE__SEND = _selinux.X_DRAWABLE__SEND
-X_DRAWABLE__RECEIVE = _selinux.X_DRAWABLE__RECEIVE
-X_SCREEN__GETATTR = _selinux.X_SCREEN__GETATTR
-X_SCREEN__SETATTR = _selinux.X_SCREEN__SETATTR
-X_SCREEN__HIDE_CURSOR = _selinux.X_SCREEN__HIDE_CURSOR
-X_SCREEN__SHOW_CURSOR = _selinux.X_SCREEN__SHOW_CURSOR
-X_SCREEN__SAVER_GETATTR = _selinux.X_SCREEN__SAVER_GETATTR
-X_SCREEN__SAVER_SETATTR = _selinux.X_SCREEN__SAVER_SETATTR
-X_SCREEN__SAVER_HIDE = _selinux.X_SCREEN__SAVER_HIDE
-X_SCREEN__SAVER_SHOW = _selinux.X_SCREEN__SAVER_SHOW
-X_GC__CREATE = _selinux.X_GC__CREATE
-X_GC__DESTROY = _selinux.X_GC__DESTROY
-X_GC__GETATTR = _selinux.X_GC__GETATTR
-X_GC__SETATTR = _selinux.X_GC__SETATTR
-X_GC__USE = _selinux.X_GC__USE
-X_FONT__CREATE = _selinux.X_FONT__CREATE
-X_FONT__DESTROY = _selinux.X_FONT__DESTROY
-X_FONT__GETATTR = _selinux.X_FONT__GETATTR
-X_FONT__ADD_GLYPH = _selinux.X_FONT__ADD_GLYPH
-X_FONT__REMOVE_GLYPH = _selinux.X_FONT__REMOVE_GLYPH
-X_FONT__USE = _selinux.X_FONT__USE
-X_COLORMAP__CREATE = _selinux.X_COLORMAP__CREATE
-X_COLORMAP__DESTROY = _selinux.X_COLORMAP__DESTROY
-X_COLORMAP__READ = _selinux.X_COLORMAP__READ
-X_COLORMAP__WRITE = _selinux.X_COLORMAP__WRITE
-X_COLORMAP__GETATTR = _selinux.X_COLORMAP__GETATTR
-X_COLORMAP__ADD_COLOR = _selinux.X_COLORMAP__ADD_COLOR
-X_COLORMAP__REMOVE_COLOR = _selinux.X_COLORMAP__REMOVE_COLOR
-X_COLORMAP__INSTALL = _selinux.X_COLORMAP__INSTALL
-X_COLORMAP__UNINSTALL = _selinux.X_COLORMAP__UNINSTALL
-X_COLORMAP__USE = _selinux.X_COLORMAP__USE
-X_PROPERTY__CREATE = _selinux.X_PROPERTY__CREATE
-X_PROPERTY__DESTROY = _selinux.X_PROPERTY__DESTROY
-X_PROPERTY__READ = _selinux.X_PROPERTY__READ
-X_PROPERTY__WRITE = _selinux.X_PROPERTY__WRITE
-X_PROPERTY__APPEND = _selinux.X_PROPERTY__APPEND
-X_PROPERTY__GETATTR = _selinux.X_PROPERTY__GETATTR
-X_PROPERTY__SETATTR = _selinux.X_PROPERTY__SETATTR
-X_SELECTION__READ = _selinux.X_SELECTION__READ
-X_SELECTION__WRITE = _selinux.X_SELECTION__WRITE
-X_SELECTION__GETATTR = _selinux.X_SELECTION__GETATTR
-X_SELECTION__SETATTR = _selinux.X_SELECTION__SETATTR
-X_CURSOR__CREATE = _selinux.X_CURSOR__CREATE
-X_CURSOR__DESTROY = _selinux.X_CURSOR__DESTROY
-X_CURSOR__READ = _selinux.X_CURSOR__READ
-X_CURSOR__WRITE = _selinux.X_CURSOR__WRITE
-X_CURSOR__GETATTR = _selinux.X_CURSOR__GETATTR
-X_CURSOR__SETATTR = _selinux.X_CURSOR__SETATTR
-X_CURSOR__USE = _selinux.X_CURSOR__USE
-X_CLIENT__DESTROY = _selinux.X_CLIENT__DESTROY
-X_CLIENT__GETATTR = _selinux.X_CLIENT__GETATTR
-X_CLIENT__SETATTR = _selinux.X_CLIENT__SETATTR
-X_CLIENT__MANAGE = _selinux.X_CLIENT__MANAGE
-X_DEVICE__GETATTR = _selinux.X_DEVICE__GETATTR
-X_DEVICE__SETATTR = _selinux.X_DEVICE__SETATTR
-X_DEVICE__USE = _selinux.X_DEVICE__USE
-X_DEVICE__READ = _selinux.X_DEVICE__READ
-X_DEVICE__WRITE = _selinux.X_DEVICE__WRITE
-X_DEVICE__GETFOCUS = _selinux.X_DEVICE__GETFOCUS
-X_DEVICE__SETFOCUS = _selinux.X_DEVICE__SETFOCUS
-X_DEVICE__BELL = _selinux.X_DEVICE__BELL
-X_DEVICE__FORCE_CURSOR = _selinux.X_DEVICE__FORCE_CURSOR
-X_DEVICE__FREEZE = _selinux.X_DEVICE__FREEZE
-X_DEVICE__GRAB = _selinux.X_DEVICE__GRAB
-X_DEVICE__MANAGE = _selinux.X_DEVICE__MANAGE
-X_SERVER__GETATTR = _selinux.X_SERVER__GETATTR
-X_SERVER__SETATTR = _selinux.X_SERVER__SETATTR
-X_SERVER__RECORD = _selinux.X_SERVER__RECORD
-X_SERVER__DEBUG = _selinux.X_SERVER__DEBUG
-X_SERVER__GRAB = _selinux.X_SERVER__GRAB
-X_SERVER__MANAGE = _selinux.X_SERVER__MANAGE
-X_EXTENSION__QUERY = _selinux.X_EXTENSION__QUERY
-X_EXTENSION__USE = _selinux.X_EXTENSION__USE
-X_RESOURCE__READ = _selinux.X_RESOURCE__READ
-X_RESOURCE__WRITE = _selinux.X_RESOURCE__WRITE
-X_EVENT__SEND = _selinux.X_EVENT__SEND
-X_EVENT__RECEIVE = _selinux.X_EVENT__RECEIVE
-X_SYNTHETIC_EVENT__SEND = _selinux.X_SYNTHETIC_EVENT__SEND
-X_SYNTHETIC_EVENT__RECEIVE = _selinux.X_SYNTHETIC_EVENT__RECEIVE
-NETLINK_ROUTE_SOCKET__IOCTL = _selinux.NETLINK_ROUTE_SOCKET__IOCTL
-NETLINK_ROUTE_SOCKET__READ = _selinux.NETLINK_ROUTE_SOCKET__READ
-NETLINK_ROUTE_SOCKET__WRITE = _selinux.NETLINK_ROUTE_SOCKET__WRITE
-NETLINK_ROUTE_SOCKET__CREATE = _selinux.NETLINK_ROUTE_SOCKET__CREATE
-NETLINK_ROUTE_SOCKET__GETATTR = _selinux.NETLINK_ROUTE_SOCKET__GETATTR
-NETLINK_ROUTE_SOCKET__SETATTR = _selinux.NETLINK_ROUTE_SOCKET__SETATTR
-NETLINK_ROUTE_SOCKET__LOCK = _selinux.NETLINK_ROUTE_SOCKET__LOCK
-NETLINK_ROUTE_SOCKET__RELABELFROM = _selinux.NETLINK_ROUTE_SOCKET__RELABELFROM
-NETLINK_ROUTE_SOCKET__RELABELTO = _selinux.NETLINK_ROUTE_SOCKET__RELABELTO
-NETLINK_ROUTE_SOCKET__APPEND = _selinux.NETLINK_ROUTE_SOCKET__APPEND
-NETLINK_ROUTE_SOCKET__BIND = _selinux.NETLINK_ROUTE_SOCKET__BIND
-NETLINK_ROUTE_SOCKET__CONNECT = _selinux.NETLINK_ROUTE_SOCKET__CONNECT
-NETLINK_ROUTE_SOCKET__LISTEN = _selinux.NETLINK_ROUTE_SOCKET__LISTEN
-NETLINK_ROUTE_SOCKET__ACCEPT = _selinux.NETLINK_ROUTE_SOCKET__ACCEPT
-NETLINK_ROUTE_SOCKET__GETOPT = _selinux.NETLINK_ROUTE_SOCKET__GETOPT
-NETLINK_ROUTE_SOCKET__SETOPT = _selinux.NETLINK_ROUTE_SOCKET__SETOPT
-NETLINK_ROUTE_SOCKET__SHUTDOWN = _selinux.NETLINK_ROUTE_SOCKET__SHUTDOWN
-NETLINK_ROUTE_SOCKET__RECVFROM = _selinux.NETLINK_ROUTE_SOCKET__RECVFROM
-NETLINK_ROUTE_SOCKET__SENDTO = _selinux.NETLINK_ROUTE_SOCKET__SENDTO
-NETLINK_ROUTE_SOCKET__RECV_MSG = _selinux.NETLINK_ROUTE_SOCKET__RECV_MSG
-NETLINK_ROUTE_SOCKET__SEND_MSG = _selinux.NETLINK_ROUTE_SOCKET__SEND_MSG
-NETLINK_ROUTE_SOCKET__NAME_BIND = _selinux.NETLINK_ROUTE_SOCKET__NAME_BIND
-NETLINK_ROUTE_SOCKET__NLMSG_READ = _selinux.NETLINK_ROUTE_SOCKET__NLMSG_READ
-NETLINK_ROUTE_SOCKET__NLMSG_WRITE = _selinux.NETLINK_ROUTE_SOCKET__NLMSG_WRITE
-NETLINK_FIREWALL_SOCKET__IOCTL = _selinux.NETLINK_FIREWALL_SOCKET__IOCTL
-NETLINK_FIREWALL_SOCKET__READ = _selinux.NETLINK_FIREWALL_SOCKET__READ
-NETLINK_FIREWALL_SOCKET__WRITE = _selinux.NETLINK_FIREWALL_SOCKET__WRITE
-NETLINK_FIREWALL_SOCKET__CREATE = _selinux.NETLINK_FIREWALL_SOCKET__CREATE
-NETLINK_FIREWALL_SOCKET__GETATTR = _selinux.NETLINK_FIREWALL_SOCKET__GETATTR
-NETLINK_FIREWALL_SOCKET__SETATTR = _selinux.NETLINK_FIREWALL_SOCKET__SETATTR
-NETLINK_FIREWALL_SOCKET__LOCK = _selinux.NETLINK_FIREWALL_SOCKET__LOCK
-NETLINK_FIREWALL_SOCKET__RELABELFROM = _selinux.NETLINK_FIREWALL_SOCKET__RELABELFROM
-NETLINK_FIREWALL_SOCKET__RELABELTO = _selinux.NETLINK_FIREWALL_SOCKET__RELABELTO
-NETLINK_FIREWALL_SOCKET__APPEND = _selinux.NETLINK_FIREWALL_SOCKET__APPEND
-NETLINK_FIREWALL_SOCKET__BIND = _selinux.NETLINK_FIREWALL_SOCKET__BIND
-NETLINK_FIREWALL_SOCKET__CONNECT = _selinux.NETLINK_FIREWALL_SOCKET__CONNECT
-NETLINK_FIREWALL_SOCKET__LISTEN = _selinux.NETLINK_FIREWALL_SOCKET__LISTEN
-NETLINK_FIREWALL_SOCKET__ACCEPT = _selinux.NETLINK_FIREWALL_SOCKET__ACCEPT
-NETLINK_FIREWALL_SOCKET__GETOPT = _selinux.NETLINK_FIREWALL_SOCKET__GETOPT
-NETLINK_FIREWALL_SOCKET__SETOPT = _selinux.NETLINK_FIREWALL_SOCKET__SETOPT
-NETLINK_FIREWALL_SOCKET__SHUTDOWN = _selinux.NETLINK_FIREWALL_SOCKET__SHUTDOWN
-NETLINK_FIREWALL_SOCKET__RECVFROM = _selinux.NETLINK_FIREWALL_SOCKET__RECVFROM
-NETLINK_FIREWALL_SOCKET__SENDTO = _selinux.NETLINK_FIREWALL_SOCKET__SENDTO
-NETLINK_FIREWALL_SOCKET__RECV_MSG = _selinux.NETLINK_FIREWALL_SOCKET__RECV_MSG
-NETLINK_FIREWALL_SOCKET__SEND_MSG = _selinux.NETLINK_FIREWALL_SOCKET__SEND_MSG
-NETLINK_FIREWALL_SOCKET__NAME_BIND = _selinux.NETLINK_FIREWALL_SOCKET__NAME_BIND
-NETLINK_FIREWALL_SOCKET__NLMSG_READ = _selinux.NETLINK_FIREWALL_SOCKET__NLMSG_READ
-NETLINK_FIREWALL_SOCKET__NLMSG_WRITE = _selinux.NETLINK_FIREWALL_SOCKET__NLMSG_WRITE
-NETLINK_TCPDIAG_SOCKET__IOCTL = _selinux.NETLINK_TCPDIAG_SOCKET__IOCTL
-NETLINK_TCPDIAG_SOCKET__READ = _selinux.NETLINK_TCPDIAG_SOCKET__READ
-NETLINK_TCPDIAG_SOCKET__WRITE = _selinux.NETLINK_TCPDIAG_SOCKET__WRITE
-NETLINK_TCPDIAG_SOCKET__CREATE = _selinux.NETLINK_TCPDIAG_SOCKET__CREATE
-NETLINK_TCPDIAG_SOCKET__GETATTR = _selinux.NETLINK_TCPDIAG_SOCKET__GETATTR
-NETLINK_TCPDIAG_SOCKET__SETATTR = _selinux.NETLINK_TCPDIAG_SOCKET__SETATTR
-NETLINK_TCPDIAG_SOCKET__LOCK = _selinux.NETLINK_TCPDIAG_SOCKET__LOCK
-NETLINK_TCPDIAG_SOCKET__RELABELFROM = _selinux.NETLINK_TCPDIAG_SOCKET__RELABELFROM
-NETLINK_TCPDIAG_SOCKET__RELABELTO = _selinux.NETLINK_TCPDIAG_SOCKET__RELABELTO
-NETLINK_TCPDIAG_SOCKET__APPEND = _selinux.NETLINK_TCPDIAG_SOCKET__APPEND
-NETLINK_TCPDIAG_SOCKET__BIND = _selinux.NETLINK_TCPDIAG_SOCKET__BIND
-NETLINK_TCPDIAG_SOCKET__CONNECT = _selinux.NETLINK_TCPDIAG_SOCKET__CONNECT
-NETLINK_TCPDIAG_SOCKET__LISTEN = _selinux.NETLINK_TCPDIAG_SOCKET__LISTEN
-NETLINK_TCPDIAG_SOCKET__ACCEPT = _selinux.NETLINK_TCPDIAG_SOCKET__ACCEPT
-NETLINK_TCPDIAG_SOCKET__GETOPT = _selinux.NETLINK_TCPDIAG_SOCKET__GETOPT
-NETLINK_TCPDIAG_SOCKET__SETOPT = _selinux.NETLINK_TCPDIAG_SOCKET__SETOPT
-NETLINK_TCPDIAG_SOCKET__SHUTDOWN = _selinux.NETLINK_TCPDIAG_SOCKET__SHUTDOWN
-NETLINK_TCPDIAG_SOCKET__RECVFROM = _selinux.NETLINK_TCPDIAG_SOCKET__RECVFROM
-NETLINK_TCPDIAG_SOCKET__SENDTO = _selinux.NETLINK_TCPDIAG_SOCKET__SENDTO
-NETLINK_TCPDIAG_SOCKET__RECV_MSG = _selinux.NETLINK_TCPDIAG_SOCKET__RECV_MSG
-NETLINK_TCPDIAG_SOCKET__SEND_MSG = _selinux.NETLINK_TCPDIAG_SOCKET__SEND_MSG
-NETLINK_TCPDIAG_SOCKET__NAME_BIND = _selinux.NETLINK_TCPDIAG_SOCKET__NAME_BIND
-NETLINK_TCPDIAG_SOCKET__NLMSG_READ = _selinux.NETLINK_TCPDIAG_SOCKET__NLMSG_READ
-NETLINK_TCPDIAG_SOCKET__NLMSG_WRITE = _selinux.NETLINK_TCPDIAG_SOCKET__NLMSG_WRITE
-NETLINK_NFLOG_SOCKET__IOCTL = _selinux.NETLINK_NFLOG_SOCKET__IOCTL
-NETLINK_NFLOG_SOCKET__READ = _selinux.NETLINK_NFLOG_SOCKET__READ
-NETLINK_NFLOG_SOCKET__WRITE = _selinux.NETLINK_NFLOG_SOCKET__WRITE
-NETLINK_NFLOG_SOCKET__CREATE = _selinux.NETLINK_NFLOG_SOCKET__CREATE
-NETLINK_NFLOG_SOCKET__GETATTR = _selinux.NETLINK_NFLOG_SOCKET__GETATTR
-NETLINK_NFLOG_SOCKET__SETATTR = _selinux.NETLINK_NFLOG_SOCKET__SETATTR
-NETLINK_NFLOG_SOCKET__LOCK = _selinux.NETLINK_NFLOG_SOCKET__LOCK
-NETLINK_NFLOG_SOCKET__RELABELFROM = _selinux.NETLINK_NFLOG_SOCKET__RELABELFROM
-NETLINK_NFLOG_SOCKET__RELABELTO = _selinux.NETLINK_NFLOG_SOCKET__RELABELTO
-NETLINK_NFLOG_SOCKET__APPEND = _selinux.NETLINK_NFLOG_SOCKET__APPEND
-NETLINK_NFLOG_SOCKET__BIND = _selinux.NETLINK_NFLOG_SOCKET__BIND
-NETLINK_NFLOG_SOCKET__CONNECT = _selinux.NETLINK_NFLOG_SOCKET__CONNECT
-NETLINK_NFLOG_SOCKET__LISTEN = _selinux.NETLINK_NFLOG_SOCKET__LISTEN
-NETLINK_NFLOG_SOCKET__ACCEPT = _selinux.NETLINK_NFLOG_SOCKET__ACCEPT
-NETLINK_NFLOG_SOCKET__GETOPT = _selinux.NETLINK_NFLOG_SOCKET__GETOPT
-NETLINK_NFLOG_SOCKET__SETOPT = _selinux.NETLINK_NFLOG_SOCKET__SETOPT
-NETLINK_NFLOG_SOCKET__SHUTDOWN = _selinux.NETLINK_NFLOG_SOCKET__SHUTDOWN
-NETLINK_NFLOG_SOCKET__RECVFROM = _selinux.NETLINK_NFLOG_SOCKET__RECVFROM
-NETLINK_NFLOG_SOCKET__SENDTO = _selinux.NETLINK_NFLOG_SOCKET__SENDTO
-NETLINK_NFLOG_SOCKET__RECV_MSG = _selinux.NETLINK_NFLOG_SOCKET__RECV_MSG
-NETLINK_NFLOG_SOCKET__SEND_MSG = _selinux.NETLINK_NFLOG_SOCKET__SEND_MSG
-NETLINK_NFLOG_SOCKET__NAME_BIND = _selinux.NETLINK_NFLOG_SOCKET__NAME_BIND
-NETLINK_XFRM_SOCKET__IOCTL = _selinux.NETLINK_XFRM_SOCKET__IOCTL
-NETLINK_XFRM_SOCKET__READ = _selinux.NETLINK_XFRM_SOCKET__READ
-NETLINK_XFRM_SOCKET__WRITE = _selinux.NETLINK_XFRM_SOCKET__WRITE
-NETLINK_XFRM_SOCKET__CREATE = _selinux.NETLINK_XFRM_SOCKET__CREATE
-NETLINK_XFRM_SOCKET__GETATTR = _selinux.NETLINK_XFRM_SOCKET__GETATTR
-NETLINK_XFRM_SOCKET__SETATTR = _selinux.NETLINK_XFRM_SOCKET__SETATTR
-NETLINK_XFRM_SOCKET__LOCK = _selinux.NETLINK_XFRM_SOCKET__LOCK
-NETLINK_XFRM_SOCKET__RELABELFROM = _selinux.NETLINK_XFRM_SOCKET__RELABELFROM
-NETLINK_XFRM_SOCKET__RELABELTO = _selinux.NETLINK_XFRM_SOCKET__RELABELTO
-NETLINK_XFRM_SOCKET__APPEND = _selinux.NETLINK_XFRM_SOCKET__APPEND
-NETLINK_XFRM_SOCKET__BIND = _selinux.NETLINK_XFRM_SOCKET__BIND
-NETLINK_XFRM_SOCKET__CONNECT = _selinux.NETLINK_XFRM_SOCKET__CONNECT
-NETLINK_XFRM_SOCKET__LISTEN = _selinux.NETLINK_XFRM_SOCKET__LISTEN
-NETLINK_XFRM_SOCKET__ACCEPT = _selinux.NETLINK_XFRM_SOCKET__ACCEPT
-NETLINK_XFRM_SOCKET__GETOPT = _selinux.NETLINK_XFRM_SOCKET__GETOPT
-NETLINK_XFRM_SOCKET__SETOPT = _selinux.NETLINK_XFRM_SOCKET__SETOPT
-NETLINK_XFRM_SOCKET__SHUTDOWN = _selinux.NETLINK_XFRM_SOCKET__SHUTDOWN
-NETLINK_XFRM_SOCKET__RECVFROM = _selinux.NETLINK_XFRM_SOCKET__RECVFROM
-NETLINK_XFRM_SOCKET__SENDTO = _selinux.NETLINK_XFRM_SOCKET__SENDTO
-NETLINK_XFRM_SOCKET__RECV_MSG = _selinux.NETLINK_XFRM_SOCKET__RECV_MSG
-NETLINK_XFRM_SOCKET__SEND_MSG = _selinux.NETLINK_XFRM_SOCKET__SEND_MSG
-NETLINK_XFRM_SOCKET__NAME_BIND = _selinux.NETLINK_XFRM_SOCKET__NAME_BIND
-NETLINK_XFRM_SOCKET__NLMSG_READ = _selinux.NETLINK_XFRM_SOCKET__NLMSG_READ
-NETLINK_XFRM_SOCKET__NLMSG_WRITE = _selinux.NETLINK_XFRM_SOCKET__NLMSG_WRITE
-NETLINK_SELINUX_SOCKET__IOCTL = _selinux.NETLINK_SELINUX_SOCKET__IOCTL
-NETLINK_SELINUX_SOCKET__READ = _selinux.NETLINK_SELINUX_SOCKET__READ
-NETLINK_SELINUX_SOCKET__WRITE = _selinux.NETLINK_SELINUX_SOCKET__WRITE
-NETLINK_SELINUX_SOCKET__CREATE = _selinux.NETLINK_SELINUX_SOCKET__CREATE
-NETLINK_SELINUX_SOCKET__GETATTR = _selinux.NETLINK_SELINUX_SOCKET__GETATTR
-NETLINK_SELINUX_SOCKET__SETATTR = _selinux.NETLINK_SELINUX_SOCKET__SETATTR
-NETLINK_SELINUX_SOCKET__LOCK = _selinux.NETLINK_SELINUX_SOCKET__LOCK
-NETLINK_SELINUX_SOCKET__RELABELFROM = _selinux.NETLINK_SELINUX_SOCKET__RELABELFROM
-NETLINK_SELINUX_SOCKET__RELABELTO = _selinux.NETLINK_SELINUX_SOCKET__RELABELTO
-NETLINK_SELINUX_SOCKET__APPEND = _selinux.NETLINK_SELINUX_SOCKET__APPEND
-NETLINK_SELINUX_SOCKET__BIND = _selinux.NETLINK_SELINUX_SOCKET__BIND
-NETLINK_SELINUX_SOCKET__CONNECT = _selinux.NETLINK_SELINUX_SOCKET__CONNECT
-NETLINK_SELINUX_SOCKET__LISTEN = _selinux.NETLINK_SELINUX_SOCKET__LISTEN
-NETLINK_SELINUX_SOCKET__ACCEPT = _selinux.NETLINK_SELINUX_SOCKET__ACCEPT
-NETLINK_SELINUX_SOCKET__GETOPT = _selinux.NETLINK_SELINUX_SOCKET__GETOPT
-NETLINK_SELINUX_SOCKET__SETOPT = _selinux.NETLINK_SELINUX_SOCKET__SETOPT
-NETLINK_SELINUX_SOCKET__SHUTDOWN = _selinux.NETLINK_SELINUX_SOCKET__SHUTDOWN
-NETLINK_SELINUX_SOCKET__RECVFROM = _selinux.NETLINK_SELINUX_SOCKET__RECVFROM
-NETLINK_SELINUX_SOCKET__SENDTO = _selinux.NETLINK_SELINUX_SOCKET__SENDTO
-NETLINK_SELINUX_SOCKET__RECV_MSG = _selinux.NETLINK_SELINUX_SOCKET__RECV_MSG
-NETLINK_SELINUX_SOCKET__SEND_MSG = _selinux.NETLINK_SELINUX_SOCKET__SEND_MSG
-NETLINK_SELINUX_SOCKET__NAME_BIND = _selinux.NETLINK_SELINUX_SOCKET__NAME_BIND
-NETLINK_AUDIT_SOCKET__IOCTL = _selinux.NETLINK_AUDIT_SOCKET__IOCTL
-NETLINK_AUDIT_SOCKET__READ = _selinux.NETLINK_AUDIT_SOCKET__READ
-NETLINK_AUDIT_SOCKET__WRITE = _selinux.NETLINK_AUDIT_SOCKET__WRITE
-NETLINK_AUDIT_SOCKET__CREATE = _selinux.NETLINK_AUDIT_SOCKET__CREATE
-NETLINK_AUDIT_SOCKET__GETATTR = _selinux.NETLINK_AUDIT_SOCKET__GETATTR
-NETLINK_AUDIT_SOCKET__SETATTR = _selinux.NETLINK_AUDIT_SOCKET__SETATTR
-NETLINK_AUDIT_SOCKET__LOCK = _selinux.NETLINK_AUDIT_SOCKET__LOCK
-NETLINK_AUDIT_SOCKET__RELABELFROM = _selinux.NETLINK_AUDIT_SOCKET__RELABELFROM
-NETLINK_AUDIT_SOCKET__RELABELTO = _selinux.NETLINK_AUDIT_SOCKET__RELABELTO
-NETLINK_AUDIT_SOCKET__APPEND = _selinux.NETLINK_AUDIT_SOCKET__APPEND
-NETLINK_AUDIT_SOCKET__BIND = _selinux.NETLINK_AUDIT_SOCKET__BIND
-NETLINK_AUDIT_SOCKET__CONNECT = _selinux.NETLINK_AUDIT_SOCKET__CONNECT
-NETLINK_AUDIT_SOCKET__LISTEN = _selinux.NETLINK_AUDIT_SOCKET__LISTEN
-NETLINK_AUDIT_SOCKET__ACCEPT = _selinux.NETLINK_AUDIT_SOCKET__ACCEPT
-NETLINK_AUDIT_SOCKET__GETOPT = _selinux.NETLINK_AUDIT_SOCKET__GETOPT
-NETLINK_AUDIT_SOCKET__SETOPT = _selinux.NETLINK_AUDIT_SOCKET__SETOPT
-NETLINK_AUDIT_SOCKET__SHUTDOWN = _selinux.NETLINK_AUDIT_SOCKET__SHUTDOWN
-NETLINK_AUDIT_SOCKET__RECVFROM = _selinux.NETLINK_AUDIT_SOCKET__RECVFROM
-NETLINK_AUDIT_SOCKET__SENDTO = _selinux.NETLINK_AUDIT_SOCKET__SENDTO
-NETLINK_AUDIT_SOCKET__RECV_MSG = _selinux.NETLINK_AUDIT_SOCKET__RECV_MSG
-NETLINK_AUDIT_SOCKET__SEND_MSG = _selinux.NETLINK_AUDIT_SOCKET__SEND_MSG
-NETLINK_AUDIT_SOCKET__NAME_BIND = _selinux.NETLINK_AUDIT_SOCKET__NAME_BIND
-NETLINK_AUDIT_SOCKET__NLMSG_READ = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_READ
-NETLINK_AUDIT_SOCKET__NLMSG_WRITE = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_WRITE
-NETLINK_AUDIT_SOCKET__NLMSG_RELAY = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_RELAY
-NETLINK_AUDIT_SOCKET__NLMSG_READPRIV = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_READPRIV
-NETLINK_AUDIT_SOCKET__NLMSG_TTY_AUDIT = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_TTY_AUDIT
-NETLINK_IP6FW_SOCKET__IOCTL = _selinux.NETLINK_IP6FW_SOCKET__IOCTL
-NETLINK_IP6FW_SOCKET__READ = _selinux.NETLINK_IP6FW_SOCKET__READ
-NETLINK_IP6FW_SOCKET__WRITE = _selinux.NETLINK_IP6FW_SOCKET__WRITE
-NETLINK_IP6FW_SOCKET__CREATE = _selinux.NETLINK_IP6FW_SOCKET__CREATE
-NETLINK_IP6FW_SOCKET__GETATTR = _selinux.NETLINK_IP6FW_SOCKET__GETATTR
-NETLINK_IP6FW_SOCKET__SETATTR = _selinux.NETLINK_IP6FW_SOCKET__SETATTR
-NETLINK_IP6FW_SOCKET__LOCK = _selinux.NETLINK_IP6FW_SOCKET__LOCK
-NETLINK_IP6FW_SOCKET__RELABELFROM = _selinux.NETLINK_IP6FW_SOCKET__RELABELFROM
-NETLINK_IP6FW_SOCKET__RELABELTO = _selinux.NETLINK_IP6FW_SOCKET__RELABELTO
-NETLINK_IP6FW_SOCKET__APPEND = _selinux.NETLINK_IP6FW_SOCKET__APPEND
-NETLINK_IP6FW_SOCKET__BIND = _selinux.NETLINK_IP6FW_SOCKET__BIND
-NETLINK_IP6FW_SOCKET__CONNECT = _selinux.NETLINK_IP6FW_SOCKET__CONNECT
-NETLINK_IP6FW_SOCKET__LISTEN = _selinux.NETLINK_IP6FW_SOCKET__LISTEN
-NETLINK_IP6FW_SOCKET__ACCEPT = _selinux.NETLINK_IP6FW_SOCKET__ACCEPT
-NETLINK_IP6FW_SOCKET__GETOPT = _selinux.NETLINK_IP6FW_SOCKET__GETOPT
-NETLINK_IP6FW_SOCKET__SETOPT = _selinux.NETLINK_IP6FW_SOCKET__SETOPT
-NETLINK_IP6FW_SOCKET__SHUTDOWN = _selinux.NETLINK_IP6FW_SOCKET__SHUTDOWN
-NETLINK_IP6FW_SOCKET__RECVFROM = _selinux.NETLINK_IP6FW_SOCKET__RECVFROM
-NETLINK_IP6FW_SOCKET__SENDTO = _selinux.NETLINK_IP6FW_SOCKET__SENDTO
-NETLINK_IP6FW_SOCKET__RECV_MSG = _selinux.NETLINK_IP6FW_SOCKET__RECV_MSG
-NETLINK_IP6FW_SOCKET__SEND_MSG = _selinux.NETLINK_IP6FW_SOCKET__SEND_MSG
-NETLINK_IP6FW_SOCKET__NAME_BIND = _selinux.NETLINK_IP6FW_SOCKET__NAME_BIND
-NETLINK_IP6FW_SOCKET__NLMSG_READ = _selinux.NETLINK_IP6FW_SOCKET__NLMSG_READ
-NETLINK_IP6FW_SOCKET__NLMSG_WRITE = _selinux.NETLINK_IP6FW_SOCKET__NLMSG_WRITE
-NETLINK_DNRT_SOCKET__IOCTL = _selinux.NETLINK_DNRT_SOCKET__IOCTL
-NETLINK_DNRT_SOCKET__READ = _selinux.NETLINK_DNRT_SOCKET__READ
-NETLINK_DNRT_SOCKET__WRITE = _selinux.NETLINK_DNRT_SOCKET__WRITE
-NETLINK_DNRT_SOCKET__CREATE = _selinux.NETLINK_DNRT_SOCKET__CREATE
-NETLINK_DNRT_SOCKET__GETATTR = _selinux.NETLINK_DNRT_SOCKET__GETATTR
-NETLINK_DNRT_SOCKET__SETATTR = _selinux.NETLINK_DNRT_SOCKET__SETATTR
-NETLINK_DNRT_SOCKET__LOCK = _selinux.NETLINK_DNRT_SOCKET__LOCK
-NETLINK_DNRT_SOCKET__RELABELFROM = _selinux.NETLINK_DNRT_SOCKET__RELABELFROM
-NETLINK_DNRT_SOCKET__RELABELTO = _selinux.NETLINK_DNRT_SOCKET__RELABELTO
-NETLINK_DNRT_SOCKET__APPEND = _selinux.NETLINK_DNRT_SOCKET__APPEND
-NETLINK_DNRT_SOCKET__BIND = _selinux.NETLINK_DNRT_SOCKET__BIND
-NETLINK_DNRT_SOCKET__CONNECT = _selinux.NETLINK_DNRT_SOCKET__CONNECT
-NETLINK_DNRT_SOCKET__LISTEN = _selinux.NETLINK_DNRT_SOCKET__LISTEN
-NETLINK_DNRT_SOCKET__ACCEPT = _selinux.NETLINK_DNRT_SOCKET__ACCEPT
-NETLINK_DNRT_SOCKET__GETOPT = _selinux.NETLINK_DNRT_SOCKET__GETOPT
-NETLINK_DNRT_SOCKET__SETOPT = _selinux.NETLINK_DNRT_SOCKET__SETOPT
-NETLINK_DNRT_SOCKET__SHUTDOWN = _selinux.NETLINK_DNRT_SOCKET__SHUTDOWN
-NETLINK_DNRT_SOCKET__RECVFROM = _selinux.NETLINK_DNRT_SOCKET__RECVFROM
-NETLINK_DNRT_SOCKET__SENDTO = _selinux.NETLINK_DNRT_SOCKET__SENDTO
-NETLINK_DNRT_SOCKET__RECV_MSG = _selinux.NETLINK_DNRT_SOCKET__RECV_MSG
-NETLINK_DNRT_SOCKET__SEND_MSG = _selinux.NETLINK_DNRT_SOCKET__SEND_MSG
-NETLINK_DNRT_SOCKET__NAME_BIND = _selinux.NETLINK_DNRT_SOCKET__NAME_BIND
-DBUS__ACQUIRE_SVC = _selinux.DBUS__ACQUIRE_SVC
-DBUS__SEND_MSG = _selinux.DBUS__SEND_MSG
-NSCD__GETPWD = _selinux.NSCD__GETPWD
-NSCD__GETGRP = _selinux.NSCD__GETGRP
-NSCD__GETHOST = _selinux.NSCD__GETHOST
-NSCD__GETSTAT = _selinux.NSCD__GETSTAT
-NSCD__ADMIN = _selinux.NSCD__ADMIN
-NSCD__SHMEMPWD = _selinux.NSCD__SHMEMPWD
-NSCD__SHMEMGRP = _selinux.NSCD__SHMEMGRP
-NSCD__SHMEMHOST = _selinux.NSCD__SHMEMHOST
-NSCD__GETSERV = _selinux.NSCD__GETSERV
-NSCD__SHMEMSERV = _selinux.NSCD__SHMEMSERV
-ASSOCIATION__SENDTO = _selinux.ASSOCIATION__SENDTO
-ASSOCIATION__RECVFROM = _selinux.ASSOCIATION__RECVFROM
-ASSOCIATION__SETCONTEXT = _selinux.ASSOCIATION__SETCONTEXT
-ASSOCIATION__POLMATCH = _selinux.ASSOCIATION__POLMATCH
-NETLINK_KOBJECT_UEVENT_SOCKET__IOCTL = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__IOCTL
-NETLINK_KOBJECT_UEVENT_SOCKET__READ = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__READ
-NETLINK_KOBJECT_UEVENT_SOCKET__WRITE = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__WRITE
-NETLINK_KOBJECT_UEVENT_SOCKET__CREATE = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__CREATE
-NETLINK_KOBJECT_UEVENT_SOCKET__GETATTR = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__GETATTR
-NETLINK_KOBJECT_UEVENT_SOCKET__SETATTR = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SETATTR
-NETLINK_KOBJECT_UEVENT_SOCKET__LOCK = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__LOCK
-NETLINK_KOBJECT_UEVENT_SOCKET__RELABELFROM = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RELABELFROM
-NETLINK_KOBJECT_UEVENT_SOCKET__RELABELTO = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RELABELTO
-NETLINK_KOBJECT_UEVENT_SOCKET__APPEND = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__APPEND
-NETLINK_KOBJECT_UEVENT_SOCKET__BIND = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__BIND
-NETLINK_KOBJECT_UEVENT_SOCKET__CONNECT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__CONNECT
-NETLINK_KOBJECT_UEVENT_SOCKET__LISTEN = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__LISTEN
-NETLINK_KOBJECT_UEVENT_SOCKET__ACCEPT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__ACCEPT
-NETLINK_KOBJECT_UEVENT_SOCKET__GETOPT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__GETOPT
-NETLINK_KOBJECT_UEVENT_SOCKET__SETOPT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SETOPT
-NETLINK_KOBJECT_UEVENT_SOCKET__SHUTDOWN = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SHUTDOWN
-NETLINK_KOBJECT_UEVENT_SOCKET__RECVFROM = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RECVFROM
-NETLINK_KOBJECT_UEVENT_SOCKET__SENDTO = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SENDTO
-NETLINK_KOBJECT_UEVENT_SOCKET__RECV_MSG = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RECV_MSG
-NETLINK_KOBJECT_UEVENT_SOCKET__SEND_MSG = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SEND_MSG
-NETLINK_KOBJECT_UEVENT_SOCKET__NAME_BIND = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__NAME_BIND
-APPLETALK_SOCKET__IOCTL = _selinux.APPLETALK_SOCKET__IOCTL
-APPLETALK_SOCKET__READ = _selinux.APPLETALK_SOCKET__READ
-APPLETALK_SOCKET__WRITE = _selinux.APPLETALK_SOCKET__WRITE
-APPLETALK_SOCKET__CREATE = _selinux.APPLETALK_SOCKET__CREATE
-APPLETALK_SOCKET__GETATTR = _selinux.APPLETALK_SOCKET__GETATTR
-APPLETALK_SOCKET__SETATTR = _selinux.APPLETALK_SOCKET__SETATTR
-APPLETALK_SOCKET__LOCK = _selinux.APPLETALK_SOCKET__LOCK
-APPLETALK_SOCKET__RELABELFROM = _selinux.APPLETALK_SOCKET__RELABELFROM
-APPLETALK_SOCKET__RELABELTO = _selinux.APPLETALK_SOCKET__RELABELTO
-APPLETALK_SOCKET__APPEND = _selinux.APPLETALK_SOCKET__APPEND
-APPLETALK_SOCKET__BIND = _selinux.APPLETALK_SOCKET__BIND
-APPLETALK_SOCKET__CONNECT = _selinux.APPLETALK_SOCKET__CONNECT
-APPLETALK_SOCKET__LISTEN = _selinux.APPLETALK_SOCKET__LISTEN
-APPLETALK_SOCKET__ACCEPT = _selinux.APPLETALK_SOCKET__ACCEPT
-APPLETALK_SOCKET__GETOPT = _selinux.APPLETALK_SOCKET__GETOPT
-APPLETALK_SOCKET__SETOPT = _selinux.APPLETALK_SOCKET__SETOPT
-APPLETALK_SOCKET__SHUTDOWN = _selinux.APPLETALK_SOCKET__SHUTDOWN
-APPLETALK_SOCKET__RECVFROM = _selinux.APPLETALK_SOCKET__RECVFROM
-APPLETALK_SOCKET__SENDTO = _selinux.APPLETALK_SOCKET__SENDTO
-APPLETALK_SOCKET__RECV_MSG = _selinux.APPLETALK_SOCKET__RECV_MSG
-APPLETALK_SOCKET__SEND_MSG = _selinux.APPLETALK_SOCKET__SEND_MSG
-APPLETALK_SOCKET__NAME_BIND = _selinux.APPLETALK_SOCKET__NAME_BIND
-PACKET__SEND = _selinux.PACKET__SEND
-PACKET__RECV = _selinux.PACKET__RECV
-PACKET__RELABELTO = _selinux.PACKET__RELABELTO
-PACKET__FLOW_IN = _selinux.PACKET__FLOW_IN
-PACKET__FLOW_OUT = _selinux.PACKET__FLOW_OUT
-PACKET__FORWARD_IN = _selinux.PACKET__FORWARD_IN
-PACKET__FORWARD_OUT = _selinux.PACKET__FORWARD_OUT
-KEY__VIEW = _selinux.KEY__VIEW
-KEY__READ = _selinux.KEY__READ
-KEY__WRITE = _selinux.KEY__WRITE
-KEY__SEARCH = _selinux.KEY__SEARCH
-KEY__LINK = _selinux.KEY__LINK
-KEY__SETATTR = _selinux.KEY__SETATTR
-KEY__CREATE = _selinux.KEY__CREATE
-CONTEXT__TRANSLATE = _selinux.CONTEXT__TRANSLATE
-CONTEXT__CONTAINS = _selinux.CONTEXT__CONTAINS
-DCCP_SOCKET__IOCTL = _selinux.DCCP_SOCKET__IOCTL
-DCCP_SOCKET__READ = _selinux.DCCP_SOCKET__READ
-DCCP_SOCKET__WRITE = _selinux.DCCP_SOCKET__WRITE
-DCCP_SOCKET__CREATE = _selinux.DCCP_SOCKET__CREATE
-DCCP_SOCKET__GETATTR = _selinux.DCCP_SOCKET__GETATTR
-DCCP_SOCKET__SETATTR = _selinux.DCCP_SOCKET__SETATTR
-DCCP_SOCKET__LOCK = _selinux.DCCP_SOCKET__LOCK
-DCCP_SOCKET__RELABELFROM = _selinux.DCCP_SOCKET__RELABELFROM
-DCCP_SOCKET__RELABELTO = _selinux.DCCP_SOCKET__RELABELTO
-DCCP_SOCKET__APPEND = _selinux.DCCP_SOCKET__APPEND
-DCCP_SOCKET__BIND = _selinux.DCCP_SOCKET__BIND
-DCCP_SOCKET__CONNECT = _selinux.DCCP_SOCKET__CONNECT
-DCCP_SOCKET__LISTEN = _selinux.DCCP_SOCKET__LISTEN
-DCCP_SOCKET__ACCEPT = _selinux.DCCP_SOCKET__ACCEPT
-DCCP_SOCKET__GETOPT = _selinux.DCCP_SOCKET__GETOPT
-DCCP_SOCKET__SETOPT = _selinux.DCCP_SOCKET__SETOPT
-DCCP_SOCKET__SHUTDOWN = _selinux.DCCP_SOCKET__SHUTDOWN
-DCCP_SOCKET__RECVFROM = _selinux.DCCP_SOCKET__RECVFROM
-DCCP_SOCKET__SENDTO = _selinux.DCCP_SOCKET__SENDTO
-DCCP_SOCKET__RECV_MSG = _selinux.DCCP_SOCKET__RECV_MSG
-DCCP_SOCKET__SEND_MSG = _selinux.DCCP_SOCKET__SEND_MSG
-DCCP_SOCKET__NAME_BIND = _selinux.DCCP_SOCKET__NAME_BIND
-DCCP_SOCKET__NODE_BIND = _selinux.DCCP_SOCKET__NODE_BIND
-DCCP_SOCKET__NAME_CONNECT = _selinux.DCCP_SOCKET__NAME_CONNECT
-MEMPROTECT__MMAP_ZERO = _selinux.MEMPROTECT__MMAP_ZERO
-DB_DATABASE__CREATE = _selinux.DB_DATABASE__CREATE
-DB_DATABASE__DROP = _selinux.DB_DATABASE__DROP
-DB_DATABASE__GETATTR = _selinux.DB_DATABASE__GETATTR
-DB_DATABASE__SETATTR = _selinux.DB_DATABASE__SETATTR
-DB_DATABASE__RELABELFROM = _selinux.DB_DATABASE__RELABELFROM
-DB_DATABASE__RELABELTO = _selinux.DB_DATABASE__RELABELTO
-DB_DATABASE__ACCESS = _selinux.DB_DATABASE__ACCESS
-DB_DATABASE__INSTALL_MODULE = _selinux.DB_DATABASE__INSTALL_MODULE
-DB_DATABASE__LOAD_MODULE = _selinux.DB_DATABASE__LOAD_MODULE
-DB_DATABASE__GET_PARAM = _selinux.DB_DATABASE__GET_PARAM
-DB_DATABASE__SET_PARAM = _selinux.DB_DATABASE__SET_PARAM
-DB_TABLE__CREATE = _selinux.DB_TABLE__CREATE
-DB_TABLE__DROP = _selinux.DB_TABLE__DROP
-DB_TABLE__GETATTR = _selinux.DB_TABLE__GETATTR
-DB_TABLE__SETATTR = _selinux.DB_TABLE__SETATTR
-DB_TABLE__RELABELFROM = _selinux.DB_TABLE__RELABELFROM
-DB_TABLE__RELABELTO = _selinux.DB_TABLE__RELABELTO
-DB_TABLE__USE = _selinux.DB_TABLE__USE
-DB_TABLE__SELECT = _selinux.DB_TABLE__SELECT
-DB_TABLE__UPDATE = _selinux.DB_TABLE__UPDATE
-DB_TABLE__INSERT = _selinux.DB_TABLE__INSERT
-DB_TABLE__DELETE = _selinux.DB_TABLE__DELETE
-DB_TABLE__LOCK = _selinux.DB_TABLE__LOCK
-DB_PROCEDURE__CREATE = _selinux.DB_PROCEDURE__CREATE
-DB_PROCEDURE__DROP = _selinux.DB_PROCEDURE__DROP
-DB_PROCEDURE__GETATTR = _selinux.DB_PROCEDURE__GETATTR
-DB_PROCEDURE__SETATTR = _selinux.DB_PROCEDURE__SETATTR
-DB_PROCEDURE__RELABELFROM = _selinux.DB_PROCEDURE__RELABELFROM
-DB_PROCEDURE__RELABELTO = _selinux.DB_PROCEDURE__RELABELTO
-DB_PROCEDURE__EXECUTE = _selinux.DB_PROCEDURE__EXECUTE
-DB_PROCEDURE__ENTRYPOINT = _selinux.DB_PROCEDURE__ENTRYPOINT
-DB_COLUMN__CREATE = _selinux.DB_COLUMN__CREATE
-DB_COLUMN__DROP = _selinux.DB_COLUMN__DROP
-DB_COLUMN__GETATTR = _selinux.DB_COLUMN__GETATTR
-DB_COLUMN__SETATTR = _selinux.DB_COLUMN__SETATTR
-DB_COLUMN__RELABELFROM = _selinux.DB_COLUMN__RELABELFROM
-DB_COLUMN__RELABELTO = _selinux.DB_COLUMN__RELABELTO
-DB_COLUMN__USE = _selinux.DB_COLUMN__USE
-DB_COLUMN__SELECT = _selinux.DB_COLUMN__SELECT
-DB_COLUMN__UPDATE = _selinux.DB_COLUMN__UPDATE
-DB_COLUMN__INSERT = _selinux.DB_COLUMN__INSERT
-DB_TUPLE__RELABELFROM = _selinux.DB_TUPLE__RELABELFROM
-DB_TUPLE__RELABELTO = _selinux.DB_TUPLE__RELABELTO
-DB_TUPLE__USE = _selinux.DB_TUPLE__USE
-DB_TUPLE__SELECT = _selinux.DB_TUPLE__SELECT
-DB_TUPLE__UPDATE = _selinux.DB_TUPLE__UPDATE
-DB_TUPLE__INSERT = _selinux.DB_TUPLE__INSERT
-DB_TUPLE__DELETE = _selinux.DB_TUPLE__DELETE
-DB_BLOB__CREATE = _selinux.DB_BLOB__CREATE
-DB_BLOB__DROP = _selinux.DB_BLOB__DROP
-DB_BLOB__GETATTR = _selinux.DB_BLOB__GETATTR
-DB_BLOB__SETATTR = _selinux.DB_BLOB__SETATTR
-DB_BLOB__RELABELFROM = _selinux.DB_BLOB__RELABELFROM
-DB_BLOB__RELABELTO = _selinux.DB_BLOB__RELABELTO
-DB_BLOB__READ = _selinux.DB_BLOB__READ
-DB_BLOB__WRITE = _selinux.DB_BLOB__WRITE
-DB_BLOB__IMPORT = _selinux.DB_BLOB__IMPORT
-DB_BLOB__EXPORT = _selinux.DB_BLOB__EXPORT
-PEER__RECV = _selinux.PEER__RECV
-X_APPLICATION_DATA__PASTE = _selinux.X_APPLICATION_DATA__PASTE
-X_APPLICATION_DATA__PASTE_AFTER_CONFIRM = _selinux.X_APPLICATION_DATA__PASTE_AFTER_CONFIRM
-X_APPLICATION_DATA__COPY = _selinux.X_APPLICATION_DATA__COPY
class context_s_t(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, context_s_t, name, value)
@@ -1492,107 +472,6 @@
def context_user_set(*args):
return _selinux.context_user_set(*args)
context_user_set = _selinux.context_user_set
-SECCLASS_SECURITY = _selinux.SECCLASS_SECURITY
-SECCLASS_PROCESS = _selinux.SECCLASS_PROCESS
-SECCLASS_SYSTEM = _selinux.SECCLASS_SYSTEM
-SECCLASS_CAPABILITY = _selinux.SECCLASS_CAPABILITY
-SECCLASS_FILESYSTEM = _selinux.SECCLASS_FILESYSTEM
-SECCLASS_FILE = _selinux.SECCLASS_FILE
-SECCLASS_DIR = _selinux.SECCLASS_DIR
-SECCLASS_FD = _selinux.SECCLASS_FD
-SECCLASS_LNK_FILE = _selinux.SECCLASS_LNK_FILE
-SECCLASS_CHR_FILE = _selinux.SECCLASS_CHR_FILE
-SECCLASS_BLK_FILE = _selinux.SECCLASS_BLK_FILE
-SECCLASS_SOCK_FILE = _selinux.SECCLASS_SOCK_FILE
-SECCLASS_FIFO_FILE = _selinux.SECCLASS_FIFO_FILE
-SECCLASS_SOCKET = _selinux.SECCLASS_SOCKET
-SECCLASS_TCP_SOCKET = _selinux.SECCLASS_TCP_SOCKET
-SECCLASS_UDP_SOCKET = _selinux.SECCLASS_UDP_SOCKET
-SECCLASS_RAWIP_SOCKET = _selinux.SECCLASS_RAWIP_SOCKET
-SECCLASS_NODE = _selinux.SECCLASS_NODE
-SECCLASS_NETIF = _selinux.SECCLASS_NETIF
-SECCLASS_NETLINK_SOCKET = _selinux.SECCLASS_NETLINK_SOCKET
-SECCLASS_PACKET_SOCKET = _selinux.SECCLASS_PACKET_SOCKET
-SECCLASS_KEY_SOCKET = _selinux.SECCLASS_KEY_SOCKET
-SECCLASS_UNIX_STREAM_SOCKET = _selinux.SECCLASS_UNIX_STREAM_SOCKET
-SECCLASS_UNIX_DGRAM_SOCKET = _selinux.SECCLASS_UNIX_DGRAM_SOCKET
-SECCLASS_SEM = _selinux.SECCLASS_SEM
-SECCLASS_MSG = _selinux.SECCLASS_MSG
-SECCLASS_MSGQ = _selinux.SECCLASS_MSGQ
-SECCLASS_SHM = _selinux.SECCLASS_SHM
-SECCLASS_IPC = _selinux.SECCLASS_IPC
-SECCLASS_PASSWD = _selinux.SECCLASS_PASSWD
-SECCLASS_X_DRAWABLE = _selinux.SECCLASS_X_DRAWABLE
-SECCLASS_X_SCREEN = _selinux.SECCLASS_X_SCREEN
-SECCLASS_X_GC = _selinux.SECCLASS_X_GC
-SECCLASS_X_FONT = _selinux.SECCLASS_X_FONT
-SECCLASS_X_COLORMAP = _selinux.SECCLASS_X_COLORMAP
-SECCLASS_X_PROPERTY = _selinux.SECCLASS_X_PROPERTY
-SECCLASS_X_SELECTION = _selinux.SECCLASS_X_SELECTION
-SECCLASS_X_CURSOR = _selinux.SECCLASS_X_CURSOR
-SECCLASS_X_CLIENT = _selinux.SECCLASS_X_CLIENT
-SECCLASS_X_DEVICE = _selinux.SECCLASS_X_DEVICE
-SECCLASS_X_SERVER = _selinux.SECCLASS_X_SERVER
-SECCLASS_X_EXTENSION = _selinux.SECCLASS_X_EXTENSION
-SECCLASS_NETLINK_ROUTE_SOCKET = _selinux.SECCLASS_NETLINK_ROUTE_SOCKET
-SECCLASS_NETLINK_FIREWALL_SOCKET = _selinux.SECCLASS_NETLINK_FIREWALL_SOCKET
-SECCLASS_NETLINK_TCPDIAG_SOCKET = _selinux.SECCLASS_NETLINK_TCPDIAG_SOCKET
-SECCLASS_NETLINK_NFLOG_SOCKET = _selinux.SECCLASS_NETLINK_NFLOG_SOCKET
-SECCLASS_NETLINK_XFRM_SOCKET = _selinux.SECCLASS_NETLINK_XFRM_SOCKET
-SECCLASS_NETLINK_SELINUX_SOCKET = _selinux.SECCLASS_NETLINK_SELINUX_SOCKET
-SECCLASS_NETLINK_AUDIT_SOCKET = _selinux.SECCLASS_NETLINK_AUDIT_SOCKET
-SECCLASS_NETLINK_IP6FW_SOCKET = _selinux.SECCLASS_NETLINK_IP6FW_SOCKET
-SECCLASS_NETLINK_DNRT_SOCKET = _selinux.SECCLASS_NETLINK_DNRT_SOCKET
-SECCLASS_DBUS = _selinux.SECCLASS_DBUS
-SECCLASS_NSCD = _selinux.SECCLASS_NSCD
-SECCLASS_ASSOCIATION = _selinux.SECCLASS_ASSOCIATION
-SECCLASS_NETLINK_KOBJECT_UEVENT_SOCKET = _selinux.SECCLASS_NETLINK_KOBJECT_UEVENT_SOCKET
-SECCLASS_APPLETALK_SOCKET = _selinux.SECCLASS_APPLETALK_SOCKET
-SECCLASS_PACKET = _selinux.SECCLASS_PACKET
-SECCLASS_KEY = _selinux.SECCLASS_KEY
-SECCLASS_CONTEXT = _selinux.SECCLASS_CONTEXT
-SECCLASS_DCCP_SOCKET = _selinux.SECCLASS_DCCP_SOCKET
-SECCLASS_MEMPROTECT = _selinux.SECCLASS_MEMPROTECT
-SECCLASS_DB_DATABASE = _selinux.SECCLASS_DB_DATABASE
-SECCLASS_DB_TABLE = _selinux.SECCLASS_DB_TABLE
-SECCLASS_DB_PROCEDURE = _selinux.SECCLASS_DB_PROCEDURE
-SECCLASS_DB_COLUMN = _selinux.SECCLASS_DB_COLUMN
-SECCLASS_DB_TUPLE = _selinux.SECCLASS_DB_TUPLE
-SECCLASS_DB_BLOB = _selinux.SECCLASS_DB_BLOB
-SECCLASS_PEER = _selinux.SECCLASS_PEER
-SECCLASS_CAPABILITY2 = _selinux.SECCLASS_CAPABILITY2
-SECCLASS_X_RESOURCE = _selinux.SECCLASS_X_RESOURCE
-SECCLASS_X_EVENT = _selinux.SECCLASS_X_EVENT
-SECCLASS_X_SYNTHETIC_EVENT = _selinux.SECCLASS_X_SYNTHETIC_EVENT
-SECCLASS_X_APPLICATION_DATA = _selinux.SECCLASS_X_APPLICATION_DATA
-SECINITSID_KERNEL = _selinux.SECINITSID_KERNEL
-SECINITSID_SECURITY = _selinux.SECINITSID_SECURITY
-SECINITSID_UNLABELED = _selinux.SECINITSID_UNLABELED
-SECINITSID_FS = _selinux.SECINITSID_FS
-SECINITSID_FILE = _selinux.SECINITSID_FILE
-SECINITSID_FILE_LABELS = _selinux.SECINITSID_FILE_LABELS
-SECINITSID_INIT = _selinux.SECINITSID_INIT
-SECINITSID_ANY_SOCKET = _selinux.SECINITSID_ANY_SOCKET
-SECINITSID_PORT = _selinux.SECINITSID_PORT
-SECINITSID_NETIF = _selinux.SECINITSID_NETIF
-SECINITSID_NETMSG = _selinux.SECINITSID_NETMSG
-SECINITSID_NODE = _selinux.SECINITSID_NODE
-SECINITSID_IGMP_PACKET = _selinux.SECINITSID_IGMP_PACKET
-SECINITSID_ICMP_SOCKET = _selinux.SECINITSID_ICMP_SOCKET
-SECINITSID_TCP_SOCKET = _selinux.SECINITSID_TCP_SOCKET
-SECINITSID_SYSCTL_MODPROBE = _selinux.SECINITSID_SYSCTL_MODPROBE
-SECINITSID_SYSCTL = _selinux.SECINITSID_SYSCTL
-SECINITSID_SYSCTL_FS = _selinux.SECINITSID_SYSCTL_FS
-SECINITSID_SYSCTL_KERNEL = _selinux.SECINITSID_SYSCTL_KERNEL
-SECINITSID_SYSCTL_NET = _selinux.SECINITSID_SYSCTL_NET
-SECINITSID_SYSCTL_NET_UNIX = _selinux.SECINITSID_SYSCTL_NET_UNIX
-SECINITSID_SYSCTL_VM = _selinux.SECINITSID_SYSCTL_VM
-SECINITSID_SYSCTL_DEV = _selinux.SECINITSID_SYSCTL_DEV
-SECINITSID_KMOD = _selinux.SECINITSID_KMOD
-SECINITSID_POLICY = _selinux.SECINITSID_POLICY
-SECINITSID_SCMP_PACKET = _selinux.SECINITSID_SCMP_PACKET
-SECINITSID_DEVNULL = _selinux.SECINITSID_DEVNULL
-SECINITSID_NUM = _selinux.SECINITSID_NUM
SELINUX_DEFAULTUSER = _selinux.SELINUX_DEFAULTUSER
def get_ordered_context_list(*args):
@@ -1644,6 +523,7 @@
SELABEL_OPT_BASEONLY = _selinux.SELABEL_OPT_BASEONLY
SELABEL_OPT_PATH = _selinux.SELABEL_OPT_PATH
SELABEL_OPT_SUBSET = _selinux.SELABEL_OPT_SUBSET
+SELABEL_OPT_DIGEST = _selinux.SELABEL_OPT_DIGEST
SELABEL_NOPT = _selinux.SELABEL_NOPT
def selabel_open(*args):
@@ -1674,6 +554,18 @@
return _selinux.selabel_lookup_best_match_raw(*args)
selabel_lookup_best_match_raw = _selinux.selabel_lookup_best_match_raw
+def selabel_digest(*args):
+ return _selinux.selabel_digest(*args)
+selabel_digest = _selinux.selabel_digest
+SELABEL_SUBSET = _selinux.SELABEL_SUBSET
+SELABEL_EQUAL = _selinux.SELABEL_EQUAL
+SELABEL_SUPERSET = _selinux.SELABEL_SUPERSET
+SELABEL_INCOMPARABLE = _selinux.SELABEL_INCOMPARABLE
+
+def selabel_cmp(*args):
+ return _selinux.selabel_cmp(*args)
+selabel_cmp = _selinux.selabel_cmp
+
def selabel_stats(*args):
return _selinux.selabel_stats(*args)
selabel_stats = _selinux.selabel_stats
@@ -2317,6 +1209,10 @@
return _selinux.selinux_sepgsql_context_path()
selinux_sepgsql_context_path = _selinux.selinux_sepgsql_context_path
+def selinux_openssh_contexts_path():
+ return _selinux.selinux_openssh_contexts_path()
+selinux_openssh_contexts_path = _selinux.selinux_openssh_contexts_path
+
def selinux_systemd_contexts_path():
return _selinux.selinux_systemd_contexts_path()
selinux_systemd_contexts_path = _selinux.selinux_systemd_contexts_path
diff --git a/lib/python2.7/site-packages/selinux/_selinux.so b/lib/python2.7/site-packages/selinux/_selinux.so
index f4a045f..c313d2b 100755
--- a/lib/python2.7/site-packages/selinux/_selinux.so
+++ b/lib/python2.7/site-packages/selinux/_selinux.so
Binary files differ
diff --git a/lib/python2.7/site-packages/selinux/audit2why.so b/lib/python2.7/site-packages/selinux/audit2why.so
index e7daaab..2c91578 100755
--- a/lib/python2.7/site-packages/selinux/audit2why.so
+++ b/lib/python2.7/site-packages/selinux/audit2why.so
Binary files differ
diff --git a/lib/python2.7/site-packages/setools/__init__.py b/lib/python2.7/site-packages/setools/__init__.py
index 4d03553..1108706 100644
--- a/lib/python2.7/site-packages/setools/__init__.py
+++ b/lib/python2.7/site-packages/setools/__init__.py
@@ -17,13 +17,12 @@
# License along with SETools. If not, see
# <http://www.gnu.org/licenses/>.
#
-#try:
-# import pkg_resources
-# # pylint: disable=no-member
-# __version__ = pkg_resources.get_distribution("setools").version
-#except ImportError: # pragma: no cover
-# __version__ = "unknown"
-__version__ = "3.3.8"
+try:
+ import pkg_resources
+ # pylint: disable=no-member
+ __version__ = pkg_resources.get_distribution("setools").version
+except ImportError: # pragma: no cover
+ __version__ = "unknown"
# Python classes for policy representation
from . import policyrep
diff --git a/lib/python2.7/site-packages/setools/dta.py b/lib/python2.7/site-packages/setools/dta.py
index 271efc4..53328f4 100644
--- a/lib/python2.7/site-packages/setools/dta.py
+++ b/lib/python2.7/site-packages/setools/dta.py
@@ -16,6 +16,8 @@
# License along with SETools. If not, see
# <http://www.gnu.org/licenses/>.
#
+# pylint: disable=unsubscriptable-object
+
import itertools
import logging
from collections import defaultdict, namedtuple
@@ -232,12 +234,12 @@
"""
Get the domain transition graph statistics.
- Return: tuple(nodes, edges)
-
- nodes The number of nodes (types) in the graph.
- edges The number of edges (domain transitions) in the graph.
+ Return: str
"""
- return (self.G.number_of_nodes(), self.G.number_of_edges())
+ if self.rebuildgraph:
+ self._build_graph()
+
+ return nx.info(self.G)
#
# Internal functions follow
@@ -357,6 +359,7 @@
#
def _build_graph(self):
self.G.clear()
+ self.G.name = "Domain transition graph for {0}.".format(self.policy)
self.log.info("Building graph from {0}...".format(self.policy))
diff --git a/lib/python2.7/site-packages/setools/fsusequery.py b/lib/python2.7/site-packages/setools/fsusequery.py
index 6825a45..f79f50e 100644
--- a/lib/python2.7/site-packages/setools/fsusequery.py
+++ b/lib/python2.7/site-packages/setools/fsusequery.py
@@ -20,7 +20,7 @@
import re
from . import contextquery
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor
+from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor, RuletypeDescriptor
class FSUseQuery(contextquery.ContextQuery):
@@ -56,7 +56,7 @@
No effect if not using set operations.
"""
- ruletype = None
+ ruletype = RuletypeDescriptor("validate_fs_use_ruletype")
fs = CriteriaDescriptor("fs_regex")
fs_regex = False
diff --git a/lib/python2.7/site-packages/setools/infoflow.py b/lib/python2.7/site-packages/setools/infoflow.py
index ea3ec32..5812828 100644
--- a/lib/python2.7/site-packages/setools/infoflow.py
+++ b/lib/python2.7/site-packages/setools/infoflow.py
@@ -18,7 +18,6 @@
#
import itertools
import logging
-from collections import namedtuple
import networkx as nx
from networkx.exception import NetworkXError, NetworkXNoPath
@@ -27,12 +26,6 @@
__all__ = ['InfoFlowAnalysis']
-# Return values for the analysis
-# are in the following tuple format:
-step_output = namedtuple("step", ["source",
- "target",
- "rules"])
-
class InfoFlowAnalysis(object):
@@ -233,7 +226,7 @@
if self.rebuildsubgraph:
self._build_subgraph()
- self.log.info("Generating all infoflows out of {0}...".format(s))
+ self.log.info("Generating all infoflows {0} {1}".format("out of" if out else "into", s))
if out:
flows = self.subG.out_edges_iter(s)
@@ -242,8 +235,7 @@
try:
for source, target in flows:
- edge = Edge(self.subG, source, target)
- yield step_output(source, target, edge.rules)
+ yield Edge(self.subG, source, target)
except NetworkXError:
# NetworkXError: the type is valid but not in graph, e.g.
# excluded or disconnected due to min weight
@@ -253,13 +245,12 @@
"""
Get the information flow graph statistics.
- Return: tuple(nodes, edges)
-
- nodes The number of nodes (types) in the graph.
- edges The number of edges (information flows between types)
- in the graph.
+ Return: str
"""
- return (self.G.number_of_nodes(), self.G.number_of_edges())
+ if self.rebuildgraph:
+ self._build_graph()
+
+ return nx.info(self.G)
#
# Internal functions follow
@@ -280,8 +271,7 @@
rules The list of rules creating this information flow step.
"""
for s in range(1, len(path)):
- edge = Edge(self.subG, path[s - 1], path[s])
- yield step_output(edge.source, edge.target, edge.rules)
+ yield Edge(self.subG, path[s - 1], path[s])
#
#
@@ -300,6 +290,7 @@
def _build_graph(self):
self.G.clear()
+ self.G.name = "Information flow graph for {0}.".format(self.policy)
self.perm_map.map_policy(self.policy)
diff --git a/lib/python2.7/site-packages/setools/mixins.py b/lib/python2.7/site-packages/setools/mixins.py
index a31d420..99dc9ff 100644
--- a/lib/python2.7/site-packages/setools/mixins.py
+++ b/lib/python2.7/site-packages/setools/mixins.py
@@ -75,6 +75,7 @@
perms = CriteriaSetDescriptor("perms_regex")
perms_equal = False
perms_regex = False
+ perms_subset = False
def _match_perms(self, obj):
"""
@@ -88,4 +89,8 @@
# if there is no criteria, everything matches.
return True
- return self._match_regex_or_set(obj.perms, self.perms, self.perms_equal, self.perms_regex)
+ if self.perms_subset:
+ return obj.perms >= self.perms
+ else:
+ return self._match_regex_or_set(obj.perms, self.perms, self.perms_equal,
+ self.perms_regex)
diff --git a/lib/python2.7/site-packages/setools/policyrep/__init__.py b/lib/python2.7/site-packages/setools/policyrep/__init__.py
index b03e524..3a4b9a1 100644
--- a/lib/python2.7/site-packages/setools/policyrep/__init__.py
+++ b/lib/python2.7/site-packages/setools/policyrep/__init__.py
@@ -124,16 +124,15 @@
@staticmethod
def _potential_policies():
"""Generate a list of potential policies to use."""
- # Start with binary policies in the standard location
+ # try libselinux for current policy
+ if selinux.selinuxfs_exists():
+ yield selinux.selinux_current_policy_path()
+
+ # otherwise look through the supported policy versions
base_policy_path = selinux.selinux_binary_policy_path()
for version in range(qpol.QPOL_POLICY_MAX_VERSION, qpol.QPOL_POLICY_MIN_VERSION-1, -1):
yield "{0}.{1}".format(base_policy_path, version)
- # Last chance, try selinuxfs. This is not first, to avoid
- # holding kernel memory for a long time
- if selinux.selinuxfs_exists():
- yield selinux.selinux_current_policy_path()
-
def _load_running_policy(self):
"""Try to load the current running policy."""
self.log.info("Attempting to locate current running policy.")
@@ -509,6 +508,11 @@
constraint.validate_ruletype(types)
@staticmethod
+ def validate_fs_use_ruletype(types):
+ """Validate fs_use_* rule types."""
+ fscontext.validate_ruletype(types)
+
+ @staticmethod
def validate_mls_ruletype(types):
"""Validate MLS rule types."""
mlsrule.validate_ruletype(types)
diff --git a/lib/python2.7/site-packages/setools/policyrep/_qpol.so b/lib/python2.7/site-packages/setools/policyrep/_qpol.so
index aaccf28..aad9de3 100755
--- a/lib/python2.7/site-packages/setools/policyrep/_qpol.so
+++ b/lib/python2.7/site-packages/setools/policyrep/_qpol.so
Binary files differ
diff --git a/lib/python2.7/site-packages/setools/policyrep/exception.py b/lib/python2.7/site-packages/setools/policyrep/exception.py
index ce367c0..6935873 100644
--- a/lib/python2.7/site-packages/setools/policyrep/exception.py
+++ b/lib/python2.7/site-packages/setools/policyrep/exception.py
@@ -157,6 +157,12 @@
pass
+class InvalidFSUseType(InvalidSymbol):
+
+ """Exception for invalid fs_use_* types."""
+ pass
+
+
class InvalidMLSRuleType(InvalidRuleType):
"""Exception for invalid MLS rule types."""
diff --git a/lib/python2.7/site-packages/setools/policyrep/fscontext.py b/lib/python2.7/site-packages/setools/policyrep/fscontext.py
index a17b0bc..215dcd7 100644
--- a/lib/python2.7/site-packages/setools/policyrep/fscontext.py
+++ b/lib/python2.7/site-packages/setools/policyrep/fscontext.py
@@ -18,11 +18,19 @@
#
import stat
+from . import exception
from . import qpol
from . import symbol
from . import context
+def validate_ruletype(types):
+ """Validate fs_use_* rule types."""
+ for t in types:
+ if t not in ["fs_use_xattr", "fs_use_trans", "fs_use_task"]:
+ raise exception.InvalidConstraintType("{0} is not a valid fs_use_* type.".format(t))
+
+
def fs_use_factory(policy, name):
"""Factory function for creating fs_use_* objects."""
diff --git a/lib/python2.7/site-packages/setools/policyrep/mls.py b/lib/python2.7/site-packages/setools/policyrep/mls.py
index 2541704..cc24026 100644
--- a/lib/python2.7/site-packages/setools/policyrep/mls.py
+++ b/lib/python2.7/site-packages/setools/policyrep/mls.py
@@ -244,6 +244,10 @@
stmt += ";"
return stmt
+ def __lt__(self, other):
+ """Comparison based on their index instead of their names."""
+ return self._value < other._value
+
class Sensitivity(BaseMLSComponent):
diff --git a/lib/python2.7/site-packages/setools/terulequery.py b/lib/python2.7/site-packages/setools/terulequery.py
index 7f3eccf..3694160 100644
--- a/lib/python2.7/site-packages/setools/terulequery.py
+++ b/lib/python2.7/site-packages/setools/terulequery.py
@@ -62,6 +62,10 @@
Default is false.
perms_regex If true, regular expression matching will be used
on the permission names instead of set logic.
+ Default is false.
+ perms_subset If true, the rule matches if the permissions criteria
+ is a subset of the rule's permission set.
+ Default is false.
default The name of the default type to match.
default_regex If true, regular expression matching will be
used on the default type.
diff --git a/lib/python2.7/site-packages/setoolsgui/apol/excludetypes.py b/lib/python2.7/site-packages/setoolsgui/apol/excludetypes.py
new file mode 100644
index 0000000..c0b821c
--- /dev/null
+++ b/lib/python2.7/site-packages/setoolsgui/apol/excludetypes.py
@@ -0,0 +1,76 @@
+# Copyright 2015, Tresys Technology, LLC
+#
+# This file is part of SETools.
+#
+# SETools is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as
+# published by the Free Software Foundation, either version 2.1 of
+# the License, or (at your option) any later version.
+#
+# SETools is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with SETools. If not, see
+# <http://www.gnu.org/licenses/>.
+#
+
+import logging
+
+from PyQt5.QtWidgets import QDialog
+
+from ..widget import SEToolsWidget
+
+
+class ExcludeTypes(SEToolsWidget, QDialog):
+
+ """Dialog for choosing excluded types."""
+
+ def __init__(self, parent, policy):
+ super(ExcludeTypes, self).__init__(parent)
+ self.log = logging.getLogger(self.__class__.__name__)
+ self.parent = parent
+ self.policy = policy
+ self.excluded_list = [str(e) for e in self.parent.query.exclude]
+ self.setupUi()
+
+ def setupUi(self):
+ self.load_ui("exclude_types.ui")
+ self.exclude_a_type.clicked.connect(self.exclude_clicked)
+ self.include_a_type.clicked.connect(self.include_clicked)
+ self.buttonBox.accepted.connect(self.ok_clicked)
+
+ # populate the lists:
+ self.included_types.clear()
+ for item in self.policy.types():
+ if item not in self.excluded_list:
+ self.included_types.addItem(str(item))
+
+ self.excluded_types.clear()
+ for item in self.excluded_list:
+ self.excluded_types.addItem(item)
+
+ def include_clicked(self):
+ for item in self.excluded_types.selectedItems():
+ self.included_types.addItem(item.text())
+ self.excluded_types.takeItem(self.excluded_types.row(item))
+
+ def exclude_clicked(self):
+ for item in self.included_types.selectedItems():
+ self.excluded_types.addItem(item.text())
+ self.included_types.takeItem(self.included_types.row(item))
+
+ def ok_clicked(self):
+ exclude = []
+
+ item = self.excluded_types.takeItem(0)
+ while item:
+ exclude.append(item.text())
+ item = self.excluded_types.takeItem(0)
+
+ self.log.debug("Chosen for exclusion: {0!r}".format(exclude))
+
+ self.parent.query.exclude = exclude
+ self.accept()
diff --git a/lib/python2.7/site-packages/setoolsgui/apol/infoflow.py b/lib/python2.7/site-packages/setoolsgui/apol/infoflow.py
new file mode 100644
index 0000000..6e3c547
--- /dev/null
+++ b/lib/python2.7/site-packages/setoolsgui/apol/infoflow.py
@@ -0,0 +1,265 @@
+# Copyright 2015, Tresys Technology, LLC
+#
+# This file is part of SETools.
+#
+# SETools is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as
+# published by the Free Software Foundation, either version 2.1 of
+# the License, or (at your option) any later version.
+#
+# SETools is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with SETools. If not, see
+# <http://www.gnu.org/licenses/>.
+#
+
+import logging
+
+from PyQt5.QtCore import pyqtSignal, Qt, QObject, QStringListModel, QThread
+from PyQt5.QtGui import QPalette, QTextCursor
+from PyQt5.QtWidgets import QCompleter, QHeaderView, QMessageBox, QProgressDialog, QScrollArea
+from setools import InfoFlowAnalysis
+
+from .excludetypes import ExcludeTypes
+from ..widget import SEToolsWidget
+
+
+class InfoFlowAnalysisTab(SEToolsWidget, QScrollArea):
+
+ """An information flow analysis tab."""
+
+ def __init__(self, parent, policy, perm_map):
+ super(InfoFlowAnalysisTab, self).__init__(parent)
+ self.log = logging.getLogger(self.__class__.__name__)
+ self.policy = policy
+ self.query = InfoFlowAnalysis(policy, perm_map)
+ self.setupUi()
+
+ def __del__(self):
+ self.thread.quit()
+ self.thread.wait(5000)
+
+ def setupUi(self):
+ self.log.debug("Initializing UI.")
+ self.load_ui("infoflow.ui")
+
+ # set up source/target autocompletion
+ type_completion_list = [str(t) for t in self.policy.types()]
+ type_completer_model = QStringListModel(self)
+ type_completer_model.setStringList(sorted(type_completion_list))
+ self.type_completion = QCompleter()
+ self.type_completion.setModel(type_completer_model)
+ self.source.setCompleter(self.type_completion)
+ self.target.setCompleter(self.type_completion)
+
+ # setup indications of errors on source/target/default
+ self.orig_palette = self.source.palette()
+ self.error_palette = self.source.palette()
+ self.error_palette.setColor(QPalette.Base, Qt.red)
+ self.clear_source_error()
+ self.clear_target_error()
+
+ # set up processing thread
+ self.thread = QThread()
+ self.worker = ResultsUpdater(self.query)
+ self.worker.moveToThread(self.thread)
+ self.worker.raw_line.connect(self.raw_results.appendPlainText)
+ self.worker.finished.connect(self.thread.quit)
+ self.thread.started.connect(self.worker.update)
+ self.thread.finished.connect(self.update_complete)
+
+ # create a "busy, please wait" dialog
+ self.busy = QProgressDialog(self)
+ self.busy.setModal(True)
+ self.busy.setLabelText("Processing analysis...")
+ self.busy.setRange(0, 0)
+ self.busy.setMinimumDuration(0)
+ self.busy.canceled.connect(self.thread.requestInterruption)
+
+ # Ensure settings are consistent with the initial .ui state
+ self.max_path_length.setEnabled(self.all_paths.isChecked())
+ self.source.setEnabled(not self.flows_in.isChecked())
+ self.target.setEnabled(not self.flows_out.isChecked())
+ self.criteria_frame.setHidden(not self.criteria_expander.isChecked())
+ self.results_frame.setHidden(not self.results_expander.isChecked())
+ self.notes.setHidden(not self.notes_expander.isChecked())
+
+ # connect signals
+ self.buttonBox.clicked.connect(self.run)
+ self.source.textEdited.connect(self.clear_source_error)
+ self.source.editingFinished.connect(self.set_source)
+ self.target.textEdited.connect(self.clear_target_error)
+ self.target.editingFinished.connect(self.set_target)
+ self.all_paths.toggled.connect(self.all_paths_toggled)
+ self.flows_in.toggled.connect(self.flows_in_toggled)
+ self.flows_out.toggled.connect(self.flows_out_toggled)
+ self.min_perm_weight.valueChanged.connect(self.set_min_weight)
+ self.exclude_types.clicked.connect(self.choose_excluded_types)
+
+ #
+ # Analysis mode
+ #
+ def all_paths_toggled(self, value):
+ self.max_path_length.setEnabled(value)
+
+ def flows_in_toggled(self, value):
+ self.source.setEnabled(not value)
+
+ def flows_out_toggled(self, value):
+ self.target.setEnabled(not value)
+
+ #
+ # Source criteria
+ #
+
+ def clear_source_error(self):
+ self.source.setToolTip("The target type of the analysis.")
+ self.source.setPalette(self.orig_palette)
+
+ def set_source(self):
+ try:
+ # look up the type here, so invalid types can be caught immediately
+ text = self.source.text()
+ if text:
+ self.query.source = self.policy.lookup_type(text)
+ else:
+ self.query.source = None
+ except Exception as ex:
+ self.source.setToolTip("Error: " + str(ex))
+ self.source.setPalette(self.error_palette)
+
+ #
+ # Target criteria
+ #
+
+ def clear_target_error(self):
+ self.target.setToolTip("The source type of the analysis.")
+ self.target.setPalette(self.orig_palette)
+
+ def set_target(self):
+ try:
+ # look up the type here, so invalid types can be caught immediately
+ text = self.target.text()
+ if text:
+ self.query.target = self.policy.lookup_type(text)
+ else:
+ self.query.target = None
+ except Exception as ex:
+ self.target.setToolTip("Error: " + str(ex))
+ self.target.setPalette(self.error_palette)
+
+ #
+ # Options
+ #
+ def set_min_weight(self, value):
+ self.query.min_weight = value
+
+ def choose_excluded_types(self):
+ chooser = ExcludeTypes(self, self.policy)
+ chooser.show()
+
+ #
+ # Results runner
+ #
+
+ def run(self, button):
+ # right now there is only one button.
+ for mode in [self.all_paths, self.all_shortest_paths, self.flows_in, self.flows_out]:
+ if mode.isChecked():
+ break
+
+ self.query.mode = mode.objectName()
+ self.query.max_path_len = self.max_path_length.value()
+ self.query.limit = self.limit_paths.value()
+
+ # start processing
+ self.busy.show()
+ self.raw_results.clear()
+ self.thread.start()
+
+ def update_complete(self):
+ # update location of result display
+ self.raw_results.moveCursor(QTextCursor.Start)
+
+ self.busy.reset()
+
+
+class ResultsUpdater(QObject):
+
+ """
+ Thread for processing queries and updating result widgets.
+
+ Parameters:
+ query The query object
+ model The model for the results
+
+ Qt signals:
+ finished The update has completed.
+ raw_line (str) A string to be appended to the raw results.
+ """
+
+ finished = pyqtSignal()
+ raw_line = pyqtSignal(str)
+
+ def __init__(self, query):
+ super(ResultsUpdater, self).__init__()
+ self.query = query
+
+ def update(self):
+ """Run the query and update results."""
+
+ assert self.query.limit, "Code doesn't currently handle unlimited (limit=0) paths."
+ if self.query.mode == "all_paths":
+ self.transitive(self.query.all_paths(self.query.source, self.query.target,
+ self.query.max_path_len))
+ elif self.query.mode == "all_shortest_paths":
+ self.transitive(self.query.all_shortest_paths(self.query.source, self.query.target))
+ elif self.query.mode == "flows_out":
+ self.direct(self.query.infoflows(self.query.source, out=True))
+ else: # flows_in
+ self.direct(self.query.infoflows(self.query.target, out=False))
+
+ self.finished.emit()
+
+ def transitive(self, paths):
+ pathnum = 0
+ for pathnum, path in enumerate(paths, start=1):
+ self.raw_line.emit("Flow {0}:".format(pathnum))
+ for stepnum, step in enumerate(path, start=1):
+ self.raw_line.emit(" Step {0}: {1} -> {2}".format(stepnum,
+ step.source,
+ step.target))
+
+ for rule in sorted(step.rules):
+ self.raw_line.emit(" {0}".format(rule))
+
+ self.raw_line.emit("")
+
+ if QThread.currentThread().isInterruptionRequested() or (pathnum >= self.query.limit):
+ break
+ else:
+ QThread.yieldCurrentThread()
+
+ self.raw_line.emit("")
+
+ self.raw_line.emit("{0} information flow path(s) found.\n".format(pathnum))
+
+ def direct(self, flows):
+ flownum = 0
+ for flownum, flow in enumerate(flows, start=1):
+ self.raw_line.emit("Flow {0}: {1} -> {2}".format(flownum, flow.source, flow.target))
+ for rule in sorted(flow.rules):
+ self.raw_line.emit(" {0}".format(rule))
+
+ self.raw_line.emit("")
+
+ if QThread.currentThread().isInterruptionRequested() or (flownum >= self.query.limit):
+ break
+ else:
+ QThread.yieldCurrentThread()
+
+ self.raw_line.emit("{0} information flow(s) found.\n".format(flownum))
diff --git a/lib/python2.7/site-packages/setoolsgui/apol/mainwindow.py b/lib/python2.7/site-packages/setoolsgui/apol/mainwindow.py
index 53b9f87..69a76ce 100644
--- a/lib/python2.7/site-packages/setoolsgui/apol/mainwindow.py
+++ b/lib/python2.7/site-packages/setoolsgui/apol/mainwindow.py
@@ -25,6 +25,8 @@
from setools import PermissionMap, SELinuxPolicy
from ..widget import SEToolsWidget
+# Analysis tabs:
+from .infoflow import InfoFlowAnalysisTab
from .terulequery import TERuleQueryTab
@@ -136,7 +138,7 @@
newtab = QWidget()
newtab.setObjectName(counted_name)
- newanalysis = tabclass(newtab, self._policy)
+ newanalysis = tabclass(newtab, self._policy, self._permmap)
# create a vertical layout in the tab, place the analysis ui inside.
tabLayout = QVBoxLayout()
@@ -192,35 +194,40 @@
The item_mapping attribute will be populated to
map the tree list items to the analysis tab widgets.
"""
- _components_map = {"Attributes (Type)": TERuleQueryTab,
- "Booleans": TERuleQueryTab,
- "Categories": TERuleQueryTab,
- "Common Permission Sets": TERuleQueryTab,
- "Object Classes": TERuleQueryTab,
- "Policy Capabilities": TERuleQueryTab,
- "Roles": TERuleQueryTab,
- "Types": TERuleQueryTab,
- "Users": TERuleQueryTab}
+# _components_map = {"Attributes (Type)": TERuleQueryTab,
+# "Booleans": TERuleQueryTab,
+# "Categories": TERuleQueryTab,
+# "Common Permission Sets": TERuleQueryTab,
+# "Object Classes": TERuleQueryTab,
+# "Policy Capabilities": TERuleQueryTab,
+# "Roles": TERuleQueryTab,
+# "Types": TERuleQueryTab,
+# "Users": TERuleQueryTab}
+#
+# _rule_map = {"TE Rules": TERuleQueryTab,
+# "RBAC Rules": TERuleQueryTab,
+# "MLS Rules": TERuleQueryTab,
+# "Constraints": TERuleQueryTab}
+#
+# _analysis_map = {"Domain Transition Analysis": TERuleQueryTab,
+# "Information Flow Analysis": TERuleQueryTab}
+#
+# _labeling_map = {"fs_use Statements": TERuleQueryTab,
+# "Genfscon Statements": TERuleQueryTab,
+# "Initial SID Statements": TERuleQueryTab,
+# "Netifcon Statements": TERuleQueryTab,
+# "Nodecon Statements": TERuleQueryTab,
+# "Portcon Statements": TERuleQueryTab}
+#
+# _analysis_choices = {"Components": _components_map,
+# "Rules": _rule_map,
+# "Analysis": _analysis_map,
+# "Labeling Statements": _labeling_map}
- _rule_map = {"TE Rules": TERuleQueryTab,
- "RBAC Rules": TERuleQueryTab,
- "MLS Rules": TERuleQueryTab,
- "Constraints": TERuleQueryTab}
-
- _analysis_map = {"Domain Transition Analysis": TERuleQueryTab,
- "Information Flow Analysis": TERuleQueryTab}
-
- _labeling_map = {"fs_use Statements": TERuleQueryTab,
- "Genfscon Statements": TERuleQueryTab,
- "Initial SID Statements": TERuleQueryTab,
- "Netifcon Statements": TERuleQueryTab,
- "Nodecon Statements": TERuleQueryTab,
- "Portcon Statements": TERuleQueryTab}
-
- _analysis_choices = {"Components": _components_map,
- "Rules": _rule_map,
- "Analysis": _analysis_map,
- "Labeling Statements": _labeling_map}
+ _analysis_map = {"Information Flow Analysis": InfoFlowAnalysisTab}
+ _rule_map = {"TE Rules": TERuleQueryTab}
+ _analysis_choices = {"Rules": _rule_map,
+ "Analyses": _analysis_map}
def __init__(self, parent):
super(ChooseAnalysis, self).__init__(parent)
diff --git a/lib/python2.7/site-packages/setoolsgui/apol/models.py b/lib/python2.7/site-packages/setoolsgui/apol/models.py
index 2744ad6..f8a5a5d 100644
--- a/lib/python2.7/site-packages/setoolsgui/apol/models.py
+++ b/lib/python2.7/site-packages/setoolsgui/apol/models.py
@@ -17,11 +17,20 @@
# <http://www.gnu.org/licenses/>.
#
-from PyQt5 import QtCore
-from PyQt5.QtCore import QAbstractListModel, QModelIndex, QStringListModel, Qt
+from PyQt5.QtCore import QAbstractListModel, QItemSelectionModel, QModelIndex, QStringListModel, Qt
from setools.policyrep.exception import NoCommon
+def invert_list_selection(selection_model):
+ """Invert the selection of a list-based model."""
+
+ model = selection_model.model()
+ rowcount = model.rowCount()
+ for row in range(rowcount):
+ index = model.createIndex(row, 0)
+ selection_model.select(index, QItemSelectionModel.Toggle)
+
+
class SEToolsListModel(QAbstractListModel):
"""
diff --git a/lib/python2.7/site-packages/setoolsgui/apol/rulemodels.py b/lib/python2.7/site-packages/setoolsgui/apol/rulemodels.py
index 4367cfb..a340040 100644
--- a/lib/python2.7/site-packages/setoolsgui/apol/rulemodels.py
+++ b/lib/python2.7/site-packages/setoolsgui/apol/rulemodels.py
@@ -24,7 +24,7 @@
class RuleResultModel(QAbstractTableModel):
def __init__(self, parent):
super(RuleResultModel, self).__init__(parent)
- self.resultlist = None
+ self.resultlist = []
def rowCount(self, parent=QModelIndex()):
if self.resultlist:
@@ -85,11 +85,6 @@
# get the whole rule for user role
return self.resultlist[row].statement()
- def set_rules(self, result_list):
- self.beginResetModel()
- self.resultlist = result_list
- self.endResetModel()
-
class TERuleListModel(RuleResultModel):
diff --git a/lib/python2.7/site-packages/setoolsgui/apol/terulequery.py b/lib/python2.7/site-packages/setoolsgui/apol/terulequery.py
index 75148fc..70d32d2 100644
--- a/lib/python2.7/site-packages/setoolsgui/apol/terulequery.py
+++ b/lib/python2.7/site-packages/setoolsgui/apol/terulequery.py
@@ -19,24 +19,31 @@
import logging
-from PyQt5.QtCore import Qt, QSortFilterProxyModel, QStringListModel
+from PyQt5.QtCore import pyqtSignal, Qt, QObject, QSortFilterProxyModel, QStringListModel, QThread
from PyQt5.QtGui import QPalette, QTextCursor
-from PyQt5.QtWidgets import QCompleter, QHeaderView, QScrollArea
+from PyQt5.QtWidgets import QCompleter, QHeaderView, QMessageBox, QProgressDialog, QScrollArea
from setools import TERuleQuery
from ..widget import SEToolsWidget
from .rulemodels import TERuleListModel
-from .models import PermListModel, SEToolsListModel
+from .models import PermListModel, SEToolsListModel, invert_list_selection
class TERuleQueryTab(SEToolsWidget, QScrollArea):
- def __init__(self, parent, policy):
+
+ """A Type Enforcement rule query."""
+
+ def __init__(self, parent, policy, perm_map):
super(TERuleQueryTab, self).__init__(parent)
self.log = logging.getLogger(self.__class__.__name__)
self.policy = policy
self.query = TERuleQuery(policy)
self.setupUi()
+ def __del__(self):
+ self.thread.quit()
+ self.thread.wait(5000)
+
def setupUi(self):
self.load_ui("terulequery.ui")
@@ -86,6 +93,22 @@
self.sort_proxy.setSourceModel(self.table_results_model)
self.table_results.setModel(self.sort_proxy)
+ # set up processing thread
+ self.thread = QThread()
+ self.worker = ResultsUpdater(self.query, self.table_results_model)
+ self.worker.moveToThread(self.thread)
+ self.worker.raw_line.connect(self.raw_results.appendPlainText)
+ self.worker.finished.connect(self.update_complete)
+ self.worker.finished.connect(self.thread.quit)
+ self.thread.started.connect(self.worker.update)
+
+ # create a "busy, please wait" dialog
+ self.busy = QProgressDialog(self)
+ self.busy.setModal(True)
+ self.busy.setRange(0, 0)
+ self.busy.setMinimumDuration(0)
+ self.busy.canceled.connect(self.thread.requestInterruption)
+
# Ensure settings are consistent with the initial .ui state
self.set_source_regex(self.source_regex.isChecked())
self.set_target_regex(self.target_regex.isChecked())
@@ -105,7 +128,9 @@
self.target.editingFinished.connect(self.set_target)
self.target_regex.toggled.connect(self.set_target_regex)
self.tclass.selectionModel().selectionChanged.connect(self.set_tclass)
+ self.invert_class.clicked.connect(self.invert_tclass_selection)
self.perms.selectionModel().selectionChanged.connect(self.set_perms)
+ self.invert_perms.clicked.connect(self.invert_perms_selection)
self.default_type.textEdited.connect(self.clear_default_error)
self.default_type.editingFinished.connect(self.set_default_type)
self.default_regex.toggled.connect(self.set_default_regex)
@@ -184,6 +209,9 @@
self.query.tclass = selected_classes
self.perms_model.set_classes(selected_classes)
+ def invert_tclass_selection(self):
+ invert_list_selection(self.tclass.selectionModel())
+
#
# Permissions criteria
#
@@ -195,6 +223,9 @@
self.query.perms = selected_perms
+ def invert_perms_selection(self):
+ invert_list_selection(self.perms.selectionModel())
+
#
# Default criteria
#
@@ -236,21 +267,29 @@
def run(self, button):
# right now there is only one button.
rule_types = []
+ max_results = 0
if self.allow.isChecked():
rule_types.append("allow")
+ max_results += self.policy.allow_count
if self.auditallow.isChecked():
rule_types.append("auditallow")
+ max_results += self.policy.auditallow_count
if self.neverallow.isChecked():
rule_types.append("neverallow")
+ max_results += self.policy.neverallow_count
if self.dontaudit.isChecked():
rule_types.append("dontaudit")
+ max_results += self.policy.dontaudit_count
if self.type_transition.isChecked():
rule_types.append("type_transition")
+ max_results += self.policy.type_transition_count
if self.type_member.isChecked():
rule_types.append("type_member")
+ max_results += self.policy.type_member_count
if self.type_change.isChecked():
rule_types.append("type_change")
+ max_results += self.policy.type_change_count
self.query.ruletype = rule_types
self.query.source_indirect = self.source_indirect.isChecked()
@@ -258,14 +297,89 @@
self.query.perms_equal = self.perms_equal.isChecked()
self.query.boolean_equal = self.bools_equal.isChecked()
- # update results table
- results = list(self.query.results())
- self.table_results_model.set_rules(results)
- self.table_results.resizeColumnsToContents()
+ # if query is broad, show warning.
+ if not self.query.source and not self.query.target and not self.query.tclass and \
+ not self.query.perms and not self.query.default and not self.query.boolean:
+ reply = QMessageBox.question(
+ self, "Continue?",
+ "This is a broad query, estimated to return {0} results. Continue?".
+ format(max_results), QMessageBox.Yes | QMessageBox.No)
- # update raw results
+ if reply == QMessageBox.No:
+ return
+
+ # start processing
+ self.busy.setLabelText("Processing query...")
+ self.busy.show()
self.raw_results.clear()
- for line in results:
- self.raw_results.appendPlainText(str(line))
+ self.thread.start()
- self.raw_results.moveCursor(QTextCursor.Start)
+ def update_complete(self):
+ # update sizes/location of result displays
+ if not self.busy.wasCanceled():
+ self.busy.setLabelText("Resizing the result table's columns; GUI may be unresponsive")
+ self.busy.repaint()
+ self.table_results.resizeColumnsToContents()
+ # If the permissions column width is too long, pull back
+ # to a reasonable size
+ header = self.table_results.horizontalHeader()
+ if header.sectionSize(4) > 400:
+ header.resizeSection(4, 400)
+
+ if not self.busy.wasCanceled():
+ self.busy.setLabelText("Resizing the result table's rows; GUI may be unresponsive")
+ self.busy.repaint()
+ self.table_results.resizeRowsToContents()
+
+ if not self.busy.wasCanceled():
+ self.busy.setLabelText("Moving the raw result to top; GUI may be unresponsive")
+ self.busy.repaint()
+ self.raw_results.moveCursor(QTextCursor.Start)
+
+ self.busy.reset()
+
+
+class ResultsUpdater(QObject):
+
+ """
+ Thread for processing queries and updating result widgets.
+
+ Parameters:
+ query The query object
+ model The model for the results
+
+ Qt signals:
+ finished The update has completed.
+ raw_line (str) A string to be appended to the raw results.
+ """
+
+ finished = pyqtSignal()
+ raw_line = pyqtSignal(str)
+
+ def __init__(self, query, model):
+ super(ResultsUpdater, self).__init__()
+ self.query = query
+ self.table_results_model = model
+
+ def update(self):
+ """Run the query and update results."""
+ self.table_results_model.beginResetModel()
+
+ results = []
+ counter = 0
+
+ for counter, item in enumerate(self.query.results(), start=1):
+ results.append(item)
+
+ self.raw_line.emit(str(item))
+
+ if QThread.currentThread().isInterruptionRequested():
+ break
+ elif not counter % 10:
+ # yield execution every 10 rules
+ QThread.yieldCurrentThread()
+
+ self.table_results_model.resultlist = results
+ self.table_results_model.endResetModel()
+
+ self.finished.emit()
diff --git a/lib/python2.7/site-packages/setoolsgui/libselinux.so.1 b/lib/python2.7/site-packages/setoolsgui/libselinux.so.1
deleted file mode 100755
index dc9280d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/libselinux.so.1
+++ /dev/null
Binary files differ
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/__init__.py
deleted file mode 100644
index 7090794..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/__init__.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""
-NetworkX
-========
-
- NetworkX (NX) is a Python package for the creation, manipulation, and
- study of the structure, dynamics, and functions of complex networks.
-
- https://networkx.lanl.gov/
-
-Using
------
-
- Just write in Python
-
- >>> import networkx as nx
- >>> G=nx.Graph()
- >>> G.add_edge(1,2)
- >>> G.add_node(42)
- >>> print(sorted(G.nodes()))
- [1, 2, 42]
- >>> print(sorted(G.edges()))
- [(1, 2)]
-"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-#
-# Add platform dependent shared library path to sys.path
-#
-
-from __future__ import absolute_import
-
-import sys
-if sys.version_info[:2] < (2, 6):
- m = "Python version 2.6 or later is required for NetworkX (%d.%d detected)."
- raise ImportError(m % sys.version_info[:2])
-del sys
-
-# Release data
-from networkx import release
-
-__author__ = '%s <%s>\n%s <%s>\n%s <%s>' % \
- ( release.authors['Hagberg'] + release.authors['Schult'] + \
- release.authors['Swart'] )
-__license__ = release.license
-
-__date__ = release.date
-__version__ = release.version
-
-#These are import orderwise
-from networkx.exception import *
-import networkx.external
-import networkx.utils
-# these packages work with Python >= 2.6
-
-import networkx.classes
-from networkx.classes import *
-
-
-import networkx.convert
-from networkx.convert import *
-
-import networkx.relabel
-from networkx.relabel import *
-
-import networkx.generators
-from networkx.generators import *
-
-import networkx.readwrite
-from networkx.readwrite import *
-
-#Need to test with SciPy, when available
-import networkx.algorithms
-from networkx.algorithms import *
-import networkx.linalg
-
-from networkx.linalg import *
-from networkx.tests.test import run as test
-
-import networkx.drawing
-from networkx.drawing import *
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/__init__.py
deleted file mode 100644
index 6230da3..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/__init__.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from networkx.algorithms.assortativity import *
-from networkx.algorithms.block import *
-from networkx.algorithms.boundary import *
-from networkx.algorithms.centrality import *
-from networkx.algorithms.cluster import *
-from networkx.algorithms.clique import *
-from networkx.algorithms.community import *
-from networkx.algorithms.components import *
-from networkx.algorithms.connectivity import *
-from networkx.algorithms.core import *
-from networkx.algorithms.cycles import *
-from networkx.algorithms.dag import *
-from networkx.algorithms.distance_measures import *
-from networkx.algorithms.flow import *
-from networkx.algorithms.hierarchy import *
-from networkx.algorithms.matching import *
-from networkx.algorithms.mis import *
-from networkx.algorithms.mst import *
-from networkx.algorithms.link_analysis import *
-from networkx.algorithms.operators import *
-from networkx.algorithms.shortest_paths import *
-from networkx.algorithms.smetric import *
-from networkx.algorithms.traversal import *
-from networkx.algorithms.isolate import *
-from networkx.algorithms.euler import *
-from networkx.algorithms.vitality import *
-from networkx.algorithms.chordal import *
-from networkx.algorithms.richclub import *
-from networkx.algorithms.distance_regular import *
-from networkx.algorithms.swap import *
-from networkx.algorithms.graphical import *
-from networkx.algorithms.simple_paths import *
-
-import networkx.algorithms.assortativity
-import networkx.algorithms.bipartite
-import networkx.algorithms.centrality
-import networkx.algorithms.cluster
-import networkx.algorithms.clique
-import networkx.algorithms.components
-import networkx.algorithms.connectivity
-import networkx.algorithms.flow
-import networkx.algorithms.isomorphism
-import networkx.algorithms.link_analysis
-import networkx.algorithms.shortest_paths
-import networkx.algorithms.traversal
-import networkx.algorithms.chordal
-import networkx.algorithms.operators
-
-from networkx.algorithms.bipartite import projected_graph,project,is_bipartite
-from networkx.algorithms.isomorphism import is_isomorphic,could_be_isomorphic,\
- fast_could_be_isomorphic,faster_could_be_isomorphic
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/__init__.py
deleted file mode 100644
index eb797c2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from networkx.algorithms.approximation.clique import *
-from networkx.algorithms.approximation.dominating_set import *
-from networkx.algorithms.approximation.independent_set import *
-from networkx.algorithms.approximation.matching import *
-from networkx.algorithms.approximation.ramsey import *
-from networkx.algorithms.approximation.vertex_cover import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/clique.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/clique.py
deleted file mode 100644
index be363f6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/clique.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Cliques.
-"""
-# Copyright (C) 2011-2012 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.algorithms.approximation import ramsey
-__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
-__all__ = ["clique_removal","max_clique"]
-
-def max_clique(G):
- r"""Find the Maximum Clique
-
- Finds the `O(|V|/(log|V|)^2)` apx of maximum clique/independent set
- in the worst case.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- clique : set
- The apx-maximum clique of the graph
-
- Notes
- ------
- A clique in an undirected graph G = (V, E) is a subset of the vertex set
- `C \subseteq V`, such that for every two vertices in C, there exists an edge
- connecting the two. This is equivalent to saying that the subgraph
- induced by C is complete (in some cases, the term clique may also refer
- to the subgraph).
-
- A maximum clique is a clique of the largest possible size in a given graph.
- The clique number `\omega(G)` of a graph G is the number of
- vertices in a maximum clique in G. The intersection number of
- G is the smallest number of cliques that together cover all edges of G.
-
- http://en.wikipedia.org/wiki/Maximum_clique
-
- References
- ----------
- .. [1] Boppana, R., & Halldórsson, M. M. (1992).
- Approximating maximum independent sets by excluding subgraphs.
- BIT Numerical Mathematics, 32(2), 180–196. Springer.
- doi:10.1007/BF01994876
- """
- if G is None:
- raise ValueError("Expected NetworkX graph!")
-
- # finding the maximum clique in a graph is equivalent to finding
- # the independent set in the complementary graph
- cgraph = nx.complement(G)
- iset, _ = clique_removal(cgraph)
- return iset
-
-def clique_removal(G):
- """ Repeatedly remove cliques from the graph.
-
- Results in a `O(|V|/(\log |V|)^2)` approximation of maximum clique
- & independent set. Returns the largest independent set found, along
- with found maximal cliques.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- max_ind_cliques : (set, list) tuple
- Maximal independent set and list of maximal cliques (sets) in the graph.
-
- References
- ----------
- .. [1] Boppana, R., & Halldórsson, M. M. (1992).
- Approximating maximum independent sets by excluding subgraphs.
- BIT Numerical Mathematics, 32(2), 180–196. Springer.
- """
- graph = G.copy()
- c_i, i_i = ramsey.ramsey_R2(graph)
- cliques = [c_i]
- isets = [i_i]
- while graph:
- graph.remove_nodes_from(c_i)
- c_i, i_i = ramsey.ramsey_R2(graph)
- if c_i:
- cliques.append(c_i)
- if i_i:
- isets.append(i_i)
-
- maxiset = max(isets)
- return maxiset, cliques
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/dominating_set.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/dominating_set.py
deleted file mode 100644
index 6a167e2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/dominating_set.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-**************************************
-Minimum Vertex and Edge Dominating Set
-**************************************
-
-
-A dominating set for a graph G = (V, E) is a subset D of V such that every
-vertex not in D is joined to at least one member of D by some edge. The
-domination number gamma(G) is the number of vertices in a smallest dominating
-set for G. Given a graph G = (V, E) find a minimum weight dominating set V'.
-
-http://en.wikipedia.org/wiki/Dominating_set
-
-An edge dominating set for a graph G = (V, E) is a subset D of E such that
-every edge not in D is adjacent to at least one edge in D.
-
-http://en.wikipedia.org/wiki/Edge_dominating_set
-"""
-# Copyright (C) 2011-2012 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__all__ = ["min_weighted_dominating_set",
- "min_edge_dominating_set"]
-__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
-
-
-def min_weighted_dominating_set(G, weight=None):
- r"""Return minimum weight vertex dominating set.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- weight : None or string, optional (default = None)
- If None, every edge has weight/distance/weight 1. If a string, use this
- edge attribute as the edge weight. Any edge attribute not present
- defaults to 1.
-
- Returns
- -------
- min_weight_dominating_set : set
- Returns a set of vertices whose weight sum is no more than log w(V) * OPT
-
- Notes
- -----
- This algorithm computes an approximate minimum weighted dominating set
- for the graph G. The upper-bound on the size of the solution is
- log w(V) * OPT. Runtime of the algorithm is `O(|E|)`.
-
- References
- ----------
- .. [1] Vazirani, Vijay Approximation Algorithms (2001)
- """
- if not G:
- raise ValueError("Expected non-empty NetworkX graph!")
-
- # min cover = min dominating set
- dom_set = set([])
- cost_func = dict((node, nd.get(weight, 1)) \
- for node, nd in G.nodes_iter(data=True))
-
- vertices = set(G)
- sets = dict((node, set([node]) | set(G[node])) for node in G)
-
- def _cost(subset):
- """ Our cost effectiveness function for sets given its weight
- """
- cost = sum(cost_func[node] for node in subset)
- return cost / float(len(subset - dom_set))
-
- while vertices:
- # find the most cost effective set, and the vertex that for that set
- dom_node, min_set = min(sets.items(),
- key=lambda x: (x[0], _cost(x[1])))
- alpha = _cost(min_set)
-
- # reduce the cost for the rest
- for node in min_set - dom_set:
- cost_func[node] = alpha
-
- # add the node to the dominating set and reduce what we must cover
- dom_set.add(dom_node)
- del sets[dom_node]
- vertices = vertices - min_set
-
- return dom_set
-
-
-def min_edge_dominating_set(G):
- r"""Return minimum cardinality edge dominating set.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- min_edge_dominating_set : set
- Returns a set of dominating edges whose size is no more than 2 * OPT.
-
- Notes
- -----
- The algorithm computes an approximate solution to the edge dominating set
- problem. The result is no more than 2 * OPT in terms of size of the set.
- Runtime of the algorithm is `O(|E|)`.
- """
- if not G:
- raise ValueError("Expected non-empty NetworkX graph!")
- return nx.maximal_matching(G)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/independent_set.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/independent_set.py
deleted file mode 100644
index 3b18ade..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/independent_set.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Independent Set
-
-Independent set or stable set is a set of vertices in a graph, no two of
-which are adjacent. That is, it is a set I of vertices such that for every
-two vertices in I, there is no edge connecting the two. Equivalently, each
-edge in the graph has at most one endpoint in I. The size of an independent
-set is the number of vertices it contains.
-
-A maximum independent set is a largest independent set for a given graph G
-and its size is denoted α(G). The problem of finding such a set is called
-the maximum independent set problem and is an NP-hard optimization problem.
-As such, it is unlikely that there exists an efficient algorithm for finding
-a maximum independent set of a graph.
-
-http://en.wikipedia.org/wiki/Independent_set_(graph_theory)
-
-Independent set algorithm is based on the following paper:
-
-`O(|V|/(log|V|)^2)` apx of maximum clique/independent set.
-
-Boppana, R., & Halldórsson, M. M. (1992).
-Approximating maximum independent sets by excluding subgraphs.
-BIT Numerical Mathematics, 32(2), 180–196. Springer.
-doi:10.1007/BF01994876
-
-"""
-# Copyright (C) 2011-2012 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-from networkx.algorithms.approximation import clique_removal
-__all__ = ["maximum_independent_set"]
-__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
-
-
-def maximum_independent_set(G):
- """Return an approximate maximum independent set.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- iset : Set
- The apx-maximum independent set
-
- Notes
- -----
- Finds the `O(|V|/(log|V|)^2)` apx of independent set in the worst case.
-
-
- References
- ----------
- .. [1] Boppana, R., & Halldórsson, M. M. (1992).
- Approximating maximum independent sets by excluding subgraphs.
- BIT Numerical Mathematics, 32(2), 180–196. Springer.
- """
- iset, _ = clique_removal(G)
- return iset
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/matching.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/matching.py
deleted file mode 100644
index 231d501..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/matching.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-**************
-Graph Matching
-**************
-
-Given a graph G = (V,E), a matching M in G is a set of pairwise non-adjacent
-edges; that is, no two edges share a common vertex.
-
-http://en.wikipedia.org/wiki/Matching_(graph_theory)
-"""
-# Copyright (C) 2011-2012 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__all__ = ["min_maximal_matching"]
-__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
-
-def min_maximal_matching(G):
- r"""Returns the minimum maximal matching of G. That is, out of all maximal
- matchings of the graph G, the smallest is returned.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- min_maximal_matching : set
- Returns a set of edges such that no two edges share a common endpoint
- and every edge not in the set shares some common endpoint in the set.
- Cardinality will be 2*OPT in the worst case.
-
- Notes
- -----
- The algorithm computes an approximate solution fo the minimum maximal
- cardinality matching problem. The solution is no more than 2 * OPT in size.
- Runtime is `O(|E|)`.
-
- References
- ----------
- .. [1] Vazirani, Vijay Approximation Algorithms (2001)
- """
- return nx.maximal_matching(G)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/ramsey.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/ramsey.py
deleted file mode 100644
index 03535ce..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/ramsey.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Ramsey numbers.
-"""
-# Copyright (C) 2011 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__all__ = ["ramsey_R2"]
-__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
-
-def ramsey_R2(G):
- r"""Approximately computes the Ramsey number `R(2;s,t)` for graph.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- max_pair : (set, set) tuple
- Maximum clique, Maximum independent set.
- """
- if not G:
- return (set([]), set([]))
-
- node = next(G.nodes_iter())
- nbrs = nx.all_neighbors(G, node)
- nnbrs = nx.non_neighbors(G, node)
- c_1, i_1 = ramsey_R2(G.subgraph(nbrs))
- c_2, i_2 = ramsey_R2(G.subgraph(nnbrs))
-
- c_1.add(node)
- i_2.add(node)
- return (max([c_1, c_2]), max([i_1, i_2]))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_clique.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_clique.py
deleted file mode 100644
index 0f384a5..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_clique.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from nose.tools import *
-import networkx as nx
-import networkx.algorithms.approximation as apxa
-
-def test_clique_removal():
- graph = nx.complete_graph(10)
- i, cs = apxa.clique_removal(graph)
- idens = nx.density(graph.subgraph(i))
- eq_(idens, 0.0, "i-set not found by clique_removal!")
- for clique in cs:
- cdens = nx.density(graph.subgraph(clique))
- eq_(cdens, 1.0, "clique not found by clique_removal!")
-
- graph = nx.trivial_graph(nx.Graph())
- i, cs = apxa.clique_removal(graph)
- idens = nx.density(graph.subgraph(i))
- eq_(idens, 0.0, "i-set not found by ramsey!")
- # we should only have 1-cliques. Just singleton nodes.
- for clique in cs:
- cdens = nx.density(graph.subgraph(clique))
- eq_(cdens, 0.0, "clique not found by clique_removal!")
-
- graph = nx.barbell_graph(10, 5, nx.Graph())
- i, cs = apxa.clique_removal(graph)
- idens = nx.density(graph.subgraph(i))
- eq_(idens, 0.0, "i-set not found by ramsey!")
- for clique in cs:
- cdens = nx.density(graph.subgraph(clique))
- eq_(cdens, 1.0, "clique not found by clique_removal!")
-
-def test_max_clique_smoke():
- # smoke test
- G = nx.Graph()
- assert_equal(len(apxa.max_clique(G)),0)
-
-def test_max_clique():
- # create a complete graph
- graph = nx.complete_graph(30)
- # this should return the entire graph
- mc = apxa.max_clique(graph)
- assert_equals(30, len(mc))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_dominating_set.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_dominating_set.py
deleted file mode 100644
index 0dbc79f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_dominating_set.py
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-import networkx.algorithms.approximation as apxa
-
-
-class TestMinWeightDominatingSet:
-
- def test_min_weighted_dominating_set(self):
- graph = nx.Graph()
- graph.add_edge(1, 2)
- graph.add_edge(1, 5)
- graph.add_edge(2, 3)
- graph.add_edge(2, 5)
- graph.add_edge(3, 4)
- graph.add_edge(3, 6)
- graph.add_edge(5, 6)
-
- vertices = set([1, 2, 3, 4, 5, 6])
- # due to ties, this might be hard to test tight bounds
- dom_set = apxa.min_weighted_dominating_set(graph)
- for vertex in vertices - dom_set:
- neighbors = set(graph.neighbors(vertex))
- ok_(len(neighbors & dom_set) > 0, "Non dominating set found!")
-
- def test_min_edge_dominating_set(self):
- graph = nx.path_graph(5)
- dom_set = apxa.min_edge_dominating_set(graph)
-
- # this is a crappy way to test, but good enough for now.
- for edge in graph.edges_iter():
- if edge in dom_set:
- continue
- else:
- u, v = edge
- found = False
- for dom_edge in dom_set:
- found |= u == dom_edge[0] or u == dom_edge[1]
- ok_(found, "Non adjacent edge found!")
-
- graph = nx.complete_graph(10)
- dom_set = apxa.min_edge_dominating_set(graph)
-
- # this is a crappy way to test, but good enough for now.
- for edge in graph.edges_iter():
- if edge in dom_set:
- continue
- else:
- u, v = edge
- found = False
- for dom_edge in dom_set:
- found |= u == dom_edge[0] or u == dom_edge[1]
- ok_(found, "Non adjacent edge found!")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_independent_set.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_independent_set.py
deleted file mode 100644
index 8825ec8..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_independent_set.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from nose.tools import *
-import networkx as nx
-import networkx.algorithms.approximation as a
-
-def test_independent_set():
- # smoke test
- G = nx.Graph()
- assert_equal(len(a.maximum_independent_set(G)),0)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_matching.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_matching.py
deleted file mode 100644
index b768c39..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_matching.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from nose.tools import *
-import networkx as nx
-import networkx.algorithms.approximation as a
-
-def test_min_maximal_matching():
- # smoke test
- G = nx.Graph()
- assert_equal(len(a.min_maximal_matching(G)),0)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_ramsey.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_ramsey.py
deleted file mode 100644
index 7ab8dac..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_ramsey.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from nose.tools import *
-import networkx as nx
-import networkx.algorithms.approximation as apxa
-
-def test_ramsey():
- # this should only find the complete graph
- graph = nx.complete_graph(10)
- c, i = apxa.ramsey_R2(graph)
- cdens = nx.density(graph.subgraph(c))
- eq_(cdens, 1.0, "clique not found by ramsey!")
- idens = nx.density(graph.subgraph(i))
- eq_(idens, 0.0, "i-set not found by ramsey!")
-
- # this trival graph has no cliques. should just find i-sets
- graph = nx.trivial_graph(nx.Graph())
- c, i = apxa.ramsey_R2(graph)
- cdens = nx.density(graph.subgraph(c))
- eq_(cdens, 0.0, "clique not found by ramsey!")
- idens = nx.density(graph.subgraph(i))
- eq_(idens, 0.0, "i-set not found by ramsey!")
-
- graph = nx.barbell_graph(10, 5, nx.Graph())
- c, i = apxa.ramsey_R2(graph)
- cdens = nx.density(graph.subgraph(c))
- eq_(cdens, 1.0, "clique not found by ramsey!")
- idens = nx.density(graph.subgraph(i))
- eq_(idens, 0.0, "i-set not found by ramsey!")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_vertex_cover.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_vertex_cover.py
deleted file mode 100644
index 74b3f51..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/test_vertex_cover.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx.algorithms import approximation as a
-
-class TestMWVC:
-
- def test_min_vertex_cover(self):
- # create a simple star graph
- size = 50
- sg = nx.star_graph(size)
- cover = a.min_weighted_vertex_cover(sg)
- assert_equals(2, len(cover))
- for u, v in sg.edges_iter():
- ok_((u in cover or v in cover), "Node node covered!")
-
- wg = nx.Graph()
- wg.add_node(0, weight=10)
- wg.add_node(1, weight=1)
- wg.add_node(2, weight=1)
- wg.add_node(3, weight=1)
- wg.add_node(4, weight=1)
-
- wg.add_edge(0, 1)
- wg.add_edge(0, 2)
- wg.add_edge(0, 3)
- wg.add_edge(0, 4)
-
- wg.add_edge(1,2)
- wg.add_edge(2,3)
- wg.add_edge(3,4)
- wg.add_edge(4,1)
-
- cover = a.min_weighted_vertex_cover(wg, weight="weight")
- csum = sum(wg.node[node]["weight"] for node in cover)
- assert_equals(4, csum)
-
- for u, v in wg.edges_iter():
- ok_((u in cover or v in cover), "Node node covered!")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/vertex_cover.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/vertex_cover.py
deleted file mode 100644
index c588e18..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/vertex_cover.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-************
-Vertex Cover
-************
-
-Given an undirected graph `G = (V, E)` and a function w assigning nonnegative
-weights to its vertices, find a minimum weight subset of V such that each edge
-in E is incident to at least one vertex in the subset.
-
-http://en.wikipedia.org/wiki/Vertex_cover
-"""
-# Copyright (C) 2011-2012 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-from networkx.utils import *
-__all__ = ["min_weighted_vertex_cover"]
-__author__ = """Nicholas Mancuso (nick.mancuso@gmail.com)"""
-
-@not_implemented_for('directed')
-def min_weighted_vertex_cover(G, weight=None):
- r"""2-OPT Local Ratio for Minimum Weighted Vertex Cover
-
- Find an approximate minimum weighted vertex cover of a graph.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- weight : None or string, optional (default = None)
- If None, every edge has weight/distance/cost 1. If a string, use this
- edge attribute as the edge weight. Any edge attribute not present
- defaults to 1.
-
- Returns
- -------
- min_weighted_cover : set
- Returns a set of vertices whose weight sum is no more than 2 * OPT.
-
- Notes
- -----
- Local-Ratio algorithm for computing an approximate vertex cover.
- Algorithm greedily reduces the costs over edges and iteratively
- builds a cover. Worst-case runtime is `O(|E|)`.
-
- References
- ----------
- .. [1] Bar-Yehuda, R., & Even, S. (1985). A local-ratio theorem for
- approximating the weighted vertex cover problem.
- Annals of Discrete Mathematics, 25, 27–46
- http://www.cs.technion.ac.il/~reuven/PDF/vc_lr.pdf
- """
- weight_func = lambda nd: nd.get(weight, 1)
- cost = dict((n, weight_func(nd)) for n, nd in G.nodes(data=True))
-
- # while there are edges uncovered, continue
- for u,v in G.edges_iter():
- # select some uncovered edge
- min_cost = min([cost[u], cost[v]])
- cost[u] -= min_cost
- cost[v] -= min_cost
-
- return set(u for u in cost if cost[u] == 0)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/__init__.py
deleted file mode 100644
index 4d98886..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from networkx.algorithms.assortativity.connectivity import *
-from networkx.algorithms.assortativity.correlation import *
-from networkx.algorithms.assortativity.mixing import *
-from networkx.algorithms.assortativity.neighbor_degree import *
-from networkx.algorithms.assortativity.pairs import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/connectivity.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/connectivity.py
deleted file mode 100644
index 17b0265..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/connectivity.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#-*- coding: utf-8 -*-
-# Copyright (C) 2011 by
-# Jordi Torrents <jtorrents@milnou.net>
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-from collections import defaultdict
-import networkx as nx
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Aric Hagberg (hagberg@lanl.gov)'])
-__all__ = ['average_degree_connectivity',
- 'k_nearest_neighbors']
-
-def _avg_deg_conn(G, neighbors, source_degree, target_degree,
- nodes=None, weight=None):
- # "k nearest neighbors, or neighbor_connectivity
- dsum = defaultdict(float)
- dnorm = defaultdict(float)
- for n,k in source_degree(nodes).items():
- nbrdeg = target_degree(neighbors(n))
- if weight is None:
- s = float(sum(nbrdeg.values()))
- else: # weight nbr degree by weight of (n,nbr) edge
- if neighbors == G.neighbors:
- s = float(sum((G[n][nbr].get(weight,1)*d
- for nbr,d in nbrdeg.items())))
- elif neighbors == G.successors:
- s = float(sum((G[n][nbr].get(weight,1)*d
- for nbr,d in nbrdeg.items())))
- elif neighbors == G.predecessors:
- s = float(sum((G[nbr][n].get(weight,1)*d
- for nbr,d in nbrdeg.items())))
- dnorm[k] += source_degree(n, weight=weight)
- dsum[k] += s
-
- # normalize
- dc = {}
- for k,avg in dsum.items():
- dc[k]=avg
- norm = dnorm[k]
- if avg > 0 and norm > 0:
- dc[k]/=norm
- return dc
-
-def average_degree_connectivity(G, source="in+out", target="in+out",
- nodes=None, weight=None):
- r"""Compute the average degree connectivity of graph.
-
- The average degree connectivity is the average nearest neighbor degree of
- nodes with degree k. For weighted graphs, an analogous measure can
- be computed using the weighted average neighbors degree defined in
- [1]_, for a node `i`, as:
-
- .. math::
-
- k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
-
- where `s_i` is the weighted degree of node `i`,
- `w_{ij}` is the weight of the edge that links `i` and `j`,
- and `N(i)` are the neighbors of node `i`.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : "in"|"out"|"in+out" (default:"in+out")
- Directed graphs only. Use "in"- or "out"-degree for source node.
-
- target : "in"|"out"|"in+out" (default:"in+out"
- Directed graphs only. Use "in"- or "out"-degree for target node.
-
- nodes: list or iterable (optional)
- Compute neighbor connectivity for these nodes. The default is all nodes.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used as a weight.
- If None, then each edge has weight 1.
-
- Returns
- -------
- d: dict
- A dictionary keyed by degree k with the value of average connectivity.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> G.edge[1][2]['weight'] = 3
- >>> nx.k_nearest_neighbors(G)
- {1: 2.0, 2: 1.5}
- >>> nx.k_nearest_neighbors(G, weight='weight')
- {1: 2.0, 2: 1.75}
-
- See also
- --------
- neighbors_average_degree
-
- Notes
- -----
- This algorithm is sometimes called "k nearest neighbors'.
-
- References
- ----------
- .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
- "The architecture of complex weighted networks".
- PNAS 101 (11): 3747–3752 (2004).
- """
- source_degree = G.degree
- target_degree = G.degree
- neighbors = G.neighbors
- if G.is_directed():
- direction = {'out':G.out_degree,
- 'in':G.in_degree,
- 'in+out': G.degree}
- source_degree = direction[source]
- target_degree = direction[target]
- if source == 'in':
- neighbors=G.predecessors
- elif source == 'out':
- neighbors=G.successors
- return _avg_deg_conn(G, neighbors, source_degree, target_degree,
- nodes=nodes, weight=weight)
-
-k_nearest_neighbors=average_degree_connectivity
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/correlation.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/correlation.py
deleted file mode 100644
index 6d471c9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/correlation.py
+++ /dev/null
@@ -1,298 +0,0 @@
-#-*- coding: utf-8 -*-
-"""Node assortativity coefficients and correlation measures.
-"""
-import networkx as nx
-from networkx.algorithms.assortativity.mixing import degree_mixing_matrix, \
- attribute_mixing_matrix, numeric_mixing_matrix
-from networkx.algorithms.assortativity.pairs import node_degree_xy, \
- node_attribute_xy
-__author__ = ' '.join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Oleguer Sagarra <oleguer.sagarra@gmail.com>'])
-__all__ = ['degree_pearson_correlation_coefficient',
- 'degree_assortativity_coefficient',
- 'attribute_assortativity_coefficient',
- 'numeric_assortativity_coefficient']
-
-def degree_assortativity_coefficient(G, x='out', y='in', weight=None,
- nodes=None):
- """Compute degree assortativity of graph.
-
- Assortativity measures the similarity of connections
- in the graph with respect to the node degree.
-
- Parameters
- ----------
- G : NetworkX graph
-
- x: string ('in','out')
- The degree type for source node (directed graphs only).
-
- y: string ('in','out')
- The degree type for target node (directed graphs only).
-
- weight: string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- nodes: list or iterable (optional)
- Compute degree assortativity only for nodes in container.
- The default is all nodes.
-
- Returns
- -------
- r : float
- Assortativity of graph by degree.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> r=nx.degree_assortativity_coefficient(G)
- >>> print("%3.1f"%r)
- -0.5
-
- See Also
- --------
- attribute_assortativity_coefficient
- numeric_assortativity_coefficient
- neighbor_connectivity
- degree_mixing_dict
- degree_mixing_matrix
-
- Notes
- -----
- This computes Eq. (21) in Ref. [1]_ , where e is the joint
- probability distribution (mixing matrix) of the degrees. If G is
- directed than the matrix e is the joint probability of the
- user-specified degree type for the source and target.
-
- References
- ----------
- .. [1] M. E. J. Newman, Mixing patterns in networks,
- Physical Review E, 67 026126, 2003
- .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
- Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
- """
- M = degree_mixing_matrix(G, x=x, y=y, nodes=nodes, weight=weight)
- return numeric_ac(M)
-
-
-def degree_pearson_correlation_coefficient(G, x='out', y='in',
- weight=None, nodes=None):
- """Compute degree assortativity of graph.
-
- Assortativity measures the similarity of connections
- in the graph with respect to the node degree.
-
- This is the same as degree_assortativity_coefficient but uses the
- potentially faster scipy.stats.pearsonr function.
-
- Parameters
- ----------
- G : NetworkX graph
-
- x: string ('in','out')
- The degree type for source node (directed graphs only).
-
- y: string ('in','out')
- The degree type for target node (directed graphs only).
-
- weight: string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- nodes: list or iterable (optional)
- Compute pearson correlation of degrees only for specified nodes.
- The default is all nodes.
-
- Returns
- -------
- r : float
- Assortativity of graph by degree.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> r=nx.degree_pearson_correlation_coefficient(G)
- >>> r
- -0.5
-
- Notes
- -----
- This calls scipy.stats.pearsonr.
-
- References
- ----------
- .. [1] M. E. J. Newman, Mixing patterns in networks
- Physical Review E, 67 026126, 2003
- .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
- Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
- """
- try:
- import scipy.stats as stats
- except ImportError:
- raise ImportError(
- "Assortativity requires SciPy: http://scipy.org/ ")
- xy=node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
- x,y=zip(*xy)
- return stats.pearsonr(x,y)[0]
-
-
-def attribute_assortativity_coefficient(G,attribute,nodes=None):
- """Compute assortativity for node attributes.
-
- Assortativity measures the similarity of connections
- in the graph with respect to the given attribute.
-
- Parameters
- ----------
- G : NetworkX graph
-
- attribute : string
- Node attribute key
-
- nodes: list or iterable (optional)
- Compute attribute assortativity for nodes in container.
- The default is all nodes.
-
- Returns
- -------
- r: float
- Assortativity of graph for given attribute
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_nodes_from([0,1],color='red')
- >>> G.add_nodes_from([2,3],color='blue')
- >>> G.add_edges_from([(0,1),(2,3)])
- >>> print(nx.attribute_assortativity_coefficient(G,'color'))
- 1.0
-
- Notes
- -----
- This computes Eq. (2) in Ref. [1]_ , trace(M)-sum(M))/(1-sum(M),
- where M is the joint probability distribution (mixing matrix)
- of the specified attribute.
-
- References
- ----------
- .. [1] M. E. J. Newman, Mixing patterns in networks,
- Physical Review E, 67 026126, 2003
- """
- M = attribute_mixing_matrix(G,attribute,nodes)
- return attribute_ac(M)
-
-
-def numeric_assortativity_coefficient(G, attribute, nodes=None):
- """Compute assortativity for numerical node attributes.
-
- Assortativity measures the similarity of connections
- in the graph with respect to the given numeric attribute.
-
- Parameters
- ----------
- G : NetworkX graph
-
- attribute : string
- Node attribute key
-
- nodes: list or iterable (optional)
- Compute numeric assortativity only for attributes of nodes in
- container. The default is all nodes.
-
- Returns
- -------
- r: float
- Assortativity of graph for given attribute
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_nodes_from([0,1],size=2)
- >>> G.add_nodes_from([2,3],size=3)
- >>> G.add_edges_from([(0,1),(2,3)])
- >>> print(nx.numeric_assortativity_coefficient(G,'size'))
- 1.0
-
- Notes
- -----
- This computes Eq. (21) in Ref. [1]_ , for the mixing matrix of
- of the specified attribute.
-
- References
- ----------
- .. [1] M. E. J. Newman, Mixing patterns in networks
- Physical Review E, 67 026126, 2003
- """
- a = numeric_mixing_matrix(G,attribute,nodes)
- return numeric_ac(a)
-
-
-def attribute_ac(M):
- """Compute assortativity for attribute matrix M.
-
- Parameters
- ----------
- M : numpy array or matrix
- Attribute mixing matrix.
-
- Notes
- -----
- This computes Eq. (2) in Ref. [1]_ , (trace(e)-sum(e))/(1-sum(e)),
- where e is the joint probability distribution (mixing matrix)
- of the specified attribute.
-
- References
- ----------
- .. [1] M. E. J. Newman, Mixing patterns in networks,
- Physical Review E, 67 026126, 2003
- """
- try:
- import numpy
- except ImportError:
- raise ImportError(
- "attribute_assortativity requires NumPy: http://scipy.org/ ")
- if M.sum() != 1.0:
- M=M/float(M.sum())
- M=numpy.asmatrix(M)
- s=(M*M).sum()
- t=M.trace()
- r=(t-s)/(1-s)
- return float(r)
-
-
-def numeric_ac(M):
- # M is a numpy matrix or array
- # numeric assortativity coefficient, pearsonr
- try:
- import numpy
- except ImportError:
- raise ImportError('numeric_assortativity requires ',
- 'NumPy: http://scipy.org/')
- if M.sum() != 1.0:
- M=M/float(M.sum())
- nx,ny=M.shape # nx=ny
- x=numpy.arange(nx)
- y=numpy.arange(ny)
- a=M.sum(axis=0)
- b=M.sum(axis=1)
- vara=(a*x**2).sum()-((a*x).sum())**2
- varb=(b*x**2).sum()-((b*x).sum())**2
- xy=numpy.outer(x,y)
- ab=numpy.outer(a,b)
- return (xy*(M-ab)).sum()/numpy.sqrt(vara*varb)
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/mixing.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/mixing.py
deleted file mode 100644
index 2c0e4f0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/mixing.py
+++ /dev/null
@@ -1,248 +0,0 @@
-#-*- coding: utf-8 -*-
-"""
-Mixing matrices for node attributes and degree.
-"""
-import networkx as nx
-from networkx.utils import dict_to_numpy_array
-from networkx.algorithms.assortativity.pairs import node_degree_xy, \
- node_attribute_xy
-__author__ = ' '.join(['Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['attribute_mixing_matrix',
- 'attribute_mixing_dict',
- 'degree_mixing_matrix',
- 'degree_mixing_dict',
- 'numeric_mixing_matrix',
- 'mixing_dict']
-
-def attribute_mixing_dict(G,attribute,nodes=None,normalized=False):
- """Return dictionary representation of mixing matrix for attribute.
-
- Parameters
- ----------
- G : graph
- NetworkX graph object.
-
- attribute : string
- Node attribute key.
-
- nodes: list or iterable (optional)
- Unse nodes in container to build the dict. The default is all nodes.
-
- normalized : bool (default=False)
- Return counts if False or probabilities if True.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_nodes_from([0,1],color='red')
- >>> G.add_nodes_from([2,3],color='blue')
- >>> G.add_edge(1,3)
- >>> d=nx.attribute_mixing_dict(G,'color')
- >>> print(d['red']['blue'])
- 1
- >>> print(d['blue']['red']) # d symmetric for undirected graphs
- 1
-
- Returns
- -------
- d : dictionary
- Counts or joint probability of occurrence of attribute pairs.
- """
- xy_iter=node_attribute_xy(G,attribute,nodes)
- return mixing_dict(xy_iter,normalized=normalized)
-
-
-def attribute_mixing_matrix(G,attribute,nodes=None,mapping=None,
- normalized=True):
- """Return mixing matrix for attribute.
-
- Parameters
- ----------
- G : graph
- NetworkX graph object.
-
- attribute : string
- Node attribute key.
-
- nodes: list or iterable (optional)
- Use only nodes in container to build the matrix. The default is
- all nodes.
-
- mapping : dictionary, optional
- Mapping from node attribute to integer index in matrix.
- If not specified, an arbitrary ordering will be used.
-
- normalized : bool (default=False)
- Return counts if False or probabilities if True.
-
- Returns
- -------
- m: numpy array
- Counts or joint probability of occurrence of attribute pairs.
- """
- d=attribute_mixing_dict(G,attribute,nodes)
- a=dict_to_numpy_array(d,mapping=mapping)
- if normalized:
- a=a/a.sum()
- return a
-
-
-def degree_mixing_dict(G, x='out', y='in', weight=None,
- nodes=None, normalized=False):
- """Return dictionary representation of mixing matrix for degree.
-
- Parameters
- ----------
- G : graph
- NetworkX graph object.
-
- x: string ('in','out')
- The degree type for source node (directed graphs only).
-
- y: string ('in','out')
- The degree type for target node (directed graphs only).
-
- weight: string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- normalized : bool (default=False)
- Return counts if False or probabilities if True.
-
- Returns
- -------
- d: dictionary
- Counts or joint probability of occurrence of degree pairs.
- """
- xy_iter=node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
- return mixing_dict(xy_iter,normalized=normalized)
-
-
-
-def degree_mixing_matrix(G, x='out', y='in', weight=None,
- nodes=None, normalized=True):
- """Return mixing matrix for attribute.
-
- Parameters
- ----------
- G : graph
- NetworkX graph object.
-
- x: string ('in','out')
- The degree type for source node (directed graphs only).
-
- y: string ('in','out')
- The degree type for target node (directed graphs only).
-
- nodes: list or iterable (optional)
- Build the matrix using only nodes in container.
- The default is all nodes.
-
- weight: string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- normalized : bool (default=False)
- Return counts if False or probabilities if True.
-
- Returns
- -------
- m: numpy array
- Counts, or joint probability, of occurrence of node degree.
- """
- d=degree_mixing_dict(G, x=x, y=y, nodes=nodes, weight=weight)
- s=set(d.keys())
- for k,v in d.items():
- s.update(v.keys())
- m=max(s)
- mapping=dict(zip(range(m+1),range(m+1)))
- a=dict_to_numpy_array(d,mapping=mapping)
- if normalized:
- a=a/a.sum()
- return a
-
-def numeric_mixing_matrix(G,attribute,nodes=None,normalized=True):
- """Return numeric mixing matrix for attribute.
-
- Parameters
- ----------
- G : graph
- NetworkX graph object.
-
- attribute : string
- Node attribute key.
-
- nodes: list or iterable (optional)
- Build the matrix only with nodes in container. The default is all nodes.
-
- normalized : bool (default=False)
- Return counts if False or probabilities if True.
-
- Returns
- -------
- m: numpy array
- Counts, or joint, probability of occurrence of node attribute pairs.
- """
- d=attribute_mixing_dict(G,attribute,nodes)
- s=set(d.keys())
- for k,v in d.items():
- s.update(v.keys())
- m=max(s)
- mapping=dict(zip(range(m+1),range(m+1)))
- a=dict_to_numpy_array(d,mapping=mapping)
- if normalized:
- a=a/a.sum()
- return a
-
-def mixing_dict(xy,normalized=False):
- """Return a dictionary representation of mixing matrix.
-
- Parameters
- ----------
- xy : list or container of two-tuples
- Pairs of (x,y) items.
-
- attribute : string
- Node attribute key
-
- normalized : bool (default=False)
- Return counts if False or probabilities if True.
-
- Returns
- -------
- d: dictionary
- Counts or Joint probability of occurrence of values in xy.
- """
- d={}
- psum=0.0
- for x,y in xy:
- if x not in d:
- d[x]={}
- if y not in d:
- d[y]={}
- v = d[x].get(y,0)
- d[x][y] = v+1
- psum+=1
-
-
- if normalized:
- for k,jdict in d.items():
- for j in jdict:
- jdict[j]/=psum
- return d
-
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/neighbor_degree.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/neighbor_degree.py
deleted file mode 100644
index 9257954..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/neighbor_degree.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#-*- coding: utf-8 -*-
-# Copyright (C) 2011 by
-# Jordi Torrents <jtorrents@milnou.net>
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Aric Hagberg (hagberg@lanl.gov)'])
-__all__ = ["average_neighbor_degree"]
-
-
-def _average_nbr_deg(G, source_degree, target_degree, nodes=None, weight=None):
- # average degree of neighbors
- avg = {}
- for n,deg in source_degree(nodes,weight=weight).items():
- # normalize but not by zero degree
- if deg == 0:
- deg = 1
- nbrdeg = target_degree(G[n])
- if weight is None:
- avg[n] = sum(nbrdeg.values())/float(deg)
- else:
- avg[n] = sum((G[n][nbr].get(weight,1)*d
- for nbr,d in nbrdeg.items()))/float(deg)
- return avg
-
-def average_neighbor_degree(G, source='out', target='out',
- nodes=None, weight=None):
- r"""Returns the average degree of the neighborhood of each node.
-
- The average degree of a node `i` is
-
- .. math::
-
- k_{nn,i} = \frac{1}{|N(i)|} \sum_{j \in N(i)} k_j
-
- where `N(i)` are the neighbors of node `i` and `k_j` is
- the degree of node `j` which belongs to `N(i)`. For weighted
- graphs, an analogous measure can be defined [1]_,
-
- .. math::
-
- k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
-
- where `s_i` is the weighted degree of node `i`, `w_{ij}`
- is the weight of the edge that links `i` and `j` and
- `N(i)` are the neighbors of node `i`.
-
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : string ("in"|"out")
- Directed graphs only.
- Use "in"- or "out"-degree for source node.
-
- target : string ("in"|"out")
- Directed graphs only.
- Use "in"- or "out"-degree for target node.
-
- nodes : list or iterable, optional
- Compute neighbor degree for specified nodes. The default is
- all nodes in the graph.
-
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used as a weight.
- If None, then each edge has weight 1.
-
- Returns
- -------
- d: dict
- A dictionary keyed by node with average neighbors degree value.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> G.edge[0][1]['weight'] = 5
- >>> G.edge[2][3]['weight'] = 3
-
- >>> nx.average_neighbor_degree(G)
- {0: 2.0, 1: 1.5, 2: 1.5, 3: 2.0}
- >>> nx.average_neighbor_degree(G, weight='weight')
- {0: 2.0, 1: 1.1666666666666667, 2: 1.25, 3: 2.0}
-
- >>> G=nx.DiGraph()
- >>> G.add_path([0,1,2,3])
- >>> nx.average_neighbor_degree(G, source='in', target='in')
- {0: 1.0, 1: 1.0, 2: 1.0, 3: 0.0}
-
- >>> nx.average_neighbor_degree(G, source='out', target='out')
- {0: 1.0, 1: 1.0, 2: 0.0, 3: 0.0}
-
- Notes
- -----
- For directed graphs you can also specify in-degree or out-degree
- by passing keyword arguments.
-
- See Also
- --------
- average_degree_connectivity
-
- References
- ----------
- .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
- "The architecture of complex weighted networks".
- PNAS 101 (11): 3747–3752 (2004).
- """
- source_degree = G.degree
- target_degree = G.degree
- if G.is_directed():
- direction = {'out':G.out_degree,
- 'in':G.in_degree}
- source_degree = direction[source]
- target_degree = direction[target]
- return _average_nbr_deg(G, source_degree, target_degree,
- nodes=nodes, weight=weight)
-
-# obsolete
-# def average_neighbor_in_degree(G, nodes=None, weight=None):
-# if not G.is_directed():
-# raise nx.NetworkXError("Not defined for undirected graphs.")
-# return _average_nbr_deg(G, G.in_degree, G.in_degree, nodes, weight)
-# average_neighbor_in_degree.__doc__=average_neighbor_degree.__doc__
-
-# def average_neighbor_out_degree(G, nodes=None, weight=None):
-# if not G.is_directed():
-# raise nx.NetworkXError("Not defined for undirected graphs.")
-# return _average_nbr_deg(G, G.out_degree, G.out_degree, nodes, weight)
-# average_neighbor_out_degree.__doc__=average_neighbor_degree.__doc__
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/pairs.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/pairs.py
deleted file mode 100644
index 0ed0fa9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/pairs.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#-*- coding: utf-8 -*-
-"""Generators of x-y pairs of node data."""
-import networkx as nx
-from networkx.utils import dict_to_numpy_array
-__author__ = ' '.join(['Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['node_attribute_xy',
- 'node_degree_xy']
-
-def node_attribute_xy(G, attribute, nodes=None):
- """Return iterator of node-attribute pairs for all edges in G.
-
- Parameters
- ----------
- G: NetworkX graph
-
- attribute: key
- The node attribute key.
-
- nodes: list or iterable (optional)
- Use only edges that are adjacency to specified nodes.
- The default is all nodes.
-
- Returns
- -------
- (x,y): 2-tuple
- Generates 2-tuple of (attribute,attribute) values.
-
- Examples
- --------
- >>> G = nx.DiGraph()
- >>> G.add_node(1,color='red')
- >>> G.add_node(2,color='blue')
- >>> G.add_edge(1,2)
- >>> list(nx.node_attribute_xy(G,'color'))
- [('red', 'blue')]
-
- Notes
- -----
- For undirected graphs each edge is produced twice, once for each edge
- representation (u,v) and (v,u), with the exception of self-loop edges
- which only appear once.
- """
- if nodes is None:
- nodes = set(G)
- else:
- nodes = set(nodes)
- node = G.node
- for u,nbrsdict in G.adjacency_iter():
- if u not in nodes:
- continue
- uattr = node[u].get(attribute,None)
- if G.is_multigraph():
- for v,keys in nbrsdict.items():
- vattr = node[v].get(attribute,None)
- for k,d in keys.items():
- yield (uattr,vattr)
- else:
- for v,eattr in nbrsdict.items():
- vattr = node[v].get(attribute,None)
- yield (uattr,vattr)
-
-
-def node_degree_xy(G, x='out', y='in', weight=None, nodes=None):
- """Generate node degree-degree pairs for edges in G.
-
- Parameters
- ----------
- G: NetworkX graph
-
- x: string ('in','out')
- The degree type for source node (directed graphs only).
-
- y: string ('in','out')
- The degree type for target node (directed graphs only).
-
- weight: string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- nodes: list or iterable (optional)
- Use only edges that are adjacency to specified nodes.
- The default is all nodes.
-
- Returns
- -------
- (x,y): 2-tuple
- Generates 2-tuple of (degree,degree) values.
-
-
- Examples
- --------
- >>> G = nx.DiGraph()
- >>> G.add_edge(1,2)
- >>> list(nx.node_degree_xy(G,x='out',y='in'))
- [(1, 1)]
- >>> list(nx.node_degree_xy(G,x='in',y='out'))
- [(0, 0)]
-
- Notes
- -----
- For undirected graphs each edge is produced twice, once for each edge
- representation (u,v) and (v,u), with the exception of self-loop edges
- which only appear once.
- """
- if nodes is None:
- nodes = set(G)
- else:
- nodes = set(nodes)
- xdeg = G.degree_iter
- ydeg = G.degree_iter
- if G.is_directed():
- direction = {'out':G.out_degree_iter,
- 'in':G.in_degree_iter}
- xdeg = direction[x]
- ydeg = direction[y]
-
- for u,degu in xdeg(nodes, weight=weight):
- neighbors = (nbr for _,nbr in G.edges_iter(u) if nbr in nodes)
- for v,degv in ydeg(neighbors, weight=weight):
- yield degu,degv
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/base_test.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/base_test.py
deleted file mode 100644
index 2e16544..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/base_test.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import networkx as nx
-
-class BaseTestAttributeMixing(object):
-
- def setUp(self):
- G=nx.Graph()
- G.add_nodes_from([0,1],fish='one')
- G.add_nodes_from([2,3],fish='two')
- G.add_nodes_from([4],fish='red')
- G.add_nodes_from([5],fish='blue')
- G.add_edges_from([(0,1),(2,3),(0,4),(2,5)])
- self.G=G
-
- D=nx.DiGraph()
- D.add_nodes_from([0,1],fish='one')
- D.add_nodes_from([2,3],fish='two')
- D.add_nodes_from([4],fish='red')
- D.add_nodes_from([5],fish='blue')
- D.add_edges_from([(0,1),(2,3),(0,4),(2,5)])
- self.D=D
-
- M=nx.MultiGraph()
- M.add_nodes_from([0,1],fish='one')
- M.add_nodes_from([2,3],fish='two')
- M.add_nodes_from([4],fish='red')
- M.add_nodes_from([5],fish='blue')
- M.add_edges_from([(0,1),(0,1),(2,3)])
- self.M=M
-
- S=nx.Graph()
- S.add_nodes_from([0,1],fish='one')
- S.add_nodes_from([2,3],fish='two')
- S.add_nodes_from([4],fish='red')
- S.add_nodes_from([5],fish='blue')
- S.add_edge(0,0)
- S.add_edge(2,2)
- self.S=S
-
-class BaseTestDegreeMixing(object):
-
- def setUp(self):
- self.P4=nx.path_graph(4)
- self.D=nx.DiGraph()
- self.D.add_edges_from([(0, 2), (0, 3), (1, 3), (2, 3)])
- self.M=nx.MultiGraph()
- self.M.add_path(list(range(4)))
- self.M.add_edge(0,1)
- self.S=nx.Graph()
- self.S.add_edges_from([(0,0),(1,1)])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_connectivity.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_connectivity.py
deleted file mode 100644
index 5091161..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_connectivity.py
+++ /dev/null
@@ -1,121 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestNeighborConnectivity(object):
-
- def test_degree_p4(self):
- G=nx.path_graph(4)
- answer={1:2.0,2:1.5}
- nd = nx.average_degree_connectivity(G)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- answer={2:2.0,4:1.5}
- nd = nx.average_degree_connectivity(D)
- assert_equal(nd,answer)
-
- answer={1:2.0,2:1.5}
- D=G.to_directed()
- nd = nx.average_degree_connectivity(D, source='in', target='in')
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_degree_connectivity(D, source='in', target='in')
- assert_equal(nd,answer)
-
- def test_degree_p4_weighted(self):
- G=nx.path_graph(4)
- G[1][2]['weight']=4
- answer={1:2.0,2:1.8}
- nd = nx.average_degree_connectivity(G,weight='weight')
- assert_equal(nd,answer)
- answer={1:2.0,2:1.5}
- nd = nx.average_degree_connectivity(G)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- answer={2:2.0,4:1.8}
- nd = nx.average_degree_connectivity(D,weight='weight')
- assert_equal(nd,answer)
-
- answer={1:2.0,2:1.8}
- D=G.to_directed()
- nd = nx.average_degree_connectivity(D,weight='weight', source='in',
- target='in')
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_degree_connectivity(D,source='in',target='out',
- weight='weight')
- assert_equal(nd,answer)
-
- def test_weight_keyword(self):
- G=nx.path_graph(4)
- G[1][2]['other']=4
- answer={1:2.0,2:1.8}
- nd = nx.average_degree_connectivity(G,weight='other')
- assert_equal(nd,answer)
- answer={1:2.0,2:1.5}
- nd = nx.average_degree_connectivity(G,weight=None)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- answer={2:2.0,4:1.8}
- nd = nx.average_degree_connectivity(D,weight='other')
- assert_equal(nd,answer)
-
- answer={1:2.0,2:1.8}
- D=G.to_directed()
- nd = nx.average_degree_connectivity(D,weight='other', source='in',
- target='in')
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_degree_connectivity(D,weight='other',source='in',
- target='in')
- assert_equal(nd,answer)
-
- def test_degree_barrat(self):
- G=nx.star_graph(5)
- G.add_edges_from([(5,6),(5,7),(5,8),(5,9)])
- G[0][5]['weight']=5
- nd = nx.average_degree_connectivity(G)[5]
- assert_equal(nd,1.8)
- nd = nx.average_degree_connectivity(G,weight='weight')[5]
- assert_almost_equal(nd,3.222222,places=5)
- nd = nx.k_nearest_neighbors(G,weight='weight')[5]
- assert_almost_equal(nd,3.222222,places=5)
-
- def test_zero_deg(self):
- G=nx.DiGraph()
- G.add_edge(1,2)
- G.add_edge(1,3)
- G.add_edge(1,4)
- c = nx.average_degree_connectivity(G)
- assert_equal(c,{1:0,3:1})
- c = nx.average_degree_connectivity(G, source='in', target='in')
- assert_equal(c,{0:0,1:0})
- c = nx.average_degree_connectivity(G, source='in', target='out')
- assert_equal(c,{0:0,1:3})
- c = nx.average_degree_connectivity(G, source='in', target='in+out')
- assert_equal(c,{0:0,1:3})
- c = nx.average_degree_connectivity(G, source='out', target='out')
- assert_equal(c,{0:0,3:0})
- c = nx.average_degree_connectivity(G, source='out', target='in')
- assert_equal(c,{0:0,3:1})
- c = nx.average_degree_connectivity(G, source='out', target='in+out')
- assert_equal(c,{0:0,3:1})
-
-
- def test_in_out_weight(self):
- from itertools import permutations
- G=nx.DiGraph()
- G.add_edge(1,2,weight=1)
- G.add_edge(1,3,weight=1)
- G.add_edge(3,1,weight=1)
- for s,t in permutations(['in','out','in+out'],2):
- c = nx.average_degree_connectivity(G, source=s, target=t)
- cw = nx.average_degree_connectivity(G,source=s, target=t,
- weight='weight')
- assert_equal(c,cw)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_correlation.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_correlation.py
deleted file mode 100644
index fbb2d51..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_correlation.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing
-from networkx.algorithms.assortativity.correlation import attribute_ac
-
-
-class TestDegreeMixingCorrelation(BaseTestDegreeMixing):
- @classmethod
- def setupClass(cls):
- global np
- global npt
- try:
- import numpy as np
- import numpy.testing as npt
- except ImportError:
- raise SkipTest('NumPy not available.')
- try:
- import scipy
- import scipy.stats
- except ImportError:
- raise SkipTest('SciPy not available.')
-
-
-
- def test_degree_assortativity_undirected(self):
- r=nx.degree_assortativity_coefficient(self.P4)
- npt.assert_almost_equal(r,-1.0/2,decimal=4)
-
- def test_degree_assortativity_directed(self):
- r=nx.degree_assortativity_coefficient(self.D)
- npt.assert_almost_equal(r,-0.57735,decimal=4)
-
- def test_degree_assortativity_multigraph(self):
- r=nx.degree_assortativity_coefficient(self.M)
- npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
-
-
- def test_degree_assortativity_undirected(self):
- r=nx.degree_pearson_correlation_coefficient(self.P4)
- npt.assert_almost_equal(r,-1.0/2,decimal=4)
-
- def test_degree_assortativity_directed(self):
- r=nx.degree_pearson_correlation_coefficient(self.D)
- npt.assert_almost_equal(r,-0.57735,decimal=4)
-
- def test_degree_assortativity_multigraph(self):
- r=nx.degree_pearson_correlation_coefficient(self.M)
- npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
-
-
-
-class TestAttributeMixingCorrelation(BaseTestAttributeMixing):
- @classmethod
- def setupClass(cls):
- global np
- global npt
- try:
- import numpy as np
- import numpy.testing as npt
-
- except ImportError:
- raise SkipTest('NumPy not available.')
-
-
- def test_attribute_assortativity_undirected(self):
- r=nx.attribute_assortativity_coefficient(self.G,'fish')
- assert_equal(r,6.0/22.0)
-
- def test_attribute_assortativity_directed(self):
- r=nx.attribute_assortativity_coefficient(self.D,'fish')
- assert_equal(r,1.0/3.0)
-
- def test_attribute_assortativity_multigraph(self):
- r=nx.attribute_assortativity_coefficient(self.M,'fish')
- assert_equal(r,1.0)
-
- def test_attribute_assortativity_coefficient(self):
- # from "Mixing patterns in networks"
- a=np.array([[0.258,0.016,0.035,0.013],
- [0.012,0.157,0.058,0.019],
- [0.013,0.023,0.306,0.035],
- [0.005,0.007,0.024,0.016]])
- r=attribute_ac(a)
- npt.assert_almost_equal(r,0.623,decimal=3)
-
- def test_attribute_assortativity_coefficient2(self):
- a=np.array([[0.18,0.02,0.01,0.03],
- [0.02,0.20,0.03,0.02],
- [0.01,0.03,0.16,0.01],
- [0.03,0.02,0.01,0.22]])
-
- r=attribute_ac(a)
- npt.assert_almost_equal(r,0.68,decimal=2)
-
- def test_attribute_assortativity(self):
- a=np.array([[50,50,0],[50,50,0],[0,0,2]])
- r=attribute_ac(a)
- npt.assert_almost_equal(r,0.029,decimal=3)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_mixing.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_mixing.py
deleted file mode 100644
index ce60a94..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_mixing.py
+++ /dev/null
@@ -1,186 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing
-
-
-class TestDegreeMixingDict(BaseTestDegreeMixing):
-
-
- def test_degree_mixing_dict_undirected(self):
- d=nx.degree_mixing_dict(self.P4)
- d_result={1:{2:2},
- 2:{1:2,2:2},
- }
- assert_equal(d,d_result)
-
- def test_degree_mixing_dict_undirected_normalized(self):
- d=nx.degree_mixing_dict(self.P4, normalized=True)
- d_result={1:{2:1.0/3},
- 2:{1:1.0/3,2:1.0/3},
- }
- assert_equal(d,d_result)
-
- def test_degree_mixing_dict_directed(self):
- d=nx.degree_mixing_dict(self.D)
- print(d)
- d_result={1:{3:2},
- 2:{1:1,3:1},
- 3:{}
- }
- assert_equal(d,d_result)
-
- def test_degree_mixing_dict_multigraph(self):
- d=nx.degree_mixing_dict(self.M)
- d_result={1:{2:1},
- 2:{1:1,3:3},
- 3:{2:3}
- }
- assert_equal(d,d_result)
-
-
-class TestDegreeMixingMatrix(BaseTestDegreeMixing):
-
- @classmethod
- def setupClass(cls):
- global np
- global npt
- try:
- import numpy as np
- import numpy.testing as npt
-
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_degree_mixing_matrix_undirected(self):
- a_result=np.array([[0,0,0],
- [0,0,2],
- [0,2,2]]
- )
- a=nx.degree_mixing_matrix(self.P4,normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.degree_mixing_matrix(self.P4)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
- def test_degree_mixing_matrix_directed(self):
- a_result=np.array([[0,0,0,0],
- [0,0,0,2],
- [0,1,0,1],
- [0,0,0,0]]
- )
- a=nx.degree_mixing_matrix(self.D,normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.degree_mixing_matrix(self.D)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
- def test_degree_mixing_matrix_multigraph(self):
- a_result=np.array([[0,0,0,0],
- [0,0,1,0],
- [0,1,0,3],
- [0,0,3,0]]
- )
- a=nx.degree_mixing_matrix(self.M,normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.degree_mixing_matrix(self.M)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
-
- def test_degree_mixing_matrix_selfloop(self):
- a_result=np.array([[0,0,0],
- [0,0,0],
- [0,0,2]]
- )
- a=nx.degree_mixing_matrix(self.S,normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.degree_mixing_matrix(self.S)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
-
-class TestAttributeMixingDict(BaseTestAttributeMixing):
-
- def test_attribute_mixing_dict_undirected(self):
- d=nx.attribute_mixing_dict(self.G,'fish')
- d_result={'one':{'one':2,'red':1},
- 'two':{'two':2,'blue':1},
- 'red':{'one':1},
- 'blue':{'two':1}
- }
- assert_equal(d,d_result)
-
- def test_attribute_mixing_dict_directed(self):
- d=nx.attribute_mixing_dict(self.D,'fish')
- d_result={'one':{'one':1,'red':1},
- 'two':{'two':1,'blue':1},
- 'red':{},
- 'blue':{}
- }
- assert_equal(d,d_result)
-
-
- def test_attribute_mixing_dict_multigraph(self):
- d=nx.attribute_mixing_dict(self.M,'fish')
- d_result={'one':{'one':4},
- 'two':{'two':2},
- }
- assert_equal(d,d_result)
-
-
-
-class TestAttributeMixingMatrix(BaseTestAttributeMixing):
- @classmethod
- def setupClass(cls):
- global np
- global npt
- try:
- import numpy as np
- import numpy.testing as npt
-
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_attribute_mixing_matrix_undirected(self):
- mapping={'one':0,'two':1,'red':2,'blue':3}
- a_result=np.array([[2,0,1,0],
- [0,2,0,1],
- [1,0,0,0],
- [0,1,0,0]]
- )
- a=nx.attribute_mixing_matrix(self.G,'fish',
- mapping=mapping,
- normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.attribute_mixing_matrix(self.G,'fish',
- mapping=mapping)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
- def test_attribute_mixing_matrix_directed(self):
- mapping={'one':0,'two':1,'red':2,'blue':3}
- a_result=np.array([[1,0,1,0],
- [0,1,0,1],
- [0,0,0,0],
- [0,0,0,0]]
- )
- a=nx.attribute_mixing_matrix(self.D,'fish',
- mapping=mapping,
- normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.attribute_mixing_matrix(self.D,'fish',
- mapping=mapping)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
- def test_attribute_mixing_matrix_multigraph(self):
- mapping={'one':0,'two':1,'red':2,'blue':3}
- a_result=np.array([[4,0,0,0],
- [0,2,0,0],
- [0,0,0,0],
- [0,0,0,0]]
- )
- a=nx.attribute_mixing_matrix(self.M,'fish',
- mapping=mapping,
- normalized=False)
- npt.assert_equal(a,a_result)
- a=nx.attribute_mixing_matrix(self.M,'fish',
- mapping=mapping)
- npt.assert_equal(a,a_result/float(a_result.sum()))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_neighbor_degree.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_neighbor_degree.py
deleted file mode 100644
index 7ab99fb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_neighbor_degree.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestAverageNeighbor(object):
-
- def test_degree_p4(self):
- G=nx.path_graph(4)
- answer={0:2,1:1.5,2:1.5,3:2}
- nd = nx.average_neighbor_degree(G)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D, source='in', target='in')
- assert_equal(nd,answer)
-
- def test_degree_p4_weighted(self):
- G=nx.path_graph(4)
- G[1][2]['weight']=4
- answer={0:2,1:1.8,2:1.8,3:2}
- nd = nx.average_neighbor_degree(G,weight='weight')
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D,weight='weight')
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D,weight='weight')
- assert_equal(nd,answer)
- nd = nx.average_neighbor_degree(D,source='out',target='out',
- weight='weight')
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D,source='in',target='in',
- weight='weight')
- assert_equal(nd,answer)
-
-
- def test_degree_k4(self):
- G=nx.complete_graph(4)
- answer={0:3,1:3,2:3,3:3}
- nd = nx.average_neighbor_degree(G)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D)
- assert_equal(nd,answer)
-
- D=G.to_directed()
- nd = nx.average_neighbor_degree(D,source='in',target='in')
- assert_equal(nd,answer)
-
- def test_degree_k4_nodes(self):
- G=nx.complete_graph(4)
- answer={1:3.0,2:3.0}
- nd = nx.average_neighbor_degree(G,nodes=[1,2])
- assert_equal(nd,answer)
-
- def test_degree_barrat(self):
- G=nx.star_graph(5)
- G.add_edges_from([(5,6),(5,7),(5,8),(5,9)])
- G[0][5]['weight']=5
- nd = nx.average_neighbor_degree(G)[5]
- assert_equal(nd,1.8)
- nd = nx.average_neighbor_degree(G,weight='weight')[5]
- assert_almost_equal(nd,3.222222,places=5)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_pairs.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_pairs.py
deleted file mode 100644
index fa67a45..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/test_pairs.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing
-
-class TestAttributeMixingXY(BaseTestAttributeMixing):
-
- def test_node_attribute_xy_undirected(self):
- attrxy=sorted(nx.node_attribute_xy(self.G,'fish'))
- attrxy_result=sorted([('one','one'),
- ('one','one'),
- ('two','two'),
- ('two','two'),
- ('one','red'),
- ('red','one'),
- ('blue','two'),
- ('two','blue')
- ])
- assert_equal(attrxy,attrxy_result)
-
- def test_node_attribute_xy_undirected_nodes(self):
- attrxy=sorted(nx.node_attribute_xy(self.G,'fish',
- nodes=['one','yellow']))
- attrxy_result=sorted( [
- ])
- assert_equal(attrxy,attrxy_result)
-
-
- def test_node_attribute_xy_directed(self):
- attrxy=sorted(nx.node_attribute_xy(self.D,'fish'))
- attrxy_result=sorted([('one','one'),
- ('two','two'),
- ('one','red'),
- ('two','blue')
- ])
- assert_equal(attrxy,attrxy_result)
-
- def test_node_attribute_xy_multigraph(self):
- attrxy=sorted(nx.node_attribute_xy(self.M,'fish'))
- attrxy_result=[('one','one'),
- ('one','one'),
- ('one','one'),
- ('one','one'),
- ('two','two'),
- ('two','two')
- ]
- assert_equal(attrxy,attrxy_result)
-
- def test_node_attribute_xy_selfloop(self):
- attrxy=sorted(nx.node_attribute_xy(self.S,'fish'))
- attrxy_result=[('one','one'),
- ('two','two')
- ]
- assert_equal(attrxy,attrxy_result)
-
-
-class TestDegreeMixingXY(BaseTestDegreeMixing):
-
- def test_node_degree_xy_undirected(self):
- xy=sorted(nx.node_degree_xy(self.P4))
- xy_result=sorted([(1,2),
- (2,1),
- (2,2),
- (2,2),
- (1,2),
- (2,1)])
- assert_equal(xy,xy_result)
-
- def test_node_degree_xy_undirected_nodes(self):
- xy=sorted(nx.node_degree_xy(self.P4,nodes=[0,1,-1]))
- xy_result=sorted([(1,2),
- (2,1),])
- assert_equal(xy,xy_result)
-
-
- def test_node_degree_xy_directed(self):
- xy=sorted(nx.node_degree_xy(self.D))
- xy_result=sorted([(2,1),
- (2,3),
- (1,3),
- (1,3)])
- assert_equal(xy,xy_result)
-
- def test_node_degree_xy_multigraph(self):
- xy=sorted(nx.node_degree_xy(self.M))
- xy_result=sorted([(2,3),
- (2,3),
- (3,2),
- (3,2),
- (2,3),
- (3,2),
- (1,2),
- (2,1)])
- assert_equal(xy,xy_result)
-
-
- def test_node_degree_xy_selfloop(self):
- xy=sorted(nx.node_degree_xy(self.S))
- xy_result=sorted([(2,2),
- (2,2)])
- assert_equal(xy,xy_result)
-
- def test_node_degree_xy_weighted(self):
- G = nx.Graph()
- G.add_edge(1,2,weight=7)
- G.add_edge(2,3,weight=10)
- xy=sorted(nx.node_degree_xy(G,weight='weight'))
- xy_result=sorted([(7,17),
- (17,10),
- (17,7),
- (10,17)])
- assert_equal(xy,xy_result)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/__init__.py
deleted file mode 100644
index 53ba9d3..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/__init__.py
+++ /dev/null
@@ -1,93 +0,0 @@
-r""" This module provides functions and operations for bipartite
-graphs. Bipartite graphs `B = (U, V, E)` have two node sets `U,V` and edges in
-`E` that only connect nodes from opposite sets. It is common in the literature
-to use an spatial analogy referring to the two node sets as top and bottom nodes.
-
-The bipartite algorithms are not imported into the networkx namespace
-at the top level so the easiest way to use them is with:
-
->>> import networkx as nx
->>> from networkx.algorithms import bipartite
-
-NetworkX does not have a custom bipartite graph class but the Graph()
-or DiGraph() classes can be used to represent bipartite graphs. However,
-you have to keep track of which set each node belongs to, and make
-sure that there is no edge between nodes of the same set. The convention used
-in NetworkX is to use a node attribute named "bipartite" with values 0 or 1 to
-identify the sets each node belongs to.
-
-For example:
-
->>> B = nx.Graph()
->>> B.add_nodes_from([1,2,3,4], bipartite=0) # Add the node attribute "bipartite"
->>> B.add_nodes_from(['a','b','c'], bipartite=1)
->>> B.add_edges_from([(1,'a'), (1,'b'), (2,'b'), (2,'c'), (3,'c'), (4,'a')])
-
-Many algorithms of the bipartite module of NetworkX require, as an argument, a
-container with all the nodes that belong to one set, in addition to the bipartite
-graph `B`. If `B` is connected, you can find the node sets using a two-coloring
-algorithm:
-
->>> nx.is_connected(B)
-True
->>> bottom_nodes, top_nodes = bipartite.sets(B)
-
-list(top_nodes)
-[1, 2, 3, 4]
-list(bottom_nodes)
-['a', 'c', 'b']
-
-However, if the input graph is not connected, there are more than one possible
-colorations. Thus, the following result is correct:
-
->>> B.remove_edge(2,'c')
->>> nx.is_connected(B)
-False
->>> bottom_nodes, top_nodes = bipartite.sets(B)
-
-list(top_nodes)
-[1, 2, 4, 'c']
-list(bottom_nodes)
-['a', 3, 'b']
-
-Using the "bipartite" node attribute, you can easily get the two node sets:
-
->>> top_nodes = set(n for n,d in B.nodes(data=True) if d['bipartite']==0)
->>> bottom_nodes = set(B) - top_nodes
-
-list(top_nodes)
-[1, 2, 3, 4]
-list(bottom_nodes)
-['a', 'c', 'b']
-
-So you can easily use the bipartite algorithms that require, as an argument, a
-container with all nodes that belong to one node set:
-
->>> print(round(bipartite.density(B, bottom_nodes),2))
-0.42
->>> G = bipartite.projected_graph(B, top_nodes)
->>> G.edges()
-[(1, 2), (1, 4)]
-
-All bipartite graph generators in NetworkX build bipartite graphs with the
-"bipartite" node attribute. Thus, you can use the same approach:
-
->>> RB = nx.bipartite_random_graph(5, 7, 0.2)
->>> RB_top = set(n for n,d in RB.nodes(data=True) if d['bipartite']==0)
->>> RB_bottom = set(RB) - RB_top
->>> list(RB_top)
-[0, 1, 2, 3, 4]
->>> list(RB_bottom)
-[5, 6, 7, 8, 9, 10, 11]
-
-For other bipartite graph generators see the bipartite section of
-:doc:`generators`.
-
-"""
-
-from networkx.algorithms.bipartite.basic import *
-from networkx.algorithms.bipartite.centrality import *
-from networkx.algorithms.bipartite.cluster import *
-from networkx.algorithms.bipartite.projection import *
-from networkx.algorithms.bipartite.redundancy import *
-from networkx.algorithms.bipartite.spectral import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/basic.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/basic.py
deleted file mode 100644
index e902889..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/basic.py
+++ /dev/null
@@ -1,335 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-==========================
-Bipartite Graph Algorithms
-==========================
-"""
-# Copyright (C) 2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from itertools import count
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = [ 'is_bipartite',
- 'is_bipartite_node_set',
- 'color',
- 'sets',
- 'density',
- 'degrees',
- 'biadjacency_matrix']
-
-def biadjacency_matrix(G, row_order, column_order=None,
- weight='weight', dtype=None):
- r"""Return the biadjacency matrix of the bipartite graph G.
-
- Let `G = (U, V, E)` be a bipartite graph with node sets
- `U = u_{1},...,u_{r}` and `V = v_{1},...,v_{s}`. The biadjacency
- matrix [1] is the `r` x `s` matrix `B` in which `b_{i,j} = 1`
- if, and only if, `(u_i, v_j) \in E`. If the parameter `weight` is
- not `None` and matches the name of an edge attribute, its value is
- used instead of 1.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- row_order : list of nodes
- The rows of the matrix are ordered according to the list of nodes.
-
- column_order : list, optional
- The columns of the matrix are ordered according to the list of nodes.
- If column_order is None, then the ordering of columns is arbitrary.
-
- weight : string or None, optional (default='weight')
- The edge data key used to provide each value in the matrix.
- If None, then each edge has weight 1.
-
- dtype : NumPy data type, optional
- A valid single NumPy data type used to initialize the array.
- This must be a simple type such as int or numpy.float64 and
- not a compound data type (see to_numpy_recarray)
- If None, then the NumPy default is used.
-
- Returns
- -------
- B : numpy matrix
- Biadjacency matrix representation of the bipartite graph G.
-
- Notes
- -----
- No attempt is made to check that the input graph is bipartite.
-
- For directed bipartite graphs only successors are considered as neighbors.
- To obtain an adjacency matrix with ones (or weight values) for both
- predecessors and successors you have to generate two biadjacency matrices
- where the rows of one of them are the columns of the other, and then add
- one to the transpose of the other.
-
- See Also
- --------
- to_numpy_matrix
- adjacency_matrix
-
- References
- ----------
- [1] http://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError('adjacency_matrix() requires numpy ',
- 'http://scipy.org/')
- if column_order is None:
- column_order = list(set(G) - set(row_order))
- row = dict(zip(row_order,count()))
- col = dict(zip(column_order,count()))
- M = np.zeros((len(row),len(col)), dtype=dtype)
- for u in row_order:
- for v, d in G[u].items():
- M[row[u],col[v]] = d.get(weight, 1)
- return np.asmatrix(M)
-
-def color(G):
- """Returns a two-coloring of the graph.
-
- Raises an exception if the graph is not bipartite.
-
- Parameters
- ----------
- G : NetworkX graph
-
- Returns
- -------
- color : dictionary
- A dictionary keyed by node with a 1 or 0 as data for each node color.
-
- Raises
- ------
- NetworkXError if the graph is not two-colorable.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.path_graph(4)
- >>> c = bipartite.color(G)
- >>> print(c)
- {0: 1, 1: 0, 2: 1, 3: 0}
-
- You can use this to set a node attribute indicating the biparite set:
-
- >>> nx.set_node_attributes(G, 'bipartite', c)
- >>> print(G.node[0]['bipartite'])
- 1
- >>> print(G.node[1]['bipartite'])
- 0
- """
- if G.is_directed():
- import itertools
- def neighbors(v):
- return itertools.chain.from_iterable([G.predecessors_iter(v),
- G.successors_iter(v)])
- else:
- neighbors=G.neighbors_iter
-
- color = {}
- for n in G: # handle disconnected graphs
- if n in color or len(G[n])==0: # skip isolates
- continue
- queue = [n]
- color[n] = 1 # nodes seen with color (1 or 0)
- while queue:
- v = queue.pop()
- c = 1 - color[v] # opposite color of node v
- for w in neighbors(v):
- if w in color:
- if color[w] == color[v]:
- raise nx.NetworkXError("Graph is not bipartite.")
- else:
- color[w] = c
- queue.append(w)
- # color isolates with 0
- color.update(dict.fromkeys(nx.isolates(G),0))
- return color
-
-def is_bipartite(G):
- """ Returns True if graph G is bipartite, False if not.
-
- Parameters
- ----------
- G : NetworkX graph
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.path_graph(4)
- >>> print(bipartite.is_bipartite(G))
- True
-
- See Also
- --------
- color, is_bipartite_node_set
- """
- try:
- color(G)
- return True
- except nx.NetworkXError:
- return False
-
-def is_bipartite_node_set(G,nodes):
- """Returns True if nodes and G/nodes are a bipartition of G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nodes: list or container
- Check if nodes are a one of a bipartite set.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.path_graph(4)
- >>> X = set([1,3])
- >>> bipartite.is_bipartite_node_set(G,X)
- True
-
- Notes
- -----
- For connected graphs the bipartite sets are unique. This function handles
- disconnected graphs.
- """
- S=set(nodes)
- for CC in nx.connected_component_subgraphs(G):
- X,Y=sets(CC)
- if not ( (X.issubset(S) and Y.isdisjoint(S)) or
- (Y.issubset(S) and X.isdisjoint(S)) ):
- return False
- return True
-
-
-def sets(G):
- """Returns bipartite node sets of graph G.
-
- Raises an exception if the graph is not bipartite.
-
- Parameters
- ----------
- G : NetworkX graph
-
- Returns
- -------
- (X,Y) : two-tuple of sets
- One set of nodes for each part of the bipartite graph.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.path_graph(4)
- >>> X, Y = bipartite.sets(G)
- >>> list(X)
- [0, 2]
- >>> list(Y)
- [1, 3]
-
- See Also
- --------
- color
- """
- c = color(G)
- X = set(n for n in c if c[n]) # c[n] == 1
- Y = set(n for n in c if not c[n]) # c[n] == 0
- return (X, Y)
-
-def density(B, nodes):
- """Return density of bipartite graph B.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nodes: list or container
- Nodes in one set of the bipartite graph.
-
- Returns
- -------
- d : float
- The bipartite density
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.complete_bipartite_graph(3,2)
- >>> X=set([0,1,2])
- >>> bipartite.density(G,X)
- 1.0
- >>> Y=set([3,4])
- >>> bipartite.density(G,Y)
- 1.0
-
- See Also
- --------
- color
- """
- n=len(B)
- m=nx.number_of_edges(B)
- nb=len(nodes)
- nt=n-nb
- if m==0: # includes cases n==0 and n==1
- d=0.0
- else:
- if B.is_directed():
- d=m/(2.0*float(nb*nt))
- else:
- d= m/float(nb*nt)
- return d
-
-def degrees(B, nodes, weight=None):
- """Return the degrees of the two node sets in the bipartite graph B.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nodes: list or container
- Nodes in one set of the bipartite graph.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used as a weight.
- If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- (degX,degY) : tuple of dictionaries
- The degrees of the two bipartite sets as dictionaries keyed by node.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.complete_bipartite_graph(3,2)
- >>> Y=set([3,4])
- >>> degX,degY=bipartite.degrees(G,Y)
- >>> degX
- {0: 2, 1: 2, 2: 2}
-
- See Also
- --------
- color, density
- """
- bottom=set(nodes)
- top=set(B)-bottom
- return (B.degree(top,weight),B.degree(bottom,weight))
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/centrality.py
deleted file mode 100644
index 0b885a7..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/centrality.py
+++ /dev/null
@@ -1,266 +0,0 @@
-#-*- coding: utf-8 -*-
-# Copyright (C) 2011 by
-# Jordi Torrents <jtorrents@milnou.net>
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Aric Hagberg (hagberg@lanl.gov)'])
-__all__=['degree_centrality',
- 'betweenness_centrality',
- 'closeness_centrality']
-
-def degree_centrality(G, nodes):
- r"""Compute the degree centrality for nodes in a bipartite network.
-
- The degree centrality for a node `v` is the fraction of nodes
- connected to it.
-
- Parameters
- ----------
- G : graph
- A bipartite network
-
- nodes : list or container
- Container with all nodes in one bipartite node set.
-
- Returns
- -------
- centrality : dictionary
- Dictionary keyed by node with bipartite degree centrality as the value.
-
- See Also
- --------
- betweenness_centrality,
- closeness_centrality,
- sets,
- is_bipartite
-
- Notes
- -----
- The nodes input parameter must conatin all nodes in one bipartite node set,
- but the dictionary returned contains all nodes from both bipartite node
- sets.
-
- For unipartite networks, the degree centrality values are
- normalized by dividing by the maximum possible degree (which is
- `n-1` where `n` is the number of nodes in G).
-
- In the bipartite case, the maximum possible degree of a node in a
- bipartite node set is the number of nodes in the opposite node set
- [1]_. The degree centrality for a node `v` in the bipartite
- sets `U` with `n` nodes and `V` with `m` nodes is
-
- .. math::
-
- d_{v} = \frac{deg(v)}{m}, \mbox{for} v \in U ,
-
- d_{v} = \frac{deg(v)}{n}, \mbox{for} v \in V ,
-
-
- where `deg(v)` is the degree of node `v`.
-
- References
- ----------
- .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
- Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
- of Social Network Analysis. Sage Publications.
- http://www.steveborgatti.com/papers/bhaffiliations.pdf
- """
- top = set(nodes)
- bottom = set(G) - top
- s = 1.0/len(bottom)
- centrality = dict((n,d*s) for n,d in G.degree_iter(top))
- s = 1.0/len(top)
- centrality.update(dict((n,d*s) for n,d in G.degree_iter(bottom)))
- return centrality
-
-
-def betweenness_centrality(G, nodes):
- r"""Compute betweenness centrality for nodes in a bipartite network.
-
- Betweenness centrality of a node `v` is the sum of the
- fraction of all-pairs shortest paths that pass through `v`.
-
- Values of betweenness are normalized by the maximum possible
- value which for bipartite graphs is limited by the relative size
- of the two node sets [1]_.
-
- Let `n` be the number of nodes in the node set `U` and
- `m` be the number of nodes in the node set `V`, then
- nodes in `U` are normalized by dividing by
-
- .. math::
-
- \frac{1}{2} [m^2 (s + 1)^2 + m (s + 1)(2t - s - 1) - t (2s - t + 3)] ,
-
- where
-
- .. math::
-
- s = (n - 1) \div m , t = (n - 1) \mod m ,
-
- and nodes in `V` are normalized by dividing by
-
- .. math::
-
- \frac{1}{2} [n^2 (p + 1)^2 + n (p + 1)(2r - p - 1) - r (2p - r + 3)] ,
-
- where,
-
- .. math::
-
- p = (m - 1) \div n , r = (m - 1) \mod n .
-
- Parameters
- ----------
- G : graph
- A bipartite graph
-
- nodes : list or container
- Container with all nodes in one bipartite node set.
-
- Returns
- -------
- betweenness : dictionary
- Dictionary keyed by node with bipartite betweenness centrality
- as the value.
-
- See Also
- --------
- degree_centrality,
- closeness_centrality,
- sets,
- is_bipartite
-
- Notes
- -----
- The nodes input parameter must contain all nodes in one bipartite node set,
- but the dictionary returned contains all nodes from both node sets.
-
- References
- ----------
- .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
- Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
- of Social Network Analysis. Sage Publications.
- http://www.steveborgatti.com/papers/bhaffiliations.pdf
- """
- top = set(nodes)
- bottom = set(G) - top
- n = float(len(top))
- m = float(len(bottom))
- s = (n-1) // m
- t = (n-1) % m
- bet_max_top = (((m**2)*((s+1)**2))+
- (m*(s+1)*(2*t-s-1))-
- (t*((2*s)-t+3)))/2.0
- p = (m-1) // n
- r = (m-1) % n
- bet_max_bot = (((n**2)*((p+1)**2))+
- (n*(p+1)*(2*r-p-1))-
- (r*((2*p)-r+3)))/2.0
- betweenness = nx.betweenness_centrality(G, normalized=False,
- weight=None)
- for node in top:
- betweenness[node]/=bet_max_top
- for node in bottom:
- betweenness[node]/=bet_max_bot
- return betweenness
-
-def closeness_centrality(G, nodes, normalized=True):
- r"""Compute the closeness centrality for nodes in a bipartite network.
-
- The closeness of a node is the distance to all other nodes in the
- graph or in the case that the graph is not connected to all other nodes
- in the connected component containing that node.
-
- Parameters
- ----------
- G : graph
- A bipartite network
-
- nodes : list or container
- Container with all nodes in one bipartite node set.
-
- normalized : bool, optional
- If True (default) normalize by connected component size.
-
- Returns
- -------
- closeness : dictionary
- Dictionary keyed by node with bipartite closeness centrality
- as the value.
-
- See Also
- --------
- betweenness_centrality,
- degree_centrality
- sets,
- is_bipartite
-
- Notes
- -----
- The nodes input parameter must conatin all nodes in one bipartite node set,
- but the dictionary returned contains all nodes from both node sets.
-
- Closeness centrality is normalized by the minimum distance possible.
- In the bipartite case the minimum distance for a node in one bipartite
- node set is 1 from all nodes in the other node set and 2 from all
- other nodes in its own set [1]_. Thus the closeness centrality
- for node `v` in the two bipartite sets `U` with
- `n` nodes and `V` with `m` nodes is
-
- .. math::
-
- c_{v} = \frac{m + 2(n - 1)}{d}, \mbox{for} v \in U,
-
- c_{v} = \frac{n + 2(m - 1)}{d}, \mbox{for} v \in V,
-
- where `d` is the sum of the distances from `v` to all
- other nodes.
-
- Higher values of closeness indicate higher centrality.
-
- As in the unipartite case, setting normalized=True causes the
- values to normalized further to n-1 / size(G)-1 where n is the
- number of nodes in the connected part of graph containing the
- node. If the graph is not completely connected, this algorithm
- computes the closeness centrality for each connected part
- separately.
-
- References
- ----------
- .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
- Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
- of Social Network Analysis. Sage Publications.
- http://www.steveborgatti.com/papers/bhaffiliations.pdf
- """
- closeness={}
- path_length=nx.single_source_shortest_path_length
- top = set(nodes)
- bottom = set(G) - top
- n = float(len(top))
- m = float(len(bottom))
- for node in top:
- sp=path_length(G,node)
- totsp=sum(sp.values())
- if totsp > 0.0 and len(G) > 1:
- closeness[node]= (m + 2*(n-1)) / totsp
- if normalized:
- s=(len(sp)-1.0) / ( len(G) - 1 )
- closeness[node] *= s
- else:
- closeness[n]=0.0
- for node in bottom:
- sp=path_length(G,node)
- totsp=sum(sp.values())
- if totsp > 0.0 and len(G) > 1:
- closeness[node]= (n + 2*(m-1)) / totsp
- if normalized:
- s=(len(sp)-1.0) / ( len(G) - 1 )
- closeness[node] *= s
- else:
- closeness[n]=0.0
- return closeness
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/cluster.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/cluster.py
deleted file mode 100644
index 8adf92d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/cluster.py
+++ /dev/null
@@ -1,266 +0,0 @@
-#-*- coding: utf-8 -*-
-# Copyright (C) 2011 by
-# Jordi Torrents <jtorrents@milnou.net>
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-import itertools
-import networkx as nx
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Aric Hagberg (hagberg@lanl.gov)'])
-__all__ = [ 'clustering',
- 'average_clustering',
- 'latapy_clustering',
- 'robins_alexander_clustering']
-
-# functions for computing clustering of pairs
-def cc_dot(nu,nv):
- return float(len(nu & nv))/len(nu | nv)
-
-def cc_max(nu,nv):
- return float(len(nu & nv))/max(len(nu),len(nv))
-
-def cc_min(nu,nv):
- return float(len(nu & nv))/min(len(nu),len(nv))
-
-modes={'dot':cc_dot,
- 'min':cc_min,
- 'max':cc_max}
-
-def latapy_clustering(G, nodes=None, mode='dot'):
- r"""Compute a bipartite clustering coefficient for nodes.
-
- The bipartie clustering coefficient is a measure of local density
- of connections defined as [1]_:
-
- .. math::
-
- c_u = \frac{\sum_{v \in N(N(v))} c_{uv} }{|N(N(u))|}
-
- where `N(N(u))` are the second order neighbors of `u` in `G` excluding `u`,
- and `c_{uv}` is the pairwise clustering coefficient between nodes
- `u` and `v`.
-
- The mode selects the function for `c_{uv}` which can be:
-
- `dot`:
-
- .. math::
-
- c_{uv}=\frac{|N(u)\cap N(v)|}{|N(u) \cup N(v)|}
-
- `min`:
-
- .. math::
-
- c_{uv}=\frac{|N(u)\cap N(v)|}{min(|N(u)|,|N(v)|)}
-
- `max`:
-
- .. math::
-
- c_{uv}=\frac{|N(u)\cap N(v)|}{max(|N(u)|,|N(v)|)}
-
-
- Parameters
- ----------
- G : graph
- A bipartite graph
-
- nodes : list or iterable (optional)
- Compute bipartite clustering for these nodes. The default
- is all nodes in G.
-
- mode : string
- The pariwise bipartite clustering method to be used in the computation.
- It must be "dot", "max", or "min".
-
- Returns
- -------
- clustering : dictionary
- A dictionary keyed by node with the clustering coefficient value.
-
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.path_graph(4) # path graphs are bipartite
- >>> c = bipartite.clustering(G)
- >>> c[0]
- 0.5
- >>> c = bipartite.clustering(G,mode='min')
- >>> c[0]
- 1.0
-
- See Also
- --------
- robins_alexander_clustering
- square_clustering
- average_clustering
-
- References
- ----------
- .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008).
- Basic notions for the analysis of large two-mode networks.
- Social Networks 30(1), 31--48.
- """
- if not nx.algorithms.bipartite.is_bipartite(G):
- raise nx.NetworkXError("Graph is not bipartite")
-
- try:
- cc_func = modes[mode]
- except KeyError:
- raise nx.NetworkXError(\
- "Mode for bipartite clustering must be: dot, min or max")
-
- if nodes is None:
- nodes = G
- ccs = {}
- for v in nodes:
- cc = 0.0
- nbrs2=set([u for nbr in G[v] for u in G[nbr]])-set([v])
- for u in nbrs2:
- cc += cc_func(set(G[u]),set(G[v]))
- if cc > 0.0: # len(nbrs2)>0
- cc /= len(nbrs2)
- ccs[v] = cc
- return ccs
-
-clustering = latapy_clustering
-
-def average_clustering(G, nodes=None, mode='dot'):
- r"""Compute the average bipartite clustering coefficient.
-
- A clustering coefficient for the whole graph is the average,
-
- .. math::
-
- C = \frac{1}{n}\sum_{v \in G} c_v,
-
- where `n` is the number of nodes in `G`.
-
- Similar measures for the two bipartite sets can be defined [1]_
-
- .. math::
-
- C_X = \frac{1}{|X|}\sum_{v \in X} c_v,
-
- where `X` is a bipartite set of `G`.
-
- Parameters
- ----------
- G : graph
- a bipartite graph
-
- nodes : list or iterable, optional
- A container of nodes to use in computing the average.
- The nodes should be either the entire graph (the default) or one of the
- bipartite sets.
-
- mode : string
- The pariwise bipartite clustering method.
- It must be "dot", "max", or "min"
-
- Returns
- -------
- clustering : float
- The average bipartite clustering for the given set of nodes or the
- entire graph if no nodes are specified.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G=nx.star_graph(3) # star graphs are bipartite
- >>> bipartite.average_clustering(G)
- 0.75
- >>> X,Y=bipartite.sets(G)
- >>> bipartite.average_clustering(G,X)
- 0.0
- >>> bipartite.average_clustering(G,Y)
- 1.0
-
- See Also
- --------
- clustering
-
- Notes
- -----
- The container of nodes passed to this function must contain all of the nodes
- in one of the bipartite sets ("top" or "bottom") in order to compute
- the correct average bipartite clustering coefficients.
-
- References
- ----------
- .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008).
- Basic notions for the analysis of large two-mode networks.
- Social Networks 30(1), 31--48.
- """
- if nodes is None:
- nodes=G
- ccs=latapy_clustering(G, nodes=nodes, mode=mode)
- return float(sum(ccs[v] for v in nodes))/len(nodes)
-
-def robins_alexander_clustering(G):
- r"""Compute the bipartite clustering of G.
-
- Robins and Alexander [1]_ defined bipartite clustering coefficient as
- four times the number of four cycles `C_4` divided by the number of
- three paths `L_3` in a bipartite graph:
-
- .. math::
-
- CC_4 = \frac{4 * C_4}{L_3}
-
- Parameters
- ----------
- G : graph
- a bipartite graph
-
- Returns
- -------
- clustering : float
- The Robins and Alexander bipartite clustering for the input graph.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.davis_southern_women_graph()
- >>> print(round(bipartite.robins_alexander_clustering(G), 3))
- 0.468
-
- See Also
- --------
- latapy_clustering
- square_clustering
-
- References
- ----------
- .. [1] Robins, G. and M. Alexander (2004). Small worlds among interlocking
- directors: Network structure and distance in bipartite graphs.
- Computational & Mathematical Organization Theory 10(1), 69–94.
-
- """
- if G.order() < 4 or G.size() < 3:
- return 0
- L_3 = _threepaths(G)
- if L_3 == 0:
- return 0
- C_4 = _four_cycles(G)
- return (4. * C_4) / L_3
-
-def _four_cycles(G):
- cycles = 0
- for v in G:
- for u, w in itertools.combinations(G[v], 2):
- cycles += len((set(G[u]) & set(G[w])) - set([v]))
- return cycles / 4
-
-def _threepaths(G):
- paths = 0
- for v in G:
- for u in G[v]:
- for w in set(G[u]) - set([v]):
- paths += len(set(G[w]) - set([v, u]))
- # Divide by two because we count each three path twice
- # one for each possible starting point
- return paths / 2
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/projection.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/projection.py
deleted file mode 100644
index 7f08244..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/projection.py
+++ /dev/null
@@ -1,497 +0,0 @@
-# -*- coding: utf-8 -*-
-"""One-mode (unipartite) projections of bipartite graphs.
-"""
-import networkx as nx
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Jordi Torrents <jtorrents@milnou.net>'])
-__all__ = ['project',
- 'projected_graph',
- 'weighted_projected_graph',
- 'collaboration_weighted_projected_graph',
- 'overlap_weighted_projected_graph',
- 'generic_weighted_projected_graph']
-
-def projected_graph(B, nodes, multigraph=False):
- r"""Returns the projection of B onto one of its node sets.
-
- Returns the graph G that is the projection of the bipartite graph B
- onto the specified nodes. They retain their attributes and are connected
- in G if they have a common neighbor in B.
-
- Parameters
- ----------
- B : NetworkX graph
- The input graph should be bipartite.
-
- nodes : list or iterable
- Nodes to project onto (the "bottom" nodes).
-
- multigraph: bool (default=False)
- If True return a multigraph where the multiple edges represent multiple
- shared neighbors. They edge key in the multigraph is assigned to the
- label of the neighbor.
-
- Returns
- -------
- Graph : NetworkX graph or multigraph
- A graph that is the projection onto the given nodes.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> B = nx.path_graph(4)
- >>> G = bipartite.projected_graph(B, [1,3])
- >>> print(G.nodes())
- [1, 3]
- >>> print(G.edges())
- [(1, 3)]
-
- If nodes `a`, and `b` are connected through both nodes 1 and 2 then
- building a multigraph results in two edges in the projection onto
- [`a`,`b`]:
-
- >>> B = nx.Graph()
- >>> B.add_edges_from([('a', 1), ('b', 1), ('a', 2), ('b', 2)])
- >>> G = bipartite.projected_graph(B, ['a', 'b'], multigraph=True)
- >>> print([sorted((u,v)) for u,v in G.edges()])
- [['a', 'b'], ['a', 'b']]
-
- Notes
- ------
- No attempt is made to verify that the input graph B is bipartite.
- Returns a simple graph that is the projection of the bipartite graph B
- onto the set of nodes given in list nodes. If multigraph=True then
- a multigraph is returned with an edge for every shared neighbor.
-
- Directed graphs are allowed as input. The output will also then
- be a directed graph with edges if there is a directed path between
- the nodes.
-
- The graph and node properties are (shallow) copied to the projected graph.
-
- See Also
- --------
- is_bipartite,
- is_bipartite_node_set,
- sets,
- weighted_projected_graph,
- collaboration_weighted_projected_graph,
- overlap_weighted_projected_graph,
- generic_weighted_projected_graph
- """
- if B.is_multigraph():
- raise nx.NetworkXError("not defined for multigraphs")
- if B.is_directed():
- directed=True
- if multigraph:
- G=nx.MultiDiGraph()
- else:
- G=nx.DiGraph()
- else:
- directed=False
- if multigraph:
- G=nx.MultiGraph()
- else:
- G=nx.Graph()
- G.graph.update(B.graph)
- G.add_nodes_from((n,B.node[n]) for n in nodes)
- for u in nodes:
- nbrs2=set((v for nbr in B[u] for v in B[nbr])) -set([u])
- if multigraph:
- for n in nbrs2:
- if directed:
- links=set(B[u]) & set(B.pred[n])
- else:
- links=set(B[u]) & set(B[n])
- for l in links:
- if not G.has_edge(u,n,l):
- G.add_edge(u,n,key=l)
- else:
- G.add_edges_from((u,n) for n in nbrs2)
- return G
-
-def weighted_projected_graph(B, nodes, ratio=False):
- r"""Returns a weighted projection of B onto one of its node sets.
-
- The weighted projected graph is the projection of the bipartite
- network B onto the specified nodes with weights representing the
- number of shared neighbors or the ratio between actual shared
- neighbors and possible shared neighbors if ratio=True [1]_. The
- nodes retain their attributes and are connected in the resulting graph
- if they have an edge to a common node in the original graph.
-
- Parameters
- ----------
- B : NetworkX graph
- The input graph should be bipartite.
-
- nodes : list or iterable
- Nodes to project onto (the "bottom" nodes).
-
- ratio: Bool (default=False)
- If True, edge weight is the ratio between actual shared neighbors
- and possible shared neighbors. If False, edges weight is the number
- of shared neighbors.
-
- Returns
- -------
- Graph : NetworkX graph
- A graph that is the projection onto the given nodes.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> B = nx.path_graph(4)
- >>> G = bipartite.weighted_projected_graph(B, [1,3])
- >>> print(G.nodes())
- [1, 3]
- >>> print(G.edges(data=True))
- [(1, 3, {'weight': 1})]
- >>> G = bipartite.weighted_projected_graph(B, [1,3], ratio=True)
- >>> print(G.edges(data=True))
- [(1, 3, {'weight': 0.5})]
-
- Notes
- ------
- No attempt is made to verify that the input graph B is bipartite.
- The graph and node properties are (shallow) copied to the projected graph.
-
- See Also
- --------
- is_bipartite,
- is_bipartite_node_set,
- sets,
- collaboration_weighted_projected_graph,
- overlap_weighted_projected_graph,
- generic_weighted_projected_graph
- projected_graph
-
- References
- ----------
- .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
- Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
- of Social Network Analysis. Sage Publications.
- """
- if B.is_multigraph():
- raise nx.NetworkXError("not defined for multigraphs")
- if B.is_directed():
- pred=B.pred
- G=nx.DiGraph()
- else:
- pred=B.adj
- G=nx.Graph()
- G.graph.update(B.graph)
- G.add_nodes_from((n,B.node[n]) for n in nodes)
- n_top = float(len(B) - len(nodes))
- for u in nodes:
- unbrs = set(B[u])
- nbrs2 = set((n for nbr in unbrs for n in B[nbr])) - set([u])
- for v in nbrs2:
- vnbrs = set(pred[v])
- common = unbrs & vnbrs
- if not ratio:
- weight = len(common)
- else:
- weight = len(common) / n_top
- G.add_edge(u,v,weight=weight)
- return G
-
-def collaboration_weighted_projected_graph(B, nodes):
- r"""Newman's weighted projection of B onto one of its node sets.
-
- The collaboration weighted projection is the projection of the
- bipartite network B onto the specified nodes with weights assigned
- using Newman's collaboration model [1]_:
-
- .. math::
-
- w_{v,u} = \sum_k \frac{\delta_{v}^{w} \delta_{w}^{k}}{k_w - 1}
-
- where `v` and `u` are nodes from the same bipartite node set,
- and `w` is a node of the opposite node set.
- The value `k_w` is the degree of node `w` in the bipartite
- network and `\delta_{v}^{w}` is 1 if node `v` is
- linked to node `w` in the original bipartite graph or 0 otherwise.
-
- The nodes retain their attributes and are connected in the resulting
- graph if have an edge to a common node in the original bipartite
- graph.
-
- Parameters
- ----------
- B : NetworkX graph
- The input graph should be bipartite.
-
- nodes : list or iterable
- Nodes to project onto (the "bottom" nodes).
-
- Returns
- -------
- Graph : NetworkX graph
- A graph that is the projection onto the given nodes.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> B = nx.path_graph(5)
- >>> B.add_edge(1,5)
- >>> G = bipartite.collaboration_weighted_projected_graph(B, [0, 2, 4, 5])
- >>> print(G.nodes())
- [0, 2, 4, 5]
- >>> for edge in G.edges(data=True): print(edge)
- ...
- (0, 2, {'weight': 0.5})
- (0, 5, {'weight': 0.5})
- (2, 4, {'weight': 1.0})
- (2, 5, {'weight': 0.5})
-
- Notes
- ------
- No attempt is made to verify that the input graph B is bipartite.
- The graph and node properties are (shallow) copied to the projected graph.
-
- See Also
- --------
- is_bipartite,
- is_bipartite_node_set,
- sets,
- weighted_projected_graph,
- overlap_weighted_projected_graph,
- generic_weighted_projected_graph,
- projected_graph
-
- References
- ----------
- .. [1] Scientific collaboration networks: II.
- Shortest paths, weighted networks, and centrality,
- M. E. J. Newman, Phys. Rev. E 64, 016132 (2001).
- """
- if B.is_multigraph():
- raise nx.NetworkXError("not defined for multigraphs")
- if B.is_directed():
- pred=B.pred
- G=nx.DiGraph()
- else:
- pred=B.adj
- G=nx.Graph()
- G.graph.update(B.graph)
- G.add_nodes_from((n,B.node[n]) for n in nodes)
- for u in nodes:
- unbrs = set(B[u])
- nbrs2 = set((n for nbr in unbrs for n in B[nbr])) - set([u])
- for v in nbrs2:
- vnbrs = set(pred[v])
- common = unbrs & vnbrs
- weight = sum([1.0/(len(B[n]) - 1) for n in common if len(B[n])>1])
- G.add_edge(u,v,weight=weight)
- return G
-
-def overlap_weighted_projected_graph(B, nodes, jaccard=True):
- r"""Overlap weighted projection of B onto one of its node sets.
-
- The overlap weighted projection is the projection of the bipartite
- network B onto the specified nodes with weights representing
- the Jaccard index between the neighborhoods of the two nodes in the
- original bipartite network [1]_:
-
- .. math::
-
- w_{v,u} = \frac{|N(u) \cap N(v)|}{|N(u) \cup N(v)|}
-
- or if the parameter 'jaccard' is False, the fraction of common
- neighbors by minimum of both nodes degree in the original
- bipartite graph [1]_:
-
- .. math::
-
- w_{v,u} = \frac{|N(u) \cap N(v)|}{min(|N(u)|,|N(v)|)}
-
- The nodes retain their attributes and are connected in the resulting
- graph if have an edge to a common node in the original bipartite graph.
-
- Parameters
- ----------
- B : NetworkX graph
- The input graph should be bipartite.
-
- nodes : list or iterable
- Nodes to project onto (the "bottom" nodes).
-
- jaccard: Bool (default=True)
-
- Returns
- -------
- Graph : NetworkX graph
- A graph that is the projection onto the given nodes.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> B = nx.path_graph(5)
- >>> G = bipartite.overlap_weighted_projected_graph(B, [0, 2, 4])
- >>> print(G.nodes())
- [0, 2, 4]
- >>> print(G.edges(data=True))
- [(0, 2, {'weight': 0.5}), (2, 4, {'weight': 0.5})]
- >>> G = bipartite.overlap_weighted_projected_graph(B, [0, 2, 4], jaccard=False)
- >>> print(G.edges(data=True))
- [(0, 2, {'weight': 1.0}), (2, 4, {'weight': 1.0})]
-
- Notes
- ------
- No attempt is made to verify that the input graph B is bipartite.
- The graph and node properties are (shallow) copied to the projected graph.
-
- See Also
- --------
- is_bipartite,
- is_bipartite_node_set,
- sets,
- weighted_projected_graph,
- collaboration_weighted_projected_graph,
- generic_weighted_projected_graph,
- projected_graph
-
- References
- ----------
- .. [1] Borgatti, S.P. and Halgin, D. In press. Analyzing Affiliation
- Networks. In Carrington, P. and Scott, J. (eds) The Sage Handbook
- of Social Network Analysis. Sage Publications.
-
- """
- if B.is_multigraph():
- raise nx.NetworkXError("not defined for multigraphs")
- if B.is_directed():
- pred=B.pred
- G=nx.DiGraph()
- else:
- pred=B.adj
- G=nx.Graph()
- G.graph.update(B.graph)
- G.add_nodes_from((n,B.node[n]) for n in nodes)
- for u in nodes:
- unbrs = set(B[u])
- nbrs2 = set((n for nbr in unbrs for n in B[nbr])) - set([u])
- for v in nbrs2:
- vnbrs = set(pred[v])
- if jaccard:
- weight = float(len(unbrs & vnbrs)) / len(unbrs | vnbrs)
- else:
- weight = float(len(unbrs & vnbrs)) / min(len(unbrs),len(vnbrs))
- G.add_edge(u,v,weight=weight)
- return G
-
-def generic_weighted_projected_graph(B, nodes, weight_function=None):
- r"""Weighted projection of B with a user-specified weight function.
-
- The bipartite network B is projected on to the specified nodes
- with weights computed by a user-specified function. This function
- must accept as a parameter the neighborhood sets of two nodes and
- return an integer or a float.
-
- The nodes retain their attributes and are connected in the resulting graph
- if they have an edge to a common node in the original graph.
-
- Parameters
- ----------
- B : NetworkX graph
- The input graph should be bipartite.
-
- nodes : list or iterable
- Nodes to project onto (the "bottom" nodes).
-
- weight_function: function
- This function must accept as parameters the same input graph
- that this function, and two nodes; and return an integer or a float.
- The default function computes the number of shared neighbors.
-
- Returns
- -------
- Graph : NetworkX graph
- A graph that is the projection onto the given nodes.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> # Define some custom weight functions
- >>> def jaccard(G, u, v):
- ... unbrs = set(G[u])
- ... vnbrs = set(G[v])
- ... return float(len(unbrs & vnbrs)) / len(unbrs | vnbrs)
- ...
- >>> def my_weight(G, u, v, weight='weight'):
- ... w = 0
- ... for nbr in set(G[u]) & set(G[v]):
- ... w += G.edge[u][nbr].get(weight, 1) + G.edge[v][nbr].get(weight, 1)
- ... return w
- ...
- >>> # A complete bipartite graph with 4 nodes and 4 edges
- >>> B = nx.complete_bipartite_graph(2,2)
- >>> # Add some arbitrary weight to the edges
- >>> for i,(u,v) in enumerate(B.edges()):
- ... B.edge[u][v]['weight'] = i + 1
- ...
- >>> for edge in B.edges(data=True):
- ... print(edge)
- ...
- (0, 2, {'weight': 1})
- (0, 3, {'weight': 2})
- (1, 2, {'weight': 3})
- (1, 3, {'weight': 4})
- >>> # Without specifying a function, the weight is equal to # shared partners
- >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1])
- >>> print(G.edges(data=True))
- [(0, 1, {'weight': 2})]
- >>> # To specify a custom weight function use the weight_function parameter
- >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1], weight_function=jaccard)
- >>> print(G.edges(data=True))
- [(0, 1, {'weight': 1.0})]
- >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1], weight_function=my_weight)
- >>> print(G.edges(data=True))
- [(0, 1, {'weight': 10})]
-
- Notes
- ------
- No attempt is made to verify that the input graph B is bipartite.
- The graph and node properties are (shallow) copied to the projected graph.
-
- See Also
- --------
- is_bipartite,
- is_bipartite_node_set,
- sets,
- weighted_projected_graph,
- collaboration_weighted_projected_graph,
- overlap_weighted_projected_graph,
- projected_graph
-
- """
- if B.is_multigraph():
- raise nx.NetworkXError("not defined for multigraphs")
- if B.is_directed():
- pred=B.pred
- G=nx.DiGraph()
- else:
- pred=B.adj
- G=nx.Graph()
- if weight_function is None:
- def weight_function(G, u, v):
- # Notice that we use set(pred[v]) for handling the directed case.
- return len(set(G[u]) & set(pred[v]))
- G.graph.update(B.graph)
- G.add_nodes_from((n,B.node[n]) for n in nodes)
- for u in nodes:
- nbrs2 = set((n for nbr in set(B[u]) for n in B[nbr])) - set([u])
- for v in nbrs2:
- weight = weight_function(B, u, v)
- G.add_edge(u,v,weight=weight)
- return G
-
-def project(B, nodes, create_using=None):
- return projected_graph(B, nodes)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/redundancy.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/redundancy.py
deleted file mode 100644
index 055fdcb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/redundancy.py
+++ /dev/null
@@ -1,84 +0,0 @@
-#-*- coding: utf-8 -*-
-"""Node redundancy for bipartite graphs."""
-# Copyright (C) 2011 by
-# Jordi Torrents <jtorrents@milnou.net>
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-from itertools import combinations
-import networkx as nx
-
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Aric Hagberg (hagberg@lanl.gov)'])
-__all__ = ['node_redundancy']
-
-def node_redundancy(G, nodes=None):
- r"""Compute bipartite node redundancy coefficient.
-
- The redundancy coefficient of a node `v` is the fraction of pairs of
- neighbors of `v` that are both linked to other nodes. In a one-mode
- projection these nodes would be linked together even if `v` were
- not there.
-
- .. math::
-
- rc(v) = \frac{|\{\{u,w\} \subseteq N(v),
- \: \exists v' \neq v,\: (v',u) \in E\:
- \mathrm{and}\: (v',w) \in E\}|}{ \frac{|N(v)|(|N(v)|-1)}{2}}
-
- where `N(v)` are the neighbors of `v` in `G`.
-
- Parameters
- ----------
- G : graph
- A bipartite graph
-
- nodes : list or iterable (optional)
- Compute redundancy for these nodes. The default is all nodes in G.
-
- Returns
- -------
- redundancy : dictionary
- A dictionary keyed by node with the node redundancy value.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.cycle_graph(4)
- >>> rc = bipartite.node_redundancy(G)
- >>> rc[0]
- 1.0
-
- Compute the average redundancy for the graph:
-
- >>> sum(rc.values())/len(G)
- 1.0
-
- Compute the average redundancy for a set of nodes:
-
- >>> nodes = [0, 2]
- >>> sum(rc[n] for n in nodes)/len(nodes)
- 1.0
-
- References
- ----------
- .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008).
- Basic notions for the analysis of large two-mode networks.
- Social Networks 30(1), 31--48.
- """
- if nodes is None:
- nodes = G
- rc = {}
- for v in nodes:
- overlap = 0.0
- for u, w in combinations(G[v], 2):
- if len((set(G[u]) & set(G[w])) - set([v])) > 0:
- overlap += 1
- if overlap > 0:
- n = len(G[v])
- norm = 2.0/(n*(n-1))
- else:
- norm = 1.0
- rc[v] = overlap*norm
- return rc
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/spectral.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/spectral.py
deleted file mode 100644
index d0ebdd4..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/spectral.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Spectral bipartivity measure.
-"""
-import networkx as nx
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__all__ = ['spectral_bipartivity']
-
-def spectral_bipartivity(G, nodes=None, weight='weight'):
- """Returns the spectral bipartivity.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nodes : list or container optional(default is all nodes)
- Nodes to return value of spectral bipartivity contribution.
-
- weight : string or None optional (default = 'weight')
- Edge data key to use for edge weights. If None, weights set to 1.
-
- Returns
- -------
- sb : float or dict
- A single number if the keyword nodes is not specified, or
- a dictionary keyed by node with the spectral bipartivity contribution
- of that node as the value.
-
- Examples
- --------
- >>> from networkx.algorithms import bipartite
- >>> G = nx.path_graph(4)
- >>> bipartite.spectral_bipartivity(G)
- 1.0
-
- Notes
- -----
- This implementation uses Numpy (dense) matrices which are not efficient
- for storing large sparse graphs.
-
- See Also
- --------
- color
-
- References
- ----------
- .. [1] E. Estrada and J. A. Rodríguez-Velázquez, "Spectral measures of
- bipartivity in complex networks", PhysRev E 72, 046105 (2005)
- """
- try:
- import scipy.linalg
- except ImportError:
- raise ImportError('spectral_bipartivity() requires SciPy: ',
- 'http://scipy.org/')
- nodelist = G.nodes() # ordering of nodes in matrix
- A = nx.to_numpy_matrix(G, nodelist, weight=weight)
- expA = scipy.linalg.expm(A)
- expmA = scipy.linalg.expm(-A)
- coshA = 0.5 * (expA + expmA)
- if nodes is None:
- # return single number for entire graph
- return coshA.diagonal().sum() / expA.diagonal().sum()
- else:
- # contribution for individual nodes
- index = dict(zip(nodelist, range(len(nodelist))))
- sb = {}
- for n in nodes:
- i = index[n]
- sb[n] = coshA[i, i] / expA[i, i]
- return sb
-
-def setup_module(module):
- """Fixture for nose tests."""
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_basic.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_basic.py
deleted file mode 100644
index 5b22e9a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_basic.py
+++ /dev/null
@@ -1,117 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-from nose.plugins.attrib import attr
-import networkx as nx
-from networkx.algorithms import bipartite
-class TestBipartiteBasic:
-
- def test_is_bipartite(self):
- assert_true(bipartite.is_bipartite(nx.path_graph(4)))
- assert_true(bipartite.is_bipartite(nx.DiGraph([(1,0)])))
- assert_false(bipartite.is_bipartite(nx.complete_graph(3)))
-
-
- def test_bipartite_color(self):
- G=nx.path_graph(4)
- c=bipartite.color(G)
- assert_equal(c,{0: 1, 1: 0, 2: 1, 3: 0})
-
- @raises(nx.NetworkXError)
- def test_not_bipartite_color(self):
- c=bipartite.color(nx.complete_graph(4))
-
-
- def test_bipartite_directed(self):
- G = nx.bipartite_random_graph(10, 10, 0.1, directed=True)
- assert_true(bipartite.is_bipartite(G))
-
- def test_bipartite_sets(self):
- G=nx.path_graph(4)
- X,Y=bipartite.sets(G)
- assert_equal(X,set([0,2]))
- assert_equal(Y,set([1,3]))
-
- def test_is_bipartite_node_set(self):
- G=nx.path_graph(4)
- assert_true(bipartite.is_bipartite_node_set(G,[0,2]))
- assert_true(bipartite.is_bipartite_node_set(G,[1,3]))
- assert_false(bipartite.is_bipartite_node_set(G,[1,2]))
- G.add_path([10,20])
- assert_true(bipartite.is_bipartite_node_set(G,[0,2,10]))
- assert_true(bipartite.is_bipartite_node_set(G,[0,2,20]))
- assert_true(bipartite.is_bipartite_node_set(G,[1,3,10]))
- assert_true(bipartite.is_bipartite_node_set(G,[1,3,20]))
-
- def test_bipartite_density(self):
- G=nx.path_graph(5)
- X,Y=bipartite.sets(G)
- density=float(len(G.edges()))/(len(X)*len(Y))
- assert_equal(bipartite.density(G,X),density)
- D = nx.DiGraph(G.edges())
- assert_equal(bipartite.density(D,X),density/2.0)
- assert_equal(bipartite.density(nx.Graph(),{}),0.0)
-
- def test_bipartite_degrees(self):
- G=nx.path_graph(5)
- X=set([1,3])
- Y=set([0,2,4])
- u,d=bipartite.degrees(G,Y)
- assert_equal(u,{1:2,3:2})
- assert_equal(d,{0:1,2:2,4:1})
-
- def test_bipartite_weighted_degrees(self):
- G=nx.path_graph(5)
- G.add_edge(0,1,weight=0.1,other=0.2)
- X=set([1,3])
- Y=set([0,2,4])
- u,d=bipartite.degrees(G,Y,weight='weight')
- assert_equal(u,{1:1.1,3:2})
- assert_equal(d,{0:0.1,2:2,4:1})
- u,d=bipartite.degrees(G,Y,weight='other')
- assert_equal(u,{1:1.2,3:2})
- assert_equal(d,{0:0.2,2:2,4:1})
-
-
- @attr('numpy')
- def test_biadjacency_matrix_weight(self):
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
- G=nx.path_graph(5)
- G.add_edge(0,1,weight=2,other=4)
- X=[1,3]
- Y=[0,2,4]
- M = bipartite.biadjacency_matrix(G,X,weight='weight')
- assert_equal(M[0,0], 2)
- M = bipartite.biadjacency_matrix(G, X, weight='other')
- assert_equal(M[0,0], 4)
-
- @attr('numpy')
- def test_biadjacency_matrix(self):
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
- tops = [2,5,10]
- bots = [5,10,15]
- for i in range(len(tops)):
- G = nx.bipartite_random_graph(tops[i], bots[i], 0.2)
- top = [n for n,d in G.nodes(data=True) if d['bipartite']==0]
- M = bipartite.biadjacency_matrix(G, top)
- assert_equal(M.shape[0],tops[i])
- assert_equal(M.shape[1],bots[i])
-
- @attr('numpy')
- def test_biadjacency_matrix_order(self):
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
- G=nx.path_graph(5)
- G.add_edge(0,1,weight=2)
- X=[3,1]
- Y=[4,2,0]
- M = bipartite.biadjacency_matrix(G,X,Y,weight='weight')
- assert_equal(M[1,2], 2)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_centrality.py
deleted file mode 100644
index 992d643..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_centrality.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from nose.tools import *
-import networkx as nx
-from networkx.algorithms import bipartite
-
-class TestBipartiteCentrality(object):
-
- def setUp(self):
- self.P4 = nx.path_graph(4)
- self.K3 = nx.complete_bipartite_graph(3,3)
- self.C4 = nx.cycle_graph(4)
- self.davis = nx.davis_southern_women_graph()
- self.top_nodes = [n for n,d in self.davis.nodes(data=True)
- if d['bipartite']==0]
-
- def test_degree_centrality(self):
- d = bipartite.degree_centrality(self.P4, [1,3])
- answer = {0: 0.5, 1: 1.0, 2: 1.0, 3: 0.5}
- assert_equal(d, answer)
- d = bipartite.degree_centrality(self.K3, [0,1,2])
- answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
- assert_equal(d, answer)
- d = bipartite.degree_centrality(self.C4, [0,2])
- answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
- assert_equal(d,answer)
-
- def test_betweenness_centrality(self):
- c = bipartite.betweenness_centrality(self.P4, [1,3])
- answer = {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.0}
- assert_equal(c, answer)
- c = bipartite.betweenness_centrality(self.K3, [0,1,2])
- answer = {0: 0.125, 1: 0.125, 2: 0.125, 3: 0.125, 4: 0.125, 5: 0.125}
- assert_equal(c, answer)
- c = bipartite.betweenness_centrality(self.C4, [0,2])
- answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
- assert_equal(c, answer)
-
- def test_closeness_centrality(self):
- c = bipartite.closeness_centrality(self.P4, [1,3])
- answer = {0: 2.0/3, 1: 1.0, 2: 1.0, 3:2.0/3}
- assert_equal(c, answer)
- c = bipartite.closeness_centrality(self.K3, [0,1,2])
- answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
- assert_equal(c, answer)
- c = bipartite.closeness_centrality(self.C4, [0,2])
- answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
- assert_equal(c, answer)
- G = nx.Graph()
- G.add_node(0)
- G.add_node(1)
- c = bipartite.closeness_centrality(G, [0])
- assert_equal(c, {1: 0.0})
- c = bipartite.closeness_centrality(G, [1])
- assert_equal(c, {1: 0.0})
-
- def test_davis_degree_centrality(self):
- G = self.davis
- deg = bipartite.degree_centrality(G, self.top_nodes)
- answer = {'E8':0.78,
- 'E9':0.67,
- 'E7':0.56,
- 'Nora Fayette':0.57,
- 'Evelyn Jefferson':0.57,
- 'Theresa Anderson':0.57,
- 'E6':0.44,
- 'Sylvia Avondale':0.50,
- 'Laura Mandeville':0.50,
- 'Brenda Rogers':0.50,
- 'Katherina Rogers':0.43,
- 'E5':0.44,
- 'Helen Lloyd':0.36,
- 'E3':0.33,
- 'Ruth DeSand':0.29,
- 'Verne Sanderson':0.29,
- 'E12':0.33,
- 'Myra Liddel':0.29,
- 'E11':0.22,
- 'Eleanor Nye':0.29,
- 'Frances Anderson':0.29,
- 'Pearl Oglethorpe':0.21,
- 'E4':0.22,
- 'Charlotte McDowd':0.29,
- 'E10':0.28,
- 'Olivia Carleton':0.14,
- 'Flora Price':0.14,
- 'E2':0.17,
- 'E1':0.17,
- 'Dorothy Murchison':0.14,
- 'E13':0.17,
- 'E14':0.17}
- for node, value in answer.items():
- assert_almost_equal(value, deg[node], places=2)
-
- def test_davis_betweenness_centrality(self):
- G = self.davis
- bet = bipartite.betweenness_centrality(G, self.top_nodes)
- answer = {'E8':0.24,
- 'E9':0.23,
- 'E7':0.13,
- 'Nora Fayette':0.11,
- 'Evelyn Jefferson':0.10,
- 'Theresa Anderson':0.09,
- 'E6':0.07,
- 'Sylvia Avondale':0.07,
- 'Laura Mandeville':0.05,
- 'Brenda Rogers':0.05,
- 'Katherina Rogers':0.05,
- 'E5':0.04,
- 'Helen Lloyd':0.04,
- 'E3':0.02,
- 'Ruth DeSand':0.02,
- 'Verne Sanderson':0.02,
- 'E12':0.02,
- 'Myra Liddel':0.02,
- 'E11':0.02,
- 'Eleanor Nye':0.01,
- 'Frances Anderson':0.01,
- 'Pearl Oglethorpe':0.01,
- 'E4':0.01,
- 'Charlotte McDowd':0.01,
- 'E10':0.01,
- 'Olivia Carleton':0.01,
- 'Flora Price':0.01,
- 'E2':0.00,
- 'E1':0.00,
- 'Dorothy Murchison':0.00,
- 'E13':0.00,
- 'E14':0.00}
- for node, value in answer.items():
- assert_almost_equal(value, bet[node], places=2)
-
- def test_davis_closeness_centrality(self):
- G = self.davis
- clos = bipartite.closeness_centrality(G, self.top_nodes)
- answer = {'E8':0.85,
- 'E9':0.79,
- 'E7':0.73,
- 'Nora Fayette':0.80,
- 'Evelyn Jefferson':0.80,
- 'Theresa Anderson':0.80,
- 'E6':0.69,
- 'Sylvia Avondale':0.77,
- 'Laura Mandeville':0.73,
- 'Brenda Rogers':0.73,
- 'Katherina Rogers':0.73,
- 'E5':0.59,
- 'Helen Lloyd':0.73,
- 'E3':0.56,
- 'Ruth DeSand':0.71,
- 'Verne Sanderson':0.71,
- 'E12':0.56,
- 'Myra Liddel':0.69,
- 'E11':0.54,
- 'Eleanor Nye':0.67,
- 'Frances Anderson':0.67,
- 'Pearl Oglethorpe':0.67,
- 'E4':0.54,
- 'Charlotte McDowd':0.60,
- 'E10':0.55,
- 'Olivia Carleton':0.59,
- 'Flora Price':0.59,
- 'E2':0.52,
- 'E1':0.52,
- 'Dorothy Murchison':0.65,
- 'E13':0.52,
- 'E14':0.52}
- for node, value in answer.items():
- assert_almost_equal(value, clos[node], places=2)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_cluster.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_cluster.py
deleted file mode 100644
index aa158f9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_cluster.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import networkx as nx
-from nose.tools import *
-from networkx.algorithms.bipartite.cluster import cc_dot,cc_min,cc_max
-import networkx.algorithms.bipartite as bipartite
-
-def test_pairwise_bipartite_cc_functions():
- # Test functions for different kinds of bipartite clustering coefficients
- # between pairs of nodes using 3 example graphs from figure 5 p. 40
- # Latapy et al (2008)
- G1 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7)])
- G2 = nx.Graph([(0,2),(0,3),(0,4),(1,3),(1,4),(1,5)])
- G3 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7),(1,8),(1,9)])
- result = {0:[1/3.0, 2/3.0, 2/5.0],
- 1:[1/2.0, 2/3.0, 2/3.0],
- 2:[2/8.0, 2/5.0, 2/5.0]}
- for i, G in enumerate([G1, G2, G3]):
- assert(bipartite.is_bipartite(G))
- assert(cc_dot(set(G[0]), set(G[1])) == result[i][0])
- assert(cc_min(set(G[0]), set(G[1])) == result[i][1])
- assert(cc_max(set(G[0]), set(G[1])) == result[i][2])
-
-def test_star_graph():
- G=nx.star_graph(3)
- # all modes are the same
- answer={0:0,1:1,2:1,3:1}
- assert_equal(bipartite.clustering(G,mode='dot'),answer)
- assert_equal(bipartite.clustering(G,mode='min'),answer)
- assert_equal(bipartite.clustering(G,mode='max'),answer)
-
-@raises(nx.NetworkXError)
-def test_not_bipartite():
- bipartite.clustering(nx.complete_graph(4))
-
-@raises(nx.NetworkXError)
-def test_bad_mode():
- bipartite.clustering(nx.path_graph(4),mode='foo')
-
-def test_path_graph():
- G=nx.path_graph(4)
- answer={0:0.5,1:0.5,2:0.5,3:0.5}
- assert_equal(bipartite.clustering(G,mode='dot'),answer)
- assert_equal(bipartite.clustering(G,mode='max'),answer)
- answer={0:1,1:1,2:1,3:1}
- assert_equal(bipartite.clustering(G,mode='min'),answer)
-
-def test_average_path_graph():
- G=nx.path_graph(4)
- assert_equal(bipartite.average_clustering(G,mode='dot'),0.5)
- assert_equal(bipartite.average_clustering(G,mode='max'),0.5)
- assert_equal(bipartite.average_clustering(G,mode='min'),1)
-
-def test_ra_clustering_davis():
- G = nx.davis_southern_women_graph()
- cc4 = round(bipartite.robins_alexander_clustering(G), 3)
- assert_equal(cc4, 0.468)
-
-def test_ra_clustering_square():
- G = nx.path_graph(4)
- G.add_edge(0, 3)
- assert_equal(bipartite.robins_alexander_clustering(G), 1.0)
-
-def test_ra_clustering_zero():
- G = nx.Graph()
- assert_equal(bipartite.robins_alexander_clustering(G), 0)
- G.add_nodes_from(range(4))
- assert_equal(bipartite.robins_alexander_clustering(G), 0)
- G.add_edges_from([(0,1),(2,3),(3,4)])
- assert_equal(bipartite.robins_alexander_clustering(G), 0)
- G.add_edge(1,2)
- assert_equal(bipartite.robins_alexander_clustering(G), 0)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_project.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_project.py
deleted file mode 100644
index 52a93b2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_project.py
+++ /dev/null
@@ -1,363 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx.algorithms import bipartite
-from networkx.testing import *
-
-class TestBipartiteProject:
-
- def test_path_projected_graph(self):
- G=nx.path_graph(4)
- P=bipartite.projected_graph(G,[1,3])
- assert_equal(sorted(P.nodes()),[1,3])
- assert_equal(sorted(P.edges()),[(1,3)])
- P=bipartite.projected_graph(G,[0,2])
- assert_equal(sorted(P.nodes()),[0,2])
- assert_equal(sorted(P.edges()),[(0,2)])
-
- def test_path_projected_properties_graph(self):
- G=nx.path_graph(4)
- G.add_node(1,name='one')
- G.add_node(2,name='two')
- P=bipartite.projected_graph(G,[1,3])
- assert_equal(sorted(P.nodes()),[1,3])
- assert_equal(sorted(P.edges()),[(1,3)])
- assert_equal(P.node[1]['name'],G.node[1]['name'])
- P=bipartite.projected_graph(G,[0,2])
- assert_equal(sorted(P.nodes()),[0,2])
- assert_equal(sorted(P.edges()),[(0,2)])
- assert_equal(P.node[2]['name'],G.node[2]['name'])
-
- def test_path_collaboration_projected_graph(self):
- G=nx.path_graph(4)
- P=bipartite.collaboration_weighted_projected_graph(G,[1,3])
- assert_equal(sorted(P.nodes()),[1,3])
- assert_equal(sorted(P.edges()),[(1,3)])
- P[1][3]['weight']=1
- P=bipartite.collaboration_weighted_projected_graph(G,[0,2])
- assert_equal(sorted(P.nodes()),[0,2])
- assert_equal(sorted(P.edges()),[(0,2)])
- P[0][2]['weight']=1
-
- def test_directed_path_collaboration_projected_graph(self):
- G=nx.DiGraph()
- G.add_path(list(range(4)))
- P=bipartite.collaboration_weighted_projected_graph(G,[1,3])
- assert_equal(sorted(P.nodes()),[1,3])
- assert_equal(sorted(P.edges()),[(1,3)])
- P[1][3]['weight']=1
- P=bipartite.collaboration_weighted_projected_graph(G,[0,2])
- assert_equal(sorted(P.nodes()),[0,2])
- assert_equal(sorted(P.edges()),[(0,2)])
- P[0][2]['weight']=1
-
- def test_path_weighted_projected_graph(self):
- G=nx.path_graph(4)
- P=bipartite.weighted_projected_graph(G,[1,3])
- assert_equal(sorted(P.nodes()),[1,3])
- assert_equal(sorted(P.edges()),[(1,3)])
- P[1][3]['weight']=1
- P=bipartite.weighted_projected_graph(G,[0,2])
- assert_equal(sorted(P.nodes()),[0,2])
- assert_equal(sorted(P.edges()),[(0,2)])
- P[0][2]['weight']=1
-
- def test_path_weighted_projected_directed_graph(self):
- G=nx.DiGraph()
- G.add_path(list(range(4)))
- P=bipartite.weighted_projected_graph(G,[1,3])
- assert_equal(sorted(P.nodes()),[1,3])
- assert_equal(sorted(P.edges()),[(1,3)])
- P[1][3]['weight']=1
- P=bipartite.weighted_projected_graph(G,[0,2])
- assert_equal(sorted(P.nodes()),[0,2])
- assert_equal(sorted(P.edges()),[(0,2)])
- P[0][2]['weight']=1
-
-
- def test_star_projected_graph(self):
- G=nx.star_graph(3)
- P=bipartite.projected_graph(G,[1,2,3])
- assert_equal(sorted(P.nodes()),[1,2,3])
- assert_equal(sorted(P.edges()),[(1,2),(1,3),(2,3)])
- P=bipartite.weighted_projected_graph(G,[1,2,3])
- assert_equal(sorted(P.nodes()),[1,2,3])
- assert_equal(sorted(P.edges()),[(1,2),(1,3),(2,3)])
-
- P=bipartite.projected_graph(G,[0])
- assert_equal(sorted(P.nodes()),[0])
- assert_equal(sorted(P.edges()),[])
-
- def test_project_multigraph(self):
- G=nx.Graph()
- G.add_edge('a',1)
- G.add_edge('b',1)
- G.add_edge('a',2)
- G.add_edge('b',2)
- P=bipartite.projected_graph(G,'ab')
- assert_edges_equal(P.edges(),[('a','b')])
- P=bipartite.weighted_projected_graph(G,'ab')
- assert_edges_equal(P.edges(),[('a','b')])
- P=bipartite.projected_graph(G,'ab',multigraph=True)
- assert_edges_equal(P.edges(),[('a','b'),('a','b')])
-
- def test_project_collaboration(self):
- G=nx.Graph()
- G.add_edge('a',1)
- G.add_edge('b',1)
- G.add_edge('b',2)
- G.add_edge('c',2)
- G.add_edge('c',3)
- G.add_edge('c',4)
- G.add_edge('b',4)
- P=bipartite.collaboration_weighted_projected_graph(G,'abc')
- assert_equal(P['a']['b']['weight'],1)
- assert_equal(P['b']['c']['weight'],2)
-
- def test_directed_projection(self):
- G=nx.DiGraph()
- G.add_edge('A',1)
- G.add_edge(1,'B')
- G.add_edge('A',2)
- G.add_edge('B',2)
- P=bipartite.projected_graph(G,'AB')
- assert_equal(sorted(P.edges()),[('A','B')])
- P=bipartite.weighted_projected_graph(G,'AB')
- assert_equal(sorted(P.edges()),[('A','B')])
- assert_equal(P['A']['B']['weight'],1)
-
- P=bipartite.projected_graph(G,'AB',multigraph=True)
- assert_equal(sorted(P.edges()),[('A','B')])
-
- G=nx.DiGraph()
- G.add_edge('A',1)
- G.add_edge(1,'B')
- G.add_edge('A',2)
- G.add_edge(2,'B')
- P=bipartite.projected_graph(G,'AB')
- assert_equal(sorted(P.edges()),[('A','B')])
- P=bipartite.weighted_projected_graph(G,'AB')
- assert_equal(sorted(P.edges()),[('A','B')])
- assert_equal(P['A']['B']['weight'],2)
-
- P=bipartite.projected_graph(G,'AB',multigraph=True)
- assert_equal(sorted(P.edges()),[('A','B'),('A','B')])
-
-
-class TestBipartiteWeightedProjection:
-
- def setUp(self):
- # Tore Opsahl's example
- # http://toreopsahl.com/2009/05/01/projecting-two-mode-networks-onto-weighted-one-mode-networks/
- self.G=nx.Graph()
- self.G.add_edge('A',1)
- self.G.add_edge('A',2)
- self.G.add_edge('B',1)
- self.G.add_edge('B',2)
- self.G.add_edge('B',3)
- self.G.add_edge('B',4)
- self.G.add_edge('B',5)
- self.G.add_edge('C',1)
- self.G.add_edge('D',3)
- self.G.add_edge('E',4)
- self.G.add_edge('E',5)
- self.G.add_edge('E',6)
- self.G.add_edge('F',6)
- # Graph based on figure 6 from Newman (2001)
- self.N=nx.Graph()
- self.N.add_edge('A',1)
- self.N.add_edge('A',2)
- self.N.add_edge('A',3)
- self.N.add_edge('B',1)
- self.N.add_edge('B',2)
- self.N.add_edge('B',3)
- self.N.add_edge('C',1)
- self.N.add_edge('D',1)
- self.N.add_edge('E',3)
-
- def test_project_weighted_shared(self):
- edges=[('A','B',2),
- ('A','C',1),
- ('B','C',1),
- ('B','D',1),
- ('B','E',2),
- ('E','F',1)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.weighted_projected_graph(self.G,'ABCDEF')
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- edges=[('A','B',3),
- ('A','E',1),
- ('A','C',1),
- ('A','D',1),
- ('B','E',1),
- ('B','C',1),
- ('B','D',1),
- ('C','D',1)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.weighted_projected_graph(self.N,'ABCDE')
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- def test_project_weighted_newman(self):
- edges=[('A','B',1.5),
- ('A','C',0.5),
- ('B','C',0.5),
- ('B','D',1),
- ('B','E',2),
- ('E','F',1)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.collaboration_weighted_projected_graph(self.G,'ABCDEF')
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- edges=[('A','B',11/6.0),
- ('A','E',1/2.0),
- ('A','C',1/3.0),
- ('A','D',1/3.0),
- ('B','E',1/2.0),
- ('B','C',1/3.0),
- ('B','D',1/3.0),
- ('C','D',1/3.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.collaboration_weighted_projected_graph(self.N,'ABCDE')
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- def test_project_weighted_ratio(self):
- edges=[('A','B',2/6.0),
- ('A','C',1/6.0),
- ('B','C',1/6.0),
- ('B','D',1/6.0),
- ('B','E',2/6.0),
- ('E','F',1/6.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.weighted_projected_graph(self.G, 'ABCDEF', ratio=True)
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- edges=[('A','B',3/3.0),
- ('A','E',1/3.0),
- ('A','C',1/3.0),
- ('A','D',1/3.0),
- ('B','E',1/3.0),
- ('B','C',1/3.0),
- ('B','D',1/3.0),
- ('C','D',1/3.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.weighted_projected_graph(self.N, 'ABCDE', ratio=True)
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- def test_project_weighted_overlap(self):
- edges=[('A','B',2/2.0),
- ('A','C',1/1.0),
- ('B','C',1/1.0),
- ('B','D',1/1.0),
- ('B','E',2/3.0),
- ('E','F',1/1.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.overlap_weighted_projected_graph(self.G,'ABCDEF', jaccard=False)
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- edges=[('A','B',3/3.0),
- ('A','E',1/1.0),
- ('A','C',1/1.0),
- ('A','D',1/1.0),
- ('B','E',1/1.0),
- ('B','C',1/1.0),
- ('B','D',1/1.0),
- ('C','D',1/1.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.overlap_weighted_projected_graph(self.N,'ABCDE', jaccard=False)
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- def test_project_weighted_jaccard(self):
- edges=[('A','B',2/5.0),
- ('A','C',1/2.0),
- ('B','C',1/5.0),
- ('B','D',1/5.0),
- ('B','E',2/6.0),
- ('E','F',1/3.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.overlap_weighted_projected_graph(self.G,'ABCDEF')
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- edges=[('A','B',3/3.0),
- ('A','E',1/3.0),
- ('A','C',1/3.0),
- ('A','D',1/3.0),
- ('B','E',1/3.0),
- ('B','C',1/3.0),
- ('B','D',1/3.0),
- ('C','D',1/1.0)]
- Panswer=nx.Graph()
- Panswer.add_weighted_edges_from(edges)
- P=bipartite.overlap_weighted_projected_graph(self.N,'ABCDE')
- assert_equal(P.edges(),Panswer.edges())
- for u,v in P.edges():
- assert_equal(P[u][v]['weight'],Panswer[u][v]['weight'])
-
- def test_generic_weighted_projected_graph_simple(self):
- def shared(G, u, v):
- return len(set(G[u]) & set(G[v]))
- B = nx.path_graph(5)
- G = bipartite.generic_weighted_projected_graph(B, [0, 2, 4], weight_function=shared)
- assert_equal(sorted(G.nodes()), [0, 2, 4])
- assert_equal(G.edges(data=True),
- [(0, 2, {'weight': 1}), (2, 4, {'weight': 1})] )
-
- G = bipartite.generic_weighted_projected_graph(B, [0, 2, 4])
- assert_equal(sorted(G.nodes()), [0, 2, 4])
- assert_equal(G.edges(data=True),
- [(0, 2, {'weight': 1}), (2, 4, {'weight': 1})] )
- B = nx.DiGraph()
- B.add_path(list(range(5)))
- G = bipartite.generic_weighted_projected_graph(B, [0, 2, 4])
- assert_equal(sorted(G.nodes()), [0, 2, 4])
- assert_equal(G.edges(data=True),
- [(0, 2, {'weight': 1}), (2, 4, {'weight': 1})] )
-
- def test_generic_weighted_projected_graph_custom(self):
- def jaccard(G, u, v):
- unbrs = set(G[u])
- vnbrs = set(G[v])
- return float(len(unbrs & vnbrs)) / len(unbrs | vnbrs)
- def my_weight(G, u, v, weight='weight'):
- w = 0
- for nbr in set(G[u]) & set(G[v]):
- w += G.edge[u][nbr].get(weight, 1) + G.edge[v][nbr].get(weight, 1)
- return w
- B = nx.complete_bipartite_graph(2,2)
- for i,(u,v) in enumerate(B.edges()):
- B.edge[u][v]['weight'] = i + 1
- G = bipartite.generic_weighted_projected_graph(B, [0, 1],
- weight_function=jaccard)
- assert_equal(G.edges(data=True), [(0, 1, {'weight': 1.0})])
- G = bipartite.generic_weighted_projected_graph(B, [0, 1],
- weight_function=my_weight)
- assert_equal(G.edges(data=True), [(0, 1, {'weight': 10})])
- G = bipartite.generic_weighted_projected_graph(B, [0, 1])
- assert_equal(G.edges(data=True), [(0, 1, {'weight': 2})])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_spectral_bipartivity.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_spectral_bipartivity.py
deleted file mode 100644
index e244a42..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/test_spectral_bipartivity.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# -*- coding: utf-8 -*-
-from nose import SkipTest
-from nose.tools import *
-import networkx as nx
-from networkx.algorithms.bipartite import spectral_bipartivity as sb
-
-# Examples from Figure 1
-# E. Estrada and J. A. Rodríguez-Velázquez, "Spectral measures of
-# bipartivity in complex networks", PhysRev E 72, 046105 (2005)
-
-class TestSpectralBipartivity(object):
- @classmethod
- def setupClass(cls):
- global scipy
- global assert_equal
- global assert_almost_equal
- try:
- import scipy.linalg
- except ImportError:
- raise SkipTest('SciPy not available.')
-
-
- def test_star_like(self):
- # star-like
-
- G=nx.star_graph(2)
- G.add_edge(1,2)
- assert_almost_equal(sb(G),0.843,places=3)
-
- G=nx.star_graph(3)
- G.add_edge(1,2)
- assert_almost_equal(sb(G),0.871,places=3)
-
- G=nx.star_graph(4)
- G.add_edge(1,2)
- assert_almost_equal(sb(G),0.890,places=3)
-
-
- def k23_like(self):
- # K2,3-like
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(0,1)
- assert_almost_equal(sb(G),0.769,places=3)
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(2,4)
- assert_almost_equal(sb(G),0.829,places=3)
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(2,4)
- G.add_edge(3,4)
- assert_almost_equal(sb(G),0.731,places=3)
-
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(0,1)
- G.add_edge(2,4)
- assert_almost_equal(sb(G),0.692,places=3)
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(2,4)
- G.add_edge(3,4)
- G.add_edge(0,1)
- assert_almost_equal(sb(G),0.645,places=3)
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(2,4)
- G.add_edge(3,4)
- G.add_edge(2,3)
- assert_almost_equal(sb(G),0.645,places=3)
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(2,4)
- G.add_edge(3,4)
- G.add_edge(2,3)
- G.add_edge(0,1)
- assert_almost_equal(sb(G),0.597,places=3)
-
- def test_single_nodes(self):
-
- # single nodes
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(2,4)
- sbn=sb(G,nodes=[1,2])
- assert_almost_equal(sbn[1],0.85,places=2)
- assert_almost_equal(sbn[2],0.77,places=2)
-
- G=nx.complete_bipartite_graph(2,3)
- G.add_edge(0,1)
- sbn=sb(G,nodes=[1,2])
- assert_almost_equal(sbn[1],0.73,places=2)
- assert_almost_equal(sbn[2],0.82,places=2)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/block.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/block.py
deleted file mode 100644
index cc2ff1d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/block.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# encoding: utf-8
-"""
-Functions for creating network blockmodels from node partitions.
-
-Created by Drew Conway <drew.conway@nyu.edu>
-Copyright (c) 2010. All rights reserved.
-"""
-__author__ = """\n""".join(['Drew Conway <drew.conway@nyu.edu>',
- 'Aric Hagberg <hagberg@lanl.gov>'])
-__all__=['blockmodel']
-
-import networkx as nx
-
-def blockmodel(G,partitions,multigraph=False):
- """Returns a reduced graph constructed using the generalized block modeling
- technique.
-
- The blockmodel technique collapses nodes into blocks based on a
- given partitioning of the node set. Each partition of nodes
- (block) is represented as a single node in the reduced graph.
-
- Edges between nodes in the block graph are added according to the
- edges in the original graph. If the parameter multigraph is False
- (the default) a single edge is added with a weight equal to the
- sum of the edge weights between nodes in the original graph
- The default is a weight of 1 if weights are not specified. If the
- parameter multigraph is True then multiple edges are added each
- with the edge data from the original graph.
-
- Parameters
- ----------
- G : graph
- A networkx Graph or DiGraph
- partitions : list of lists, or list of sets
- The partition of the nodes. Must be non-overlapping.
- multigraph : bool, optional
- If True return a MultiGraph with the edge data of the original
- graph applied to each corresponding edge in the new graph.
- If False return a Graph with the sum of the edge weights, or a
- count of the edges if the original graph is unweighted.
-
- Returns
- -------
- blockmodel : a Networkx graph object
-
- Examples
- --------
- >>> G=nx.path_graph(6)
- >>> partition=[[0,1],[2,3],[4,5]]
- >>> M=nx.blockmodel(G,partition)
-
- References
- ----------
- .. [1] Patrick Doreian, Vladimir Batagelj, and Anuska Ferligoj
- "Generalized Blockmodeling",Cambridge University Press, 2004.
- """
- # Create sets of node partitions
- part=list(map(set,partitions))
-
- # Check for overlapping node partitions
- u=set()
- for p1,p2 in zip(part[:-1],part[1:]):
- u.update(p1)
- #if not u.isdisjoint(p2): # Python 2.6 required
- if len (u.intersection(p2))>0:
- raise nx.NetworkXException("Overlapping node partitions.")
-
- # Initialize blockmodel graph
- if multigraph:
- if G.is_directed():
- M=nx.MultiDiGraph()
- else:
- M=nx.MultiGraph()
- else:
- if G.is_directed():
- M=nx.DiGraph()
- else:
- M=nx.Graph()
-
- # Add nodes and properties to blockmodel
- # The blockmodel nodes are node-induced subgraphs of G
- # Label them with integers starting at 0
- for i,p in zip(range(len(part)),part):
- M.add_node(i)
- # The node-induced subgraph is stored as the node 'graph' attribute
- SG=G.subgraph(p)
- M.node[i]['graph']=SG
- M.node[i]['nnodes']=SG.number_of_nodes()
- M.node[i]['nedges']=SG.number_of_edges()
- M.node[i]['density']=nx.density(SG)
-
- # Create mapping between original node labels and new blockmodel node labels
- block_mapping={}
- for n in M:
- nodes_in_block=M.node[n]['graph'].nodes()
- block_mapping.update(dict.fromkeys(nodes_in_block,n))
-
- # Add edges to block graph
- for u,v,d in G.edges(data=True):
- bmu=block_mapping[u]
- bmv=block_mapping[v]
- if bmu==bmv: # no self loops
- continue
- if multigraph:
- # For multigraphs add an edge for each edge in original graph
- M.add_edge(bmu,bmv,attr_dict=d)
- else:
- # For graphs and digraphs add single weighted edge
- weight=d.get('weight',1.0) # default to 1 if no weight specified
- if M.has_edge(bmu,bmv):
- M[bmu][bmv]['weight']+=weight
- else:
- M.add_edge(bmu,bmv,weight=weight)
- return M
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/boundary.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/boundary.py
deleted file mode 100644
index ec7b11c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/boundary.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""
-Routines to find the boundary of a set of nodes.
-
-Edge boundaries are edges that have only one end
-in the set of nodes.
-
-Node boundaries are nodes outside the set of nodes
-that have an edge to a node in the set.
-
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)\nDan Schult (dschult@colgate.edu)"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__=['edge_boundary','node_boundary']
-
-def edge_boundary(G, nbunch1, nbunch2=None):
- """Return the edge boundary.
-
- Edge boundaries are edges that have only one end
- in the given set of nodes.
-
- Parameters
- -----------
- G : graph
- A networkx graph
-
- nbunch1 : list, container
- Interior node set
-
- nbunch2 : list, container
- Exterior node set. If None then it is set to all of the
- nodes in G not in nbunch1.
-
- Returns
- -------
- elist : list
- List of edges
-
- Notes
- ------
- Nodes in nbunch1 and nbunch2 that are not in G are ignored.
-
- nbunch1 and nbunch2 are usually meant to be disjoint,
- but in the interest of speed and generality, that is
- not required here.
-
- """
- if nbunch2 is None: # Then nbunch2 is complement of nbunch1
- nset1=set((n for n in nbunch1 if n in G))
- return [(n1,n2) for n1 in nset1 for n2 in G[n1] \
- if n2 not in nset1]
-
- nset2=set(nbunch2)
- return [(n1,n2) for n1 in nbunch1 if n1 in G for n2 in G[n1] \
- if n2 in nset2]
-
-def node_boundary(G, nbunch1, nbunch2=None):
- """Return the node boundary.
-
- The node boundary is all nodes in the edge boundary of a given
- set of nodes that are in the set.
-
- Parameters
- -----------
- G : graph
- A networkx graph
-
- nbunch1 : list, container
- Interior node set
-
- nbunch2 : list, container
- Exterior node set. If None then it is set to all of the
- nodes in G not in nbunch1.
-
- Returns
- -------
- nlist : list
- List of nodes.
-
- Notes
- ------
- Nodes in nbunch1 and nbunch2 that are not in G are ignored.
-
- nbunch1 and nbunch2 are usually meant to be disjoint,
- but in the interest of speed and generality, that is
- not required here.
-
- """
- nset1=set(n for n in nbunch1 if n in G)
- bdy=set()
- for n1 in nset1:
- bdy.update(G[n1])
- bdy -= nset1
- if nbunch2 is not None: # else nbunch2 is complement of nbunch1
- bdy &= set(nbunch2)
- return list(bdy)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/__init__.py
deleted file mode 100644
index d60154b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from networkx.algorithms.centrality.betweenness import *
-from networkx.algorithms.centrality.betweenness_subset import *
-from networkx.algorithms.centrality.closeness import *
-from networkx.algorithms.centrality.current_flow_closeness import *
-from networkx.algorithms.centrality.current_flow_betweenness import *
-from networkx.algorithms.centrality.current_flow_betweenness_subset import *
-from networkx.algorithms.centrality.degree_alg import *
-from networkx.algorithms.centrality.eigenvector import *
-from networkx.algorithms.centrality.katz import *
-from networkx.algorithms.centrality.load import *
-from networkx.algorithms.centrality.communicability_alg import *
-import networkx.algorithms.centrality.betweenness
-import networkx.algorithms.centrality.closeness
-import networkx.algorithms.centrality.current_flow_betweenness
-import networkx.algorithms.centrality.current_flow_closeness
-import networkx.algorithms.centrality.degree_alg
-import networkx.algorithms.centrality.eigenvector
-import networkx.algorithms.centrality.load
-import networkx.algorithms.centrality.communicability_alg
-import networkx.algorithms.centrality.katz
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/betweenness.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/betweenness.py
deleted file mode 100644
index af2cf6a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/betweenness.py
+++ /dev/null
@@ -1,334 +0,0 @@
-"""
-Betweenness centrality measures.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import heapq
-import networkx as nx
-import random
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-
-__all__ = ['betweenness_centrality',
- 'edge_betweenness_centrality',
- 'edge_betweenness']
-
-def betweenness_centrality(G, k=None, normalized=True, weight=None,
- endpoints=False,
- seed=None):
- r"""Compute the shortest-path betweenness centrality for nodes.
-
- Betweenness centrality of a node `v` is the sum of the
- fraction of all-pairs shortest paths that pass through `v`:
-
- .. math::
-
- c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|v)}{\sigma(s, t)}
-
- where `V` is the set of nodes, `\sigma(s, t)` is the number of
- shortest `(s, t)`-paths, and `\sigma(s, t|v)` is the number of those
- paths passing through some node `v` other than `s, t`.
- If `s = t`, `\sigma(s, t) = 1`, and if `v \in {s, t}`,
- `\sigma(s, t|v) = 0` [2]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- k : int, optional (default=None)
- If k is not None use k node samples to estimate betweenness.
- The value of k <= n where n is the number of nodes in the graph.
- Higher values give better approximation.
-
- normalized : bool, optional
- If True the betweenness values are normalized by `2/((n-1)(n-2))`
- for graphs, and `1/((n-1)(n-2))` for directed graphs where `n`
- is the number of nodes in G.
-
- weight : None or string, optional
- If None, all edge weights are considered equal.
- Otherwise holds the name of the edge attribute used as weight.
-
- endpoints : bool, optional
- If True include the endpoints in the shortest path counts.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with betweenness centrality as the value.
-
- See Also
- --------
- edge_betweenness_centrality
- load_centrality
-
- Notes
- -----
- The algorithm is from Ulrik Brandes [1]_.
- See [2]_ for details on algorithms for variations and related metrics.
-
- For approximate betweenness calculations set k=#samples to use
- k nodes ("pivots") to estimate the betweenness values. For an estimate
- of the number of pivots needed see [3]_.
-
- For weighted graphs the edge weights must be greater than zero.
- Zero edge weights can produce an infinite number of equal length
- paths between pairs of nodes.
-
- References
- ----------
- .. [1] A Faster Algorithm for Betweenness Centrality.
- Ulrik Brandes,
- Journal of Mathematical Sociology 25(2):163-177, 2001.
- http://www.inf.uni-konstanz.de/algo/publications/b-fabc-01.pdf
- .. [2] Ulrik Brandes: On Variants of Shortest-Path Betweenness
- Centrality and their Generic Computation.
- Social Networks 30(2):136-145, 2008.
- http://www.inf.uni-konstanz.de/algo/publications/b-vspbc-08.pdf
- .. [3] Ulrik Brandes and Christian Pich:
- Centrality Estimation in Large Networks.
- International Journal of Bifurcation and Chaos 17(7):2303-2318, 2007.
- http://www.inf.uni-konstanz.de/algo/publications/bp-celn-06.pdf
- """
- betweenness=dict.fromkeys(G,0.0) # b[v]=0 for v in G
- if k is None:
- nodes = G
- else:
- random.seed(seed)
- nodes = random.sample(G.nodes(), k)
- for s in nodes:
- # single source shortest paths
- if weight is None: # use BFS
- S,P,sigma=_single_source_shortest_path_basic(G,s)
- else: # use Dijkstra's algorithm
- S,P,sigma=_single_source_dijkstra_path_basic(G,s,weight)
- # accumulation
- if endpoints:
- betweenness=_accumulate_endpoints(betweenness,S,P,sigma,s)
- else:
- betweenness=_accumulate_basic(betweenness,S,P,sigma,s)
- # rescaling
- betweenness=_rescale(betweenness, len(G),
- normalized=normalized,
- directed=G.is_directed(),
- k=k)
- return betweenness
-
-
-def edge_betweenness_centrality(G,normalized=True,weight=None):
- r"""Compute betweenness centrality for edges.
-
- Betweenness centrality of an edge `e` is the sum of the
- fraction of all-pairs shortest paths that pass through `e`:
-
- .. math::
-
- c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|e)}{\sigma(s, t)}
-
- where `V` is the set of nodes,`\sigma(s, t)` is the number of
- shortest `(s, t)`-paths, and `\sigma(s, t|e)` is the number of
- those paths passing through edge `e` [2]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- normalized : bool, optional
- If True the betweenness values are normalized by `2/(n(n-1))`
- for graphs, and `1/(n(n-1))` for directed graphs where `n`
- is the number of nodes in G.
-
- weight : None or string, optional
- If None, all edge weights are considered equal.
- Otherwise holds the name of the edge attribute used as weight.
-
- Returns
- -------
- edges : dictionary
- Dictionary of edges with betweenness centrality as the value.
-
- See Also
- --------
- betweenness_centrality
- edge_load
-
- Notes
- -----
- The algorithm is from Ulrik Brandes [1]_.
-
- For weighted graphs the edge weights must be greater than zero.
- Zero edge weights can produce an infinite number of equal length
- paths between pairs of nodes.
-
- References
- ----------
- .. [1] A Faster Algorithm for Betweenness Centrality. Ulrik Brandes,
- Journal of Mathematical Sociology 25(2):163-177, 2001.
- http://www.inf.uni-konstanz.de/algo/publications/b-fabc-01.pdf
- .. [2] Ulrik Brandes: On Variants of Shortest-Path Betweenness
- Centrality and their Generic Computation.
- Social Networks 30(2):136-145, 2008.
- http://www.inf.uni-konstanz.de/algo/publications/b-vspbc-08.pdf
- """
- betweenness=dict.fromkeys(G,0.0) # b[v]=0 for v in G
- # b[e]=0 for e in G.edges()
- betweenness.update(dict.fromkeys(G.edges(),0.0))
- for s in G:
- # single source shortest paths
- if weight is None: # use BFS
- S,P,sigma=_single_source_shortest_path_basic(G,s)
- else: # use Dijkstra's algorithm
- S,P,sigma=_single_source_dijkstra_path_basic(G,s,weight)
- # accumulation
- betweenness=_accumulate_edges(betweenness,S,P,sigma,s)
- # rescaling
- for n in G: # remove nodes to only return edges
- del betweenness[n]
- betweenness=_rescale_e(betweenness, len(G),
- normalized=normalized,
- directed=G.is_directed())
- return betweenness
-
-# obsolete name
-def edge_betweenness(G,normalized=True,weight=None):
- return edge_betweenness_centrality(G,normalized,weight)
-
-
-# helpers for betweenness centrality
-
-def _single_source_shortest_path_basic(G,s):
- S=[]
- P={}
- for v in G:
- P[v]=[]
- sigma=dict.fromkeys(G,0.0) # sigma[v]=0 for v in G
- D={}
- sigma[s]=1.0
- D[s]=0
- Q=[s]
- while Q: # use BFS to find shortest paths
- v=Q.pop(0)
- S.append(v)
- Dv=D[v]
- sigmav=sigma[v]
- for w in G[v]:
- if w not in D:
- Q.append(w)
- D[w]=Dv+1
- if D[w]==Dv+1: # this is a shortest path, count paths
- sigma[w] += sigmav
- P[w].append(v) # predecessors
- return S,P,sigma
-
-
-
-def _single_source_dijkstra_path_basic(G,s,weight='weight'):
- # modified from Eppstein
- S=[]
- P={}
- for v in G:
- P[v]=[]
- sigma=dict.fromkeys(G,0.0) # sigma[v]=0 for v in G
- D={}
- sigma[s]=1.0
- push=heapq.heappush
- pop=heapq.heappop
- seen = {s:0}
- Q=[] # use Q as heap with (distance,node id) tuples
- push(Q,(0,s,s))
- while Q:
- (dist,pred,v)=pop(Q)
- if v in D:
- continue # already searched this node.
- sigma[v] += sigma[pred] # count paths
- S.append(v)
- D[v] = dist
- for w,edgedata in G[v].items():
- vw_dist = dist + edgedata.get(weight,1)
- if w not in D and (w not in seen or vw_dist < seen[w]):
- seen[w] = vw_dist
- push(Q,(vw_dist,v,w))
- sigma[w]=0.0
- P[w]=[v]
- elif vw_dist==seen[w]: # handle equal paths
- sigma[w] += sigma[v]
- P[w].append(v)
- return S,P,sigma
-
-def _accumulate_basic(betweenness,S,P,sigma,s):
- delta=dict.fromkeys(S,0)
- while S:
- w=S.pop()
- coeff=(1.0+delta[w])/sigma[w]
- for v in P[w]:
- delta[v] += sigma[v]*coeff
- if w != s:
- betweenness[w]+=delta[w]
- return betweenness
-
-def _accumulate_endpoints(betweenness,S,P,sigma,s):
- betweenness[s]+=len(S)-1
- delta=dict.fromkeys(S,0)
- while S:
- w=S.pop()
- coeff=(1.0+delta[w])/sigma[w]
- for v in P[w]:
- delta[v] += sigma[v]*coeff
- if w != s:
- betweenness[w] += delta[w]+1
- return betweenness
-
-def _accumulate_edges(betweenness,S,P,sigma,s):
- delta=dict.fromkeys(S,0)
- while S:
- w=S.pop()
- coeff=(1.0+delta[w])/sigma[w]
- for v in P[w]:
- c=sigma[v]*coeff
- if (v,w) not in betweenness:
- betweenness[(w,v)]+=c
- else:
- betweenness[(v,w)]+=c
- delta[v]+=c
- if w != s:
- betweenness[w]+=delta[w]
- return betweenness
-
-def _rescale(betweenness,n,normalized,directed=False,k=None):
- if normalized is True:
- if n <=2:
- scale=None # no normalization b=0 for all nodes
- else:
- scale=1.0/((n-1)*(n-2))
- else: # rescale by 2 for undirected graphs
- if not directed:
- scale=1.0/2.0
- else:
- scale=None
- if scale is not None:
- if k is not None:
- scale=scale*n/k
- for v in betweenness:
- betweenness[v] *= scale
- return betweenness
-
-def _rescale_e(betweenness,n,normalized,directed=False):
- if normalized is True:
- if n <=1:
- scale=None # no normalization b=0 for all nodes
- else:
- scale=1.0/(n*(n-1))
- else: # rescale by 2 for undirected graphs
- if not directed:
- scale=1.0/2.0
- else:
- scale=None
- if scale is not None:
- for v in betweenness:
- betweenness[v] *= scale
- return betweenness
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/betweenness_subset.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/betweenness_subset.py
deleted file mode 100644
index cd4f8a2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/betweenness_subset.py
+++ /dev/null
@@ -1,263 +0,0 @@
-"""
-Betweenness centrality measures for subsets of nodes.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-
-__all__ = ['betweenness_centrality_subset',
- 'edge_betweenness_centrality_subset',
- 'betweenness_centrality_source']
-
-import networkx as nx
-
-from networkx.algorithms.centrality.betweenness import\
- _single_source_dijkstra_path_basic as dijkstra
-from networkx.algorithms.centrality.betweenness import\
- _single_source_shortest_path_basic as shortest_path
-
-
-def betweenness_centrality_subset(G,sources,targets,
- normalized=False,
- weight=None):
- """Compute betweenness centrality for a subset of nodes.
-
- .. math::
-
- c_B(v) =\sum_{s\in S, t \in T} \frac{\sigma(s, t|v)}{\sigma(s, t)}
-
- where `S` is the set of sources, `T` is the set of targets,
- `\sigma(s, t)` is the number of shortest `(s, t)`-paths,
- and `\sigma(s, t|v)` is the number of those paths
- passing through some node `v` other than `s, t`.
- If `s = t`, `\sigma(s, t) = 1`,
- and if `v \in {s, t}`, `\sigma(s, t|v) = 0` [2]_.
-
-
- Parameters
- ----------
- G : graph
-
- sources: list of nodes
- Nodes to use as sources for shortest paths in betweenness
-
- targets: list of nodes
- Nodes to use as targets for shortest paths in betweenness
-
- normalized : bool, optional
- If True the betweenness values are normalized by `2/((n-1)(n-2))`
- for graphs, and `1/((n-1)(n-2))` for directed graphs where `n`
- is the number of nodes in G.
-
- weight : None or string, optional
- If None, all edge weights are considered equal.
- Otherwise holds the name of the edge attribute used as weight.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with betweenness centrality as the value.
-
- See Also
- --------
- edge_betweenness_centrality
- load_centrality
-
- Notes
- -----
- The basic algorithm is from [1]_.
-
- For weighted graphs the edge weights must be greater than zero.
- Zero edge weights can produce an infinite number of equal length
- paths between pairs of nodes.
-
- The normalization might seem a little strange but it is the same
- as in betweenness_centrality() and is designed to make
- betweenness_centrality(G) be the same as
- betweenness_centrality_subset(G,sources=G.nodes(),targets=G.nodes()).
-
-
- References
- ----------
- .. [1] Ulrik Brandes, A Faster Algorithm for Betweenness Centrality.
- Journal of Mathematical Sociology 25(2):163-177, 2001.
- http://www.inf.uni-konstanz.de/algo/publications/b-fabc-01.pdf
- .. [2] Ulrik Brandes: On Variants of Shortest-Path Betweenness
- Centrality and their Generic Computation.
- Social Networks 30(2):136-145, 2008.
- http://www.inf.uni-konstanz.de/algo/publications/b-vspbc-08.pdf
- """
- b=dict.fromkeys(G,0.0) # b[v]=0 for v in G
- for s in sources:
- # single source shortest paths
- if weight is None: # use BFS
- S,P,sigma=shortest_path(G,s)
- else: # use Dijkstra's algorithm
- S,P,sigma=dijkstra(G,s,weight)
- b=_accumulate_subset(b,S,P,sigma,s,targets)
- b=_rescale(b,len(G),normalized=normalized,directed=G.is_directed())
- return b
-
-
-def edge_betweenness_centrality_subset(G,sources,targets,
- normalized=False,
- weight=None):
- """Compute betweenness centrality for edges for a subset of nodes.
-
- .. math::
-
- c_B(v) =\sum_{s\in S,t \in T} \frac{\sigma(s, t|e)}{\sigma(s, t)}
-
- where `S` is the set of sources, `T` is the set of targets,
- `\sigma(s, t)` is the number of shortest `(s, t)`-paths,
- and `\sigma(s, t|e)` is the number of those paths
- passing through edge `e` [2]_.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- sources: list of nodes
- Nodes to use as sources for shortest paths in betweenness
-
- targets: list of nodes
- Nodes to use as targets for shortest paths in betweenness
-
- normalized : bool, optional
- If True the betweenness values are normalized by `2/(n(n-1))`
- for graphs, and `1/(n(n-1))` for directed graphs where `n`
- is the number of nodes in G.
-
- weight : None or string, optional
- If None, all edge weights are considered equal.
- Otherwise holds the name of the edge attribute used as weight.
-
- Returns
- -------
- edges : dictionary
- Dictionary of edges with Betweenness centrality as the value.
-
- See Also
- --------
- betweenness_centrality
- edge_load
-
- Notes
- -----
- The basic algorithm is from [1]_.
-
- For weighted graphs the edge weights must be greater than zero.
- Zero edge weights can produce an infinite number of equal length
- paths between pairs of nodes.
-
- The normalization might seem a little strange but it is the same
- as in edge_betweenness_centrality() and is designed to make
- edge_betweenness_centrality(G) be the same as
- edge_betweenness_centrality_subset(G,sources=G.nodes(),targets=G.nodes()).
-
- References
- ----------
- .. [1] Ulrik Brandes, A Faster Algorithm for Betweenness Centrality.
- Journal of Mathematical Sociology 25(2):163-177, 2001.
- http://www.inf.uni-konstanz.de/algo/publications/b-fabc-01.pdf
- .. [2] Ulrik Brandes: On Variants of Shortest-Path Betweenness
- Centrality and their Generic Computation.
- Social Networks 30(2):136-145, 2008.
- http://www.inf.uni-konstanz.de/algo/publications/b-vspbc-08.pdf
-
- """
-
- b=dict.fromkeys(G,0.0) # b[v]=0 for v in G
- b.update(dict.fromkeys(G.edges(),0.0)) # b[e] for e in G.edges()
- for s in sources:
- # single source shortest paths
- if weight is None: # use BFS
- S,P,sigma=shortest_path(G,s)
- else: # use Dijkstra's algorithm
- S,P,sigma=dijkstra(G,s,weight)
- b=_accumulate_edges_subset(b,S,P,sigma,s,targets)
- for n in G: # remove nodes to only return edges
- del b[n]
- b=_rescale_e(b,len(G),normalized=normalized,directed=G.is_directed())
- return b
-
-# obsolete name
-def betweenness_centrality_source(G,normalized=True,weight=None,sources=None):
- if sources is None:
- sources=G.nodes()
- targets=G.nodes()
- return betweenness_centrality_subset(G,sources,targets,normalized,weight)
-
-
-def _accumulate_subset(betweenness,S,P,sigma,s,targets):
- delta=dict.fromkeys(S,0)
- target_set=set(targets)
- while S:
- w=S.pop()
- for v in P[w]:
- if w in target_set:
- delta[v]+=(sigma[v]/sigma[w])*(1.0+delta[w])
- else:
- delta[v]+=delta[w]/len(P[w])
- if w != s:
- betweenness[w]+=delta[w]
- return betweenness
-
-def _accumulate_edges_subset(betweenness,S,P,sigma,s,targets):
- delta=dict.fromkeys(S,0)
- target_set=set(targets)
- while S:
- w=S.pop()
- for v in P[w]:
- if w in target_set:
- c=(sigma[v]/sigma[w])*(1.0+delta[w])
- else:
- c=delta[w]/len(P[w])
- if (v,w) not in betweenness:
- betweenness[(w,v)]+=c
- else:
- betweenness[(v,w)]+=c
- delta[v]+=c
- if w != s:
- betweenness[w]+=delta[w]
- return betweenness
-
-
-
-
-def _rescale(betweenness,n,normalized,directed=False):
- if normalized is True:
- if n <=2:
- scale=None # no normalization b=0 for all nodes
- else:
- scale=1.0/((n-1)*(n-2))
- else: # rescale by 2 for undirected graphs
- if not directed:
- scale=1.0/2.0
- else:
- scale=None
- if scale is not None:
- for v in betweenness:
- betweenness[v] *= scale
- return betweenness
-
-def _rescale_e(betweenness,n,normalized,directed=False):
- if normalized is True:
- if n <=1:
- scale=None # no normalization b=0 for all nodes
- else:
- scale=1.0/(n*(n-1))
- else: # rescale by 2 for undirected graphs
- if not directed:
- scale=1.0/2.0
- else:
- scale=None
- if scale is not None:
- for v in betweenness:
- betweenness[v] *= scale
- return betweenness
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/closeness.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/closeness.py
deleted file mode 100644
index 67d8089..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/closeness.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-Closeness centrality measures.
-"""
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import functools
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Pieter Swart (swart@lanl.gov)',
- 'Sasha Gutfraind (ag362@cornell.edu)'])
-__all__ = ['closeness_centrality']
-
-
-def closeness_centrality(G, u=None, distance=None, normalized=True):
- r"""Compute closeness centrality for nodes.
-
- Closeness centrality [1]_ of a node `u` is the reciprocal of the
- sum of the shortest path distances from `u` to all `n-1` other nodes.
- Since the sum of distances depends on the number of nodes in the
- graph, closeness is normalized by the sum of minimum possible
- distances `n-1`.
-
- .. math::
-
- C(u) = \frac{n - 1}{\sum_{v=1}^{n} d(v, u)},
-
- where `d(v, u)` is the shortest-path distance between `v` and `u`,
- and `n` is the number of nodes in the graph.
-
- Notice that higher values of closeness indicate higher centrality.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- u : node, optional
- Return only the value for node u
- distance : edge attribute key, optional (default=None)
- Use the specified edge attribute as the edge distance in shortest
- path calculations
- normalized : bool, optional
- If True (default) normalize by the number of nodes in the connected
- part of the graph.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with closeness centrality as the value.
-
- See Also
- --------
- betweenness_centrality, load_centrality, eigenvector_centrality,
- degree_centrality
-
- Notes
- -----
- The closeness centrality is normalized to `(n-1)/(|G|-1)` where
- `n` is the number of nodes in the connected part of graph
- containing the node. If the graph is not completely connected,
- this algorithm computes the closeness centrality for each
- connected part separately.
-
- If the 'distance' keyword is set to an edge attribute key then the
- shortest-path length will be computed using Dijkstra's algorithm with
- that edge attribute as the edge weight.
-
- References
- ----------
- .. [1] Freeman, L.C., 1979. Centrality in networks: I.
- Conceptual clarification. Social Networks 1, 215--239.
- http://www.soc.ucsb.edu/faculty/friedkin/Syllabi/Soc146/Freeman78.PDF
- """
- if distance is not None:
- # use Dijkstra's algorithm with specified attribute as edge weight
- path_length = functools.partial(nx.single_source_dijkstra_path_length,
- weight=distance)
- else:
- path_length = nx.single_source_shortest_path_length
-
- if u is None:
- nodes = G.nodes()
- else:
- nodes = [u]
- closeness_centrality = {}
- for n in nodes:
- sp = path_length(G,n)
- totsp = sum(sp.values())
- if totsp > 0.0 and len(G) > 1:
- closeness_centrality[n] = (len(sp)-1.0) / totsp
- # normalize to number of nodes-1 in connected part
- if normalized:
- s = (len(sp)-1.0) / ( len(G) - 1 )
- closeness_centrality[n] *= s
- else:
- closeness_centrality[n] = 0.0
- if u is not None:
- return closeness_centrality[u]
- else:
- return closeness_centrality
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/communicability_alg.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/communicability_alg.py
deleted file mode 100644
index 3c8fc6b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/communicability_alg.py
+++ /dev/null
@@ -1,495 +0,0 @@
-"""
-Communicability and centrality measures.
-"""
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import *
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Franck Kalala (franckkalala@yahoo.fr'])
-__all__ = ['communicability_centrality_exp',
- 'communicability_centrality',
- 'communicability_betweenness_centrality',
- 'communicability',
- 'communicability_exp',
- 'estrada_index',
- ]
-
-@require('scipy')
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def communicability_centrality_exp(G):
- r"""Return the communicability centrality for each node of G
-
- Communicability centrality, also called subgraph centrality, of a node `n`
- is the sum of closed walks of all lengths starting and ending at node `n`.
-
- Parameters
- ----------
- G: graph
-
- Returns
- -------
- nodes:dictionary
- Dictionary of nodes with communicability centrality as the value.
-
- Raises
- ------
- NetworkXError
- If the graph is not undirected and simple.
-
- See Also
- --------
- communicability:
- Communicability between all pairs of nodes in G.
- communicability_centrality:
- Communicability centrality for each node of G.
-
- Notes
- -----
- This version of the algorithm exponentiates the adjacency matrix.
- The communicability centrality of a node `u` in G can be found using
- the matrix exponential of the adjacency matrix of G [1]_ [2]_,
-
- .. math::
-
- SC(u)=(e^A)_{uu} .
-
- References
- ----------
- .. [1] Ernesto Estrada, Juan A. Rodriguez-Velazquez,
- "Subgraph centrality in complex networks",
- Physical Review E 71, 056103 (2005).
- http://arxiv.org/abs/cond-mat/0504730
-
- .. [2] Ernesto Estrada, Naomichi Hatano,
- "Communicability in complex networks",
- Phys. Rev. E 77, 036111 (2008).
- http://arxiv.org/abs/0707.0756
-
- Examples
- --------
- >>> G = nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
- >>> sc = nx.communicability_centrality_exp(G)
- """
- # alternative implementation that calculates the matrix exponential
- import scipy.linalg
- nodelist = G.nodes() # ordering of nodes in matrix
- A = nx.to_numpy_matrix(G,nodelist)
- # convert to 0-1 matrix
- A[A!=0.0] = 1
- expA = scipy.linalg.expm(A)
- # convert diagonal to dictionary keyed by node
- sc = dict(zip(nodelist,map(float,expA.diagonal())))
- return sc
-
-@require('numpy')
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def communicability_centrality(G):
- r"""Return communicability centrality for each node in G.
-
- Communicability centrality, also called subgraph centrality, of a node `n`
- is the sum of closed walks of all lengths starting and ending at node `n`.
-
- Parameters
- ----------
- G: graph
-
- Returns
- -------
- nodes: dictionary
- Dictionary of nodes with communicability centrality as the value.
-
- Raises
- ------
- NetworkXError
- If the graph is not undirected and simple.
-
- See Also
- --------
- communicability:
- Communicability between all pairs of nodes in G.
- communicability_centrality:
- Communicability centrality for each node of G.
-
- Notes
- -----
- This version of the algorithm computes eigenvalues and eigenvectors
- of the adjacency matrix.
-
- Communicability centrality of a node `u` in G can be found using
- a spectral decomposition of the adjacency matrix [1]_ [2]_,
-
- .. math::
-
- SC(u)=\sum_{j=1}^{N}(v_{j}^{u})^2 e^{\lambda_{j}},
-
- where `v_j` is an eigenvector of the adjacency matrix `A` of G
- corresponding corresponding to the eigenvalue `\lambda_j`.
-
- Examples
- --------
- >>> G = nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
- >>> sc = nx.communicability_centrality(G)
-
- References
- ----------
- .. [1] Ernesto Estrada, Juan A. Rodriguez-Velazquez,
- "Subgraph centrality in complex networks",
- Physical Review E 71, 056103 (2005).
- http://arxiv.org/abs/cond-mat/0504730
- .. [2] Ernesto Estrada, Naomichi Hatano,
- "Communicability in complex networks",
- Phys. Rev. E 77, 036111 (2008).
- http://arxiv.org/abs/0707.0756
- """
- import numpy
- import numpy.linalg
- nodelist = G.nodes() # ordering of nodes in matrix
- A = nx.to_numpy_matrix(G,nodelist)
- # convert to 0-1 matrix
- A[A!=0.0] = 1
- w,v = numpy.linalg.eigh(A)
- vsquare = numpy.array(v)**2
- expw = numpy.exp(w)
- xg = numpy.dot(vsquare,expw)
- # convert vector dictionary keyed by node
- sc = dict(zip(nodelist,map(float,xg)))
- return sc
-
-@require('scipy')
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def communicability_betweenness_centrality(G, normalized=True):
- r"""Return communicability betweenness for all pairs of nodes in G.
-
- Communicability betweenness measure makes use of the number of walks
- connecting every pair of nodes as the basis of a betweenness centrality
- measure.
-
- Parameters
- ----------
- G: graph
-
- Returns
- -------
- nodes:dictionary
- Dictionary of nodes with communicability betweenness as the value.
-
- Raises
- ------
- NetworkXError
- If the graph is not undirected and simple.
-
- See Also
- --------
- communicability:
- Communicability between all pairs of nodes in G.
- communicability_centrality:
- Communicability centrality for each node of G using matrix exponential.
- communicability_centrality_exp:
- Communicability centrality for each node in G using
- spectral decomposition.
-
- Notes
- -----
- Let `G=(V,E)` be a simple undirected graph with `n` nodes and `m` edges,
- and `A` denote the adjacency matrix of `G`.
-
- Let `G(r)=(V,E(r))` be the graph resulting from
- removing all edges connected to node `r` but not the node itself.
-
- The adjacency matrix for `G(r)` is `A+E(r)`, where `E(r)` has nonzeros
- only in row and column `r`.
-
- The communicability betweenness of a node `r` is [1]_
-
- .. math::
- \omega_{r} = \frac{1}{C}\sum_{p}\sum_{q}\frac{G_{prq}}{G_{pq}},
- p\neq q, q\neq r,
-
- where
- `G_{prq}=(e^{A}_{pq} - (e^{A+E(r)})_{pq}` is the number of walks
- involving node r,
- `G_{pq}=(e^{A})_{pq}` is the number of closed walks starting
- at node `p` and ending at node `q`,
- and `C=(n-1)^{2}-(n-1)` is a normalization factor equal to the
- number of terms in the sum.
-
- The resulting `\omega_{r}` takes values between zero and one.
- The lower bound cannot be attained for a connected
- graph, and the upper bound is attained in the star graph.
-
- References
- ----------
- .. [1] Ernesto Estrada, Desmond J. Higham, Naomichi Hatano,
- "Communicability Betweenness in Complex Networks"
- Physica A 388 (2009) 764-774.
- http://arxiv.org/abs/0905.4102
-
- Examples
- --------
- >>> G = nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
- >>> cbc = nx.communicability_betweenness_centrality(G)
- """
- import scipy
- import scipy.linalg
- nodelist = G.nodes() # ordering of nodes in matrix
- n = len(nodelist)
- A = nx.to_numpy_matrix(G,nodelist)
- # convert to 0-1 matrix
- A[A!=0.0] = 1
- expA = scipy.linalg.expm(A)
- mapping = dict(zip(nodelist,range(n)))
- sc = {}
- for v in G:
- # remove row and col of node v
- i = mapping[v]
- row = A[i,:].copy()
- col = A[:,i].copy()
- A[i,:] = 0
- A[:,i] = 0
- B = (expA - scipy.linalg.expm(A)) / expA
- # sum with row/col of node v and diag set to zero
- B[i,:] = 0
- B[:,i] = 0
- B -= scipy.diag(scipy.diag(B))
- sc[v] = float(B.sum())
- # put row and col back
- A[i,:] = row
- A[:,i] = col
- # rescaling
- sc = _rescale(sc,normalized=normalized)
- return sc
-
-def _rescale(sc,normalized):
- # helper to rescale betweenness centrality
- if normalized is True:
- order=len(sc)
- if order <=2:
- scale=None
- else:
- scale=1.0/((order-1.0)**2-(order-1.0))
- if scale is not None:
- for v in sc:
- sc[v] *= scale
- return sc
-
-
-@require('numpy','scipy')
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def communicability(G):
- r"""Return communicability between all pairs of nodes in G.
-
- The communicability between pairs of nodes in G is the sum of
- closed walks of different lengths starting at node u and ending at node v.
-
- Parameters
- ----------
- G: graph
-
- Returns
- -------
- comm: dictionary of dictionaries
- Dictionary of dictionaries keyed by nodes with communicability
- as the value.
-
- Raises
- ------
- NetworkXError
- If the graph is not undirected and simple.
-
- See Also
- --------
- communicability_centrality_exp:
- Communicability centrality for each node of G using matrix exponential.
- communicability_centrality:
- Communicability centrality for each node in G using spectral
- decomposition.
- communicability:
- Communicability between pairs of nodes in G.
-
- Notes
- -----
- This algorithm uses a spectral decomposition of the adjacency matrix.
- Let G=(V,E) be a simple undirected graph. Using the connection between
- the powers of the adjacency matrix and the number of walks in the graph,
- the communicability between nodes `u` and `v` based on the graph spectrum
- is [1]_
-
- .. math::
- C(u,v)=\sum_{j=1}^{n}\phi_{j}(u)\phi_{j}(v)e^{\lambda_{j}},
-
- where `\phi_{j}(u)` is the `u\rm{th}` element of the `j\rm{th}` orthonormal
- eigenvector of the adjacency matrix associated with the eigenvalue
- `\lambda_{j}`.
-
- References
- ----------
- .. [1] Ernesto Estrada, Naomichi Hatano,
- "Communicability in complex networks",
- Phys. Rev. E 77, 036111 (2008).
- http://arxiv.org/abs/0707.0756
-
- Examples
- --------
- >>> G = nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
- >>> c = nx.communicability(G)
- """
- import numpy
- import scipy.linalg
- nodelist = G.nodes() # ordering of nodes in matrix
- A = nx.to_numpy_matrix(G,nodelist)
- # convert to 0-1 matrix
- A[A!=0.0] = 1
- w,vec = numpy.linalg.eigh(A)
- expw = numpy.exp(w)
- mapping = dict(zip(nodelist,range(len(nodelist))))
- sc={}
- # computing communicabilities
- for u in G:
- sc[u]={}
- for v in G:
- s = 0
- p = mapping[u]
- q = mapping[v]
- for j in range(len(nodelist)):
- s += vec[:,j][p,0]*vec[:,j][q,0]*expw[j]
- sc[u][v] = float(s)
- return sc
-
-@require('scipy')
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def communicability_exp(G):
- r"""Return communicability between all pairs of nodes in G.
-
- Communicability between pair of node (u,v) of node in G is the sum of
- closed walks of different lengths starting at node u and ending at node v.
-
- Parameters
- ----------
- G: graph
-
- Returns
- -------
- comm: dictionary of dictionaries
- Dictionary of dictionaries keyed by nodes with communicability
- as the value.
-
- Raises
- ------
- NetworkXError
- If the graph is not undirected and simple.
-
- See Also
- --------
- communicability_centrality_exp:
- Communicability centrality for each node of G using matrix exponential.
- communicability_centrality:
- Communicability centrality for each node in G using spectral
- decomposition.
- communicability_exp:
- Communicability between all pairs of nodes in G using spectral
- decomposition.
-
- Notes
- -----
- This algorithm uses matrix exponentiation of the adjacency matrix.
-
- Let G=(V,E) be a simple undirected graph. Using the connection between
- the powers of the adjacency matrix and the number of walks in the graph,
- the communicability between nodes u and v is [1]_,
-
- .. math::
- C(u,v) = (e^A)_{uv},
-
- where `A` is the adjacency matrix of G.
-
- References
- ----------
- .. [1] Ernesto Estrada, Naomichi Hatano,
- "Communicability in complex networks",
- Phys. Rev. E 77, 036111 (2008).
- http://arxiv.org/abs/0707.0756
-
- Examples
- --------
- >>> G = nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
- >>> c = nx.communicability_exp(G)
- """
- import scipy.linalg
- nodelist = G.nodes() # ordering of nodes in matrix
- A = nx.to_numpy_matrix(G,nodelist)
- # convert to 0-1 matrix
- A[A!=0.0] = 1
- # communicability matrix
- expA = scipy.linalg.expm(A)
- mapping = dict(zip(nodelist,range(len(nodelist))))
- sc = {}
- for u in G:
- sc[u]={}
- for v in G:
- sc[u][v] = float(expA[mapping[u],mapping[v]])
- return sc
-
-@require('numpy')
-def estrada_index(G):
- r"""Return the Estrada index of a the graph G.
-
- Parameters
- ----------
- G: graph
-
- Returns
- -------
- estrada index: float
-
- Raises
- ------
- NetworkXError
- If the graph is not undirected and simple.
-
- See also
- --------
- estrada_index_exp
-
- Notes
- -----
- Let `G=(V,E)` be a simple undirected graph with `n` nodes and let
- `\lambda_{1}\leq\lambda_{2}\leq\cdots\lambda_{n}`
- be a non-increasing ordering of the eigenvalues of its adjacency
- matrix `A`. The Estrada index is
-
- .. math::
- EE(G)=\sum_{j=1}^n e^{\lambda _j}.
-
- References
- ----------
- .. [1] E. Estrada, Characterization of 3D molecular structure,
- Chem. Phys. Lett. 319, 713 (2000).
-
- Examples
- --------
- >>> G=nx.Graph([(0,1),(1,2),(1,5),(5,4),(2,4),(2,3),(4,3),(3,6)])
- >>> ei=nx.estrada_index(G)
- """
- return sum(communicability_centrality(G).values())
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_betweenness.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_betweenness.py
deleted file mode 100644
index 23cb5bf..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_betweenness.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"""
-Current-flow betweenness centrality measures.
-"""
-# Copyright (C) 2010-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import random
-import networkx as nx
-from networkx.algorithms.centrality.flow_matrix import *
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-
-__all__ = ['current_flow_betweenness_centrality',
- 'approximate_current_flow_betweenness_centrality',
- 'edge_current_flow_betweenness_centrality']
-
-
-def approximate_current_flow_betweenness_centrality(G, normalized=True,
- weight='weight',
- dtype=float, solver='full',
- epsilon=0.5, kmax=10000):
- r"""Compute the approximate current-flow betweenness centrality for nodes.
-
- Approximates the current-flow betweenness centrality within absolute
- error of epsilon with high probability [1]_.
-
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- normalized : bool, optional (default=True)
- If True the betweenness values are normalized by 2/[(n-1)(n-2)] where
- n is the number of nodes in G.
-
- weight : string or None, optional (default='weight')
- Key for edge data used as the edge weight.
- If None, then use 1 as each edge weight.
-
- dtype: data type (float)
- Default data type for internal matrices.
- Set to np.float32 for lower memory consumption.
-
- solver: string (default='lu')
- Type of linear solver to use for computing the flow matrix.
- Options are "full" (uses most memory), "lu" (recommended), and
- "cg" (uses least memory).
-
- epsilon: float
- Absolute error tolerance.
-
- kmax: int
- Maximum number of sample node pairs to use for approximation.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with betweenness centrality as the value.
-
- See Also
- --------
- current_flow_betweenness_centrality
-
- Notes
- -----
- The running time is `O((1/\epsilon^2)m{\sqrt k} \log n)`
- and the space required is `O(m)` for n nodes and m edges.
-
- If the edges have a 'weight' attribute they will be used as
- weights in this algorithm. Unspecified weights are set to 1.
-
- References
- ----------
- .. [1] Centrality Measures Based on Current Flow.
- Ulrik Brandes and Daniel Fleischer,
- Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
- LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
- http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
- """
- from networkx.utils import reverse_cuthill_mckee_ordering
- try:
- import numpy as np
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires NumPy ',
- 'http://scipy.org/')
- try:
- from scipy import sparse
- from scipy.sparse import linalg
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires SciPy ',
- 'http://scipy.org/')
- if G.is_directed():
- raise nx.NetworkXError('current_flow_betweenness_centrality() ',
- 'not defined for digraphs.')
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected.")
- solvername={"full" :FullInverseLaplacian,
- "lu": SuperLUInverseLaplacian,
- "cg": CGInverseLaplacian}
- n = G.number_of_nodes()
- ordering = list(reverse_cuthill_mckee_ordering(G))
- # make a copy with integer labels according to rcm ordering
- # this could be done without a copy if we really wanted to
- H = nx.relabel_nodes(G,dict(zip(ordering,range(n))))
- L = laplacian_sparse_matrix(H, nodelist=range(n), weight=weight,
- dtype=dtype, format='csc')
- C = solvername[solver](L, dtype=dtype) # initialize solver
- betweenness = dict.fromkeys(H,0.0)
- nb = (n-1.0)*(n-2.0) # normalization factor
- cstar = n*(n-1)/nb
- l = 1 # parameter in approximation, adjustable
- k = l*int(np.ceil((cstar/epsilon)**2*np.log(n)))
- if k > kmax:
- raise nx.NetworkXError('Number random pairs k>kmax (%d>%d) '%(k,kmax),
- 'Increase kmax or epsilon')
- cstar2k = cstar/(2*k)
- for i in range(k):
- s,t = random.sample(range(n),2)
- b = np.zeros(n, dtype=dtype)
- b[s] = 1
- b[t] = -1
- p = C.solve(b)
- for v in H:
- if v==s or v==t:
- continue
- for nbr in H[v]:
- w = H[v][nbr].get(weight,1.0)
- betweenness[v] += w*np.abs(p[v]-p[nbr])*cstar2k
- if normalized:
- factor = 1.0
- else:
- factor = nb/2.0
- # remap to original node names and "unnormalize" if required
- return dict((ordering[k],float(v*factor)) for k,v in betweenness.items())
-
-
-def current_flow_betweenness_centrality(G, normalized=True, weight='weight',
- dtype=float, solver='full'):
- r"""Compute current-flow betweenness centrality for nodes.
-
- Current-flow betweenness centrality uses an electrical current
- model for information spreading in contrast to betweenness
- centrality which uses shortest paths.
-
- Current-flow betweenness centrality is also known as
- random-walk betweenness centrality [2]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- normalized : bool, optional (default=True)
- If True the betweenness values are normalized by 2/[(n-1)(n-2)] where
- n is the number of nodes in G.
-
- weight : string or None, optional (default='weight')
- Key for edge data used as the edge weight.
- If None, then use 1 as each edge weight.
-
- dtype: data type (float)
- Default data type for internal matrices.
- Set to np.float32 for lower memory consumption.
-
- solver: string (default='lu')
- Type of linear solver to use for computing the flow matrix.
- Options are "full" (uses most memory), "lu" (recommended), and
- "cg" (uses least memory).
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with betweenness centrality as the value.
-
- See Also
- --------
- approximate_current_flow_betweenness_centrality
- betweenness_centrality
- edge_betweenness_centrality
- edge_current_flow_betweenness_centrality
-
- Notes
- -----
- Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)`
- time [1]_, where `I(n-1)` is the time needed to compute the
- inverse Laplacian. For a full matrix this is `O(n^3)` but using
- sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the
- Laplacian matrix condition number.
-
- The space required is `O(nw) where `w` is the width of the sparse
- Laplacian matrix. Worse case is `w=n` for `O(n^2)`.
-
- If the edges have a 'weight' attribute they will be used as
- weights in this algorithm. Unspecified weights are set to 1.
-
- References
- ----------
- .. [1] Centrality Measures Based on Current Flow.
- Ulrik Brandes and Daniel Fleischer,
- Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
- LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
- http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
-
- .. [2] A measure of betweenness centrality based on random walks,
- M. E. J. Newman, Social Networks 27, 39-54 (2005).
- """
- from networkx.utils import reverse_cuthill_mckee_ordering
- try:
- import numpy as np
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires NumPy ',
- 'http://scipy.org/')
- try:
- import scipy
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires SciPy ',
- 'http://scipy.org/')
- if G.is_directed():
- raise nx.NetworkXError('current_flow_betweenness_centrality() ',
- 'not defined for digraphs.')
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected.")
- n = G.number_of_nodes()
- ordering = list(reverse_cuthill_mckee_ordering(G))
- # make a copy with integer labels according to rcm ordering
- # this could be done without a copy if we really wanted to
- H = nx.relabel_nodes(G,dict(zip(ordering,range(n))))
- betweenness = dict.fromkeys(H,0.0) # b[v]=0 for v in H
- for row,(s,t) in flow_matrix_row(H, weight=weight, dtype=dtype,
- solver=solver):
- pos = dict(zip(row.argsort()[::-1],range(n)))
- for i in range(n):
- betweenness[s] += (i-pos[i])*row[i]
- betweenness[t] += (n-i-1-pos[i])*row[i]
- if normalized:
- nb = (n-1.0)*(n-2.0) # normalization factor
- else:
- nb = 2.0
- for i,v in enumerate(H): # map integers to nodes
- betweenness[v] = float((betweenness[v]-i)*2.0/nb)
- return dict((ordering[k],v) for k,v in betweenness.items())
-
-
-def edge_current_flow_betweenness_centrality(G, normalized=True,
- weight='weight',
- dtype=float, solver='full'):
- """Compute current-flow betweenness centrality for edges.
-
- Current-flow betweenness centrality uses an electrical current
- model for information spreading in contrast to betweenness
- centrality which uses shortest paths.
-
- Current-flow betweenness centrality is also known as
- random-walk betweenness centrality [2]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- normalized : bool, optional (default=True)
- If True the betweenness values are normalized by 2/[(n-1)(n-2)] where
- n is the number of nodes in G.
-
- weight : string or None, optional (default='weight')
- Key for edge data used as the edge weight.
- If None, then use 1 as each edge weight.
-
- dtype: data type (float)
- Default data type for internal matrices.
- Set to np.float32 for lower memory consumption.
-
- solver: string (default='lu')
- Type of linear solver to use for computing the flow matrix.
- Options are "full" (uses most memory), "lu" (recommended), and
- "cg" (uses least memory).
-
- Returns
- -------
- nodes : dictionary
- Dictionary of edge tuples with betweenness centrality as the value.
-
- See Also
- --------
- betweenness_centrality
- edge_betweenness_centrality
- current_flow_betweenness_centrality
-
- Notes
- -----
- Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)`
- time [1]_, where `I(n-1)` is the time needed to compute the
- inverse Laplacian. For a full matrix this is `O(n^3)` but using
- sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the
- Laplacian matrix condition number.
-
- The space required is `O(nw) where `w` is the width of the sparse
- Laplacian matrix. Worse case is `w=n` for `O(n^2)`.
-
- If the edges have a 'weight' attribute they will be used as
- weights in this algorithm. Unspecified weights are set to 1.
-
- References
- ----------
- .. [1] Centrality Measures Based on Current Flow.
- Ulrik Brandes and Daniel Fleischer,
- Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
- LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
- http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
-
- .. [2] A measure of betweenness centrality based on random walks,
- M. E. J. Newman, Social Networks 27, 39-54 (2005).
- """
- from networkx.utils import reverse_cuthill_mckee_ordering
- try:
- import numpy as np
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires NumPy ',
- 'http://scipy.org/')
- try:
- import scipy
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires SciPy ',
- 'http://scipy.org/')
- if G.is_directed():
- raise nx.NetworkXError('edge_current_flow_betweenness_centrality ',
- 'not defined for digraphs.')
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected.")
- n = G.number_of_nodes()
- ordering = list(reverse_cuthill_mckee_ordering(G))
- # make a copy with integer labels according to rcm ordering
- # this could be done without a copy if we really wanted to
- H = nx.relabel_nodes(G,dict(zip(ordering,range(n))))
- betweenness=(dict.fromkeys(H.edges(),0.0))
- if normalized:
- nb=(n-1.0)*(n-2.0) # normalization factor
- else:
- nb=2.0
- for row,(e) in flow_matrix_row(H, weight=weight, dtype=dtype,
- solver=solver):
- pos=dict(zip(row.argsort()[::-1],range(1,n+1)))
- for i in range(n):
- betweenness[e]+=(i+1-pos[i])*row[i]
- betweenness[e]+=(n-i-pos[i])*row[i]
- betweenness[e]/=nb
- return dict(((ordering[s],ordering[t]),float(v))
- for (s,t),v in betweenness.items())
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- import scipy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_betweenness_subset.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_betweenness_subset.py
deleted file mode 100644
index 7902f30..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_betweenness_subset.py
+++ /dev/null
@@ -1,263 +0,0 @@
-"""
-Current-flow betweenness centrality measures for subsets of nodes.
-"""
-# Copyright (C) 2010-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-
-__all__ = ['current_flow_betweenness_centrality_subset',
- 'edge_current_flow_betweenness_centrality_subset']
-
-import itertools
-import networkx as nx
-from networkx.algorithms.centrality.flow_matrix import *
-
-
-def current_flow_betweenness_centrality_subset(G,sources,targets,
- normalized=True,
- weight='weight',
- dtype=float, solver='lu'):
- r"""Compute current-flow betweenness centrality for subsets of nodes.
-
- Current-flow betweenness centrality uses an electrical current
- model for information spreading in contrast to betweenness
- centrality which uses shortest paths.
-
- Current-flow betweenness centrality is also known as
- random-walk betweenness centrality [2]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- sources: list of nodes
- Nodes to use as sources for current
-
- targets: list of nodes
- Nodes to use as sinks for current
-
- normalized : bool, optional (default=True)
- If True the betweenness values are normalized by b=b/(n-1)(n-2) where
- n is the number of nodes in G.
-
- weight : string or None, optional (default='weight')
- Key for edge data used as the edge weight.
- If None, then use 1 as each edge weight.
-
- dtype: data type (float)
- Default data type for internal matrices.
- Set to np.float32 for lower memory consumption.
-
- solver: string (default='lu')
- Type of linear solver to use for computing the flow matrix.
- Options are "full" (uses most memory), "lu" (recommended), and
- "cg" (uses least memory).
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with betweenness centrality as the value.
-
- See Also
- --------
- approximate_current_flow_betweenness_centrality
- betweenness_centrality
- edge_betweenness_centrality
- edge_current_flow_betweenness_centrality
-
- Notes
- -----
- Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)`
- time [1]_, where `I(n-1)` is the time needed to compute the
- inverse Laplacian. For a full matrix this is `O(n^3)` but using
- sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the
- Laplacian matrix condition number.
-
- The space required is `O(nw) where `w` is the width of the sparse
- Laplacian matrix. Worse case is `w=n` for `O(n^2)`.
-
- If the edges have a 'weight' attribute they will be used as
- weights in this algorithm. Unspecified weights are set to 1.
-
- References
- ----------
- .. [1] Centrality Measures Based on Current Flow.
- Ulrik Brandes and Daniel Fleischer,
- Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
- LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
- http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
-
- .. [2] A measure of betweenness centrality based on random walks,
- M. E. J. Newman, Social Networks 27, 39-54 (2005).
- """
- from networkx.utils import reverse_cuthill_mckee_ordering
- try:
- import numpy as np
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires NumPy ',
- 'http://scipy.org/')
- try:
- import scipy
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires SciPy ',
- 'http://scipy.org/')
- if G.is_directed():
- raise nx.NetworkXError('current_flow_betweenness_centrality() ',
- 'not defined for digraphs.')
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected.")
- n = G.number_of_nodes()
- ordering = list(reverse_cuthill_mckee_ordering(G))
- # make a copy with integer labels according to rcm ordering
- # this could be done without a copy if we really wanted to
- mapping=dict(zip(ordering,range(n)))
- H = nx.relabel_nodes(G,mapping)
- betweenness = dict.fromkeys(H,0.0) # b[v]=0 for v in H
- for row,(s,t) in flow_matrix_row(H, weight=weight, dtype=dtype,
- solver=solver):
- for ss in sources:
- i=mapping[ss]
- for tt in targets:
- j=mapping[tt]
- betweenness[s]+=0.5*np.abs(row[i]-row[j])
- betweenness[t]+=0.5*np.abs(row[i]-row[j])
- if normalized:
- nb=(n-1.0)*(n-2.0) # normalization factor
- else:
- nb=2.0
- for v in H:
- betweenness[v]=betweenness[v]/nb+1.0/(2-n)
- return dict((ordering[k],v) for k,v in betweenness.items())
-
-
-def edge_current_flow_betweenness_centrality_subset(G, sources, targets,
- normalized=True,
- weight='weight',
- dtype=float, solver='lu'):
- """Compute current-flow betweenness centrality for edges using subsets
- of nodes.
-
- Current-flow betweenness centrality uses an electrical current
- model for information spreading in contrast to betweenness
- centrality which uses shortest paths.
-
- Current-flow betweenness centrality is also known as
- random-walk betweenness centrality [2]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- sources: list of nodes
- Nodes to use as sources for current
-
- targets: list of nodes
- Nodes to use as sinks for current
-
- normalized : bool, optional (default=True)
- If True the betweenness values are normalized by b=b/(n-1)(n-2) where
- n is the number of nodes in G.
-
- weight : string or None, optional (default='weight')
- Key for edge data used as the edge weight.
- If None, then use 1 as each edge weight.
-
- dtype: data type (float)
- Default data type for internal matrices.
- Set to np.float32 for lower memory consumption.
-
- solver: string (default='lu')
- Type of linear solver to use for computing the flow matrix.
- Options are "full" (uses most memory), "lu" (recommended), and
- "cg" (uses least memory).
-
- Returns
- -------
- nodes : dictionary
- Dictionary of edge tuples with betweenness centrality as the value.
-
- See Also
- --------
- betweenness_centrality
- edge_betweenness_centrality
- current_flow_betweenness_centrality
-
- Notes
- -----
- Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)`
- time [1]_, where `I(n-1)` is the time needed to compute the
- inverse Laplacian. For a full matrix this is `O(n^3)` but using
- sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the
- Laplacian matrix condition number.
-
- The space required is `O(nw) where `w` is the width of the sparse
- Laplacian matrix. Worse case is `w=n` for `O(n^2)`.
-
- If the edges have a 'weight' attribute they will be used as
- weights in this algorithm. Unspecified weights are set to 1.
-
- References
- ----------
- .. [1] Centrality Measures Based on Current Flow.
- Ulrik Brandes and Daniel Fleischer,
- Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
- LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
- http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
-
- .. [2] A measure of betweenness centrality based on random walks,
- M. E. J. Newman, Social Networks 27, 39-54 (2005).
- """
- from networkx.utils import reverse_cuthill_mckee_ordering
- try:
- import numpy as np
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires NumPy ',
- 'http://scipy.org/')
- try:
- import scipy
- except ImportError:
- raise ImportError('current_flow_betweenness_centrality requires SciPy ',
- 'http://scipy.org/')
- if G.is_directed():
- raise nx.NetworkXError('edge_current_flow_betweenness_centrality ',
- 'not defined for digraphs.')
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected.")
- n = G.number_of_nodes()
- ordering = list(reverse_cuthill_mckee_ordering(G))
- # make a copy with integer labels according to rcm ordering
- # this could be done without a copy if we really wanted to
- mapping=dict(zip(ordering,range(n)))
- H = nx.relabel_nodes(G,mapping)
- betweenness=(dict.fromkeys(H.edges(),0.0))
- if normalized:
- nb=(n-1.0)*(n-2.0) # normalization factor
- else:
- nb=2.0
- for row,(e) in flow_matrix_row(H, weight=weight, dtype=dtype,
- solver=solver):
- for ss in sources:
- i=mapping[ss]
- for tt in targets:
- j=mapping[tt]
- betweenness[e]+=0.5*np.abs(row[i]-row[j])
- betweenness[e]/=nb
- return dict(((ordering[s],ordering[t]),v)
- for (s,t),v in betweenness.items())
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- import scipy
- except:
- raise SkipTest("NumPy not available")
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_closeness.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_closeness.py
deleted file mode 100644
index 1a484b2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/current_flow_closeness.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""
-Current-flow closeness centrality measures.
-
-"""
-# Copyright (C) 2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """Aric Hagberg <aric.hagberg@gmail.com>"""
-
-__all__ = ['current_flow_closeness_centrality','information_centrality']
-
-import networkx as nx
-from networkx.algorithms.centrality.flow_matrix import *
-
-
-def current_flow_closeness_centrality(G, normalized=True, weight='weight',
- dtype=float, solver='lu'):
- """Compute current-flow closeness centrality for nodes.
-
- A variant of closeness centrality based on effective
- resistance between nodes in a network. This metric
- is also known as information centrality.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- normalized : bool, optional
- If True the values are normalized by 1/(n-1) where n is the
- number of nodes in G.
-
- dtype: data type (float)
- Default data type for internal matrices.
- Set to np.float32 for lower memory consumption.
-
- solver: string (default='lu')
- Type of linear solver to use for computing the flow matrix.
- Options are "full" (uses most memory), "lu" (recommended), and
- "cg" (uses least memory).
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with current flow closeness centrality as the value.
-
- See Also
- --------
- closeness_centrality
-
- Notes
- -----
- The algorithm is from Brandes [1]_.
-
- See also [2]_ for the original definition of information centrality.
-
- References
- ----------
- .. [1] Ulrik Brandes and Daniel Fleischer,
- Centrality Measures Based on Current Flow.
- Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
- LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
- http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
-
- .. [2] Stephenson, K. and Zelen, M.
- Rethinking centrality: Methods and examples.
- Social Networks. Volume 11, Issue 1, March 1989, pp. 1-37
- http://dx.doi.org/10.1016/0378-8733(89)90016-6
- """
- from networkx.utils import reverse_cuthill_mckee_ordering
- try:
- import numpy as np
- except ImportError:
- raise ImportError('current_flow_closeness_centrality requires NumPy ',
- 'http://scipy.org/')
- try:
- import scipy
- except ImportError:
- raise ImportError('current_flow_closeness_centrality requires SciPy ',
- 'http://scipy.org/')
- if G.is_directed():
- raise nx.NetworkXError('current_flow_closeness_centrality ',
- 'not defined for digraphs.')
- if G.is_directed():
- raise nx.NetworkXError(\
- "current_flow_closeness_centrality() not defined for digraphs.")
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected.")
- solvername={"full" :FullInverseLaplacian,
- "lu": SuperLUInverseLaplacian,
- "cg": CGInverseLaplacian}
- n = G.number_of_nodes()
- ordering = list(reverse_cuthill_mckee_ordering(G))
- # make a copy with integer labels according to rcm ordering
- # this could be done without a copy if we really wanted to
- H = nx.relabel_nodes(G,dict(zip(ordering,range(n))))
- betweenness = dict.fromkeys(H,0.0) # b[v]=0 for v in H
- n = G.number_of_nodes()
- L = laplacian_sparse_matrix(H, nodelist=range(n), weight=weight,
- dtype=dtype, format='csc')
- C2 = solvername[solver](L, width=1, dtype=dtype) # initialize solver
- for v in H:
- col=C2.get_row(v)
- for w in H:
- betweenness[v]+=col[v]-2*col[w]
- betweenness[w]+=col[v]
-
- if normalized:
- nb=len(betweenness)-1.0
- else:
- nb=1.0
- for v in H:
- betweenness[v]=nb/(betweenness[v])
- return dict((ordering[k],float(v)) for k,v in betweenness.items())
-
-information_centrality=current_flow_closeness_centrality
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/degree_alg.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/degree_alg.py
deleted file mode 100644
index c65d967..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/degree_alg.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""
-Degree centrality measures.
-
-"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Sasha Gutfraind (ag362@cornell.edu)'])
-
-__all__ = ['degree_centrality',
- 'in_degree_centrality',
- 'out_degree_centrality']
-
-import networkx as nx
-
-def degree_centrality(G):
- """Compute the degree centrality for nodes.
-
- The degree centrality for a node v is the fraction of nodes it
- is connected to.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with degree centrality as the value.
-
- See Also
- --------
- betweenness_centrality, load_centrality, eigenvector_centrality
-
- Notes
- -----
- The degree centrality values are normalized by dividing by the maximum
- possible degree in a simple graph n-1 where n is the number of nodes in G.
-
- For multigraphs or graphs with self loops the maximum degree might
- be higher than n-1 and values of degree centrality greater than 1
- are possible.
- """
- centrality={}
- s=1.0/(len(G)-1.0)
- centrality=dict((n,d*s) for n,d in G.degree_iter())
- return centrality
-
-def in_degree_centrality(G):
- """Compute the in-degree centrality for nodes.
-
- The in-degree centrality for a node v is the fraction of nodes its
- incoming edges are connected to.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with in-degree centrality as values.
-
- See Also
- --------
- degree_centrality, out_degree_centrality
-
- Notes
- -----
- The degree centrality values are normalized by dividing by the maximum
- possible degree in a simple graph n-1 where n is the number of nodes in G.
-
- For multigraphs or graphs with self loops the maximum degree might
- be higher than n-1 and values of degree centrality greater than 1
- are possible.
- """
- if not G.is_directed():
- raise nx.NetworkXError(\
- "in_degree_centrality() not defined for undirected graphs.")
- centrality={}
- s=1.0/(len(G)-1.0)
- centrality=dict((n,d*s) for n,d in G.in_degree_iter())
- return centrality
-
-
-def out_degree_centrality(G):
- """Compute the out-degree centrality for nodes.
-
- The out-degree centrality for a node v is the fraction of nodes its
- outgoing edges are connected to.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with out-degree centrality as values.
-
- See Also
- --------
- degree_centrality, in_degree_centrality
-
- Notes
- -----
- The degree centrality values are normalized by dividing by the maximum
- possible degree in a simple graph n-1 where n is the number of nodes in G.
-
- For multigraphs or graphs with self loops the maximum degree might
- be higher than n-1 and values of degree centrality greater than 1
- are possible.
- """
- if not G.is_directed():
- raise nx.NetworkXError(\
- "out_degree_centrality() not defined for undirected graphs.")
- centrality={}
- s=1.0/(len(G)-1.0)
- centrality=dict((n,d*s) for n,d in G.out_degree_iter())
- return centrality
-
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/eigenvector.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/eigenvector.py
deleted file mode 100644
index 28e0013..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/eigenvector.py
+++ /dev/null
@@ -1,169 +0,0 @@
-"""
-Eigenvector centrality.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Sasha Gutfraind (ag362@cornell.edu)'])
-__all__ = ['eigenvector_centrality',
- 'eigenvector_centrality_numpy']
-
-def eigenvector_centrality(G,max_iter=100,tol=1.0e-6,nstart=None):
- """Compute the eigenvector centrality for the graph G.
-
- Uses the power method to find the eigenvector for the
- largest eigenvalue of the adjacency matrix of G.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- max_iter : interger, optional
- Maximum number of iterations in power method.
-
- tol : float, optional
- Error tolerance used to check convergence in power method iteration.
-
- nstart : dictionary, optional
- Starting value of eigenvector iteration for each node.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with eigenvector centrality as the value.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> centrality=nx.eigenvector_centrality(G)
- >>> print(['%s %0.2f'%(node,centrality[node]) for node in centrality])
- ['0 0.37', '1 0.60', '2 0.60', '3 0.37']
-
- Notes
- ------
- The eigenvector calculation is done by the power iteration method
- and has no guarantee of convergence. The iteration will stop
- after max_iter iterations or an error tolerance of
- number_of_nodes(G)*tol has been reached.
-
- For directed graphs this is "right" eigevector centrality. For
- "left" eigenvector centrality, first reverse the graph with
- G.reverse().
-
- See Also
- --------
- eigenvector_centrality_numpy
- pagerank
- hits
- """
- from math import sqrt
- if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
- raise nx.NetworkXException("Not defined for multigraphs.")
-
- if len(G)==0:
- raise nx.NetworkXException("Empty graph.")
-
- if nstart is None:
- # choose starting vector with entries of 1/len(G)
- x=dict([(n,1.0/len(G)) for n in G])
- else:
- x=nstart
- # normalize starting vector
- s=1.0/sum(x.values())
- for k in x: x[k]*=s
- nnodes=G.number_of_nodes()
- # make up to max_iter iterations
- for i in range(max_iter):
- xlast=x
- x=dict.fromkeys(xlast, 0)
- # do the multiplication y=Ax
- for n in x:
- for nbr in G[n]:
- x[n]+=xlast[nbr]*G[n][nbr].get('weight',1)
- # normalize vector
- try:
- s=1.0/sqrt(sum(v**2 for v in x.values()))
- # this should never be zero?
- except ZeroDivisionError:
- s=1.0
- for n in x: x[n]*=s
- # check convergence
- err=sum([abs(x[n]-xlast[n]) for n in x])
- if err < nnodes*tol:
- return x
-
- raise nx.NetworkXError("""eigenvector_centrality():
-power iteration failed to converge in %d iterations."%(i+1))""")
-
-
-def eigenvector_centrality_numpy(G):
- """Compute the eigenvector centrality for the graph G.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with eigenvector centrality as the value.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> centrality=nx.eigenvector_centrality_numpy(G)
- >>> print(['%s %0.2f'%(node,centrality[node]) for node in centrality])
- ['0 0.37', '1 0.60', '2 0.60', '3 0.37']
-
- Notes
- ------
- This algorithm uses the NumPy eigenvalue solver.
-
- For directed graphs this is "right" eigevector centrality. For
- "left" eigenvector centrality, first reverse the graph with
- G.reverse().
-
- See Also
- --------
- eigenvector_centrality
- pagerank
- hits
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError('Requires NumPy: http://scipy.org/')
-
- if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
- raise nx.NetworkXException('Not defined for multigraphs.')
-
- if len(G)==0:
- raise nx.NetworkXException('Empty graph.')
-
- A=nx.adj_matrix(G,nodelist=G.nodes())
- eigenvalues,eigenvectors=np.linalg.eig(A)
- # eigenvalue indices in reverse sorted order
- ind=eigenvalues.argsort()[::-1]
- # eigenvector of largest eigenvalue at ind[0], normalized
- largest=np.array(eigenvectors[:,ind[0]]).flatten().real
- norm=np.sign(largest.sum())*np.linalg.norm(largest)
- centrality=dict(zip(G,map(float,largest/norm)))
- return centrality
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- import numpy.linalg
- except:
- raise SkipTest("numpy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/flow_matrix.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/flow_matrix.py
deleted file mode 100644
index d886182..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/flow_matrix.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# Helpers for current-flow betweenness and current-flow closness
-# Lazy computations for inverse Laplacian and flow-matrix rows.
-import networkx as nx
-
-def flow_matrix_row(G, weight='weight', dtype=float, solver='lu'):
- # Generate a row of the current-flow matrix
- import numpy as np
- from scipy import sparse
- from scipy.sparse import linalg
- solvername={"full" :FullInverseLaplacian,
- "lu": SuperLUInverseLaplacian,
- "cg": CGInverseLaplacian}
- n = G.number_of_nodes()
- L = laplacian_sparse_matrix(G, nodelist=range(n), weight=weight,
- dtype=dtype, format='csc')
- C = solvername[solver](L, dtype=dtype) # initialize solver
- w = C.w # w is the Laplacian matrix width
- # row-by-row flow matrix
- for u,v,d in G.edges_iter(data=True):
- B = np.zeros(w, dtype=dtype)
- c = d.get(weight,1.0)
- B[u%w] = c
- B[v%w] = -c
- # get only the rows needed in the inverse laplacian
- # and multiply to get the flow matrix row
- row = np.dot(B, C.get_rows(u,v))
- yield row,(u,v)
-
-
-# Class to compute the inverse laplacian only for specified rows
-# Allows computation of the current-flow matrix without storing entire
-# inverse laplacian matrix
-class InverseLaplacian(object):
- def __init__(self, L, width=None, dtype=None):
- global np
- import numpy as np
- (n,n) = L.shape
- self.dtype = dtype
- self.n = n
- if width is None:
- self.w = self.width(L)
- else:
- self.w = width
- self.C = np.zeros((self.w,n), dtype=dtype)
- self.L1 = L[1:,1:]
- self.init_solver(L)
-
- def init_solver(self,L):
- pass
-
- def solve(self,r):
- raise("Implement solver")
-
- def solve_inverse(self,r):
- raise("Implement solver")
-
-
- def get_rows(self, r1, r2):
- for r in range(r1, r2+1):
- self.C[r%self.w, 1:] = self.solve_inverse(r)
- return self.C
-
- def get_row(self, r):
- self.C[r%self.w, 1:] = self.solve_inverse(r)
- return self.C[r%self.w]
-
-
- def width(self,L):
- m=0
- for i,row in enumerate(L):
- w=0
- x,y = np.nonzero(row)
- if len(y) > 0:
- v = y-i
- w=v.max()-v.min()+1
- m = max(w,m)
- return m
-
-class FullInverseLaplacian(InverseLaplacian):
- def init_solver(self,L):
- self.IL = np.zeros(L.shape, dtype=self.dtype)
- self.IL[1:,1:] = np.linalg.inv(self.L1.todense())
-
- def solve(self,rhs):
- s = np.zeros(rhs.shape, dtype=self.dtype)
- s = np.dot(self.IL,rhs)
- return s
-
- def solve_inverse(self,r):
- return self.IL[r,1:]
-
-
-class SuperLUInverseLaplacian(InverseLaplacian):
- def init_solver(self,L):
- from scipy.sparse import linalg
- self.lusolve = linalg.factorized(self.L1.tocsc())
-
- def solve_inverse(self,r):
- rhs = np.zeros(self.n, dtype=self.dtype)
- rhs[r]=1
- return self.lusolve(rhs[1:])
-
- def solve(self,rhs):
- s = np.zeros(rhs.shape, dtype=self.dtype)
- s[1:]=self.lusolve(rhs[1:])
- return s
-
-
-
-class CGInverseLaplacian(InverseLaplacian):
- def init_solver(self,L):
- global linalg
- from scipy.sparse import linalg
- ilu= linalg.spilu(self.L1.tocsc())
- n=self.n-1
- self.M = linalg.LinearOperator(shape=(n,n), matvec=ilu.solve)
-
- def solve(self,rhs):
- s = np.zeros(rhs.shape, dtype=self.dtype)
- s[1:]=linalg.cg(self.L1, rhs[1:], M=self.M)[0]
- return s
-
- def solve_inverse(self,r):
- rhs = np.zeros(self.n, self.dtype)
- rhs[r] = 1
- return linalg.cg(self.L1, rhs[1:], M=self.M)[0]
-
-
-# graph laplacian, sparse version, will move to linalg/laplacianmatrix.py
-def laplacian_sparse_matrix(G, nodelist=None, weight='weight', dtype=None,
- format='csr'):
- import numpy as np
- import scipy.sparse
- A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
- dtype=dtype, format=format)
- (n,n) = A.shape
- data = np.asarray(A.sum(axis=1).T)
- D = scipy.sparse.spdiags(data,0,n,n, format=format)
- return D - A
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/katz.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/katz.py
deleted file mode 100644
index 4babe25..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/katz.py
+++ /dev/null
@@ -1,296 +0,0 @@
-"""
-Katz centrality.
-"""
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import *
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Sasha Gutfraind (ag362@cornell.edu)',
- 'Vincent Gauthier (vgauthier@luxbulb.org)'])
-
-__all__ = ['katz_centrality',
- 'katz_centrality_numpy']
-
-@not_implemented_for('multigraph')
-def katz_centrality(G, alpha=0.1, beta=1.0,
- max_iter=1000, tol=1.0e-6, nstart=None, normalized=True):
- r"""Compute the Katz centrality for the nodes of the graph G.
-
-
- Katz centrality is related to eigenvalue centrality and PageRank.
- The Katz centrality for node `i` is
-
- .. math::
-
- x_i = \alpha \sum_{j} A_{ij} x_j + \beta,
-
- where `A` is the adjacency matrix of the graph G with eigenvalues `\lambda`.
-
- The parameter `\beta` controls the initial centrality and
-
- .. math::
-
- \alpha < \frac{1}{\lambda_{max}}.
-
-
- Katz centrality computes the relative influence of a node within a
- network by measuring the number of the immediate neighbors (first
- degree nodes) and also all other nodes in the network that connect
- to the node under consideration through these immediate neighbors.
-
- Extra weight can be provided to immediate neighbors through the
- parameter :math:`\beta`. Connections made with distant neighbors
- are, however, penalized by an attenuation factor `\alpha` which
- should be strictly less than the inverse largest eigenvalue of the
- adjacency matrix in order for the Katz centrality to be computed
- correctly. More information is provided in [1]_ .
-
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- alpha : float
- Attenuation factor
-
- beta : scalar or dictionary, optional (default=1.0)
- Weight attributed to the immediate neighborhood. If not a scalar the
- dictionary must have an value for every node.
-
- max_iter : integer, optional (default=1000)
- Maximum number of iterations in power method.
-
- tol : float, optional (default=1.0e-6)
- Error tolerance used to check convergence in power method iteration.
-
- nstart : dictionary, optional
- Starting value of Katz iteration for each node.
-
- normalized : bool, optional (default=True)
- If True normalize the resulting values.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with Katz centrality as the value.
-
- Examples
- --------
- >>> import math
- >>> G = nx.path_graph(4)
- >>> phi = (1+math.sqrt(5))/2.0 # largest eigenvalue of adj matrix
- >>> centrality = nx.katz_centrality(G,1/phi-0.01)
- >>> for n,c in sorted(centrality.items()):
- ... print("%d %0.2f"%(n,c))
- 0 0.37
- 1 0.60
- 2 0.60
- 3 0.37
-
- Notes
- -----
- This algorithm it uses the power method to find the eigenvector
- corresponding to the largest eigenvalue of the adjacency matrix of G.
- The constant alpha should be strictly less than the inverse of largest
- eigenvalue of the adjacency matrix for the algorithm to converge.
- The iteration will stop after max_iter iterations or an error tolerance of
- number_of_nodes(G)*tol has been reached.
-
- When `\alpha = 1/\lambda_{max}` and `\beta=1` Katz centrality is the same as
- eigenvector centrality.
-
- References
- ----------
- .. [1] M. Newman, Networks: An Introduction. Oxford University Press,
- USA, 2010, p. 720.
-
- See Also
- --------
- katz_centrality_numpy
- eigenvector_centrality
- eigenvector_centrality_numpy
- pagerank
- hits
- """
- from math import sqrt
-
- if len(G)==0:
- return {}
-
- nnodes=G.number_of_nodes()
-
- if nstart is None:
- # choose starting vector with entries of 0
- x=dict([(n,0) for n in G])
- else:
- x=nstart
-
- try:
- b = dict.fromkeys(G,float(beta))
- except (TypeError,ValueError):
- b = beta
- if set(beta) != set(G):
- raise nx.NetworkXError('beta dictionary '
- 'must have a value for every node')
-
- # make up to max_iter iterations
- for i in range(max_iter):
- xlast=x
- x=dict.fromkeys(xlast, 0)
- # do the multiplication y = Alpha * Ax - Beta
- for n in x:
- for nbr in G[n]:
- x[n] += xlast[nbr] * G[n][nbr].get('weight',1)
- x[n] = alpha*x[n] + b[n]
-
- # check convergence
- err=sum([abs(x[n]-xlast[n]) for n in x])
- if err < nnodes*tol:
- if normalized:
- # normalize vector
- try:
- s=1.0/sqrt(sum(v**2 for v in x.values()))
- # this should never be zero?
- except ZeroDivisionError:
- s=1.0
- else:
- s = 1
- for n in x:
- x[n]*=s
- return x
-
- raise nx.NetworkXError('Power iteration failed to converge in ',
- '%d iterations."%(i+1))')
-
-@not_implemented_for('multigraph')
-def katz_centrality_numpy(G, alpha=0.1, beta=1.0, normalized=True):
- r"""Compute the Katz centrality for the graph G.
-
-
- Katz centrality is related to eigenvalue centrality and PageRank.
- The Katz centrality for node `i` is
-
- .. math::
-
- x_i = \alpha \sum_{j} A_{ij} x_j + \beta,
-
- where `A` is the adjacency matrix of the graph G with eigenvalues `\lambda`.
-
- The parameter `\beta` controls the initial centrality and
-
- .. math::
-
- \alpha < \frac{1}{\lambda_{max}}.
-
-
- Katz centrality computes the relative influence of a node within a
- network by measuring the number of the immediate neighbors (first
- degree nodes) and also all other nodes in the network that connect
- to the node under consideration through these immediate neighbors.
-
- Extra weight can be provided to immediate neighbors through the
- parameter :math:`\beta`. Connections made with distant neighbors
- are, however, penalized by an attenuation factor `\alpha` which
- should be strictly less than the inverse largest eigenvalue of the
- adjacency matrix in order for the Katz centrality to be computed
- correctly. More information is provided in [1]_ .
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- alpha : float
- Attenuation factor
-
- beta : scalar or dictionary, optional (default=1.0)
- Weight attributed to the immediate neighborhood. If not a scalar the
- dictionary must have an value for every node.
-
- normalized : bool
- If True normalize the resulting values.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with Katz centrality as the value.
-
- Examples
- --------
- >>> import math
- >>> G = nx.path_graph(4)
- >>> phi = (1+math.sqrt(5))/2.0 # largest eigenvalue of adj matrix
- >>> centrality = nx.katz_centrality_numpy(G,1/phi)
- >>> for n,c in sorted(centrality.items()):
- ... print("%d %0.2f"%(n,c))
- 0 0.37
- 1 0.60
- 2 0.60
- 3 0.37
-
- Notes
- ------
- This algorithm uses a direct linear solver to solve the above equation.
- The constant alpha should be strictly less than the inverse of largest
- eigenvalue of the adjacency matrix for there to be a solution. When
- `\alpha = 1/\lambda_{max}` and `\beta=1` Katz centrality is the same as
- eigenvector centrality.
-
- References
- ----------
- .. [1] M. Newman, Networks: An Introduction. Oxford University Press,
- USA, 2010, p. 720.
-
- See Also
- --------
- katz_centrality
- eigenvector_centrality_numpy
- eigenvector_centrality
- pagerank
- hits
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError('Requires NumPy: http://scipy.org/')
- if len(G)==0:
- return {}
- try:
- nodelist = beta.keys()
- if set(nodelist) != set(G):
- raise nx.NetworkXError('beta dictionary '
- 'must have a value for every node')
- b = np.array(list(beta.values()),dtype=float)
- except AttributeError:
- nodelist = G.nodes()
- try:
- b = np.ones((len(nodelist),1))*float(beta)
- except (TypeError,ValueError):
- raise nx.NetworkXError('beta must be a number')
-
- A=nx.adj_matrix(G, nodelist=nodelist)
- n = np.array(A).shape[0]
- centrality = np.linalg.solve( np.eye(n,n) - (alpha * A) , b)
- if normalized:
- norm = np.sign(sum(centrality)) * np.linalg.norm(centrality)
- else:
- norm = 1.0
- centrality=dict(zip(nodelist, map(float,centrality/norm)))
- return centrality
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- import numpy.linalg
- except:
- raise SkipTest("numpy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/load.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/load.py
deleted file mode 100644
index 2d279e8..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/load.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""
-Load centrality.
-
-"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Sasha Gutfraind (ag362@cornell.edu)'])
-
-__all__ = ['load_centrality',
- 'edge_load']
-
-import networkx as nx
-
-def newman_betweenness_centrality(G,v=None,cutoff=None,
- normalized=True,
- weight=None):
- """Compute load centrality for nodes.
-
- The load centrality of a node is the fraction of all shortest
- paths that pass through that node.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- normalized : bool, optional
- If True the betweenness values are normalized by b=b/(n-1)(n-2) where
- n is the number of nodes in G.
-
- weight : None or string, optional
- If None, edge weights are ignored.
- Otherwise holds the name of the edge attribute used as weight.
-
- cutoff : bool, optional
- If specified, only consider paths of length <= cutoff.
-
- Returns
- -------
- nodes : dictionary
- Dictionary of nodes with centrality as the value.
-
-
- See Also
- --------
- betweenness_centrality()
-
- Notes
- -----
- Load centrality is slightly different than betweenness.
- For this load algorithm see the reference
- Scientific collaboration networks: II.
- Shortest paths, weighted networks, and centrality,
- M. E. J. Newman, Phys. Rev. E 64, 016132 (2001).
-
- """
- if v is not None: # only one node
- betweenness=0.0
- for source in G:
- ubetween = _node_betweenness(G, source, cutoff, False, weight)
- betweenness += ubetween[v] if v in ubetween else 0
- if normalized:
- order = G.order()
- if order <= 2:
- return betweenness # no normalization b=0 for all nodes
- betweenness *= 1.0 / ((order-1) * (order-2))
- return betweenness
- else:
- betweenness = {}.fromkeys(G,0.0)
- for source in betweenness:
- ubetween = _node_betweenness(G, source, cutoff, False, weight)
- for vk in ubetween:
- betweenness[vk] += ubetween[vk]
- if normalized:
- order = G.order()
- if order <= 2:
- return betweenness # no normalization b=0 for all nodes
- scale = 1.0 / ((order-1) * (order-2))
- for v in betweenness:
- betweenness[v] *= scale
- return betweenness # all nodes
-
-def _node_betweenness(G,source,cutoff=False,normalized=True,weight=None):
- """Node betweenness helper:
- see betweenness_centrality for what you probably want.
-
- This actually computes "load" and not betweenness.
- See https://networkx.lanl.gov/ticket/103
-
- This calculates the load of each node for paths from a single source.
- (The fraction of number of shortests paths from source that go
- through each node.)
-
- To get the load for a node you need to do all-pairs shortest paths.
-
- If weight is not None then use Dijkstra for finding shortest paths.
- In this case a cutoff is not implemented and so is ignored.
-
- """
-
- # get the predecessor and path length data
- if weight is None:
- (pred,length)=nx.predecessor(G,source,cutoff=cutoff,return_seen=True)
- else:
- (pred,length)=nx.dijkstra_predecessor_and_distance(G,source,weight=weight)
-
- # order the nodes by path length
- onodes = [ (l,vert) for (vert,l) in length.items() ]
- onodes.sort()
- onodes[:] = [vert for (l,vert) in onodes if l>0]
-
- # intialize betweenness
- between={}.fromkeys(length,1.0)
-
- while onodes:
- v=onodes.pop()
- if v in pred:
- num_paths=len(pred[v]) # Discount betweenness if more than
- for x in pred[v]: # one shortest path.
- if x==source: # stop if hit source because all remaining v
- break # also have pred[v]==[source]
- between[x]+=between[v]/float(num_paths)
- # remove source
- for v in between:
- between[v]-=1
- # rescale to be between 0 and 1
- if normalized:
- l=len(between)
- if l > 2:
- scale=1.0/float((l-1)*(l-2)) # 1/the number of possible paths
- for v in between:
- between[v] *= scale
- return between
-
-
-load_centrality=newman_betweenness_centrality
-
-
-def edge_load(G,nodes=None,cutoff=False):
- """Compute edge load.
-
- WARNING:
-
- This module is for demonstration and testing purposes.
-
- """
- betweenness={}
- if not nodes: # find betweenness for every node in graph
- nodes=G.nodes() # that probably is what you want...
- for source in nodes:
- ubetween=_edge_betweenness(G,source,nodes,cutoff=cutoff)
- for v in ubetween.keys():
- b=betweenness.setdefault(v,0) # get or set default
- betweenness[v]=ubetween[v]+b # cumulative total
- return betweenness
-
-def _edge_betweenness(G,source,nodes,cutoff=False):
- """
- Edge betweenness helper.
- """
- between={}
- # get the predecessor data
- #(pred,length)=_fast_predecessor(G,source,cutoff=cutoff)
- (pred,length)=nx.predecessor(G,source,cutoff=cutoff,return_seen=True)
- # order the nodes by path length
- onodes = [ nn for dd,nn in sorted( (dist,n) for n,dist in length.items() )]
- # intialize betweenness, doesn't account for any edge weights
- for u,v in G.edges(nodes):
- between[(u,v)]=1.0
- between[(v,u)]=1.0
-
- while onodes: # work through all paths
- v=onodes.pop()
- if v in pred:
- num_paths=len(pred[v]) # Discount betweenness if more than
- for w in pred[v]: # one shortest path.
- if w in pred:
- num_paths=len(pred[w]) # Discount betweenness, mult path
- for x in pred[w]:
- between[(w,x)]+=between[(v,w)]/num_paths
- between[(x,w)]+=between[(w,v)]/num_paths
- return between
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_betweenness_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_betweenness_centrality.py
deleted file mode 100644
index b0169a9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_betweenness_centrality.py
+++ /dev/null
@@ -1,462 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-def weighted_G():
- G=nx.Graph();
- G.add_edge(0,1,weight=3)
- G.add_edge(0,2,weight=2)
- G.add_edge(0,3,weight=6)
- G.add_edge(0,4,weight=4)
- G.add_edge(1,3,weight=5)
- G.add_edge(1,5,weight=5)
- G.add_edge(2,4,weight=1)
- G.add_edge(3,4,weight=2)
- G.add_edge(3,5,weight=1)
- G.add_edge(4,5,weight=4)
-
- return G
-
-
-class TestBetweennessCentrality(object):
-
- def test_K5(self):
- """Betweenness centrality: K5"""
- G=nx.complete_graph(5)
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False)
- b_answer={0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_K5_endpoints(self):
- """Betweenness centrality: K5 endpoints"""
- G=nx.complete_graph(5)
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False,
- endpoints=True)
- b_answer={0: 4.0, 1: 4.0, 2: 4.0, 3: 4.0, 4: 4.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P3_normalized(self):
- """Betweenness centrality: P3 normalized"""
- G=nx.path_graph(3)
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=True)
- b_answer={0: 0.0, 1: 1.0, 2: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P3(self):
- """Betweenness centrality: P3"""
- G=nx.path_graph(3)
- b_answer={0: 0.0, 1: 1.0, 2: 0.0}
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P3_endpoints(self):
- """Betweenness centrality: P3 endpoints"""
- G=nx.path_graph(3)
- b_answer={0: 2.0, 1: 3.0, 2: 2.0}
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False,
- endpoints=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_krackhardt_kite_graph(self):
- """Betweenness centrality: Krackhardt kite graph"""
- G=nx.krackhardt_kite_graph()
- b_answer={0: 1.667,1: 1.667,2: 0.000,3: 7.333,4: 0.000,
- 5: 16.667,6: 16.667,7: 28.000,8: 16.000,9: 0.000}
- for b in b_answer:
- b_answer[b]/=2.0
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False)
-
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_krackhardt_kite_graph_normalized(self):
- """Betweenness centrality: Krackhardt kite graph normalized"""
- G=nx.krackhardt_kite_graph()
- b_answer={0:0.023,1:0.023,2:0.000,3:0.102,4:0.000,
- 5:0.231,6:0.231,7:0.389,8:0.222,9:0.000}
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=True)
-
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_florentine_families_graph(self):
- """Betweenness centrality: Florentine families graph"""
- G=nx.florentine_families_graph()
- b_answer=\
- {'Acciaiuoli': 0.000,
- 'Albizzi': 0.212,
- 'Barbadori': 0.093,
- 'Bischeri': 0.104,
- 'Castellani': 0.055,
- 'Ginori': 0.000,
- 'Guadagni': 0.255,
- 'Lamberteschi': 0.000,
- 'Medici': 0.522,
- 'Pazzi': 0.000,
- 'Peruzzi': 0.022,
- 'Ridolfi': 0.114,
- 'Salviati': 0.143,
- 'Strozzi': 0.103,
- 'Tornabuoni': 0.092}
-
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_ladder_graph(self):
- """Betweenness centrality: Ladder graph"""
- G = nx.Graph() # ladder_graph(3)
- G.add_edges_from([(0,1), (0,2), (1,3), (2,3),
- (2,4), (4,5), (3,5)])
- b_answer={0:1.667,1: 1.667,2: 6.667,
- 3: 6.667,4: 1.667,5: 1.667}
- for b in b_answer:
- b_answer[b]/=2.0
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
- def test_disconnected_path(self):
- """Betweenness centrality: disconnected path"""
- G=nx.Graph()
- G.add_path([0,1,2])
- G.add_path([3,4,5,6])
- b_answer={0:0,1:1,2:0,3:0,4:2,5:2,6:0}
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_disconnected_path_endpoints(self):
- """Betweenness centrality: disconnected path endpoints"""
- G=nx.Graph()
- G.add_path([0,1,2])
- G.add_path([3,4,5,6])
- b_answer={0:2,1:3,2:2,3:3,4:5,5:5,6:3}
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False,
- endpoints=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_directed_path(self):
- """Betweenness centrality: directed path"""
- G=nx.DiGraph()
- G.add_path([0,1,2])
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=False)
- b_answer={0: 0.0, 1: 1.0, 2: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_directed_path_normalized(self):
- """Betweenness centrality: directed path normalized"""
- G=nx.DiGraph()
- G.add_path([0,1,2])
- b=nx.betweenness_centrality(G,
- weight=None,
- normalized=True)
- b_answer={0: 0.0, 1: 0.5, 2: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-
-class TestWeightedBetweennessCentrality(object):
-
- def test_K5(self):
- """Weighted betweenness centrality: K5"""
- G=nx.complete_graph(5)
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=False)
- b_answer={0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P3_normalized(self):
- """Weighted betweenness centrality: P3 normalized"""
- G=nx.path_graph(3)
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=True)
- b_answer={0: 0.0, 1: 1.0, 2: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P3(self):
- """Weighted betweenness centrality: P3"""
- G=nx.path_graph(3)
- b_answer={0: 0.0, 1: 1.0, 2: 0.0}
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_krackhardt_kite_graph(self):
- """Weighted betweenness centrality: Krackhardt kite graph"""
- G=nx.krackhardt_kite_graph()
- b_answer={0: 1.667,1: 1.667,2: 0.000,3: 7.333,4: 0.000,
- 5: 16.667,6: 16.667,7: 28.000,8: 16.000,9: 0.000}
- for b in b_answer:
- b_answer[b]/=2.0
-
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=False)
-
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_krackhardt_kite_graph_normalized(self):
- """Weighted betweenness centrality:
- Krackhardt kite graph normalized
- """
- G=nx.krackhardt_kite_graph()
- b_answer={0:0.023,1:0.023,2:0.000,3:0.102,4:0.000,
- 5:0.231,6:0.231,7:0.389,8:0.222,9:0.000}
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=True)
-
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_florentine_families_graph(self):
- """Weighted betweenness centrality:
- Florentine families graph"""
- G=nx.florentine_families_graph()
- b_answer=\
- {'Acciaiuoli': 0.000,
- 'Albizzi': 0.212,
- 'Barbadori': 0.093,
- 'Bischeri': 0.104,
- 'Castellani': 0.055,
- 'Ginori': 0.000,
- 'Guadagni': 0.255,
- 'Lamberteschi': 0.000,
- 'Medici': 0.522,
- 'Pazzi': 0.000,
- 'Peruzzi': 0.022,
- 'Ridolfi': 0.114,
- 'Salviati': 0.143,
- 'Strozzi': 0.103,
- 'Tornabuoni': 0.092}
-
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_ladder_graph(self):
- """Weighted betweenness centrality: Ladder graph"""
- G = nx.Graph() # ladder_graph(3)
- G.add_edges_from([(0,1), (0,2), (1,3), (2,3),
- (2,4), (4,5), (3,5)])
- b_answer={0:1.667,1: 1.667,2: 6.667,
- 3: 6.667,4: 1.667,5: 1.667}
- for b in b_answer:
- b_answer[b]/=2.0
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
- def test_G(self):
- """Weighted betweenness centrality: G"""
- G = weighted_G()
- b_answer={0: 2.0, 1: 0.0, 2: 4.0, 3: 3.0, 4: 4.0, 5: 0.0}
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_G2(self):
- """Weighted betweenness centrality: G2"""
- G=nx.DiGraph()
- G.add_weighted_edges_from([('s','u',10) ,('s','x',5) ,
- ('u','v',1) ,('u','x',2) ,
- ('v','y',1) ,('x','u',3) ,
- ('x','v',5) ,('x','y',2) ,
- ('y','s',7) ,('y','v',6)])
-
- b_answer={'y':5.0,'x':5.0,'s':4.0,'u':2.0,'v':2.0}
-
- b=nx.betweenness_centrality(G,
- weight='weight',
- normalized=False)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-class TestEdgeBetweennessCentrality(object):
-
- def test_K5(self):
- """Edge betweenness centrality: K5"""
- G=nx.complete_graph(5)
- b=nx.edge_betweenness_centrality(G, weight=None, normalized=False)
- b_answer=dict.fromkeys(G.edges(),1)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_normalized_K5(self):
- """Edge betweenness centrality: K5"""
- G=nx.complete_graph(5)
- b=nx.edge_betweenness_centrality(G, weight=None, normalized=True)
- b_answer=dict.fromkeys(G.edges(),1/10.0)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_C4(self):
- """Edge betweenness centrality: C4"""
- G=nx.cycle_graph(4)
- b=nx.edge_betweenness_centrality(G, weight=None, normalized=True)
- b_answer={(0, 1):2,(0, 3):2, (1, 2):2, (2, 3): 2}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n]/6.0)
-
- def test_P4(self):
- """Edge betweenness centrality: P4"""
- G=nx.path_graph(4)
- b=nx.edge_betweenness_centrality(G, weight=None, normalized=False)
- b_answer={(0, 1):3,(1, 2):4, (2, 3):3}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_normalized_P4(self):
- """Edge betweenness centrality: P4"""
- G=nx.path_graph(4)
- b=nx.edge_betweenness_centrality(G, weight=None, normalized=True)
- b_answer={(0, 1):3,(1, 2):4, (2, 3):3}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n]/6.0)
-
-
- def test_balanced_tree(self):
- """Edge betweenness centrality: balanced tree"""
- G=nx.balanced_tree(r=2,h=2)
- b=nx.edge_betweenness_centrality(G, weight=None, normalized=False)
- b_answer={(0, 1):12,(0, 2):12,
- (1, 3):6,(1, 4):6,(2, 5):6,(2,6):6}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
-class TestWeightedEdgeBetweennessCentrality(object):
-
- def test_K5(self):
- """Edge betweenness centrality: K5"""
- G=nx.complete_graph(5)
- b=nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
- b_answer=dict.fromkeys(G.edges(),1)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_C4(self):
- """Edge betweenness centrality: C4"""
- G=nx.cycle_graph(4)
- b=nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
- b_answer={(0, 1):2,(0, 3):2, (1, 2):2, (2, 3): 2}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P4(self):
- """Edge betweenness centrality: P4"""
- G=nx.path_graph(4)
- b=nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
- b_answer={(0, 1):3,(1, 2):4, (2, 3):3}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_balanced_tree(self):
- """Edge betweenness centrality: balanced tree"""
- G=nx.balanced_tree(r=2,h=2)
- b=nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
- b_answer={(0, 1):12,(0, 2):12,
- (1, 3):6,(1, 4):6,(2, 5):6,(2,6):6}
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_weighted_graph(self):
- eList = [(0, 1, 5), (0, 2, 4), (0, 3, 3),
- (0, 4, 2), (1, 2, 4), (1, 3, 1),
- (1, 4, 3), (2, 4, 5), (3, 4, 4)]
- G = nx.Graph()
- G.add_weighted_edges_from(eList)
- b = nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
- b_answer={(0, 1):0.0,
- (0, 2):1.0,
- (0, 3):2.0,
- (0, 4):1.0,
- (1, 2):2.0,
- (1, 3):3.5,
- (1, 4):1.5,
- (2, 4):1.0,
- (3, 4):0.5}
-
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_normalized_weighted_graph(self):
- eList = [(0, 1, 5), (0, 2, 4), (0, 3, 3),
- (0, 4, 2), (1, 2, 4), (1, 3, 1),
- (1, 4, 3), (2, 4, 5), (3, 4, 4)]
- G = nx.Graph()
- G.add_weighted_edges_from(eList)
- b = nx.edge_betweenness_centrality(G, weight='weight', normalized=True)
- b_answer={(0, 1):0.0,
- (0, 2):1.0,
- (0, 3):2.0,
- (0, 4):1.0,
- (1, 2):2.0,
- (1, 3):3.5,
- (1, 4):1.5,
- (2, 4):1.0,
- (3, 4):0.5}
-
- norm = len(G)*(len(G)-1)/2.0
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n]/norm)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py
deleted file mode 100644
index 762b873..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py
+++ /dev/null
@@ -1,258 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-from networkx import betweenness_centrality_subset,\
- edge_betweenness_centrality_subset
-
-class TestSubsetBetweennessCentrality:
-
- def test_K5(self):
- """Betweenness centrality: K5"""
- G=networkx.complete_graph(5)
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[1,3],
- weight=None)
- b_answer={0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P5_directed(self):
- """Betweenness centrality: P5 directed"""
- G=networkx.DiGraph()
- G.add_path(list(range(5)))
- b_answer={0:0,1:1,2:1,3:0,4:0,5:0}
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[3],
- weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P5(self):
- """Betweenness centrality: P5"""
- G=networkx.Graph()
- G.add_path(list(range(5)))
- b_answer={0:0,1:0.5,2:0.5,3:0,4:0,5:0}
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[3],
- weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P5_multiple_target(self):
- """Betweenness centrality: P5 multiple target"""
- G=networkx.Graph()
- G.add_path(list(range(5)))
- b_answer={0:0,1:1,2:1,3:0.5,4:0,5:0}
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[3,4],
- weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_box(self):
- """Betweenness centrality: box"""
- G=networkx.Graph()
- G.add_edge(0,1)
- G.add_edge(0,2)
- G.add_edge(1,3)
- G.add_edge(2,3)
- b_answer={0:0,1:0.25,2:0.25,3:0}
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[3],
- weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_box_and_path(self):
- """Betweenness centrality: box and path"""
- G=networkx.Graph()
- G.add_edge(0,1)
- G.add_edge(0,2)
- G.add_edge(1,3)
- G.add_edge(2,3)
- G.add_edge(3,4)
- G.add_edge(4,5)
- b_answer={0:0,1:0.5,2:0.5,3:0.5,4:0,5:0}
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[3,4],
- weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_box_and_path2(self):
- """Betweenness centrality: box and path multiple target"""
- G=networkx.Graph()
- G.add_edge(0,1)
- G.add_edge(1,2)
- G.add_edge(2,3)
- G.add_edge(1,20)
- G.add_edge(20,3)
- G.add_edge(3,4)
- b_answer={0:0,1:1.0,2:0.5,20:0.5,3:0.5,4:0}
- b=betweenness_centrality_subset(G,
- sources=[0],
- targets=[3,4])
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-class TestBetweennessCentralitySources:
- def test_K5(self):
- """Betweenness centrality: K5"""
- G=networkx.complete_graph(5)
- b=networkx.betweenness_centrality_source(G,
- weight=None,
- normalized=False)
- b_answer={0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P3(self):
- """Betweenness centrality: P3"""
- G=networkx.path_graph(3)
- b_answer={0: 0.0, 1: 1.0, 2: 0.0}
- b=networkx.betweenness_centrality_source(G,
- weight=None,
- normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-
-
-class TestEdgeSubsetBetweennessCentrality:
-
- def test_K5(self):
- """Edge betweenness centrality: K5"""
- G=networkx.complete_graph(5)
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[1,3],
- weight=None)
- b_answer=dict.fromkeys(G.edges(),0)
- b_answer[(0,3)]=0.5
- b_answer[(0,1)]=0.5
- for n in sorted(G.edges()):
- print(n,b[n])
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P5_directed(self):
- """Edge betweenness centrality: P5 directed"""
- G=networkx.DiGraph()
- G.add_path(list(range(5)))
- b_answer=dict.fromkeys(G.edges(),0)
- b_answer[(0,1)]=1
- b_answer[(1,2)]=1
- b_answer[(2,3)]=1
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[3],
- weight=None)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P5(self):
- """Edge betweenness centrality: P5"""
- G=networkx.Graph()
- G.add_path(list(range(5)))
- b_answer=dict.fromkeys(G.edges(),0)
- b_answer[(0,1)]=0.5
- b_answer[(1,2)]=0.5
- b_answer[(2,3)]=0.5
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[3],
- weight=None)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_P5_multiple_target(self):
- """Edge betweenness centrality: P5 multiple target"""
- G=networkx.Graph()
- G.add_path(list(range(5)))
- b_answer=dict.fromkeys(G.edges(),0)
- b_answer[(0,1)]=1
- b_answer[(1,2)]=1
- b_answer[(2,3)]=1
- b_answer[(3,4)]=0.5
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[3,4],
- weight=None)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_box(self):
- """Edge etweenness centrality: box"""
- G=networkx.Graph()
- G.add_edge(0,1)
- G.add_edge(0,2)
- G.add_edge(1,3)
- G.add_edge(2,3)
- b_answer=dict.fromkeys(G.edges(),0)
-
- b_answer[(0,1)]=0.25
- b_answer[(0,2)]=0.25
- b_answer[(1,3)]=0.25
- b_answer[(2,3)]=0.25
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[3],
- weight=None)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_box_and_path(self):
- """Edge etweenness centrality: box and path"""
- G=networkx.Graph()
- G.add_edge(0,1)
- G.add_edge(0,2)
- G.add_edge(1,3)
- G.add_edge(2,3)
- G.add_edge(3,4)
- G.add_edge(4,5)
- b_answer=dict.fromkeys(G.edges(),0)
- b_answer[(0,1)]=1.0/2
- b_answer[(0,2)]=1.0/2
- b_answer[(1,3)]=1.0/2
- b_answer[(2,3)]=1.0/2
- b_answer[(3,4)]=1.0/2
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[3,4],
- weight=None)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_box_and_path2(self):
- """Edge betweenness centrality: box and path multiple target"""
- G=networkx.Graph()
- G.add_edge(0,1)
- G.add_edge(1,2)
- G.add_edge(2,3)
- G.add_edge(1,20)
- G.add_edge(20,3)
- G.add_edge(3,4)
- b_answer=dict.fromkeys(G.edges(),0)
- b_answer[(0,1)]=1.0
- b_answer[(1,20)]=1.0/2
- b_answer[(3,20)]=1.0/2
- b_answer[(1,2)]=1.0/2
- b_answer[(2,3)]=1.0/2
- b_answer[(3,4)]=1.0/2
- b=edge_betweenness_centrality_subset(G,
- sources=[0],
- targets=[3,4],
- weight=None)
- for n in sorted(G.edges()):
- assert_almost_equal(b[n],b_answer[n])
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_closeness_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_closeness_centrality.py
deleted file mode 100644
index 71b0009..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_closeness_centrality.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""
-Tests for degree centrality.
-"""
-from nose.tools import *
-import networkx as nx
-
-class TestClosenessCentrality:
- def setUp(self):
-
- self.K = nx.krackhardt_kite_graph()
- self.P3 = nx.path_graph(3)
- self.P4 = nx.path_graph(4)
- self.K5 = nx.complete_graph(5)
-
- self.C4=nx.cycle_graph(4)
- self.T=nx.balanced_tree(r=2, h=2)
- self.Gb = nx.Graph()
- self.Gb.add_edges_from([(0,1), (0,2), (1,3), (2,3),
- (2,4), (4,5), (3,5)])
-
-
- F = nx.florentine_families_graph()
- self.F = F
-
-
- def test_k5_closeness(self):
- c=nx.closeness_centrality(self.K5)
- d={0: 1.000,
- 1: 1.000,
- 2: 1.000,
- 3: 1.000,
- 4: 1.000}
- for n in sorted(self.K5):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_p3_closeness(self):
- c=nx.closeness_centrality(self.P3)
- d={0: 0.667,
- 1: 1.000,
- 2: 0.667}
- for n in sorted(self.P3):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_krackhardt_closeness(self):
- c=nx.closeness_centrality(self.K)
- d={0: 0.529,
- 1: 0.529,
- 2: 0.500,
- 3: 0.600,
- 4: 0.500,
- 5: 0.643,
- 6: 0.643,
- 7: 0.600,
- 8: 0.429,
- 9: 0.310}
- for n in sorted(self.K):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_florentine_families_closeness(self):
- c=nx.closeness_centrality(self.F)
- d={'Acciaiuoli': 0.368,
- 'Albizzi': 0.483,
- 'Barbadori': 0.4375,
- 'Bischeri': 0.400,
- 'Castellani': 0.389,
- 'Ginori': 0.333,
- 'Guadagni': 0.467,
- 'Lamberteschi': 0.326,
- 'Medici': 0.560,
- 'Pazzi': 0.286,
- 'Peruzzi': 0.368,
- 'Ridolfi': 0.500,
- 'Salviati': 0.389,
- 'Strozzi': 0.4375,
- 'Tornabuoni': 0.483}
- for n in sorted(self.F):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_weighted_closeness(self):
- XG=nx.Graph()
- XG.add_weighted_edges_from([('s','u',10), ('s','x',5), ('u','v',1),
- ('u','x',2), ('v','y',1), ('x','u',3),
- ('x','v',5), ('x','y',2), ('y','s',7),
- ('y','v',6)])
- c=nx.closeness_centrality(XG,distance='weight')
- d={'y': 0.200,
- 'x': 0.286,
- 's': 0.138,
- 'u': 0.235,
- 'v': 0.200}
- for n in sorted(XG):
- assert_almost_equal(c[n],d[n],places=3)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_communicability.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_communicability.py
deleted file mode 100644
index d03be65..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_communicability.py
+++ /dev/null
@@ -1,134 +0,0 @@
-from collections import defaultdict
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-from networkx.algorithms.centrality.communicability_alg import *
-
-class TestCommunicability:
- @classmethod
- def setupClass(cls):
- global numpy
- global scipy
- try:
- import numpy
- except ImportError:
- raise SkipTest('NumPy not available.')
- try:
- import scipy
- except ImportError:
- raise SkipTest('SciPy not available.')
-
-
- def test_communicability_centrality(self):
- answer={0: 1.5430806348152433, 1: 1.5430806348152433}
- result=communicability_centrality(nx.path_graph(2))
- for k,v in result.items():
- assert_almost_equal(answer[k],result[k],places=7)
-
- answer1={'1': 1.6445956054135658,
- 'Albert': 2.4368257358712189,
- 'Aric': 2.4368257358712193,
- 'Dan':3.1306328496328168,
- 'Franck': 2.3876142275231915}
- G1=nx.Graph([('Franck','Aric'),('Aric','Dan'),('Dan','Albert'),
- ('Albert','Franck'),('Dan','1'),('Franck','Albert')])
- result1=communicability_centrality(G1)
- for k,v in result1.items():
- assert_almost_equal(answer1[k],result1[k],places=7)
- result1=communicability_centrality_exp(G1)
- for k,v in result1.items():
- assert_almost_equal(answer1[k],result1[k],places=7)
-
- def test_communicability_betweenness_centrality(self):
- answer={0: 0.07017447951484615, 1: 0.71565598701107991,
- 2: 0.71565598701107991, 3: 0.07017447951484615}
- result=communicability_betweenness_centrality(nx.path_graph(4))
- for k,v in result.items():
- assert_almost_equal(answer[k],result[k],places=7)
-
- answer1={'1': 0.060039074193949521,
- 'Albert': 0.315470761661372,
- 'Aric': 0.31547076166137211,
- 'Dan': 0.68297778678316201,
- 'Franck': 0.21977926617449497}
- G1=nx.Graph([('Franck','Aric'),
- ('Aric','Dan'),('Dan','Albert'),('Albert','Franck'),
- ('Dan','1'),('Franck','Albert')])
- result1=communicability_betweenness_centrality(G1)
- for k,v in result1.items():
- assert_almost_equal(answer1[k],result1[k],places=7)
-
- def test_communicability_betweenness_centrality_small(self):
- G = nx.Graph([(1,2)])
- result=communicability_betweenness_centrality(G)
- assert_equal(result, {1:0,2:0})
-
-
- def test_communicability(self):
- answer={0 :{0: 1.5430806348152435,
- 1: 1.1752011936438012
- },
- 1 :{0: 1.1752011936438012,
- 1: 1.5430806348152435
- }
- }
-# answer={(0, 0): 1.5430806348152435,
-# (0, 1): 1.1752011936438012,
-# (1, 0): 1.1752011936438012,
-# (1, 1): 1.5430806348152435}
-
- result=communicability(nx.path_graph(2))
- for k1,val in result.items():
- for k2 in val:
- assert_almost_equal(answer[k1][k2],result[k1][k2],places=7)
-
- def test_communicability2(self):
-
- answer_orig ={('1', '1'): 1.6445956054135658,
- ('1', 'Albert'): 0.7430186221096251,
- ('1', 'Aric'): 0.7430186221096251,
- ('1', 'Dan'): 1.6208126320442937,
- ('1', 'Franck'): 0.42639707170035257,
- ('Albert', '1'): 0.7430186221096251,
- ('Albert', 'Albert'): 2.4368257358712189,
- ('Albert', 'Aric'): 1.4368257358712191,
- ('Albert', 'Dan'): 2.0472097037446453,
- ('Albert', 'Franck'): 1.8340111678944691,
- ('Aric', '1'): 0.7430186221096251,
- ('Aric', 'Albert'): 1.4368257358712191,
- ('Aric', 'Aric'): 2.4368257358712193,
- ('Aric', 'Dan'): 2.0472097037446457,
- ('Aric', 'Franck'): 1.8340111678944691,
- ('Dan', '1'): 1.6208126320442937,
- ('Dan', 'Albert'): 2.0472097037446453,
- ('Dan', 'Aric'): 2.0472097037446457,
- ('Dan', 'Dan'): 3.1306328496328168,
- ('Dan', 'Franck'): 1.4860372442192515,
- ('Franck', '1'): 0.42639707170035257,
- ('Franck', 'Albert'): 1.8340111678944691,
- ('Franck', 'Aric'): 1.8340111678944691,
- ('Franck', 'Dan'): 1.4860372442192515,
- ('Franck', 'Franck'): 2.3876142275231915}
-
- answer=defaultdict(dict)
- for (k1,k2),v in answer_orig.items():
- answer[k1][k2]=v
-
- G1=nx.Graph([('Franck','Aric'),('Aric','Dan'),('Dan','Albert'),
- ('Albert','Franck'),('Dan','1'),('Franck','Albert')])
-
- result=communicability(G1)
- for k1,val in result.items():
- for k2 in val:
- assert_almost_equal(answer[k1][k2],result[k1][k2],places=7)
-
- result=communicability_exp(G1)
- for k1,val in result.items():
- for k2 in val:
- assert_almost_equal(answer[k1][k2],result[k1][k2],places=7)
-
-
- def test_estrada_index(self):
- answer=1041.2470334195475
- result=estrada_index(nx.karate_club_graph())
- assert_almost_equal(answer,result,places=7)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality.py
deleted file mode 100644
index 46e8a2d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality.py
+++ /dev/null
@@ -1,211 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx
-from nose.plugins.attrib import attr
-
-from networkx import edge_current_flow_betweenness_centrality \
- as edge_current_flow
-
-from networkx import approximate_current_flow_betweenness_centrality \
- as approximate_cfbc
-
-class TestFlowBetweennessCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- import scipy
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_K4_normalized(self):
- """Betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- b_answer={0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
- G.add_edge(0,1,{'weight':0.5,'other':0.3})
- b=networkx.current_flow_betweenness_centrality(G,normalized=True,weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
- wb_answer={0: 0.2222222, 1: 0.2222222, 2: 0.30555555, 3: 0.30555555}
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],wb_answer[n])
- wb_answer={0: 0.2051282, 1: 0.2051282, 2: 0.33974358, 3: 0.33974358}
- b=networkx.current_flow_betweenness_centrality(G,normalized=True,weight='other')
- for n in sorted(G):
- assert_almost_equal(b[n],wb_answer[n])
-
- def test_K4(self):
- """Betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- for solver in ['full','lu','cg']:
- b=networkx.current_flow_betweenness_centrality(G, normalized=False,
- solver=solver)
- b_answer={0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P4_normalized(self):
- """Betweenness centrality: P4 normalized"""
- G=networkx.path_graph(4)
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- b_answer={0: 0, 1: 2./3, 2: 2./3, 3:0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P4(self):
- """Betweenness centrality: P4"""
- G=networkx.path_graph(4)
- b=networkx.current_flow_betweenness_centrality(G,normalized=False)
- b_answer={0: 0, 1: 2, 2: 2, 3: 0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_star(self):
- """Betweenness centrality: star """
- G=networkx.Graph()
- G.add_star(['a','b','c','d'])
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- b_answer={'a': 1.0, 'b': 0.0, 'c': 0.0, 'd':0.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-
- def test_solers(self):
- """Betweenness centrality: alternate solvers"""
- G=networkx.complete_graph(4)
- for solver in ['full','lu','cg']:
- b=networkx.current_flow_betweenness_centrality(G,normalized=False,
- solver=solver)
- b_answer={0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-
-class TestApproximateFlowBetweennessCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- global assert_allclose
- try:
- import numpy as np
- import scipy
- from numpy.testing import assert_allclose
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_K4_normalized(self):
- "Approximate current-flow betweenness centrality: K4 normalized"
- G=networkx.complete_graph(4)
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- epsilon=0.1
- ba = approximate_cfbc(G,normalized=True, epsilon=0.5*epsilon)
- for n in sorted(G):
- assert_allclose(b[n],ba[n],atol=epsilon)
-
- def test_K4(self):
- "Approximate current-flow betweenness centrality: K4"
- G=networkx.complete_graph(4)
- b=networkx.current_flow_betweenness_centrality(G,normalized=False)
- epsilon=0.1
- ba = approximate_cfbc(G,normalized=False, epsilon=0.5*epsilon)
- for n in sorted(G):
- assert_allclose(b[n],ba[n],atol=epsilon*len(G)**2)
-
- def test_star(self):
- "Approximate current-flow betweenness centrality: star"
- G=networkx.Graph()
- G.add_star(['a','b','c','d'])
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- epsilon=0.1
- ba = approximate_cfbc(G,normalized=True, epsilon=0.5*epsilon)
- for n in sorted(G):
- assert_allclose(b[n],ba[n],atol=epsilon)
-
- def test_grid(self):
- "Approximate current-flow betweenness centrality: 2d grid"
- G=networkx.grid_2d_graph(4,4)
- b=networkx.current_flow_betweenness_centrality(G,normalized=True)
- epsilon=0.1
- ba = approximate_cfbc(G,normalized=True, epsilon=0.5*epsilon)
- for n in sorted(G):
- assert_allclose(b[n],ba[n],atol=epsilon)
-
- def test_solvers(self):
- "Approximate current-flow betweenness centrality: solvers"
- G=networkx.complete_graph(4)
- epsilon=0.1
- for solver in ['full','lu','cg']:
- b=approximate_cfbc(G,normalized=False,solver=solver,
- epsilon=0.5*epsilon)
- b_answer={0: 0.75, 1: 0.75, 2: 0.75, 3: 0.75}
- for n in sorted(G):
- assert_allclose(b[n],b_answer[n],atol=epsilon)
-
-
-
-
-
-class TestWeightedFlowBetweennessCentrality(object):
- pass
-
-
-class TestEdgeFlowBetweennessCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- import scipy
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_K4(self):
- """Edge flow betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=edge_current_flow(G,normalized=True)
- b_answer=dict.fromkeys(G.edges(),0.25)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
- def test_K4_normalized(self):
- """Edge flow betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=edge_current_flow(G,normalized=False)
- b_answer=dict.fromkeys(G.edges(),0.75)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
- def test_C4(self):
- """Edge flow betweenness centrality: C4"""
- G=networkx.cycle_graph(4)
- b=edge_current_flow(G,normalized=False)
- b_answer={(0, 1):1.25,(0, 3):1.25, (1, 2):1.25, (2, 3): 1.25}
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
-
- def test_P4(self):
- """Edge betweenness centrality: P4"""
- G=networkx.path_graph(4)
- b=edge_current_flow(G,normalized=False)
- b_answer={(0, 1):1.5,(1, 2):2.0, (2, 3):1.5}
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality_subset.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality_subset.py
deleted file mode 100644
index 4c7acd6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality_subset.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx
-from nose.plugins.attrib import attr
-
-from networkx import edge_current_flow_betweenness_centrality \
- as edge_current_flow
-
-from networkx import edge_current_flow_betweenness_centrality_subset \
- as edge_current_flow_subset
-
-class TestFlowBetweennessCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- import scipy
- except ImportError:
- raise SkipTest('NumPy not available.')
-
-
- def test_K4_normalized(self):
- """Betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True)
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_K4(self):
- """Betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True)
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
- # test weighted network
- G.add_edge(0,1,{'weight':0.5,'other':0.3})
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True,
- weight=None)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True)
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True,
- weight='other')
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True,weight='other')
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P4_normalized(self):
- """Betweenness centrality: P4 normalized"""
- G=networkx.path_graph(4)
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True)
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P4(self):
- """Betweenness centrality: P4"""
- G=networkx.path_graph(4)
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True)
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_star(self):
- """Betweenness centrality: star """
- G=networkx.Graph()
- G.add_star(['a','b','c','d'])
- b=networkx.current_flow_betweenness_centrality_subset(G,
- G.nodes(),
- G.nodes(),
- normalized=True)
- b_answer=networkx.current_flow_betweenness_centrality(G,normalized=True)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-
-# class TestWeightedFlowBetweennessCentrality():
-# pass
-
-
-class TestEdgeFlowBetweennessCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- import scipy
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_K4_normalized(self):
- """Betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=True)
- b_answer=edge_current_flow(G,normalized=True)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
- def test_K4(self):
- """Betweenness centrality: K4"""
- G=networkx.complete_graph(4)
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=False)
- b_answer=edge_current_flow(G,normalized=False)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
- # test weighted network
- G.add_edge(0,1,{'weight':0.5,'other':0.3})
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=False,weight=None)
- # weight is None => same as unweighted network
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=False)
- b_answer=edge_current_flow(G,normalized=False)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=False,weight='other')
- b_answer=edge_current_flow(G,normalized=False,weight='other')
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
-
- def test_C4(self):
- """Edge betweenness centrality: C4"""
- G=networkx.cycle_graph(4)
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=True)
- b_answer=edge_current_flow(G,normalized=True)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
-
- def test_P4(self):
- """Edge betweenness centrality: P4"""
- G=networkx.path_graph(4)
- b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=True)
- b_answer=edge_current_flow(G,normalized=True)
- for (s,t),v1 in b_answer.items():
- v2=b.get((s,t),b.get((t,s)))
- assert_almost_equal(v1,v2)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_closeness.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_closeness.py
deleted file mode 100644
index 28598d4..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_current_flow_closeness.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx
-
-class TestFlowClosenessCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- import scipy
- except ImportError:
- raise SkipTest('NumPy not available.')
-
-
- def test_K4(self):
- """Closeness centrality: K4"""
- G=networkx.complete_graph(4)
- b=networkx.current_flow_closeness_centrality(G,normalized=True)
- b_answer={0: 2.0, 1: 2.0, 2: 2.0, 3: 2.0}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P4_normalized(self):
- """Closeness centrality: P4 normalized"""
- G=networkx.path_graph(4)
- b=networkx.current_flow_closeness_centrality(G,normalized=True)
- b_answer={0: 1./2, 1: 3./4, 2: 3./4, 3:1./2}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- def test_P4(self):
- """Closeness centrality: P4"""
- G=networkx.path_graph(4)
- b=networkx.current_flow_closeness_centrality(G,normalized=False)
- b_answer={0: 1.0/6, 1: 1.0/4, 2: 1.0/4, 3:1.0/6}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
- def test_star(self):
- """Closeness centrality: star """
- G=networkx.Graph()
- G.add_star(['a','b','c','d'])
- b=networkx.current_flow_closeness_centrality(G,normalized=True)
- b_answer={'a': 1.0, 'b': 0.6, 'c': 0.6, 'd':0.6}
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
-
-class TestWeightedFlowClosenessCentrality(object):
- pass
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_degree_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_degree_centrality.py
deleted file mode 100644
index 9109ddc..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_degree_centrality.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""
- Unit tests for degree centrality.
-"""
-
-from nose.tools import *
-
-import networkx as nx
-
-
-class TestDegreeCentrality:
- def __init__(self):
-
- self.K = nx.krackhardt_kite_graph()
- self.P3 = nx.path_graph(3)
- self.K5 = nx.complete_graph(5)
-
- F = nx.Graph() # Florentine families
- F.add_edge('Acciaiuoli','Medici')
- F.add_edge('Castellani','Peruzzi')
- F.add_edge('Castellani','Strozzi')
- F.add_edge('Castellani','Barbadori')
- F.add_edge('Medici','Barbadori')
- F.add_edge('Medici','Ridolfi')
- F.add_edge('Medici','Tornabuoni')
- F.add_edge('Medici','Albizzi')
- F.add_edge('Medici','Salviati')
- F.add_edge('Salviati','Pazzi')
- F.add_edge('Peruzzi','Strozzi')
- F.add_edge('Peruzzi','Bischeri')
- F.add_edge('Strozzi','Ridolfi')
- F.add_edge('Strozzi','Bischeri')
- F.add_edge('Ridolfi','Tornabuoni')
- F.add_edge('Tornabuoni','Guadagni')
- F.add_edge('Albizzi','Ginori')
- F.add_edge('Albizzi','Guadagni')
- F.add_edge('Bischeri','Guadagni')
- F.add_edge('Guadagni','Lamberteschi')
- self.F = F
-
- G = nx.DiGraph()
- G.add_edge(0,5)
- G.add_edge(1,5)
- G.add_edge(2,5)
- G.add_edge(3,5)
- G.add_edge(4,5)
- G.add_edge(5,6)
- G.add_edge(5,7)
- G.add_edge(5,8)
- self.G = G
-
- def test_degree_centrality_1(self):
- d = nx.degree_centrality(self.K5)
- exact = dict(zip(range(5), [1]*5))
- for n,dc in d.items():
- assert_almost_equal(exact[n], dc)
-
- def test_degree_centrality_2(self):
- d = nx.degree_centrality(self.P3)
- exact = {0:0.5, 1:1, 2:0.5}
- for n,dc in d.items():
- assert_almost_equal(exact[n], dc)
-
- def test_degree_centrality_3(self):
- d = nx.degree_centrality(self.K)
- exact = {0:.444, 1:.444, 2:.333, 3:.667, 4:.333,
- 5:.556, 6:.556, 7:.333, 8:.222, 9:.111}
- for n,dc in d.items():
- assert_almost_equal(exact[n], float("%5.3f" % dc))
-
- def test_degree_centrality_4(self):
- d = nx.degree_centrality(self.F)
- names = sorted(self.F.nodes())
- dcs = [0.071, 0.214, 0.143, 0.214, 0.214, 0.071, 0.286,
- 0.071, 0.429, 0.071, 0.214, 0.214, 0.143, 0.286, 0.214]
- exact = dict(zip(names, dcs))
- for n,dc in d.items():
- assert_almost_equal(exact[n], float("%5.3f" % dc))
-
- def test_indegree_centrality(self):
- d = nx.in_degree_centrality(self.G)
- exact = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0,
- 5: 0.625, 6: 0.125, 7: 0.125, 8: 0.125}
- for n,dc in d.items():
- assert_almost_equal(exact[n], dc)
-
- def test_outdegree_centrality(self):
- d = nx.out_degree_centrality(self.G)
- exact = {0: 0.125, 1: 0.125, 2: 0.125, 3: 0.125,
- 4: 0.125, 5: 0.375, 6: 0.0, 7: 0.0, 8: 0.0}
- for n,dc in d.items():
- assert_almost_equal(exact[n], dc)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py
deleted file mode 100644
index 22b859c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#!/usr/bin/env python
-import math
-from nose import SkipTest
-from nose.tools import *
-import networkx
-
-class TestEigenvectorCentrality(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_K5(self):
- """Eigenvector centrality: K5"""
- G=networkx.complete_graph(5)
- b=networkx.eigenvector_centrality(G)
- v=math.sqrt(1/5.0)
- b_answer=dict.fromkeys(G,v)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
- nstart = dict([(n,1) for n in G])
- b=networkx.eigenvector_centrality(G,nstart=nstart)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n])
-
-
- b=networkx.eigenvector_centrality_numpy(G)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=3)
-
-
- def test_P3(self):
- """Eigenvector centrality: P3"""
- G=networkx.path_graph(3)
- b_answer={0: 0.5, 1: 0.7071, 2: 0.5}
- b=networkx.eigenvector_centrality_numpy(G)
- for n in sorted(G):
- assert_almost_equal(b[n],b_answer[n],places=4)
-
-
- @raises(networkx.NetworkXError)
- def test_maxiter(self):
- G=networkx.path_graph(3)
- b=networkx.eigenvector_centrality(G,max_iter=0)
-
-class TestEigenvectorCentralityDirected(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def setUp(self):
-
- G=networkx.DiGraph()
-
- edges=[(1,2),(1,3),(2,4),(3,2),(3,5),(4,2),(4,5),(4,6),\
- (5,6),(5,7),(5,8),(6,8),(7,1),(7,5),\
- (7,8),(8,6),(8,7)]
-
- G.add_edges_from(edges,weight=2.0)
- self.G=G
- self.G.evc=[0.25368793, 0.19576478, 0.32817092, 0.40430835,
- 0.48199885, 0.15724483, 0.51346196, 0.32475403]
-
- H=networkx.DiGraph()
-
- edges=[(1,2),(1,3),(2,4),(3,2),(3,5),(4,2),(4,5),(4,6),\
- (5,6),(5,7),(5,8),(6,8),(7,1),(7,5),\
- (7,8),(8,6),(8,7)]
-
- G.add_edges_from(edges)
- self.H=G
- self.H.evc=[0.25368793, 0.19576478, 0.32817092, 0.40430835,
- 0.48199885, 0.15724483, 0.51346196, 0.32475403]
-
-
- def test_eigenvector_centrality_weighted(self):
- G=self.G
- p=networkx.eigenvector_centrality_numpy(G)
- for (a,b) in zip(list(p.values()),self.G.evc):
- assert_almost_equal(a,b)
-
- def test_eigenvector_centrality_unweighted(self):
- G=self.H
- p=networkx.eigenvector_centrality_numpy(G)
- for (a,b) in zip(list(p.values()),self.G.evc):
- assert_almost_equal(a,b)
-
-
-class TestEigenvectorCentralityExceptions(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- except ImportError:
- raise SkipTest('NumPy not available.')
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @raises(networkx.NetworkXException)
- def test_multigraph(self):
- e = networkx.eigenvector_centrality(networkx.MultiGraph())
-
- @raises(networkx.NetworkXException)
- def test_multigraph_numpy(self):
- e = networkx.eigenvector_centrality_numpy(networkx.MultiGraph())
-
-
- @raises(networkx.NetworkXException)
- def test_empty(self):
- e = networkx.eigenvector_centrality(networkx.Graph())
-
- @raises(networkx.NetworkXException)
- def test_empty_numpy(self):
- e = networkx.eigenvector_centrality_numpy(networkx.Graph())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_katz_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_katz_centrality.py
deleted file mode 100644
index 9e8e6d2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_katz_centrality.py
+++ /dev/null
@@ -1,289 +0,0 @@
-# -*- coding: utf-8 -*-
-import math
-from nose import SkipTest
-from nose.tools import *
-import networkx
-
-class TestKatzCentrality(object):
-
- def test_K5(self):
- """Katz centrality: K5"""
- G = networkx.complete_graph(5)
- alpha = 0.1
- b = networkx.katz_centrality(G, alpha)
- v = math.sqrt(1 / 5.0)
- b_answer = dict.fromkeys(G, v)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n])
- nstart = dict([(n, 1) for n in G])
- b = networkx.katz_centrality(G, alpha, nstart=nstart)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n])
-
- def test_P3(self):
- """Katz centrality: P3"""
- alpha = 0.1
- G = networkx.path_graph(3)
- b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162}
- b = networkx.katz_centrality(G, alpha)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=4)
-
- @raises(networkx.NetworkXError)
- def test_maxiter(self):
- alpha = 0.1
- G = networkx.path_graph(3)
- b = networkx.katz_centrality(G, alpha, max_iter=0)
-
- def test_beta_as_scalar(self):
- alpha = 0.1
- beta = 0.1
- b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162}
- G = networkx.path_graph(3)
- b = networkx.katz_centrality(G, alpha, beta)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=4)
-
- def test_beta_as_dict(self):
- alpha = 0.1
- beta = {0: 1.0, 1: 1.0, 2: 1.0}
- b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162}
- G = networkx.path_graph(3)
- b = networkx.katz_centrality(G, alpha, beta)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=4)
-
-
- def test_multiple_alpha(self):
- alpha_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
- for alpha in alpha_list:
- b_answer = {0.1: {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162},
- 0.2: {0: 0.5454545454545454, 1: 0.6363636363636365,
- 2: 0.5454545454545454},
- 0.3: {0: 0.5333964609104419, 1: 0.6564879518897746,
- 2: 0.5333964609104419},
- 0.4: {0: 0.5232045649263551, 1: 0.6726915834767423,
- 2: 0.5232045649263551},
- 0.5: {0: 0.5144957746691622, 1: 0.6859943117075809,
- 2: 0.5144957746691622},
- 0.6: {0: 0.5069794004195823, 1: 0.6970966755769258,
- 2: 0.5069794004195823}}
- G = networkx.path_graph(3)
- b = networkx.katz_centrality(G, alpha)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[alpha][n], places=4)
-
- @raises(networkx.NetworkXException)
- def test_multigraph(self):
- e = networkx.katz_centrality(networkx.MultiGraph(), 0.1)
-
- def test_empty(self):
- e = networkx.katz_centrality(networkx.Graph(), 0.1)
- assert_equal(e, {})
-
- @raises(networkx.NetworkXException)
- def test_bad_beta(self):
- G = networkx.Graph([(0,1)])
- beta = {0:77}
- e = networkx.katz_centrality(G, 0.1,beta=beta)
-
- @raises(networkx.NetworkXException)
- def test_bad_beta_numbe(self):
- G = networkx.Graph([(0,1)])
- e = networkx.katz_centrality(G, 0.1,beta='foo')
-
-
-class TestKatzCentralityNumpy(object):
- numpy = 1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_K5(self):
- """Katz centrality: K5"""
- G = networkx.complete_graph(5)
- alpha = 0.1
- b = networkx.katz_centrality(G, alpha)
- v = math.sqrt(1 / 5.0)
- b_answer = dict.fromkeys(G, v)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n])
- nstart = dict([(n, 1) for n in G])
- b = networkx.eigenvector_centrality_numpy(G)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=3)
-
- def test_P3(self):
- """Katz centrality: P3"""
- alpha = 0.1
- G = networkx.path_graph(3)
- b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162}
- b = networkx.katz_centrality_numpy(G, alpha)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=4)
-
-
- def test_beta_as_scalar(self):
- alpha = 0.1
- beta = 0.1
- b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162}
- G = networkx.path_graph(3)
- b = networkx.katz_centrality_numpy(G, alpha, beta)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=4)
-
-
- def test_beta_as_dict(self):
- alpha = 0.1
- beta = {0: 1.0, 1: 1.0, 2: 1.0}
- b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162}
- G = networkx.path_graph(3)
- b = networkx.katz_centrality_numpy(G, alpha, beta)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[n], places=4)
-
-
- def test_multiple_alpha(self):
- alpha_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
- for alpha in alpha_list:
- b_answer = {0.1: {0: 0.5598852584152165, 1: 0.6107839182711449,
- 2: 0.5598852584152162},
- 0.2: {0: 0.5454545454545454, 1: 0.6363636363636365,
- 2: 0.5454545454545454},
- 0.3: {0: 0.5333964609104419, 1: 0.6564879518897746,
- 2: 0.5333964609104419},
- 0.4: {0: 0.5232045649263551, 1: 0.6726915834767423,
- 2: 0.5232045649263551},
- 0.5: {0: 0.5144957746691622, 1: 0.6859943117075809,
- 2: 0.5144957746691622},
- 0.6: {0: 0.5069794004195823, 1: 0.6970966755769258,
- 2: 0.5069794004195823}}
- G = networkx.path_graph(3)
- b = networkx.katz_centrality_numpy(G, alpha)
- for n in sorted(G):
- assert_almost_equal(b[n], b_answer[alpha][n], places=4)
-
- @raises(networkx.NetworkXException)
- def test_multigraph(self):
- e = networkx.katz_centrality(networkx.MultiGraph(), 0.1)
-
- def test_empty(self):
- e = networkx.katz_centrality(networkx.Graph(), 0.1)
- assert_equal(e, {})
-
- @raises(networkx.NetworkXException)
- def test_bad_beta(self):
- G = networkx.Graph([(0,1)])
- beta = {0:77}
- e = networkx.katz_centrality_numpy(G, 0.1,beta=beta)
-
- @raises(networkx.NetworkXException)
- def test_bad_beta_numbe(self):
- G = networkx.Graph([(0,1)])
- e = networkx.katz_centrality_numpy(G, 0.1,beta='foo')
-
-
-class TestKatzCentralityDirected(object):
- def setUp(self):
- G = networkx.DiGraph()
- edges = [(1, 2),(1, 3),(2, 4),(3, 2),(3, 5),(4, 2),(4, 5),(4, 6),(5, 6),
- (5, 7),(5, 8),(6, 8),(7, 1),(7, 5),(7, 8),(8, 6),(8, 7)]
- G.add_edges_from(edges, weight=2.0)
- self.G = G
- self.G.alpha = 0.1
- self.G.evc = [
- 0.3289589783189635,
- 0.2832077296243516,
- 0.3425906003685471,
- 0.3970420865198392,
- 0.41074871061646284,
- 0.272257430756461,
- 0.4201989685435462,
- 0.34229059218038554,
- ]
-
- H = networkx.DiGraph(edges)
- self.H = G
- self.H.alpha = 0.1
- self.H.evc = [
- 0.3289589783189635,
- 0.2832077296243516,
- 0.3425906003685471,
- 0.3970420865198392,
- 0.41074871061646284,
- 0.272257430756461,
- 0.4201989685435462,
- 0.34229059218038554,
- ]
-
- def test_eigenvector_centrality_weighted(self):
- G = self.G
- alpha = self.G.alpha
- p = networkx.katz_centrality(G, alpha)
- for (a, b) in zip(list(p.values()), self.G.evc):
- assert_almost_equal(a, b)
-
- def test_eigenvector_centrality_unweighted(self):
- G = self.H
- alpha = self.H.alpha
- p = networkx.katz_centrality(G, alpha)
- for (a, b) in zip(list(p.values()), self.G.evc):
- assert_almost_equal(a, b)
-
-
-class TestKatzCentralityDirectedNumpy(TestKatzCentralityDirected):
- numpy = 1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
-
- @classmethod
- def setupClass(cls):
- global np
- try:
- import numpy as np
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_eigenvector_centrality_weighted(self):
- G = self.G
- alpha = self.G.alpha
- p = networkx.katz_centrality_numpy(G, alpha)
- for (a, b) in zip(list(p.values()), self.G.evc):
- assert_almost_equal(a, b)
-
- def test_eigenvector_centrality_unweighted(self):
- G = self.H
- alpha = self.H.alpha
- p = networkx.katz_centrality_numpy(G, alpha)
- for (a, b) in zip(list(p.values()), self.G.evc):
- assert_almost_equal(a, b)
-
-class TestKatzEigenvectorVKatz(object):
- numpy = 1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
-
- @classmethod
- def setupClass(cls):
- global np
- global eigvals
- try:
- import numpy as np
- from numpy.linalg import eigvals
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_eigenvector_v_katz_random(self):
- G = networkx.gnp_random_graph(10,0.5)
- l = float(max(eigvals(networkx.adjacency_matrix(G))))
- e = networkx.eigenvector_centrality_numpy(G)
- k = networkx.katz_centrality_numpy(G, 1.0/l)
- for n in G:
- assert_almost_equal(e[n], k[n])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_load_centrality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_load_centrality.py
deleted file mode 100644
index 8939d26..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/test_load_centrality.py
+++ /dev/null
@@ -1,273 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-
-class TestLoadCentrality:
-
- def setUp(self):
-
- G=nx.Graph();
- G.add_edge(0,1,weight=3)
- G.add_edge(0,2,weight=2)
- G.add_edge(0,3,weight=6)
- G.add_edge(0,4,weight=4)
- G.add_edge(1,3,weight=5)
- G.add_edge(1,5,weight=5)
- G.add_edge(2,4,weight=1)
- G.add_edge(3,4,weight=2)
- G.add_edge(3,5,weight=1)
- G.add_edge(4,5,weight=4)
- self.G=G
- self.exact_weighted={0: 4.0, 1: 0.0, 2: 8.0, 3: 6.0, 4: 8.0, 5: 0.0}
- self.K = nx.krackhardt_kite_graph()
- self.P3 = nx.path_graph(3)
- self.P4 = nx.path_graph(4)
- self.K5 = nx.complete_graph(5)
-
- self.C4=nx.cycle_graph(4)
- self.T=nx.balanced_tree(r=2, h=2)
- self.Gb = nx.Graph()
- self.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3),
- (2, 4), (4, 5), (3, 5)])
- self.F = nx.florentine_families_graph()
- self.D = nx.cycle_graph(3, create_using=nx.DiGraph())
- self.D.add_edges_from([(3, 0), (4, 3)])
-
- def test_not_strongly_connected(self):
- b = nx.load_centrality(self.D)
- result = {0: 5./12,
- 1: 1./4,
- 2: 1./12,
- 3: 1./4,
- 4: 0.000}
- for n in sorted(self.D):
- assert_almost_equal(result[n], b[n], places=3)
- assert_almost_equal(result[n], nx.load_centrality(self.D, n), places=3)
-
- def test_weighted_load(self):
- b=nx.load_centrality(self.G,weight='weight',normalized=False)
- for n in sorted(self.G):
- assert_equal(b[n],self.exact_weighted[n])
-
- def test_k5_load(self):
- G=self.K5
- c=nx.load_centrality(G)
- d={0: 0.000,
- 1: 0.000,
- 2: 0.000,
- 3: 0.000,
- 4: 0.000}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_p3_load(self):
- G=self.P3
- c=nx.load_centrality(G)
- d={0: 0.000,
- 1: 1.000,
- 2: 0.000}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
- c=nx.load_centrality(G,v=1)
- assert_almost_equal(c,1.0)
- c=nx.load_centrality(G,v=1,normalized=True)
- assert_almost_equal(c,1.0)
-
-
- def test_p2_load(self):
- G=nx.path_graph(2)
- c=nx.load_centrality(G)
- d={0: 0.000,
- 1: 0.000}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
-
- def test_krackhardt_load(self):
- G=self.K
- c=nx.load_centrality(G)
- d={0: 0.023,
- 1: 0.023,
- 2: 0.000,
- 3: 0.102,
- 4: 0.000,
- 5: 0.231,
- 6: 0.231,
- 7: 0.389,
- 8: 0.222,
- 9: 0.000}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_florentine_families_load(self):
- G=self.F
- c=nx.load_centrality(G)
- d={'Acciaiuoli': 0.000,
- 'Albizzi': 0.211,
- 'Barbadori': 0.093,
- 'Bischeri': 0.104,
- 'Castellani': 0.055,
- 'Ginori': 0.000,
- 'Guadagni': 0.251,
- 'Lamberteschi': 0.000,
- 'Medici': 0.522,
- 'Pazzi': 0.000,
- 'Peruzzi': 0.022,
- 'Ridolfi': 0.117,
- 'Salviati': 0.143,
- 'Strozzi': 0.106,
- 'Tornabuoni': 0.090}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
-
- def test_unnormalized_k5_load(self):
- G=self.K5
- c=nx.load_centrality(G,normalized=False)
- d={0: 0.000,
- 1: 0.000,
- 2: 0.000,
- 3: 0.000,
- 4: 0.000}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_unnormalized_p3_load(self):
- G=self.P3
- c=nx.load_centrality(G,normalized=False)
- d={0: 0.000,
- 1: 2.000,
- 2: 0.000}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
-
- def test_unnormalized_krackhardt_load(self):
- G=self.K
- c=nx.load_centrality(G,normalized=False)
- d={0: 1.667,
- 1: 1.667,
- 2: 0.000,
- 3: 7.333,
- 4: 0.000,
- 5: 16.667,
- 6: 16.667,
- 7: 28.000,
- 8: 16.000,
- 9: 0.000}
-
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_unnormalized_florentine_families_load(self):
- G=self.F
- c=nx.load_centrality(G,normalized=False)
-
- d={'Acciaiuoli': 0.000,
- 'Albizzi': 38.333,
- 'Barbadori': 17.000,
- 'Bischeri': 19.000,
- 'Castellani': 10.000,
- 'Ginori': 0.000,
- 'Guadagni': 45.667,
- 'Lamberteschi': 0.000,
- 'Medici': 95.000,
- 'Pazzi': 0.000,
- 'Peruzzi': 4.000,
- 'Ridolfi': 21.333,
- 'Salviati': 26.000,
- 'Strozzi': 19.333,
- 'Tornabuoni': 16.333}
- for n in sorted(G):
- assert_almost_equal(c[n],d[n],places=3)
-
-
- def test_load_betweenness_difference(self):
- # Difference Between Load and Betweenness
- # --------------------------------------- The smallest graph
- # that shows the difference between load and betweenness is
- # G=ladder_graph(3) (Graph B below)
-
- # Graph A and B are from Tao Zhou, Jian-Guo Liu, Bing-Hong
- # Wang: Comment on ``Scientific collaboration
- # networks. II. Shortest paths, weighted networks, and
- # centrality". http://arxiv.org/pdf/physics/0511084
-
- # Notice that unlike here, their calculation adds to 1 to the
- # betweennes of every node i for every path from i to every
- # other node. This is exactly what it should be, based on
- # Eqn. (1) in their paper: the eqn is B(v) = \sum_{s\neq t,
- # s\neq v}{\frac{\sigma_{st}(v)}{\sigma_{st}}}, therefore,
- # they allow v to be the target node.
-
- # We follow Brandes 2001, who follows Freeman 1977 that make
- # the sum for betweenness of v exclude paths where v is either
- # the source or target node. To agree with their numbers, we
- # must additionally, remove edge (4,8) from the graph, see AC
- # example following (there is a mistake in the figure in their
- # paper - personal communication).
-
- # A = nx.Graph()
- # A.add_edges_from([(0,1), (1,2), (1,3), (2,4),
- # (3,5), (4,6), (4,7), (4,8),
- # (5,8), (6,9), (7,9), (8,9)])
- B = nx.Graph() # ladder_graph(3)
- B.add_edges_from([(0,1), (0,2), (1,3), (2,3), (2,4), (4,5), (3,5)])
- c = nx.load_centrality(B,normalized=False)
- d={0: 1.750,
- 1: 1.750,
- 2: 6.500,
- 3: 6.500,
- 4: 1.750,
- 5: 1.750}
- for n in sorted(B):
- assert_almost_equal(c[n],d[n],places=3)
-
-
- def test_c4_edge_load(self):
- G=self.C4
- c = nx.edge_load(G)
- d={(0, 1): 6.000,
- (0, 3): 6.000,
- (1, 2): 6.000,
- (2, 3): 6.000}
- for n in G.edges():
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_p4_edge_load(self):
- G=self.P4
- c = nx.edge_load(G)
- d={(0, 1): 6.000,
- (1, 2): 8.000,
- (2, 3): 6.000}
- for n in G.edges():
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_k5_edge_load(self):
- G=self.K5
- c = nx.edge_load(G)
- d={(0, 1): 5.000,
- (0, 2): 5.000,
- (0, 3): 5.000,
- (0, 4): 5.000,
- (1, 2): 5.000,
- (1, 3): 5.000,
- (1, 4): 5.000,
- (2, 3): 5.000,
- (2, 4): 5.000,
- (3, 4): 5.000}
- for n in G.edges():
- assert_almost_equal(c[n],d[n],places=3)
-
- def test_tree_edge_load(self):
- G=self.T
- c = nx.edge_load(G)
- d={(0, 1): 24.000,
- (0, 2): 24.000,
- (1, 3): 12.000,
- (1, 4): 12.000,
- (2, 5): 12.000,
- (2, 6): 12.000}
- for n in G.edges():
- assert_almost_equal(c[n],d[n],places=3)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/__init__.py
deleted file mode 100644
index cf8e951..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from networkx.algorithms.chordal.chordal_alg import *
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/chordal_alg.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/chordal_alg.py
deleted file mode 100644
index 8eb6404..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/chordal_alg.py
+++ /dev/null
@@ -1,347 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Algorithms for chordal graphs.
-
-A graph is chordal if every cycle of length at least 4 has a chord
-(an edge joining two nodes not adjacent in the cycle).
-http://en.wikipedia.org/wiki/Chordal_graph
-"""
-import networkx as nx
-import random
-import sys
-
-__authors__ = "\n".join(['Jesus Cerquides <cerquide@iiia.csic.es>'])
-# Copyright (C) 2010 by
-# Jesus Cerquides <cerquide@iiia.csic.es>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['is_chordal',
- 'find_induced_nodes',
- 'chordal_graph_cliques',
- 'chordal_graph_treewidth',
- 'NetworkXTreewidthBoundExceeded']
-
-class NetworkXTreewidthBoundExceeded(nx.NetworkXException):
- """Exception raised when a treewidth bound has been provided and it has
- been exceeded"""
-
-
-def is_chordal(G):
- """Checks whether G is a chordal graph.
-
- A graph is chordal if every cycle of length at least 4 has a chord
- (an edge joining two nodes not adjacent in the cycle).
-
- Parameters
- ----------
- G : graph
- A NetworkX graph.
-
- Returns
- -------
- chordal : bool
- True if G is a chordal graph and False otherwise.
-
- Raises
- ------
- NetworkXError
- The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
- If the input graph is an instance of one of these classes, a
- NetworkXError is raised.
-
- Examples
- --------
- >>> import networkx as nx
- >>> e=[(1,2),(1,3),(2,3),(2,4),(3,4),(3,5),(3,6),(4,5),(4,6),(5,6)]
- >>> G=nx.Graph(e)
- >>> nx.is_chordal(G)
- True
-
- Notes
- -----
- The routine tries to go through every node following maximum cardinality
- search. It returns False when it finds that the separator for any node
- is not a clique. Based on the algorithms in [1]_.
-
- References
- ----------
- .. [1] R. E. Tarjan and M. Yannakakis, Simple linear-time algorithms
- to test chordality of graphs, test acyclicity of hypergraphs, and
- selectively reduce acyclic hypergraphs, SIAM J. Comput., 13 (1984),
- pp. 566–579.
- """
- if G.is_directed():
- raise nx.NetworkXError('Directed graphs not supported')
- if G.is_multigraph():
- raise nx.NetworkXError('Multiply connected graphs not supported.')
- if len(_find_chordality_breaker(G))==0:
- return True
- else:
- return False
-
-def find_induced_nodes(G,s,t,treewidth_bound=sys.maxsize):
- """Returns the set of induced nodes in the path from s to t.
-
- Parameters
- ----------
- G : graph
- A chordal NetworkX graph
- s : node
- Source node to look for induced nodes
- t : node
- Destination node to look for induced nodes
- treewith_bound: float
- Maximum treewidth acceptable for the graph H. The search
- for induced nodes will end as soon as the treewidth_bound is exceeded.
-
- Returns
- -------
- I : Set of nodes
- The set of induced nodes in the path from s to t in G
-
- Raises
- ------
- NetworkXError
- The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
- If the input graph is an instance of one of these classes, a
- NetworkXError is raised.
- The algorithm can only be applied to chordal graphs. If
- the input graph is found to be non-chordal, a NetworkXError is raised.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G=nx.Graph()
- >>> G = nx.generators.classic.path_graph(10)
- >>> I = nx.find_induced_nodes(G,1,9,2)
- >>> list(I)
- [1, 2, 3, 4, 5, 6, 7, 8, 9]
-
- Notes
- -----
- G must be a chordal graph and (s,t) an edge that is not in G.
-
- If a treewidth_bound is provided, the search for induced nodes will end
- as soon as the treewidth_bound is exceeded.
-
- The algorithm is inspired by Algorithm 4 in [1]_.
- A formal definition of induced node can also be found on that reference.
-
- References
- ----------
- .. [1] Learning Bounded Treewidth Bayesian Networks.
- Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.
- http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf
- """
- if not is_chordal(G):
- raise nx.NetworkXError("Input graph is not chordal.")
-
- H = nx.Graph(G)
- H.add_edge(s,t)
- I = set()
- triplet = _find_chordality_breaker(H,s,treewidth_bound)
- while triplet:
- (u,v,w) = triplet
- I.update(triplet)
- for n in triplet:
- if n!=s:
- H.add_edge(s,n)
- triplet = _find_chordality_breaker(H,s,treewidth_bound)
- if I:
- # Add t and the second node in the induced path from s to t.
- I.add(t)
- for u in G[s]:
- if len(I & set(G[u]))==2:
- I.add(u)
- break
- return I
-
-def chordal_graph_cliques(G):
- """Returns the set of maximal cliques of a chordal graph.
-
- The algorithm breaks the graph in connected components and performs a
- maximum cardinality search in each component to get the cliques.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- Returns
- -------
- cliques : A set containing the maximal cliques in G.
-
- Raises
- ------
- NetworkXError
- The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
- If the input graph is an instance of one of these classes, a
- NetworkXError is raised.
- The algorithm can only be applied to chordal graphs. If the
- input graph is found to be non-chordal, a NetworkXError is raised.
-
- Examples
- --------
- >>> import networkx as nx
- >>> e= [(1,2),(1,3),(2,3),(2,4),(3,4),(3,5),(3,6),(4,5),(4,6),(5,6),(7,8)]
- >>> G = nx.Graph(e)
- >>> G.add_node(9)
- >>> setlist = nx.chordal_graph_cliques(G)
- """
- if not is_chordal(G):
- raise nx.NetworkXError("Input graph is not chordal.")
-
- cliques = set()
- for C in nx.connected.connected_component_subgraphs(G):
- cliques |= _connected_chordal_graph_cliques(C)
-
- return cliques
-
-
-def chordal_graph_treewidth(G):
- """Returns the treewidth of the chordal graph G.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- Returns
- -------
- treewidth : int
- The size of the largest clique in the graph minus one.
-
- Raises
- ------
- NetworkXError
- The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
- If the input graph is an instance of one of these classes, a
- NetworkXError is raised.
- The algorithm can only be applied to chordal graphs. If
- the input graph is found to be non-chordal, a NetworkXError is raised.
-
- Examples
- --------
- >>> import networkx as nx
- >>> e = [(1,2),(1,3),(2,3),(2,4),(3,4),(3,5),(3,6),(4,5),(4,6),(5,6),(7,8)]
- >>> G = nx.Graph(e)
- >>> G.add_node(9)
- >>> nx.chordal_graph_treewidth(G)
- 3
-
- References
- ----------
- .. [1] http://en.wikipedia.org/wiki/Tree_decomposition#Treewidth
- """
- if not is_chordal(G):
- raise nx.NetworkXError("Input graph is not chordal.")
-
- max_clique = -1
- for clique in nx.chordal_graph_cliques(G):
- max_clique = max(max_clique,len(clique))
- return max_clique - 1
-
-def _is_complete_graph(G):
- """Returns True if G is a complete graph."""
- if G.number_of_selfloops()>0:
- raise nx.NetworkXError("Self loop found in _is_complete_graph()")
- n = G.number_of_nodes()
- if n < 2:
- return True
- e = G.number_of_edges()
- max_edges = ((n * (n-1))/2)
- return e == max_edges
-
-
-def _find_missing_edge(G):
- """ Given a non-complete graph G, returns a missing edge."""
- nodes=set(G)
- for u in G:
- missing=nodes-set(list(G[u].keys())+[u])
- if missing:
- return (u,missing.pop())
-
-
-def _max_cardinality_node(G,choices,wanna_connect):
- """Returns a the node in choices that has more connections in G
- to nodes in wanna_connect.
- """
-# max_number = None
- max_number = -1
- for x in choices:
- number=len([y for y in G[x] if y in wanna_connect])
- if number > max_number:
- max_number = number
- max_cardinality_node = x
- return max_cardinality_node
-
-
-def _find_chordality_breaker(G,s=None,treewidth_bound=sys.maxsize):
- """ Given a graph G, starts a max cardinality search
- (starting from s if s is given and from a random node otherwise)
- trying to find a non-chordal cycle.
-
- If it does find one, it returns (u,v,w) where u,v,w are the three
- nodes that together with s are involved in the cycle.
- """
-
- unnumbered = set(G)
- if s is None:
- s = random.choice(list(unnumbered))
- unnumbered.remove(s)
- numbered = set([s])
-# current_treewidth = None
- current_treewidth = -1
- while unnumbered:# and current_treewidth <= treewidth_bound:
- v = _max_cardinality_node(G,unnumbered,numbered)
- unnumbered.remove(v)
- numbered.add(v)
- clique_wanna_be = set(G[v]) & numbered
- sg = G.subgraph(clique_wanna_be)
- if _is_complete_graph(sg):
- # The graph seems to be chordal by now. We update the treewidth
- current_treewidth = max(current_treewidth,len(clique_wanna_be))
- if current_treewidth > treewidth_bound:
- raise nx.NetworkXTreewidthBoundExceeded(\
- "treewidth_bound exceeded: %s"%current_treewidth)
- else:
- # sg is not a clique,
- # look for an edge that is not included in sg
- (u,w) = _find_missing_edge(sg)
- return (u,v,w)
- return ()
-
-
-
-def _connected_chordal_graph_cliques(G):
- """Return the set of maximal cliques of a connected chordal graph."""
- if G.number_of_nodes() == 1:
- x = frozenset(G.nodes())
- return set([x])
- else:
- cliques = set()
- unnumbered = set(G.nodes())
- v = random.choice(list(unnumbered))
- unnumbered.remove(v)
- numbered = set([v])
- clique_wanna_be = set([v])
- while unnumbered:
- v = _max_cardinality_node(G,unnumbered,numbered)
- unnumbered.remove(v)
- numbered.add(v)
- new_clique_wanna_be = set(G.neighbors(v)) & numbered
- sg = G.subgraph(clique_wanna_be)
- if _is_complete_graph(sg):
- new_clique_wanna_be.add(v)
- if not new_clique_wanna_be >= clique_wanna_be:
- cliques.add(frozenset(clique_wanna_be))
- clique_wanna_be = new_clique_wanna_be
- else:
- raise nx.NetworkXError("Input graph is not chordal.")
- cliques.add(frozenset(clique_wanna_be))
- return cliques
-
-
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/tests/test_chordal.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/tests/test_chordal.py
deleted file mode 100644
index 4ec0b5b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/tests/test_chordal.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestMCS:
-
- def setUp(self):
- # simple graph
- connected_chordal_G=nx.Graph()
- connected_chordal_G.add_edges_from([(1,2),(1,3),(2,3),(2,4),(3,4),
- (3,5),(3,6),(4,5),(4,6),(5,6)])
- self.connected_chordal_G=connected_chordal_G
-
- chordal_G = nx.Graph()
- chordal_G.add_edges_from([(1,2),(1,3),(2,3),(2,4),(3,4),
- (3,5),(3,6),(4,5),(4,6),(5,6),(7,8)])
- chordal_G.add_node(9)
- self.chordal_G=chordal_G
-
- non_chordal_G = nx.Graph()
- non_chordal_G.add_edges_from([(1,2),(1,3),(2,4),(2,5),(3,4),(3,5)])
- self.non_chordal_G = non_chordal_G
-
- def test_is_chordal(self):
- assert_false(nx.is_chordal(self.non_chordal_G))
- assert_true(nx.is_chordal(self.chordal_G))
- assert_true(nx.is_chordal(self.connected_chordal_G))
- assert_true(nx.is_chordal(nx.complete_graph(3)))
- assert_true(nx.is_chordal(nx.cycle_graph(3)))
- assert_false(nx.is_chordal(nx.cycle_graph(5)))
-
- def test_induced_nodes(self):
- G = nx.generators.classic.path_graph(10)
- I = nx.find_induced_nodes(G,1,9,2)
- assert_equal(I,set([1,2,3,4,5,6,7,8,9]))
- assert_raises(nx.NetworkXTreewidthBoundExceeded,
- nx.find_induced_nodes,G,1,9,1)
- I = nx.find_induced_nodes(self.chordal_G,1,6)
- assert_equal(I,set([1,2,4,6]))
- assert_raises(nx.NetworkXError,
- nx.find_induced_nodes,self.non_chordal_G,1,5)
-
- def test_chordal_find_cliques(self):
- cliques = set([frozenset([9]),frozenset([7,8]),frozenset([1,2,3]),
- frozenset([2,3,4]),frozenset([3,4,5,6])])
- assert_equal(nx.chordal_graph_cliques(self.chordal_G),cliques)
-
- def test_chordal_find_cliques_path(self):
- G = nx.path_graph(10)
- cliqueset = nx.chordal_graph_cliques(G)
- for (u,v) in G.edges_iter():
- assert_true(frozenset([u,v]) in cliqueset
- or frozenset([v,u]) in cliqueset)
-
- def test_chordal_find_cliquesCC(self):
- cliques = set([frozenset([1,2,3]),frozenset([2,3,4]),
- frozenset([3,4,5,6])])
- assert_equal(nx.chordal_graph_cliques(self.connected_chordal_G),cliques)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/clique.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/clique.py
deleted file mode 100644
index 08d9ade..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/clique.py
+++ /dev/null
@@ -1,516 +0,0 @@
-"""
-=======
-Cliques
-=======
-
-Find and manipulate cliques of graphs.
-
-Note that finding the largest clique of a graph has been
-shown to be an NP-complete problem; the algorithms here
-could take a long time to run.
-
-http://en.wikipedia.org/wiki/Clique_problem
-"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx
-from networkx.utils.decorators import *
-__author__ = """Dan Schult (dschult@colgate.edu)"""
-__all__ = ['find_cliques', 'find_cliques_recursive', 'make_max_clique_graph',
- 'make_clique_bipartite' ,'graph_clique_number',
- 'graph_number_of_cliques', 'node_clique_number',
- 'number_of_cliques', 'cliques_containing_node',
- 'project_down', 'project_up']
-
-
-@not_implemented_for('directed')
-def find_cliques(G):
- """Search for all maximal cliques in a graph.
-
- Maximal cliques are the largest complete subgraph containing
- a given node. The largest maximal clique is sometimes called
- the maximum clique.
-
- Returns
- -------
- generator of lists: genetor of member list for each maximal clique
-
- See Also
- --------
- find_cliques_recursive :
- A recursive version of the same algorithm
-
- Notes
- -----
- To obtain a list of cliques, use list(find_cliques(G)).
-
- Based on the algorithm published by Bron & Kerbosch (1973) [1]_
- as adapated by Tomita, Tanaka and Takahashi (2006) [2]_
- and discussed in Cazals and Karande (2008) [3]_.
- The method essentially unrolls the recursion used in
- the references to avoid issues of recursion stack depth.
-
- This algorithm is not suitable for directed graphs.
-
- This algorithm ignores self-loops and parallel edges as
- clique is not conventionally defined with such edges.
-
- There are often many cliques in graphs. This algorithm can
- run out of memory for large graphs.
-
- References
- ----------
- .. [1] Bron, C. and Kerbosch, J. 1973.
- Algorithm 457: finding all cliques of an undirected graph.
- Commun. ACM 16, 9 (Sep. 1973), 575-577.
- http://portal.acm.org/citation.cfm?doid=362342.362367
-
- .. [2] Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi,
- The worst-case time complexity for generating all maximal
- cliques and computational experiments,
- Theoretical Computer Science, Volume 363, Issue 1,
- Computing and Combinatorics,
- 10th Annual International Conference on
- Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28-42
- http://dx.doi.org/10.1016/j.tcs.2006.06.015
-
- .. [3] F. Cazals, C. Karande,
- A note on the problem of reporting maximal cliques,
- Theoretical Computer Science,
- Volume 407, Issues 1-3, 6 November 2008, Pages 564-568,
- http://dx.doi.org/10.1016/j.tcs.2008.05.010
- """
- # Cache nbrs and find first pivot (highest degree)
- maxconn=-1
- nnbrs={}
- pivotnbrs=set() # handle empty graph
- for n,nbrs in G.adjacency_iter():
- nbrs=set(nbrs)
- nbrs.discard(n)
- conn = len(nbrs)
- if conn > maxconn:
- nnbrs[n] = pivotnbrs = nbrs
- maxconn = conn
- else:
- nnbrs[n] = nbrs
- # Initial setup
- cand=set(nnbrs)
- smallcand = set(cand - pivotnbrs)
- done=set()
- stack=[]
- clique_so_far=[]
- # Start main loop
- while smallcand or stack:
- try:
- # Any nodes left to check?
- n=smallcand.pop()
- except KeyError:
- # back out clique_so_far
- cand,done,smallcand = stack.pop()
- clique_so_far.pop()
- continue
- # Add next node to clique
- clique_so_far.append(n)
- cand.remove(n)
- done.add(n)
- nn=nnbrs[n]
- new_cand = cand & nn
- new_done = done & nn
- # check if we have more to search
- if not new_cand:
- if not new_done:
- # Found a clique!
- yield clique_so_far[:]
- clique_so_far.pop()
- continue
- # Shortcut--only one node left!
- if not new_done and len(new_cand)==1:
- yield clique_so_far + list(new_cand)
- clique_so_far.pop()
- continue
- # find pivot node (max connected in cand)
- # look in done nodes first
- numb_cand=len(new_cand)
- maxconndone=-1
- for n in new_done:
- cn = new_cand & nnbrs[n]
- conn=len(cn)
- if conn > maxconndone:
- pivotdonenbrs=cn
- maxconndone=conn
- if maxconndone==numb_cand:
- break
- # Shortcut--this part of tree already searched
- if maxconndone == numb_cand:
- clique_so_far.pop()
- continue
- # still finding pivot node
- # look in cand nodes second
- maxconn=-1
- for n in new_cand:
- cn = new_cand & nnbrs[n]
- conn=len(cn)
- if conn > maxconn:
- pivotnbrs=cn
- maxconn=conn
- if maxconn == numb_cand-1:
- break
- # pivot node is max connected in cand from done or cand
- if maxconndone > maxconn:
- pivotnbrs = pivotdonenbrs
- # save search status for later backout
- stack.append( (cand, done, smallcand) )
- cand=new_cand
- done=new_done
- smallcand = cand - pivotnbrs
-
-
-def find_cliques_recursive(G):
- """Recursive search for all maximal cliques in a graph.
-
- Maximal cliques are the largest complete subgraph containing
- a given point. The largest maximal clique is sometimes called
- the maximum clique.
-
- Returns
- -------
- list of lists: list of members in each maximal clique
-
- See Also
- --------
- find_cliques : An nonrecursive version of the same algorithm
-
- Notes
- -----
- Based on the algorithm published by Bron & Kerbosch (1973) [1]_
- as adapated by Tomita, Tanaka and Takahashi (2006) [2]_
- and discussed in Cazals and Karande (2008) [3]_.
-
- This implementation returns a list of lists each of
- which contains the members of a maximal clique.
-
- This algorithm ignores self-loops and parallel edges as
- clique is not conventionally defined with such edges.
-
- References
- ----------
- .. [1] Bron, C. and Kerbosch, J. 1973.
- Algorithm 457: finding all cliques of an undirected graph.
- Commun. ACM 16, 9 (Sep. 1973), 575-577.
- http://portal.acm.org/citation.cfm?doid=362342.362367
-
- .. [2] Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi,
- The worst-case time complexity for generating all maximal
- cliques and computational experiments,
- Theoretical Computer Science, Volume 363, Issue 1,
- Computing and Combinatorics,
- 10th Annual International Conference on
- Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28-42
- http://dx.doi.org/10.1016/j.tcs.2006.06.015
-
- .. [3] F. Cazals, C. Karande,
- A note on the problem of reporting maximal cliques,
- Theoretical Computer Science,
- Volume 407, Issues 1-3, 6 November 2008, Pages 564-568,
- http://dx.doi.org/10.1016/j.tcs.2008.05.010
- """
- nnbrs={}
- for n,nbrs in G.adjacency_iter():
- nbrs=set(nbrs)
- nbrs.discard(n)
- nnbrs[n]=nbrs
- if not nnbrs: return [] # empty graph
- cand=set(nnbrs)
- done=set()
- clique_so_far=[]
- cliques=[]
- _extend(nnbrs,cand,done,clique_so_far,cliques)
- return cliques
-
-def _extend(nnbrs,cand,done,so_far,cliques):
- # find pivot node (max connections in cand)
- maxconn=-1
- numb_cand=len(cand)
- for n in done:
- cn = cand & nnbrs[n]
- conn=len(cn)
- if conn > maxconn:
- pivotnbrs=cn
- maxconn=conn
- if conn==numb_cand:
- # All possible cliques already found
- return
- for n in cand:
- cn = cand & nnbrs[n]
- conn=len(cn)
- if conn > maxconn:
- pivotnbrs=cn
- maxconn=conn
- # Use pivot to reduce number of nodes to examine
- smallercand = set(cand - pivotnbrs)
- for n in smallercand:
- cand.remove(n)
- so_far.append(n)
- nn=nnbrs[n]
- new_cand=cand & nn
- new_done=done & nn
- if not new_cand and not new_done:
- # Found the clique
- cliques.append(so_far[:])
- elif not new_done and len(new_cand) is 1:
- # shortcut if only one node left
- cliques.append(so_far+list(new_cand))
- else:
- _extend(nnbrs, new_cand, new_done, so_far, cliques)
- done.add(so_far.pop())
-
-
-def make_max_clique_graph(G,create_using=None,name=None):
- """ Create the maximal clique graph of a graph.
-
- Finds the maximal cliques and treats these as nodes.
- The nodes are connected if they have common members in
- the original graph. Theory has done a lot with clique
- graphs, but I haven't seen much on maximal clique graphs.
-
- Notes
- -----
- This should be the same as make_clique_bipartite followed
- by project_up, but it saves all the intermediate steps.
- """
- cliq=list(map(set,find_cliques(G)))
- if create_using:
- B=create_using
- B.clear()
- else:
- B=networkx.Graph()
- if name is not None:
- B.name=name
-
- for i,cl in enumerate(cliq):
- B.add_node(i+1)
- for j,other_cl in enumerate(cliq[:i]):
- # if not cl.isdisjoint(other_cl): #Requires 2.6
- intersect=cl & other_cl
- if intersect: # Not empty
- B.add_edge(i+1,j+1)
- return B
-
-def make_clique_bipartite(G,fpos=None,create_using=None,name=None):
- """Create a bipartite clique graph from a graph G.
-
- Nodes of G are retained as the "bottom nodes" of B and
- cliques of G become "top nodes" of B.
- Edges are present if a bottom node belongs to the clique
- represented by the top node.
-
- Returns a Graph with additional attribute dict B.node_type
- which is keyed by nodes to "Bottom" or "Top" appropriately.
-
- if fpos is not None, a second additional attribute dict B.pos
- is created to hold the position tuple of each node for viewing
- the bipartite graph.
- """
- cliq=list(find_cliques(G))
- if create_using:
- B=create_using
- B.clear()
- else:
- B=networkx.Graph()
- if name is not None:
- B.name=name
-
- B.add_nodes_from(G)
- B.node_type={} # New Attribute for B
- for n in B:
- B.node_type[n]="Bottom"
-
- if fpos:
- B.pos={} # New Attribute for B
- delta_cpos=1./len(cliq)
- delta_ppos=1./G.order()
- cpos=0.
- ppos=0.
- for i,cl in enumerate(cliq):
- name= -i-1 # Top nodes get negative names
- B.add_node(name)
- B.node_type[name]="Top"
- if fpos:
- if name not in B.pos:
- B.pos[name]=(0.2,cpos)
- cpos +=delta_cpos
- for v in cl:
- B.add_edge(name,v)
- if fpos is not None:
- if v not in B.pos:
- B.pos[v]=(0.8,ppos)
- ppos +=delta_ppos
- return B
-
-def project_down(B,create_using=None,name=None):
- """Project a bipartite graph B down onto its "bottom nodes".
-
- The nodes retain their names and are connected if they
- share a common top node in the bipartite graph.
-
- Returns a Graph.
- """
- if create_using:
- G=create_using
- G.clear()
- else:
- G=networkx.Graph()
- if name is not None:
- G.name=name
-
- for v,Bvnbrs in B.adjacency_iter():
- if B.node_type[v]=="Bottom":
- G.add_node(v)
- for cv in Bvnbrs:
- G.add_edges_from([(v,u) for u in B[cv] if u!=v])
- return G
-
-def project_up(B,create_using=None,name=None):
- """Project a bipartite graph B down onto its "bottom nodes".
-
- The nodes retain their names and are connected if they
- share a common Bottom Node in the Bipartite Graph.
-
- Returns a Graph.
- """
- if create_using:
- G=create_using
- G.clear()
- else:
- G=networkx.Graph()
- if name is not None:
- G.name=name
-
- for v,Bvnbrs in B.adjacency_iter():
- if B.node_type[v]=="Top":
- vname= -v #Change sign of name for Top Nodes
- G.add_node(vname)
- for cv in Bvnbrs:
- # Note: -u changes the name (not Top node anymore)
- G.add_edges_from([(vname,-u) for u in B[cv] if u!=v])
- return G
-
-def graph_clique_number(G,cliques=None):
- """Return the clique number (size of the largest clique) for G.
-
- An optional list of cliques can be input if already computed.
- """
- if cliques is None:
- cliques=find_cliques(G)
- return max( [len(c) for c in cliques] )
-
-
-def graph_number_of_cliques(G,cliques=None):
- """Returns the number of maximal cliques in G.
-
- An optional list of cliques can be input if already computed.
- """
- if cliques is None:
- cliques=list(find_cliques(G))
- return len(cliques)
-
-
-def node_clique_number(G,nodes=None,cliques=None):
- """ Returns the size of the largest maximal clique containing
- each given node.
-
- Returns a single or list depending on input nodes.
- Optional list of cliques can be input if already computed.
- """
- if cliques is None:
- if nodes is not None:
- # Use ego_graph to decrease size of graph
- if isinstance(nodes,list):
- d={}
- for n in nodes:
- H=networkx.ego_graph(G,n)
- d[n]=max( (len(c) for c in find_cliques(H)) )
- else:
- H=networkx.ego_graph(G,nodes)
- d=max( (len(c) for c in find_cliques(H)) )
- return d
- # nodes is None--find all cliques
- cliques=list(find_cliques(G))
-
- if nodes is None:
- nodes=G.nodes() # none, get entire graph
-
- if not isinstance(nodes, list): # check for a list
- v=nodes
- # assume it is a single value
- d=max([len(c) for c in cliques if v in c])
- else:
- d={}
- for v in nodes:
- d[v]=max([len(c) for c in cliques if v in c])
- return d
-
- # if nodes is None: # none, use entire graph
- # nodes=G.nodes()
- # elif not isinstance(nodes, list): # check for a list
- # nodes=[nodes] # assume it is a single value
-
- # if cliques is None:
- # cliques=list(find_cliques(G))
- # d={}
- # for v in nodes:
- # d[v]=max([len(c) for c in cliques if v in c])
-
- # if nodes in G:
- # return d[v] #return single value
- # return d
-
-
-def number_of_cliques(G,nodes=None,cliques=None):
- """Returns the number of maximal cliques for each node.
-
- Returns a single or list depending on input nodes.
- Optional list of cliques can be input if already computed.
- """
- if cliques is None:
- cliques=list(find_cliques(G))
-
- if nodes is None:
- nodes=G.nodes() # none, get entire graph
-
- if not isinstance(nodes, list): # check for a list
- v=nodes
- # assume it is a single value
- numcliq=len([1 for c in cliques if v in c])
- else:
- numcliq={}
- for v in nodes:
- numcliq[v]=len([1 for c in cliques if v in c])
- return numcliq
-
-
-def cliques_containing_node(G,nodes=None,cliques=None):
- """Returns a list of cliques containing the given node.
-
- Returns a single list or list of lists depending on input nodes.
- Optional list of cliques can be input if already computed.
- """
- if cliques is None:
- cliques=list(find_cliques(G))
-
- if nodes is None:
- nodes=G.nodes() # none, get entire graph
-
- if not isinstance(nodes, list): # check for a list
- v=nodes
- # assume it is a single value
- vcliques=[c for c in cliques if v in c]
- else:
- vcliques={}
- for v in nodes:
- vcliques[v]=[c for c in cliques if v in c]
- return vcliques
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/cluster.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/cluster.py
deleted file mode 100644
index a442431..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/cluster.py
+++ /dev/null
@@ -1,363 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Algorithms to characterize the number of triangles in a graph."""
-from itertools import combinations
-import networkx as nx
-from networkx import NetworkXError
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Dan Schult (dschult@colgate.edu)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Jordi Torrents <jtorrents@milnou.net>'])
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__all__= ['triangles', 'average_clustering', 'clustering', 'transitivity',
- 'square_clustering']
-
-def triangles(G, nodes=None):
- """Compute the number of triangles.
-
- Finds the number of triangles that include a node as one vertex.
-
- Parameters
- ----------
- G : graph
- A networkx graph
- nodes : container of nodes, optional (default= all nodes in G)
- Compute triangles for nodes in this container.
-
- Returns
- -------
- out : dictionary
- Number of triangles keyed by node label.
-
- Examples
- --------
- >>> G=nx.complete_graph(5)
- >>> print(nx.triangles(G,0))
- 6
- >>> print(nx.triangles(G))
- {0: 6, 1: 6, 2: 6, 3: 6, 4: 6}
- >>> print(list(nx.triangles(G,(0,1)).values()))
- [6, 6]
-
- Notes
- -----
- When computing triangles for the entire graph each triangle is counted
- three times, once at each node. Self loops are ignored.
-
- """
- if G.is_directed():
- raise NetworkXError("triangles() is not defined for directed graphs.")
- if nodes in G:
- # return single value
- return next(_triangles_and_degree_iter(G,nodes))[2] // 2
- return dict( (v,t // 2) for v,d,t in _triangles_and_degree_iter(G,nodes))
-
-def _triangles_and_degree_iter(G,nodes=None):
- """ Return an iterator of (node, degree, triangles).
-
- This double counts triangles so you may want to divide by 2.
- See degree() and triangles() for definitions and details.
-
- """
- if G.is_multigraph():
- raise NetworkXError("Not defined for multigraphs.")
-
- if nodes is None:
- nodes_nbrs = G.adj.items()
- else:
- nodes_nbrs= ( (n,G[n]) for n in G.nbunch_iter(nodes) )
-
- for v,v_nbrs in nodes_nbrs:
- vs=set(v_nbrs)-set([v])
- ntriangles=0
- for w in vs:
- ws=set(G[w])-set([w])
- ntriangles+=len(vs.intersection(ws))
- yield (v,len(vs),ntriangles)
-
-
-def _weighted_triangles_and_degree_iter(G, nodes=None, weight='weight'):
- """ Return an iterator of (node, degree, weighted_triangles).
-
- Used for weighted clustering.
-
- """
- if G.is_multigraph():
- raise NetworkXError("Not defined for multigraphs.")
-
- if weight is None or G.edges()==[]:
- max_weight=1.0
- else:
- max_weight=float(max(d.get(weight,1.0)
- for u,v,d in G.edges(data=True)))
- if nodes is None:
- nodes_nbrs = G.adj.items()
- else:
- nodes_nbrs= ( (n,G[n]) for n in G.nbunch_iter(nodes) )
-
- for i,nbrs in nodes_nbrs:
- inbrs=set(nbrs)-set([i])
- weighted_triangles=0.0
- seen=set()
- for j in inbrs:
- wij=G[i][j].get(weight,1.0)/max_weight
- seen.add(j)
- jnbrs=set(G[j])-seen # this keeps from double counting
- for k in inbrs&jnbrs:
- wjk=G[j][k].get(weight,1.0)/max_weight
- wki=G[i][k].get(weight,1.0)/max_weight
- weighted_triangles+=(wij*wjk*wki)**(1.0/3.0)
- yield (i,len(inbrs),weighted_triangles*2)
-
-
-def average_clustering(G, nodes=None, weight=None, count_zeros=True):
- r"""Compute the average clustering coefficient for the graph G.
-
- The clustering coefficient for the graph is the average,
-
- .. math::
-
- C = \frac{1}{n}\sum_{v \in G} c_v,
-
- where `n` is the number of nodes in `G`.
-
- Parameters
- ----------
- G : graph
-
- nodes : container of nodes, optional (default=all nodes in G)
- Compute average clustering for nodes in this container.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used as a weight.
- If None, then each edge has weight 1.
-
- count_zeros : bool (default=False)
- If False include only the nodes with nonzero clustering in the average.
-
- Returns
- -------
- avg : float
- Average clustering
-
- Examples
- --------
- >>> G=nx.complete_graph(5)
- >>> print(nx.average_clustering(G))
- 1.0
-
- Notes
- -----
- This is a space saving routine; it might be faster
- to use the clustering function to get a list and then take the average.
-
- Self loops are ignored.
-
- References
- ----------
- .. [1] Generalizations of the clustering coefficient to weighted
- complex networks by J. Saramäki, M. Kivelä, J.-P. Onnela,
- K. Kaski, and J. Kertész, Physical Review E, 75 027105 (2007).
- http://jponnela.com/web_documents/a9.pdf
- .. [2] Marcus Kaiser, Mean clustering coefficients: the role of isolated
- nodes and leafs on clustering measures for small-world networks.
- http://arxiv.org/abs/0802.2512
- """
- c=clustering(G,nodes,weight=weight).values()
- if not count_zeros:
- c = [v for v in c if v > 0]
- return sum(c)/float(len(c))
-
-def clustering(G, nodes=None, weight=None):
- r"""Compute the clustering coefficient for nodes.
-
- For unweighted graphs, the clustering of a node `u`
- is the fraction of possible triangles through that node that exist,
-
- .. math::
-
- c_u = \frac{2 T(u)}{deg(u)(deg(u)-1)},
-
- where `T(u)` is the number of triangles through node `u` and
- `deg(u)` is the degree of `u`.
-
- For weighted graphs, the clustering is defined
- as the geometric average of the subgraph edge weights [1]_,
-
- .. math::
-
- c_u = \frac{1}{deg(u)(deg(u)-1))}
- \sum_{uv} (\hat{w}_{uv} \hat{w}_{uw} \hat{w}_{vw})^{1/3}.
-
- The edge weights `\hat{w}_{uv}` are normalized by the maximum weight in the
- network `\hat{w}_{uv} = w_{uv}/\max(w)`.
-
- The value of `c_u` is assigned to 0 if `deg(u) < 2`.
-
- Parameters
- ----------
- G : graph
-
- nodes : container of nodes, optional (default=all nodes in G)
- Compute clustering for nodes in this container.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used as a weight.
- If None, then each edge has weight 1.
-
- Returns
- -------
- out : float, or dictionary
- Clustering coefficient at specified nodes
-
- Examples
- --------
- >>> G=nx.complete_graph(5)
- >>> print(nx.clustering(G,0))
- 1.0
- >>> print(nx.clustering(G))
- {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0}
-
- Notes
- -----
- Self loops are ignored.
-
- References
- ----------
- .. [1] Generalizations of the clustering coefficient to weighted
- complex networks by J. Saramäki, M. Kivelä, J.-P. Onnela,
- K. Kaski, and J. Kertész, Physical Review E, 75 027105 (2007).
- http://jponnela.com/web_documents/a9.pdf
- """
- if G.is_directed():
- raise NetworkXError('Clustering algorithms are not defined ',
- 'for directed graphs.')
- if weight is not None:
- td_iter=_weighted_triangles_and_degree_iter(G,nodes,weight)
- else:
- td_iter=_triangles_and_degree_iter(G,nodes)
-
- clusterc={}
-
- for v,d,t in td_iter:
- if t==0:
- clusterc[v]=0.0
- else:
- clusterc[v]=t/float(d*(d-1))
-
- if nodes in G:
- return list(clusterc.values())[0] # return single value
- return clusterc
-
-def transitivity(G):
- r"""Compute graph transitivity, the fraction of all possible triangles
- present in G.
-
- Possible triangles are identified by the number of "triads"
- (two edges with a shared vertex).
-
- The transitivity is
-
- .. math::
-
- T = 3\frac{\#triangles}{\#triads}.
-
- Parameters
- ----------
- G : graph
-
- Returns
- -------
- out : float
- Transitivity
-
- Examples
- --------
- >>> G = nx.complete_graph(5)
- >>> print(nx.transitivity(G))
- 1.0
- """
- triangles=0 # 6 times number of triangles
- contri=0 # 2 times number of connected triples
- for v,d,t in _triangles_and_degree_iter(G):
- contri += d*(d-1)
- triangles += t
- if triangles==0: # we had no triangles or possible triangles
- return 0.0
- else:
- return triangles/float(contri)
-
-def square_clustering(G, nodes=None):
- r""" Compute the squares clustering coefficient for nodes.
-
- For each node return the fraction of possible squares that exist at
- the node [1]_
-
- .. math::
- C_4(v) = \frac{ \sum_{u=1}^{k_v}
- \sum_{w=u+1}^{k_v} q_v(u,w) }{ \sum_{u=1}^{k_v}
- \sum_{w=u+1}^{k_v} [a_v(u,w) + q_v(u,w)]},
-
- where `q_v(u,w)` are the number of common neighbors of `u` and `w`
- other than `v` (ie squares), and
- `a_v(u,w) = (k_u - (1+q_v(u,w)+\theta_{uv}))(k_w - (1+q_v(u,w)+\theta_{uw}))`,
- where `\theta_{uw} = 1` if `u` and `w` are connected and 0 otherwise.
-
- Parameters
- ----------
- G : graph
-
- nodes : container of nodes, optional (default=all nodes in G)
- Compute clustering for nodes in this container.
-
- Returns
- -------
- c4 : dictionary
- A dictionary keyed by node with the square clustering coefficient value.
-
- Examples
- --------
- >>> G=nx.complete_graph(5)
- >>> print(nx.square_clustering(G,0))
- 1.0
- >>> print(nx.square_clustering(G))
- {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0}
-
- Notes
- -----
- While `C_3(v)` (triangle clustering) gives the probability that
- two neighbors of node v are connected with each other, `C_4(v)` is
- the probability that two neighbors of node v share a common
- neighbor different from v. This algorithm can be applied to both
- bipartite and unipartite networks.
-
- References
- ----------
- .. [1] Pedro G. Lind, Marta C. González, and Hans J. Herrmann. 2005
- Cycles and clustering in bipartite networks.
- Physical Review E (72) 056127.
- """
- if nodes is None:
- node_iter = G
- else:
- node_iter = G.nbunch_iter(nodes)
- clustering = {}
- for v in node_iter:
- clustering[v] = 0.0
- potential=0
- for u,w in combinations(G[v], 2):
- squares = len((set(G[u]) & set(G[w])) - set([v]))
- clustering[v] += squares
- degm = squares + 1.0
- if w in G[u]:
- degm += 1
- potential += (len(G[u]) - degm) * (len(G[w]) - degm) + squares
- if potential > 0:
- clustering[v] /= potential
- if nodes in G:
- return list(clustering.values())[0] # return single value
- return clustering
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/__init__.py
deleted file mode 100644
index c3c2285..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from networkx.algorithms.community.kclique import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/kclique.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/kclique.py
deleted file mode 100644
index dc95b58..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/kclique.py
+++ /dev/null
@@ -1,82 +0,0 @@
-#-*- coding: utf-8 -*-
-# Copyright (C) 2011 by
-# Conrad Lee <conradlee@gmail.com>
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-from collections import defaultdict
-import networkx as nx
-__author__ = """\n""".join(['Conrad Lee <conradlee@gmail.com>',
- 'Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['k_clique_communities']
-
-def k_clique_communities(G, k, cliques=None):
- """Find k-clique communities in graph using the percolation method.
-
- A k-clique community is the union of all cliques of size k that
- can be reached through adjacent (sharing k-1 nodes) k-cliques.
-
- Parameters
- ----------
- G : NetworkX graph
-
- k : int
- Size of smallest clique
-
- cliques: list or generator
- Precomputed cliques (use networkx.find_cliques(G))
-
- Returns
- -------
- Yields sets of nodes, one for each k-clique community.
-
- Examples
- --------
- >>> G = nx.complete_graph(5)
- >>> K5 = nx.convert_node_labels_to_integers(G,first_label=2)
- >>> G.add_edges_from(K5.edges())
- >>> c = list(nx.k_clique_communities(G, 4))
- >>> list(c[0])
- [0, 1, 2, 3, 4, 5, 6]
- >>> list(nx.k_clique_communities(G, 6))
- []
-
- References
- ----------
- .. [1] Gergely Palla, Imre Derényi, Illés Farkas1, and Tamás Vicsek,
- Uncovering the overlapping community structure of complex networks
- in nature and society Nature 435, 814-818, 2005,
- doi:10.1038/nature03607
- """
- if k < 2:
- raise nx.NetworkXError("k=%d, k must be greater than 1."%k)
- if cliques is None:
- cliques = nx.find_cliques(G)
- cliques = [frozenset(c) for c in cliques if len(c) >= k]
-
- # First index which nodes are in which cliques
- membership_dict = defaultdict(list)
- for clique in cliques:
- for node in clique:
- membership_dict[node].append(clique)
-
- # For each clique, see which adjacent cliques percolate
- perc_graph = nx.Graph()
- perc_graph.add_nodes_from(cliques)
- for clique in cliques:
- for adj_clique in _get_adjacent_cliques(clique, membership_dict):
- if len(clique.intersection(adj_clique)) >= (k - 1):
- perc_graph.add_edge(clique, adj_clique)
-
- # Connected components of clique graph with perc edges
- # are the percolated cliques
- for component in nx.connected_components(perc_graph):
- yield(frozenset.union(*component))
-
-def _get_adjacent_cliques(clique, membership_dict):
- adjacent_cliques = set()
- for n in clique:
- for adj_clique in membership_dict[n]:
- if clique != adj_clique:
- adjacent_cliques.add(adj_clique)
- return adjacent_cliques
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/tests/test_kclique.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/tests/test_kclique.py
deleted file mode 100644
index 8debca6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/tests/test_kclique.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from itertools import combinations
-from networkx import k_clique_communities
-
-def test_overlaping_K5():
- G = nx.Graph()
- G.add_edges_from(combinations(range(5), 2)) # Add a five clique
- G.add_edges_from(combinations(range(2,7), 2)) # Add another five clique
- c = list(nx.k_clique_communities(G, 4))
- assert_equal(c,[frozenset([0, 1, 2, 3, 4, 5, 6])])
- c= list(nx.k_clique_communities(G, 5))
- assert_equal(set(c),set([frozenset([0,1,2,3,4]),frozenset([2,3,4,5,6])]))
-
-def test_isolated_K5():
- G = nx.Graph()
- G.add_edges_from(combinations(range(0,5), 2)) # Add a five clique
- G.add_edges_from(combinations(range(5,10), 2)) # Add another five clique
- c= list(nx.k_clique_communities(G, 5))
- assert_equal(set(c),set([frozenset([0,1,2,3,4]),frozenset([5,6,7,8,9])]))
-
-def test_zachary():
- z = nx.karate_club_graph()
- # clique percolation with k=2 is just connected components
- zachary_k2_ground_truth = set([frozenset(z.nodes())])
- zachary_k3_ground_truth = set([frozenset([0, 1, 2, 3, 7, 8, 12, 13, 14,
- 15, 17, 18, 19, 20, 21, 22, 23,
- 26, 27, 28, 29, 30, 31, 32, 33]),
- frozenset([0, 4, 5, 6, 10, 16]),
- frozenset([24, 25, 31])])
- zachary_k4_ground_truth = set([frozenset([0, 1, 2, 3, 7, 13]),
- frozenset([8, 32, 30, 33]),
- frozenset([32, 33, 29, 23])])
- zachary_k5_ground_truth = set([frozenset([0, 1, 2, 3, 7, 13])])
- zachary_k6_ground_truth = set([])
-
- assert set(k_clique_communities(z, 2)) == zachary_k2_ground_truth
- assert set(k_clique_communities(z, 3)) == zachary_k3_ground_truth
- assert set(k_clique_communities(z, 4)) == zachary_k4_ground_truth
- assert set(k_clique_communities(z, 5)) == zachary_k5_ground_truth
- assert set(k_clique_communities(z, 6)) == zachary_k6_ground_truth
-
-@raises(nx.NetworkXError)
-def test_bad_k():
- c = list(k_clique_communities(nx.Graph(),1))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/__init__.py
deleted file mode 100644
index 36c9391..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from networkx.algorithms.components.connected import *
-from networkx.algorithms.components.strongly_connected import *
-from networkx.algorithms.components.weakly_connected import *
-from networkx.algorithms.components.attracting import *
-from networkx.algorithms.components.biconnected import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/attracting.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/attracting.py
deleted file mode 100644
index d0e75c2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/attracting.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Attracting components.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__authors__ = "\n".join(['Christopher Ellison'])
-__all__ = ['number_attracting_components',
- 'attracting_components',
- 'is_attracting_component',
- 'attracting_component_subgraphs',
- ]
-
-def attracting_components(G):
- """Returns a list of attracting components in `G`.
-
- An attracting component in a directed graph `G` is a strongly connected
- component with the property that a random walker on the graph will never
- leave the component, once it enters the component.
-
- The nodes in attracting components can also be thought of as recurrent
- nodes. If a random walker enters the attractor containing the node, then
- the node will be visited infinitely often.
-
- Parameters
- ----------
- G : DiGraph, MultiDiGraph
- The graph to be analyzed.
-
- Returns
- -------
- attractors : list
- The list of attracting components, sorted from largest attracting
- component to smallest attracting component.
-
- See Also
- --------
- number_attracting_components
- is_attracting_component
- attracting_component_subgraphs
-
- """
- scc = nx.strongly_connected_components(G)
- cG = nx.condensation(G, scc)
- attractors = [scc[n] for n in cG if cG.out_degree(n) == 0]
- attractors.sort(key=len,reverse=True)
- return attractors
-
-
-def number_attracting_components(G):
- """Returns the number of attracting components in `G`.
-
- Parameters
- ----------
- G : DiGraph, MultiDiGraph
- The graph to be analyzed.
-
- Returns
- -------
- n : int
- The number of attracting components in G.
-
- See Also
- --------
- attracting_components
- is_attracting_component
- attracting_component_subgraphs
-
- """
- n = len(attracting_components(G))
- return n
-
-
-def is_attracting_component(G):
- """Returns True if `G` consists of a single attracting component.
-
- Parameters
- ----------
- G : DiGraph, MultiDiGraph
- The graph to be analyzed.
-
- Returns
- -------
- attracting : bool
- True if `G` has a single attracting component. Otherwise, False.
-
- See Also
- --------
- attracting_components
- number_attracting_components
- attracting_component_subgraphs
-
- """
- ac = attracting_components(G)
- if len(ac[0]) == len(G):
- attracting = True
- else:
- attracting = False
- return attracting
-
-
-def attracting_component_subgraphs(G):
- """Returns a list of attracting component subgraphs from `G`.
-
- Parameters
- ----------
- G : DiGraph, MultiDiGraph
- The graph to be analyzed.
-
- Returns
- -------
- subgraphs : list
- A list of node-induced subgraphs of the attracting components of `G`.
-
- Notes
- -----
- Graph, node, and edge attributes are copied to the subgraphs.
-
- See Also
- --------
- attracting_components
- number_attracting_components
- is_attracting_component
-
- """
- subgraphs = [G.subgraph(ac).copy() for ac in attracting_components(G)]
- return subgraphs
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/biconnected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/biconnected.py
deleted file mode 100644
index 0185c2c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/biconnected.py
+++ /dev/null
@@ -1,417 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Biconnected components and articulation points.
-"""
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from itertools import chain
-import networkx as nx
-__author__ = '\n'.join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Dan Schult <dschult@colgate.edu>',
- 'Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['biconnected_components',
- 'biconnected_component_edges',
- 'biconnected_component_subgraphs',
- 'is_biconnected',
- 'articulation_points',
- ]
-
-def is_biconnected(G):
- """Return True if the graph is biconnected, False otherwise.
-
- A graph is biconnected if, and only if, it cannot be disconnected by
- removing only one node (and all edges incident on that node). If
- removing a node increases the number of disconnected components
- in the graph, that node is called an articulation point, or cut
- vertex. A biconnected graph has no articulation points.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- biconnected : bool
- True if the graph is biconnected, False otherwise.
-
- Raises
- ------
- NetworkXError :
- If the input graph is not undirected.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> print(nx.is_biconnected(G))
- False
- >>> G.add_edge(0,3)
- >>> print(nx.is_biconnected(G))
- True
-
- See Also
- --------
- biconnected_components,
- articulation_points,
- biconnected_component_edges,
- biconnected_component_subgraphs
-
- Notes
- -----
- The algorithm to find articulation points and biconnected
- components is implemented using a non-recursive depth-first-search
- (DFS) that keeps track of the highest level that back edges reach
- in the DFS tree. A node `n` is an articulation point if, and only
- if, there exists a subtree rooted at `n` such that there is no
- back edge from any successor of `n` that links to a predecessor of
- `n` in the DFS tree. By keeping track of all the edges traversed
- by the DFS we can obtain the biconnected components because all
- edges of a bicomponent will be traversed consecutively between
- articulation points.
-
- References
- ----------
- .. [1] Hopcroft, J.; Tarjan, R. (1973).
- "Efficient algorithms for graph manipulation".
- Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
- """
- bcc = list(biconnected_components(G))
- if not bcc: # No bicomponents (it could be an empty graph)
- return False
- return len(bcc[0]) == len(G)
-
-def biconnected_component_edges(G):
- """Return a generator of lists of edges, one list for each biconnected
- component of the input graph.
-
- Biconnected components are maximal subgraphs such that the removal of a
- node (and all edges incident on that node) will not disconnect the
- subgraph. Note that nodes may be part of more than one biconnected
- component. Those nodes are articulation points, or cut vertices. However,
- each edge belongs to one, and only one, biconnected component.
-
- Notice that by convention a dyad is considered a biconnected component.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- edges : generator
- Generator of lists of edges, one list for each bicomponent.
-
- Raises
- ------
- NetworkXError :
- If the input graph is not undirected.
-
- Examples
- --------
- >>> G = nx.barbell_graph(4,2)
- >>> print(nx.is_biconnected(G))
- False
- >>> components = nx.biconnected_component_edges(G)
- >>> G.add_edge(2,8)
- >>> print(nx.is_biconnected(G))
- True
- >>> components = nx.biconnected_component_edges(G)
-
- See Also
- --------
- is_biconnected,
- biconnected_components,
- articulation_points,
- biconnected_component_subgraphs
-
- Notes
- -----
- The algorithm to find articulation points and biconnected
- components is implemented using a non-recursive depth-first-search
- (DFS) that keeps track of the highest level that back edges reach
- in the DFS tree. A node `n` is an articulation point if, and only
- if, there exists a subtree rooted at `n` such that there is no
- back edge from any successor of `n` that links to a predecessor of
- `n` in the DFS tree. By keeping track of all the edges traversed
- by the DFS we can obtain the biconnected components because all
- edges of a bicomponent will be traversed consecutively between
- articulation points.
-
- References
- ----------
- .. [1] Hopcroft, J.; Tarjan, R. (1973).
- "Efficient algorithms for graph manipulation".
- Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
- """
- return sorted(_biconnected_dfs(G,components=True), key=len, reverse=True)
-
-def biconnected_components(G):
- """Return a generator of sets of nodes, one set for each biconnected
- component of the graph
-
- Biconnected components are maximal subgraphs such that the removal of a
- node (and all edges incident on that node) will not disconnect the
- subgraph. Note that nodes may be part of more than one biconnected
- component. Those nodes are articulation points, or cut vertices. The
- removal of articulation points will increase the number of connected
- components of the graph.
-
- Notice that by convention a dyad is considered a biconnected component.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- nodes : generator
- Generator of sets of nodes, one set for each biconnected component.
-
- Raises
- ------
- NetworkXError :
- If the input graph is not undirected.
-
- Examples
- --------
- >>> G = nx.barbell_graph(4,2)
- >>> print(nx.is_biconnected(G))
- False
- >>> components = nx.biconnected_components(G)
- >>> G.add_edge(2,8)
- >>> print(nx.is_biconnected(G))
- True
- >>> components = nx.biconnected_components(G)
-
- See Also
- --------
- is_biconnected,
- articulation_points,
- biconnected_component_edges,
- biconnected_component_subgraphs
-
- Notes
- -----
- The algorithm to find articulation points and biconnected
- components is implemented using a non-recursive depth-first-search
- (DFS) that keeps track of the highest level that back edges reach
- in the DFS tree. A node `n` is an articulation point if, and only
- if, there exists a subtree rooted at `n` such that there is no
- back edge from any successor of `n` that links to a predecessor of
- `n` in the DFS tree. By keeping track of all the edges traversed
- by the DFS we can obtain the biconnected components because all
- edges of a bicomponent will be traversed consecutively between
- articulation points.
-
- References
- ----------
- .. [1] Hopcroft, J.; Tarjan, R. (1973).
- "Efficient algorithms for graph manipulation".
- Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
- """
- bicomponents = (set(chain.from_iterable(comp))
- for comp in _biconnected_dfs(G,components=True))
- return sorted(bicomponents, key=len, reverse=True)
-
-def biconnected_component_subgraphs(G):
- """Return a generator of graphs, one graph for each biconnected component
- of the input graph.
-
- Biconnected components are maximal subgraphs such that the removal of a
- node (and all edges incident on that node) will not disconnect the
- subgraph. Note that nodes may be part of more than one biconnected
- component. Those nodes are articulation points, or cut vertices. The
- removal of articulation points will increase the number of connected
- components of the graph.
-
- Notice that by convention a dyad is considered a biconnected component.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- graphs : generator
- Generator of graphs, one graph for each biconnected component.
-
- Raises
- ------
- NetworkXError :
- If the input graph is not undirected.
-
- Examples
- --------
- >>> G = nx.barbell_graph(4,2)
- >>> print(nx.is_biconnected(G))
- False
- >>> subgraphs = nx.biconnected_component_subgraphs(G)
-
- See Also
- --------
- is_biconnected,
- articulation_points,
- biconnected_component_edges,
- biconnected_components
-
- Notes
- -----
- The algorithm to find articulation points and biconnected
- components is implemented using a non-recursive depth-first-search
- (DFS) that keeps track of the highest level that back edges reach
- in the DFS tree. A node `n` is an articulation point if, and only
- if, there exists a subtree rooted at `n` such that there is no
- back edge from any successor of `n` that links to a predecessor of
- `n` in the DFS tree. By keeping track of all the edges traversed
- by the DFS we can obtain the biconnected components because all
- edges of a bicomponent will be traversed consecutively between
- articulation points.
-
- Graph, node, and edge attributes are copied to the subgraphs.
-
- References
- ----------
- .. [1] Hopcroft, J.; Tarjan, R. (1973).
- "Efficient algorithms for graph manipulation".
- Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
- """
- def edge_subgraph(G,edges):
- # create new graph and copy subgraph into it
- H = G.__class__()
- for u,v in edges:
- H.add_edge(u,v,attr_dict=G[u][v])
- for n in H:
- H.node[n]=G.node[n].copy()
- H.graph=G.graph.copy()
- return H
- return (edge_subgraph(G,edges) for edges in
- sorted(_biconnected_dfs(G,components=True), key=len, reverse=True))
-
-def articulation_points(G):
- """Return a generator of articulation points, or cut vertices, of a graph.
-
- An articulation point or cut vertex is any node whose removal (along with
- all its incident edges) increases the number of connected components of
- a graph. An undirected connected graph without articulation points is
- biconnected. Articulation points belong to more than one biconnected
- component of a graph.
-
- Notice that by convention a dyad is considered a biconnected component.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- articulation points : generator
- generator of nodes
-
- Raises
- ------
- NetworkXError :
- If the input graph is not undirected.
-
- Examples
- --------
- >>> G = nx.barbell_graph(4,2)
- >>> print(nx.is_biconnected(G))
- False
- >>> list(nx.articulation_points(G))
- [6, 5, 4, 3]
- >>> G.add_edge(2,8)
- >>> print(nx.is_biconnected(G))
- True
- >>> list(nx.articulation_points(G))
- []
-
- See Also
- --------
- is_biconnected,
- biconnected_components,
- biconnected_component_edges,
- biconnected_component_subgraphs
-
- Notes
- -----
- The algorithm to find articulation points and biconnected
- components is implemented using a non-recursive depth-first-search
- (DFS) that keeps track of the highest level that back edges reach
- in the DFS tree. A node `n` is an articulation point if, and only
- if, there exists a subtree rooted at `n` such that there is no
- back edge from any successor of `n` that links to a predecessor of
- `n` in the DFS tree. By keeping track of all the edges traversed
- by the DFS we can obtain the biconnected components because all
- edges of a bicomponent will be traversed consecutively between
- articulation points.
-
- References
- ----------
- .. [1] Hopcroft, J.; Tarjan, R. (1973).
- "Efficient algorithms for graph manipulation".
- Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
- """
- return _biconnected_dfs(G,components=False)
-
-def _biconnected_dfs(G, components=True):
- # depth-first search algorithm to generate articulation points
- # and biconnected components
- if G.is_directed():
- raise nx.NetworkXError('Not allowed for directed graph G. '
- 'Use UG=G.to_undirected() to create an '
- 'undirected graph.')
- visited = set()
- for start in G:
- if start in visited:
- continue
- discovery = {start:0} # "time" of first discovery of node during search
- low = {start:0}
- root_children = 0
- visited.add(start)
- edge_stack = []
- stack = [(start, start, iter(G[start]))]
- while stack:
- grandparent, parent, children = stack[-1]
- try:
- child = next(children)
- if grandparent == child:
- continue
- if child in visited:
- if discovery[child] <= discovery[parent]: # back edge
- low[parent] = min(low[parent],discovery[child])
- if components:
- edge_stack.append((parent,child))
- else:
- low[child] = discovery[child] = len(discovery)
- visited.add(child)
- stack.append((parent, child, iter(G[child])))
- if components:
- edge_stack.append((parent,child))
- except StopIteration:
- stack.pop()
- if len(stack) > 1:
- if low[parent] >= discovery[grandparent]:
- if components:
- ind = edge_stack.index((grandparent,parent))
- yield edge_stack[ind:]
- edge_stack=edge_stack[:ind]
- else:
- yield grandparent
- low[grandparent] = min(low[parent], low[grandparent])
- elif stack: # length 1 so grandparent is root
- root_children += 1
- if components:
- ind = edge_stack.index((grandparent,parent))
- yield edge_stack[ind:]
- if not components:
- # root node is articulation point if it has more than 1 child
- if root_children > 1:
- yield start
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/connected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/connected.py
deleted file mode 100644
index 088a99e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/connected.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Connected components.
-"""
-__authors__ = "\n".join(['Eben Kenah',
- 'Aric Hagberg (hagberg@lanl.gov)'
- 'Christopher Ellison'])
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['number_connected_components',
- 'connected_components',
- 'connected_component_subgraphs',
- 'is_connected',
- 'node_connected_component',
- ]
-
-import networkx as nx
-
-def connected_components(G):
- """Return nodes in connected components of graph.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- comp : list of lists
- A list of nodes for each component of G.
-
- See Also
- --------
- strongly_connected_components
-
- Notes
- -----
- The list is ordered from largest connected component to smallest.
- For undirected graphs only.
- """
- if G.is_directed():
- raise nx.NetworkXError("""Not allowed for directed graph G.
- Use UG=G.to_undirected() to create an undirected graph.""")
- seen={}
- components=[]
- for v in G:
- if v not in seen:
- c=nx.single_source_shortest_path_length(G,v)
- components.append(list(c.keys()))
- seen.update(c)
- components.sort(key=len,reverse=True)
- return components
-
-
-def number_connected_components(G):
- """Return number of connected components in graph.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- n : integer
- Number of connected components
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- For undirected graphs only.
- """
- return len(connected_components(G))
-
-
-def is_connected(G):
- """Test graph connectivity.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- connected : bool
- True if the graph is connected, false otherwise.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> print(nx.is_connected(G))
- True
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- For undirected graphs only.
- """
- if G.is_directed():
- raise nx.NetworkXError(\
- """Not allowed for directed graph G.
-Use UG=G.to_undirected() to create an undirected graph.""")
-
- if len(G)==0:
- raise nx.NetworkXPointlessConcept(
- """Connectivity is undefined for the null graph.""")
-
- return len(nx.single_source_shortest_path_length(G,
- next(G.nodes_iter())))==len(G)
-
-
-def connected_component_subgraphs(G):
- """Return connected components as subgraphs.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- Returns
- -------
- glist : list
- A list of graphs, one for each connected component of G.
-
- Examples
- --------
- Get largest connected component as subgraph
-
- >>> G=nx.path_graph(4)
- >>> G.add_edge(5,6)
- >>> H=nx.connected_component_subgraphs(G)[0]
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- The list is ordered from largest connected component to smallest.
- For undirected graphs only.
-
- Graph, node, and edge attributes are copied to the subgraphs.
- """
- cc=connected_components(G)
- graph_list=[]
- for c in cc:
- graph_list.append(G.subgraph(c).copy())
- return graph_list
-
-
-def node_connected_component(G,n):
- """Return nodes in connected components of graph containing node n.
-
- Parameters
- ----------
- G : NetworkX Graph
- An undirected graph.
-
- n : node label
- A node in G
-
- Returns
- -------
- comp : lists
- A list of nodes in component of G containing node n.
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- For undirected graphs only.
- """
- if G.is_directed():
- raise nx.NetworkXError("""Not allowed for directed graph G.
- Use UG=G.to_undirected() to create an undirected graph.""")
- return list(nx.single_source_shortest_path_length(G,n).keys())
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/strongly_connected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/strongly_connected.py
deleted file mode 100644
index fbdec13..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/strongly_connected.py
+++ /dev/null
@@ -1,359 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Strongly connected components.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__authors__ = "\n".join(['Eben Kenah',
- 'Aric Hagberg (hagberg@lanl.gov)'
- 'Christopher Ellison',
- 'Ben Edwards (bedwards@cs.unm.edu)'])
-
-__all__ = ['number_strongly_connected_components',
- 'strongly_connected_components',
- 'strongly_connected_component_subgraphs',
- 'is_strongly_connected',
- 'strongly_connected_components_recursive',
- 'kosaraju_strongly_connected_components',
- 'condensation']
-
-def strongly_connected_components(G):
- """Return nodes in strongly connected components of graph.
-
- Parameters
- ----------
- G : NetworkX Graph
- An directed graph.
-
- Returns
- -------
- comp : list of lists
- A list of nodes for each component of G.
- The list is ordered from largest connected component to smallest.
-
- Raises
- ------
- NetworkXError: If G is undirected.
-
- See Also
- --------
- connected_components, weakly_connected_components
-
- Notes
- -----
- Uses Tarjan's algorithm with Nuutila's modifications.
- Nonrecursive version of algorithm.
-
- References
- ----------
- .. [1] Depth-first search and linear graph algorithms, R. Tarjan
- SIAM Journal of Computing 1(2):146-160, (1972).
-
- .. [2] On finding the strongly connected components in a directed graph.
- E. Nuutila and E. Soisalon-Soinen
- Information Processing Letters 49(1): 9-14, (1994)..
- """
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- Use connected_components() """)
- preorder={}
- lowlink={}
- scc_found={}
- scc_queue = []
- scc_list=[]
- i=0 # Preorder counter
- for source in G:
- if source not in scc_found:
- queue=[source]
- while queue:
- v=queue[-1]
- if v not in preorder:
- i=i+1
- preorder[v]=i
- done=1
- v_nbrs=G[v]
- for w in v_nbrs:
- if w not in preorder:
- queue.append(w)
- done=0
- break
- if done==1:
- lowlink[v]=preorder[v]
- for w in v_nbrs:
- if w not in scc_found:
- if preorder[w]>preorder[v]:
- lowlink[v]=min([lowlink[v],lowlink[w]])
- else:
- lowlink[v]=min([lowlink[v],preorder[w]])
- queue.pop()
- if lowlink[v]==preorder[v]:
- scc_found[v]=True
- scc=[v]
- while scc_queue and preorder[scc_queue[-1]]>preorder[v]:
- k=scc_queue.pop()
- scc_found[k]=True
- scc.append(k)
- scc_list.append(scc)
- else:
- scc_queue.append(v)
- scc_list.sort(key=len,reverse=True)
- return scc_list
-
-
-def kosaraju_strongly_connected_components(G,source=None):
- """Return nodes in strongly connected components of graph.
-
- Parameters
- ----------
- G : NetworkX Graph
- An directed graph.
-
- Returns
- -------
- comp : list of lists
- A list of nodes for each component of G.
- The list is ordered from largest connected component to smallest.
-
- Raises
- ------
- NetworkXError: If G is undirected
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- Uses Kosaraju's algorithm.
- """
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- Use connected_components() """)
- components=[]
- G=G.reverse(copy=False)
- post=list(nx.dfs_postorder_nodes(G,source=source))
- G=G.reverse(copy=False)
- seen={}
- while post:
- r=post.pop()
- if r in seen:
- continue
- c=nx.dfs_preorder_nodes(G,r)
- new=[v for v in c if v not in seen]
- seen.update([(u,True) for u in new])
- components.append(new)
- components.sort(key=len,reverse=True)
- return components
-
-
-def strongly_connected_components_recursive(G):
- """Return nodes in strongly connected components of graph.
-
- Recursive version of algorithm.
-
- Parameters
- ----------
- G : NetworkX Graph
- An directed graph.
-
- Returns
- -------
- comp : list of lists
- A list of nodes for each component of G.
- The list is ordered from largest connected component to smallest.
-
- Raises
- ------
- NetworkXError : If G is undirected
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- Uses Tarjan's algorithm with Nuutila's modifications.
-
- References
- ----------
- .. [1] Depth-first search and linear graph algorithms, R. Tarjan
- SIAM Journal of Computing 1(2):146-160, (1972).
-
- .. [2] On finding the strongly connected components in a directed graph.
- E. Nuutila and E. Soisalon-Soinen
- Information Processing Letters 49(1): 9-14, (1994)..
- """
- def visit(v,cnt):
- root[v]=cnt
- visited[v]=cnt
- cnt+=1
- stack.append(v)
- for w in G[v]:
- if w not in visited: visit(w,cnt)
- if w not in component:
- root[v]=min(root[v],root[w])
- if root[v]==visited[v]:
- component[v]=root[v]
- tmpc=[v] # hold nodes in this component
- while stack[-1]!=v:
- w=stack.pop()
- component[w]=root[v]
- tmpc.append(w)
- stack.remove(v)
- scc.append(tmpc) # add to scc list
-
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- Use connected_components() """)
-
- scc=[]
- visited={}
- component={}
- root={}
- cnt=0
- stack=[]
- for source in G:
- if source not in visited:
- visit(source,cnt)
-
- scc.sort(key=len,reverse=True)
- return scc
-
-
-def strongly_connected_component_subgraphs(G):
- """Return strongly connected components as subgraphs.
-
- Parameters
- ----------
- G : NetworkX Graph
- A graph.
-
- Returns
- -------
- glist : list
- A list of graphs, one for each strongly connected component of G.
-
- See Also
- --------
- connected_component_subgraphs
-
- Notes
- -----
- The list is ordered from largest strongly connected component to smallest.
-
- Graph, node, and edge attributes are copied to the subgraphs.
- """
- cc=strongly_connected_components(G)
- graph_list=[]
- for c in cc:
- graph_list.append(G.subgraph(c).copy())
- return graph_list
-
-
-def number_strongly_connected_components(G):
- """Return number of strongly connected components in graph.
-
- Parameters
- ----------
- G : NetworkX graph
- A directed graph.
-
- Returns
- -------
- n : integer
- Number of strongly connected components
-
- See Also
- --------
- connected_components
-
- Notes
- -----
- For directed graphs only.
- """
- return len(strongly_connected_components(G))
-
-
-def is_strongly_connected(G):
- """Test directed graph for strong connectivity.
-
- Parameters
- ----------
- G : NetworkX Graph
- A directed graph.
-
- Returns
- -------
- connected : bool
- True if the graph is strongly connected, False otherwise.
-
- See Also
- --------
- strongly_connected_components
-
- Notes
- -----
- For directed graphs only.
- """
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- See is_connected() for connectivity test.""")
-
- if len(G)==0:
- raise nx.NetworkXPointlessConcept(
- """Connectivity is undefined for the null graph.""")
-
- return len(strongly_connected_components(G)[0])==len(G)
-
-def condensation(G, scc=None):
- """Returns the condensation of G.
-
- The condensation of G is the graph with each of the strongly connected
- components contracted into a single node.
-
- Parameters
- ----------
- G : NetworkX DiGraph
- A directed graph.
-
- scc: list (optional, default=None)
- A list of strongly connected components. If provided, the elements in
- `scc` must partition the nodes in `G`. If not provided, it will be
- calculated as scc=nx.strongly_connected_components(G).
-
- Returns
- -------
- C : NetworkX DiGraph
- The condensation of G. The node labels are integers corresponding
- to the index of the component in the list of strongly connected
- components.
-
- Raises
- ------
- NetworkXError: If G is not directed
-
- Notes
- -----
- After contracting all strongly connected components to a single node,
- the resulting graph is a directed acyclic graph.
- """
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- See is_connected() for connectivity test.""")
- if scc is None:
- scc = nx.strongly_connected_components(G)
- mapping = {}
- C = nx.DiGraph()
- for i,component in enumerate(scc):
- for n in component:
- mapping[n] = i
- C.add_nodes_from(range(len(scc)))
- for u,v in G.edges():
- if mapping[u] != mapping[v]:
- C.add_edge(mapping[u],mapping[v])
- return C
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_attracting.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_attracting.py
deleted file mode 100644
index c2108dd..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_attracting.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-
-class TestAttractingComponents(object):
- def setUp(self):
- self.G1 = nx.DiGraph()
- self.G1.add_edges_from([(5,11),(11,2),(11,9),(11,10),
- (7,11),(7,8),(8,9),(3,8),(3,10)])
- self.G2 = nx.DiGraph()
- self.G2.add_edges_from([(0,1),(0,2),(1,1),(1,2),(2,1)])
-
- self.G3 = nx.DiGraph()
- self.G3.add_edges_from([(0,1),(1,2),(2,1),(0,3),(3,4),(4,3)])
-
- def test_attracting_components(self):
- ac = nx.attracting_components(self.G1)
- assert_true([2] in ac)
- assert_true([9] in ac)
- assert_true([10] in ac)
-
- ac = nx.attracting_components(self.G2)
- ac = [tuple(sorted(x)) for x in ac]
- assert_true(ac == [(1,2)])
-
- ac = nx.attracting_components(self.G3)
- ac = [tuple(sorted(x)) for x in ac]
- assert_true((1,2) in ac)
- assert_true((3,4) in ac)
- assert_equal(len(ac), 2)
-
- def test_number_attacting_components(self):
- assert_equal(len(nx.attracting_components(self.G1)), 3)
- assert_equal(len(nx.attracting_components(self.G2)), 1)
- assert_equal(len(nx.attracting_components(self.G3)), 2)
-
- def test_is_attracting_component(self):
- assert_false(nx.is_attracting_component(self.G1))
- assert_false(nx.is_attracting_component(self.G2))
- assert_false(nx.is_attracting_component(self.G3))
- g2 = self.G3.subgraph([1,2])
- assert_true(nx.is_attracting_component(g2))
-
- def test_attracting_component_subgraphs(self):
- subgraphs = nx.attracting_component_subgraphs(self.G1)
- for subgraph in subgraphs:
- assert_equal(len(subgraph), 1)
-
- self.G2.add_edge(1,2,eattr='red') # test attrs copied to subgraphs
- self.G2.node[2]['nattr']='blue'
- self.G2.graph['gattr']='green'
- subgraphs = nx.attracting_component_subgraphs(self.G2)
- assert_equal(len(subgraphs), 1)
- SG2=subgraphs[0]
- assert_true(1 in SG2)
- assert_true(2 in SG2)
- assert_equal(SG2[1][2]['eattr'],'red')
- assert_equal(SG2.node[2]['nattr'],'blue')
- assert_equal(SG2.graph['gattr'],'green')
- SG2.add_edge(1,2,eattr='blue')
- assert_equal(SG2[1][2]['eattr'],'blue')
- assert_equal(self.G2[1][2]['eattr'],'red')
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_biconnected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_biconnected.py
deleted file mode 100644
index 85f967a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_biconnected.py
+++ /dev/null
@@ -1,191 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx.algorithms.components import biconnected
-
-def assert_components_equal(x,y):
- sx = set((frozenset([frozenset(e) for e in c]) for c in x))
- sy = set((frozenset([frozenset(e) for e in c]) for c in y))
- assert_equal(sx,sy)
-
-def test_barbell():
- G=nx.barbell_graph(8,4)
- G.add_path([7,20,21,22])
- G.add_cycle([22,23,24,25])
- pts=set(biconnected.articulation_points(G))
- assert_equal(pts,set([7,8,9,10,11,12,20,21,22]))
-
- answer = [set([12, 13, 14, 15, 16, 17, 18, 19]),
- set([0, 1, 2, 3, 4, 5, 6, 7]),
- set([22, 23, 24, 25]),
- set([11, 12]),
- set([10, 11]),
- set([9, 10]),
- set([8, 9]),
- set([7, 8]),
- set([21, 22]),
- set([20, 21]),
- set([7, 20])]
- bcc=list(biconnected.biconnected_components(G))
- bcc.sort(key=len, reverse=True)
- assert_equal(bcc,answer)
-
- G.add_edge(2,17)
- pts=set(biconnected.articulation_points(G))
- assert_equal(pts,set([7,20,21,22]))
-
-def test_articulation_points_cycle():
- G=nx.cycle_graph(3)
- G.add_cycle([1,3,4])
- pts=set(biconnected.articulation_points(G))
- assert_equal(pts,set([1]))
-
-def test_is_biconnected():
- G=nx.cycle_graph(3)
- assert_true(biconnected.is_biconnected(G))
- G.add_cycle([1,3,4])
- assert_false(biconnected.is_biconnected(G))
-
-def test_empty_is_biconnected():
- G=nx.empty_graph(5)
- assert_false(biconnected.is_biconnected(G))
- G.add_edge(0,1)
- assert_false(biconnected.is_biconnected(G))
-
-def test_biconnected_components_cycle():
- G=nx.cycle_graph(3)
- G.add_cycle([1,3,4])
- pts = set(map(frozenset,biconnected.biconnected_components(G)))
- assert_equal(pts,set([frozenset([0,1,2]),frozenset([1,3,4])]))
-
-def test_biconnected_component_subgraphs_cycle():
- G=nx.cycle_graph(3)
- G.add_cycle([1,3,4,5])
- G.add_edge(1,3,eattr='red') # test copying of edge data
- G.node[1]['nattr']='blue'
- G.graph['gattr']='green'
- Gc = set(biconnected.biconnected_component_subgraphs(G))
- assert_equal(len(Gc),2)
- g1,g2=Gc
- if 0 in g1:
- assert_true(nx.is_isomorphic(g1,nx.Graph([(0,1),(0,2),(1,2)])))
- assert_true(nx.is_isomorphic(g2,nx.Graph([(1,3),(1,5),(3,4),(4,5)])))
- assert_equal(g2[1][3]['eattr'],'red')
- assert_equal(g2.node[1]['nattr'],'blue')
- assert_equal(g2.graph['gattr'],'green')
- g2[1][3]['eattr']='blue'
- assert_equal(g2[1][3]['eattr'],'blue')
- assert_equal(G[1][3]['eattr'],'red')
- else:
- assert_true(nx.is_isomorphic(g1,nx.Graph([(1,3),(1,5),(3,4),(4,5)])))
- assert_true(nx.is_isomorphic(g2,nx.Graph([(0,1),(0,2),(1,2)])))
- assert_equal(g1[1][3]['eattr'],'red')
- assert_equal(g1.node[1]['nattr'],'blue')
- assert_equal(g1.graph['gattr'],'green')
- g1[1][3]['eattr']='blue'
- assert_equal(g1[1][3]['eattr'],'blue')
- assert_equal(G[1][3]['eattr'],'red')
-
-
-def test_biconnected_components1():
- # graph example from
- # http://www.ibluemojo.com/school/articul_algorithm.html
- edges=[(0,1),
- (0,5),
- (0,6),
- (0,14),
- (1,5),
- (1,6),
- (1,14),
- (2,4),
- (2,10),
- (3,4),
- (3,15),
- (4,6),
- (4,7),
- (4,10),
- (5,14),
- (6,14),
- (7,9),
- (8,9),
- (8,12),
- (8,13),
- (10,15),
- (11,12),
- (11,13),
- (12,13)]
- G=nx.Graph(edges)
- pts = set(biconnected.articulation_points(G))
- assert_equal(pts,set([4,6,7,8,9]))
- comps = list(biconnected.biconnected_component_edges(G))
- answer = [
- [(3,4),(15,3),(10,15),(10,4),(2,10),(4,2)],
- [(13,12),(13,8),(11,13),(12,11),(8,12)],
- [(9,8)],
- [(7,9)],
- [(4,7)],
- [(6,4)],
- [(14,0),(5,1),(5,0),(14,5),(14,1),(6,14),(6,0),(1,6),(0,1)],
- ]
- assert_components_equal(comps,answer)
-
-def test_biconnected_components2():
- G=nx.Graph()
- G.add_cycle('ABC')
- G.add_cycle('CDE')
- G.add_cycle('FIJHG')
- G.add_cycle('GIJ')
- G.add_edge('E','G')
- comps = list(biconnected.biconnected_component_edges(G))
- answer = [
- [tuple('GF'),tuple('FI'),tuple('IG'),tuple('IJ'),tuple('JG'),tuple('JH'),tuple('HG')],
- [tuple('EG')],
- [tuple('CD'),tuple('DE'),tuple('CE')],
- [tuple('AB'),tuple('BC'),tuple('AC')]
- ]
- assert_components_equal(comps,answer)
-
-def test_biconnected_davis():
- D = nx.davis_southern_women_graph()
- bcc = list(biconnected.biconnected_components(D))[0]
- assert_true(set(D) == bcc) # All nodes in a giant bicomponent
- # So no articulation points
- assert_equal(list(biconnected.articulation_points(D)),[])
-
-def test_biconnected_karate():
- K = nx.karate_club_graph()
- answer = [set([0, 1, 2, 3, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19,
- 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]),
- set([0, 4, 5, 6, 10, 16]),
- set([0, 11])]
- bcc = list(biconnected.biconnected_components(K))
- bcc.sort(key=len, reverse=True)
- assert_true(list(biconnected.biconnected_components(K)) == answer)
- assert_equal(list(biconnected.articulation_points(K)),[0])
-
-def test_biconnected_eppstein():
- # tests from http://www.ics.uci.edu/~eppstein/PADS/Biconnectivity.py
- G1 = nx.Graph({
- 0: [1,2,5],
- 1: [0,5],
- 2: [0,3,4],
- 3: [2,4,5,6],
- 4: [2,3,5,6],
- 5: [0,1,3,4],
- 6: [3,4]})
- G2 = nx.Graph({
- 0: [2,5],
- 1: [3,8],
- 2: [0,3,5],
- 3: [1,2,6,8],
- 4: [7],
- 5: [0,2],
- 6: [3,8],
- 7: [4],
- 8: [1,3,6]})
- assert_true(biconnected.is_biconnected(G1))
- assert_false(biconnected.is_biconnected(G2))
- answer_G2 = [set([1, 3, 6, 8]), set([0, 2, 5]), set([2, 3]), set([4, 7])]
- bcc = list(biconnected.biconnected_components(G2))
- bcc.sort(key=len, reverse=True)
- assert_equal(bcc, answer_G2)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_connected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_connected.py
deleted file mode 100644
index ae0247b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_connected.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx import convert_node_labels_to_integers as cnlti
-from networkx import NetworkXError
-
-class TestConnected:
-
- def setUp(self):
- G1=cnlti(nx.grid_2d_graph(2,2),first_label=0,ordering="sorted")
- G2=cnlti(nx.lollipop_graph(3,3),first_label=4,ordering="sorted")
- G3=cnlti(nx.house_graph(),first_label=10,ordering="sorted")
- self.G=nx.union(G1,G2)
- self.G=nx.union(self.G,G3)
- self.DG=nx.DiGraph([(1,2),(1,3),(2,3)])
- self.grid=cnlti(nx.grid_2d_graph(4,4),first_label=1)
-
- def test_connected_components(self):
- cc=nx.connected_components
- G=self.G
- C=[[0, 1, 2, 3], [4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]
- assert_equal(sorted([sorted(g) for g in cc(G)]),sorted(C))
-
- def test_number_connected_components(self):
- ncc=nx.number_connected_components
- assert_equal(ncc(self.G),3)
-
- def test_number_connected_components2(self):
- ncc=nx.number_connected_components
- assert_equal(ncc(self.grid),1)
-
- def test_connected_components2(self):
- cc=nx.connected_components
- G=self.grid
- C=[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
- assert_equal(sorted([sorted(g) for g in cc(G)]),sorted(C))
-
- def test_node_connected_components(self):
- ncc=nx.node_connected_component
- G=self.grid
- C=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
- assert_equal(sorted(ncc(G,1)),sorted(C))
-
- def test_connected_component_subgraphs(self):
- G=self.grid
- G.add_edge(1,2,eattr='red') # test attributes copied to subgraphs
- G.node[1]['nattr']='blue'
- G.graph['gattr']='green'
- ccs=nx.connected_component_subgraphs(G)
- assert_equal(len(ccs),1)
- sg=ccs[0]
- assert_equal(sorted(sg.nodes()),list(range(1,17)))
- assert_equal(sg[1][2]['eattr'],'red')
- assert_equal(sg.node[1]['nattr'],'blue')
- assert_equal(sg.graph['gattr'],'green')
- sg[1][2]['eattr']='blue'
- assert_equal(G[1][2]['eattr'],'red')
- assert_equal(sg[1][2]['eattr'],'blue')
-
-
- def test_is_connected(self):
- assert_true(nx.is_connected(self.grid))
- G=nx.Graph()
- G.add_nodes_from([1,2])
- assert_false(nx.is_connected(G))
-
- def test_connected_raise(self):
- assert_raises(NetworkXError,nx.connected_components,self.DG)
- assert_raises(NetworkXError,nx.number_connected_components,self.DG)
- assert_raises(NetworkXError,nx.connected_component_subgraphs,self.DG)
- assert_raises(NetworkXError,nx.node_connected_component,self.DG,1)
- assert_raises(NetworkXError,nx.is_connected,self.DG)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_strongly_connected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_strongly_connected.py
deleted file mode 100644
index d2569a9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_strongly_connected.py
+++ /dev/null
@@ -1,138 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx import NetworkXError
-
-class TestStronglyConnected:
-
- def setUp(self):
- self.gc=[]
- G=nx.DiGraph()
- G.add_edges_from([(1,2),(2,3),(2,8),(3,4),(3,7),
- (4,5),(5,3),(5,6),(7,4),(7,6),(8,1),(8,7)])
- C=[[3, 4, 5, 7], [1, 2, 8], [6]]
- self.gc.append((G,C))
-
- G= nx.DiGraph()
- G.add_edges_from([(1,2),(1,3),(1,4),(4,2),(3,4),(2,3)])
- C = [[2, 3, 4],[1]]
- self.gc.append((G,C))
-
- G = nx.DiGraph()
- G.add_edges_from([(1,2),(2,3),(3,2),(2,1)])
- C = [[1, 2, 3]]
- self.gc.append((G,C))
-
- # Eppstein's tests
- G = nx.DiGraph({ 0:[1],1:[2,3],2:[4,5],3:[4,5],4:[6],5:[],6:[]})
- C = [[0],[1],[2],[3],[4],[5],[6]]
- self.gc.append((G,C))
-
- G = nx.DiGraph({0:[1],1:[2,3,4],2:[0,3],3:[4],4:[3]})
- C = [[0,1,2],[3,4]]
- self.gc.append((G,C))
-
-
- def test_tarjan(self):
- scc=nx.strongly_connected_components
- for G,C in self.gc:
- assert_equal(sorted([sorted(g) for g in scc(G)]),sorted(C))
-
-
- def test_tarjan_recursive(self):
- scc=nx.strongly_connected_components_recursive
- for G,C in self.gc:
- assert_equal(sorted([sorted(g) for g in scc(G)]),sorted(C))
-
-
- def test_kosaraju(self):
- scc=nx.kosaraju_strongly_connected_components
- for G,C in self.gc:
- assert_equal(sorted([sorted(g) for g in scc(G)]),sorted(C))
-
- def test_number_strongly_connected_components(self):
- ncc=nx.number_strongly_connected_components
- for G,C in self.gc:
- assert_equal(ncc(G),len(C))
-
- def test_is_strongly_connected(self):
- for G,C in self.gc:
- if len(C)==1:
- assert_true(nx.is_strongly_connected(G))
- else:
- assert_false(nx.is_strongly_connected(G))
-
-
- def test_strongly_connected_component_subgraphs(self):
- scc=nx.strongly_connected_component_subgraphs
- for G,C in self.gc:
- assert_equal(sorted([sorted(g.nodes()) for g in scc(G)]),sorted(C))
- G,C=self.gc[0]
- G.add_edge(1,2,eattr='red')
- G.node[1]['nattr']='blue'
- G.graph['gattr']='green'
- sgs=scc(G)[1]
- assert_equal(sgs[1][2]['eattr'],'red')
- assert_equal(sgs.node[1]['nattr'],'blue')
- assert_equal(sgs.graph['gattr'],'green')
- sgs[1][2]['eattr']='blue'
- assert_equal(G[1][2]['eattr'],'red')
- assert_equal(sgs[1][2]['eattr'],'blue')
-
- def test_contract_scc1(self):
- G = nx.DiGraph()
- G.add_edges_from([(1,2),(2,3),(2,11),(2,12),(3,4),(4,3),(4,5),
- (5,6),(6,5),(6,7),(7,8),(7,9),(7,10),(8,9),
- (9,7),(10,6),(11,2),(11,4),(11,6),(12,6),(12,11)])
- scc = nx.strongly_connected_components(G)
- cG = nx.condensation(G, scc)
- # DAG
- assert_true(nx.is_directed_acyclic_graph(cG))
- # # nodes
- assert_equal(sorted(cG.nodes()),[0,1,2,3])
- # # edges
- mapping={}
- for i,component in enumerate(scc):
- for n in component:
- mapping[n] = i
- edge=(mapping[2],mapping[3])
- assert_true(cG.has_edge(*edge))
- edge=(mapping[2],mapping[5])
- assert_true(cG.has_edge(*edge))
- edge=(mapping[3],mapping[5])
- assert_true(cG.has_edge(*edge))
-
- def test_contract_scc_isolate(self):
- # Bug found and fixed in [1687].
- G = nx.DiGraph()
- G.add_edge(1,2)
- G.add_edge(2,1)
- scc = nx.strongly_connected_components(G)
- cG = nx.condensation(G, scc)
- assert_equal(cG.nodes(),[0])
- assert_equal(cG.edges(),[])
-
- def test_contract_scc_edge(self):
- G = nx.DiGraph()
- G.add_edge(1,2)
- G.add_edge(2,1)
- G.add_edge(2,3)
- G.add_edge(3,4)
- G.add_edge(4,3)
- scc = nx.strongly_connected_components(G)
- cG = nx.condensation(G, scc)
- assert_equal(cG.nodes(),[0,1])
- if 1 in scc[0]:
- edge = (0,1)
- else:
- edge = (1,0)
- assert_equal(cG.edges(),[edge])
-
- def test_connected_raise(self):
- G=nx.Graph()
- assert_raises(NetworkXError,nx.strongly_connected_components,G)
- assert_raises(NetworkXError,nx.kosaraju_strongly_connected_components,G)
- assert_raises(NetworkXError,nx.strongly_connected_components_recursive,G)
- assert_raises(NetworkXError,nx.strongly_connected_component_subgraphs,G)
- assert_raises(NetworkXError,nx.is_strongly_connected,G)
- assert_raises(NetworkXError,nx.condensation,G)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_weakly_connected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_weakly_connected.py
deleted file mode 100644
index d05e8ed..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/test_weakly_connected.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx import NetworkXError
-
-class TestWeaklyConnected:
-
- def setUp(self):
- self.gc=[]
- G=nx.DiGraph()
- G.add_edges_from([(1,2),(2,3),(2,8),(3,4),(3,7),
- (4,5),(5,3),(5,6),(7,4),(7,6),(8,1),(8,7)])
- C=[[3, 4, 5, 7], [1, 2, 8], [6]]
- self.gc.append((G,C))
-
- G= nx.DiGraph()
- G.add_edges_from([(1,2),(1,3),(1,4),(4,2),(3,4),(2,3)])
- C = [[2, 3, 4],[1]]
- self.gc.append((G,C))
-
- G = nx.DiGraph()
- G.add_edges_from([(1,2),(2,3),(3,2),(2,1)])
- C = [[1, 2, 3]]
- self.gc.append((G,C))
-
- # Eppstein's tests
- G = nx.DiGraph({ 0:[1],1:[2,3],2:[4,5],3:[4,5],4:[6],5:[],6:[]})
- C = [[0],[1],[2],[3],[4],[5],[6]]
- self.gc.append((G,C))
-
- G = nx.DiGraph({0:[1],1:[2,3,4],2:[0,3],3:[4],4:[3]})
- C = [[0,1,2],[3,4]]
- self.gc.append((G,C))
-
-
- def test_weakly_connected_components(self):
- wcc=nx.weakly_connected_components
- cc=nx.connected_components
- for G,C in self.gc:
- U=G.to_undirected()
- w=sorted([sorted(g) for g in wcc(G)])
- c=sorted([sorted(g) for g in cc(U)])
- assert_equal(w,c)
-
- def test_number_weakly_connected_components(self):
- wcc=nx.number_weakly_connected_components
- cc=nx.number_connected_components
- for G,C in self.gc:
- U=G.to_undirected()
- w=wcc(G)
- c=cc(U)
- assert_equal(w,c)
-
- def test_weakly_connected_component_subgraphs(self):
- wcc=nx.weakly_connected_component_subgraphs
- cc=nx.connected_component_subgraphs
- for G,C in self.gc:
- U=G.to_undirected()
- w=sorted([sorted(g.nodes()) for g in wcc(G)])
- c=sorted([sorted(g.nodes()) for g in cc(U)])
- assert_equal(w,c)
- G,C=self.gc[0]
- G.add_edge(1,2,eattr='red')
- G.node[1]['nattr']='blue'
- G.graph['gattr']='green'
- sgs=wcc(G)[0]
- assert_equal(sgs[1][2]['eattr'],'red')
- assert_equal(sgs.node[1]['nattr'],'blue')
- assert_equal(sgs.graph['gattr'],'green')
- sgs[1][2]['eattr']='blue'
- assert_equal(G[1][2]['eattr'],'red')
- assert_equal(sgs[1][2]['eattr'],'blue')
-
- def test_is_weakly_connected(self):
- wcc=nx.is_weakly_connected
- cc=nx.is_connected
- for G,C in self.gc:
- U=G.to_undirected()
- assert_equal(wcc(G),cc(U))
-
-
- def test_connected_raise(self):
- G=nx.Graph()
- assert_raises(NetworkXError,nx.weakly_connected_components,G)
- assert_raises(NetworkXError,nx.number_weakly_connected_components,G)
- assert_raises(NetworkXError,nx.weakly_connected_component_subgraphs,G)
- assert_raises(NetworkXError,nx.is_weakly_connected,G)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/weakly_connected.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/weakly_connected.py
deleted file mode 100644
index 410a6c2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/weakly_connected.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Weakly connected components.
-"""
-__authors__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)'
- 'Christopher Ellison'])
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['number_weakly_connected_components',
- 'weakly_connected_components',
- 'weakly_connected_component_subgraphs',
- 'is_weakly_connected'
- ]
-
-import networkx as nx
-
-def weakly_connected_components(G):
- """Return weakly connected components of G.
- """
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- Use connected_components() """)
- seen={}
- components=[]
- for v in G:
- if v not in seen:
- c=_single_source_shortest_unipath_length(G,v)
- components.append(list(c.keys()))
- seen.update(c)
- components.sort(key=len,reverse=True)
- return components
-
-
-def number_weakly_connected_components(G):
- """Return the number of connected components in G.
- For directed graphs only.
- """
- return len(weakly_connected_components(G))
-
-def weakly_connected_component_subgraphs(G):
- """Return weakly connected components as subgraphs.
-
- Graph, node, and edge attributes are copied to the subgraphs.
- """
- wcc=weakly_connected_components(G)
- graph_list=[]
- for c in wcc:
- graph_list.append(G.subgraph(c).copy())
- return graph_list
-
-def is_weakly_connected(G):
- """Test directed graph for weak connectivity.
-
- Parameters
- ----------
- G : NetworkX Graph
- A directed graph.
-
- Returns
- -------
- connected : bool
- True if the graph is weakly connected, False otherwise.
-
- See Also
- --------
- strongly_connected_components
-
- Notes
- -----
- For directed graphs only.
- """
- if not G.is_directed():
- raise nx.NetworkXError("""Not allowed for undirected graph G.
- See is_connected() for connectivity test.""")
-
- if len(G)==0:
- raise nx.NetworkXPointlessConcept(
- """Connectivity is undefined for the null graph.""")
-
- return len(weakly_connected_components(G)[0])==len(G)
-
-def _single_source_shortest_unipath_length(G,source,cutoff=None):
- """Compute the shortest path lengths from source to all reachable nodes.
-
- The direction of the edge between nodes is ignored.
-
- For directed graphs only.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path
-
- cutoff : integer, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- lengths : dictionary
- Dictionary of shortest path lengths keyed by target.
- """
- # namespace speedups
- Gsucc = G.succ
- Gpred = G.pred
-
- seen={} # level (number of hops) when seen in BFS
- level=0 # the current level
- nextlevel = set([source]) # set of nodes to check at next level
- while nextlevel:
- thislevel=nextlevel # advance to next level
- nextlevel = set() # and start a new list (fringe)
- for v in thislevel:
- if v not in seen:
- seen[v]=level # set the level of vertex v
- nextlevel.update(Gsucc[v]) # add successors of v
- nextlevel.update(Gpred[v]) # add predecessors of v
- if (cutoff is not None and cutoff <= level): break
- level=level+1
- return seen # return all path lengths as dictionary
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/__init__.py
deleted file mode 100644
index d14c33e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""Flow based connectivity and cut algorithms
-"""
-from networkx.algorithms.connectivity.connectivity import *
-from networkx.algorithms.connectivity.cuts import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/connectivity.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/connectivity.py
deleted file mode 100644
index bf6f272..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/connectivity.py
+++ /dev/null
@@ -1,607 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Flow based connectivity algorithms
-"""
-import itertools
-import networkx as nx
-
-__author__ = '\n'.join(['Jordi Torrents <jtorrents@milnou.net>'])
-
-__all__ = [ 'average_node_connectivity',
- 'local_node_connectivity',
- 'node_connectivity',
- 'local_edge_connectivity',
- 'edge_connectivity',
- 'all_pairs_node_connectivity_matrix',
- 'dominating_set',
- ]
-
-def average_node_connectivity(G):
- r"""Returns the average connectivity of a graph G.
-
- The average connectivity `\bar{\kappa}` of a graph G is the average
- of local node connectivity over all pairs of nodes of G [1]_ .
-
- .. math::
-
- \bar{\kappa}(G) = \frac{\sum_{u,v} \kappa_{G}(u,v)}{{n \choose 2}}
-
- Parameters
- ----------
-
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- K : float
- Average node connectivity
-
- See also
- --------
- local_node_connectivity
- node_connectivity
- local_edge_connectivity
- edge_connectivity
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Beineke, L., O. Oellermann, and R. Pippert (2002). The average
- connectivity of a graph. Discrete mathematics 252(1-3), 31-45.
- http://www.sciencedirect.com/science/article/pii/S0012365X01001807
-
- """
- if G.is_directed():
- iter_func = itertools.permutations
- else:
- iter_func = itertools.combinations
-
- H, mapping = _aux_digraph_node_connectivity(G)
- num = 0.
- den = 0.
- for u,v in iter_func(G, 2):
- den += 1
- num += local_node_connectivity(G, u, v, aux_digraph=H, mapping=mapping)
-
- if den == 0: # Null Graph
- return 0
- return num/den
-
-def _aux_digraph_node_connectivity(G):
- r""" Creates a directed graph D from an undirected graph G to compute flow
- based node connectivity.
-
- For an undirected graph G having `n` nodes and `m` edges we derive a
- directed graph D with 2n nodes and 2m+n arcs by replacing each
- original node `v` with two nodes `vA`,`vB` linked by an (internal)
- arc in D. Then for each edge (u,v) in G we add two arcs (uB,vA)
- and (vB,uA) in D. Finally we set the attribute capacity = 1 for each
- arc in D [1].
-
- For a directed graph having `n` nodes and `m` arcs we derive a
- directed graph D with 2n nodes and m+n arcs by replacing each
- original node `v` with two nodes `vA`,`vB` linked by an (internal)
- arc `(vA,vB)` in D. Then for each arc (u,v) in G we add one arc (uB,vA)
- in D. Finally we set the attribute capacity = 1 for each arc in D.
-
- References
- ----------
- .. [1] Kammer, Frank and Hanjo Taubig. Graph Connectivity. in Brandes and
- Erlebach, 'Network Analysis: Methodological Foundations', Lecture
- Notes in Computer Science, Volume 3418, Springer-Verlag, 2005.
- http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
-
- """
- directed = G.is_directed()
-
- mapping = {}
- D = nx.DiGraph()
- for i,node in enumerate(G):
- mapping[node] = i
- D.add_node('%dA' % i,id=node)
- D.add_node('%dB' % i,id=node)
- D.add_edge('%dA' % i, '%dB' % i, capacity=1)
-
- edges = []
- for (source, target) in G.edges():
- edges.append(('%sB' % mapping[source], '%sA' % mapping[target]))
- if not directed:
- edges.append(('%sB' % mapping[target], '%sA' % mapping[source]))
-
- D.add_edges_from(edges, capacity=1)
- return D, mapping
-
-def local_node_connectivity(G, s, t, aux_digraph=None, mapping=None):
- r"""Computes local node connectivity for nodes s and t.
-
- Local node connectivity for two non adjacent nodes s and t is the
- minimum number of nodes that must be removed (along with their incident
- edges) to disconnect them.
-
- This is a flow based implementation of node connectivity. We compute the
- maximum flow on an auxiliary digraph build from the original input
- graph (see below for details). This is equal to the local node
- connectivity because the value of a maximum s-t-flow is equal to the
- capacity of a minimum s-t-cut (Ford and Fulkerson theorem) [1]_ .
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- s : node
- Source node
-
- t : node
- Target node
-
- aux_digraph : NetworkX DiGraph (default=None)
- Auxiliary digraph to compute flow based node connectivity. If None
- the auxiliary digraph is build.
-
- mapping : dict (default=None)
- Dictionary with a mapping of node names in G and in the auxiliary digraph.
-
- Returns
- -------
- K : integer
- local node connectivity for nodes s and t
-
- Examples
- --------
- >>> # Platonic icosahedral graph has node connectivity 5
- >>> # for each non adjacent node pair
- >>> G = nx.icosahedral_graph()
- >>> nx.local_node_connectivity(G,0,6)
- 5
-
- Notes
- -----
- This is a flow based implementation of node connectivity. We compute the
- maximum flow using the Ford and Fulkerson algorithm on an auxiliary digraph
- build from the original input graph:
-
- For an undirected graph G having `n` nodes and `m` edges we derive a
- directed graph D with 2n nodes and 2m+n arcs by replacing each
- original node `v` with two nodes `v_A`, `v_B` linked by an (internal)
- arc in `D`. Then for each edge (`u`, `v`) in G we add two arcs
- (`u_B`, `v_A`) and (`v_B`, `u_A`) in `D`. Finally we set the attribute
- capacity = 1 for each arc in `D` [1]_ .
-
- For a directed graph G having `n` nodes and `m` arcs we derive a
- directed graph `D` with `2n` nodes and `m+n` arcs by replacing each
- original node `v` with two nodes `v_A`, `v_B` linked by an (internal)
- arc `(v_A, v_B)` in D. Then for each arc `(u,v)` in G we add one arc
- `(u_B,v_A)` in `D`. Finally we set the attribute capacity = 1 for
- each arc in `D`.
-
- This is equal to the local node connectivity because the value of
- a maximum s-t-flow is equal to the capacity of a minimum s-t-cut (Ford
- and Fulkerson theorem).
-
- See also
- --------
- node_connectivity
- all_pairs_node_connectivity_matrix
- local_edge_connectivity
- edge_connectivity
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Kammer, Frank and Hanjo Taubig. Graph Connectivity. in Brandes and
- Erlebach, 'Network Analysis: Methodological Foundations', Lecture
- Notes in Computer Science, Volume 3418, Springer-Verlag, 2005.
- http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
-
- """
- if aux_digraph is None or mapping is None:
- H, mapping = _aux_digraph_node_connectivity(G)
- else:
- H = aux_digraph
- return nx.max_flow(H,'%sB' % mapping[s], '%sA' % mapping[t])
-
-def node_connectivity(G, s=None, t=None):
- r"""Returns node connectivity for a graph or digraph G.
-
- Node connectivity is equal to the minimum number of nodes that
- must be removed to disconnect G or render it trivial. If source
- and target nodes are provided, this function returns the local node
- connectivity: the minimum number of nodes that must be removed to break
- all paths from source to target in G.
-
- This is a flow based implementation. The algorithm is based in
- solving a number of max-flow problems (ie local st-node connectivity,
- see local_node_connectivity) to determine the capacity of the
- minimum cut on an auxiliary directed network that corresponds to the
- minimum node cut of G. It handles both directed and undirected graphs.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- s : node
- Source node. Optional (default=None)
-
- t : node
- Target node. Optional (default=None)
-
- Returns
- -------
- K : integer
- Node connectivity of G, or local node connectivity if source
- and target were provided
-
- Examples
- --------
- >>> # Platonic icosahedral graph is 5-node-connected
- >>> G = nx.icosahedral_graph()
- >>> nx.node_connectivity(G)
- 5
- >>> nx.node_connectivity(G, 3, 7)
- 5
-
- Notes
- -----
- This is a flow based implementation of node connectivity. The
- algorithm works by solving `O((n-\delta-1+\delta(\delta-1)/2)` max-flow
- problems on an auxiliary digraph. Where `\delta` is the minimum degree
- of G. For details about the auxiliary digraph and the computation of
- local node connectivity see local_node_connectivity.
-
- This implementation is based on algorithm 11 in [1]_. We use the Ford
- and Fulkerson algorithm to compute max flow (see ford_fulkerson).
-
- See also
- --------
- local_node_connectivity
- all_pairs_node_connectivity_matrix
- local_edge_connectivity
- edge_connectivity
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-
- """
- # Local node connectivity
- if s is not None and t is not None:
- if s not in G:
- raise nx.NetworkXError('node %s not in graph' % s)
- if t not in G:
- raise nx.NetworkXError('node %s not in graph' % t)
- return local_node_connectivity(G, s, t)
- # Global node connectivity
- if G.is_directed():
- if not nx.is_weakly_connected(G):
- return 0
- iter_func = itertools.permutations
- # I think that it is necessary to consider both predecessors
- # and successors for directed graphs
- def neighbors(v):
- return itertools.chain.from_iterable([G.predecessors_iter(v),
- G.successors_iter(v)])
- else:
- if not nx.is_connected(G):
- return 0
- iter_func = itertools.combinations
- neighbors = G.neighbors_iter
- # Initial guess \kappa = n - 1
- K = G.order()-1
- deg = G.degree()
- min_deg = min(deg.values())
- v = next(n for n,d in deg.items() if d==min_deg)
- # Reuse the auxiliary digraph
- H, mapping = _aux_digraph_node_connectivity(G)
- # compute local node connectivity with all non-neighbors nodes
- for w in set(G) - set(neighbors(v)) - set([v]):
- K = min(K, local_node_connectivity(G, v, w,
- aux_digraph=H, mapping=mapping))
- # Same for non adjacent pairs of neighbors of v
- for x,y in iter_func(neighbors(v), 2):
- if y in G[x]: continue
- K = min(K, local_node_connectivity(G, x, y,
- aux_digraph=H, mapping=mapping))
- return K
-
-def all_pairs_node_connectivity_matrix(G):
- """Return a numpy 2d ndarray with node connectivity between all pairs
- of nodes.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- K : 2d numpy ndarray
- node connectivity between all pairs of nodes.
-
- See also
- --------
- local_node_connectivity
- node_connectivity
- local_edge_connectivity
- edge_connectivity
- max_flow
- ford_fulkerson
-
- """
- try:
- import numpy
- except ImportError:
- raise ImportError(\
- "all_pairs_node_connectivity_matrix() requires NumPy")
-
- n = G.order()
- M = numpy.zeros((n, n), dtype=int)
- # Create auxiliary Digraph
- D, mapping = _aux_digraph_node_connectivity(G)
-
- if G.is_directed():
- for u, v in itertools.permutations(G, 2):
- K = local_node_connectivity(G, u, v, aux_digraph=D, mapping=mapping)
- M[mapping[u],mapping[v]] = K
- else:
- for u, v in itertools.combinations(G, 2):
- K = local_node_connectivity(G, u, v, aux_digraph=D, mapping=mapping)
- M[mapping[u],mapping[v]] = M[mapping[v],mapping[u]] = K
-
- return M
-
-def _aux_digraph_edge_connectivity(G):
- """Auxiliary digraph for computing flow based edge connectivity
-
- If the input graph is undirected, we replace each edge (u,v) with
- two reciprocal arcs (u,v) and (v,u) and then we set the attribute
- 'capacity' for each arc to 1. If the input graph is directed we simply
- add the 'capacity' attribute. Part of algorithm 1 in [1]_ .
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms. (this is a
- chapter, look for the reference of the book).
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
- """
- if G.is_directed():
- if nx.get_edge_attributes(G, 'capacity'):
- return G
- D = G.copy()
- capacity = dict((e,1) for e in D.edges())
- nx.set_edge_attributes(D, 'capacity', capacity)
- return D
- else:
- D = G.to_directed()
- capacity = dict((e,1) for e in D.edges())
- nx.set_edge_attributes(D, 'capacity', capacity)
- return D
-
-def local_edge_connectivity(G, u, v, aux_digraph=None):
- r"""Returns local edge connectivity for nodes s and t in G.
-
- Local edge connectivity for two nodes s and t is the minimum number
- of edges that must be removed to disconnect them.
-
- This is a flow based implementation of edge connectivity. We compute the
- maximum flow on an auxiliary digraph build from the original
- network (see below for details). This is equal to the local edge
- connectivity because the value of a maximum s-t-flow is equal to the
- capacity of a minimum s-t-cut (Ford and Fulkerson theorem) [1]_ .
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected or directed graph
-
- s : node
- Source node
-
- t : node
- Target node
-
- aux_digraph : NetworkX DiGraph (default=None)
- Auxiliary digraph to compute flow based edge connectivity. If None
- the auxiliary digraph is build.
-
- Returns
- -------
- K : integer
- local edge connectivity for nodes s and t
-
- Examples
- --------
- >>> # Platonic icosahedral graph has edge connectivity 5
- >>> # for each non adjacent node pair
- >>> G = nx.icosahedral_graph()
- >>> nx.local_edge_connectivity(G,0,6)
- 5
-
- Notes
- -----
- This is a flow based implementation of edge connectivity. We compute the
- maximum flow using the Ford and Fulkerson algorithm on an auxiliary digraph
- build from the original graph:
-
- If the input graph is undirected, we replace each edge (u,v) with
- two reciprocal arcs `(u,v)` and `(v,u)` and then we set the attribute
- 'capacity' for each arc to 1. If the input graph is directed we simply
- add the 'capacity' attribute. This is an implementation of algorithm 1
- in [1]_.
-
- The maximum flow in the auxiliary network is equal to the local edge
- connectivity because the value of a maximum s-t-flow is equal to the
- capacity of a minimum s-t-cut (Ford and Fulkerson theorem).
-
- See also
- --------
- local_node_connectivity
- node_connectivity
- edge_connectivity
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-
- """
- if aux_digraph is None:
- H = _aux_digraph_edge_connectivity(G)
- else:
- H = aux_digraph
- return nx.max_flow(H, u, v)
-
-def edge_connectivity(G, s=None, t=None):
- r"""Returns the edge connectivity of the graph or digraph G.
-
- The edge connectivity is equal to the minimum number of edges that
- must be removed to disconnect G or render it trivial. If source
- and target nodes are provided, this function returns the local edge
- connectivity: the minimum number of edges that must be removed to
- break all paths from source to target in G.
-
- This is a flow based implementation. The algorithm is based in solving
- a number of max-flow problems (ie local st-edge connectivity, see
- local_edge_connectivity) to determine the capacity of the minimum
- cut on an auxiliary directed network that corresponds to the minimum
- edge cut of G. It handles both directed and undirected graphs.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected or directed graph
-
- s : node
- Source node. Optional (default=None)
-
- t : node
- Target node. Optional (default=None)
-
- Returns
- -------
- K : integer
- Edge connectivity for G, or local edge connectivity if source
- and target were provided
-
- Examples
- --------
- >>> # Platonic icosahedral graph is 5-edge-connected
- >>> G = nx.icosahedral_graph()
- >>> nx.edge_connectivity(G)
- 5
-
- Notes
- -----
- This is a flow based implementation of global edge connectivity. For
- undirected graphs the algorithm works by finding a 'small' dominating
- set of nodes of G (see algorithm 7 in [1]_ ) and computing local max flow
- (see local_edge_connectivity) between an arbitrary node in the dominating
- set and the rest of nodes in it. This is an implementation of
- algorithm 6 in [1]_ .
-
- For directed graphs, the algorithm does n calls to the max flow function.
- This is an implementation of algorithm 8 in [1]_ . We use the Ford and
- Fulkerson algorithm to compute max flow (see ford_fulkerson).
-
- See also
- --------
- local_node_connectivity
- node_connectivity
- local_edge_connectivity
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-
- """
- # Local edge connectivity
- if s is not None and t is not None:
- if s not in G:
- raise nx.NetworkXError('node %s not in graph' % s)
- if t not in G:
- raise nx.NetworkXError('node %s not in graph' % t)
- return local_edge_connectivity(G, s, t)
- # Global edge connectivity
- if G.is_directed():
- # Algorithm 8 in [1]
- if not nx.is_weakly_connected(G):
- return 0
- # initial value for lambda is min degree (\delta(G))
- L = min(G.degree().values())
- # reuse auxiliary digraph
- H = _aux_digraph_edge_connectivity(G)
- nodes = G.nodes()
- n = len(nodes)
- for i in range(n):
- try:
- L = min(L, local_edge_connectivity(G, nodes[i],
- nodes[i+1], aux_digraph=H))
- except IndexError: # last node!
- L = min(L, local_edge_connectivity(G, nodes[i],
- nodes[0], aux_digraph=H))
- return L
- else: # undirected
- # Algorithm 6 in [1]
- if not nx.is_connected(G):
- return 0
- # initial value for lambda is min degree (\delta(G))
- L = min(G.degree().values())
- # reuse auxiliary digraph
- H = _aux_digraph_edge_connectivity(G)
- # A dominating set is \lambda-covering
- # We need a dominating set with at least two nodes
- for node in G:
- D = dominating_set(G, start_with=node)
- v = D.pop()
- if D: break
- else:
- # in complete graphs the dominating sets will always be of one node
- # thus we return min degree
- return L
- for w in D:
- L = min(L, local_edge_connectivity(G, v, w, aux_digraph=H))
- return L
-
-def dominating_set(G, start_with=None):
- # Algorithm 7 in [1]
- all_nodes = set(G)
- if start_with is None:
- v = set(G).pop() # pick a node
- else:
- if start_with not in G:
- raise nx.NetworkXError('node %s not in G' % start_with)
- v = start_with
- D = set([v])
- ND = set([nbr for nbr in G[v]])
- other = all_nodes - ND - D
- while other:
- w = other.pop()
- D.add(w)
- ND.update([nbr for nbr in G[w] if nbr not in D])
- other = all_nodes - ND - D
- return D
-
-def is_dominating_set(G, nbunch):
- # Proposed by Dan on the mailing list
- allnodes=set(G)
- testset=set(n for n in nbunch if n in G)
- nbrs=set()
- for n in testset:
- nbrs.update(G[n])
- if nbrs - allnodes: # some nodes left--not dominating
- return False
- else:
- return True
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/cuts.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/cuts.py
deleted file mode 100644
index a55bc0d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/cuts.py
+++ /dev/null
@@ -1,382 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Flow based cut algorithms
-"""
-# http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
-# http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-import itertools
-from operator import itemgetter
-import networkx as nx
-from networkx.algorithms.connectivity.connectivity import \
- _aux_digraph_node_connectivity, _aux_digraph_edge_connectivity, \
- dominating_set, node_connectivity
-
-__author__ = '\n'.join(['Jordi Torrents <jtorrents@milnou.net>'])
-
-__all__ = [ 'minimum_st_node_cut',
- 'minimum_node_cut',
- 'minimum_st_edge_cut',
- 'minimum_edge_cut',
- ]
-
-def minimum_st_edge_cut(G, s, t, capacity='capacity'):
- """Returns the edges of the cut-set of a minimum (s, t)-cut.
-
- We use the max-flow min-cut theorem, i.e., the capacity of a minimum
- capacity cut is equal to the flow value of a maximum flow.
-
- Parameters
- ----------
- G : NetworkX graph
- Edges of the graph are expected to have an attribute called
- 'capacity'. If this attribute is not present, the edge is
- considered to have infinite capacity.
-
- s : node
- Source node for the flow.
-
- t : node
- Sink node for the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- Returns
- -------
- cutset : set
- Set of edges that, if removed from the graph, will disconnect it
-
- Raises
- ------
- NetworkXUnbounded
- If the graph has a path of infinite capacity, all cuts have
- infinite capacity and the function raises a NetworkXError.
-
- Examples
- --------
- >>> G = nx.DiGraph()
- >>> G.add_edge('x','a', capacity = 3.0)
- >>> G.add_edge('x','b', capacity = 1.0)
- >>> G.add_edge('a','c', capacity = 3.0)
- >>> G.add_edge('b','c', capacity = 5.0)
- >>> G.add_edge('b','d', capacity = 4.0)
- >>> G.add_edge('d','e', capacity = 2.0)
- >>> G.add_edge('c','y', capacity = 2.0)
- >>> G.add_edge('e','y', capacity = 3.0)
- >>> sorted(nx.minimum_edge_cut(G, 'x', 'y'))
- [('c', 'y'), ('x', 'b')]
- >>> nx.min_cut(G, 'x', 'y')
- 3.0
- """
- try:
- flow, H = nx.ford_fulkerson_flow_and_auxiliary(G, s, t, capacity=capacity)
- cutset = set()
- # Compute reachable nodes from source in the residual network
- reachable = set(nx.single_source_shortest_path(H,s))
- # And unreachable nodes
- others = set(H) - reachable # - set([s])
- # Any edge in the original network linking these two partitions
- # is part of the edge cutset
- for u, nbrs in ((n, G[n]) for n in reachable):
- cutset.update((u,v) for v in nbrs if v in others)
- return cutset
- except nx.NetworkXUnbounded:
- # Should we raise any other exception or just let ford_fulkerson
- # propagate nx.NetworkXUnbounded ?
- raise nx.NetworkXUnbounded("Infinite capacity path, no minimum cut.")
-
-def minimum_st_node_cut(G, s, t, aux_digraph=None, mapping=None):
- r"""Returns a set of nodes of minimum cardinality that disconnect source
- from target in G.
-
- This function returns the set of nodes of minimum cardinality that,
- if removed, would destroy all paths among source and target in G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- s : node
- Source node.
-
- t : node
- Target node.
-
- Returns
- -------
- cutset : set
- Set of nodes that, if removed, would destroy all paths between
- source and target in G.
-
- Examples
- --------
- >>> # Platonic icosahedral graph has node connectivity 5
- >>> G = nx.icosahedral_graph()
- >>> len(nx.minimum_node_cut(G, 0, 6))
- 5
-
- Notes
- -----
- This is a flow based implementation of minimum node cut. The algorithm
- is based in solving a number of max-flow problems (ie local st-node
- connectivity, see local_node_connectivity) to determine the capacity
- of the minimum cut on an auxiliary directed network that corresponds
- to the minimum node cut of G. It handles both directed and undirected
- graphs.
-
- This implementation is based on algorithm 11 in [1]_. We use the Ford
- and Fulkerson algorithm to compute max flow (see ford_fulkerson).
-
- See also
- --------
- node_connectivity
- edge_connectivity
- minimum_edge_cut
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-
- """
- if aux_digraph is None or mapping is None:
- H, mapping = _aux_digraph_node_connectivity(G)
- else:
- H = aux_digraph
- edge_cut = minimum_st_edge_cut(H, '%sB' % mapping[s], '%sA' % mapping[t])
- # Each node in the original graph maps to two nodes of the auxiliary graph
- node_cut = set(H.node[node]['id'] for edge in edge_cut for node in edge)
- return node_cut - set([s,t])
-
-def minimum_node_cut(G, s=None, t=None):
- r"""Returns a set of nodes of minimum cardinality that disconnects G.
-
- If source and target nodes are provided, this function returns the
- set of nodes of minimum cardinality that, if removed, would destroy
- all paths among source and target in G. If not, it returns a set
- of nodes of minimum cardinality that disconnects G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- s : node
- Source node. Optional (default=None)
-
- t : node
- Target node. Optional (default=None)
-
- Returns
- -------
- cutset : set
- Set of nodes that, if removed, would disconnect G. If source
- and target nodes are provided, the set contians the nodes that
- if removed, would destroy all paths between source and target.
-
- Examples
- --------
- >>> # Platonic icosahedral graph has node connectivity 5
- >>> G = nx.icosahedral_graph()
- >>> len(nx.minimum_node_cut(G))
- 5
- >>> # this is the minimum over any pair of non adjacent nodes
- >>> from itertools import combinations
- >>> for u,v in combinations(G, 2):
- ... if v not in G[u]:
- ... assert(len(nx.minimum_node_cut(G,u,v)) == 5)
- ...
-
- Notes
- -----
- This is a flow based implementation of minimum node cut. The algorithm
- is based in solving a number of max-flow problems (ie local st-node
- connectivity, see local_node_connectivity) to determine the capacity
- of the minimum cut on an auxiliary directed network that corresponds
- to the minimum node cut of G. It handles both directed and undirected
- graphs.
-
- This implementation is based on algorithm 11 in [1]_. We use the Ford
- and Fulkerson algorithm to compute max flow (see ford_fulkerson).
-
- See also
- --------
- node_connectivity
- edge_connectivity
- minimum_edge_cut
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-
- """
- # Local minimum node cut
- if s is not None and t is not None:
- if s not in G:
- raise nx.NetworkXError('node %s not in graph' % s)
- if t not in G:
- raise nx.NetworkXError('node %s not in graph' % t)
- return minimum_st_node_cut(G, s, t)
- # Global minimum node cut
- # Analog to the algoritm 11 for global node connectivity in [1]
- if G.is_directed():
- if not nx.is_weakly_connected(G):
- raise nx.NetworkXError('Input graph is not connected')
- iter_func = itertools.permutations
- def neighbors(v):
- return itertools.chain.from_iterable([G.predecessors_iter(v),
- G.successors_iter(v)])
- else:
- if not nx.is_connected(G):
- raise nx.NetworkXError('Input graph is not connected')
- iter_func = itertools.combinations
- neighbors = G.neighbors_iter
- # Choose a node with minimum degree
- deg = G.degree()
- min_deg = min(deg.values())
- v = next(n for n,d in deg.items() if d == min_deg)
- # Initial node cutset is all neighbors of the node with minimum degree
- min_cut = set(G[v])
- # Reuse the auxiliary digraph
- H, mapping = _aux_digraph_node_connectivity(G)
- # compute st node cuts between v and all its non-neighbors nodes in G
- # and store the minimum
- for w in set(G) - set(neighbors(v)) - set([v]):
- this_cut = minimum_st_node_cut(G, v, w, aux_digraph=H, mapping=mapping)
- if len(min_cut) >= len(this_cut):
- min_cut = this_cut
- # Same for non adjacent pairs of neighbors of v
- for x,y in iter_func(neighbors(v),2):
- if y in G[x]: continue
- this_cut = minimum_st_node_cut(G, x, y, aux_digraph=H, mapping=mapping)
- if len(min_cut) >= len(this_cut):
- min_cut = this_cut
- return min_cut
-
-def minimum_edge_cut(G, s=None, t=None):
- r"""Returns a set of edges of minimum cardinality that disconnects G.
-
- If source and target nodes are provided, this function returns the
- set of edges of minimum cardinality that, if removed, would break
- all paths among source and target in G. If not, it returns a set of
- edges of minimum cardinality that disconnects G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- s : node
- Source node. Optional (default=None)
-
- t : node
- Target node. Optional (default=None)
-
- Returns
- -------
- cutset : set
- Set of edges that, if removed, would disconnect G. If source
- and target nodes are provided, the set contians the edges that
- if removed, would destroy all paths between source and target.
-
- Examples
- --------
- >>> # Platonic icosahedral graph has edge connectivity 5
- >>> G = nx.icosahedral_graph()
- >>> len(nx.minimum_edge_cut(G))
- 5
- >>> # this is the minimum over any pair of nodes
- >>> from itertools import combinations
- >>> for u,v in combinations(G, 2):
- ... assert(len(nx.minimum_edge_cut(G,u,v)) == 5)
- ...
-
- Notes
- -----
- This is a flow based implementation of minimum edge cut. For
- undirected graphs the algorithm works by finding a 'small' dominating
- set of nodes of G (see algorithm 7 in [1]_) and computing the maximum
- flow between an arbitrary node in the dominating set and the rest of
- nodes in it. This is an implementation of algorithm 6 in [1]_.
-
- For directed graphs, the algorithm does n calls to the max flow function.
- This is an implementation of algorithm 8 in [1]_. We use the Ford and
- Fulkerson algorithm to compute max flow (see ford_fulkerson).
-
- See also
- --------
- node_connectivity
- edge_connectivity
- minimum_node_cut
- max_flow
- ford_fulkerson
-
- References
- ----------
- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms.
- http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
-
- """
- # reuse auxiliary digraph
- H = _aux_digraph_edge_connectivity(G)
- # Local minimum edge cut if s and t are not None
- if s is not None and t is not None:
- if s not in G:
- raise nx.NetworkXError('node %s not in graph' % s)
- if t not in G:
- raise nx.NetworkXError('node %s not in graph' % t)
- return minimum_st_edge_cut(H, s, t)
- # Global minimum edge cut
- # Analog to the algoritm for global edge connectivity
- if G.is_directed():
- # Based on algorithm 8 in [1]
- if not nx.is_weakly_connected(G):
- raise nx.NetworkXError('Input graph is not connected')
- # Initial cutset is all edges of a node with minimum degree
- deg = G.degree()
- min_deg = min(deg.values())
- node = next(n for n,d in deg.items() if d==min_deg)
- min_cut = G.edges(node)
- nodes = G.nodes()
- n = len(nodes)
- for i in range(n):
- try:
- this_cut = minimum_st_edge_cut(H, nodes[i], nodes[i+1])
- if len(this_cut) <= len(min_cut):
- min_cut = this_cut
- except IndexError: # Last node!
- this_cut = minimum_st_edge_cut(H, nodes[i], nodes[0])
- if len(this_cut) <= len(min_cut):
- min_cut = this_cut
- return min_cut
- else: # undirected
- # Based on algorithm 6 in [1]
- if not nx.is_connected(G):
- raise nx.NetworkXError('Input graph is not connected')
- # Initial cutset is all edges of a node with minimum degree
- deg = G.degree()
- min_deg = min(deg.values())
- node = next(n for n,d in deg.items() if d==min_deg)
- min_cut = G.edges(node)
- # A dominating set is \lambda-covering
- # We need a dominating set with at least two nodes
- for node in G:
- D = dominating_set(G, start_with=node)
- v = D.pop()
- if D: break
- else:
- # in complete graphs the dominating set will always be of one node
- # thus we return min_cut, which now contains the edges of a node
- # with minimum degree
- return min_cut
- for w in D:
- this_cut = minimum_st_edge_cut(H, v, w)
- if len(this_cut) <= len(min_cut):
- min_cut = this_cut
- return min_cut
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/tests/test_connectivity.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/tests/test_connectivity.py
deleted file mode 100644
index 2745504..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/tests/test_connectivity.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from nose.tools import assert_equal, assert_true, assert_false
-import networkx as nx
-
-# helper functions for tests
-def _generate_no_biconnected(max_attempts=50):
- attempts = 0
- while True:
- G = nx.fast_gnp_random_graph(100,0.0575)
- if nx.is_connected(G) and not nx.is_biconnected(G):
- attempts = 0
- yield G
- else:
- if attempts >= max_attempts:
- msg = "Tried %d times: no suitable Graph."
- raise Exception(msg % max_attempts)
- else:
- attempts += 1
-
-def is_dominating_set(G, nbunch):
- # Proposed by Dan on the mailing list
- allnodes=set(G)
- testset=set(n for n in nbunch if n in G)
- nbrs=set()
- for n in testset:
- nbrs.update(G[n])
- if nbrs - allnodes: # some nodes left--not dominating
- return False
- else:
- return True
-
-# Tests for node and edge connectivity
-def test_average_connectivity():
- # figure 1 from:
- # Beineke, L., O. Oellermann, and R. Pippert (2002). The average
- # connectivity of a graph. Discrete mathematics 252(1-3), 31-45
- # http://www.sciencedirect.com/science/article/pii/S0012365X01001807
- G1 = nx.path_graph(3)
- G1.add_edges_from([(1,3),(1,4)])
- assert_equal(nx.average_node_connectivity(G1),1)
- G2 = nx.path_graph(3)
- G2.add_edges_from([(1,3),(1,4),(0,3),(0,4),(3,4)])
- assert_equal(nx.average_node_connectivity(G2),2.2)
- G3 = nx.Graph()
- assert_equal(nx.average_node_connectivity(G3),0)
-
-def test_articulation_points():
- Ggen = _generate_no_biconnected()
- for i in range(5):
- G = next(Ggen)
- assert_equal(nx.node_connectivity(G), 1)
-
-def test_brandes_erlebach():
- # Figure 1 chapter 7: Connectivity
- # http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
- G = nx.Graph()
- G.add_edges_from([(1,2),(1,3),(1,4),(1,5),(2,3),(2,6),(3,4),
- (3,6),(4,6),(4,7),(5,7),(6,8),(6,9),(7,8),
- (7,10),(8,11),(9,10),(9,11),(10,11)])
- assert_equal(3,nx.local_edge_connectivity(G,1,11))
- assert_equal(3,nx.edge_connectivity(G,1,11))
- assert_equal(2,nx.local_node_connectivity(G,1,11))
- assert_equal(2,nx.node_connectivity(G,1,11))
- assert_equal(2,nx.edge_connectivity(G)) # node 5 has degree 2
- assert_equal(2,nx.node_connectivity(G))
-
-def test_white_harary_1():
- # Figure 1b white and harary (2001)
- # # http://eclectic.ss.uci.edu/~drwhite/sm-w23.PDF
- # A graph with high adhesion (edge connectivity) and low cohesion
- # (vertex connectivity)
- G = nx.disjoint_union(nx.complete_graph(4), nx.complete_graph(4))
- G.remove_node(7)
- for i in range(4,7):
- G.add_edge(0,i)
- G = nx.disjoint_union(G, nx.complete_graph(4))
- G.remove_node(G.order()-1)
- for i in range(7,10):
- G.add_edge(0,i)
- assert_equal(1, nx.node_connectivity(G))
- assert_equal(3, nx.edge_connectivity(G))
-
-def test_white_harary_2():
- # Figure 8 white and harary (2001)
- # # http://eclectic.ss.uci.edu/~drwhite/sm-w23.PDF
- G = nx.disjoint_union(nx.complete_graph(4), nx.complete_graph(4))
- G.add_edge(0,4)
- # kappa <= lambda <= delta
- assert_equal(3, min(nx.core_number(G).values()))
- assert_equal(1, nx.node_connectivity(G))
- assert_equal(1, nx.edge_connectivity(G))
-
-def test_complete_graphs():
- for n in range(5, 25, 5):
- G = nx.complete_graph(n)
- assert_equal(n-1, nx.node_connectivity(G))
- assert_equal(n-1, nx.node_connectivity(G.to_directed()))
- assert_equal(n-1, nx.edge_connectivity(G))
- assert_equal(n-1, nx.edge_connectivity(G.to_directed()))
-
-def test_empty_graphs():
- for k in range(5, 25, 5):
- G = nx.empty_graph(k)
- assert_equal(0, nx.node_connectivity(G))
- assert_equal(0, nx.edge_connectivity(G))
-
-def test_petersen():
- G = nx.petersen_graph()
- assert_equal(3, nx.node_connectivity(G))
- assert_equal(3, nx.edge_connectivity(G))
-
-def test_tutte():
- G = nx.tutte_graph()
- assert_equal(3, nx.node_connectivity(G))
- assert_equal(3, nx.edge_connectivity(G))
-
-def test_dodecahedral():
- G = nx.dodecahedral_graph()
- assert_equal(3, nx.node_connectivity(G))
- assert_equal(3, nx.edge_connectivity(G))
-
-def test_octahedral():
- G=nx.octahedral_graph()
- assert_equal(4, nx.node_connectivity(G))
- assert_equal(4, nx.edge_connectivity(G))
-
-def test_icosahedral():
- G=nx.icosahedral_graph()
- assert_equal(5, nx.node_connectivity(G))
- assert_equal(5, nx.edge_connectivity(G))
-
-def test_directed_edge_connectivity():
- G = nx.cycle_graph(10,create_using=nx.DiGraph()) # only one direction
- D = nx.cycle_graph(10).to_directed() # 2 reciprocal edges
- assert_equal(1, nx.edge_connectivity(G))
- assert_equal(1, nx.local_edge_connectivity(G,1,4))
- assert_equal(1, nx.edge_connectivity(G,1,4))
- assert_equal(2, nx.edge_connectivity(D))
- assert_equal(2, nx.local_edge_connectivity(D,1,4))
- assert_equal(2, nx.edge_connectivity(D,1,4))
-
-def test_dominating_set():
- for i in range(5):
- G = nx.gnp_random_graph(100,0.1)
- D = nx.dominating_set(G)
- assert_true(is_dominating_set(G,D))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/tests/test_cuts.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/tests/test_cuts.py
deleted file mode 100644
index 0570494..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/connectivity/tests/test_cuts.py
+++ /dev/null
@@ -1,157 +0,0 @@
-from nose.tools import assert_equal, assert_true, assert_false, assert_raises
-import networkx as nx
-
-# Tests for node and edge cutsets
-def _generate_no_biconnected(max_attempts=50):
- attempts = 0
- while True:
- G = nx.fast_gnp_random_graph(100,0.0575)
- if nx.is_connected(G) and not nx.is_biconnected(G):
- attempts = 0
- yield G
- else:
- if attempts >= max_attempts:
- msg = "Tried %d times: no suitable Graph."%attempts
- raise Exception(msg % max_attempts)
- else:
- attempts += 1
-
-def test_articulation_points():
- Ggen = _generate_no_biconnected()
- for i in range(5):
- G = next(Ggen)
- cut = nx.minimum_node_cut(G)
- assert_true(len(cut) == 1)
- assert_true(cut.pop() in set(nx.articulation_points(G)))
-
-def test_brandes_erlebach_book():
- # Figure 1 chapter 7: Connectivity
- # http://www.informatik.uni-augsburg.de/thi/personen/kammer/Graph_Connectivity.pdf
- G = nx.Graph()
- G.add_edges_from([(1,2),(1,3),(1,4),(1,5),(2,3),(2,6),(3,4),
- (3,6),(4,6),(4,7),(5,7),(6,8),(6,9),(7,8),
- (7,10),(8,11),(9,10),(9,11),(10,11)])
- # edge cutsets
- assert_equal(3, len(nx.minimum_edge_cut(G,1,11)))
- edge_cut = nx.minimum_edge_cut(G)
- assert_equal(2, len(edge_cut)) # Node 5 has only two edges
- H = G.copy()
- H.remove_edges_from(edge_cut)
- assert_false(nx.is_connected(H))
- # node cuts
- assert_equal(set([6,7]), nx.minimum_st_node_cut(G,1,11))
- assert_equal(set([6,7]), nx.minimum_node_cut(G,1,11))
- node_cut = nx.minimum_node_cut(G)
- assert_equal(2,len(node_cut))
- H = G.copy()
- H.remove_nodes_from(node_cut)
- assert_false(nx.is_connected(H))
-
-def test_white_harary_paper():
- # Figure 1b white and harary (2001)
- # http://eclectic.ss.uci.edu/~drwhite/sm-w23.PDF
- # A graph with high adhesion (edge connectivity) and low cohesion
- # (node connectivity)
- G = nx.disjoint_union(nx.complete_graph(4), nx.complete_graph(4))
- G.remove_node(7)
- for i in range(4,7):
- G.add_edge(0,i)
- G = nx.disjoint_union(G, nx.complete_graph(4))
- G.remove_node(G.order()-1)
- for i in range(7,10):
- G.add_edge(0,i)
- # edge cuts
- edge_cut = nx.minimum_edge_cut(G)
- assert_equal(3, len(edge_cut))
- H = G.copy()
- H.remove_edges_from(edge_cut)
- assert_false(nx.is_connected(H))
- # node cuts
- node_cut = nx.minimum_node_cut(G)
- assert_equal(set([0]), node_cut)
- H = G.copy()
- H.remove_nodes_from(node_cut)
- assert_false(nx.is_connected(H))
-
-def test_petersen_cutset():
- G = nx.petersen_graph()
- # edge cuts
- edge_cut = nx.minimum_edge_cut(G)
- assert_equal(3, len(edge_cut))
- H = G.copy()
- H.remove_edges_from(edge_cut)
- assert_false(nx.is_connected(H))
- # node cuts
- node_cut = nx.minimum_node_cut(G)
- assert_equal(3,len(node_cut))
- H = G.copy()
- H.remove_nodes_from(node_cut)
- assert_false(nx.is_connected(H))
-
-def test_octahedral_cutset():
- G=nx.octahedral_graph()
- # edge cuts
- edge_cut = nx.minimum_edge_cut(G)
- assert_equal(4, len(edge_cut))
- H = G.copy()
- H.remove_edges_from(edge_cut)
- assert_false(nx.is_connected(H))
- # node cuts
- node_cut = nx.minimum_node_cut(G)
- assert_equal(4,len(node_cut))
- H = G.copy()
- H.remove_nodes_from(node_cut)
- assert_false(nx.is_connected(H))
-
-def test_icosahedral_cutset():
- G=nx.icosahedral_graph()
- # edge cuts
- edge_cut = nx.minimum_edge_cut(G)
- assert_equal(5, len(edge_cut))
- H = G.copy()
- H.remove_edges_from(edge_cut)
- assert_false(nx.is_connected(H))
- # node cuts
- node_cut = nx.minimum_node_cut(G)
- assert_equal(5,len(node_cut))
- H = G.copy()
- H.remove_nodes_from(node_cut)
- assert_false(nx.is_connected(H))
-
-def test_node_cutset_exception():
- G=nx.Graph()
- G.add_edges_from([(1,2),(3,4)])
- assert_raises(nx.NetworkXError, nx.minimum_node_cut,G)
-
-def test_node_cutset_random_graphs():
- for i in range(5):
- G = nx.fast_gnp_random_graph(50,0.2)
- if not nx.is_connected(G):
- ccs = iter(nx.connected_components(G))
- start = next(ccs)[0]
- G.add_edges_from( (start,c[0]) for c in ccs )
- cutset = nx.minimum_node_cut(G)
- assert_equal(nx.node_connectivity(G), len(cutset))
- G.remove_nodes_from(cutset)
- assert_false(nx.is_connected(G))
-
-def test_edge_cutset_random_graphs():
- for i in range(5):
- G = nx.fast_gnp_random_graph(50,0.2)
- if not nx.is_connected(G):
- ccs = iter(nx.connected_components(G))
- start = next(ccs)[0]
- G.add_edges_from( (start,c[0]) for c in ccs )
- cutset = nx.minimum_edge_cut(G)
- assert_equal(nx.edge_connectivity(G), len(cutset))
- G.remove_edges_from(cutset)
- assert_false(nx.is_connected(G))
-
-# Test empty graphs
-def test_empty_graphs():
- G = nx.Graph()
- D = nx.DiGraph()
- assert_raises(nx.NetworkXPointlessConcept, nx.minimum_node_cut, G)
- assert_raises(nx.NetworkXPointlessConcept, nx.minimum_node_cut, D)
- assert_raises(nx.NetworkXPointlessConcept, nx.minimum_edge_cut, G)
- assert_raises(nx.NetworkXPointlessConcept, nx.minimum_edge_cut, D)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/core.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/core.py
deleted file mode 100644
index 4daba25..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/core.py
+++ /dev/null
@@ -1,324 +0,0 @@
-"""
-Find the k-cores of a graph.
-
-The k-core is found by recursively pruning nodes with degrees less than k.
-
-See the following reference for details:
-
-An O(m) Algorithm for Cores Decomposition of Networks
-Vladimir Batagelj and Matjaz Zaversnik, 2003.
-http://arxiv.org/abs/cs.DS/0310049
-
-"""
-__author__ = "\n".join(['Dan Schult (dschult@colgate.edu)',
- 'Jason Grout (jason-sage@creativetrax.com)',
- 'Aric Hagberg (hagberg@lanl.gov)'])
-
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__all__ = ['core_number','k_core','k_shell','k_crust','k_corona','find_cores']
-
-import networkx as nx
-
-def core_number(G):
- """Return the core number for each vertex.
-
- A k-core is a maximal subgraph that contains nodes of degree k or more.
-
- The core number of a node is the largest value k of a k-core containing
- that node.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph or directed graph
-
- Returns
- -------
- core_number : dictionary
- A dictionary keyed by node to the core number.
-
- Raises
- ------
- NetworkXError
- The k-core is not defined for graphs with self loops or parallel edges.
-
- Notes
- -----
- Not implemented for graphs with parallel edges or self loops.
-
- For directed graphs the node degree is defined to be the
- in-degree + out-degree.
-
- References
- ----------
- .. [1] An O(m) Algorithm for Cores Decomposition of Networks
- Vladimir Batagelj and Matjaz Zaversnik, 2003.
- http://arxiv.org/abs/cs.DS/0310049
- """
- if G.is_multigraph():
- raise nx.NetworkXError(
- 'MultiGraph and MultiDiGraph types not supported.')
-
- if G.number_of_selfloops()>0:
- raise nx.NetworkXError(
- 'Input graph has self loops; the core number is not defined.',
- 'Consider using G.remove_edges_from(G.selfloop_edges()).')
-
- if G.is_directed():
- import itertools
- def neighbors(v):
- return itertools.chain.from_iterable([G.predecessors_iter(v),
- G.successors_iter(v)])
- else:
- neighbors=G.neighbors_iter
- degrees=G.degree()
- # sort nodes by degree
- nodes=sorted(degrees,key=degrees.get)
- bin_boundaries=[0]
- curr_degree=0
- for i,v in enumerate(nodes):
- if degrees[v]>curr_degree:
- bin_boundaries.extend([i]*(degrees[v]-curr_degree))
- curr_degree=degrees[v]
- node_pos = dict((v,pos) for pos,v in enumerate(nodes))
- # initial guesses for core is degree
- core=degrees
- nbrs=dict((v,set(neighbors(v))) for v in G)
- for v in nodes:
- for u in nbrs[v]:
- if core[u] > core[v]:
- nbrs[u].remove(v)
- pos=node_pos[u]
- bin_start=bin_boundaries[core[u]]
- node_pos[u]=bin_start
- node_pos[nodes[bin_start]]=pos
- nodes[bin_start],nodes[pos]=nodes[pos],nodes[bin_start]
- bin_boundaries[core[u]]+=1
- core[u]-=1
- return core
-
-find_cores=core_number
-
-def k_core(G,k=None,core_number=None):
- """Return the k-core of G.
-
- A k-core is a maximal subgraph that contains nodes of degree k or more.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph or directed graph
- k : int, optional
- The order of the core. If not specified return the main core.
- core_number : dictionary, optional
- Precomputed core numbers for the graph G.
-
- Returns
- -------
- G : NetworkX graph
- The k-core subgraph
-
- Raises
- ------
- NetworkXError
- The k-core is not defined for graphs with self loops or parallel edges.
-
- Notes
- -----
- The main core is the core with the largest degree.
-
- Not implemented for graphs with parallel edges or self loops.
-
- For directed graphs the node degree is defined to be the
- in-degree + out-degree.
-
- Graph, node, and edge attributes are copied to the subgraph.
-
- See Also
- --------
- core_number
-
- References
- ----------
- .. [1] An O(m) Algorithm for Cores Decomposition of Networks
- Vladimir Batagelj and Matjaz Zaversnik, 2003.
- http://arxiv.org/abs/cs.DS/0310049
- """
- if core_number is None:
- core_number=nx.core_number(G)
- if k is None:
- k=max(core_number.values()) # max core
- nodes=(n for n in core_number if core_number[n]>=k)
- return G.subgraph(nodes).copy()
-
-def k_shell(G,k=None,core_number=None):
- """Return the k-shell of G.
-
- The k-shell is the subgraph of nodes in the k-core containing
- nodes of exactly degree k.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph or directed graph.
- k : int, optional
- The order of the shell. If not specified return the main shell.
- core_number : dictionary, optional
- Precomputed core numbers for the graph G.
-
-
- Returns
- -------
- G : NetworkX graph
- The k-shell subgraph
-
- Raises
- ------
- NetworkXError
- The k-shell is not defined for graphs with self loops or parallel edges.
-
- Notes
- -----
- This is similar to k_corona but in that case only neighbors in the
- k-core are considered.
-
- Not implemented for graphs with parallel edges or self loops.
-
- For directed graphs the node degree is defined to be the
- in-degree + out-degree.
-
- Graph, node, and edge attributes are copied to the subgraph.
-
- See Also
- --------
- core_number
- k_corona
-
-
- ----------
- .. [1] A model of Internet topology using k-shell decomposition
- Shai Carmi, Shlomo Havlin, Scott Kirkpatrick, Yuval Shavitt,
- and Eran Shir, PNAS July 3, 2007 vol. 104 no. 27 11150-11154
- http://www.pnas.org/content/104/27/11150.full
- """
- if core_number is None:
- core_number=nx.core_number(G)
- if k is None:
- k=max(core_number.values()) # max core
- nodes=(n for n in core_number if core_number[n]==k)
- return G.subgraph(nodes).copy()
-
-def k_crust(G,k=None,core_number=None):
- """Return the k-crust of G.
-
- The k-crust is the graph G with the k-core removed.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph or directed graph.
- k : int, optional
- The order of the shell. If not specified return the main crust.
- core_number : dictionary, optional
- Precomputed core numbers for the graph G.
-
- Returns
- -------
- G : NetworkX graph
- The k-crust subgraph
-
- Raises
- ------
- NetworkXError
- The k-crust is not defined for graphs with self loops or parallel edges.
-
- Notes
- -----
- This definition of k-crust is different than the definition in [1]_.
- The k-crust in [1]_ is equivalent to the k+1 crust of this algorithm.
-
- Not implemented for graphs with parallel edges or self loops.
-
- For directed graphs the node degree is defined to be the
- in-degree + out-degree.
-
- Graph, node, and edge attributes are copied to the subgraph.
-
- See Also
- --------
- core_number
-
- References
- ----------
- .. [1] A model of Internet topology using k-shell decomposition
- Shai Carmi, Shlomo Havlin, Scott Kirkpatrick, Yuval Shavitt,
- and Eran Shir, PNAS July 3, 2007 vol. 104 no. 27 11150-11154
- http://www.pnas.org/content/104/27/11150.full
- """
- if core_number is None:
- core_number=nx.core_number(G)
- if k is None:
- k=max(core_number.values())-1
- nodes=(n for n in core_number if core_number[n]<=k)
- return G.subgraph(nodes).copy()
-
-
-def k_corona(G, k, core_number=None):
- """Return the k-crust of G.
-
- The k-corona is the subset of vertices in the k-core which have
- exactly k neighbours in the k-core.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph or directed graph
- k : int
- The order of the corona.
- core_number : dictionary, optional
- Precomputed core numbers for the graph G.
-
- Returns
- -------
- G : NetworkX graph
- The k-corona subgraph
-
- Raises
- ------
- NetworkXError
- The k-cornoa is not defined for graphs with self loops or
- parallel edges.
-
- Notes
- -----
- Not implemented for graphs with parallel edges or self loops.
-
- For directed graphs the node degree is defined to be the
- in-degree + out-degree.
-
- Graph, node, and edge attributes are copied to the subgraph.
-
- See Also
- --------
- core_number
-
- References
- ----------
- .. [1] k -core (bootstrap) percolation on complex networks:
- Critical phenomena and nonlocal effects,
- A. V. Goltsev, S. N. Dorogovtsev, and J. F. F. Mendes,
- Phys. Rev. E 73, 056101 (2006)
- http://link.aps.org/doi/10.1103/PhysRevE.73.056101
- """
-
- if core_number is None:
- core_number = nx.core_number(G)
- nodes = (n for n in core_number
- if core_number[n] >= k
- and len([v for v in G[n] if core_number[v] >= k]) == k)
- return G.subgraph(nodes).copy()
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/cycles.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/cycles.py
deleted file mode 100644
index 4955538..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/cycles.py
+++ /dev/null
@@ -1,317 +0,0 @@
-"""
-========================
-Cycle finding algorithms
-========================
-"""
-# Copyright (C) 2010-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import *
-from collections import defaultdict
-
-__all__ = ['cycle_basis','simple_cycles','recursive_simple_cycles']
-__author__ = "\n".join(['Jon Olav Vik <jonovik@gmail.com>',
- 'Dan Schult <dschult@colgate.edu>',
- 'Aric Hagberg <hagberg@lanl.gov>'])
-
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def cycle_basis(G,root=None):
- """ Returns a list of cycles which form a basis for cycles of G.
-
- A basis for cycles of a network is a minimal collection of
- cycles such that any cycle in the network can be written
- as a sum of cycles in the basis. Here summation of cycles
- is defined as "exclusive or" of the edges. Cycle bases are
- useful, e.g. when deriving equations for electric circuits
- using Kirchhoff's Laws.
-
- Parameters
- ----------
- G : NetworkX Graph
- root : node, optional
- Specify starting node for basis.
-
- Returns
- -------
- A list of cycle lists. Each cycle list is a list of nodes
- which forms a cycle (loop) in G.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_cycle([0,1,2,3])
- >>> G.add_cycle([0,3,4,5])
- >>> print(nx.cycle_basis(G,0))
- [[3, 4, 5, 0], [1, 2, 3, 0]]
-
- Notes
- -----
- This is adapted from algorithm CACM 491 [1]_.
-
- References
- ----------
- .. [1] Paton, K. An algorithm for finding a fundamental set of
- cycles of a graph. Comm. ACM 12, 9 (Sept 1969), 514-518.
-
- See Also
- --------
- simple_cycles
- """
- gnodes=set(G.nodes())
- cycles=[]
- while gnodes: # loop over connected components
- if root is None:
- root=gnodes.pop()
- stack=[root]
- pred={root:root}
- used={root:set()}
- while stack: # walk the spanning tree finding cycles
- z=stack.pop() # use last-in so cycles easier to find
- zused=used[z]
- for nbr in G[z]:
- if nbr not in used: # new node
- pred[nbr]=z
- stack.append(nbr)
- used[nbr]=set([z])
- elif nbr == z: # self loops
- cycles.append([z])
- elif nbr not in zused:# found a cycle
- pn=used[nbr]
- cycle=[nbr,z]
- p=pred[z]
- while p not in pn:
- cycle.append(p)
- p=pred[p]
- cycle.append(p)
- cycles.append(cycle)
- used[nbr].add(z)
- gnodes-=set(pred)
- root=None
- return cycles
-
-
-@not_implemented_for('undirected')
-def simple_cycles(G):
- """Find simple cycles (elementary circuits) of a directed graph.
-
- An simple cycle, or elementary circuit, is a closed path where no
- node appears twice, except that the first and last node are the same.
- Two elementary circuits are distinct if they are not cyclic permutations
- of each other.
-
- This is a nonrecursive, iterator/generator version of Johnson's
- algorithm [1]_. There may be better algorithms for some cases [2]_ [3]_.
-
- Parameters
- ----------
- G : NetworkX DiGraph
- A directed graph
-
- Returns
- -------
- cycle_generator: generator
- A generator that produces elementary cycles of the graph. Each cycle is
- a list of nodes with the first and last nodes being the same.
-
- Examples
- --------
- >>> G = nx.DiGraph([(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)])
- >>> list(nx.simple_cycles(G))
- [[2], [2, 1], [2, 0], [2, 0, 1], [0]]
-
- Notes
- -----
- The implementation follows pp. 79-80 in [1]_.
-
- The time complexity is O((n+e)(c+1)) for n nodes, e edges and c
- elementary circuits.
-
- To filter the cycles so that they don't include certain nodes or edges,
- copy your graph and eliminate those nodes or edges before calling.
- >>> copyG = G.copy()
- >>> copyG.remove_nodes_from([1])
- >>> copyG.remove_edges_from([(0,1)])
- >>> list(nx.simple_cycles(copyG))
- [[2], [2, 0], [0]]
-
- References
- ----------
- .. [1] Finding all the elementary circuits of a directed graph.
- D. B. Johnson, SIAM Journal on Computing 4, no. 1, 77-84, 1975.
- http://dx.doi.org/10.1137/0204007
-
- .. [2] Enumerating the cycles of a digraph: a new preprocessing strategy.
- G. Loizou and P. Thanish, Information Sciences, v. 27, 163-182, 1982.
-
- .. [3] A search strategy for the elementary cycles of a directed graph.
- J.L. Szwarcfiter and P.E. Lauer, BIT NUMERICAL MATHEMATICS,
- v. 16, no. 2, 192-204, 1976.
-
- See Also
- --------
- cycle_basis
- """
- def _unblock(thisnode,blocked,B):
- stack=set([thisnode])
- while stack:
- node=stack.pop()
- if node in blocked:
- blocked.remove(node)
- stack.update(B[node])
- B[node].clear()
-
- # Johnson's algorithm requires some ordering of the nodes.
- # We assign the arbitrary ordering given by the strongly connected comps
- # There is no need to track the ordering as each node removed as processed.
- subG=G.copy() # save the actual graph so we can mutate it here
- sccs = nx.strongly_connected_components(subG)
- while sccs:
- scc=sccs.pop()
- # order of scc determines ordering of nodes
- startnode = scc.pop()
- # Processing node runs "circuit" routine from recursive version
- path=[startnode]
- blocked = set() # vertex: blocked from search?
- closed = set() # nodes involved in a cycle
- blocked.add(startnode)
- B=defaultdict(set) # graph portions that yield no elementary circuit
- stack=[ (startnode,list(subG[startnode])) ] # subG gives component nbrs
- while stack:
- thisnode,nbrs = stack[-1]
- if nbrs:
- nextnode = nbrs.pop()
-# print thisnode,nbrs,":",nextnode,blocked,B,path,stack,startnode
-# f=raw_input("pause")
- if nextnode == startnode:
- yield path[:]
- closed.update(path)
-# print "Found a cycle",path,closed
- elif nextnode not in blocked:
- path.append(nextnode)
- stack.append( (nextnode,list(subG[nextnode])) )
- blocked.add(nextnode)
- continue
- # done with nextnode... look for more neighbors
- if not nbrs: # no more nbrs
- if thisnode in closed:
- _unblock(thisnode,blocked,B)
- else:
- for nbr in G[thisnode]:
- if thisnode not in B[nbr]:
- B[nbr].add(thisnode)
- stack.pop()
-# assert path[-1]==thisnode
- path.pop()
- # done processing this node
- subG.remove_node(startnode)
- H=subG.subgraph(scc) # make smaller to avoid work in SCC routine
- sccs.extend(nx.strongly_connected_components(H))
-
-
-@not_implemented_for('undirected')
-def recursive_simple_cycles(G):
- """Find simple cycles (elementary circuits) of a directed graph.
-
- A simple cycle, or elementary circuit, is a closed path where no
- node appears twice, except that the first and last node are the same.
- Two elementary circuits are distinct if they are not cyclic permutations
- of each other.
-
- This version uses a recursive algorithm to build a list of cycles.
- You should probably use the iterator version caled simple_cycles().
- Warning: This recursive version uses lots of RAM!
-
- Parameters
- ----------
- G : NetworkX DiGraph
- A directed graph
-
- Returns
- -------
- A list of circuits, where each circuit is a list of nodes, with the first
- and last node being the same.
-
- Example:
- >>> G = nx.DiGraph([(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)])
- >>> nx.recursive_simple_cycles(G)
- [[0], [0, 1, 2], [0, 2], [1, 2], [2]]
-
- See Also
- --------
- cycle_basis (for undirected graphs)
-
- Notes
- -----
- The implementation follows pp. 79-80 in [1]_.
-
- The time complexity is O((n+e)(c+1)) for n nodes, e edges and c
- elementary circuits.
-
- References
- ----------
- .. [1] Finding all the elementary circuits of a directed graph.
- D. B. Johnson, SIAM Journal on Computing 4, no. 1, 77-84, 1975.
- http://dx.doi.org/10.1137/0204007
-
- See Also
- --------
- simple_cycles, cycle_basis
- """
- # Jon Olav Vik, 2010-08-09
- def _unblock(thisnode):
- """Recursively unblock and remove nodes from B[thisnode]."""
- if blocked[thisnode]:
- blocked[thisnode] = False
- while B[thisnode]:
- _unblock(B[thisnode].pop())
-
- def circuit(thisnode, startnode, component):
- closed = False # set to True if elementary path is closed
- path.append(thisnode)
- blocked[thisnode] = True
- for nextnode in component[thisnode]: # direct successors of thisnode
- if nextnode == startnode:
- result.append(path[:])
- closed = True
- elif not blocked[nextnode]:
- if circuit(nextnode, startnode, component):
- closed = True
- if closed:
- _unblock(thisnode)
- else:
- for nextnode in component[thisnode]:
- if thisnode not in B[nextnode]: # TODO: use set for speedup?
- B[nextnode].append(thisnode)
- path.pop() # remove thisnode from path
- return closed
-
- path = [] # stack of nodes in current path
- blocked = defaultdict(bool) # vertex: blocked from search?
- B = defaultdict(list) # graph portions that yield no elementary circuit
- result = [] # list to accumulate the circuits found
- # Johnson's algorithm requires some ordering of the nodes.
- # They might not be sortable so we assign an arbitrary ordering.
- ordering=dict(zip(G,range(len(G))))
- for s in ordering:
- # Build the subgraph induced by s and following nodes in the ordering
- subgraph = G.subgraph(node for node in G
- if ordering[node] >= ordering[s])
- # Find the strongly connected component in the subgraph
- # that contains the least node according to the ordering
- strongcomp = nx.strongly_connected_components(subgraph)
- mincomp=min(strongcomp,
- key=lambda nodes: min(ordering[n] for n in nodes))
- component = G.subgraph(mincomp)
- if component:
- # smallest node in the component according to the ordering
- startnode = min(component,key=ordering.__getitem__)
- for node in component:
- blocked[node] = False
- B[node][:] = []
- dummy=circuit(startnode, startnode, component)
- return result
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/dag.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/dag.py
deleted file mode 100644
index 4d376e0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/dag.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# -*- coding: utf-8 -*-
-from fractions import gcd
-import networkx as nx
-"""Algorithms for directed acyclic graphs (DAGs)."""
-# Copyright (C) 2006-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Dan Schult (dschult@colgate.edu)',
- 'Ben Edwards (bedwards@cs.unm.edu)'])
-__all__ = ['descendants',
- 'ancestors',
- 'topological_sort',
- 'topological_sort_recursive',
- 'is_directed_acyclic_graph',
- 'is_aperiodic']
-
-def descendants(G, source):
- """Return all nodes reachable from `source` in G.
-
- Parameters
- ----------
- G : NetworkX DiGraph
- source : node in G
-
- Returns
- -------
- des : set()
- The descendants of source in G
- """
- if not G.has_node(source):
- raise nx.NetworkXError("The node %s is not in the graph." % source)
- des = set(nx.shortest_path_length(G, source=source).keys()) - set([source])
- return des
-
-def ancestors(G, source):
- """Return all nodes having a path to `source` in G.
-
- Parameters
- ----------
- G : NetworkX DiGraph
- source : node in G
-
- Returns
- -------
- ancestors : set()
- The ancestors of source in G
- """
- if not G.has_node(source):
- raise nx.NetworkXError("The node %s is not in the graph." % source)
- anc = set(nx.shortest_path_length(G, target=source).keys()) - set([source])
- return anc
-
-def is_directed_acyclic_graph(G):
- """Return True if the graph G is a directed acyclic graph (DAG) or
- False if not.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph
-
- Returns
- -------
- is_dag : bool
- True if G is a DAG, false otherwise
- """
- if not G.is_directed():
- return False
- try:
- topological_sort(G)
- return True
- except nx.NetworkXUnfeasible:
- return False
-
-def topological_sort(G,nbunch=None):
- """Return a list of nodes in topological sort order.
-
- A topological sort is a nonunique permutation of the nodes
- such that an edge from u to v implies that u appears before v in the
- topological sort order.
-
- Parameters
- ----------
- G : NetworkX digraph
- A directed graph
-
- nbunch : container of nodes (optional)
- Explore graph in specified order given in nbunch
-
- Raises
- ------
- NetworkXError
- Topological sort is defined for directed graphs only. If the
- graph G is undirected, a NetworkXError is raised.
-
- NetworkXUnfeasible
- If G is not a directed acyclic graph (DAG) no topological sort
- exists and a NetworkXUnfeasible exception is raised.
-
- Notes
- -----
- This algorithm is based on a description and proof in
- The Algorithm Design Manual [1]_ .
-
- See also
- --------
- is_directed_acyclic_graph
-
- References
- ----------
- .. [1] Skiena, S. S. The Algorithm Design Manual (Springer-Verlag, 1998).
- http://www.amazon.com/exec/obidos/ASIN/0387948600/ref=ase_thealgorithmrepo/
- """
- if not G.is_directed():
- raise nx.NetworkXError(
- "Topological sort not defined on undirected graphs.")
-
- # nonrecursive version
- seen = set()
- order = []
- explored = set()
-
- if nbunch is None:
- nbunch = G.nodes_iter()
- for v in nbunch: # process all vertices in G
- if v in explored:
- continue
- fringe = [v] # nodes yet to look at
- while fringe:
- w = fringe[-1] # depth first search
- if w in explored: # already looked down this branch
- fringe.pop()
- continue
- seen.add(w) # mark as seen
- # Check successors for cycles and for new nodes
- new_nodes = []
- for n in G[w]:
- if n not in explored:
- if n in seen: #CYCLE !!
- raise nx.NetworkXUnfeasible("Graph contains a cycle.")
- new_nodes.append(n)
- if new_nodes: # Add new_nodes to fringe
- fringe.extend(new_nodes)
- else: # No new nodes so w is fully explored
- explored.add(w)
- order.append(w)
- fringe.pop() # done considering this node
- return list(reversed(order))
-
-def topological_sort_recursive(G,nbunch=None):
- """Return a list of nodes in topological sort order.
-
- A topological sort is a nonunique permutation of the nodes such
- that an edge from u to v implies that u appears before v in the
- topological sort order.
-
- Parameters
- ----------
- G : NetworkX digraph
-
- nbunch : container of nodes (optional)
- Explore graph in specified order given in nbunch
-
- Raises
- ------
- NetworkXError
- Topological sort is defined for directed graphs only. If the
- graph G is undirected, a NetworkXError is raised.
-
- NetworkXUnfeasible
- If G is not a directed acyclic graph (DAG) no topological sort
- exists and a NetworkXUnfeasible exception is raised.
-
- Notes
- -----
- This is a recursive version of topological sort.
-
- See also
- --------
- topological_sort
- is_directed_acyclic_graph
-
- """
- if not G.is_directed():
- raise nx.NetworkXError(
- "Topological sort not defined on undirected graphs.")
-
- def _dfs(v):
- ancestors.add(v)
-
- for w in G[v]:
- if w in ancestors:
- raise nx.NetworkXUnfeasible("Graph contains a cycle.")
-
- if w not in explored:
- _dfs(w)
-
- ancestors.remove(v)
- explored.add(v)
- order.append(v)
-
- ancestors = set()
- explored = set()
- order = []
-
- if nbunch is None:
- nbunch = G.nodes_iter()
-
- for v in nbunch:
- if v not in explored:
- _dfs(v)
-
- return list(reversed(order))
-
-def is_aperiodic(G):
- """Return True if G is aperiodic.
-
- A directed graph is aperiodic if there is no integer k > 1 that
- divides the length of every cycle in the graph.
-
- Parameters
- ----------
- G : NetworkX DiGraph
- Graph
-
- Returns
- -------
- aperiodic : boolean
- True if the graph is aperiodic False otherwise
-
- Raises
- ------
- NetworkXError
- If G is not directed
-
- Notes
- -----
- This uses the method outlined in [1]_, which runs in O(m) time
- given m edges in G. Note that a graph is not aperiodic if it is
- acyclic as every integer trivial divides length 0 cycles.
-
- References
- ----------
- .. [1] Jarvis, J. P.; Shier, D. R. (1996),
- Graph-theoretic analysis of finite Markov chains,
- in Shier, D. R.; Wallenius, K. T., Applied Mathematical Modeling:
- A Multidisciplinary Approach, CRC Press.
- """
- if not G.is_directed():
- raise nx.NetworkXError("is_aperiodic not defined for undirected graphs")
-
- s = next(G.nodes_iter())
- levels = {s:0}
- this_level = [s]
- g = 0
- l = 1
- while this_level:
- next_level = []
- for u in this_level:
- for v in G[u]:
- if v in levels: # Non-Tree Edge
- g = gcd(g, levels[u]-levels[v] + 1)
- else: # Tree Edge
- next_level.append(v)
- levels[v] = l
- this_level = next_level
- l += 1
- if len(levels)==len(G): #All nodes in tree
- return g==1
- else:
- return g==1 and nx.is_aperiodic(G.subgraph(set(G)-set(levels)))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/distance_measures.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/distance_measures.py
deleted file mode 100644
index 33df686..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/distance_measures.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Graph diameter, radius, eccentricity and other properties.
-"""
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['eccentricity', 'diameter', 'radius', 'periphery', 'center']
-
-import networkx
-
-def eccentricity(G, v=None, sp=None):
- """Return the eccentricity of nodes in G.
-
- The eccentricity of a node v is the maximum distance from v to
- all other nodes in G.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph
-
- v : node, optional
- Return value of specified node
-
- sp : dict of dicts, optional
- All pairs shortest path lengths as a dictionary of dictionaries
-
- Returns
- -------
- ecc : dictionary
- A dictionary of eccentricity values keyed by node.
- """
-# nodes=
-# nodes=[]
-# if v is None: # none, use entire graph
-# nodes=G.nodes()
-# elif v in G: # is v a single node
-# nodes=[v]
-# else: # assume v is a container of nodes
-# nodes=v
- order=G.order()
-
- e={}
- for n in G.nbunch_iter(v):
- if sp is None:
- length=networkx.single_source_shortest_path_length(G,n)
- L = len(length)
- else:
- try:
- length=sp[n]
- L = len(length)
- except TypeError:
- raise networkx.NetworkXError('Format of "sp" is invalid.')
- if L != order:
- msg = "Graph not connected: infinite path length"
- raise networkx.NetworkXError(msg)
-
- e[n]=max(length.values())
-
- if v in G:
- return e[v] # return single value
- else:
- return e
-
-
-def diameter(G, e=None):
- """Return the diameter of the graph G.
-
- The diameter is the maximum eccentricity.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph
-
- e : eccentricity dictionary, optional
- A precomputed dictionary of eccentricities.
-
- Returns
- -------
- d : integer
- Diameter of graph
-
- See Also
- --------
- eccentricity
- """
- if e is None:
- e=eccentricity(G)
- return max(e.values())
-
-def periphery(G, e=None):
- """Return the periphery of the graph G.
-
- The periphery is the set of nodes with eccentricity equal to the diameter.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph
-
- e : eccentricity dictionary, optional
- A precomputed dictionary of eccentricities.
-
- Returns
- -------
- p : list
- List of nodes in periphery
- """
- if e is None:
- e=eccentricity(G)
- diameter=max(e.values())
- p=[v for v in e if e[v]==diameter]
- return p
-
-
-def radius(G, e=None):
- """Return the radius of the graph G.
-
- The radius is the minimum eccentricity.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph
-
- e : eccentricity dictionary, optional
- A precomputed dictionary of eccentricities.
-
- Returns
- -------
- r : integer
- Radius of graph
- """
- if e is None:
- e=eccentricity(G)
- return min(e.values())
-
-def center(G, e=None):
- """Return the center of the graph G.
-
- The center is the set of nodes with eccentricity equal to radius.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph
-
- e : eccentricity dictionary, optional
- A precomputed dictionary of eccentricities.
-
- Returns
- -------
- c : list
- List of nodes in center
- """
- if e is None:
- e=eccentricity(G)
- # order the nodes by path length
- radius=min(e.values())
- p=[v for v in e if e[v]==radius]
- return p
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/distance_regular.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/distance_regular.py
deleted file mode 100644
index 3fbcdbc..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/distance_regular.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""
-=======================
-Distance-regular graphs
-=======================
-"""
-# Copyright (C) 2011 by
-# Dheeraj M R <dheerajrav@gmail.com>
-# Aric Hagberg <aric.hagberg@gmail.com>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Dheeraj M R <dheerajrav@gmail.com>',
- 'Aric Hagberg <aric.hagberg@gmail.com>'])
-
-__all__ = ['is_distance_regular','intersection_array','global_parameters']
-
-def is_distance_regular(G):
- """Returns True if the graph is distance regular, False otherwise.
-
- A connected graph G is distance-regular if for any nodes x,y
- and any integers i,j=0,1,...,d (where d is the graph
- diameter), the number of vertices at distance i from x and
- distance j from y depends only on i,j and the graph distance
- between x and y, independently of the choice of x and y.
-
- Parameters
- ----------
- G: Networkx graph (undirected)
-
- Returns
- -------
- bool
- True if the graph is Distance Regular, False otherwise
-
- Examples
- --------
- >>> G=nx.hypercube_graph(6)
- >>> nx.is_distance_regular(G)
- True
-
- See Also
- --------
- intersection_array, global_parameters
-
- Notes
- -----
- For undirected and simple graphs only
-
- References
- ----------
- .. [1] Brouwer, A. E.; Cohen, A. M.; and Neumaier, A.
- Distance-Regular Graphs. New York: Springer-Verlag, 1989.
- .. [2] Weisstein, Eric W. "Distance-Regular Graph."
- http://mathworld.wolfram.com/Distance-RegularGraph.html
-
- """
- try:
- a=intersection_array(G)
- return True
- except nx.NetworkXError:
- return False
-
-def global_parameters(b,c):
- """Return global parameters for a given intersection array.
-
- Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
- such that for any 2 vertices x,y in G at a distance i=d(x,y), there
- are exactly c_i neighbors of y at a distance of i-1 from x and b_i
- neighbors of y at a distance of i+1 from x.
-
- Thus, a distance regular graph has the global parameters,
- [[c_0,a_0,b_0],[c_1,a_1,b_1],......,[c_d,a_d,b_d]] for the
- intersection array [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
- where a_i+b_i+c_i=k , k= degree of every vertex.
-
- Parameters
- ----------
- b,c: tuple of lists
-
- Returns
- -------
- p : list of three-tuples
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> b,c=nx.intersection_array(G)
- >>> list(nx.global_parameters(b,c))
- [(0, 0, 3), (1, 0, 2), (1, 1, 1), (1, 1, 1), (2, 0, 1), (3, 0, 0)]
-
- References
- ----------
- .. [1] Weisstein, Eric W. "Global Parameters."
- From MathWorld--A Wolfram Web Resource.
- http://mathworld.wolfram.com/GlobalParameters.html
-
- See Also
- --------
- intersection_array
- """
- d=len(b)
- ba=b[:]
- ca=c[:]
- ba.append(0)
- ca.insert(0,0)
- k = ba[0]
- aa = [k-x-y for x,y in zip(ba,ca)]
- return zip(*[ca,aa,ba])
-
-
-def intersection_array(G):
- """Returns the intersection array of a distance-regular graph.
-
- Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
- such that for any 2 vertices x,y in G at a distance i=d(x,y), there
- are exactly c_i neighbors of y at a distance of i-1 from x and b_i
- neighbors of y at a distance of i+1 from x.
-
- A distance regular graph'sintersection array is given by,
- [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
-
- Parameters
- ----------
- G: Networkx graph (undirected)
-
- Returns
- -------
- b,c: tuple of lists
-
- Examples
- --------
- >>> G=nx.icosahedral_graph()
- >>> nx.intersection_array(G)
- ([5, 2, 1], [1, 2, 5])
-
- References
- ----------
- .. [1] Weisstein, Eric W. "Intersection Array."
- From MathWorld--A Wolfram Web Resource.
- http://mathworld.wolfram.com/IntersectionArray.html
-
-
- See Also
- --------
- global_parameters
- """
- if G.is_multigraph() or G.is_directed():
- raise nx.NetworkxException('Not implemented for directed ',
- 'or multiedge graphs.')
- # test for regular graph (all degrees must be equal)
- degree = G.degree_iter()
- (_,k) = next(degree)
- for _,knext in degree:
- if knext != k:
- raise nx.NetworkXError('Graph is not distance regular.')
- k = knext
- path_length = nx.all_pairs_shortest_path_length(G)
- diameter = max([max(path_length[n].values()) for n in path_length])
- bint = {} # 'b' intersection array
- cint = {} # 'c' intersection array
- for u in G:
- for v in G:
- try:
- i = path_length[u][v]
- except KeyError: # graph must be connected
- raise nx.NetworkXError('Graph is not distance regular.')
- # number of neighbors of v at a distance of i-1 from u
- c = len([n for n in G[v] if path_length[n][u]==i-1])
- # number of neighbors of v at a distance of i+1 from u
- b = len([n for n in G[v] if path_length[n][u]==i+1])
- # b,c are independent of u and v
- if cint.get(i,c) != c or bint.get(i,b) != b:
- raise nx.NetworkXError('Graph is not distance regular')
- bint[i] = b
- cint[i] = c
- return ([bint.get(i,0) for i in range(diameter)],
- [cint.get(i+1,0) for i in range(diameter)])
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/euler.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/euler.py
deleted file mode 100644
index 4e834c7..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/euler.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Eulerian circuits and graphs.
-"""
-import networkx as nx
-
-__author__ = """\n""".join(['Nima Mohammadi (nima.irt[AT]gmail.com)',
- 'Aric Hagberg <hagberg@lanl.gov>'])
-# Copyright (C) 2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['is_eulerian', 'eulerian_circuit']
-
-def is_eulerian(G):
- """Return True if G is an Eulerian graph, False otherwise.
-
- An Eulerian graph is a graph with an Eulerian circuit.
-
- Parameters
- ----------
- G : graph
- A NetworkX Graph
-
- Examples
- --------
- >>> nx.is_eulerian(nx.DiGraph({0:[3], 1:[2], 2:[3], 3:[0, 1]}))
- True
- >>> nx.is_eulerian(nx.complete_graph(5))
- True
- >>> nx.is_eulerian(nx.petersen_graph())
- False
-
- Notes
- -----
- This implementation requires the graph to be connected
- (or strongly connected for directed graphs).
- """
- if G.is_directed():
- # Every node must have equal in degree and out degree
- for n in G.nodes_iter():
- if G.in_degree(n) != G.out_degree(n):
- return False
- # Must be strongly connected
- if not nx.is_strongly_connected(G):
- return False
- else:
- # An undirected Eulerian graph has no vertices of odd degrees
- for v,d in G.degree_iter():
- if d % 2 != 0:
- return False
- # Must be connected
- if not nx.is_connected(G):
- return False
- return True
-
-
-def eulerian_circuit(G, source=None):
- """Return the edges of an Eulerian circuit in G.
-
- An Eulerian circuit is a path that crosses every edge in G exactly once
- and finishes at the starting node.
-
- Parameters
- ----------
- G : graph
- A NetworkX Graph
- source : node, optional
- Starting node for circuit.
-
- Returns
- -------
- edges : generator
- A generator that produces edges in the Eulerian circuit.
-
- Raises
- ------
- NetworkXError
- If the graph is not Eulerian.
-
- See Also
- --------
- is_eulerian
-
- Notes
- -----
- Uses Fleury's algorithm [1]_,[2]_
-
- References
- ----------
- .. [1] Fleury, "Deux problemes de geometrie de situation",
- Journal de mathematiques elementaires (1883), 257-261.
- .. [2] http://en.wikipedia.org/wiki/Eulerian_path
-
- Examples
- --------
- >>> G=nx.complete_graph(3)
- >>> list(nx.eulerian_circuit(G))
- [(0, 1), (1, 2), (2, 0)]
- >>> list(nx.eulerian_circuit(G,source=1))
- [(1, 0), (0, 2), (2, 1)]
- >>> [u for u,v in nx.eulerian_circuit(G)] # nodes in circuit
- [0, 1, 2]
- """
- if not is_eulerian(G):
- raise nx.NetworkXError("G is not Eulerian.")
-
- g = G.__class__(G) # copy graph structure (not attributes)
-
- # set starting node
- if source is None:
- v = next(g.nodes_iter())
- else:
- v = source
-
- while g.size() > 0:
- n = v
- # sort nbrs here to provide stable ordering of alternate cycles
- nbrs = sorted([v for u,v in g.edges(n)])
- for v in nbrs:
- g.remove_edge(n,v)
- bridge = not nx.is_connected(g.to_undirected())
- if bridge:
- g.add_edge(n,v) # add this edge back and try another
- else:
- break # this edge is good, break the for loop
- if bridge:
- g.remove_edge(n,v)
- g.remove_node(n)
- yield (n,v)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/__init__.py
deleted file mode 100644
index 438ab5f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from networkx.algorithms.flow.maxflow import *
-from networkx.algorithms.flow.mincost import *
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/maxflow.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/maxflow.py
deleted file mode 100644
index ee71528..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/maxflow.py
+++ /dev/null
@@ -1,477 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Maximum flow (and minimum cut) algorithms on capacitated graphs.
-"""
-
-__author__ = """Loïc Séguin-C. <loicseguin@gmail.com>"""
-# Copyright (C) 2010 Loïc Séguin-C. <loicseguin@gmail.com>
-# All rights reserved.
-# BSD license.
-
-import networkx as nx
-
-__all__ = ['ford_fulkerson',
- 'ford_fulkerson_flow',
- 'ford_fulkerson_flow_and_auxiliary',
- 'max_flow',
- 'min_cut']
-
-def ford_fulkerson_flow_and_auxiliary(G, s, t, capacity='capacity'):
- """Find a maximum single-commodity flow using the Ford-Fulkerson
- algorithm.
-
- This function returns both the value of the maximum flow and the
- auxiliary network resulting after finding the maximum flow, which
- is also named residual network in the literature. The
- auxiliary network has edges with capacity equal to the capacity
- of the edge in the original network minus the flow that went
- throught that edge. Notice that it can happen that a flow
- from v to u is allowed in the auxiliary network, though disallowed
- in the original network. A dictionary with infinite capacity edges
- can be found as an attribute of the auxiliary network.
-
- Parameters
- ----------
- G : NetworkX graph
- Edges of the graph are expected to have an attribute called
- 'capacity'. If this attribute is not present, the edge is
- considered to have infinite capacity.
-
- s : node
- Source node for the flow.
-
- t : node
- Sink node for the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- Returns
- -------
- flow_value : integer, float
- Value of the maximum flow, i.e., net outflow from the source.
-
- auxiliary : DiGraph
- Residual/auxiliary network after finding the maximum flow.
- A dictionary with infinite capacity edges can be found as
- an attribute of this network: auxiliary.graph['inf_capacity_flows']
-
- Raises
- ------
- NetworkXError
- The algorithm does not support MultiGraph and MultiDiGraph. If
- the input graph is an instance of one of these two classes, a
- NetworkXError is raised.
-
- NetworkXUnbounded
- If the graph has a path of infinite capacity, the value of a
- feasible flow on the graph is unbounded above and the function
- raises a NetworkXUnbounded.
-
- Notes
- -----
- This algorithm uses Edmonds-Karp-Dinitz path selection rule which
- guarantees a running time of `O(nm^2)` for `n` nodes and `m` edges.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_edge('x','a', capacity=3.0)
- >>> G.add_edge('x','b', capacity=1.0)
- >>> G.add_edge('a','c', capacity=3.0)
- >>> G.add_edge('b','c', capacity=5.0)
- >>> G.add_edge('b','d', capacity=4.0)
- >>> G.add_edge('d','e', capacity=2.0)
- >>> G.add_edge('c','y', capacity=2.0)
- >>> G.add_edge('e','y', capacity=3.0)
- >>> flow, auxiliary = nx.ford_fulkerson_flow_and_auxiliary(G, 'x', 'y')
- >>> flow
- 3.0
- >>> # A dictionary with infinite capacity flows can be found as an
- >>> # attribute of the auxiliary network
- >>> inf_capacity_flows = auxiliary.graph['inf_capacity_flows']
-
- """
- if G.is_multigraph():
- raise nx.NetworkXError(
- 'MultiGraph and MultiDiGraph not supported (yet).')
-
- if s not in G:
- raise nx.NetworkXError('node %s not in graph' % str(s))
- if t not in G:
- raise nx.NetworkXError('node %s not in graph' % str(t))
-
- auxiliary = _create_auxiliary_digraph(G, capacity=capacity)
- inf_capacity_flows = auxiliary.graph['inf_capacity_flows']
-
- flow_value = 0 # Initial feasible flow.
-
- # As long as there is an (s, t)-path in the auxiliary digraph, find
- # the shortest (with respect to the number of arcs) such path and
- # augment the flow on this path.
- while True:
- try:
- path_nodes = nx.bidirectional_shortest_path(auxiliary, s, t)
- except nx.NetworkXNoPath:
- break
-
- # Get the list of edges in the shortest path.
- path_edges = list(zip(path_nodes[:-1], path_nodes[1:]))
-
- # Find the minimum capacity of an edge in the path.
- try:
- path_capacity = min([auxiliary[u][v][capacity]
- for u, v in path_edges
- if capacity in auxiliary[u][v]])
- except ValueError:
- # path of infinite capacity implies no max flow
- raise nx.NetworkXUnbounded(
- "Infinite capacity path, flow unbounded above.")
-
- flow_value += path_capacity
-
- # Augment the flow along the path.
- for u, v in path_edges:
- edge_attr = auxiliary[u][v]
- if capacity in edge_attr:
- edge_attr[capacity] -= path_capacity
- if edge_attr[capacity] == 0:
- auxiliary.remove_edge(u, v)
- else:
- inf_capacity_flows[(u, v)] += path_capacity
-
- if auxiliary.has_edge(v, u):
- if capacity in auxiliary[v][u]:
- auxiliary[v][u][capacity] += path_capacity
- else:
- auxiliary.add_edge(v, u, {capacity: path_capacity})
-
- auxiliary.graph['inf_capacity_flows'] = inf_capacity_flows
- return flow_value, auxiliary
-
-def _create_auxiliary_digraph(G, capacity='capacity'):
- """Initialize an auxiliary digraph and dict of infinite capacity
- edges for a given graph G.
- Ignore edges with capacity <= 0.
- """
- auxiliary = nx.DiGraph()
- auxiliary.add_nodes_from(G)
- inf_capacity_flows = {}
- if nx.is_directed(G):
- for edge in G.edges(data = True):
- if capacity in edge[2]:
- if edge[2][capacity] > 0:
- auxiliary.add_edge(*edge)
- else:
- auxiliary.add_edge(*edge)
- inf_capacity_flows[(edge[0], edge[1])] = 0
- else:
- for edge in G.edges(data = True):
- if capacity in edge[2]:
- if edge[2][capacity] > 0:
- auxiliary.add_edge(*edge)
- auxiliary.add_edge(edge[1], edge[0], edge[2])
- else:
- auxiliary.add_edge(*edge)
- auxiliary.add_edge(edge[1], edge[0], edge[2])
- inf_capacity_flows[(edge[0], edge[1])] = 0
- inf_capacity_flows[(edge[1], edge[0])] = 0
-
- auxiliary.graph['inf_capacity_flows'] = inf_capacity_flows
- return auxiliary
-
-
-def _create_flow_dict(G, H, capacity='capacity'):
- """Creates the flow dict of dicts on G corresponding to the
- auxiliary digraph H and infinite capacity edges flows
- inf_capacity_flows.
- """
- inf_capacity_flows = H.graph['inf_capacity_flows']
- flow = dict([(u, {}) for u in G])
-
- if G.is_directed():
- for u, v in G.edges_iter():
- if H.has_edge(u, v):
- if capacity in G[u][v]:
- flow[u][v] = max(0, G[u][v][capacity] - H[u][v][capacity])
- elif G.has_edge(v, u) and not capacity in G[v][u]:
- flow[u][v] = max(0, inf_capacity_flows[(u, v)] -
- inf_capacity_flows[(v, u)])
- else:
- flow[u][v] = max(0, H[v].get(u, {}).get(capacity, 0) -
- G[v].get(u, {}).get(capacity, 0))
- else:
- flow[u][v] = G[u][v][capacity]
-
- else: # undirected
- for u, v in G.edges_iter():
- if H.has_edge(u, v):
- if capacity in G[u][v]:
- flow[u][v] = abs(G[u][v][capacity] - H[u][v][capacity])
- else:
- flow[u][v] = abs(inf_capacity_flows[(u, v)] -
- inf_capacity_flows[(v, u)])
- else:
- flow[u][v] = G[u][v][capacity]
- flow[v][u] = flow[u][v]
-
- return flow
-
-def ford_fulkerson(G, s, t, capacity='capacity'):
- """Find a maximum single-commodity flow using the Ford-Fulkerson
- algorithm.
-
- This algorithm uses Edmonds-Karp-Dinitz path selection rule which
- guarantees a running time of `O(nm^2)` for `n` nodes and `m` edges.
-
-
- Parameters
- ----------
- G : NetworkX graph
- Edges of the graph are expected to have an attribute called
- 'capacity'. If this attribute is not present, the edge is
- considered to have infinite capacity.
-
- s : node
- Source node for the flow.
-
- t : node
- Sink node for the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- Returns
- -------
- flow_value : integer, float
- Value of the maximum flow, i.e., net outflow from the source.
-
- flow_dict : dictionary
- Dictionary of dictionaries keyed by nodes such that
- flow_dict[u][v] is the flow edge (u, v).
-
- Raises
- ------
- NetworkXError
- The algorithm does not support MultiGraph and MultiDiGraph. If
- the input graph is an instance of one of these two classes, a
- NetworkXError is raised.
-
- NetworkXUnbounded
- If the graph has a path of infinite capacity, the value of a
- feasible flow on the graph is unbounded above and the function
- raises a NetworkXUnbounded.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_edge('x','a', capacity=3.0)
- >>> G.add_edge('x','b', capacity=1.0)
- >>> G.add_edge('a','c', capacity=3.0)
- >>> G.add_edge('b','c', capacity=5.0)
- >>> G.add_edge('b','d', capacity=4.0)
- >>> G.add_edge('d','e', capacity=2.0)
- >>> G.add_edge('c','y', capacity=2.0)
- >>> G.add_edge('e','y', capacity=3.0)
- >>> flow, F = nx.ford_fulkerson(G, 'x', 'y')
- >>> flow
- 3.0
- """
- flow_value, auxiliary = ford_fulkerson_flow_and_auxiliary(G,
- s, t, capacity=capacity)
- flow_dict = _create_flow_dict(G, auxiliary, capacity=capacity)
- return flow_value, flow_dict
-
-def ford_fulkerson_flow(G, s, t, capacity='capacity'):
- """Return a maximum flow for a single-commodity flow problem.
-
- Parameters
- ----------
- G : NetworkX graph
- Edges of the graph are expected to have an attribute called
- 'capacity'. If this attribute is not present, the edge is
- considered to have infinite capacity.
-
- s : node
- Source node for the flow.
-
- t : node
- Sink node for the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- Returns
- -------
- flow_dict : dictionary
- Dictionary of dictionaries keyed by nodes such that
- flow_dict[u][v] is the flow edge (u, v).
-
- Raises
- ------
- NetworkXError
- The algorithm does not support MultiGraph and MultiDiGraph. If
- the input graph is an instance of one of these two classes, a
- NetworkXError is raised.
-
- NetworkXUnbounded
- If the graph has a path of infinite capacity, the value of a
- feasible flow on the graph is unbounded above and the function
- raises a NetworkXUnbounded.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_edge('x','a', capacity=3.0)
- >>> G.add_edge('x','b', capacity=1.0)
- >>> G.add_edge('a','c', capacity=3.0)
- >>> G.add_edge('b','c', capacity=5.0)
- >>> G.add_edge('b','d', capacity=4.0)
- >>> G.add_edge('d','e', capacity=2.0)
- >>> G.add_edge('c','y', capacity=2.0)
- >>> G.add_edge('e','y', capacity=3.0)
- >>> F = nx.ford_fulkerson_flow(G, 'x', 'y')
- >>> for u, v in sorted(G.edges_iter()):
- ... print('(%s, %s) %.2f' % (u, v, F[u][v]))
- ...
- (a, c) 2.00
- (b, c) 0.00
- (b, d) 1.00
- (c, y) 2.00
- (d, e) 1.00
- (e, y) 1.00
- (x, a) 2.00
- (x, b) 1.00
- """
- flow_value, auxiliary = ford_fulkerson_flow_and_auxiliary(G,
- s, t, capacity=capacity)
- return _create_flow_dict(G, auxiliary, capacity=capacity)
-
-def max_flow(G, s, t, capacity='capacity'):
- """Find the value of a maximum single-commodity flow.
-
- Parameters
- ----------
- G : NetworkX graph
- Edges of the graph are expected to have an attribute called
- 'capacity'. If this attribute is not present, the edge is
- considered to have infinite capacity.
-
- s : node
- Source node for the flow.
-
- t : node
- Sink node for the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- Returns
- -------
- flow_value : integer, float
- Value of the maximum flow, i.e., net outflow from the source.
-
- Raises
- ------
- NetworkXError
- The algorithm does not support MultiGraph and MultiDiGraph. If
- the input graph is an instance of one of these two classes, a
- NetworkXError is raised.
-
- NetworkXUnbounded
- If the graph has a path of infinite capacity, the value of a
- feasible flow on the graph is unbounded above and the function
- raises a NetworkXUnbounded.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_edge('x','a', capacity=3.0)
- >>> G.add_edge('x','b', capacity=1.0)
- >>> G.add_edge('a','c', capacity=3.0)
- >>> G.add_edge('b','c', capacity=5.0)
- >>> G.add_edge('b','d', capacity=4.0)
- >>> G.add_edge('d','e', capacity=2.0)
- >>> G.add_edge('c','y', capacity=2.0)
- >>> G.add_edge('e','y', capacity=3.0)
- >>> flow = nx.max_flow(G, 'x', 'y')
- >>> flow
- 3.0
- """
- return ford_fulkerson_flow_and_auxiliary(G, s, t, capacity=capacity)[0]
-
-
-def min_cut(G, s, t, capacity='capacity'):
- """Compute the value of a minimum (s, t)-cut.
-
- Use the max-flow min-cut theorem, i.e., the capacity of a minimum
- capacity cut is equal to the flow value of a maximum flow.
-
- Parameters
- ----------
- G : NetworkX graph
- Edges of the graph are expected to have an attribute called
- 'capacity'. If this attribute is not present, the edge is
- considered to have infinite capacity.
-
- s : node
- Source node for the flow.
-
- t : node
- Sink node for the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- Returns
- -------
- cutValue : integer, float
- Value of the minimum cut.
-
- Raises
- ------
- NetworkXUnbounded
- If the graph has a path of infinite capacity, all cuts have
- infinite capacity and the function raises a NetworkXError.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_edge('x','a', capacity = 3.0)
- >>> G.add_edge('x','b', capacity = 1.0)
- >>> G.add_edge('a','c', capacity = 3.0)
- >>> G.add_edge('b','c', capacity = 5.0)
- >>> G.add_edge('b','d', capacity = 4.0)
- >>> G.add_edge('d','e', capacity = 2.0)
- >>> G.add_edge('c','y', capacity = 2.0)
- >>> G.add_edge('e','y', capacity = 3.0)
- >>> nx.min_cut(G, 'x', 'y')
- 3.0
- """
-
- try:
- return ford_fulkerson_flow_and_auxiliary(G, s, t, capacity=capacity)[0]
- except nx.NetworkXUnbounded:
- raise nx.NetworkXUnbounded(
- "Infinite capacity path, no minimum cut.")
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/mincost.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/mincost.py
deleted file mode 100644
index 9e5f574..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/mincost.py
+++ /dev/null
@@ -1,802 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Minimum cost flow algorithms on directed connected graphs.
-"""
-
-__author__ = """Loïc Séguin-C. <loicseguin@gmail.com>"""
-# Copyright (C) 2010 Loïc Séguin-C. <loicseguin@gmail.com>
-# All rights reserved.
-# BSD license.
-
-
-__all__ = ['network_simplex',
- 'min_cost_flow_cost',
- 'min_cost_flow',
- 'cost_of_flow',
- 'max_flow_min_cost']
-
-import networkx as nx
-from networkx.utils import generate_unique_node
-
-def _initial_tree_solution(G, demand = 'demand', capacity = 'capacity',
- weight = 'weight'):
- """Find a initial tree solution rooted at r.
-
- The initial tree solution is obtained by considering edges (r, v)
- for all nodes v with non-negative demand and (v, r) for all nodes
- with negative demand. If these edges do not exist, we add them to
- the graph and call them artificial edges.
- """
- H = nx.DiGraph((edge for edge in G.edges(data=True) if
- edge[2].get(capacity, 1) > 0))
- demand_nodes = (node for node in G.nodes_iter(data=True) if
- node[1].get(demand, 0) != 0)
- H.add_nodes_from(demand_nodes)
- r = H.nodes()[0]
-
- T = nx.DiGraph()
- y = {r: 0}
- artificialEdges = []
- flowCost = 0
-
- n = H.number_of_nodes()
- try:
- maxWeight = max(abs(d[weight]) for u, v, d in H.edges(data = True)
- if weight in d)
- except ValueError:
- maxWeight = 0
- hugeWeight = 1 + n * maxWeight
-
- for v, d in H.nodes(data = True)[1:]:
- vDemand = d.get(demand, 0)
- if vDemand >= 0:
- if not (r, v) in H.edges():
- H.add_edge(r, v, {weight: hugeWeight, 'flow': vDemand})
- artificialEdges.append((r, v))
- y[v] = H[r][v].get(weight, 0)
- T.add_edge(r, v)
- flowCost += vDemand * H[r][v].get(weight, 0)
-
- else: # (r, v) in H.edges()
- if (not capacity in H[r][v]
- or vDemand <= H[r][v][capacity]):
- H[r][v]['flow'] = vDemand
- y[v] = H[r][v].get(weight, 0)
- T.add_edge(r, v)
- flowCost += vDemand * H[r][v].get(weight, 0)
-
- else: # existing edge does not have enough capacity
- newLabel = generate_unique_node()
- H.add_edge(r, newLabel, {weight: hugeWeight, 'flow': vDemand})
- H.add_edge(newLabel, v, {weight: hugeWeight, 'flow': vDemand})
- artificialEdges.append((r, newLabel))
- artificialEdges.append((newLabel, v))
- y[v] = 2 * hugeWeight
- y[newLabel] = hugeWeight
- T.add_edge(r, newLabel)
- T.add_edge(newLabel, v)
- flowCost += 2 * vDemand * hugeWeight
-
- else: # vDemand < 0
- if not (v, r) in H.edges():
- H.add_edge(v, r, {weight: hugeWeight, 'flow': -vDemand})
- artificialEdges.append((v, r))
- y[v] = -H[v][r].get(weight, 0)
- T.add_edge(v, r)
- flowCost += -vDemand * H[v][r].get(weight, 0)
-
- else:
- if (not capacity in H[v][r]
- or -vDemand <= H[v][r][capacity]):
- H[v][r]['flow'] = -vDemand
- y[v] = -H[v][r].get(weight, 0)
- T.add_edge(v, r)
- flowCost += -vDemand * H[v][r].get(weight, 0)
- else: # existing edge does not have enough capacity
- newLabel = generate_unique_node()
- H.add_edge(v, newLabel,
- {weight: hugeWeight, 'flow': -vDemand})
- H.add_edge(newLabel, r,
- {weight: hugeWeight, 'flow': -vDemand})
- artificialEdges.append((v, newLabel))
- artificialEdges.append((newLabel, r))
- y[v] = -2 * hugeWeight
- y[newLabel] = -hugeWeight
- T.add_edge(v, newLabel)
- T.add_edge(newLabel, r)
- flowCost += 2 * -vDemand * hugeWeight
-
- return H, T, y, artificialEdges, flowCost, r
-
-
-def _find_entering_edge(H, c, capacity = 'capacity'):
- """Find an edge which creates a negative cost cycle in the actual
- tree solution.
-
- The reduced cost of every edge gives the value of the cycle
- obtained by adding that edge to the tree solution. If that value is
- negative, we will augment the flow in the direction indicated by
- the edge. Otherwise, we will augment the flow in the reverse
- direction.
-
- If no edge is found, return and empty tuple. This will cause the
- main loop of the algorithm to terminate.
- """
- newEdge = ()
- for u, v, d in H.edges_iter(data = True):
- if d.get('flow', 0) == 0:
- if c[(u, v)] < 0:
- newEdge = (u, v)
- break
- else:
- if capacity in d:
- if (d.get('flow', 0) == d[capacity]
- and c[(u, v)] > 0):
- newEdge = (u, v)
- break
- return newEdge
-
-
-def _find_leaving_edge(H, T, cycle, newEdge, capacity = 'capacity',
- reverse=False):
- """Find an edge that will leave the basis and the value by which we
- can increase or decrease the flow on that edge.
-
- The leaving arc rule is used to prevent cycling.
-
- If cycle has no reverse edge and no forward edge of finite
- capacity, it means that cycle is a negative cost infinite capacity
- cycle. This implies that the cost of a flow satisfying all demands
- is unbounded below. An exception is raised in this case.
- """
- eps = False
- leavingEdge = ()
-
- # If cycle is a digon.
- if len(cycle) == 3:
- u, v = newEdge
- if capacity not in H[u][v] and capacity not in H[v][u]:
- raise nx.NetworkXUnbounded(
- "Negative cost cycle of infinite capacity found. "
- + "Min cost flow unbounded below.")
-
- if reverse:
- if H[u][v].get('flow', 0) > H[v][u].get('flow', 0):
- return (v, u), H[v][u].get('flow', 0)
- else:
- return (u, v), H[u][v].get('flow', 0)
- else:
- uv_residual = H[u][v].get(capacity, 0) - H[u][v].get('flow', 0)
- vu_residual = H[v][u].get(capacity, 0) - H[v][u].get('flow', 0)
- if (uv_residual > vu_residual):
- return (v, u), vu_residual
- else:
- return (u, v), uv_residual
-
- # Find the forward edge with the minimum value for capacity - 'flow'
- # and the reverse edge with the minimum value for 'flow'.
- for index, u in enumerate(cycle[:-1]):
- edgeCapacity = False
- edge = ()
- v = cycle[index + 1]
- if (u, v) in T.edges() + [newEdge]: #forward edge
- if capacity in H[u][v]: # edge (u, v) has finite capacity
- edgeCapacity = H[u][v][capacity] - H[u][v].get('flow', 0)
- edge = (u, v)
- else: #reverse edge
- edgeCapacity = H[v][u].get('flow', 0)
- edge = (v, u)
-
- # Determine if edge might be the leaving edge.
- if edge:
- if leavingEdge:
- if edgeCapacity < eps:
- eps = edgeCapacity
- leavingEdge = edge
- else:
- eps = edgeCapacity
- leavingEdge = edge
-
- if not leavingEdge:
- raise nx.NetworkXUnbounded(
- "Negative cost cycle of infinite capacity found. "
- + "Min cost flow unbounded below.")
-
- return leavingEdge, eps
-
-
-def _create_flow_dict(G, H):
- """Creates the flow dict of dicts of graph G with auxiliary graph H."""
- flowDict = dict([(u, {}) for u in G])
-
- for u in G.nodes_iter():
- for v in G.neighbors(u):
- if H.has_edge(u, v):
- flowDict[u][v] = H[u][v].get('flow', 0)
- else:
- flowDict[u][v] = 0
- return flowDict
-
-
-def network_simplex(G, demand = 'demand', capacity = 'capacity',
- weight = 'weight'):
- """Find a minimum cost flow satisfying all demands in digraph G.
-
- This is a primal network simplex algorithm that uses the leaving
- arc rule to prevent cycling.
-
- G is a digraph with edge costs and capacities and in which nodes
- have demand, i.e., they want to send or receive some amount of
- flow. A negative demand means that the node wants to send flow, a
- positive demand means that the node want to receive flow. A flow on
- the digraph G satisfies all demand if the net flow into each node
- is equal to the demand of that node.
-
- Parameters
- ----------
- G : NetworkX graph
- DiGraph on which a minimum cost flow satisfying all demands is
- to be found.
-
- demand: string
- Nodes of the graph G are expected to have an attribute demand
- that indicates how much flow a node wants to send (negative
- demand) or receive (positive demand). Note that the sum of the
- demands should be 0 otherwise the problem in not feasible. If
- this attribute is not present, a node is considered to have 0
- demand. Default value: 'demand'.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- weight: string
- Edges of the graph G are expected to have an attribute weight
- that indicates the cost incurred by sending one unit of flow on
- that edge. If not present, the weight is considered to be 0.
- Default value: 'weight'.
-
- Returns
- -------
- flowCost: integer, float
- Cost of a minimum cost flow satisfying all demands.
-
- flowDict: dictionary
- Dictionary of dictionaries keyed by nodes such that
- flowDict[u][v] is the flow edge (u, v).
-
- Raises
- ------
- NetworkXError
- This exception is raised if the input graph is not directed,
- not connected or is a multigraph.
-
- NetworkXUnfeasible
- This exception is raised in the following situations:
- * The sum of the demands is not zero. Then, there is no
- flow satisfying all demands.
- * There is no flow satisfying all demand.
-
- NetworkXUnbounded
- This exception is raised if the digraph G has a cycle of
- negative cost and infinite capacity. Then, the cost of a flow
- satisfying all demands is unbounded below.
-
- Notes
- -----
- This algorithm is not guaranteed to work if edge weights
- are floating point numbers (overflows and roundoff errors can
- cause problems).
-
- See also
- --------
- cost_of_flow, max_flow_min_cost, min_cost_flow, min_cost_flow_cost
-
- Examples
- --------
- A simple example of a min cost flow problem.
-
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_node('a', demand = -5)
- >>> G.add_node('d', demand = 5)
- >>> G.add_edge('a', 'b', weight = 3, capacity = 4)
- >>> G.add_edge('a', 'c', weight = 6, capacity = 10)
- >>> G.add_edge('b', 'd', weight = 1, capacity = 9)
- >>> G.add_edge('c', 'd', weight = 2, capacity = 5)
- >>> flowCost, flowDict = nx.network_simplex(G)
- >>> flowCost
- 24
- >>> flowDict # doctest: +SKIP
- {'a': {'c': 1, 'b': 4}, 'c': {'d': 1}, 'b': {'d': 4}, 'd': {}}
-
- The mincost flow algorithm can also be used to solve shortest path
- problems. To find the shortest path between two nodes u and v,
- give all edges an infinite capacity, give node u a demand of -1 and
- node v a demand a 1. Then run the network simplex. The value of a
- min cost flow will be the distance between u and v and edges
- carrying positive flow will indicate the path.
-
- >>> G=nx.DiGraph()
- >>> G.add_weighted_edges_from([('s','u',10), ('s','x',5),
- ... ('u','v',1), ('u','x',2),
- ... ('v','y',1), ('x','u',3),
- ... ('x','v',5), ('x','y',2),
- ... ('y','s',7), ('y','v',6)])
- >>> G.add_node('s', demand = -1)
- >>> G.add_node('v', demand = 1)
- >>> flowCost, flowDict = nx.network_simplex(G)
- >>> flowCost == nx.shortest_path_length(G, 's', 'v', weight = 'weight')
- True
- >>> sorted([(u, v) for u in flowDict for v in flowDict[u] if flowDict[u][v] > 0])
- [('s', 'x'), ('u', 'v'), ('x', 'u')]
- >>> nx.shortest_path(G, 's', 'v', weight = 'weight')
- ['s', 'x', 'u', 'v']
-
- It is possible to change the name of the attributes used for the
- algorithm.
-
- >>> G = nx.DiGraph()
- >>> G.add_node('p', spam = -4)
- >>> G.add_node('q', spam = 2)
- >>> G.add_node('a', spam = -2)
- >>> G.add_node('d', spam = -1)
- >>> G.add_node('t', spam = 2)
- >>> G.add_node('w', spam = 3)
- >>> G.add_edge('p', 'q', cost = 7, vacancies = 5)
- >>> G.add_edge('p', 'a', cost = 1, vacancies = 4)
- >>> G.add_edge('q', 'd', cost = 2, vacancies = 3)
- >>> G.add_edge('t', 'q', cost = 1, vacancies = 2)
- >>> G.add_edge('a', 't', cost = 2, vacancies = 4)
- >>> G.add_edge('d', 'w', cost = 3, vacancies = 4)
- >>> G.add_edge('t', 'w', cost = 4, vacancies = 1)
- >>> flowCost, flowDict = nx.network_simplex(G, demand = 'spam',
- ... capacity = 'vacancies',
- ... weight = 'cost')
- >>> flowCost
- 37
- >>> flowDict # doctest: +SKIP
- {'a': {'t': 4}, 'd': {'w': 2}, 'q': {'d': 1}, 'p': {'q': 2, 'a': 2}, 't': {'q': 1, 'w': 1}, 'w': {}}
-
- References
- ----------
- W. J. Cook, W. H. Cunningham, W. R. Pulleyblank and A. Schrijver.
- Combinatorial Optimization. Wiley-Interscience, 1998.
-
- """
-
- if not G.is_directed():
- raise nx.NetworkXError("Undirected graph not supported.")
- if not nx.is_connected(G.to_undirected()):
- raise nx.NetworkXError("Not connected graph not supported.")
- if G.is_multigraph():
- raise nx.NetworkXError("MultiDiGraph not supported.")
- if sum(d[demand] for v, d in G.nodes(data = True)
- if demand in d) != 0:
- raise nx.NetworkXUnfeasible("Sum of the demands should be 0.")
-
- # Fix an arbitrarily chosen root node and find an initial tree solution.
- H, T, y, artificialEdges, flowCost, r = \
- _initial_tree_solution(G, demand = demand, capacity = capacity,
- weight = weight)
-
- # Initialize the reduced costs.
- c = {}
- for u, v, d in H.edges_iter(data = True):
- c[(u, v)] = d.get(weight, 0) + y[u] - y[v]
-
- # Print stuff for debugging.
- # print('-' * 78)
- # nbIter = 0
- # print('Iteration %d' % nbIter)
- # nbIter += 1
- # print('Tree solution: %s' % T.edges())
- # print(' Edge %11s%10s' % ('Flow', 'Red Cost'))
- # for u, v, d in H.edges(data = True):
- # flag = ''
- # if (u, v) in artificialEdges:
- # flag = '*'
- # print('(%s, %s)%1s%10d%10d' % (u, v, flag, d.get('flow', 0),
- # c[(u, v)]))
- # print('Distances: %s' % y)
-
- # Main loop.
- while True:
- newEdge = _find_entering_edge(H, c, capacity = capacity)
- if not newEdge:
- break # Optimal basis found. Main loop is over.
- cycleCost = abs(c[newEdge])
-
- # Find the cycle created by adding newEdge to T.
- path1 = nx.shortest_path(T.to_undirected(), r, newEdge[0])
- path2 = nx.shortest_path(T.to_undirected(), r, newEdge[1])
- join = r
- for index, node in enumerate(path1[1:]):
- if index + 1 < len(path2) and node == path2[index + 1]:
- join = node
- else:
- break
- path1 = path1[path1.index(join):]
- path2 = path2[path2.index(join):]
- cycle = []
- if H[newEdge[0]][newEdge[1]].get('flow', 0) == 0:
- reverse = False
- path2.reverse()
- cycle = path1 + path2
- else: # newEdge is at capacity
- reverse = True
- path1.reverse()
- cycle = path2 + path1
-
- # Find the leaving edge. Will stop here if cycle is an infinite
- # capacity negative cost cycle.
- leavingEdge, eps = _find_leaving_edge(H, T, cycle, newEdge,
- capacity=capacity,
- reverse=reverse)
-
- # Actual augmentation happens here. If eps = 0, don't bother.
- if eps:
- flowCost -= cycleCost * eps
- if len(cycle) == 3:
- if reverse:
- eps = -eps
- u, v = newEdge
- H[u][v]['flow'] = H[u][v].get('flow', 0) + eps
- H[v][u]['flow'] = H[v][u].get('flow', 0) + eps
- else:
- for index, u in enumerate(cycle[:-1]):
- v = cycle[index + 1]
- if (u, v) in T.edges() + [newEdge]:
- H[u][v]['flow'] = H[u][v].get('flow', 0) + eps
- else: # (v, u) in T.edges():
- H[v][u]['flow'] -= eps
-
- # Update tree solution.
- T.add_edge(*newEdge)
- T.remove_edge(*leavingEdge)
-
- # Update distances and reduced costs.
- if newEdge != leavingEdge:
- forest = nx.DiGraph(T)
- forest.remove_edge(*newEdge)
- R, notR = nx.connected_component_subgraphs(forest.to_undirected())
- if r in notR.nodes(): # make sure r is in R
- R, notR = notR, R
- if newEdge[0] in R.nodes():
- for v in notR.nodes():
- y[v] += c[newEdge]
- else:
- for v in notR.nodes():
- y[v] -= c[newEdge]
- for u, v in H.edges():
- if u in notR.nodes() or v in notR.nodes():
- c[(u, v)] = H[u][v].get(weight, 0) + y[u] - y[v]
-
- # Print stuff for debugging.
- # print('-' * 78)
- # print('Iteration %d' % nbIter)
- # nbIter += 1
- # print('Tree solution: %s' % T.edges())
- # print('New edge: (%s, %s)' % (newEdge[0], newEdge[1]))
- # print('Leaving edge: (%s, %s)' % (leavingEdge[0], leavingEdge[1]))
- # print('Cycle: %s' % cycle)
- # print('eps: %d' % eps)
- # print(' Edge %11s%10s' % ('Flow', 'Red Cost'))
- # for u, v, d in H.edges(data = True):
- # flag = ''
- # if (u, v) in artificialEdges:
- # flag = '*'
- # print('(%s, %s)%1s%10d%10d' % (u, v, flag, d.get('flow', 0),
- # c[(u, v)]))
- # print('Distances: %s' % y)
-
-
- # If an artificial edge has positive flow, the initial problem was
- # not feasible.
- for u, v in artificialEdges:
- if H[u][v]['flow'] != 0:
- raise nx.NetworkXUnfeasible("No flow satisfying all demands.")
- H.remove_edge(u, v)
-
- for u in H.nodes():
- if not u in G:
- H.remove_node(u)
-
- flowDict = _create_flow_dict(G, H)
-
- return flowCost, flowDict
-
-
-def min_cost_flow_cost(G, demand = 'demand', capacity = 'capacity',
- weight = 'weight'):
- """Find the cost of a minimum cost flow satisfying all demands in digraph G.
-
- G is a digraph with edge costs and capacities and in which nodes
- have demand, i.e., they want to send or receive some amount of
- flow. A negative demand means that the node wants to send flow, a
- positive demand means that the node want to receive flow. A flow on
- the digraph G satisfies all demand if the net flow into each node
- is equal to the demand of that node.
-
- Parameters
- ----------
- G : NetworkX graph
- DiGraph on which a minimum cost flow satisfying all demands is
- to be found.
-
- demand: string
- Nodes of the graph G are expected to have an attribute demand
- that indicates how much flow a node wants to send (negative
- demand) or receive (positive demand). Note that the sum of the
- demands should be 0 otherwise the problem in not feasible. If
- this attribute is not present, a node is considered to have 0
- demand. Default value: 'demand'.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- weight: string
- Edges of the graph G are expected to have an attribute weight
- that indicates the cost incurred by sending one unit of flow on
- that edge. If not present, the weight is considered to be 0.
- Default value: 'weight'.
-
- Returns
- -------
- flowCost: integer, float
- Cost of a minimum cost flow satisfying all demands.
-
- Raises
- ------
- NetworkXError
- This exception is raised if the input graph is not directed or
- not connected.
-
- NetworkXUnfeasible
- This exception is raised in the following situations:
- * The sum of the demands is not zero. Then, there is no
- flow satisfying all demands.
- * There is no flow satisfying all demand.
-
- NetworkXUnbounded
- This exception is raised if the digraph G has a cycle of
- negative cost and infinite capacity. Then, the cost of a flow
- satisfying all demands is unbounded below.
-
- See also
- --------
- cost_of_flow, max_flow_min_cost, min_cost_flow, network_simplex
-
- Examples
- --------
- A simple example of a min cost flow problem.
-
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_node('a', demand = -5)
- >>> G.add_node('d', demand = 5)
- >>> G.add_edge('a', 'b', weight = 3, capacity = 4)
- >>> G.add_edge('a', 'c', weight = 6, capacity = 10)
- >>> G.add_edge('b', 'd', weight = 1, capacity = 9)
- >>> G.add_edge('c', 'd', weight = 2, capacity = 5)
- >>> flowCost = nx.min_cost_flow_cost(G)
- >>> flowCost
- 24
- """
- return network_simplex(G, demand = demand, capacity = capacity,
- weight = weight)[0]
-
-
-def min_cost_flow(G, demand = 'demand', capacity = 'capacity',
- weight = 'weight'):
- """Return a minimum cost flow satisfying all demands in digraph G.
-
- G is a digraph with edge costs and capacities and in which nodes
- have demand, i.e., they want to send or receive some amount of
- flow. A negative demand means that the node wants to send flow, a
- positive demand means that the node want to receive flow. A flow on
- the digraph G satisfies all demand if the net flow into each node
- is equal to the demand of that node.
-
- Parameters
- ----------
- G : NetworkX graph
- DiGraph on which a minimum cost flow satisfying all demands is
- to be found.
-
- demand: string
- Nodes of the graph G are expected to have an attribute demand
- that indicates how much flow a node wants to send (negative
- demand) or receive (positive demand). Note that the sum of the
- demands should be 0 otherwise the problem in not feasible. If
- this attribute is not present, a node is considered to have 0
- demand. Default value: 'demand'.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- weight: string
- Edges of the graph G are expected to have an attribute weight
- that indicates the cost incurred by sending one unit of flow on
- that edge. If not present, the weight is considered to be 0.
- Default value: 'weight'.
-
- Returns
- -------
- flowDict: dictionary
- Dictionary of dictionaries keyed by nodes such that
- flowDict[u][v] is the flow edge (u, v).
-
- Raises
- ------
- NetworkXError
- This exception is raised if the input graph is not directed or
- not connected.
-
- NetworkXUnfeasible
- This exception is raised in the following situations:
- * The sum of the demands is not zero. Then, there is no
- flow satisfying all demands.
- * There is no flow satisfying all demand.
-
- NetworkXUnbounded
- This exception is raised if the digraph G has a cycle of
- negative cost and infinite capacity. Then, the cost of a flow
- satisfying all demands is unbounded below.
-
- See also
- --------
- cost_of_flow, max_flow_min_cost, min_cost_flow_cost, network_simplex
-
- Examples
- --------
- A simple example of a min cost flow problem.
-
- >>> import networkx as nx
- >>> G = nx.DiGraph()
- >>> G.add_node('a', demand = -5)
- >>> G.add_node('d', demand = 5)
- >>> G.add_edge('a', 'b', weight = 3, capacity = 4)
- >>> G.add_edge('a', 'c', weight = 6, capacity = 10)
- >>> G.add_edge('b', 'd', weight = 1, capacity = 9)
- >>> G.add_edge('c', 'd', weight = 2, capacity = 5)
- >>> flowDict = nx.min_cost_flow(G)
- """
- return network_simplex(G, demand = demand, capacity = capacity,
- weight = weight)[1]
-
-
-def cost_of_flow(G, flowDict, weight = 'weight'):
- """Compute the cost of the flow given by flowDict on graph G.
-
- Note that this function does not check for the validity of the
- flow flowDict. This function will fail if the graph G and the
- flow don't have the same edge set.
-
- Parameters
- ----------
- G : NetworkX graph
- DiGraph on which a minimum cost flow satisfying all demands is
- to be found.
-
- weight: string
- Edges of the graph G are expected to have an attribute weight
- that indicates the cost incurred by sending one unit of flow on
- that edge. If not present, the weight is considered to be 0.
- Default value: 'weight'.
-
- flowDict: dictionary
- Dictionary of dictionaries keyed by nodes such that
- flowDict[u][v] is the flow edge (u, v).
-
- Returns
- -------
- cost: Integer, float
- The total cost of the flow. This is given by the sum over all
- edges of the product of the edge's flow and the edge's weight.
-
- See also
- --------
- max_flow_min_cost, min_cost_flow, min_cost_flow_cost, network_simplex
- """
- return sum((flowDict[u][v] * d.get(weight, 0)
- for u, v, d in G.edges_iter(data = True)))
-
-
-def max_flow_min_cost(G, s, t, capacity = 'capacity', weight = 'weight'):
- """Return a maximum (s, t)-flow of minimum cost.
-
- G is a digraph with edge costs and capacities. There is a source
- node s and a sink node t. This function finds a maximum flow from
- s to t whose total cost is minimized.
-
- Parameters
- ----------
- G : NetworkX graph
- DiGraph on which a minimum cost flow satisfying all demands is
- to be found.
-
- s: node label
- Source of the flow.
-
- t: node label
- Destination of the flow.
-
- capacity: string
- Edges of the graph G are expected to have an attribute capacity
- that indicates how much flow the edge can support. If this
- attribute is not present, the edge is considered to have
- infinite capacity. Default value: 'capacity'.
-
- weight: string
- Edges of the graph G are expected to have an attribute weight
- that indicates the cost incurred by sending one unit of flow on
- that edge. If not present, the weight is considered to be 0.
- Default value: 'weight'.
-
- Returns
- -------
- flowDict: dictionary
- Dictionary of dictionaries keyed by nodes such that
- flowDict[u][v] is the flow edge (u, v).
-
- Raises
- ------
- NetworkXError
- This exception is raised if the input graph is not directed or
- not connected.
-
- NetworkXUnbounded
- This exception is raised if there is an infinite capacity path
- from s to t in G. In this case there is no maximum flow. This
- exception is also raised if the digraph G has a cycle of
- negative cost and infinite capacity. Then, the cost of a flow
- is unbounded below.
-
- See also
- --------
- cost_of_flow, ford_fulkerson, min_cost_flow, min_cost_flow_cost,
- network_simplex
-
- Examples
- --------
- >>> G = nx.DiGraph()
- >>> G.add_edges_from([(1, 2, {'capacity': 12, 'weight': 4}),
- ... (1, 3, {'capacity': 20, 'weight': 6}),
- ... (2, 3, {'capacity': 6, 'weight': -3}),
- ... (2, 6, {'capacity': 14, 'weight': 1}),
- ... (3, 4, {'weight': 9}),
- ... (3, 5, {'capacity': 10, 'weight': 5}),
- ... (4, 2, {'capacity': 19, 'weight': 13}),
- ... (4, 5, {'capacity': 4, 'weight': 0}),
- ... (5, 7, {'capacity': 28, 'weight': 2}),
- ... (6, 5, {'capacity': 11, 'weight': 1}),
- ... (6, 7, {'weight': 8}),
- ... (7, 4, {'capacity': 6, 'weight': 6})])
- >>> mincostFlow = nx.max_flow_min_cost(G, 1, 7)
- >>> nx.cost_of_flow(G, mincostFlow)
- 373
- >>> maxFlow = nx.ford_fulkerson_flow(G, 1, 7)
- >>> nx.cost_of_flow(G, maxFlow)
- 428
- >>> mincostFlowValue = (sum((mincostFlow[u][7] for u in G.predecessors(7)))
- ... - sum((mincostFlow[7][v] for v in G.successors(7))))
- >>> mincostFlowValue == nx.max_flow(G, 1, 7)
- True
-
-
- """
- maxFlow = nx.max_flow(G, s, t, capacity = capacity)
- H = nx.DiGraph(G)
- H.add_node(s, demand = -maxFlow)
- H.add_node(t, demand = maxFlow)
- return min_cost_flow(H, capacity = capacity, weight = weight)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_maxflow.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_maxflow.py
deleted file mode 100644
index 41393c2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_maxflow.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Max flow algorithm test suite.
-
-Run with nose: nosetests -v test_max_flow.py
-"""
-
-__author__ = """Loïc Séguin-C. <loicseguin@gmail.com>"""
-# Copyright (C) 2010 Loïc Séguin-C. <loicseguin@gmail.com>
-# All rights reserved.
-# BSD license.
-
-
-import networkx as nx
-from nose.tools import *
-
-def compare_flows(G, s, t, solnFlows, solnValue):
- flowValue, flowDict = nx.ford_fulkerson(G, s, t)
- assert_equal(flowValue, solnValue)
- assert_equal(flowDict, solnFlows)
- assert_equal(nx.min_cut(G, s, t), solnValue)
- assert_equal(nx.max_flow(G, s, t), solnValue)
- assert_equal(nx.ford_fulkerson_flow(G, s, t), solnFlows)
-
-
-class TestMaxflow:
- def test_graph1(self):
- # Trivial undirected graph
- G = nx.Graph()
- G.add_edge(1,2, capacity = 1.0)
-
- solnFlows = {1: {2: 1.0},
- 2: {1: 1.0}}
-
- compare_flows(G, 1, 2, solnFlows, 1.0)
-
- def test_graph2(self):
- # A more complex undirected graph
- # adapted from www.topcoder.com/tc?module=Statc&d1=tutorials&d2=maxFlow
- G = nx.Graph()
- G.add_edge('x','a', capacity = 3.0)
- G.add_edge('x','b', capacity = 1.0)
- G.add_edge('a','c', capacity = 3.0)
- G.add_edge('b','c', capacity = 5.0)
- G.add_edge('b','d', capacity = 4.0)
- G.add_edge('d','e', capacity = 2.0)
- G.add_edge('c','y', capacity = 2.0)
- G.add_edge('e','y', capacity = 3.0)
-
- H = {'x': {'a': 3, 'b': 1},
- 'a': {'c': 3, 'x': 3},
- 'b': {'c': 1, 'd': 2, 'x': 1},
- 'c': {'a': 3, 'b': 1, 'y': 2},
- 'd': {'b': 2, 'e': 2},
- 'e': {'d': 2, 'y': 2},
- 'y': {'c': 2, 'e': 2}}
-
- compare_flows(G, 'x', 'y', H, 4.0)
-
- def test_digraph1(self):
- # The classic directed graph example
- G = nx.DiGraph()
- G.add_edge('a','b', capacity = 1000.0)
- G.add_edge('a','c', capacity = 1000.0)
- G.add_edge('b','c', capacity = 1.0)
- G.add_edge('b','d', capacity = 1000.0)
- G.add_edge('c','d', capacity = 1000.0)
-
- H = {'a': {'b': 1000.0, 'c': 1000.0},
- 'b': {'c': 0, 'd': 1000.0},
- 'c': {'d': 1000.0},
- 'd': {}}
-
- compare_flows(G, 'a', 'd', H, 2000.0)
-
- # An example in which some edges end up with zero flow.
- G = nx.DiGraph()
- G.add_edge('s', 'b', capacity = 2)
- G.add_edge('s', 'c', capacity = 1)
- G.add_edge('c', 'd', capacity = 1)
- G.add_edge('d', 'a', capacity = 1)
- G.add_edge('b', 'a', capacity = 2)
- G.add_edge('a', 't', capacity = 2)
-
- H = {'s': {'b': 2, 'c': 0},
- 'c': {'d': 0},
- 'd': {'a': 0},
- 'b': {'a': 2},
- 'a': {'t': 2},
- 't': {}}
-
- compare_flows(G, 's', 't', H, 2)
-
- def test_digraph2(self):
- # A directed graph example from Cormen et al.
- G = nx.DiGraph()
- G.add_edge('s','v1', capacity = 16.0)
- G.add_edge('s','v2', capacity = 13.0)
- G.add_edge('v1','v2', capacity = 10.0)
- G.add_edge('v2','v1', capacity = 4.0)
- G.add_edge('v1','v3', capacity = 12.0)
- G.add_edge('v3','v2', capacity = 9.0)
- G.add_edge('v2','v4', capacity = 14.0)
- G.add_edge('v4','v3', capacity = 7.0)
- G.add_edge('v3','t', capacity = 20.0)
- G.add_edge('v4','t', capacity = 4.0)
-
- H = {'s': {'v1': 12.0, 'v2': 11.0},
- 'v2': {'v1': 0, 'v4': 11.0},
- 'v1': {'v2': 0, 'v3': 12.0},
- 'v3': {'v2': 0, 't': 19.0},
- 'v4': {'v3': 7.0, 't': 4.0},
- 't': {}}
-
- compare_flows(G, 's', 't', H, 23.0)
-
- def test_digraph3(self):
- # A more complex directed graph
- # from www.topcoder.com/tc?module=Statc&d1=tutorials&d2=maxFlow
- G = nx.DiGraph()
- G.add_edge('x','a', capacity = 3.0)
- G.add_edge('x','b', capacity = 1.0)
- G.add_edge('a','c', capacity = 3.0)
- G.add_edge('b','c', capacity = 5.0)
- G.add_edge('b','d', capacity = 4.0)
- G.add_edge('d','e', capacity = 2.0)
- G.add_edge('c','y', capacity = 2.0)
- G.add_edge('e','y', capacity = 3.0)
-
- H = {'x': {'a': 2.0, 'b': 1.0},
- 'a': {'c': 2.0},
- 'b': {'c': 0, 'd': 1.0},
- 'c': {'y': 2.0},
- 'd': {'e': 1.0},
- 'e': {'y': 1.0},
- 'y': {}}
-
- compare_flows(G, 'x', 'y', H, 3.0)
-
- def test_optional_capacity(self):
- # Test optional capacity parameter.
- G = nx.DiGraph()
- G.add_edge('x','a', spam = 3.0)
- G.add_edge('x','b', spam = 1.0)
- G.add_edge('a','c', spam = 3.0)
- G.add_edge('b','c', spam = 5.0)
- G.add_edge('b','d', spam = 4.0)
- G.add_edge('d','e', spam = 2.0)
- G.add_edge('c','y', spam = 2.0)
- G.add_edge('e','y', spam = 3.0)
-
- solnFlows = {'x': {'a': 2.0, 'b': 1.0},
- 'a': {'c': 2.0},
- 'b': {'c': 0, 'd': 1.0},
- 'c': {'y': 2.0},
- 'd': {'e': 1.0},
- 'e': {'y': 1.0},
- 'y': {}}
- solnValue = 3.0
- s = 'x'
- t = 'y'
-
- flowValue, flowDict = nx.ford_fulkerson(G, s, t, capacity = 'spam')
- assert_equal(flowValue, solnValue)
- assert_equal(flowDict, solnFlows)
- assert_equal(nx.min_cut(G, s, t, capacity = 'spam'), solnValue)
- assert_equal(nx.max_flow(G, s, t, capacity = 'spam'), solnValue)
- assert_equal(nx.ford_fulkerson_flow(G, s, t, capacity = 'spam'),
- solnFlows)
-
- def test_digraph_infcap_edges(self):
- # DiGraph with infinite capacity edges
- G = nx.DiGraph()
- G.add_edge('s', 'a')
- G.add_edge('s', 'b', capacity = 30)
- G.add_edge('a', 'c', capacity = 25)
- G.add_edge('b', 'c', capacity = 12)
- G.add_edge('a', 't', capacity = 60)
- G.add_edge('c', 't')
-
- H = {'s': {'a': 85, 'b': 12},
- 'a': {'c': 25, 't': 60},
- 'b': {'c': 12},
- 'c': {'t': 37},
- 't': {}}
-
- compare_flows(G, 's', 't', H, 97)
-
- # DiGraph with infinite capacity digon
- G = nx.DiGraph()
- G.add_edge('s', 'a', capacity = 85)
- G.add_edge('s', 'b', capacity = 30)
- G.add_edge('a', 'c')
- G.add_edge('c', 'a')
- G.add_edge('b', 'c', capacity = 12)
- G.add_edge('a', 't', capacity = 60)
- G.add_edge('c', 't', capacity = 37)
-
- H = {'s': {'a': 85, 'b': 12},
- 'a': {'c': 25, 't': 60},
- 'c': {'a': 0, 't': 37},
- 'b': {'c': 12},
- 't': {}}
-
- compare_flows(G, 's', 't', H, 97)
-
-
- def test_digraph_infcap_path(self):
- # Graph with infinite capacity (s, t)-path
- G = nx.DiGraph()
- G.add_edge('s', 'a')
- G.add_edge('s', 'b', capacity = 30)
- G.add_edge('a', 'c')
- G.add_edge('b', 'c', capacity = 12)
- G.add_edge('a', 't', capacity = 60)
- G.add_edge('c', 't')
-
- assert_raises(nx.NetworkXUnbounded,
- nx.ford_fulkerson, G, 's', 't')
- assert_raises(nx.NetworkXUnbounded,
- nx.max_flow, G, 's', 't')
- assert_raises(nx.NetworkXUnbounded,
- nx.ford_fulkerson_flow, G, 's', 't')
- assert_raises(nx.NetworkXUnbounded,
- nx.min_cut, G, 's', 't')
-
- def test_graph_infcap_edges(self):
- # Undirected graph with infinite capacity edges
- G = nx.Graph()
- G.add_edge('s', 'a')
- G.add_edge('s', 'b', capacity = 30)
- G.add_edge('a', 'c', capacity = 25)
- G.add_edge('b', 'c', capacity = 12)
- G.add_edge('a', 't', capacity = 60)
- G.add_edge('c', 't')
-
- H = {'s': {'a': 85, 'b': 12},
- 'a': {'c': 25, 's': 85, 't': 60},
- 'b': {'c': 12, 's': 12},
- 'c': {'a': 25, 'b': 12, 't': 37},
- 't': {'a': 60, 'c': 37}}
-
- compare_flows(G, 's', 't', H, 97)
-
- def test_digraph4(self):
- # From ticket #429 by mfrasca.
- G = nx.DiGraph()
- G.add_edge('s', 'a', capacity = 2)
- G.add_edge('s', 'b', capacity = 2)
- G.add_edge('a', 'b', capacity = 5)
- G.add_edge('a', 't', capacity = 1)
- G.add_edge('b', 'a', capacity = 1)
- G.add_edge('b', 't', capacity = 3)
- flowSoln = {'a': {'b': 1, 't': 1},
- 'b': {'a': 0, 't': 3},
- 's': {'a': 2, 'b': 2},
- 't': {}}
- compare_flows(G, 's', 't', flowSoln, 4)
-
-
- def test_disconnected(self):
- G = nx.Graph()
- G.add_weighted_edges_from([(0,1,1),(1,2,1),(2,3,1)],weight='capacity')
- G.remove_node(1)
- assert_equal(nx.max_flow(G,0,3),0)
-
- def test_source_target_not_in_graph(self):
- G = nx.Graph()
- G.add_weighted_edges_from([(0,1,1),(1,2,1),(2,3,1)],weight='capacity')
- G.remove_node(0)
- assert_raises(nx.NetworkXError,nx.max_flow,G,0,3)
- G.add_weighted_edges_from([(0,1,1),(1,2,1),(2,3,1)],weight='capacity')
- G.remove_node(3)
- assert_raises(nx.NetworkXError,nx.max_flow,G,0,3)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_maxflow_large_graph.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_maxflow_large_graph.py
deleted file mode 100644
index 578e2df..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_maxflow_large_graph.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Max flow algorithm test suite on large graphs.
-
-Run with nose: nosetests -v test_max_flow.py
-"""
-
-__author__ = """Loïc Séguin-C. <loicseguin@gmail.com>"""
-# Copyright (C) 2010 Loïc Séguin-C. <loicseguin@gmail.com>
-# All rights reserved.
-# BSD license.
-
-
-import networkx as nx
-from nose.tools import *
-
-def gen_pyramid(N):
- # This graph admits a flow of value 1 for which every arc is at
- # capacity (except the arcs incident to the sink which have
- # infinite capacity).
- G = nx.DiGraph()
-
- for i in range(N - 1):
- cap = 1. / (i + 2)
- for j in range(i + 1):
- G.add_edge((i, j), (i + 1, j),
- capacity = cap)
- cap = 1. / (i + 1) - cap
- G.add_edge((i, j), (i + 1, j + 1),
- capacity = cap)
- cap = 1. / (i + 2) - cap
-
- for j in range(N):
- G.add_edge((N - 1, j), 't')
-
- return G
-
-
-class TestMaxflowLargeGraph:
- def test_complete_graph(self):
- N = 50
- G = nx.complete_graph(N)
- for (u, v) in G.edges():
- G[u][v]['capacity'] = 5
- assert_equal(nx.ford_fulkerson(G, 1, 2)[0], 5 * (N - 1))
-
- def test_pyramid(self):
- N = 10
-# N = 100 # this gives a graph with 5051 nodes
- G = gen_pyramid(N)
- assert_almost_equal(nx.ford_fulkerson(G, (0, 0), 't')[0], 1.)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_mincost.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_mincost.py
deleted file mode 100644
index 72df2f0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/test_mincost.py
+++ /dev/null
@@ -1,284 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import networkx as nx
-from nose.tools import assert_equal, assert_raises
-
-class TestNetworkSimplex:
- def test_simple_digraph(self):
- G = nx.DiGraph()
- G.add_node('a', demand = -5)
- G.add_node('d', demand = 5)
- G.add_edge('a', 'b', weight = 3, capacity = 4)
- G.add_edge('a', 'c', weight = 6, capacity = 10)
- G.add_edge('b', 'd', weight = 1, capacity = 9)
- G.add_edge('c', 'd', weight = 2, capacity = 5)
- flowCost, H = nx.network_simplex(G)
- soln = {'a': {'b': 4, 'c': 1},
- 'b': {'d': 4},
- 'c': {'d': 1},
- 'd': {}}
- assert_equal(flowCost, 24)
- assert_equal(nx.min_cost_flow_cost(G), 24)
- assert_equal(H, soln)
- assert_equal(nx.min_cost_flow(G), soln)
- assert_equal(nx.cost_of_flow(G, H), 24)
-
- def test_negcycle_infcap(self):
- G = nx.DiGraph()
- G.add_node('s', demand = -5)
- G.add_node('t', demand = 5)
- G.add_edge('s', 'a', weight = 1, capacity = 3)
- G.add_edge('a', 'b', weight = 3)
- G.add_edge('c', 'a', weight = -6)
- G.add_edge('b', 'd', weight = 1)
- G.add_edge('d', 'c', weight = -2)
- G.add_edge('d', 't', weight = 1, capacity = 3)
- assert_raises(nx.NetworkXUnbounded, nx.network_simplex, G)
-
- def test_sum_demands_not_zero(self):
- G = nx.DiGraph()
- G.add_node('s', demand = -5)
- G.add_node('t', demand = 4)
- G.add_edge('s', 'a', weight = 1, capacity = 3)
- G.add_edge('a', 'b', weight = 3)
- G.add_edge('a', 'c', weight = -6)
- G.add_edge('b', 'd', weight = 1)
- G.add_edge('c', 'd', weight = -2)
- G.add_edge('d', 't', weight = 1, capacity = 3)
- assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
-
- def test_no_flow_satisfying_demands(self):
- G = nx.DiGraph()
- G.add_node('s', demand = -5)
- G.add_node('t', demand = 5)
- G.add_edge('s', 'a', weight = 1, capacity = 3)
- G.add_edge('a', 'b', weight = 3)
- G.add_edge('a', 'c', weight = -6)
- G.add_edge('b', 'd', weight = 1)
- G.add_edge('c', 'd', weight = -2)
- G.add_edge('d', 't', weight = 1, capacity = 3)
- assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
-
- def test_transshipment(self):
- G = nx.DiGraph()
- G.add_node('a', demand = 1)
- G.add_node('b', demand = -2)
- G.add_node('c', demand = -2)
- G.add_node('d', demand = 3)
- G.add_node('e', demand = -4)
- G.add_node('f', demand = -4)
- G.add_node('g', demand = 3)
- G.add_node('h', demand = 2)
- G.add_node('r', demand = 3)
- G.add_edge('a', 'c', weight = 3)
- G.add_edge('r', 'a', weight = 2)
- G.add_edge('b', 'a', weight = 9)
- G.add_edge('r', 'c', weight = 0)
- G.add_edge('b', 'r', weight = -6)
- G.add_edge('c', 'd', weight = 5)
- G.add_edge('e', 'r', weight = 4)
- G.add_edge('e', 'f', weight = 3)
- G.add_edge('h', 'b', weight = 4)
- G.add_edge('f', 'd', weight = 7)
- G.add_edge('f', 'h', weight = 12)
- G.add_edge('g', 'd', weight = 12)
- G.add_edge('f', 'g', weight = -1)
- G.add_edge('h', 'g', weight = -10)
- flowCost, H = nx.network_simplex(G)
- soln = {'a': {'c': 0},
- 'b': {'a': 0, 'r': 2},
- 'c': {'d': 3},
- 'd': {},
- 'e': {'r': 3, 'f': 1},
- 'f': {'d': 0, 'g': 3, 'h': 2},
- 'g': {'d': 0},
- 'h': {'b': 0, 'g': 0},
- 'r': {'a': 1, 'c': 1}}
- assert_equal(flowCost, 41)
- assert_equal(nx.min_cost_flow_cost(G), 41)
- assert_equal(H, soln)
- assert_equal(nx.min_cost_flow(G), soln)
- assert_equal(nx.cost_of_flow(G, H), 41)
-
- def test_max_flow_min_cost(self):
- G = nx.DiGraph()
- G.add_edge('s', 'a', bandwidth = 6)
- G.add_edge('s', 'c', bandwidth = 10, cost = 10)
- G.add_edge('a', 'b', cost = 6)
- G.add_edge('b', 'd', bandwidth = 8, cost = 7)
- G.add_edge('c', 'd', cost = 10)
- G.add_edge('d', 't', bandwidth = 5, cost = 5)
- soln = {'s': {'a': 5, 'c': 0},
- 'a': {'b': 5},
- 'b': {'d': 5},
- 'c': {'d': 0},
- 'd': {'t': 5},
- 't': {}}
- flow = nx.max_flow_min_cost(G, 's', 't', capacity = 'bandwidth',
- weight = 'cost')
- assert_equal(flow, soln)
- assert_equal(nx.cost_of_flow(G, flow, weight = 'cost'), 90)
-
- def test_digraph1(self):
- # From Bradley, S. P., Hax, A. C. and Magnanti, T. L. Applied
- # Mathematical Programming. Addison-Wesley, 1977.
- G = nx.DiGraph()
- G.add_node(1, demand = -20)
- G.add_node(4, demand = 5)
- G.add_node(5, demand = 15)
- G.add_edges_from([(1, 2, {'capacity': 15, 'weight': 4}),
- (1, 3, {'capacity': 8, 'weight': 4}),
- (2, 3, {'weight': 2}),
- (2, 4, {'capacity': 4, 'weight': 2}),
- (2, 5, {'capacity': 10, 'weight': 6}),
- (3, 4, {'capacity': 15, 'weight': 1}),
- (3, 5, {'capacity': 5, 'weight': 3}),
- (4, 5, {'weight': 2}),
- (5, 3, {'capacity': 4, 'weight': 1})])
- flowCost, H = nx.network_simplex(G)
- soln = {1: {2: 12, 3: 8},
- 2: {3: 8, 4: 4, 5: 0},
- 3: {4: 11, 5: 5},
- 4: {5: 10},
- 5: {3: 0}}
- assert_equal(flowCost, 150)
- assert_equal(nx.min_cost_flow_cost(G), 150)
- assert_equal(H, soln)
- assert_equal(nx.min_cost_flow(G), soln)
- assert_equal(nx.cost_of_flow(G, H), 150)
-
- def test_digraph2(self):
- # Example from ticket #430 from mfrasca. Original source:
- # http://www.cs.princeton.edu/courses/archive/spr03/cs226/lectures/mincost.4up.pdf, slide 11.
- G = nx.DiGraph()
- G.add_edge('s', 1, capacity=12)
- G.add_edge('s', 2, capacity=6)
- G.add_edge('s', 3, capacity=14)
- G.add_edge(1, 2, capacity=11, weight=4)
- G.add_edge(2, 3, capacity=9, weight=6)
- G.add_edge(1, 4, capacity=5, weight=5)
- G.add_edge(1, 5, capacity=2, weight=12)
- G.add_edge(2, 5, capacity=4, weight=4)
- G.add_edge(2, 6, capacity=2, weight=6)
- G.add_edge(3, 6, capacity=31, weight=3)
- G.add_edge(4, 5, capacity=18, weight=4)
- G.add_edge(5, 6, capacity=9, weight=5)
- G.add_edge(4, 't', capacity=3)
- G.add_edge(5, 't', capacity=7)
- G.add_edge(6, 't', capacity=22)
- flow = nx.max_flow_min_cost(G, 's', 't')
- soln = {1: {2: 6, 4: 5, 5: 1},
- 2: {3: 6, 5: 4, 6: 2},
- 3: {6: 20},
- 4: {5: 2, 't': 3},
- 5: {6: 0, 't': 7},
- 6: {'t': 22},
- 's': {1: 12, 2: 6, 3: 14},
- 't': {}}
- assert_equal(flow, soln)
-
- def test_digraph3(self):
- """Combinatorial Optimization: Algorithms and Complexity,
- Papadimitriou Steiglitz at page 140 has an example, 7.1, but that
- admits multiple solutions, so I alter it a bit. From ticket #430
- by mfrasca."""
-
- G = nx.DiGraph()
- G.add_edge('s', 'a', {0: 2, 1: 4})
- G.add_edge('s', 'b', {0: 2, 1: 1})
- G.add_edge('a', 'b', {0: 5, 1: 2})
- G.add_edge('a', 't', {0: 1, 1: 5})
- G.add_edge('b', 'a', {0: 1, 1: 3})
- G.add_edge('b', 't', {0: 3, 1: 2})
-
- "PS.ex.7.1: testing main function"
- sol = nx.max_flow_min_cost(G, 's', 't', capacity=0, weight=1)
- flow = sum(v for v in sol['s'].values())
- assert_equal(4, flow)
- assert_equal(23, nx.cost_of_flow(G, sol, weight=1))
- assert_equal(sol['s'], {'a': 2, 'b': 2})
- assert_equal(sol['a'], {'b': 1, 't': 1})
- assert_equal(sol['b'], {'a': 0, 't': 3})
- assert_equal(sol['t'], {})
-
- def test_zero_capacity_edges(self):
- """Address issue raised in ticket #617 by arv."""
- G = nx.DiGraph()
- G.add_edges_from([(1, 2, {'capacity': 1, 'weight': 1}),
- (1, 5, {'capacity': 1, 'weight': 1}),
- (2, 3, {'capacity': 0, 'weight': 1}),
- (2, 5, {'capacity': 1, 'weight': 1}),
- (5, 3, {'capacity': 2, 'weight': 1}),
- (5, 4, {'capacity': 0, 'weight': 1}),
- (3, 4, {'capacity': 2, 'weight': 1})])
- G.node[1]['demand'] = -1
- G.node[2]['demand'] = -1
- G.node[4]['demand'] = 2
-
- flowCost, H = nx.network_simplex(G)
- soln = {1: {2: 0, 5: 1},
- 2: {3: 0, 5: 1},
- 3: {4: 2},
- 4: {},
- 5: {3: 2, 4: 0}}
- assert_equal(flowCost, 6)
- assert_equal(nx.min_cost_flow_cost(G), 6)
- assert_equal(H, soln)
- assert_equal(nx.min_cost_flow(G), soln)
- assert_equal(nx.cost_of_flow(G, H), 6)
-
- def test_digon(self):
- """Check if digons are handled properly. Taken from ticket
- #618 by arv."""
- nodes = [(1, {}),
- (2, {'demand': -4}),
- (3, {'demand': 4}),
- ]
- edges = [(1, 2, {'capacity': 3, 'weight': 600000}),
- (2, 1, {'capacity': 2, 'weight': 0}),
- (2, 3, {'capacity': 5, 'weight': 714285}),
- (3, 2, {'capacity': 2, 'weight': 0}),
- ]
- G = nx.DiGraph(edges)
- G.add_nodes_from(nodes)
- flowCost, H = nx.network_simplex(G)
- soln = {1: {2: 0},
- 2: {1: 0, 3: 4},
- 3: {2: 0}}
- assert_equal(flowCost, 2857140)
- assert_equal(nx.min_cost_flow_cost(G), 2857140)
- assert_equal(H, soln)
- assert_equal(nx.min_cost_flow(G), soln)
- assert_equal(nx.cost_of_flow(G, H), 2857140)
-
- def test_infinite_capacity_neg_digon(self):
- """An infinite capacity negative cost digon results in an unbounded
- instance."""
- nodes = [(1, {}),
- (2, {'demand': -4}),
- (3, {'demand': 4}),
- ]
- edges = [(1, 2, {'weight': -600}),
- (2, 1, {'weight': 0}),
- (2, 3, {'capacity': 5, 'weight': 714285}),
- (3, 2, {'capacity': 2, 'weight': 0}),
- ]
- G = nx.DiGraph(edges)
- G.add_nodes_from(nodes)
- assert_raises(nx.NetworkXUnbounded, nx.network_simplex, G)
-
- def test_finite_capacity_neg_digon(self):
- """The digon should receive the maximum amount of flow it can handle.
- Taken from ticket #749 by @chuongdo."""
- G = nx.DiGraph()
- G.add_edge('a', 'b', capacity=1, weight=-1)
- G.add_edge('b', 'a', capacity=1, weight=-1)
- min_cost = -2
- assert_equal(nx.min_cost_flow_cost(G), min_cost)
-
- def test_multidigraph(self):
- """Raise an exception for multidigraph."""
- G = nx.MultiDiGraph()
- G.add_weighted_edges_from([(1, 2, 1), (2, 3, 2)], weight='capacity')
- assert_raises(nx.NetworkXError, nx.network_simplex, G)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/graphical.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/graphical.py
deleted file mode 100644
index 5c82761..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/graphical.py
+++ /dev/null
@@ -1,405 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Test sequences for graphiness.
-"""
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from collections import defaultdict
-import heapq
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult (dschult@colgate.edu)'
- 'Joel Miller (joel.c.miller.research@gmail.com)'
- 'Ben Edwards'
- 'Brian Cloteaux <brian.cloteaux@nist.gov>'])
-
-__all__ = ['is_graphical',
- 'is_multigraphical',
- 'is_pseudographical',
- 'is_digraphical',
- 'is_valid_degree_sequence_erdos_gallai',
- 'is_valid_degree_sequence_havel_hakimi',
- 'is_valid_degree_sequence', # deprecated
- ]
-
-def is_graphical(sequence, method='eg'):
- """Returns True if sequence is a valid degree sequence.
-
- A degree sequence is valid if some graph can realize it.
-
- Parameters
- ----------
- sequence : list or iterable container
- A sequence of integer node degrees
-
-
- method : "eg" | "hh"
- The method used to validate the degree sequence.
- "eg" corresponds to the Erdős-Gallai algorithm, and
- "hh" to the Havel-Hakimi algorithm.
-
- Returns
- -------
- valid : bool
- True if the sequence is a valid degree sequence and False if not.
-
- Examples
- --------
- >>> G = nx.path_graph(4)
- >>> sequence = G.degree().values()
- >>> nx.is_valid_degree_sequence(sequence)
- True
-
- References
- ----------
- Erdős-Gallai
- [EG1960]_, [choudum1986]_
-
- Havel-Hakimi
- [havel1955]_, [hakimi1962]_, [CL1996]_
- """
- if method == 'eg':
- valid = is_valid_degree_sequence_erdos_gallai(list(sequence))
- elif method == 'hh':
- valid = is_valid_degree_sequence_havel_hakimi(list(sequence))
- else:
- msg = "`method` must be 'eg' or 'hh'"
- raise nx.NetworkXException(msg)
- return valid
-
-is_valid_degree_sequence = is_graphical
-
-def _basic_graphical_tests(deg_sequence):
- # Sort and perform some simple tests on the sequence
- if not nx.utils.is_list_of_ints(deg_sequence):
- raise nx.NetworkXUnfeasible
- p = len(deg_sequence)
- num_degs = [0]*p
- dmax, dmin, dsum, n = 0, p, 0, 0
- for d in deg_sequence:
- # Reject if degree is negative or larger than the sequence length
- if d<0 or d>=p:
- raise nx.NetworkXUnfeasible
- # Process only the non-zero integers
- elif d>0:
- dmax, dmin, dsum, n = max(dmax,d), min(dmin,d), dsum+d, n+1
- num_degs[d] += 1
- # Reject sequence if it has odd sum or is oversaturated
- if dsum%2 or dsum>n*(n-1):
- raise nx.NetworkXUnfeasible
- return dmax,dmin,dsum,n,num_degs
-
-def is_valid_degree_sequence_havel_hakimi(deg_sequence):
- r"""Returns True if deg_sequence can be realized by a simple graph.
-
- The validation proceeds using the Havel-Hakimi theorem.
- Worst-case run time is: O(s) where s is the sum of the sequence.
-
- Parameters
- ----------
- deg_sequence : list
- A list of integers where each element specifies the degree of a node
- in a graph.
-
- Returns
- -------
- valid : bool
- True if deg_sequence is graphical and False if not.
-
- Notes
- -----
- The ZZ condition says that for the sequence d if
-
- .. math::
- |d| >= \frac{(\max(d) + \min(d) + 1)^2}{4*\min(d)}
-
- then d is graphical. This was shown in Theorem 6 in [1]_.
-
- References
- ----------
- .. [1] I.E. Zverovich and V.E. Zverovich. "Contributions to the theory
- of graphic sequences", Discrete Mathematics, 105, pp. 292-303 (1992).
-
- [havel1955]_, [hakimi1962]_, [CL1996]_
-
- """
- try:
- dmax,dmin,dsum,n,num_degs = _basic_graphical_tests(deg_sequence)
- except nx.NetworkXUnfeasible:
- return False
- # Accept if sequence has no non-zero degrees or passes the ZZ condition
- if n==0 or 4*dmin*n >= (dmax+dmin+1) * (dmax+dmin+1):
- return True
-
- modstubs = [0]*(dmax+1)
- # Successively reduce degree sequence by removing the maximum degree
- while n > 0:
- # Retrieve the maximum degree in the sequence
- while num_degs[dmax] == 0:
- dmax -= 1;
- # If there are not enough stubs to connect to, then the sequence is
- # not graphical
- if dmax > n-1:
- return False
-
- # Remove largest stub in list
- num_degs[dmax], n = num_degs[dmax]-1, n-1
- # Reduce the next dmax largest stubs
- mslen = 0
- k = dmax
- for i in range(dmax):
- while num_degs[k] == 0:
- k -= 1
- num_degs[k], n = num_degs[k]-1, n-1
- if k > 1:
- modstubs[mslen] = k-1
- mslen += 1
- # Add back to the list any non-zero stubs that were removed
- for i in range(mslen):
- stub = modstubs[i]
- num_degs[stub], n = num_degs[stub]+1, n+1
- return True
-
-
-def is_valid_degree_sequence_erdos_gallai(deg_sequence):
- r"""Returns True if deg_sequence can be realized by a simple graph.
-
- The validation is done using the Erdős-Gallai theorem [EG1960]_.
-
- Parameters
- ----------
- deg_sequence : list
- A list of integers
-
- Returns
- -------
- valid : bool
- True if deg_sequence is graphical and False if not.
-
- Notes
- -----
-
- This implementation uses an equivalent form of the Erdős-Gallai criterion.
- Worst-case run time is: O(n) where n is the length of the sequence.
-
- Specifically, a sequence d is graphical if and only if the
- sum of the sequence is even and for all strong indices k in the sequence,
-
- .. math::
-
- \sum_{i=1}^{k} d_i \leq k(k-1) + \sum_{j=k+1}^{n} \min(d_i,k)
- = k(n-1) - ( k \sum_{j=0}^{k-1} n_j - \sum_{j=0}^{k-1} j n_j )
-
- A strong index k is any index where `d_k \geq k` and the value `n_j` is the
- number of occurrences of j in d. The maximal strong index is called the
- Durfee index.
-
- This particular rearrangement comes from the proof of Theorem 3 in [2]_.
-
- The ZZ condition says that for the sequence d if
-
- .. math::
- |d| >= \frac{(\max(d) + \min(d) + 1)^2}{4*\min(d)}
-
- then d is graphical. This was shown in Theorem 6 in [2]_.
-
- References
- ----------
- .. [1] A. Tripathi and S. Vijay. "A note on a theorem of Erdős & Gallai",
- Discrete Mathematics, 265, pp. 417-420 (2003).
- .. [2] I.E. Zverovich and V.E. Zverovich. "Contributions to the theory
- of graphic sequences", Discrete Mathematics, 105, pp. 292-303 (1992).
-
- [EG1960]_, [choudum1986]_
- """
- try:
- dmax,dmin,dsum,n,num_degs = _basic_graphical_tests(deg_sequence)
- except nx.NetworkXUnfeasible:
- return False
- # Accept if sequence has no non-zero degrees or passes the ZZ condition
- if n==0 or 4*dmin*n >= (dmax+dmin+1) * (dmax+dmin+1):
- return True
-
- # Perform the EG checks using the reformulation of Zverovich and Zverovich
- k, sum_deg, sum_nj, sum_jnj = 0, 0, 0, 0
- for dk in range(dmax, dmin-1, -1):
- if dk < k+1: # Check if already past Durfee index
- return True
- if num_degs[dk] > 0:
- run_size = num_degs[dk] # Process a run of identical-valued degrees
- if dk < k+run_size: # Check if end of run is past Durfee index
- run_size = dk-k # Adjust back to Durfee index
- sum_deg += run_size * dk
- for v in range(run_size):
- sum_nj += num_degs[k+v]
- sum_jnj += (k+v) * num_degs[k+v]
- k += run_size
- if sum_deg > k*(n-1) - k*sum_nj + sum_jnj:
- return False
- return True
-
-def is_multigraphical(sequence):
- """Returns True if some multigraph can realize the sequence.
-
- Parameters
- ----------
- deg_sequence : list
- A list of integers
-
- Returns
- -------
- valid : bool
- True if deg_sequence is a multigraphic degree sequence and False if not.
-
- Notes
- -----
- The worst-case run time is O(n) where n is the length of the sequence.
-
- References
- ----------
- .. [1] S. L. Hakimi. "On the realizability of a set of integers as
- degrees of the vertices of a linear graph", J. SIAM, 10, pp. 496-506
- (1962).
- """
- deg_sequence = list(sequence)
- if not nx.utils.is_list_of_ints(deg_sequence):
- return False
- dsum, dmax = 0, 0
- for d in deg_sequence:
- if d<0:
- return False
- dsum, dmax = dsum+d, max(dmax,d)
- if dsum%2 or dsum<2*dmax:
- return False
- return True
-
-def is_pseudographical(sequence):
- """Returns True if some pseudograph can realize the sequence.
-
- Every nonnegative integer sequence with an even sum is pseudographical
- (see [1]_).
-
- Parameters
- ----------
- sequence : list or iterable container
- A sequence of integer node degrees
-
- Returns
- -------
- valid : bool
- True if the sequence is a pseudographic degree sequence and False if not.
-
- Notes
- -----
- The worst-case run time is O(n) where n is the length of the sequence.
-
- References
- ----------
- .. [1] F. Boesch and F. Harary. "Line removal algorithms for graphs
- and their degree lists", IEEE Trans. Circuits and Systems, CAS-23(12),
- pp. 778-782 (1976).
- """
- s = list(sequence)
- if not nx.utils.is_list_of_ints(s):
- return False
- return sum(s)%2 == 0 and min(s) >= 0
-
-def is_digraphical(in_sequence, out_sequence):
- r"""Returns True if some directed graph can realize the in- and out-degree
- sequences.
-
- Parameters
- ----------
- in_sequence : list or iterable container
- A sequence of integer node in-degrees
-
- out_sequence : list or iterable container
- A sequence of integer node out-degrees
-
- Returns
- -------
- valid : bool
- True if in and out-sequences are digraphic False if not.
-
- Notes
- -----
- This algorithm is from Kleitman and Wang [1]_.
- The worst case runtime is O(s * log n) where s and n are the sum and length
- of the sequences respectively.
-
- References
- ----------
- .. [1] D.J. Kleitman and D.L. Wang
- Algorithms for Constructing Graphs and Digraphs with Given Valences
- and Factors, Discrete Mathematics, 6(1), pp. 79-88 (1973)
- """
- in_deg_sequence = list(in_sequence)
- out_deg_sequence = list(out_sequence)
- if not nx.utils.is_list_of_ints(in_deg_sequence):
- return False
- if not nx.utils.is_list_of_ints(out_deg_sequence):
- return False
- # Process the sequences and form two heaps to store degree pairs with
- # either zero or non-zero out degrees
- sumin, sumout, nin, nout = 0, 0, len(in_deg_sequence), len(out_deg_sequence)
- maxn = max(nin, nout)
- maxin = 0
- if maxn==0:
- return True
- stubheap, zeroheap = [ ], [ ]
- for n in range(maxn):
- in_deg, out_deg = 0, 0
- if n<nout:
- out_deg = out_deg_sequence[n]
- if n<nin:
- in_deg = in_deg_sequence[n]
- if in_deg<0 or out_deg<0:
- return False
- sumin, sumout, maxin = sumin+in_deg, sumout+out_deg, max(maxin, in_deg)
- if in_deg > 0:
- stubheap.append((-1*out_deg, -1*in_deg))
- elif out_deg > 0:
- zeroheap.append(-1*out_deg)
- if sumin != sumout:
- return False
- heapq.heapify(stubheap)
- heapq.heapify(zeroheap)
-
- modstubs = [(0,0)]*(maxin+1)
- # Successively reduce degree sequence by removing the maximum out degree
- while stubheap:
- # Take the first value in the sequence with non-zero in degree
- (freeout, freein) = heapq.heappop( stubheap )
- freein *= -1
- if freein > len(stubheap)+len(zeroheap):
- return False
-
- # Attach out stubs to the nodes with the most in stubs
- mslen = 0
- for i in range(freein):
- if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0]):
- stubout = heapq.heappop(zeroheap)
- stubin = 0
- else:
- (stubout, stubin) = heapq.heappop(stubheap)
- if stubout == 0:
- return False
- # Check if target is now totally connected
- if stubout+1<0 or stubin<0:
- modstubs[mslen] = (stubout+1, stubin)
- mslen += 1
-
- # Add back the nodes to the heap that still have available stubs
- for i in range(mslen):
- stub = modstubs[i]
- if stub[1] < 0:
- heapq.heappush(stubheap, stub)
- else:
- heapq.heappush(zeroheap, stub[0])
- if freeout<0:
- heapq.heappush(zeroheap, freeout)
- return True
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/hierarchy.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/hierarchy.py
deleted file mode 100644
index c38337b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/hierarchy.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Flow Hierarchy.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__authors__ = "\n".join(['Ben Edwards (bedwards@cs.unm.edu)'])
-__all__ = ['flow_hierarchy']
-
-def flow_hierarchy(G, weight=None):
- """Returns the flow hierarchy of a directed network.
-
- Flow hierarchy is defined as the fraction of edges not participating
- in cycles in a directed graph [1]_.
-
- Parameters
- ----------
- G : DiGraph or MultiDiGraph
- A directed graph
-
- weight : key,optional (default=None)
- Attribute to use for node weights. If None the weight defaults to 1.
-
- Returns
- -------
- h : float
- Flow heirarchy value
-
- Notes
- -----
- The algorithm described in [1]_ computes the flow hierarchy through
- exponentiation of the adjacency matrix. This function implements an
- alternative approach that finds strongly connected components.
- An edge is in a cycle if and only if it is in a strongly connected
- component, which can be found in `O(m)` time using Tarjan's algorithm.
-
- References
- ----------
- .. [1] Luo, J.; Magee, C.L. (2011),
- Detecting evolving patterns of self-organizing networks by flow
- hierarchy measurement, Complexity, Volume 16 Issue 6 53-61.
- DOI: 10.1002/cplx.20368
- http://web.mit.edu/~cmagee/www/documents/28-DetectingEvolvingPatterns_FlowHierarchy.pdf
- """
- if not G.is_directed():
- raise nx.NetworkXError("G must be a digraph in flow_heirarchy")
- scc = nx.strongly_connected_components(G)
- return 1.-sum(G.subgraph(c).size(weight) for c in scc)/float(G.size(weight))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isolate.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isolate.py
deleted file mode 100644
index a14178b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isolate.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# encoding: utf-8
-"""
-Functions for identifying isolate (degree zero) nodes.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Drew Conway <drew.conway@nyu.edu>',
- 'Aric Hagberg <hagberg@lanl.gov>'])
-__all__=['is_isolate','isolates']
-
-def is_isolate(G,n):
- """Determine of node n is an isolate (degree zero).
-
- Parameters
- ----------
- G : graph
- A networkx graph
- n : node
- A node in G
-
- Returns
- -------
- isolate : bool
- True if n has no neighbors, False otherwise.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_edge(1,2)
- >>> G.add_node(3)
- >>> nx.is_isolate(G,2)
- False
- >>> nx.is_isolate(G,3)
- True
- """
- return G.degree(n)==0
-
-def isolates(G):
- """Return list of isolates in the graph.
-
- Isolates are nodes with no neighbors (degree zero).
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- Returns
- -------
- isolates : list
- List of isolate nodes.
-
- Examples
- --------
- >>> G = nx.Graph()
- >>> G.add_edge(1,2)
- >>> G.add_node(3)
- >>> nx.isolates(G)
- [3]
-
- To remove all isolates in the graph use
- >>> G.remove_nodes_from(nx.isolates(G))
- >>> G.nodes()
- [1, 2]
-
- For digraphs isolates have zero in-degree and zero out_degre
- >>> G = nx.DiGraph([(0,1),(1,2)])
- >>> G.add_node(3)
- >>> nx.isolates(G)
- [3]
- """
- return [n for (n,d) in G.degree_iter() if d==0]
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/__init__.py
deleted file mode 100644
index 7821bc2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from networkx.algorithms.isomorphism.isomorph import *
-from networkx.algorithms.isomorphism.vf2userfunc import *
-from networkx.algorithms.isomorphism.matchhelpers import *
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/isomorph.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/isomorph.py
deleted file mode 100644
index 7de3308..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/isomorph.py
+++ /dev/null
@@ -1,227 +0,0 @@
-"""
-Graph isomorphism functions.
-"""
-import networkx as nx
-from networkx.exception import NetworkXError
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Christopher Ellison cellison@cse.ucdavis.edu)'])
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__all__ = ['could_be_isomorphic',
- 'fast_could_be_isomorphic',
- 'faster_could_be_isomorphic',
- 'is_isomorphic']
-
-def could_be_isomorphic(G1,G2):
- """Returns False if graphs are definitely not isomorphic.
- True does NOT guarantee isomorphism.
-
- Parameters
- ----------
- G1, G2 : graphs
- The two graphs G1 and G2 must be the same type.
-
- Notes
- -----
- Checks for matching degree, triangle, and number of cliques sequences.
- """
-
- # Check global properties
- if G1.order() != G2.order(): return False
-
- # Check local properties
- d1=G1.degree()
- t1=nx.triangles(G1)
- c1=nx.number_of_cliques(G1)
- props1=[ [d1[v], t1[v], c1[v]] for v in d1 ]
- props1.sort()
-
- d2=G2.degree()
- t2=nx.triangles(G2)
- c2=nx.number_of_cliques(G2)
- props2=[ [d2[v], t2[v], c2[v]] for v in d2 ]
- props2.sort()
-
- if props1 != props2:
- return False
-
- # OK...
- return True
-
-graph_could_be_isomorphic=could_be_isomorphic
-
-def fast_could_be_isomorphic(G1,G2):
- """Returns False if graphs are definitely not isomorphic.
-
- True does NOT guarantee isomorphism.
-
- Parameters
- ----------
- G1, G2 : graphs
- The two graphs G1 and G2 must be the same type.
-
- Notes
- -----
- Checks for matching degree and triangle sequences.
- """
- # Check global properties
- if G1.order() != G2.order(): return False
-
- # Check local properties
- d1=G1.degree()
- t1=nx.triangles(G1)
- props1=[ [d1[v], t1[v]] for v in d1 ]
- props1.sort()
-
- d2=G2.degree()
- t2=nx.triangles(G2)
- props2=[ [d2[v], t2[v]] for v in d2 ]
- props2.sort()
-
- if props1 != props2: return False
-
- # OK...
- return True
-
-fast_graph_could_be_isomorphic=fast_could_be_isomorphic
-
-def faster_could_be_isomorphic(G1,G2):
- """Returns False if graphs are definitely not isomorphic.
-
- True does NOT guarantee isomorphism.
-
- Parameters
- ----------
- G1, G2 : graphs
- The two graphs G1 and G2 must be the same type.
-
- Notes
- -----
- Checks for matching degree sequences.
- """
- # Check global properties
- if G1.order() != G2.order(): return False
-
- # Check local properties
- d1=list(G1.degree().values())
- d1.sort()
- d2=list(G2.degree().values())
- d2.sort()
-
- if d1 != d2: return False
-
- # OK...
- return True
-
-faster_graph_could_be_isomorphic=faster_could_be_isomorphic
-
-def is_isomorphic(G1, G2, node_match=None, edge_match=None):
- """Returns True if the graphs G1 and G2 are isomorphic and False otherwise.
-
- Parameters
- ----------
- G1, G2: graphs
- The two graphs G1 and G2 must be the same type.
-
- node_match : callable
- A function that returns True if node n1 in G1 and n2 in G2 should
- be considered equal during the isomorphism test.
- If node_match is not specified then node attributes are not considered.
-
- The function will be called like
-
- node_match(G1.node[n1], G2.node[n2]).
-
- That is, the function will receive the node attribute dictionaries
- for n1 and n2 as inputs.
-
- edge_match : callable
- A function that returns True if the edge attribute dictionary
- for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
- be considered equal during the isomorphism test. If edge_match is
- not specified then edge attributes are not considered.
-
- The function will be called like
-
- edge_match(G1[u1][v1], G2[u2][v2]).
-
- That is, the function will receive the edge attribute dictionaries
- of the edges under consideration.
-
- Notes
- -----
- Uses the vf2 algorithm [1]_.
-
- Examples
- --------
- >>> import networkx.algorithms.isomorphism as iso
-
- For digraphs G1 and G2, using 'weight' edge attribute (default: 1)
-
- >>> G1 = nx.DiGraph()
- >>> G2 = nx.DiGraph()
- >>> G1.add_path([1,2,3,4],weight=1)
- >>> G2.add_path([10,20,30,40],weight=2)
- >>> em = iso.numerical_edge_match('weight', 1)
- >>> nx.is_isomorphic(G1, G2) # no weights considered
- True
- >>> nx.is_isomorphic(G1, G2, edge_match=em) # match weights
- False
-
- For multidigraphs G1 and G2, using 'fill' node attribute (default: '')
-
- >>> G1 = nx.MultiDiGraph()
- >>> G2 = nx.MultiDiGraph()
- >>> G1.add_nodes_from([1,2,3],fill='red')
- >>> G2.add_nodes_from([10,20,30,40],fill='red')
- >>> G1.add_path([1,2,3,4],weight=3, linewidth=2.5)
- >>> G2.add_path([10,20,30,40],weight=3)
- >>> nm = iso.categorical_node_match('fill', 'red')
- >>> nx.is_isomorphic(G1, G2, node_match=nm)
- True
-
- For multidigraphs G1 and G2, using 'weight' edge attribute (default: 7)
-
- >>> G1.add_edge(1,2, weight=7)
- >>> G2.add_edge(10,20)
- >>> em = iso.numerical_multiedge_match('weight', 7, rtol=1e-6)
- >>> nx.is_isomorphic(G1, G2, edge_match=em)
- True
-
- For multigraphs G1 and G2, using 'weight' and 'linewidth' edge attributes
- with default values 7 and 2.5. Also using 'fill' node attribute with
- default value 'red'.
-
- >>> em = iso.numerical_multiedge_match(['weight', 'linewidth'], [7, 2.5])
- >>> nm = iso.categorical_node_match('fill', 'red')
- >>> nx.is_isomorphic(G1, G2, edge_match=em, node_match=nm)
- True
-
- See Also
- --------
- numerical_node_match, numerical_edge_match, numerical_multiedge_match
- categorical_node_match, categorical_edge_match, categorical_multiedge_match
-
- References
- ----------
- .. [1] L. P. Cordella, P. Foggia, C. Sansone, M. Vento,
- "An Improved Algorithm for Matching Large Graphs",
- 3rd IAPR-TC15 Workshop on Graph-based Representations in
- Pattern Recognition, Cuen, pp. 149-159, 2001.
- http://amalfi.dis.unina.it/graph/db/papers/vf-algorithm.pdf
- """
- if G1.is_directed() and G2.is_directed():
- GM = nx.algorithms.isomorphism.DiGraphMatcher
- elif (not G1.is_directed()) and (not G2.is_directed()):
- GM = nx.algorithms.isomorphism.GraphMatcher
- else:
- raise NetworkXError("Graphs G1 and G2 are not of the same type.")
-
- gm = GM(G1, G2, node_match=node_match, edge_match=edge_match)
-
- return gm.is_isomorphic()
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/isomorphvf2.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/isomorphvf2.py
deleted file mode 100644
index 1efe74d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/isomorphvf2.py
+++ /dev/null
@@ -1,965 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-*************
-VF2 Algorithm
-*************
-
-An implementation of VF2 algorithm for graph ismorphism testing.
-
-The simplest interface to use this module is to call networkx.is_isomorphic().
-
-Introduction
-------------
-
-The GraphMatcher and DiGraphMatcher are responsible for matching
-graphs or directed graphs in a predetermined manner. This
-usually means a check for an isomorphism, though other checks
-are also possible. For example, a subgraph of one graph
-can be checked for isomorphism to a second graph.
-
-Matching is done via syntactic feasibility. It is also possible
-to check for semantic feasibility. Feasibility, then, is defined
-as the logical AND of the two functions.
-
-To include a semantic check, the (Di)GraphMatcher class should be
-subclassed, and the semantic_feasibility() function should be
-redefined. By default, the semantic feasibility function always
-returns True. The effect of this is that semantics are not
-considered in the matching of G1 and G2.
-
-Examples
---------
-
-Suppose G1 and G2 are isomorphic graphs. Verification is as follows:
-
->>> from networkx.algorithms import isomorphism
->>> G1 = nx.path_graph(4)
->>> G2 = nx.path_graph(4)
->>> GM = isomorphism.GraphMatcher(G1,G2)
->>> GM.is_isomorphic()
-True
-
-GM.mapping stores the isomorphism mapping from G1 to G2.
-
->>> GM.mapping
-{0: 0, 1: 1, 2: 2, 3: 3}
-
-
-Suppose G1 and G2 are isomorphic directed graphs
-graphs. Verification is as follows:
-
->>> G1 = nx.path_graph(4, create_using=nx.DiGraph())
->>> G2 = nx.path_graph(4, create_using=nx.DiGraph())
->>> DiGM = isomorphism.DiGraphMatcher(G1,G2)
->>> DiGM.is_isomorphic()
-True
-
-DiGM.mapping stores the isomorphism mapping from G1 to G2.
-
->>> DiGM.mapping
-{0: 0, 1: 1, 2: 2, 3: 3}
-
-
-
-Subgraph Isomorphism
---------------------
-Graph theory literature can be ambiguious about the meaning of the
-above statement, and we seek to clarify it now.
-
-In the VF2 literature, a mapping M is said to be a graph-subgraph
-isomorphism iff M is an isomorphism between G2 and a subgraph of G1.
-Thus, to say that G1 and G2 are graph-subgraph isomorphic is to say
-that a subgraph of G1 is isomorphic to G2.
-
-Other literature uses the phrase 'subgraph isomorphic' as in 'G1 does
-not have a subgraph isomorphic to G2'. Another use is as an in adverb
-for isomorphic. Thus, to say that G1 and G2 are subgraph isomorphic
-is to say that a subgraph of G1 is isomorphic to G2.
-
-Finally, the term 'subgraph' can have multiple meanings. In this
-context, 'subgraph' always means a 'node-induced subgraph'. Edge-induced
-subgraph isomorphisms are not directly supported, but one should be
-able to perform the check by making use of nx.line_graph(). For
-subgraphs which are not induced, the term 'monomorphism' is preferred
-over 'isomorphism'. Currently, it is not possible to check for
-monomorphisms.
-
-Let G=(N,E) be a graph with a set of nodes N and set of edges E.
-
-If G'=(N',E') is a subgraph, then:
- N' is a subset of N
- E' is a subset of E
-
-If G'=(N',E') is a node-induced subgraph, then:
- N' is a subset of N
- E' is the subset of edges in E relating nodes in N'
-
-If G'=(N',E') is an edge-induced subgrpah, then:
- N' is the subset of nodes in N related by edges in E'
- E' is a subset of E
-
-References
-----------
-[1] Luigi P. Cordella, Pasquale Foggia, Carlo Sansone, Mario Vento,
- "A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs",
- IEEE Transactions on Pattern Analysis and Machine Intelligence,
- vol. 26, no. 10, pp. 1367-1372, Oct., 2004.
- http://ieeexplore.ieee.org/iel5/34/29305/01323804.pdf
-
-[2] L. P. Cordella, P. Foggia, C. Sansone, M. Vento, "An Improved
- Algorithm for Matching Large Graphs", 3rd IAPR-TC15 Workshop
- on Graph-based Representations in Pattern Recognition, Cuen,
- pp. 149-159, 2001.
- http://amalfi.dis.unina.it/graph/db/papers/vf-algorithm.pdf
-
-See Also
---------
-syntactic_feasibliity(), semantic_feasibility()
-
-Notes
------
-Modified to handle undirected graphs.
-Modified to handle multiple edges.
-
-
-In general, this problem is NP-Complete.
-
-
-
-"""
-
-# Copyright (C) 2007-2009 by the NetworkX maintainers
-# All rights reserved.
-# BSD license.
-
-# This work was originally coded by Christopher Ellison
-# as part of the Computational Mechanics Python (CMPy) project.
-# James P. Crutchfield, principal investigator.
-# Complexity Sciences Center and Physics Department, UC Davis.
-
-import sys
-import networkx as nx
-
-__all__ = ['GraphMatcher',
- 'DiGraphMatcher']
-
-class GraphMatcher(object):
- """Implementation of VF2 algorithm for matching undirected graphs.
-
- Suitable for Graph and MultiGraph instances.
- """
- def __init__(self, G1, G2):
- """Initialize GraphMatcher.
-
- Parameters
- ----------
- G1,G2: NetworkX Graph or MultiGraph instances.
- The two graphs to check for isomorphism.
-
- Examples
- --------
- To create a GraphMatcher which checks for syntactic feasibility:
-
- >>> from networkx.algorithms import isomorphism
- >>> G1 = nx.path_graph(4)
- >>> G2 = nx.path_graph(4)
- >>> GM = isomorphism.GraphMatcher(G1,G2)
- """
- self.G1 = G1
- self.G2 = G2
- self.G1_nodes = set(G1.nodes())
- self.G2_nodes = set(G2.nodes())
-
- # Set recursion limit.
- self.old_recursion_limit = sys.getrecursionlimit()
- expected_max_recursion_level = len(self.G2)
- if self.old_recursion_limit < 1.5 * expected_max_recursion_level:
- # Give some breathing room.
- sys.setrecursionlimit(int(1.5 * expected_max_recursion_level))
-
- # Declare that we will be searching for a graph-graph isomorphism.
- self.test = 'graph'
-
- # Initialize state
- self.initialize()
-
- def reset_recursion_limit(self):
- """Restores the recursion limit."""
- ### TODO:
- ### Currently, we use recursion and set the recursion level higher.
- ### It would be nice to restore the level, but because the
- ### (Di)GraphMatcher classes make use of cyclic references, garbage
- ### collection will never happen when we define __del__() to
- ### restore the recursion level. The result is a memory leak.
- ### So for now, we do not automatically restore the recursion level,
- ### and instead provide a method to do this manually. Eventually,
- ### we should turn this into a non-recursive implementation.
- sys.setrecursionlimit(self.old_recursion_limit)
-
- def candidate_pairs_iter(self):
- """Iterator over candidate pairs of nodes in G1 and G2."""
-
- # All computations are done using the current state!
-
- G1_nodes = self.G1_nodes
- G2_nodes = self.G2_nodes
-
- # First we compute the inout-terminal sets.
- T1_inout = [node for node in G1_nodes if (node in self.inout_1) and (node not in self.core_1)]
- T2_inout = [node for node in G2_nodes if (node in self.inout_2) and (node not in self.core_2)]
-
- # If T1_inout and T2_inout are both nonempty.
- # P(s) = T1_inout x {min T2_inout}
- if T1_inout and T2_inout:
- for node in T1_inout:
- yield node, min(T2_inout)
-
- else:
- # If T1_inout and T2_inout were both empty....
- # P(s) = (N_1 - M_1) x {min (N_2 - M_2)}
- ##if not (T1_inout or T2_inout): # as suggested by [2], incorrect
- if 1: # as inferred from [1], correct
- # First we determine the candidate node for G2
- other_node = min(G2_nodes - set(self.core_2))
- for node in self.G1:
- if node not in self.core_1:
- yield node, other_node
-
- # For all other cases, we don't have any candidate pairs.
-
- def initialize(self):
- """Reinitializes the state of the algorithm.
-
- This method should be redefined if using something other than GMState.
- If only subclassing GraphMatcher, a redefinition is not necessary.
-
- """
-
- # core_1[n] contains the index of the node paired with n, which is m,
- # provided n is in the mapping.
- # core_2[m] contains the index of the node paired with m, which is n,
- # provided m is in the mapping.
- self.core_1 = {}
- self.core_2 = {}
-
- # See the paper for definitions of M_x and T_x^{y}
-
- # inout_1[n] is non-zero if n is in M_1 or in T_1^{inout}
- # inout_2[m] is non-zero if m is in M_2 or in T_2^{inout}
- #
- # The value stored is the depth of the SSR tree when the node became
- # part of the corresponding set.
- self.inout_1 = {}
- self.inout_2 = {}
- # Practically, these sets simply store the nodes in the subgraph.
-
- self.state = GMState(self)
-
- # Provide a convienient way to access the isomorphism mapping.
- self.mapping = self.core_1.copy()
-
- def is_isomorphic(self):
- """Returns True if G1 and G2 are isomorphic graphs."""
-
- # Let's do two very quick checks!
- # QUESTION: Should we call faster_graph_could_be_isomorphic(G1,G2)?
- # For now, I just copy the code.
-
- # Check global properties
- if self.G1.order() != self.G2.order(): return False
-
- # Check local properties
- d1=sorted(self.G1.degree().values())
- d2=sorted(self.G2.degree().values())
- if d1 != d2: return False
-
- try:
- x = next(self.isomorphisms_iter())
- return True
- except StopIteration:
- return False
-
- def isomorphisms_iter(self):
- """Generator over isomorphisms between G1 and G2."""
- # Declare that we are looking for a graph-graph isomorphism.
- self.test = 'graph'
- self.initialize()
- for mapping in self.match():
- yield mapping
-
- def match(self):
- """Extends the isomorphism mapping.
-
- This function is called recursively to determine if a complete
- isomorphism can be found between G1 and G2. It cleans up the class
- variables after each recursive call. If an isomorphism is found,
- we yield the mapping.
-
- """
- if len(self.core_1) == len(self.G2):
- # Save the final mapping, otherwise garbage collection deletes it.
- self.mapping = self.core_1.copy()
- # The mapping is complete.
- yield self.mapping
- else:
- for G1_node, G2_node in self.candidate_pairs_iter():
- if self.syntactic_feasibility(G1_node, G2_node):
- if self.semantic_feasibility(G1_node, G2_node):
- # Recursive call, adding the feasible state.
- newstate = self.state.__class__(self, G1_node, G2_node)
- for mapping in self.match():
- yield mapping
-
- # restore data structures
- newstate.restore()
-
- def semantic_feasibility(self, G1_node, G2_node):
- """Returns True if adding (G1_node, G2_node) is symantically feasible.
-
- The semantic feasibility function should return True if it is
- acceptable to add the candidate pair (G1_node, G2_node) to the current
- partial isomorphism mapping. The logic should focus on semantic
- information contained in the edge data or a formalized node class.
-
- By acceptable, we mean that the subsequent mapping can still become a
- complete isomorphism mapping. Thus, if adding the candidate pair
- definitely makes it so that the subsequent mapping cannot become a
- complete isomorphism mapping, then this function must return False.
-
- The default semantic feasibility function always returns True. The
- effect is that semantics are not considered in the matching of G1
- and G2.
-
- The semantic checks might differ based on the what type of test is
- being performed. A keyword description of the test is stored in
- self.test. Here is a quick description of the currently implemented
- tests::
-
- test='graph'
- Indicates that the graph matcher is looking for a graph-graph
- isomorphism.
-
- test='subgraph'
- Indicates that the graph matcher is looking for a subgraph-graph
- isomorphism such that a subgraph of G1 is isomorphic to G2.
-
- Any subclass which redefines semantic_feasibility() must maintain
- the above form to keep the match() method functional. Implementations
- should consider multigraphs.
- """
- return True
-
- def subgraph_is_isomorphic(self):
- """Returns True if a subgraph of G1 is isomorphic to G2."""
- try:
- x = next(self.subgraph_isomorphisms_iter())
- return True
- except StopIteration:
- return False
-
-# subgraph_is_isomorphic.__doc__ += "\n" + subgraph.replace('\n','\n'+indent)
-
- def subgraph_isomorphisms_iter(self):
- """Generator over isomorphisms between a subgraph of G1 and G2."""
- # Declare that we are looking for graph-subgraph isomorphism.
- self.test = 'subgraph'
- self.initialize()
- for mapping in self.match():
- yield mapping
-
-# subgraph_isomorphisms_iter.__doc__ += "\n" + subgraph.replace('\n','\n'+indent)
-
- def syntactic_feasibility(self, G1_node, G2_node):
- """Returns True if adding (G1_node, G2_node) is syntactically feasible.
-
- This function returns True if it is adding the candidate pair
- to the current partial isomorphism mapping is allowable. The addition
- is allowable if the inclusion of the candidate pair does not make it
- impossible for an isomorphism to be found.
- """
-
- # The VF2 algorithm was designed to work with graphs having, at most,
- # one edge connecting any two nodes. This is not the case when
- # dealing with an MultiGraphs.
- #
- # Basically, when we test the look-ahead rules R_neighbor, we will
- # make sure that the number of edges are checked. We also add
- # a R_self check to verify that the number of selfloops is acceptable.
- #
- # Users might be comparing Graph instances with MultiGraph instances.
- # So the generic GraphMatcher class must work with MultiGraphs.
- # Care must be taken since the value in the innermost dictionary is a
- # singlet for Graph instances. For MultiGraphs, the value in the
- # innermost dictionary is a list.
-
-
- ###
- ### Test at each step to get a return value as soon as possible.
- ###
-
- ### Look ahead 0
-
- # R_self
-
- # The number of selfloops for G1_node must equal the number of
- # self-loops for G2_node. Without this check, we would fail on
- # R_neighbor at the next recursion level. But it is good to prune the
- # search tree now.
- if self.G1.number_of_edges(G1_node,G1_node) != self.G2.number_of_edges(G2_node,G2_node):
- return False
-
-
- # R_neighbor
-
- # For each neighbor n' of n in the partial mapping, the corresponding
- # node m' is a neighbor of m, and vice versa. Also, the number of
- # edges must be equal.
- for neighbor in self.G1[G1_node]:
- if neighbor in self.core_1:
- if not (self.core_1[neighbor] in self.G2[G2_node]):
- return False
- elif self.G1.number_of_edges(neighbor, G1_node) != self.G2.number_of_edges(self.core_1[neighbor], G2_node):
- return False
- for neighbor in self.G2[G2_node]:
- if neighbor in self.core_2:
- if not (self.core_2[neighbor] in self.G1[G1_node]):
- return False
- elif self.G1.number_of_edges(self.core_2[neighbor], G1_node) != self.G2.number_of_edges(neighbor, G2_node):
- return False
-
- ### Look ahead 1
-
- # R_terminout
- # The number of neighbors of n that are in T_1^{inout} is equal to the
- # number of neighbors of m that are in T_2^{inout}, and vice versa.
- num1 = 0
- for neighbor in self.G1[G1_node]:
- if (neighbor in self.inout_1) and (neighbor not in self.core_1):
- num1 += 1
- num2 = 0
- for neighbor in self.G2[G2_node]:
- if (neighbor in self.inout_2) and (neighbor not in self.core_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
-
- ### Look ahead 2
-
- # R_new
-
- # The number of neighbors of n that are neither in the core_1 nor
- # T_1^{inout} is equal to the number of neighbors of m
- # that are neither in core_2 nor T_2^{inout}.
- num1 = 0
- for neighbor in self.G1[G1_node]:
- if neighbor not in self.inout_1:
- num1 += 1
- num2 = 0
- for neighbor in self.G2[G2_node]:
- if neighbor not in self.inout_2:
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- # Otherwise, this node pair is syntactically feasible!
- return True
-
-
-class DiGraphMatcher(GraphMatcher):
- """Implementation of VF2 algorithm for matching directed graphs.
-
- Suitable for DiGraph and MultiDiGraph instances.
- """
-# __doc__ += "Notes\n%s-----" % (indent,) + sources.replace('\n','\n'+indent)
-
- def __init__(self, G1, G2):
- """Initialize DiGraphMatcher.
-
- G1 and G2 should be nx.Graph or nx.MultiGraph instances.
-
- Examples
- --------
- To create a GraphMatcher which checks for syntactic feasibility:
-
- >>> from networkx.algorithms import isomorphism
- >>> G1 = nx.DiGraph(nx.path_graph(4, create_using=nx.DiGraph()))
- >>> G2 = nx.DiGraph(nx.path_graph(4, create_using=nx.DiGraph()))
- >>> DiGM = isomorphism.DiGraphMatcher(G1,G2)
- """
- super(DiGraphMatcher, self).__init__(G1, G2)
-
- def candidate_pairs_iter(self):
- """Iterator over candidate pairs of nodes in G1 and G2."""
-
- # All computations are done using the current state!
-
- G1_nodes = self.G1_nodes
- G2_nodes = self.G2_nodes
-
- # First we compute the out-terminal sets.
- T1_out = [node for node in G1_nodes if (node in self.out_1) and (node not in self.core_1)]
- T2_out = [node for node in G2_nodes if (node in self.out_2) and (node not in self.core_2)]
-
- # If T1_out and T2_out are both nonempty.
- # P(s) = T1_out x {min T2_out}
- if T1_out and T2_out:
- node_2 = min(T2_out)
- for node_1 in T1_out:
- yield node_1, node_2
-
- # If T1_out and T2_out were both empty....
- # We compute the in-terminal sets.
-
- ##elif not (T1_out or T2_out): # as suggested by [2], incorrect
- else: # as suggested by [1], correct
- T1_in = [node for node in G1_nodes if (node in self.in_1) and (node not in self.core_1)]
- T2_in = [node for node in G2_nodes if (node in self.in_2) and (node not in self.core_2)]
-
- # If T1_in and T2_in are both nonempty.
- # P(s) = T1_out x {min T2_out}
- if T1_in and T2_in:
- node_2 = min(T2_in)
- for node_1 in T1_in:
- yield node_1, node_2
-
- # If all terminal sets are empty...
- # P(s) = (N_1 - M_1) x {min (N_2 - M_2)}
-
- ##elif not (T1_in or T2_in): # as suggested by [2], incorrect
- else: # as inferred from [1], correct
- node_2 = min(G2_nodes - set(self.core_2))
- for node_1 in G1_nodes:
- if node_1 not in self.core_1:
- yield node_1, node_2
-
- # For all other cases, we don't have any candidate pairs.
-
- def initialize(self):
- """Reinitializes the state of the algorithm.
-
- This method should be redefined if using something other than DiGMState.
- If only subclassing GraphMatcher, a redefinition is not necessary.
- """
-
- # core_1[n] contains the index of the node paired with n, which is m,
- # provided n is in the mapping.
- # core_2[m] contains the index of the node paired with m, which is n,
- # provided m is in the mapping.
- self.core_1 = {}
- self.core_2 = {}
-
- # See the paper for definitions of M_x and T_x^{y}
-
- # in_1[n] is non-zero if n is in M_1 or in T_1^{in}
- # out_1[n] is non-zero if n is in M_1 or in T_1^{out}
- #
- # in_2[m] is non-zero if m is in M_2 or in T_2^{in}
- # out_2[m] is non-zero if m is in M_2 or in T_2^{out}
- #
- # The value stored is the depth of the search tree when the node became
- # part of the corresponding set.
- self.in_1 = {}
- self.in_2 = {}
- self.out_1 = {}
- self.out_2 = {}
-
- self.state = DiGMState(self)
-
- # Provide a convienient way to access the isomorphism mapping.
- self.mapping = self.core_1.copy()
-
- def syntactic_feasibility(self, G1_node, G2_node):
- """Returns True if adding (G1_node, G2_node) is syntactically feasible.
-
- This function returns True if it is adding the candidate pair
- to the current partial isomorphism mapping is allowable. The addition
- is allowable if the inclusion of the candidate pair does not make it
- impossible for an isomorphism to be found.
- """
-
- # The VF2 algorithm was designed to work with graphs having, at most,
- # one edge connecting any two nodes. This is not the case when
- # dealing with an MultiGraphs.
- #
- # Basically, when we test the look-ahead rules R_pred and R_succ, we
- # will make sure that the number of edges are checked. We also add
- # a R_self check to verify that the number of selfloops is acceptable.
-
- # Users might be comparing DiGraph instances with MultiDiGraph
- # instances. So the generic DiGraphMatcher class must work with
- # MultiDiGraphs. Care must be taken since the value in the innermost
- # dictionary is a singlet for DiGraph instances. For MultiDiGraphs,
- # the value in the innermost dictionary is a list.
-
-
- ###
- ### Test at each step to get a return value as soon as possible.
- ###
-
-
-
- ### Look ahead 0
-
- # R_self
-
- # The number of selfloops for G1_node must equal the number of
- # self-loops for G2_node. Without this check, we would fail on R_pred
- # at the next recursion level. This should prune the tree even further.
-
- if self.G1.number_of_edges(G1_node,G1_node) != self.G2.number_of_edges(G2_node,G2_node):
- return False
-
-
- # R_pred
-
- # For each predecessor n' of n in the partial mapping, the
- # corresponding node m' is a predecessor of m, and vice versa. Also,
- # the number of edges must be equal
- for predecessor in self.G1.pred[G1_node]:
- if predecessor in self.core_1:
- if not (self.core_1[predecessor] in self.G2.pred[G2_node]):
- return False
- elif self.G1.number_of_edges(predecessor, G1_node) != self.G2.number_of_edges(self.core_1[predecessor], G2_node):
- return False
-
- for predecessor in self.G2.pred[G2_node]:
- if predecessor in self.core_2:
- if not (self.core_2[predecessor] in self.G1.pred[G1_node]):
- return False
- elif self.G1.number_of_edges(self.core_2[predecessor], G1_node) != self.G2.number_of_edges(predecessor, G2_node):
- return False
-
-
- # R_succ
-
- # For each successor n' of n in the partial mapping, the corresponding
- # node m' is a successor of m, and vice versa. Also, the number of
- # edges must be equal.
- for successor in self.G1[G1_node]:
- if successor in self.core_1:
- if not (self.core_1[successor] in self.G2[G2_node]):
- return False
- elif self.G1.number_of_edges(G1_node, successor) != self.G2.number_of_edges(G2_node, self.core_1[successor]):
- return False
-
- for successor in self.G2[G2_node]:
- if successor in self.core_2:
- if not (self.core_2[successor] in self.G1[G1_node]):
- return False
- elif self.G1.number_of_edges(G1_node, self.core_2[successor]) != self.G2.number_of_edges(G2_node, successor):
- return False
-
-
- ### Look ahead 1
-
- # R_termin
- # The number of predecessors of n that are in T_1^{in} is equal to the
- # number of predecessors of m that are in T_2^{in}.
- num1 = 0
- for predecessor in self.G1.pred[G1_node]:
- if (predecessor in self.in_1) and (predecessor not in self.core_1):
- num1 += 1
- num2 = 0
- for predecessor in self.G2.pred[G2_node]:
- if (predecessor in self.in_2) and (predecessor not in self.core_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- # The number of successors of n that are in T_1^{in} is equal to the
- # number of successors of m that are in T_2^{in}.
- num1 = 0
- for successor in self.G1[G1_node]:
- if (successor in self.in_1) and (successor not in self.core_1):
- num1 += 1
- num2 = 0
- for successor in self.G2[G2_node]:
- if (successor in self.in_2) and (successor not in self.core_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- # R_termout
-
- # The number of predecessors of n that are in T_1^{out} is equal to the
- # number of predecessors of m that are in T_2^{out}.
- num1 = 0
- for predecessor in self.G1.pred[G1_node]:
- if (predecessor in self.out_1) and (predecessor not in self.core_1):
- num1 += 1
- num2 = 0
- for predecessor in self.G2.pred[G2_node]:
- if (predecessor in self.out_2) and (predecessor not in self.core_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- # The number of successors of n that are in T_1^{out} is equal to the
- # number of successors of m that are in T_2^{out}.
- num1 = 0
- for successor in self.G1[G1_node]:
- if (successor in self.out_1) and (successor not in self.core_1):
- num1 += 1
- num2 = 0
- for successor in self.G2[G2_node]:
- if (successor in self.out_2) and (successor not in self.core_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- ### Look ahead 2
-
- # R_new
-
- # The number of predecessors of n that are neither in the core_1 nor
- # T_1^{in} nor T_1^{out} is equal to the number of predecessors of m
- # that are neither in core_2 nor T_2^{in} nor T_2^{out}.
- num1 = 0
- for predecessor in self.G1.pred[G1_node]:
- if (predecessor not in self.in_1) and (predecessor not in self.out_1):
- num1 += 1
- num2 = 0
- for predecessor in self.G2.pred[G2_node]:
- if (predecessor not in self.in_2) and (predecessor not in self.out_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- # The number of successors of n that are neither in the core_1 nor
- # T_1^{in} nor T_1^{out} is equal to the number of successors of m
- # that are neither in core_2 nor T_2^{in} nor T_2^{out}.
- num1 = 0
- for successor in self.G1[G1_node]:
- if (successor not in self.in_1) and (successor not in self.out_1):
- num1 += 1
- num2 = 0
- for successor in self.G2[G2_node]:
- if (successor not in self.in_2) and (successor not in self.out_2):
- num2 += 1
- if self.test == 'graph':
- if not (num1 == num2):
- return False
- else: # self.test == 'subgraph'
- if not (num1 >= num2):
- return False
-
- # Otherwise, this node pair is syntactically feasible!
- return True
-
-
-class GMState(object):
- """Internal representation of state for the GraphMatcher class.
-
- This class is used internally by the GraphMatcher class. It is used
- only to store state specific data. There will be at most G2.order() of
- these objects in memory at a time, due to the depth-first search
- strategy employed by the VF2 algorithm.
- """
- def __init__(self, GM, G1_node=None, G2_node=None):
- """Initializes GMState object.
-
- Pass in the GraphMatcher to which this GMState belongs and the
- new node pair that will be added to the GraphMatcher's current
- isomorphism mapping.
- """
- self.GM = GM
-
- # Initialize the last stored node pair.
- self.G1_node = None
- self.G2_node = None
- self.depth = len(GM.core_1)
-
- if G1_node is None or G2_node is None:
- # Then we reset the class variables
- GM.core_1 = {}
- GM.core_2 = {}
- GM.inout_1 = {}
- GM.inout_2 = {}
-
- # Watch out! G1_node == 0 should evaluate to True.
- if G1_node is not None and G2_node is not None:
- # Add the node pair to the isomorphism mapping.
- GM.core_1[G1_node] = G2_node
- GM.core_2[G2_node] = G1_node
-
- # Store the node that was added last.
- self.G1_node = G1_node
- self.G2_node = G2_node
-
- # Now we must update the other two vectors.
- # We will add only if it is not in there already!
- self.depth = len(GM.core_1)
-
- # First we add the new nodes...
- if G1_node not in GM.inout_1:
- GM.inout_1[G1_node] = self.depth
- if G2_node not in GM.inout_2:
- GM.inout_2[G2_node] = self.depth
-
- # Now we add every other node...
-
- # Updates for T_1^{inout}
- new_nodes = set([])
- for node in GM.core_1:
- new_nodes.update([neighbor for neighbor in GM.G1[node] if neighbor not in GM.core_1])
- for node in new_nodes:
- if node not in GM.inout_1:
- GM.inout_1[node] = self.depth
-
- # Updates for T_2^{inout}
- new_nodes = set([])
- for node in GM.core_2:
- new_nodes.update([neighbor for neighbor in GM.G2[node] if neighbor not in GM.core_2])
- for node in new_nodes:
- if node not in GM.inout_2:
- GM.inout_2[node] = self.depth
-
- def restore(self):
- """Deletes the GMState object and restores the class variables."""
- # First we remove the node that was added from the core vectors.
- # Watch out! G1_node == 0 should evaluate to True.
- if self.G1_node is not None and self.G2_node is not None:
- del self.GM.core_1[self.G1_node]
- del self.GM.core_2[self.G2_node]
-
- # Now we revert the other two vectors.
- # Thus, we delete all entries which have this depth level.
- for vector in (self.GM.inout_1, self.GM.inout_2):
- for node in list(vector.keys()):
- if vector[node] == self.depth:
- del vector[node]
-
-
-class DiGMState(object):
- """Internal representation of state for the DiGraphMatcher class.
-
- This class is used internally by the DiGraphMatcher class. It is used
- only to store state specific data. There will be at most G2.order() of
- these objects in memory at a time, due to the depth-first search
- strategy employed by the VF2 algorithm.
-
- """
- def __init__(self, GM, G1_node=None, G2_node=None):
- """Initializes DiGMState object.
-
- Pass in the DiGraphMatcher to which this DiGMState belongs and the
- new node pair that will be added to the GraphMatcher's current
- isomorphism mapping.
- """
- self.GM = GM
-
- # Initialize the last stored node pair.
- self.G1_node = None
- self.G2_node = None
- self.depth = len(GM.core_1)
-
- if G1_node is None or G2_node is None:
- # Then we reset the class variables
- GM.core_1 = {}
- GM.core_2 = {}
- GM.in_1 = {}
- GM.in_2 = {}
- GM.out_1 = {}
- GM.out_2 = {}
-
- # Watch out! G1_node == 0 should evaluate to True.
- if G1_node is not None and G2_node is not None:
- # Add the node pair to the isomorphism mapping.
- GM.core_1[G1_node] = G2_node
- GM.core_2[G2_node] = G1_node
-
- # Store the node that was added last.
- self.G1_node = G1_node
- self.G2_node = G2_node
-
- # Now we must update the other four vectors.
- # We will add only if it is not in there already!
- self.depth = len(GM.core_1)
-
- # First we add the new nodes...
- for vector in (GM.in_1, GM.out_1):
- if G1_node not in vector:
- vector[G1_node] = self.depth
- for vector in (GM.in_2, GM.out_2):
- if G2_node not in vector:
- vector[G2_node] = self.depth
-
- # Now we add every other node...
-
- # Updates for T_1^{in}
- new_nodes = set([])
- for node in GM.core_1:
- new_nodes.update([predecessor for predecessor in GM.G1.predecessors(node) if predecessor not in GM.core_1])
- for node in new_nodes:
- if node not in GM.in_1:
- GM.in_1[node] = self.depth
-
- # Updates for T_2^{in}
- new_nodes = set([])
- for node in GM.core_2:
- new_nodes.update([predecessor for predecessor in GM.G2.predecessors(node) if predecessor not in GM.core_2])
- for node in new_nodes:
- if node not in GM.in_2:
- GM.in_2[node] = self.depth
-
- # Updates for T_1^{out}
- new_nodes = set([])
- for node in GM.core_1:
- new_nodes.update([successor for successor in GM.G1.successors(node) if successor not in GM.core_1])
- for node in new_nodes:
- if node not in GM.out_1:
- GM.out_1[node] = self.depth
-
- # Updates for T_2^{out}
- new_nodes = set([])
- for node in GM.core_2:
- new_nodes.update([successor for successor in GM.G2.successors(node) if successor not in GM.core_2])
- for node in new_nodes:
- if node not in GM.out_2:
- GM.out_2[node] = self.depth
-
- def restore(self):
- """Deletes the DiGMState object and restores the class variables."""
-
- # First we remove the node that was added from the core vectors.
- # Watch out! G1_node == 0 should evaluate to True.
- if self.G1_node is not None and self.G2_node is not None:
- del self.GM.core_1[self.G1_node]
- del self.GM.core_2[self.G2_node]
-
- # Now we revert the other four vectors.
- # Thus, we delete all entries which have this depth level.
- for vector in (self.GM.in_1, self.GM.in_2, self.GM.out_1, self.GM.out_2):
- for node in list(vector.keys()):
- if vector[node] == self.depth:
- del vector[node]
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/matchhelpers.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/matchhelpers.py
deleted file mode 100644
index f9af38b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/matchhelpers.py
+++ /dev/null
@@ -1,346 +0,0 @@
-"""Functions which help end users define customize node_match and
-edge_match functions to use during isomorphism checks.
-"""
-from itertools import permutations
-import types
-import networkx as nx
-
-__all__ = ['categorical_node_match',
- 'categorical_edge_match',
- 'categorical_multiedge_match',
- 'numerical_node_match',
- 'numerical_edge_match',
- 'numerical_multiedge_match',
- 'generic_node_match',
- 'generic_edge_match',
- 'generic_multiedge_match',
- ]
-
-
-def copyfunc(f, name=None):
- """Returns a deepcopy of a function."""
- try:
- return types.FunctionType(f.func_code, f.func_globals, name or f.name,
- f.func_defaults, f.func_closure)
- except AttributeError:
- return types.FunctionType(f.__code__, f.__globals__, name or f.name,
- f.__defaults__, f.__closure__)
-
-def allclose(x, y, rtol=1.0000000000000001e-05, atol=1e-08):
- """Returns True if x and y are sufficiently close, elementwise.
-
- Parameters
- ----------
- rtol : float
- The relative error tolerance.
- atol : float
- The absolute error tolerance.
-
- """
- # assume finite weights, see numpy.allclose() for reference
- for xi, yi in zip(x,y):
- if not ( abs(xi-yi) <= atol + rtol * abs(yi) ):
- return False
- return True
-
-
-def close(x, y, rtol=1.0000000000000001e-05, atol=1e-08):
- """Returns True if x and y are sufficiently close.
-
- Parameters
- ----------
- rtol : float
- The relative error tolerance.
- atol : float
- The absolute error tolerance.
-
- """
- # assume finite weights, see numpy.allclose() for reference
- return abs(x-y) <= atol + rtol * abs(y)
-
-
-categorical_doc = """
-Returns a comparison function for a categorical node attribute.
-
-The value(s) of the attr(s) must be hashable and comparable via the ==
-operator since they are placed into a set([]) object. If the sets from
-G1 and G2 are the same, then the constructed function returns True.
-
-Parameters
-----------
-attr : string | list
- The categorical node attribute to compare, or a list of categorical
- node attributes to compare.
-default : value | list
- The default value for the categorical node attribute, or a list of
- default values for the categorical node attributes.
-
-Returns
--------
-match : function
- The customized, categorical `node_match` function.
-
-Examples
---------
->>> import networkx.algorithms.isomorphism as iso
->>> nm = iso.categorical_node_match('size', 1)
->>> nm = iso.categorical_node_match(['color', 'size'], ['red', 2])
-
-"""
-
-def categorical_node_match(attr, default):
- if nx.utils.is_string_like(attr):
- def match(data1, data2):
- return data1.get(attr, default) == data2.get(attr, default)
- else:
- attrs = list(zip(attr, default)) # Python 3
- def match(data1, data2):
- values1 = set([data1.get(attr, d) for attr, d in attrs])
- values2 = set([data2.get(attr, d) for attr, d in attrs])
- return values1 == values2
- return match
-
-categorical_edge_match = copyfunc(categorical_node_match, 'categorical_edge_match')
-
-def categorical_multiedge_match(attr, default):
- if nx.utils.is_string_like(attr):
- def match(datasets1, datasets2):
- values1 = set([data.get(attr, default) for data in datasets1.values()])
- values2 = set([data.get(attr, default) for data in datasets2.values()])
- return values1 == values2
- else:
- attrs = list(zip(attr, default)) # Python 3
- def match(datasets1, datasets2):
- values1 = set([])
- for data1 in datasets1.values():
- x = tuple( data1.get(attr, d) for attr, d in attrs )
- values1.add(x)
- values2 = set([])
- for data2 in datasets2.values():
- x = tuple( data2.get(attr, d) for attr, d in attrs )
- values2.add(x)
- return values1 == values2
- return match
-
-# Docstrings for categorical functions.
-categorical_node_match.__doc__ = categorical_doc
-categorical_edge_match.__doc__ = categorical_doc.replace('node', 'edge')
-tmpdoc = categorical_doc.replace('node', 'edge')
-tmpdoc = tmpdoc.replace('categorical_edge_match', 'categorical_multiedge_match')
-categorical_multiedge_match.__doc__ = tmpdoc
-
-
-numerical_doc = """
-Returns a comparison function for a numerical node attribute.
-
-The value(s) of the attr(s) must be numerical and sortable. If the
-sorted list of values from G1 and G2 are the same within some
-tolerance, then the constructed function returns True.
-
-Parameters
-----------
-attr : string | list
- The numerical node attribute to compare, or a list of numerical
- node attributes to compare.
-default : value | list
- The default value for the numerical node attribute, or a list of
- default values for the numerical node attributes.
-rtol : float
- The relative error tolerance.
-atol : float
- The absolute error tolerance.
-
-Returns
--------
-match : function
- The customized, numerical `node_match` function.
-
-Examples
---------
->>> import networkx.algorithms.isomorphism as iso
->>> nm = iso.numerical_node_match('weight', 1.0)
->>> nm = iso.numerical_node_match(['weight', 'linewidth'], [.25, .5])
-
-"""
-
-def numerical_node_match(attr, default, rtol=1.0000000000000001e-05, atol=1e-08):
- if nx.utils.is_string_like(attr):
- def match(data1, data2):
- return close(data1.get(attr, default),
- data2.get(attr, default),
- rtol=rtol, atol=atol)
- else:
- attrs = list(zip(attr, default)) # Python 3
- def match(data1, data2):
- values1 = [data1.get(attr, d) for attr, d in attrs]
- values2 = [data2.get(attr, d) for attr, d in attrs]
- return allclose(values1, values2, rtol=rtol, atol=atol)
- return match
-
-numerical_edge_match = copyfunc(numerical_node_match, 'numerical_edge_match')
-
-def numerical_multiedge_match(attr, default, rtol=1.0000000000000001e-05, atol=1e-08):
- if nx.utils.is_string_like(attr):
- def match(datasets1, datasets2):
- values1 = sorted([data.get(attr, default) for data in datasets1.values()])
- values2 = sorted([data.get(attr, default) for data in datasets2.values()])
- return allclose(values1, values2, rtol=rtol, atol=atol)
- else:
- attrs = list(zip(attr, default)) # Python 3
- def match(datasets1, datasets2):
- values1 = []
- for data1 in datasets1.values():
- x = tuple( data1.get(attr, d) for attr, d in attrs )
- values1.append(x)
- values2 = []
- for data2 in datasets2.values():
- x = tuple( data2.get(attr, d) for attr, d in attrs )
- values2.append(x)
- values1.sort()
- values2.sort()
- for xi, yi in zip(values1, values2):
- if not allclose(xi, yi, rtol=rtol, atol=atol):
- return False
- else:
- return True
- return match
-
-# Docstrings for numerical functions.
-numerical_node_match.__doc__ = numerical_doc
-numerical_edge_match.__doc__ = numerical_doc.replace('node', 'edge')
-tmpdoc = numerical_doc.replace('node', 'edge')
-tmpdoc = tmpdoc.replace('numerical_edge_match', 'numerical_multiedge_match')
-numerical_multiedge_match.__doc__ = tmpdoc
-
-
-generic_doc = """
-Returns a comparison function for a generic attribute.
-
-The value(s) of the attr(s) are compared using the specified
-operators. If all the attributes are equal, then the constructed
-function returns True.
-
-Parameters
-----------
-attr : string | list
- The node attribute to compare, or a list of node attributes
- to compare.
-default : value | list
- The default value for the node attribute, or a list of
- default values for the node attributes.
-op : callable | list
- The operator to use when comparing attribute values, or a list
- of operators to use when comparing values for each attribute.
-
-Returns
--------
-match : function
- The customized, generic `node_match` function.
-
-Examples
---------
->>> from operator import eq
->>> from networkx.algorithms.isomorphism.matchhelpers import close
->>> from networkx.algorithms.isomorphism import generic_node_match
->>> nm = generic_node_match('weight', 1.0, close)
->>> nm = generic_node_match('color', 'red', eq)
->>> nm = generic_node_match(['weight', 'color'], [1.0, 'red'], [close, eq])
-
-"""
-
-def generic_node_match(attr, default, op):
- if nx.utils.is_string_like(attr):
- def match(data1, data2):
- return op(data1.get(attr, default), data2.get(attr, default))
- else:
- attrs = list(zip(attr, default, op)) # Python 3
- def match(data1, data2):
- for attr, d, operator in attrs:
- if not operator(data1.get(attr, d), data2.get(attr, d)):
- return False
- else:
- return True
- return match
-
-generic_edge_match = copyfunc(generic_node_match, 'generic_edge_match')
-
-def generic_multiedge_match(attr, default, op):
- """Returns a comparison function for a generic attribute.
-
- The value(s) of the attr(s) are compared using the specified
- operators. If all the attributes are equal, then the constructed
- function returns True. Potentially, the constructed edge_match
- function can be slow since it must verify that no isomorphism
- exists between the multiedges before it returns False.
-
- Parameters
- ----------
- attr : string | list
- The edge attribute to compare, or a list of node attributes
- to compare.
- default : value | list
- The default value for the edge attribute, or a list of
- default values for the dgeattributes.
- op : callable | list
- The operator to use when comparing attribute values, or a list
- of operators to use when comparing values for each attribute.
-
- Returns
- -------
- match : function
- The customized, generic `edge_match` function.
-
- Examples
- --------
- >>> from operator import eq
- >>> from networkx.algorithms.isomorphism.matchhelpers import close
- >>> from networkx.algorithms.isomorphism import generic_node_match
- >>> nm = generic_node_match('weight', 1.0, close)
- >>> nm = generic_node_match('color', 'red', eq)
- >>> nm = generic_node_match(['weight', 'color'],
- ... [1.0, 'red'],
- ... [close, eq])
- ...
-
- """
-
- # This is slow, but generic.
- # We must test every possible isomorphism between the edges.
- if nx.utils.is_string_like(attr):
- def match(datasets1, datasets2):
- values1 = [data.get(attr, default) for data in datasets1.values()]
- values2 = [data.get(attr, default) for data in datasets2.values()]
- for vals2 in permutations(values2):
- for xi, yi in zip(values1, vals2):
- if not op(xi, yi):
- # This is not an isomorphism, go to next permutation.
- break
- else:
- # Then we found an isomorphism.
- return True
- else:
- # Then there are no isomorphisms between the multiedges.
- return False
- else:
- attrs = list(zip(attr, default)) # Python 3
- def match(datasets1, datasets2):
- values1 = []
- for data1 in datasets1.values():
- x = tuple( data1.get(attr, d) for attr, d in attrs )
- values1.append(x)
- values2 = []
- for data2 in datasets2.values():
- x = tuple( data2.get(attr, d) for attr, d in attrs )
- values2.append(x)
- for vals2 in permutations(values2):
- for xi, yi, operator in zip(values1, vals2, op):
- if not operator(xi, yi):
- return False
- else:
- return True
- return match
-
-# Docstrings for numerical functions.
-generic_node_match.__doc__ = generic_doc
-generic_edge_match.__doc__ = generic_doc.replace('node', 'edge')
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/iso_r01_s80.A99 b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/iso_r01_s80.A99
deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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+++ /dev/null
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diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/si2_b06_m200.B99 b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/si2_b06_m200.B99
deleted file mode 100644
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diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_isomorphism.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_isomorphism.py
deleted file mode 100644
index b9c7e63..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_isomorphism.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx.algorithms import isomorphism as iso
-
-class TestIsomorph:
-
- def setUp(self):
- self.G1=nx.Graph()
- self.G2=nx.Graph()
- self.G3=nx.Graph()
- self.G4=nx.Graph()
- self.G1.add_edges_from([ [1,2],[1,3],[1,5],[2,3] ])
- self.G2.add_edges_from([ [10,20],[20,30],[10,30],[10,50] ])
- self.G3.add_edges_from([ [1,2],[1,3],[1,5],[2,5] ])
- self.G4.add_edges_from([ [1,2],[1,3],[1,5],[2,4] ])
-
- def test_could_be_isomorphic(self):
- assert_true(iso.could_be_isomorphic(self.G1,self.G2))
- assert_true(iso.could_be_isomorphic(self.G1,self.G3))
- assert_false(iso.could_be_isomorphic(self.G1,self.G4))
- assert_true(iso.could_be_isomorphic(self.G3,self.G2))
-
- def test_fast_could_be_isomorphic(self):
- assert_true(iso.fast_could_be_isomorphic(self.G3,self.G2))
-
- def test_faster_could_be_isomorphic(self):
- assert_true(iso.faster_could_be_isomorphic(self.G3,self.G2))
-
- def test_is_isomorphic(self):
- assert_true(iso.is_isomorphic(self.G1,self.G2))
- assert_false(iso.is_isomorphic(self.G1,self.G4))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py
deleted file mode 100644
index 562d396..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py
+++ /dev/null
@@ -1,217 +0,0 @@
-"""
- Tests for VF2 isomorphism algorithm.
-"""
-
-import os
-import struct
-import random
-
-from nose.tools import assert_true, assert_equal
-import networkx as nx
-from networkx.algorithms import isomorphism as iso
-
-class TestWikipediaExample(object):
- # Source: http://en.wikipedia.org/wiki/Graph_isomorphism
-
- # Nodes 'a', 'b', 'c' and 'd' form a column.
- # Nodes 'g', 'h', 'i' and 'j' form a column.
- g1edges = [['a','g'], ['a','h'], ['a','i'],
- ['b','g'], ['b','h'], ['b','j'],
- ['c','g'], ['c','i'], ['c','j'],
- ['d','h'], ['d','i'], ['d','j']]
-
- # Nodes 1,2,3,4 form the clockwise corners of a large square.
- # Nodes 5,6,7,8 form the clockwise corners of a small square
- g2edges = [[1,2], [2,3], [3,4], [4,1],
- [5,6], [6,7], [7,8], [8,5],
- [1,5], [2,6], [3,7], [4,8]]
-
- def test_graph(self):
- g1 = nx.Graph()
- g2 = nx.Graph()
- g1.add_edges_from(self.g1edges)
- g2.add_edges_from(self.g2edges)
- gm = iso.GraphMatcher(g1,g2)
- assert_true(gm.is_isomorphic())
-
- mapping = sorted(gm.mapping.items())
-# this mapping is only one of the possibilies
-# so this test needs to be reconsidered
-# isomap = [('a', 1), ('b', 6), ('c', 3), ('d', 8),
-# ('g', 2), ('h', 5), ('i', 4), ('j', 7)]
-# assert_equal(mapping, isomap)
-
- def test_subgraph(self):
- g1 = nx.Graph()
- g2 = nx.Graph()
- g1.add_edges_from(self.g1edges)
- g2.add_edges_from(self.g2edges)
- g3 = g2.subgraph([1,2,3,4])
- gm = iso.GraphMatcher(g1,g3)
- assert_true(gm.subgraph_is_isomorphic())
-
-class TestVF2GraphDB(object):
- # http://amalfi.dis.unina.it/graph/db/
-
- @staticmethod
- def create_graph(filename):
- """Creates a Graph instance from the filename."""
-
- # The file is assumed to be in the format from the VF2 graph database.
- # Each file is composed of 16-bit numbers (unsigned short int).
- # So we will want to read 2 bytes at a time.
-
- # We can read the number as follows:
- # number = struct.unpack('<H', file.read(2))
- # This says, expect the data in little-endian encoding
- # as an unsigned short int and unpack 2 bytes from the file.
-
- fh = open(filename, mode='rb')
-
- # Grab the number of nodes.
- # Node numeration is 0-based, so the first node has index 0.
- nodes = struct.unpack('<H', fh.read(2))[0]
-
- graph = nx.Graph()
- for from_node in range(nodes):
- # Get the number of edges.
- edges = struct.unpack('<H', fh.read(2))[0]
- for edge in range(edges):
- # Get the terminal node.
- to_node = struct.unpack('<H', fh.read(2))[0]
- graph.add_edge(from_node, to_node)
-
- fh.close()
- return graph
-
- def test_graph(self):
- head,tail = os.path.split(__file__)
- g1 = self.create_graph(os.path.join(head,'iso_r01_s80.A99'))
- g2 = self.create_graph(os.path.join(head,'iso_r01_s80.B99'))
- gm = iso.GraphMatcher(g1,g2)
- assert_true(gm.is_isomorphic())
-
- def test_subgraph(self):
- # A is the subgraph
- # B is the full graph
- head,tail = os.path.split(__file__)
- subgraph = self.create_graph(os.path.join(head,'si2_b06_m200.A99'))
- graph = self.create_graph(os.path.join(head,'si2_b06_m200.B99'))
- gm = iso.GraphMatcher(graph, subgraph)
- assert_true(gm.subgraph_is_isomorphic())
-
-def test_graph_atlas():
- #Atlas = nx.graph_atlas_g()[0:208] # 208, 6 nodes or less
- Atlas = nx.graph_atlas_g()[0:100]
- alphabet = list(range(26))
- for graph in Atlas:
- nlist = graph.nodes()
- labels = alphabet[:len(nlist)]
- for s in range(10):
- random.shuffle(labels)
- d = dict(zip(nlist,labels))
- relabel = nx.relabel_nodes(graph, d)
- gm = iso.GraphMatcher(graph, relabel)
- assert_true(gm.is_isomorphic())
-
-def test_multiedge():
- # Simple test for multigraphs
- # Need something much more rigorous
- edges = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5),
- (5, 6), (6, 7), (7, 8), (8, 9), (9, 10),
- (10, 11), (10, 11), (11, 12), (11, 12),
- (12, 13), (12, 13), (13, 14), (13, 14),
- (14, 15), (14, 15), (15, 16), (15, 16),
- (16, 17), (16, 17), (17, 18), (17, 18),
- (18, 19), (18, 19), (19, 0), (19, 0)]
- nodes = list(range(20))
-
- for g1 in [nx.MultiGraph(), nx.MultiDiGraph()]:
- g1.add_edges_from(edges)
- for _ in range(10):
- new_nodes = list(nodes)
- random.shuffle(new_nodes)
- d = dict(zip(nodes, new_nodes))
- g2 = nx.relabel_nodes(g1, d)
- if not g1.is_directed():
- gm = iso.GraphMatcher(g1,g2)
- else:
- gm = iso.DiGraphMatcher(g1,g2)
- assert_true(gm.is_isomorphic())
-
-def test_selfloop():
- # Simple test for graphs with selfloops
- edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 2),
- (2, 4), (3, 1), (3, 2), (4, 2), (4, 5), (5, 4)]
- nodes = list(range(6))
-
- for g1 in [nx.Graph(), nx.DiGraph()]:
- g1.add_edges_from(edges)
- for _ in range(100):
- new_nodes = list(nodes)
- random.shuffle(new_nodes)
- d = dict(zip(nodes, new_nodes))
- g2 = nx.relabel_nodes(g1, d)
- if not g1.is_directed():
- gm = iso.GraphMatcher(g1,g2)
- else:
- gm = iso.DiGraphMatcher(g1,g2)
- assert_true(gm.is_isomorphic())
-
-def test_isomorphism_iter1():
- # As described in:
- # http://groups.google.com/group/networkx-discuss/browse_thread/thread/2ff65c67f5e3b99f/d674544ebea359bb?fwc=1
- g1 = nx.DiGraph()
- g2 = nx.DiGraph()
- g3 = nx.DiGraph()
- g1.add_edge('A','B')
- g1.add_edge('B','C')
- g2.add_edge('Y','Z')
- g3.add_edge('Z','Y')
- gm12 = iso.DiGraphMatcher(g1,g2)
- gm13 = iso.DiGraphMatcher(g1,g3)
- x = list(gm12.subgraph_isomorphisms_iter())
- y = list(gm13.subgraph_isomorphisms_iter())
- assert_true({'A':'Y','B':'Z'} in x)
- assert_true({'B':'Y','C':'Z'} in x)
- assert_true({'A':'Z','B':'Y'} in y)
- assert_true({'B':'Z','C':'Y'} in y)
- assert_equal(len(x),len(y))
- assert_equal(len(x),2)
-
-def test_isomorphism_iter2():
- # Path
- for L in range(2,10):
- g1 = nx.path_graph(L)
- gm = iso.GraphMatcher(g1,g1)
- s = len(list(gm.isomorphisms_iter()))
- assert_equal(s,2)
- # Cycle
- for L in range(3,10):
- g1 = nx.cycle_graph(L)
- gm = iso.GraphMatcher(g1,g1)
- s = len(list(gm.isomorphisms_iter()))
- assert_equal(s, 2*L)
-
-def test_multiple():
- # Verify that we can use the graph matcher multiple times
- edges = [('A','B'),('B','A'),('B','C')]
- for g1,g2 in [(nx.Graph(),nx.Graph()), (nx.DiGraph(),nx.DiGraph())]:
- g1.add_edges_from(edges)
- g2.add_edges_from(edges)
- g3 = nx.subgraph(g2, ['A','B'])
- if not g1.is_directed():
- gmA = iso.GraphMatcher(g1,g2)
- gmB = iso.GraphMatcher(g1,g3)
- else:
- gmA = iso.DiGraphMatcher(g1,g2)
- gmB = iso.DiGraphMatcher(g1,g3)
- assert_true(gmA.is_isomorphic())
- g2.remove_node('C')
- assert_true(gmA.subgraph_is_isomorphic())
- assert_true(gmB.subgraph_is_isomorphic())
-# for m in [gmB.mapping, gmB.mapping]:
-# assert_true(m['A'] == 'A')
-# assert_true(m['B'] == 'B')
-# assert_true('C' not in m)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py
deleted file mode 100644
index ca4f6bd..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""
- Tests for VF2 isomorphism algorithm for weighted graphs.
-"""
-
-from nose.tools import assert_true, assert_false
-from operator import eq
-
-import networkx as nx
-import networkx.algorithms.isomorphism as iso
-def test_simple():
- # 16 simple tests
- w = 'weight'
- edges = [(0,0,1),(0,0,1.5),(0,1,2),(1,0,3)]
- for g1 in [nx.Graph(),
- nx.DiGraph(),
- nx.MultiGraph(),
- nx.MultiDiGraph(),
- ]:
-
- g1.add_weighted_edges_from(edges)
- g2 = g1.subgraph(g1.nodes())
- if g1.is_multigraph():
- em = iso.numerical_multiedge_match('weight', 1)
- else:
- em = iso.numerical_edge_match('weight', 1)
- assert_true( nx.is_isomorphic(g1,g2,edge_match=em) )
-
- for mod1, mod2 in [(False, True), (True, False), (True, True)]:
- # mod1 tests a regular edge
- # mod2 tests a selfloop
- if g2.is_multigraph():
- if mod1:
- data1 = {0:{'weight':10}}
- if mod2:
- data2 = {0:{'weight':1},1:{'weight':2.5}}
- else:
- if mod1:
- data1 = {'weight':10}
- if mod2:
- data2 = {'weight':2.5}
-
- g2 = g1.subgraph(g1.nodes())
- if mod1:
- if not g1.is_directed():
- g2.adj[1][0] = data1
- g2.adj[0][1] = data1
- else:
- g2.succ[1][0] = data1
- g2.pred[0][1] = data1
- if mod2:
- if not g1.is_directed():
- g2.adj[0][0] = data2
- else:
- g2.succ[0][0] = data2
- g2.pred[0][0] = data2
-
- assert_false(nx.is_isomorphic(g1,g2,edge_match=em))
-
-def test_weightkey():
- g1 = nx.DiGraph()
- g2 = nx.DiGraph()
-
- g1.add_edge('A','B', weight=1)
- g2.add_edge('C','D', weight=0)
-
- assert_true( nx.is_isomorphic(g1, g2) )
- em = iso.numerical_edge_match('nonexistent attribute', 1)
- assert_true( nx.is_isomorphic(g1, g2, edge_match=em) )
- em = iso.numerical_edge_match('weight', 1)
- assert_false( nx.is_isomorphic(g1, g2, edge_match=em) )
-
- g2 = nx.DiGraph()
- g2.add_edge('C','D')
- assert_true( nx.is_isomorphic(g1, g2, edge_match=em) )
-
-class TestNodeMatch_Graph(object):
- def setUp(self):
- self.g1 = nx.Graph()
- self.g2 = nx.Graph()
- self.build()
-
- def build(self):
-
- self.nm = iso.categorical_node_match('color', '')
- self.em = iso.numerical_edge_match('weight', 1)
-
- self.g1.add_node('A', color='red')
- self.g2.add_node('C', color='blue')
-
- self.g1.add_edge('A','B', weight=1)
- self.g2.add_edge('C','D', weight=1)
-
- def test_noweight_nocolor(self):
- assert_true( nx.is_isomorphic(self.g1, self.g2) )
-
- def test_color1(self):
- assert_false( nx.is_isomorphic(self.g1, self.g2, node_match=self.nm) )
-
- def test_color2(self):
- self.g1.node['A']['color'] = 'blue'
- assert_true( nx.is_isomorphic(self.g1, self.g2, node_match=self.nm) )
-
- def test_weight1(self):
- assert_true( nx.is_isomorphic(self.g1, self.g2, edge_match=self.em) )
-
- def test_weight2(self):
- self.g1.add_edge('A', 'B', weight=2)
- assert_false( nx.is_isomorphic(self.g1, self.g2, edge_match=self.em) )
-
- def test_colorsandweights1(self):
- iso = nx.is_isomorphic(self.g1, self.g2,
- node_match=self.nm, edge_match=self.em)
- assert_false(iso)
-
- def test_colorsandweights2(self):
- self.g1.node['A']['color'] = 'blue'
- iso = nx.is_isomorphic(self.g1, self.g2,
- node_match=self.nm, edge_match=self.em)
- assert_true(iso)
-
- def test_colorsandweights3(self):
- # make the weights disagree
- self.g1.add_edge('A', 'B', weight=2)
- assert_false( nx.is_isomorphic(self.g1, self.g2,
- node_match=self.nm, edge_match=self.em) )
-
-class TestEdgeMatch_MultiGraph(object):
- def setUp(self):
- self.g1 = nx.MultiGraph()
- self.g2 = nx.MultiGraph()
- self.GM = iso.MultiGraphMatcher
- self.build()
-
- def build(self):
- g1 = self.g1
- g2 = self.g2
-
- # We will assume integer weights only.
- g1.add_edge('A', 'B', color='green', weight=0, size=.5)
- g1.add_edge('A', 'B', color='red', weight=1, size=.35)
- g1.add_edge('A', 'B', color='red', weight=2, size=.65)
-
- g2.add_edge('C', 'D', color='green', weight=1, size=.5)
- g2.add_edge('C', 'D', color='red', weight=0, size=.45)
- g2.add_edge('C', 'D', color='red', weight=2, size=.65)
-
- if g1.is_multigraph():
- self.em = iso.numerical_multiedge_match('weight', 1)
- self.emc = iso.categorical_multiedge_match('color', '')
- self.emcm = iso.categorical_multiedge_match(['color', 'weight'], ['', 1])
- self.emg1 = iso.generic_multiedge_match('color', 'red', eq)
- self.emg2 = iso.generic_multiedge_match(['color', 'weight', 'size'], ['red', 1, .5], [eq, eq, iso.matchhelpers.close])
- else:
- self.em = iso.numerical_edge_match('weight', 1)
- self.emc = iso.categorical_edge_match('color', '')
- self.emcm = iso.categorical_edge_match(['color', 'weight'], ['', 1])
- self.emg1 = iso.generic_multiedge_match('color', 'red', eq)
- self.emg2 = iso.generic_edge_match(['color', 'weight', 'size'], ['red', 1, .5], [eq, eq, iso.matchhelpers.close])
-
- def test_weights_only(self):
- assert_true( nx.is_isomorphic(self.g1, self.g2, edge_match=self.em) )
-
- def test_colors_only(self):
- gm = self.GM(self.g1, self.g2, edge_match=self.emc)
- assert_true( gm.is_isomorphic() )
-
- def test_colorsandweights(self):
- gm = self.GM(self.g1, self.g2, edge_match=self.emcm)
- assert_false( gm.is_isomorphic() )
-
- def test_generic1(self):
- gm = self.GM(self.g1, self.g2, edge_match=self.emg1)
- assert_true( gm.is_isomorphic() )
-
- def test_generic2(self):
- gm = self.GM(self.g1, self.g2, edge_match=self.emg2)
- assert_false( gm.is_isomorphic() )
-
-
-class TestEdgeMatch_DiGraph(TestNodeMatch_Graph):
- def setUp(self):
- self.g1 = nx.DiGraph()
- self.g2 = nx.DiGraph()
- self.build()
-
-class TestEdgeMatch_MultiDiGraph(TestEdgeMatch_MultiGraph):
- def setUp(self):
- self.g1 = nx.MultiDiGraph()
- self.g2 = nx.MultiDiGraph()
- self.GM = iso.MultiDiGraphMatcher
- self.build()
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/vf2userfunc.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/vf2userfunc.py
deleted file mode 100644
index 64685c7..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/isomorphism/vf2userfunc.py
+++ /dev/null
@@ -1,198 +0,0 @@
-"""
- Module to simplify the specification of user-defined equality functions for
- node and edge attributes during isomorphism checks.
-
- During the construction of an isomorphism, the algorithm considers two
- candidate nodes n1 in G1 and n2 in G2. The graphs G1 and G2 are then
- compared with respect to properties involving n1 and n2, and if the outcome
- is good, then the candidate nodes are considered isomorphic. NetworkX
- provides a simple mechanism for users to extend the comparisons to include
- node and edge attributes.
-
- Node attributes are handled by the node_match keyword. When considering
- n1 and n2, the algorithm passes their node attribute dictionaries to
- node_match, and if it returns False, then n1 and n2 cannot be
- considered to be isomorphic.
-
- Edge attributes are handled by the edge_match keyword. When considering
- n1 and n2, the algorithm must verify that outgoing edges from n1 are
- commensurate with the outgoing edges for n2. If the graph is directed,
- then a similar check is also performed for incoming edges.
-
- Focusing only on outgoing edges, we consider pairs of nodes (n1, v1) from
- G1 and (n2, v2) from G2. For graphs and digraphs, there is only one edge
- between (n1, v1) and only one edge between (n2, v2). Those edge attribute
- dictionaries are passed to edge_match, and if it returns False, then
- n1 and n2 cannot be considered isomorphic. For multigraphs and
- multidigraphs, there can be multiple edges between (n1, v1) and also
- multiple edges between (n2, v2). Now, there must exist an isomorphism
- from "all the edges between (n1, v1)" to "all the edges between (n2, v2)".
- So, all of the edge attribute dictionaries are passed to edge_match, and
- it must determine if there is an isomorphism between the two sets of edges.
-"""
-
-import networkx as nx
-
-from . import isomorphvf2 as vf2
-
-__all__ = ['GraphMatcher',
- 'DiGraphMatcher',
- 'MultiGraphMatcher',
- 'MultiDiGraphMatcher',
- ]
-
-
-def _semantic_feasibility(self, G1_node, G2_node):
- """Returns True if mapping G1_node to G2_node is semantically feasible.
- """
- # Make sure the nodes match
- if self.node_match is not None:
- nm = self.node_match(self.G1.node[G1_node], self.G2.node[G2_node])
- if not nm:
- return False
-
- # Make sure the edges match
- if self.edge_match is not None:
-
- # Cached lookups
- G1_adj = self.G1_adj
- G2_adj = self.G2_adj
- core_1 = self.core_1
- edge_match = self.edge_match
-
- for neighbor in G1_adj[G1_node]:
- # G1_node is not in core_1, so we must handle R_self separately
- if neighbor == G1_node:
- if not edge_match(G1_adj[G1_node][G1_node],
- G2_adj[G2_node][G2_node]):
- return False
- elif neighbor in core_1:
- if not edge_match(G1_adj[G1_node][neighbor],
- G2_adj[G2_node][core_1[neighbor]]):
- return False
- # syntactic check has already verified that neighbors are symmetric
-
- return True
-
-
-class GraphMatcher(vf2.GraphMatcher):
- """VF2 isomorphism checker for undirected graphs.
- """
- def __init__(self, G1, G2, node_match=None, edge_match=None):
- """Initialize graph matcher.
-
- Parameters
- ----------
- G1, G2: graph
- The graphs to be tested.
-
- node_match: callable
- A function that returns True iff node n1 in G1 and n2 in G2
- should be considered equal during the isomorphism test. The
- function will be called like::
-
- node_match(G1.node[n1], G2.node[n2])
-
- That is, the function will receive the node attribute dictionaries
- of the nodes under consideration. If None, then no attributes are
- considered when testing for an isomorphism.
-
- edge_match: callable
- A function that returns True iff the edge attribute dictionary for
- the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should be
- considered equal during the isomorphism test. The function will be
- called like::
-
- edge_match(G1[u1][v1], G2[u2][v2])
-
- That is, the function will receive the edge attribute dictionaries
- of the edges under consideration. If None, then no attributes are
- considered when testing for an isomorphism.
-
- """
- vf2.GraphMatcher.__init__(self, G1, G2)
-
- self.node_match = node_match
- self.edge_match = edge_match
-
- # These will be modified during checks to minimize code repeat.
- self.G1_adj = self.G1.adj
- self.G2_adj = self.G2.adj
-
- semantic_feasibility = _semantic_feasibility
-
-
-class DiGraphMatcher(vf2.DiGraphMatcher):
- """VF2 isomorphism checker for directed graphs.
- """
- def __init__(self, G1, G2, node_match=None, edge_match=None):
- """Initialize graph matcher.
-
- Parameters
- ----------
- G1, G2 : graph
- The graphs to be tested.
-
- node_match : callable
- A function that returns True iff node n1 in G1 and n2 in G2
- should be considered equal during the isomorphism test. The
- function will be called like::
-
- node_match(G1.node[n1], G2.node[n2])
-
- That is, the function will receive the node attribute dictionaries
- of the nodes under consideration. If None, then no attributes are
- considered when testing for an isomorphism.
-
- edge_match : callable
- A function that returns True iff the edge attribute dictionary for
- the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should be
- considered equal during the isomorphism test. The function will be
- called like::
-
- edge_match(G1[u1][v1], G2[u2][v2])
-
- That is, the function will receive the edge attribute dictionaries
- of the edges under consideration. If None, then no attributes are
- considered when testing for an isomorphism.
-
- """
- vf2.DiGraphMatcher.__init__(self, G1, G2)
-
- self.node_match = node_match
- self.edge_match = edge_match
-
- # These will be modified during checks to minimize code repeat.
- self.G1_adj = self.G1.adj
- self.G2_adj = self.G2.adj
-
-
- def semantic_feasibility(self, G1_node, G2_node):
- """Returns True if mapping G1_node to G2_node is semantically feasible."""
-
- # Test node_match and also test edge_match on successors
- feasible = _semantic_feasibility(self, G1_node, G2_node)
- if not feasible:
- return False
-
- # Test edge_match on predecessors
- self.G1_adj = self.G1.pred
- self.G2_adj = self.G2.pred
- feasible = _semantic_feasibility(self, G1_node, G2_node)
- self.G1_adj = self.G1.adj
- self.G2_adj = self.G2.adj
-
- return feasible
-
-## The "semantics" of edge_match are different for multi(di)graphs, but
-## the implementation is the same. So, technically we do not need to
-## provide "multi" versions, but we do so to match NetworkX's base classes.
-
-class MultiGraphMatcher(GraphMatcher):
- """VF2 isomorphism checker for undirected multigraphs. """
- pass
-
-class MultiDiGraphMatcher(DiGraphMatcher):
- """VF2 isomorphism checker for directed multigraphs. """
- pass
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/__init__.py
deleted file mode 100644
index d7e67c3..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from networkx.algorithms.link_analysis.pagerank_alg import *
-from networkx.algorithms.link_analysis.hits_alg import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/hits_alg.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/hits_alg.py
deleted file mode 100644
index 5b707d7..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/hits_alg.py
+++ /dev/null
@@ -1,308 +0,0 @@
-"""Hubs and authorities analysis of graph structure.
-"""
-# Copyright (C) 2008-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-# NetworkX:http://networkx.lanl.gov/
-import networkx as nx
-from networkx.exception import NetworkXError
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-__all__ = ['hits','hits_numpy','hits_scipy','authority_matrix','hub_matrix']
-
-def hits(G,max_iter=100,tol=1.0e-8,nstart=None,normalized=True):
- """Return HITS hubs and authorities values for nodes.
-
- The HITS algorithm computes two numbers for a node.
- Authorities estimates the node value based on the incoming links.
- Hubs estimates the node value based on outgoing links.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- max_iter : interger, optional
- Maximum number of iterations in power method.
-
- tol : float, optional
- Error tolerance used to check convergence in power method iteration.
-
- nstart : dictionary, optional
- Starting value of each node for power method iteration.
-
- normalized : bool (default=True)
- Normalize results by the sum of all of the values.
-
- Returns
- -------
- (hubs,authorities) : two-tuple of dictionaries
- Two dictionaries keyed by node containing the hub and authority
- values.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> h,a=nx.hits(G)
-
- Notes
- -----
- The eigenvector calculation is done by the power iteration method
- and has no guarantee of convergence. The iteration will stop
- after max_iter iterations or an error tolerance of
- number_of_nodes(G)*tol has been reached.
-
- The HITS algorithm was designed for directed graphs but this
- algorithm does not check if the input graph is directed and will
- execute on undirected graphs.
-
- References
- ----------
- .. [1] A. Langville and C. Meyer,
- "A survey of eigenvector methods of web information retrieval."
- http://citeseer.ist.psu.edu/713792.html
- .. [2] Jon Kleinberg,
- Authoritative sources in a hyperlinked environment
- Journal of the ACM 46 (5): 604-32, 1999.
- doi:10.1145/324133.324140.
- http://www.cs.cornell.edu/home/kleinber/auth.pdf.
- """
- if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
- raise Exception("hits() not defined for graphs with multiedges.")
- if len(G) == 0:
- return {},{}
- # choose fixed starting vector if not given
- if nstart is None:
- h=dict.fromkeys(G,1.0/G.number_of_nodes())
- else:
- h=nstart
- # normalize starting vector
- s=1.0/sum(h.values())
- for k in h:
- h[k]*=s
- i=0
- while True: # power iteration: make up to max_iter iterations
- hlast=h
- h=dict.fromkeys(hlast.keys(),0)
- a=dict.fromkeys(hlast.keys(),0)
- # this "matrix multiply" looks odd because it is
- # doing a left multiply a^T=hlast^T*G
- for n in h:
- for nbr in G[n]:
- a[nbr]+=hlast[n]*G[n][nbr].get('weight',1)
- # now multiply h=Ga
- for n in h:
- for nbr in G[n]:
- h[n]+=a[nbr]*G[n][nbr].get('weight',1)
- # normalize vector
- s=1.0/max(h.values())
- for n in h: h[n]*=s
- # normalize vector
- s=1.0/max(a.values())
- for n in a: a[n]*=s
- # check convergence, l1 norm
- err=sum([abs(h[n]-hlast[n]) for n in h])
- if err < tol:
- break
- if i>max_iter:
- raise NetworkXError(\
- "HITS: power iteration failed to converge in %d iterations."%(i+1))
- i+=1
- if normalized:
- s = 1.0/sum(a.values())
- for n in a:
- a[n] *= s
- s = 1.0/sum(h.values())
- for n in h:
- h[n] *= s
- return h,a
-
-def authority_matrix(G,nodelist=None):
- """Return the HITS authority matrix."""
- M=nx.to_numpy_matrix(G,nodelist=nodelist)
- return M.T*M
-
-def hub_matrix(G,nodelist=None):
- """Return the HITS hub matrix."""
- M=nx.to_numpy_matrix(G,nodelist=nodelist)
- return M*M.T
-
-def hits_numpy(G,normalized=True):
- """Return HITS hubs and authorities values for nodes.
-
- The HITS algorithm computes two numbers for a node.
- Authorities estimates the node value based on the incoming links.
- Hubs estimates the node value based on outgoing links.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- normalized : bool (default=True)
- Normalize results by the sum of all of the values.
-
- Returns
- -------
- (hubs,authorities) : two-tuple of dictionaries
- Two dictionaries keyed by node containing the hub and authority
- values.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> h,a=nx.hits(G)
-
- Notes
- -----
- The eigenvector calculation uses NumPy's interface to LAPACK.
-
- The HITS algorithm was designed for directed graphs but this
- algorithm does not check if the input graph is directed and will
- execute on undirected graphs.
-
- References
- ----------
- .. [1] A. Langville and C. Meyer,
- "A survey of eigenvector methods of web information retrieval."
- http://citeseer.ist.psu.edu/713792.html
- .. [2] Jon Kleinberg,
- Authoritative sources in a hyperlinked environment
- Journal of the ACM 46 (5): 604-32, 1999.
- doi:10.1145/324133.324140.
- http://www.cs.cornell.edu/home/kleinber/auth.pdf.
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(\
- "hits_numpy() requires NumPy: http://scipy.org/")
- if len(G) == 0:
- return {},{}
- H=nx.hub_matrix(G,G.nodes())
- e,ev=np.linalg.eig(H)
- m=e.argsort()[-1] # index of maximum eigenvalue
- h=np.array(ev[:,m]).flatten()
- A=nx.authority_matrix(G,G.nodes())
- e,ev=np.linalg.eig(A)
- m=e.argsort()[-1] # index of maximum eigenvalue
- a=np.array(ev[:,m]).flatten()
- if normalized:
- h = h/h.sum()
- a = a/a.sum()
- else:
- h = h/h.max()
- a = a/a.max()
- hubs=dict(zip(G.nodes(),map(float,h)))
- authorities=dict(zip(G.nodes(),map(float,a)))
- return hubs,authorities
-
-def hits_scipy(G,max_iter=100,tol=1.0e-6,normalized=True):
- """Return HITS hubs and authorities values for nodes.
-
- The HITS algorithm computes two numbers for a node.
- Authorities estimates the node value based on the incoming links.
- Hubs estimates the node value based on outgoing links.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- max_iter : interger, optional
- Maximum number of iterations in power method.
-
- tol : float, optional
- Error tolerance used to check convergence in power method iteration.
-
- nstart : dictionary, optional
- Starting value of each node for power method iteration.
-
- normalized : bool (default=True)
- Normalize results by the sum of all of the values.
-
- Returns
- -------
- (hubs,authorities) : two-tuple of dictionaries
- Two dictionaries keyed by node containing the hub and authority
- values.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> h,a=nx.hits(G)
-
- Notes
- -----
- This implementation uses SciPy sparse matrices.
-
- The eigenvector calculation is done by the power iteration method
- and has no guarantee of convergence. The iteration will stop
- after max_iter iterations or an error tolerance of
- number_of_nodes(G)*tol has been reached.
-
- The HITS algorithm was designed for directed graphs but this
- algorithm does not check if the input graph is directed and will
- execute on undirected graphs.
-
- References
- ----------
- .. [1] A. Langville and C. Meyer,
- "A survey of eigenvector methods of web information retrieval."
- http://citeseer.ist.psu.edu/713792.html
- .. [2] Jon Kleinberg,
- Authoritative sources in a hyperlinked environment
- Journal of the ACM 46 (5): 604-632, 1999.
- doi:10.1145/324133.324140.
- http://www.cs.cornell.edu/home/kleinber/auth.pdf.
- """
- try:
- import scipy.sparse
- import numpy as np
- except ImportError:
- raise ImportError(\
- "hits_scipy() requires SciPy: http://scipy.org/")
- if len(G) == 0:
- return {},{}
- M=nx.to_scipy_sparse_matrix(G,nodelist=G.nodes())
- (n,m)=M.shape # should be square
- A=M.T*M # authority matrix
- x=scipy.ones((n,1))/n # initial guess
- # power iteration on authority matrix
- i=0
- while True:
- xlast=x
- x=A*x
- x=x/x.max()
- # check convergence, l1 norm
- err=scipy.absolute(x-xlast).sum()
- if err < tol:
- break
- if i>max_iter:
- raise NetworkXError(\
- "HITS: power iteration failed to converge in %d iterations."%(i+1))
- i+=1
-
- a=np.asarray(x).flatten()
- # h=M*a
- h=np.asarray(M*a).flatten()
- if normalized:
- h = h/h.sum()
- a = a/a.sum()
- hubs=dict(zip(G.nodes(),map(float,h)))
- authorities=dict(zip(G.nodes(),map(float,a)))
- return hubs,authorities
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/pagerank_alg.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/pagerank_alg.py
deleted file mode 100644
index 244350a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/pagerank_alg.py
+++ /dev/null
@@ -1,399 +0,0 @@
-"""PageRank analysis of graph structure. """
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-# NetworkX:http://networkx.lanl.gov/
-import networkx as nx
-from networkx.exception import NetworkXError
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-__all__ = ['pagerank','pagerank_numpy','pagerank_scipy','google_matrix']
-
-def pagerank(G,alpha=0.85,personalization=None,
- max_iter=100,tol=1.0e-8,nstart=None,weight='weight'):
- """Return the PageRank of the nodes in the graph.
-
- PageRank computes a ranking of the nodes in the graph G based on
- the structure of the incoming links. It was originally designed as
- an algorithm to rank web pages.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- alpha : float, optional
- Damping parameter for PageRank, default=0.85
-
- personalization: dict, optional
- The "personalization vector" consisting of a dictionary with a
- key for every graph node and nonzero personalization value for each node.
-
- max_iter : integer, optional
- Maximum number of iterations in power method eigenvalue solver.
-
- tol : float, optional
- Error tolerance used to check convergence in power method solver.
-
- nstart : dictionary, optional
- Starting value of PageRank iteration for each node.
-
- weight : key, optional
- Edge data key to use as weight. If None weights are set to 1.
-
- Returns
- -------
- pagerank : dictionary
- Dictionary of nodes with PageRank as value
-
- Examples
- --------
- >>> G=nx.DiGraph(nx.path_graph(4))
- >>> pr=nx.pagerank(G,alpha=0.9)
-
- Notes
- -----
- The eigenvector calculation is done by the power iteration method
- and has no guarantee of convergence. The iteration will stop
- after max_iter iterations or an error tolerance of
- number_of_nodes(G)*tol has been reached.
-
- The PageRank algorithm was designed for directed graphs but this
- algorithm does not check if the input graph is directed and will
- execute on undirected graphs by converting each oriented edge in the
- directed graph to two edges.
-
- See Also
- --------
- pagerank_numpy, pagerank_scipy, google_matrix
-
- References
- ----------
- .. [1] A. Langville and C. Meyer,
- "A survey of eigenvector methods of web information retrieval."
- http://citeseer.ist.psu.edu/713792.html
- .. [2] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry,
- The PageRank citation ranking: Bringing order to the Web. 1999
- http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf
- """
- if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
- raise Exception("pagerank() not defined for graphs with multiedges.")
-
- if len(G) == 0:
- return {}
-
- if not G.is_directed():
- D=G.to_directed()
- else:
- D=G
-
- # create a copy in (right) stochastic form
- W=nx.stochastic_graph(D, weight=weight)
- scale=1.0/W.number_of_nodes()
-
- # choose fixed starting vector if not given
- if nstart is None:
- x=dict.fromkeys(W,scale)
- else:
- x=nstart
- # normalize starting vector to 1
- s=1.0/sum(x.values())
- for k in x: x[k]*=s
-
- # assign uniform personalization/teleportation vector if not given
- if personalization is None:
- p=dict.fromkeys(W,scale)
- else:
- p=personalization
- # normalize starting vector to 1
- s=1.0/sum(p.values())
- for k in p:
- p[k]*=s
- if set(p)!=set(G):
- raise NetworkXError('Personalization vector '
- 'must have a value for every node')
-
-
- # "dangling" nodes, no links out from them
- out_degree=W.out_degree()
- dangle=[n for n in W if out_degree[n]==0.0]
- i=0
- while True: # power iteration: make up to max_iter iterations
- xlast=x
- x=dict.fromkeys(xlast.keys(),0)
- danglesum=alpha*scale*sum(xlast[n] for n in dangle)
- for n in x:
- # this matrix multiply looks odd because it is
- # doing a left multiply x^T=xlast^T*W
- for nbr in W[n]:
- x[nbr]+=alpha*xlast[n]*W[n][nbr][weight]
- x[n]+=danglesum+(1.0-alpha)*p[n]
- # normalize vector
- s=1.0/sum(x.values())
- for n in x:
- x[n]*=s
- # check convergence, l1 norm
- err=sum([abs(x[n]-xlast[n]) for n in x])
- if err < tol:
- break
- if i>max_iter:
- raise NetworkXError('pagerank: power iteration failed to converge '
- 'in %d iterations.'%(i-1))
- i+=1
- return x
-
-
-def google_matrix(G, alpha=0.85, personalization=None,
- nodelist=None, weight='weight'):
- """Return the Google matrix of the graph.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- alpha : float
- The damping factor
-
- personalization: dict, optional
- The "personalization vector" consisting of a dictionary with a
- key for every graph node and nonzero personalization value for each node.
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in nodelist.
- If nodelist is None, then the ordering is produced by G.nodes().
-
- weight : key, optional
- Edge data key to use as weight. If None weights are set to 1.
-
- Returns
- -------
- A : NumPy matrix
- Google matrix of the graph
-
- See Also
- --------
- pagerank, pagerank_numpy, pagerank_scipy
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(\
- "google_matrix() requires NumPy: http://scipy.org/")
- # choose ordering in matrix
- if personalization is None: # use G.nodes() ordering
- nodelist=G.nodes()
- else: # use personalization "vector" ordering
- nodelist=personalization.keys()
- if set(nodelist)!=set(G):
- raise NetworkXError('Personalization vector dictionary'
- 'must have a value for every node')
- M=nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight)
- (n,m)=M.shape # should be square
- if n == 0:
- return M
- # add constant to dangling nodes' row
- dangling=np.where(M.sum(axis=1)==0)
- for d in dangling[0]:
- M[d]=1.0/n
- # normalize
- M=M/M.sum(axis=1)
- # add "teleportation"/personalization
- e=np.ones((n))
- if personalization is not None:
- v=np.array(list(personalization.values()),dtype=float)
- else:
- v=e
- v=v/v.sum()
- P=alpha*M+(1-alpha)*np.outer(e,v)
- return P
-
-
-def pagerank_numpy(G, alpha=0.85, personalization=None, weight='weight'):
- """Return the PageRank of the nodes in the graph.
-
- PageRank computes a ranking of the nodes in the graph G based on
- the structure of the incoming links. It was originally designed as
- an algorithm to rank web pages.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- alpha : float, optional
- Damping parameter for PageRank, default=0.85
-
- personalization: dict, optional
- The "personalization vector" consisting of a dictionary with a
- key for every graph node and nonzero personalization value for each node.
-
- weight : key, optional
- Edge data key to use as weight. If None weights are set to 1.
-
- Returns
- -------
- pagerank : dictionary
- Dictionary of nodes with PageRank as value
-
- Examples
- --------
- >>> G=nx.DiGraph(nx.path_graph(4))
- >>> pr=nx.pagerank_numpy(G,alpha=0.9)
-
- Notes
- -----
- The eigenvector calculation uses NumPy's interface to the LAPACK
- eigenvalue solvers. This will be the fastest and most accurate
- for small graphs.
-
- This implementation works with Multi(Di)Graphs.
-
- See Also
- --------
- pagerank, pagerank_scipy, google_matrix
-
- References
- ----------
- .. [1] A. Langville and C. Meyer,
- "A survey of eigenvector methods of web information retrieval."
- http://citeseer.ist.psu.edu/713792.html
- .. [2] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry,
- The PageRank citation ranking: Bringing order to the Web. 1999
- http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError("pagerank_numpy() requires NumPy: http://scipy.org/")
- if len(G) == 0:
- return {}
- # choose ordering in matrix
- if personalization is None: # use G.nodes() ordering
- nodelist=G.nodes()
- else: # use personalization "vector" ordering
- nodelist=personalization.keys()
- M=google_matrix(G, alpha, personalization=personalization,
- nodelist=nodelist, weight=weight)
- # use numpy LAPACK solver
- eigenvalues,eigenvectors=np.linalg.eig(M.T)
- ind=eigenvalues.argsort()
- # eigenvector of largest eigenvalue at ind[-1], normalized
- largest=np.array(eigenvectors[:,ind[-1]]).flatten().real
- norm=float(largest.sum())
- centrality=dict(zip(nodelist,map(float,largest/norm)))
- return centrality
-
-
-def pagerank_scipy(G, alpha=0.85, personalization=None,
- max_iter=100, tol=1.0e-6, weight='weight'):
- """Return the PageRank of the nodes in the graph.
-
- PageRank computes a ranking of the nodes in the graph G based on
- the structure of the incoming links. It was originally designed as
- an algorithm to rank web pages.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- alpha : float, optional
- Damping parameter for PageRank, default=0.85
-
- personalization: dict, optional
- The "personalization vector" consisting of a dictionary with a
- key for every graph node and nonzero personalization value for each node.
-
- max_iter : integer, optional
- Maximum number of iterations in power method eigenvalue solver.
-
- tol : float, optional
- Error tolerance used to check convergence in power method solver.
-
- weight : key, optional
- Edge data key to use as weight. If None weights are set to 1.
-
- Returns
- -------
- pagerank : dictionary
- Dictionary of nodes with PageRank as value
-
- Examples
- --------
- >>> G=nx.DiGraph(nx.path_graph(4))
- >>> pr=nx.pagerank_scipy(G,alpha=0.9)
-
- Notes
- -----
- The eigenvector calculation uses power iteration with a SciPy
- sparse matrix representation.
-
- See Also
- --------
- pagerank, pagerank_numpy, google_matrix
-
- References
- ----------
- .. [1] A. Langville and C. Meyer,
- "A survey of eigenvector methods of web information retrieval."
- http://citeseer.ist.psu.edu/713792.html
- .. [2] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry,
- The PageRank citation ranking: Bringing order to the Web. 1999
- http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf
- """
- try:
- import scipy.sparse
- except ImportError:
- raise ImportError("pagerank_scipy() requires SciPy: http://scipy.org/")
- if len(G) == 0:
- return {}
- # choose ordering in matrix
- if personalization is None: # use G.nodes() ordering
- nodelist=G.nodes()
- else: # use personalization "vector" ordering
- nodelist=personalization.keys()
- M=nx.to_scipy_sparse_matrix(G,nodelist=nodelist,weight=weight,dtype='f')
- (n,m)=M.shape # should be square
- S=scipy.array(M.sum(axis=1)).flatten()
-# for i, j, v in zip( *scipy.sparse.find(M) ):
-# M[i,j] = v / S[i]
- S[S>0] = 1.0 / S[S>0]
- Q = scipy.sparse.spdiags(S.T, 0, *M.shape, format='csr')
- M = Q * M
- x=scipy.ones((n))/n # initial guess
- dangle=scipy.array(scipy.where(M.sum(axis=1)==0,1.0/n,0)).flatten()
- # add "teleportation"/personalization
- if personalization is not None:
- v=scipy.array(list(personalization.values()),dtype=float)
- v=v/v.sum()
- else:
- v=x
- i=0
- while i <= max_iter:
- # power iteration: make up to max_iter iterations
- xlast=x
- x=alpha*(x*M+scipy.dot(dangle,xlast))+(1-alpha)*v
- x=x/x.sum()
- # check convergence, l1 norm
- err=scipy.absolute(x-xlast).sum()
- if err < n*tol:
- return dict(zip(nodelist,map(float,x)))
- i+=1
- raise NetworkXError('pagerank_scipy: power iteration failed to converge'
- 'in %d iterations.'%(i+1))
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_hits.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_hits.py
deleted file mode 100644
index 7092d1d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_hits.py
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-from nose.plugins.attrib import attr
-import networkx
-
-# Example from
-# A. Langville and C. Meyer, "A survey of eigenvector methods of web
-# information retrieval." http://citeseer.ist.psu.edu/713792.html
-
-
-class TestHITS:
-
- def setUp(self):
-
- G=networkx.DiGraph()
-
- edges=[(1,3),(1,5),\
- (2,1),\
- (3,5),\
- (5,4),(5,3),\
- (6,5)]
-
- G.add_edges_from(edges,weight=1)
- self.G=G
- self.G.a=dict(zip(G,[0.000000, 0.000000, 0.366025,
- 0.133975, 0.500000, 0.000000]))
- self.G.h=dict(zip(G,[ 0.366025, 0.000000, 0.211325,
- 0.000000, 0.211325, 0.211325]))
-
-
- def test_hits(self):
- G=self.G
- h,a=networkx.hits(G,tol=1.e-08)
- for n in G:
- assert_almost_equal(h[n],G.h[n],places=4)
- for n in G:
- assert_almost_equal(a[n],G.a[n],places=4)
-
- def test_hits_nstart(self):
- G = self.G
- nstart = dict([(i, 1./2) for i in G])
- h, a = networkx.hits(G, nstart = nstart)
-
- @attr('numpy')
- def test_hits_numpy(self):
- try:
- import numpy as np
- except ImportError:
- raise SkipTest('NumPy not available.')
-
-
- G=self.G
- h,a=networkx.hits_numpy(G)
- for n in G:
- assert_almost_equal(h[n],G.h[n],places=4)
- for n in G:
- assert_almost_equal(a[n],G.a[n],places=4)
-
-
- def test_hits_scipy(self):
- try:
- import scipy as sp
- except ImportError:
- raise SkipTest('SciPy not available.')
-
- G=self.G
- h,a=networkx.hits_scipy(G,tol=1.e-08)
- for n in G:
- assert_almost_equal(h[n],G.h[n],places=4)
- for n in G:
- assert_almost_equal(a[n],G.a[n],places=4)
-
-
- @attr('numpy')
- def test_empty(self):
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
- G=networkx.Graph()
- assert_equal(networkx.hits(G),({},{}))
- assert_equal(networkx.hits_numpy(G),({},{}))
- assert_equal(networkx.authority_matrix(G).shape,(0,0))
- assert_equal(networkx.hub_matrix(G).shape,(0,0))
-
- def test_empty_scipy(self):
- try:
- import scipy
- except ImportError:
- raise SkipTest('scipy not available.')
- G=networkx.Graph()
- assert_equal(networkx.hits_scipy(G),({},{}))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_pagerank.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_pagerank.py
deleted file mode 100644
index 7b409ff..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/link_analysis/tests/test_pagerank.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-from nose.plugins.attrib import attr
-import random
-import networkx
-
-# Example from
-# A. Langville and C. Meyer, "A survey of eigenvector methods of web
-# information retrieval." http://citeseer.ist.psu.edu/713792.html
-
-
-class TestPageRank:
-
- def setUp(self):
- G=networkx.DiGraph()
- edges=[(1,2),(1,3),\
- (3,1),(3,2),(3,5),\
- (4,5),(4,6),\
- (5,4),(5,6),\
- (6,4)]
- G.add_edges_from(edges)
- self.G=G
- self.G.pagerank=dict(zip(G,
- [0.03721197,0.05395735,0.04150565,
- 0.37508082,0.20599833, 0.28624589]))
-
- def test_pagerank(self):
- G=self.G
- p=networkx.pagerank(G,alpha=0.9,tol=1.e-08)
- for n in G:
- assert_almost_equal(p[n],G.pagerank[n],places=4)
-
- nstart = dict((n,random.random()) for n in G)
- p=networkx.pagerank(G,alpha=0.9,tol=1.e-08, nstart=nstart)
- for n in G:
- assert_almost_equal(p[n],G.pagerank[n],places=4)
-
- assert_raises(networkx.NetworkXError,networkx.pagerank,G,
- max_iter=0)
-
-
- @attr('numpy')
- def test_numpy_pagerank(self):
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
- G=self.G
- p=networkx.pagerank_numpy(G,alpha=0.9)
- for n in G:
- assert_almost_equal(p[n],G.pagerank[n],places=4)
- personalize = dict((n,random.random()) for n in G)
- p=networkx.pagerank_numpy(G,alpha=0.9, personalization=personalize)
-
-
-
- @attr('numpy')
- def test_google_matrix(self):
- try:
- import numpy.linalg
- except ImportError:
- raise SkipTest('numpy not available.')
- G=self.G
- M=networkx.google_matrix(G,alpha=0.9)
- e,ev=numpy.linalg.eig(M.T)
- p=numpy.array(ev[:,0]/ev[:,0].sum())[:,0]
- for (a,b) in zip(p,self.G.pagerank.values()):
- assert_almost_equal(a,b)
-
- personalize = dict((n,random.random()) for n in G)
- M=networkx.google_matrix(G,alpha=0.9, personalization=personalize)
- _ = personalize.pop(1)
- assert_raises(networkx.NetworkXError,networkx.google_matrix,G,
- personalization=personalize)
-
- def test_scipy_pagerank(self):
- G=self.G
- try:
- import scipy
- except ImportError:
- raise SkipTest('scipy not available.')
- p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08)
- for n in G:
- assert_almost_equal(p[n],G.pagerank[n],places=4)
- personalize = dict((n,random.random()) for n in G)
- p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08,
- personalization=personalize)
-
- assert_raises(networkx.NetworkXError,networkx.pagerank_scipy,G,
- max_iter=0)
-
- def test_personalization(self):
- G=networkx.complete_graph(4)
- personalize={0:1,1:1,2:4,3:4}
- answer={0:0.1,1:0.1,2:0.4,3:0.4}
- p=networkx.pagerank(G,alpha=0.0,personalization=personalize)
- for n in G:
- assert_almost_equal(p[n],answer[n],places=4)
- _ = personalize.pop(0)
- assert_raises(networkx.NetworkXError,networkx.pagerank,G,
- personalization=personalize)
-
-
- @attr('numpy')
- def test_empty(self):
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
- G=networkx.Graph()
- assert_equal(networkx.pagerank(G),{})
- assert_equal(networkx.pagerank_numpy(G),{})
- assert_equal(networkx.google_matrix(G).shape,(0,0))
-
- def test_empty_scipy(self):
- try:
- import scipy
- except ImportError:
- raise SkipTest('scipy not available.')
- G=networkx.Graph()
- assert_equal(networkx.pagerank_scipy(G),{})
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/matching.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/matching.py
deleted file mode 100644
index 70c424f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/matching.py
+++ /dev/null
@@ -1,825 +0,0 @@
-"""
-********
-Matching
-********
-"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-# Copyright (C) 2011 by
-# Nicholas Mancuso <nick.mancuso@gmail.com>
-# All rights reserved.
-# BSD license.
-from itertools import repeat
-__author__ = """\n""".join(['Joris van Rantwijk',
- 'Nicholas Mancuso (nick.mancuso@gmail.com)'])
-
-_all__ = ['max_weight_matching', 'maximal_matching']
-
-
-def maximal_matching(G):
- r"""Find a maximal cardinality matching in the graph.
-
- A matching is a subset of edges in which no node occurs more than once.
- The cardinality of a matching is the number of matched edges.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- Returns
- -------
- matching : set
- A maximal matching of the graph.
-
- Notes
- -----
- The algorithm greedily selects a maximal matching M of the graph G
- (i.e. no superset of M exists). It runs in `O(|E|)` time.
- """
- matching = set([])
- edges = set([])
- for edge in G.edges_iter():
- # If the edge isn't covered, add it to the matching
- # then remove neighborhood of u and v from consideration.
- if edge not in edges:
- u, v = edge
- matching.add(edge)
- edges |= set(G.edges(u))
- edges |= set(G.edges(v))
-
- return matching
-
-
-def max_weight_matching(G, maxcardinality=False):
- """Compute a maximum-weighted matching of G.
-
- A matching is a subset of edges in which no node occurs more than once.
- The cardinality of a matching is the number of matched edges.
- The weight of a matching is the sum of the weights of its edges.
-
- Parameters
- ----------
- G : NetworkX graph
- Undirected graph
-
- maxcardinality: bool, optional
- If maxcardinality is True, compute the maximum-cardinality matching
- with maximum weight among all maximum-cardinality matchings.
-
- Returns
- -------
- mate : dictionary
- The matching is returned as a dictionary, mate, such that
- mate[v] == w if node v is matched to node w. Unmatched nodes do not
- occur as a key in mate.
-
-
- Notes
- ------
- If G has edges with 'weight' attribute the edge data are used as
- weight values else the weights are assumed to be 1.
-
- This function takes time O(number_of_nodes ** 3).
-
- If all edge weights are integers, the algorithm uses only integer
- computations. If floating point weights are used, the algorithm
- could return a slightly suboptimal matching due to numeric
- precision errors.
-
- This method is based on the "blossom" method for finding augmenting
- paths and the "primal-dual" method for finding a matching of maximum
- weight, both methods invented by Jack Edmonds [1]_.
-
- References
- ----------
- .. [1] "Efficient Algorithms for Finding Maximum Matching in Graphs",
- Zvi Galil, ACM Computing Surveys, 1986.
- """
- #
- # The algorithm is taken from "Efficient Algorithms for Finding Maximum
- # Matching in Graphs" by Zvi Galil, ACM Computing Surveys, 1986.
- # It is based on the "blossom" method for finding augmenting paths and
- # the "primal-dual" method for finding a matching of maximum weight, both
- # methods invented by Jack Edmonds.
- #
- # A C program for maximum weight matching by Ed Rothberg was used
- # extensively to validate this new code.
- #
- # Many terms used in the code comments are explained in the paper
- # by Galil. You will probably need the paper to make sense of this code.
- #
-
- class NoNode:
- """Dummy value which is different from any node."""
- pass
-
- class Blossom:
- """Representation of a non-trivial blossom or sub-blossom."""
-
- __slots__ = [ 'childs', 'edges', 'mybestedges' ]
-
- # b.childs is an ordered list of b's sub-blossoms, starting with
- # the base and going round the blossom.
-
- # b.edges is the list of b's connecting edges, such that
- # b.edges[i] = (v, w) where v is a vertex in b.childs[i]
- # and w is a vertex in b.childs[wrap(i+1)].
-
- # If b is a top-level S-blossom,
- # b.mybestedges is a list of least-slack edges to neighbouring
- # S-blossoms, or None if no such list has been computed yet.
- # This is used for efficient computation of delta3.
-
- # Generate the blossom's leaf vertices.
- def leaves(self):
- for t in self.childs:
- if isinstance(t, Blossom):
- for v in t.leaves():
- yield v
- else:
- yield t
-
- # Get a list of vertices.
- gnodes = G.nodes()
- if not gnodes:
- return { } # don't bother with empty graphs
-
- # Find the maximum edge weight.
- maxweight = 0
- allinteger = True
- for i,j,d in G.edges_iter(data=True):
- wt=d.get('weight',1)
- if i != j and wt > maxweight:
- maxweight = wt
- allinteger = allinteger and (str(type(wt)).split("'")[1]
- in ('int', 'long'))
-
- # If v is a matched vertex, mate[v] is its partner vertex.
- # If v is a single vertex, v does not occur as a key in mate.
- # Initially all vertices are single; updated during augmentation.
- mate = { }
-
- # If b is a top-level blossom,
- # label.get(b) is None if b is unlabeled (free),
- # 1 if b is an S-blossom,
- # 2 if b is a T-blossom.
- # The label of a vertex is found by looking at the label of its top-level
- # containing blossom.
- # If v is a vertex inside a T-blossom, label[v] is 2 iff v is reachable
- # from an S-vertex outside the blossom.
- # Labels are assigned during a stage and reset after each augmentation.
- label = { }
-
- # If b is a labeled top-level blossom,
- # labeledge[b] = (v, w) is the edge through which b obtained its label
- # such that w is a vertex in b, or None if b's base vertex is single.
- # If w is a vertex inside a T-blossom and label[w] == 2,
- # labeledge[w] = (v, w) is an edge through which w is reachable from
- # outside the blossom.
- labeledge = { }
-
- # If v is a vertex, inblossom[v] is the top-level blossom to which v
- # belongs.
- # If v is a top-level vertex, inblossom[v] == v since v is itself
- # a (trivial) top-level blossom.
- # Initially all vertices are top-level trivial blossoms.
- inblossom = dict(zip(gnodes, gnodes))
-
- # If b is a sub-blossom,
- # blossomparent[b] is its immediate parent (sub-)blossom.
- # If b is a top-level blossom, blossomparent[b] is None.
- blossomparent = dict(zip(gnodes, repeat(None)))
-
- # If b is a (sub-)blossom,
- # blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).
- blossombase = dict(zip(gnodes, gnodes))
-
- # If w is a free vertex (or an unreached vertex inside a T-blossom),
- # bestedge[w] = (v, w) is the least-slack edge from an S-vertex,
- # or None if there is no such edge.
- # If b is a (possibly trivial) top-level S-blossom,
- # bestedge[b] = (v, w) is the least-slack edge to a different S-blossom
- # (v inside b), or None if there is no such edge.
- # This is used for efficient computation of delta2 and delta3.
- bestedge = { }
-
- # If v is a vertex,
- # dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual
- # optimization problem (if all edge weights are integers, multiplication
- # by two ensures that all values remain integers throughout the algorithm).
- # Initially, u(v) = maxweight / 2.
- dualvar = dict(zip(gnodes, repeat(maxweight)))
-
- # If b is a non-trivial blossom,
- # blossomdual[b] = z(b) where z(b) is b's variable in the dual
- # optimization problem.
- blossomdual = { }
-
- # If (v, w) in allowedge or (w, v) in allowedg, then the edge
- # (v, w) is known to have zero slack in the optimization problem;
- # otherwise the edge may or may not have zero slack.
- allowedge = { }
-
- # Queue of newly discovered S-vertices.
- queue = [ ]
-
- # Return 2 * slack of edge (v, w) (does not work inside blossoms).
- def slack(v, w):
- return dualvar[v] + dualvar[w] - 2 * G[v][w].get('weight',1)
-
- # Assign label t to the top-level blossom containing vertex w,
- # coming through an edge from vertex v.
- def assignLabel(w, t, v):
- b = inblossom[w]
- assert label.get(w) is None and label.get(b) is None
- label[w] = label[b] = t
- if v is not None:
- labeledge[w] = labeledge[b] = (v, w)
- else:
- labeledge[w] = labeledge[b] = None
- bestedge[w] = bestedge[b] = None
- if t == 1:
- # b became an S-vertex/blossom; add it(s vertices) to the queue.
- if isinstance(b, Blossom):
- queue.extend(b.leaves())
- else:
- queue.append(b)
- elif t == 2:
- # b became a T-vertex/blossom; assign label S to its mate.
- # (If b is a non-trivial blossom, its base is the only vertex
- # with an external mate.)
- base = blossombase[b]
- assignLabel(mate[base], 1, base)
-
- # Trace back from vertices v and w to discover either a new blossom
- # or an augmenting path. Return the base vertex of the new blossom,
- # or NoNode if an augmenting path was found.
- def scanBlossom(v, w):
- # Trace back from v and w, placing breadcrumbs as we go.
- path = [ ]
- base = NoNode
- while v is not NoNode:
- # Look for a breadcrumb in v's blossom or put a new breadcrumb.
- b = inblossom[v]
- if label[b] & 4:
- base = blossombase[b]
- break
- assert label[b] == 1
- path.append(b)
- label[b] = 5
- # Trace one step back.
- if labeledge[b] is None:
- # The base of blossom b is single; stop tracing this path.
- assert blossombase[b] not in mate
- v = NoNode
- else:
- assert labeledge[b][0] == mate[blossombase[b]]
- v = labeledge[b][0]
- b = inblossom[v]
- assert label[b] == 2
- # b is a T-blossom; trace one more step back.
- v = labeledge[b][0]
- # Swap v and w so that we alternate between both paths.
- if w is not NoNode:
- v, w = w, v
- # Remove breadcrumbs.
- for b in path:
- label[b] = 1
- # Return base vertex, if we found one.
- return base
-
- # Construct a new blossom with given base, through S-vertices v and w.
- # Label the new blossom as S; set its dual variable to zero;
- # relabel its T-vertices to S and add them to the queue.
- def addBlossom(base, v, w):
- bb = inblossom[base]
- bv = inblossom[v]
- bw = inblossom[w]
- # Create blossom.
- b = Blossom()
- blossombase[b] = base
- blossomparent[b] = None
- blossomparent[bb] = b
- # Make list of sub-blossoms and their interconnecting edge endpoints.
- b.childs = path = [ ]
- b.edges = edgs = [ (v, w) ]
- # Trace back from v to base.
- while bv != bb:
- # Add bv to the new blossom.
- blossomparent[bv] = b
- path.append(bv)
- edgs.append(labeledge[bv])
- assert label[bv] == 2 or (label[bv] == 1 and labeledge[bv][0] == mate[blossombase[bv]])
- # Trace one step back.
- v = labeledge[bv][0]
- bv = inblossom[v]
- # Add base sub-blossom; reverse lists.
- path.append(bb)
- path.reverse()
- edgs.reverse()
- # Trace back from w to base.
- while bw != bb:
- # Add bw to the new blossom.
- blossomparent[bw] = b
- path.append(bw)
- edgs.append((labeledge[bw][1], labeledge[bw][0]))
- assert label[bw] == 2 or (label[bw] == 1 and labeledge[bw][0] == mate[blossombase[bw]])
- # Trace one step back.
- w = labeledge[bw][0]
- bw = inblossom[w]
- # Set label to S.
- assert label[bb] == 1
- label[b] = 1
- labeledge[b] = labeledge[bb]
- # Set dual variable to zero.
- blossomdual[b] = 0
- # Relabel vertices.
- for v in b.leaves():
- if label[inblossom[v]] == 2:
- # This T-vertex now turns into an S-vertex because it becomes
- # part of an S-blossom; add it to the queue.
- queue.append(v)
- inblossom[v] = b
- # Compute b.mybestedges.
- bestedgeto = { }
- for bv in path:
- if isinstance(bv, Blossom):
- if bv.mybestedges is not None:
- # Walk this subblossom's least-slack edges.
- nblist = bv.mybestedges
- # The sub-blossom won't need this data again.
- bv.mybestedges = None
- else:
- # This subblossom does not have a list of least-slack
- # edges; get the information from the vertices.
- nblist = [ (v, w)
- for v in bv.leaves()
- for w in G.neighbors_iter(v)
- if v != w ]
- else:
- nblist = [ (bv, w)
- for w in G.neighbors_iter(bv)
- if bv != w ]
- for k in nblist:
- (i, j) = k
- if inblossom[j] == b:
- i, j = j, i
- bj = inblossom[j]
- if (bj != b and label.get(bj) == 1 and
- ((bj not in bestedgeto) or
- slack(i, j) < slack(*bestedgeto[bj]))):
- bestedgeto[bj] = k
- # Forget about least-slack edge of the subblossom.
- bestedge[bv] = None
- b.mybestedges = list(bestedgeto.values())
- # Select bestedge[b].
- mybestedge = None
- bestedge[b] = None
- for k in b.mybestedges:
- kslack = slack(*k)
- if mybestedge is None or kslack < mybestslack:
- mybestedge = k
- mybestslack = kslack
- bestedge[b] = mybestedge
-
- # Expand the given top-level blossom.
- def expandBlossom(b, endstage):
- # Convert sub-blossoms into top-level blossoms.
- for s in b.childs:
- blossomparent[s] = None
- if isinstance(s, Blossom):
- if endstage and blossomdual[s] == 0:
- # Recursively expand this sub-blossom.
- expandBlossom(s, endstage)
- else:
- for v in s.leaves():
- inblossom[v] = s
- else:
- inblossom[s] = s
- # If we expand a T-blossom during a stage, its sub-blossoms must be
- # relabeled.
- if (not endstage) and label.get(b) == 2:
- # Start at the sub-blossom through which the expanding
- # blossom obtained its label, and relabel sub-blossoms untili
- # we reach the base.
- # Figure out through which sub-blossom the expanding blossom
- # obtained its label initially.
- entrychild = inblossom[labeledge[b][1]]
- # Decide in which direction we will go round the blossom.
- j = b.childs.index(entrychild)
- if j & 1:
- # Start index is odd; go forward and wrap.
- j -= len(b.childs)
- jstep = 1
- else:
- # Start index is even; go backward.
- jstep = -1
- # Move along the blossom until we get to the base.
- v, w = labeledge[b]
- while j != 0:
- # Relabel the T-sub-blossom.
- if jstep == 1:
- p, q = b.edges[j]
- else:
- q, p = b.edges[j-1]
- label[w] = None
- label[q] = None
- assignLabel(w, 2, v)
- # Step to the next S-sub-blossom and note its forward edge.
- allowedge[(p, q)] = allowedge[(q, p)] = True
- j += jstep
- if jstep == 1:
- v, w = b.edges[j]
- else:
- w, v = b.edges[j-1]
- # Step to the next T-sub-blossom.
- allowedge[(v, w)] = allowedge[(w, v)] = True
- j += jstep
- # Relabel the base T-sub-blossom WITHOUT stepping through to
- # its mate (so don't call assignLabel).
- bw = b.childs[j]
- label[w] = label[bw] = 2
- labeledge[w] = labeledge[bw] = (v, w)
- bestedge[bw] = None
- # Continue along the blossom until we get back to entrychild.
- j += jstep
- while b.childs[j] != entrychild:
- # Examine the vertices of the sub-blossom to see whether
- # it is reachable from a neighbouring S-vertex outside the
- # expanding blossom.
- bv = b.childs[j]
- if label.get(bv) == 1:
- # This sub-blossom just got label S through one of its
- # neighbours; leave it be.
- j += jstep
- continue
- if isinstance(bv, Blossom):
- for v in bv.leaves():
- if label.get(v):
- break
- else:
- v = bv
- # If the sub-blossom contains a reachable vertex, assign
- # label T to the sub-blossom.
- if label.get(v):
- assert label[v] == 2
- assert inblossom[v] == bv
- label[v] = None
- label[mate[blossombase[bv]]] = None
- assignLabel(v, 2, labeledge[v][0])
- j += jstep
- # Remove the expanded blossom entirely.
- label.pop(b, None)
- labeledge.pop(b, None)
- bestedge.pop(b, None)
- del blossomparent[b]
- del blossombase[b]
- del blossomdual[b]
-
- # Swap matched/unmatched edges over an alternating path through blossom b
- # between vertex v and the base vertex. Keep blossom bookkeeping consistent.
- def augmentBlossom(b, v):
- # Bubble up through the blossom tree from vertex v to an immediate
- # sub-blossom of b.
- t = v
- while blossomparent[t] != b:
- t = blossomparent[t]
- # Recursively deal with the first sub-blossom.
- if isinstance(t, Blossom):
- augmentBlossom(t, v)
- # Decide in which direction we will go round the blossom.
- i = j = b.childs.index(t)
- if i & 1:
- # Start index is odd; go forward and wrap.
- j -= len(b.childs)
- jstep = 1
- else:
- # Start index is even; go backward.
- jstep = -1
- # Move along the blossom until we get to the base.
- while j != 0:
- # Step to the next sub-blossom and augment it recursively.
- j += jstep
- t = b.childs[j]
- if jstep == 1:
- w, x = b.edges[j]
- else:
- x, w = b.edges[j-1]
- if isinstance(t, Blossom):
- augmentBlossom(t, w)
- # Step to the next sub-blossom and augment it recursively.
- j += jstep
- t = b.childs[j]
- if isinstance(t, Blossom):
- augmentBlossom(t, x)
- # Match the edge connecting those sub-blossoms.
- mate[w] = x
- mate[x] = w
- # Rotate the list of sub-blossoms to put the new base at the front.
- b.childs = b.childs[i:] + b.childs[:i]
- b.edges = b.edges[i:] + b.edges[:i]
- blossombase[b] = blossombase[b.childs[0]]
- assert blossombase[b] == v
-
- # Swap matched/unmatched edges over an alternating path between two
- # single vertices. The augmenting path runs through S-vertices v and w.
- def augmentMatching(v, w):
- for (s, j) in ((v, w), (w, v)):
- # Match vertex s to vertex j. Then trace back from s
- # until we find a single vertex, swapping matched and unmatched
- # edges as we go.
- while 1:
- bs = inblossom[s]
- assert label[bs] == 1
- assert (labeledge[bs] is None and blossombase[bs] not in mate) or (labeledge[bs][0] == mate[blossombase[bs]])
- # Augment through the S-blossom from s to base.
- if isinstance(bs, Blossom):
- augmentBlossom(bs, s)
- # Update mate[s]
- mate[s] = j
- # Trace one step back.
- if labeledge[bs] is None:
- # Reached single vertex; stop.
- break
- t = labeledge[bs][0]
- bt = inblossom[t]
- assert label[bt] == 2
- # Trace one more step back.
- s, j = labeledge[bt]
- # Augment through the T-blossom from j to base.
- assert blossombase[bt] == t
- if isinstance(bt, Blossom):
- augmentBlossom(bt, j)
- # Update mate[j]
- mate[j] = s
-
- # Verify that the optimum solution has been reached.
- def verifyOptimum():
- if maxcardinality:
- # Vertices may have negative dual;
- # find a constant non-negative number to add to all vertex duals.
- vdualoffset = max(0, -min(dualvar.values()))
- else:
- vdualoffset = 0
- # 0. all dual variables are non-negative
- assert min(dualvar.values()) + vdualoffset >= 0
- assert len(blossomdual) == 0 or min(blossomdual.values()) >= 0
- # 0. all edges have non-negative slack and
- # 1. all matched edges have zero slack;
- for i,j,d in G.edges_iter(data=True):
- wt=d.get('weight',1)
- if i == j:
- continue # ignore self-loops
- s = dualvar[i] + dualvar[j] - 2 * wt
- iblossoms = [ i ]
- jblossoms = [ j ]
- while blossomparent[iblossoms[-1]] is not None:
- iblossoms.append(blossomparent[iblossoms[-1]])
- while blossomparent[jblossoms[-1]] is not None:
- jblossoms.append(blossomparent[jblossoms[-1]])
- iblossoms.reverse()
- jblossoms.reverse()
- for (bi, bj) in zip(iblossoms, jblossoms):
- if bi != bj:
- break
- s += 2 * blossomdual[bi]
- assert s >= 0
- if mate.get(i) == j or mate.get(j) == i:
- assert mate[i] == j and mate[j] == i
- assert s == 0
- # 2. all single vertices have zero dual value;
- for v in gnodes:
- assert (v in mate) or dualvar[v] + vdualoffset == 0
- # 3. all blossoms with positive dual value are full.
- for b in blossomdual:
- if blossomdual[b] > 0:
- assert len(b.edges) % 2 == 1
- for (i, j) in b.edges[1::2]:
- assert mate[i] == j and mate[j] == i
- # Ok.
-
- # Main loop: continue until no further improvement is possible.
- while 1:
-
- # Each iteration of this loop is a "stage".
- # A stage finds an augmenting path and uses that to improve
- # the matching.
-
- # Remove labels from top-level blossoms/vertices.
- label.clear()
- labeledge.clear()
-
- # Forget all about least-slack edges.
- bestedge.clear()
- for b in blossomdual:
- b.mybestedges = None
-
- # Loss of labeling means that we can not be sure that currently
- # allowable edges remain allowable througout this stage.
- allowedge.clear()
-
- # Make queue empty.
- queue[:] = [ ]
-
- # Label single blossoms/vertices with S and put them in the queue.
- for v in gnodes:
- if (v not in mate) and label.get(inblossom[v]) is None:
- assignLabel(v, 1, None)
-
-
- # Loop until we succeed in augmenting the matching.
- augmented = 0
- while 1:
-
- # Each iteration of this loop is a "substage".
- # A substage tries to find an augmenting path;
- # if found, the path is used to improve the matching and
- # the stage ends. If there is no augmenting path, the
- # primal-dual method is used to pump some slack out of
- # the dual variables.
-
- # Continue labeling until all vertices which are reachable
- # through an alternating path have got a label.
- while queue and not augmented:
-
- # Take an S vertex from the queue.
- v = queue.pop()
- assert label[inblossom[v]] == 1
-
- # Scan its neighbours:
- for w in G.neighbors_iter(v):
- if w == v:
- continue # ignore self-loops
- # w is a neighbour to v
- bv = inblossom[v]
- bw = inblossom[w]
- if bv == bw:
- # this edge is internal to a blossom; ignore it
- continue
- if (v, w) not in allowedge:
- kslack = slack(v, w)
- if kslack <= 0:
- # edge k has zero slack => it is allowable
- allowedge[(v, w)] = allowedge[(w, v)] = True
- if (v, w) in allowedge:
- if label.get(bw) is None:
- # (C1) w is a free vertex;
- # label w with T and label its mate with S (R12).
- assignLabel(w, 2, v)
- elif label.get(bw) == 1:
- # (C2) w is an S-vertex (not in the same blossom);
- # follow back-links to discover either an
- # augmenting path or a new blossom.
- base = scanBlossom(v, w)
- if base is not NoNode:
- # Found a new blossom; add it to the blossom
- # bookkeeping and turn it into an S-blossom.
- addBlossom(base, v, w)
- else:
- # Found an augmenting path; augment the
- # matching and end this stage.
- augmentMatching(v, w)
- augmented = 1
- break
- elif label.get(w) is None:
- # w is inside a T-blossom, but w itself has not
- # yet been reached from outside the blossom;
- # mark it as reached (we need this to relabel
- # during T-blossom expansion).
- assert label[bw] == 2
- label[w] = 2
- labeledge[w] = (v, w)
- elif label.get(bw) == 1:
- # keep track of the least-slack non-allowable edge to
- # a different S-blossom.
- if bestedge.get(bv) is None or kslack < slack(*bestedge[bv]):
- bestedge[bv] = (v, w)
- elif label.get(w) is None:
- # w is a free vertex (or an unreached vertex inside
- # a T-blossom) but we can not reach it yet;
- # keep track of the least-slack edge that reaches w.
- if bestedge.get(w) is None or kslack < slack(*bestedge[w]):
- bestedge[w] = (v, w)
-
- if augmented:
- break
-
- # There is no augmenting path under these constraints;
- # compute delta and reduce slack in the optimization problem.
- # (Note that our vertex dual variables, edge slacks and delta's
- # are pre-multiplied by two.)
- deltatype = -1
- delta = deltaedge = deltablossom = None
-
- # Compute delta1: the minumum value of any vertex dual.
- if not maxcardinality:
- deltatype = 1
- delta = min(dualvar.values())
-
- # Compute delta2: the minimum slack on any edge between
- # an S-vertex and a free vertex.
- for v in G.nodes_iter():
- if label.get(inblossom[v]) is None and bestedge.get(v) is not None:
- d = slack(*bestedge[v])
- if deltatype == -1 or d < delta:
- delta = d
- deltatype = 2
- deltaedge = bestedge[v]
-
- # Compute delta3: half the minimum slack on any edge between
- # a pair of S-blossoms.
- for b in blossomparent:
- if ( blossomparent[b] is None and label.get(b) == 1 and
- bestedge.get(b) is not None ):
- kslack = slack(*bestedge[b])
- if allinteger:
- assert (kslack % 2) == 0
- d = kslack // 2
- else:
- d = kslack / 2.0
- if deltatype == -1 or d < delta:
- delta = d
- deltatype = 3
- deltaedge = bestedge[b]
-
- # Compute delta4: minimum z variable of any T-blossom.
- for b in blossomdual:
- if ( blossomparent[b] is None and label.get(b) == 2 and
- (deltatype == -1 or blossomdual[b] < delta) ):
- delta = blossomdual[b]
- deltatype = 4
- deltablossom = b
-
- if deltatype == -1:
- # No further improvement possible; max-cardinality optimum
- # reached. Do a final delta update to make the optimum
- # verifyable.
- assert maxcardinality
- deltatype = 1
- delta = max(0, min(dualvar.values()))
-
- # Update dual variables according to delta.
- for v in gnodes:
- if label.get(inblossom[v]) == 1:
- # S-vertex: 2*u = 2*u - 2*delta
- dualvar[v] -= delta
- elif label.get(inblossom[v]) == 2:
- # T-vertex: 2*u = 2*u + 2*delta
- dualvar[v] += delta
- for b in blossomdual:
- if blossomparent[b] is None:
- if label.get(b) == 1:
- # top-level S-blossom: z = z + 2*delta
- blossomdual[b] += delta
- elif label.get(b) == 2:
- # top-level T-blossom: z = z - 2*delta
- blossomdual[b] -= delta
-
- # Take action at the point where minimum delta occurred.
- if deltatype == 1:
- # No further improvement possible; optimum reached.
- break
- elif deltatype == 2:
- # Use the least-slack edge to continue the search.
- (v, w) = deltaedge
- assert label[inblossom[v]] == 1
- allowedge[(v, w)] = allowedge[(w, v)] = True
- queue.append(v)
- elif deltatype == 3:
- # Use the least-slack edge to continue the search.
- (v, w) = deltaedge
- allowedge[(v, w)] = allowedge[(w, v)] = True
- assert label[inblossom[v]] == 1
- queue.append(v)
- elif deltatype == 4:
- # Expand the least-z blossom.
- expandBlossom(deltablossom, False)
-
- # End of a this substage.
-
- # Paranoia check that the matching is symmetric.
- for v in mate:
- assert mate[mate[v]] == v
-
- # Stop when no more augmenting path can be found.
- if not augmented:
- break
-
- # End of a stage; expand all S-blossoms which have zero dual.
- for b in list(blossomdual.keys()):
- if b not in blossomdual:
- continue # already expanded
- if ( blossomparent[b] is None and label.get(b) == 1 and
- blossomdual[b] == 0 ):
- expandBlossom(b, True)
-
- # Verify that we reached the optimum solution (only for integer weights).
- if allinteger:
- verifyOptimum()
-
- return mate
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/mis.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/mis.py
deleted file mode 100644
index d02c2d5..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/mis.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# -*- coding: utf-8 -*-
-# $Id: maximalIndependentSet.py 576 2011-03-01 05:50:34Z lleeoo $
-"""
-Algorithm to find a maximal (not maximum) independent set.
-
-"""
-# Leo Lopes <leo.lopes@monash.edu>
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__author__ = "\n".join(["Leo Lopes <leo.lopes@monash.edu>",
- "Loïc Séguin-C. <loicseguin@gmail.com>"])
-
-__all__ = ['maximal_independent_set']
-
-import random
-import networkx as nx
-
-def maximal_independent_set(G, nodes=None):
- """Return a random maximal independent set guaranteed to contain
- a given set of nodes.
-
- An independent set is a set of nodes such that the subgraph
- of G induced by these nodes contains no edges. A maximal
- independent set is an independent set such that it is not possible
- to add a new node and still get an independent set.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nodes : list or iterable
- Nodes that must be part of the independent set. This set of nodes
- must be independent.
-
- Returns
- -------
- indep_nodes : list
- List of nodes that are part of a maximal independent set.
-
- Raises
- ------
- NetworkXUnfeasible
- If the nodes in the provided list are not part of the graph or
- do not form an independent set, an exception is raised.
-
- Examples
- --------
- >>> G = nx.path_graph(5)
- >>> nx.maximal_independent_set(G) # doctest: +SKIP
- [4, 0, 2]
- >>> nx.maximal_independent_set(G, [1]) # doctest: +SKIP
- [1, 3]
-
- Notes
- ------
- This algorithm does not solve the maximum independent set problem.
-
- """
- if not nodes:
- nodes = set([random.choice(G.nodes())])
- else:
- nodes = set(nodes)
- if not nodes.issubset(G):
- raise nx.NetworkXUnfeasible(
- "%s is not a subset of the nodes of G" % nodes)
- neighbors = set.union(*[set(G.neighbors(v)) for v in nodes])
- if set.intersection(neighbors, nodes):
- raise nx.NetworkXUnfeasible(
- "%s is not an independent set of G" % nodes)
- indep_nodes = list(nodes)
- available_nodes = set(G.nodes()).difference(neighbors.union(nodes))
- while available_nodes:
- node = random.choice(list(available_nodes))
- indep_nodes.append(node)
- available_nodes.difference_update(G.neighbors(node) + [node])
- return indep_nodes
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/mst.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/mst.py
deleted file mode 100644
index 03ca5e8..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/mst.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Computes minimum spanning tree of a weighted graph.
-
-"""
-# Copyright (C) 2009-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# Loïc Séguin-C. <loicseguin@gmail.com>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['kruskal_mst',
- 'minimum_spanning_edges',
- 'minimum_spanning_tree',
- 'prim_mst_edges', 'prim_mst']
-
-import networkx as nx
-from heapq import heappop, heappush
-
-def minimum_spanning_edges(G,weight='weight',data=True):
- """Generate edges in a minimum spanning forest of an undirected
- weighted graph.
-
- A minimum spanning tree is a subgraph of the graph (a tree)
- with the minimum sum of edge weights. A spanning forest is a
- union of the spanning trees for each connected component of the graph.
-
- Parameters
- ----------
- G : NetworkX Graph
-
- weight : string
- Edge data key to use for weight (default 'weight').
-
- data : bool, optional
- If True yield the edge data along with the edge.
-
- Returns
- -------
- edges : iterator
- A generator that produces edges in the minimum spanning tree.
- The edges are three-tuples (u,v,w) where w is the weight.
-
- Examples
- --------
- >>> G=nx.cycle_graph(4)
- >>> G.add_edge(0,3,weight=2) # assign weight 2 to edge 0-3
- >>> mst=nx.minimum_spanning_edges(G,data=False) # a generator of MST edges
- >>> edgelist=list(mst) # make a list of the edges
- >>> print(sorted(edgelist))
- [(0, 1), (1, 2), (2, 3)]
-
- Notes
- -----
- Uses Kruskal's algorithm.
-
- If the graph edges do not have a weight attribute a default weight of 1
- will be used.
-
- Modified code from David Eppstein, April 2006
- http://www.ics.uci.edu/~eppstein/PADS/
- """
- # Modified code from David Eppstein, April 2006
- # http://www.ics.uci.edu/~eppstein/PADS/
- # Kruskal's algorithm: sort edges by weight, and add them one at a time.
- # We use Kruskal's algorithm, first because it is very simple to
- # implement once UnionFind exists, and second, because the only slow
- # part (the sort) is sped up by being built in to Python.
- from networkx.utils import UnionFind
- if G.is_directed():
- raise nx.NetworkXError(
- "Mimimum spanning tree not defined for directed graphs.")
-
- subtrees = UnionFind()
- edges = sorted(G.edges(data=True),key=lambda t: t[2].get(weight,1))
- for u,v,d in edges:
- if subtrees[u] != subtrees[v]:
- if data:
- yield (u,v,d)
- else:
- yield (u,v)
- subtrees.union(u,v)
-
-
-def minimum_spanning_tree(G,weight='weight'):
- """Return a minimum spanning tree or forest of an undirected
- weighted graph.
-
- A minimum spanning tree is a subgraph of the graph (a tree) with
- the minimum sum of edge weights.
-
- If the graph is not connected a spanning forest is constructed. A
- spanning forest is a union of the spanning trees for each
- connected component of the graph.
-
- Parameters
- ----------
- G : NetworkX Graph
-
- weight : string
- Edge data key to use for weight (default 'weight').
-
- Returns
- -------
- G : NetworkX Graph
- A minimum spanning tree or forest.
-
- Examples
- --------
- >>> G=nx.cycle_graph(4)
- >>> G.add_edge(0,3,weight=2) # assign weight 2 to edge 0-3
- >>> T=nx.minimum_spanning_tree(G)
- >>> print(sorted(T.edges(data=True)))
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
-
- Notes
- -----
- Uses Kruskal's algorithm.
-
- If the graph edges do not have a weight attribute a default weight of 1
- will be used.
- """
- T=nx.Graph(nx.minimum_spanning_edges(G,weight=weight,data=True))
- # Add isolated nodes
- if len(T)!=len(G):
- T.add_nodes_from([n for n,d in G.degree().items() if d==0])
- # Add node and graph attributes as shallow copy
- for n in T:
- T.node[n]=G.node[n].copy()
- T.graph=G.graph.copy()
- return T
-
-kruskal_mst=minimum_spanning_tree
-
-def prim_mst_edges(G, weight = 'weight', data = True):
- """Generate edges in a minimum spanning forest of an undirected
- weighted graph.
-
- A minimum spanning tree is a subgraph of the graph (a tree)
- with the minimum sum of edge weights. A spanning forest is a
- union of the spanning trees for each connected component of the graph.
-
- Parameters
- ----------
- G : NetworkX Graph
-
- weight : string
- Edge data key to use for weight (default 'weight').
-
- data : bool, optional
- If True yield the edge data along with the edge.
-
- Returns
- -------
- edges : iterator
- A generator that produces edges in the minimum spanning tree.
- The edges are three-tuples (u,v,w) where w is the weight.
-
- Examples
- --------
- >>> G=nx.cycle_graph(4)
- >>> G.add_edge(0,3,weight=2) # assign weight 2 to edge 0-3
- >>> mst=nx.prim_mst_edges(G,data=False) # a generator of MST edges
- >>> edgelist=list(mst) # make a list of the edges
- >>> print(sorted(edgelist))
- [(0, 1), (1, 2), (2, 3)]
-
- Notes
- -----
- Uses Prim's algorithm.
-
- If the graph edges do not have a weight attribute a default weight of 1
- will be used.
- """
-
- if G.is_directed():
- raise nx.NetworkXError(
- "Mimimum spanning tree not defined for directed graphs.")
-
- nodes = G.nodes()
-
- while nodes:
- u = nodes.pop(0)
- frontier = []
- visited = [u]
- for u, v in G.edges(u):
- heappush(frontier, (G[u][v].get(weight, 1), u, v))
-
- while frontier:
- W, u, v = heappop(frontier)
- if v in visited:
- continue
- visited.append(v)
- nodes.remove(v)
- for v, w in G.edges(v):
- if not w in visited:
- heappush(frontier, (G[v][w].get(weight, 1), v, w))
- if data:
- yield u, v, G[u][v]
- else:
- yield u, v
-
-
-def prim_mst(G, weight = 'weight'):
- """Return a minimum spanning tree or forest of an undirected
- weighted graph.
-
- A minimum spanning tree is a subgraph of the graph (a tree) with
- the minimum sum of edge weights.
-
- If the graph is not connected a spanning forest is constructed. A
- spanning forest is a union of the spanning trees for each
- connected component of the graph.
-
- Parameters
- ----------
- G : NetworkX Graph
-
- weight : string
- Edge data key to use for weight (default 'weight').
-
- Returns
- -------
- G : NetworkX Graph
- A minimum spanning tree or forest.
-
- Examples
- --------
- >>> G=nx.cycle_graph(4)
- >>> G.add_edge(0,3,weight=2) # assign weight 2 to edge 0-3
- >>> T=nx.prim_mst(G)
- >>> print(sorted(T.edges(data=True)))
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
-
- Notes
- -----
- Uses Prim's algorithm.
-
- If the graph edges do not have a weight attribute a default weight of 1
- will be used.
- """
-
- T=nx.Graph(nx.prim_mst_edges(G,weight=weight,data=True))
- # Add isolated nodes
- if len(T)!=len(G):
- T.add_nodes_from([n for n,d in G.degree().items() if d==0])
- # Add node and graph attributes as shallow copy
- for n in T:
- T.node[n]=G.node[n].copy()
- T.graph=G.graph.copy()
- return T
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/__init__.py
deleted file mode 100644
index 0ebc6ab..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from networkx.algorithms.operators.all import *
-from networkx.algorithms.operators.binary import *
-from networkx.algorithms.operators.product import *
-from networkx.algorithms.operators.unary import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/all.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/all.py
deleted file mode 100644
index 755256b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/all.py
+++ /dev/null
@@ -1,151 +0,0 @@
-"""Operations on many graphs.
-"""
-# Copyright (C) 2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-try:
- from itertools import izip_longest as zip_longest
-except ImportError: # Python3 has zip_longest
- from itertools import zip_longest
-import networkx as nx
-from networkx.utils import is_string_like
-
-__author__ = """\n""".join([ 'Robert King <kingrobertking@gmail.com>',
- 'Aric Hagberg <aric.hagberg@gmail.com>'])
-
-__all__ = ['union_all', 'compose_all', 'disjoint_union_all',
- 'intersection_all']
-
-def union_all(graphs, rename=(None,) , name=None):
- """Return the union of all graphs.
-
- The graphs must be disjoint, otherwise an exception is raised.
-
- Parameters
- ----------
- graphs : list of graphs
- List of NetworkX graphs
-
- rename : bool , default=(None, None)
- Node names of G and H can be changed by specifying the tuple
- rename=('G-','H-') (for example). Node "u" in G is then renamed
- "G-u" and "v" in H is renamed "H-v".
-
- name : string
- Specify the name for the union graph@not_implemnted_for('direct
-
- Returns
- -------
- U : a graph with the same type as the first graph in list
-
- Notes
- -----
- To force a disjoint union with node relabeling, use
- disjoint_union_all(G,H) or convert_node_labels_to integers().
-
- Graph, edge, and node attributes are propagated to the union graph.
- If a graph attribute is present in multiple graphs, then the value
- from the last graph in the list with that attribute is used.
-
- See Also
- --------
- union
- disjoint_union_all
- """
- graphs_names = zip_longest(graphs,rename)
- U, gname = next(graphs_names)
- for H,hname in graphs_names:
- U = nx.union(U, H, (gname,hname),name=name)
- gname = None
- return U
-
-def disjoint_union_all(graphs):
- """Return the disjoint union of all graphs.
-
- This operation forces distinct integer node labels starting with 0
- for the first graph in the list and numbering consecutively.
-
- Parameters
- ----------
- graphs : list
- List of NetworkX graphs
-
- Returns
- -------
- U : A graph with the same type as the first graph in list
-
- Notes
- -----
- It is recommended that the graphs be either all directed or all undirected.
-
- Graph, edge, and node attributes are propagated to the union graph.
- If a graph attribute is present in multiple graphs, then the value
- from the last graph in the list with that attribute is used.
- """
- graphs = iter(graphs)
- U = next(graphs)
- for H in graphs:
- U = nx.disjoint_union(U, H)
- return U
-
-def compose_all(graphs, name=None):
- """Return the composition of all graphs.
-
- Composition is the simple union of the node sets and edge sets.
- The node sets of the supplied graphs need not be disjoint.
-
- Parameters
- ----------
- graphs : list
- List of NetworkX graphs
-
- name : string
- Specify name for new graph
-
- Returns
- -------
- C : A graph with the same type as the first graph in list
-
- Notes
- -----
- It is recommended that the supplied graphs be either all directed or all
- undirected.
-
- Graph, edge, and node attributes are propagated to the union graph.
- If a graph attribute is present in multiple graphs, then the value
- from the last graph in the list with that attribute is used.
- """
- graphs = iter(graphs)
- C = next(graphs)
- for H in graphs:
- C = nx.compose(C, H, name=name)
- return C
-
-def intersection_all(graphs):
- """Return a new graph that contains only the edges that exist in
- all graphs.
-
- All supplied graphs must have the same node set.
-
- Parameters
- ----------
- graphs_list : list
- List of NetworkX graphs
-
- Returns
- -------
- R : A new graph with the same type as the first graph in list
-
- Notes
- -----
- Attributes from the graph, nodes, and edges are not copied to the new
- graph.
- """
- graphs = iter(graphs)
- R = next(graphs)
- for H in graphs:
- R = nx.intersection(R, H)
- return R
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/binary.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/binary.py
deleted file mode 100644
index a710008..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/binary.py
+++ /dev/null
@@ -1,329 +0,0 @@
-"""
-Operations on graphs including union, intersection, difference.
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import is_string_like
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-__all__ = ['union', 'compose', 'disjoint_union', 'intersection',
- 'difference', 'symmetric_difference']
-
-def union(G, H, rename=(None, None), name=None):
- """ Return the union of graphs G and H.
-
- Graphs G and H must be disjoint, otherwise an exception is raised.
-
- Parameters
- ----------
- G,H : graph
- A NetworkX graph
-
- create_using : NetworkX graph
- Use specified graph for result. Otherwise
-
- rename : bool , default=(None, None)
- Node names of G and H can be changed by specifying the tuple
- rename=('G-','H-') (for example). Node "u" in G is then renamed
- "G-u" and "v" in H is renamed "H-v".
-
- name : string
- Specify the name for the union graph
-
- Returns
- -------
- U : A union graph with the same type as G.
-
- Notes
- -----
- To force a disjoint union with node relabeling, use
- disjoint_union(G,H) or convert_node_labels_to integers().
-
- Graph, edge, and node attributes are propagated from G and H
- to the union graph. If a graph attribute is present in both
- G and H the value from H is used.
-
- See Also
- --------
- disjoint_union
- """
- # Union is the same type as G
- R = G.__class__()
- if name is None:
- name = "union( %s, %s )"%(G.name,H.name)
- R.name = name
-
- # rename graph to obtain disjoint node labels
- def add_prefix(graph, prefix):
- if prefix is None:
- return graph
- def label(x):
- if is_string_like(x):
- name=prefix+x
- else:
- name=prefix+repr(x)
- return name
- return nx.relabel_nodes(graph, label)
- G = add_prefix(G,rename[0])
- H = add_prefix(H,rename[1])
- if set(G) & set(H):
- raise nx.NetworkXError('The node sets of G and H are not disjoint.',
- 'Use appropriate rename=(Gprefix,Hprefix)'
- 'or use disjoint_union(G,H).')
- if G.is_multigraph():
- G_edges = G.edges_iter(keys=True, data=True)
- else:
- G_edges = G.edges_iter(data=True)
- if H.is_multigraph():
- H_edges = H.edges_iter(keys=True, data=True)
- else:
- H_edges = H.edges_iter(data=True)
-
- # add nodes
- R.add_nodes_from(G)
- R.add_edges_from(G_edges)
- # add edges
- R.add_nodes_from(H)
- R.add_edges_from(H_edges)
- # add node attributes
- R.node.update(G.node)
- R.node.update(H.node)
- # add graph attributes, H attributes take precedent over G attributes
- R.graph.update(G.graph)
- R.graph.update(H.graph)
-
-
- return R
-
-def disjoint_union(G,H):
- """ Return the disjoint union of graphs G and H.
-
- This algorithm forces distinct integer node labels.
-
- Parameters
- ----------
- G,H : graph
- A NetworkX graph
-
- Returns
- -------
- U : A union graph with the same type as G.
-
- Notes
- -----
- A new graph is created, of the same class as G. It is recommended
- that G and H be either both directed or both undirected.
-
- The nodes of G are relabeled 0 to len(G)-1, and the nodes of H are
- relabeled len(G) to len(G)+len(H)-1.
-
- Graph, edge, and node attributes are propagated from G and H
- to the union graph. If a graph attribute is present in both
- G and H the value from H is used.
- """
- R1=nx.convert_node_labels_to_integers(G)
- R2=nx.convert_node_labels_to_integers(H,first_label=len(R1))
- R=union(R1,R2)
- R.name="disjoint_union( %s, %s )"%(G.name,H.name)
- R.graph.update(G.graph)
- R.graph.update(H.graph)
- return R
-
-
-def intersection(G, H):
- """Return a new graph that contains only the edges that exist in
- both G and H.
-
- The node sets of H and G must be the same.
-
- Parameters
- ----------
- G,H : graph
- A NetworkX graph. G and H must have the same node sets.
-
- Returns
- -------
- GH : A new graph with the same type as G.
-
- Notes
- -----
- Attributes from the graph, nodes, and edges are not copied to the new
- graph. If you want a new graph of the intersection of G and H
- with the attributes (including edge data) from G use remove_nodes_from()
- as follows
-
- >>> G=nx.path_graph(3)
- >>> H=nx.path_graph(5)
- >>> R=G.copy()
- >>> R.remove_nodes_from(n for n in G if n not in H)
- """
- # create new graph
- R=nx.create_empty_copy(G)
-
- R.name="Intersection of (%s and %s)"%(G.name, H.name)
-
- if set(G)!=set(H):
- raise nx.NetworkXError("Node sets of graphs are not equal")
-
- if G.number_of_edges()<=H.number_of_edges():
- if G.is_multigraph():
- edges=G.edges_iter(keys=True)
- else:
- edges=G.edges_iter()
- for e in edges:
- if H.has_edge(*e):
- R.add_edge(*e)
- else:
- if H.is_multigraph():
- edges=H.edges_iter(keys=True)
- else:
- edges=H.edges_iter()
- for e in edges:
- if G.has_edge(*e):
- R.add_edge(*e)
-
- return R
-
-def difference(G, H):
- """Return a new graph that contains the edges that exist in G but not in H.
-
- The node sets of H and G must be the same.
-
- Parameters
- ----------
- G,H : graph
- A NetworkX graph. G and H must have the same node sets.
-
- Returns
- -------
- D : A new graph with the same type as G.
-
- Notes
- -----
- Attributes from the graph, nodes, and edges are not copied to the new
- graph. If you want a new graph of the difference of G and H with
- with the attributes (including edge data) from G use remove_nodes_from()
- as follows:
-
- >>> G=nx.path_graph(3)
- >>> H=nx.path_graph(5)
- >>> R=G.copy()
- >>> R.remove_nodes_from(n for n in G if n in H)
- """
- # create new graph
- R=nx.create_empty_copy(G)
- R.name="Difference of (%s and %s)"%(G.name, H.name)
-
- if set(G)!=set(H):
- raise nx.NetworkXError("Node sets of graphs not equal")
-
- if G.is_multigraph():
- edges=G.edges_iter(keys=True)
- else:
- edges=G.edges_iter()
- for e in edges:
- if not H.has_edge(*e):
- R.add_edge(*e)
- return R
-
-def symmetric_difference(G, H):
- """Return new graph with edges that exist in either G or H but not both.
-
- The node sets of H and G must be the same.
-
- Parameters
- ----------
- G,H : graph
- A NetworkX graph. G and H must have the same node sets.
-
- Returns
- -------
- D : A new graph with the same type as G.
-
- Notes
- -----
- Attributes from the graph, nodes, and edges are not copied to the new
- graph.
- """
- # create new graph
- R=nx.create_empty_copy(G)
- R.name="Symmetric difference of (%s and %s)"%(G.name, H.name)
-
- if set(G)!=set(H):
- raise nx.NetworkXError("Node sets of graphs not equal")
-
- gnodes=set(G) # set of nodes in G
- hnodes=set(H) # set of nodes in H
- nodes=gnodes.symmetric_difference(hnodes)
- R.add_nodes_from(nodes)
-
- if G.is_multigraph():
- edges=G.edges_iter(keys=True)
- else:
- edges=G.edges_iter()
- # we could copy the data here but then this function doesn't
- # match intersection and difference
- for e in edges:
- if not H.has_edge(*e):
- R.add_edge(*e)
-
- if H.is_multigraph():
- edges=H.edges_iter(keys=True)
- else:
- edges=H.edges_iter()
- for e in edges:
- if not G.has_edge(*e):
- R.add_edge(*e)
- return R
-
-def compose(G, H, name=None):
- """Return a new graph of G composed with H.
-
- Composition is the simple union of the node sets and edge sets.
- The node sets of G and H need not be disjoint.
-
- Parameters
- ----------
- G,H : graph
- A NetworkX graph
-
- name : string
- Specify name for new graph
-
- Returns
- -------
- C: A new graph with the same type as G
-
- Notes
- -----
- It is recommended that G and H be either both directed or both undirected.
- Attributes from H take precedent over attributes from G.
- """
- if name is None:
- name="compose( %s, %s )"%(G.name,H.name)
- R=G.__class__()
- R.name=name
- R.add_nodes_from(H.nodes())
- R.add_nodes_from(G.nodes())
- if H.is_multigraph():
- R.add_edges_from(H.edges_iter(keys=True,data=True))
- else:
- R.add_edges_from(H.edges_iter(data=True))
- if G.is_multigraph():
- R.add_edges_from(G.edges_iter(keys=True,data=True))
- else:
- R.add_edges_from(G.edges_iter(data=True))
-
- # add node attributes, H attributes take precedent over G attributes
- R.node.update(G.node)
- R.node.update(H.node)
- # add graph attributes, H attributes take precedent over G attributes
- R.graph.update(G.graph)
- R.graph.update(H.graph)
- return R
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/product.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/product.py
deleted file mode 100644
index 0fa8a17..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/product.py
+++ /dev/null
@@ -1,330 +0,0 @@
-"""
-Graph products.
-"""
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from itertools import product
-
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'
- 'Ben Edwards(bedwards@cs.unm.edu)'])
-
-__all__ = ['tensor_product','cartesian_product',
- 'lexicographic_product', 'strong_product']
-
-def _dict_product(d1,d2):
- return dict((k,(d1.get(k),d2.get(k))) for k in set(d1)|set(d2))
-
-
-# Generators for producting graph products
-def _node_product(G,H):
- for u,v in product(G, H):
- yield ((u,v), _dict_product(G.node[u], H.node[v]))
-
-def _directed_edges_cross_edges(G,H):
- if not G.is_multigraph() and not H.is_multigraph():
- for u,v,c in G.edges_iter(data=True):
- for x,y,d in H.edges_iter(data=True):
- yield (u,x),(v,y),_dict_product(c,d)
- if not G.is_multigraph() and H.is_multigraph():
- for u,v,c in G.edges_iter(data=True):
- for x,y,k,d in H.edges_iter(data=True,keys=True):
- yield (u,x),(v,y),k,_dict_product(c,d)
- if G.is_multigraph() and not H.is_multigraph():
- for u,v,k,c in G.edges_iter(data=True,keys=True):
- for x,y,d in H.edges_iter(data=True):
- yield (u,x),(v,y),k,_dict_product(c,d)
- if G.is_multigraph() and H.is_multigraph():
- for u,v,j,c in G.edges_iter(data=True,keys=True):
- for x,y,k,d in H.edges_iter(data=True,keys=True):
- yield (u,x),(v,y),(j,k),_dict_product(c,d)
-
-def _undirected_edges_cross_edges(G,H):
- if not G.is_multigraph() and not H.is_multigraph():
- for u,v,c in G.edges_iter(data=True):
- for x,y,d in H.edges_iter(data=True):
- yield (v,x),(u,y),_dict_product(c,d)
- if not G.is_multigraph() and H.is_multigraph():
- for u,v,c in G.edges_iter(data=True):
- for x,y,k,d in H.edges_iter(data=True,keys=True):
- yield (v,x),(u,y),k,_dict_product(c,d)
- if G.is_multigraph() and not H.is_multigraph():
- for u,v,k,c in G.edges_iter(data=True,keys=True):
- for x,y,d in H.edges_iter(data=True):
- yield (v,x),(u,y),k,_dict_product(c,d)
- if G.is_multigraph() and H.is_multigraph():
- for u,v,j,c in G.edges_iter(data=True,keys=True):
- for x,y,k,d in H.edges_iter(data=True,keys=True):
- yield (v,x),(u,y),(j,k),_dict_product(c,d)
-
-def _edges_cross_nodes(G,H):
- if G.is_multigraph():
- for u,v,k,d in G.edges_iter(data=True,keys=True):
- for x in H:
- yield (u,x),(v,x),k,d
- else:
- for u,v,d in G.edges_iter(data=True):
- for x in H:
- if H.is_multigraph():
- yield (u,x),(v,x),None,d
- else:
- yield (u,x),(v,x),d
-
-
-def _nodes_cross_edges(G,H):
- if H.is_multigraph():
- for x in G:
- for u,v,k,d in H.edges_iter(data=True,keys=True):
- yield (x,u),(x,v),k,d
- else:
- for x in G:
- for u,v,d in H.edges_iter(data=True):
- if G.is_multigraph():
- yield (x,u),(x,v),None,d
- else:
- yield (x,u),(x,v),d
-
-def _edges_cross_nodes_and_nodes(G,H):
- if G.is_multigraph():
- for u,v,k,d in G.edges_iter(data=True,keys=True):
- for x in H:
- for y in H:
- yield (u,x),(v,y),k,d
- else:
- for u,v,d in G.edges_iter(data=True):
- for x in H:
- for y in H:
- if H.is_multigraph():
- yield (u,x),(v,y),None,d
- else:
- yield (u,x),(v,y),d
-
-def _init_product_graph(G,H):
- if not G.is_directed() == H.is_directed():
- raise nx.NetworkXError("G and H must be both directed or",
- "both undirected")
- if G.is_multigraph() or H.is_multigraph():
- GH = nx.MultiGraph()
- else:
- GH = nx.Graph()
- if G.is_directed():
- GH = GH.to_directed()
- return GH
-
-
-def tensor_product(G,H):
- r"""Return the tensor product of G and H.
-
- The tensor product P of the graphs G and H has a node set that
- is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.
- P has an edge ((u,v),(x,y)) if and only if (u,v) is an edge in G
- and (x,y) is an edge in H.
-
- Sometimes referred to as the categorical product.
-
-
- Parameters
- ----------
- G, H: graphs
- Networkx graphs.
-
- Returns
- -------
- P: NetworkX graph
- The tensor product of G and H. P will be a multi-graph if either G
- or H is a multi-graph. Will be a directed if G and H are directed,
- and undirected if G and H are undirected.
-
- Raises
- ------
- NetworkXError
- If G and H are not both directed or both undirected.
-
- Notes
- -----
- Node attributes in P are two-tuple of the G and H node attributes.
- Missing attributes are assigned None.
-
- For example
- >>> G = nx.Graph()
- >>> H = nx.Graph()
- >>> G.add_node(0,a1=True)
- >>> H.add_node('a',a2='Spam')
- >>> P = nx.tensor_product(G,H)
- >>> P.nodes()
- [(0, 'a')]
-
- Edge attributes and edge keys (for multigraphs) are also copied to the
- new product graph
- """
- GH = _init_product_graph(G,H)
- GH.add_nodes_from(_node_product(G,H))
- GH.add_edges_from(_directed_edges_cross_edges(G,H))
- if not GH.is_directed():
- GH.add_edges_from(_undirected_edges_cross_edges(G,H))
- GH.name = "Tensor product("+G.name+","+H.name+")"
- return GH
-
-def cartesian_product(G,H):
- """Return the Cartesian product of G and H.
-
- The tensor product P of the graphs G and H has a node set that
- is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.
- P has an edge ((u,v),(x,y)) if and only if (u,v) is an edge in G
- and x==y or and (x,y) is an edge in H and u==v.
- and (x,y) is an edge in H.
-
- Parameters
- ----------
- G, H: graphs
- Networkx graphs.
-
- Returns
- -------
- P: NetworkX graph
- The Cartesian product of G and H. P will be a multi-graph if either G
- or H is a multi-graph. Will be a directed if G and H are directed,
- and undirected if G and H are undirected.
-
- Raises
- ------
- NetworkXError
- If G and H are not both directed or both undirected.
-
- Notes
- -----
- Node attributes in P are two-tuple of the G and H node attributes.
- Missing attributes are assigned None.
-
- For example
- >>> G = nx.Graph()
- >>> H = nx.Graph()
- >>> G.add_node(0,a1=True)
- >>> H.add_node('a',a2='Spam')
- >>> P = nx.cartesian_product(G,H)
- >>> P.nodes()
- [(0, 'a')]
-
- Edge attributes and edge keys (for multigraphs) are also copied to the
- new product graph
- """
- if not G.is_directed() == H.is_directed():
- raise nx.NetworkXError("G and H must be both directed or",
- "both undirected")
- GH = _init_product_graph(G,H)
- GH.add_nodes_from(_node_product(G,H))
- GH.add_edges_from(_edges_cross_nodes(G,H))
- GH.add_edges_from(_nodes_cross_edges(G,H))
- GH.name = "Cartesian product("+G.name+","+H.name+")"
- return GH
-
-def lexicographic_product(G,H):
- """Return the lexicographic product of G and H.
-
- The lexicographical product P of the graphs G and H has a node set that
- is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.
- P has an edge ((u,v),(x,y)) if and only if (u,v) is an edge in G
- or u==v and (x,y) is an edge in H.
-
- Parameters
- ----------
- G, H: graphs
- Networkx graphs.
-
- Returns
- -------
- P: NetworkX graph
- The Cartesian product of G and H. P will be a multi-graph if either G
- or H is a multi-graph. Will be a directed if G and H are directed,
- and undirected if G and H are undirected.
-
- Raises
- ------
- NetworkXError
- If G and H are not both directed or both undirected.
-
- Notes
- -----
- Node attributes in P are two-tuple of the G and H node attributes.
- Missing attributes are assigned None.
-
- For example
- >>> G = nx.Graph()
- >>> H = nx.Graph()
- >>> G.add_node(0,a1=True)
- >>> H.add_node('a',a2='Spam')
- >>> P = nx.lexicographic_product(G,H)
- >>> P.nodes()
- [(0, 'a')]
-
- Edge attributes and edge keys (for multigraphs) are also copied to the
- new product graph
- """
- GH = _init_product_graph(G,H)
- GH.add_nodes_from(_node_product(G,H))
- # Edges in G regardless of H designation
- GH.add_edges_from(_edges_cross_nodes_and_nodes(G,H))
- # For each x in G, only if there is an edge in H
- GH.add_edges_from(_nodes_cross_edges(G,H))
- GH.name = "Lexicographic product("+G.name+","+H.name+")"
- return GH
-
-def strong_product(G,H):
- """Return the strong product of G and H.
-
- The strong product P of the graphs G and H has a node set that
- is the Cartesian product of the node sets, $V(P)=V(G) \times V(H)$.
- P has an edge ((u,v),(x,y)) if and only if
- u==v and (x,y) is an edge in H, or
- x==y and (u,v) is an edge in G, or
- (u,v) is an edge in G and (x,y) is an edge in H.
-
- Parameters
- ----------
- G, H: graphs
- Networkx graphs.
-
- Returns
- -------
- P: NetworkX graph
- The Cartesian product of G and H. P will be a multi-graph if either G
- or H is a multi-graph. Will be a directed if G and H are directed,
- and undirected if G and H are undirected.
-
- Raises
- ------
- NetworkXError
- If G and H are not both directed or both undirected.
-
- Notes
- -----
- Node attributes in P are two-tuple of the G and H node attributes.
- Missing attributes are assigned None.
-
- For example
- >>> G = nx.Graph()
- >>> H = nx.Graph()
- >>> G.add_node(0,a1=True)
- >>> H.add_node('a',a2='Spam')
- >>> P = nx.strong_product(G,H)
- >>> P.nodes()
- [(0, 'a')]
-
- Edge attributes and edge keys (for multigraphs) are also copied to the
- new product graph
- """
- GH = _init_product_graph(G,H)
- GH.add_nodes_from(_node_product(G,H))
- GH.add_edges_from(_nodes_cross_edges(G,H))
- GH.add_edges_from(_edges_cross_nodes(G,H))
- GH.add_edges_from(_directed_edges_cross_edges(G,H))
- if not GH.is_directed():
- GH.add_edges_from(_undirected_edges_cross_edges(G,H))
- GH.name = "Strong product("+G.name+","+H.name+")"
- return GH
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_all.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_all.py
deleted file mode 100644
index fc4fa4a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_all.py
+++ /dev/null
@@ -1,167 +0,0 @@
-from nose.tools import *
-import networkx as nx
-from networkx.testing import *
-
-def test_union_all_attributes():
- g = nx.Graph()
- g.add_node(0, x=4)
- g.add_node(1, x=5)
- g.add_edge(0, 1, size=5)
- g.graph['name'] = 'g'
-
- h = g.copy()
- h.graph['name'] = 'h'
- h.graph['attr'] = 'attr'
- h.node[0]['x'] = 7
-
- j = g.copy()
- j.graph['name'] = 'j'
- j.graph['attr'] = 'attr'
- j.node[0]['x'] = 7
-
- ghj = nx.union_all([g, h, j], rename=('g', 'h', 'j'))
- assert_equal( set(ghj.nodes()) , set(['h0', 'h1', 'g0', 'g1', 'j0', 'j1']) )
- for n in ghj:
- graph, node = n
- assert_equal( ghj.node[n], eval(graph).node[int(node)] )
-
- assert_equal(ghj.graph['attr'],'attr')
- assert_equal(ghj.graph['name'],'j') # j graph attributes take precendent
-
-
-
-def test_intersection_all():
- G=nx.Graph()
- H=nx.Graph()
- R=nx.Graph()
- G.add_nodes_from([1,2,3,4])
- G.add_edge(1,2)
- G.add_edge(2,3)
- H.add_nodes_from([1,2,3,4])
- H.add_edge(2,3)
- H.add_edge(3,4)
- R.add_nodes_from([1,2,3,4])
- R.add_edge(2,3)
- R.add_edge(4,1)
- I=nx.intersection_all([G,H,R])
- assert_equal( set(I.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(I.edges()) , [(2,3)] )
-
-
-def test_intersection_all_attributes():
- g = nx.Graph()
- g.add_node(0, x=4)
- g.add_node(1, x=5)
- g.add_edge(0, 1, size=5)
- g.graph['name'] = 'g'
-
- h = g.copy()
- h.graph['name'] = 'h'
- h.graph['attr'] = 'attr'
- h.node[0]['x'] = 7
-
- gh = nx.intersection_all([g, h])
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , sorted(g.edges()) )
-
- h.remove_node(0)
- assert_raises(nx.NetworkXError, nx.intersection, g, h)
-
-def test_intersection_all_multigraph_attributes():
- g = nx.MultiGraph()
- g.add_edge(0, 1, key=0)
- g.add_edge(0, 1, key=1)
- g.add_edge(0, 1, key=2)
- h = nx.MultiGraph()
- h.add_edge(0, 1, key=0)
- h.add_edge(0, 1, key=3)
- gh = nx.intersection_all([g, h])
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , [(0,1)] )
- assert_equal( sorted(gh.edges(keys=True)) , [(0,1,0)] )
-
-def test_union_all_and_compose_all():
- K3=nx.complete_graph(3)
- P3=nx.path_graph(3)
-
- G1=nx.DiGraph()
- G1.add_edge('A','B')
- G1.add_edge('A','C')
- G1.add_edge('A','D')
- G2=nx.DiGraph()
- G2.add_edge('1','2')
- G2.add_edge('1','3')
- G2.add_edge('1','4')
-
- G=nx.union_all([G1,G2])
- H=nx.compose_all([G1,G2])
- assert_edges_equal(G.edges(),H.edges())
- assert_false(G.has_edge('A','1'))
- assert_raises(nx.NetworkXError, nx.union, K3, P3)
- H1=nx.union_all([H,G1],rename=('H','G1'))
- assert_equal(sorted(H1.nodes()),
- ['G1A', 'G1B', 'G1C', 'G1D',
- 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])
-
- H2=nx.union_all([H,G2],rename=("H",""))
- assert_equal(sorted(H2.nodes()),
- ['1', '2', '3', '4',
- 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])
-
- assert_false(H1.has_edge('NB','NA'))
-
- G=nx.compose_all([G,G])
- assert_edges_equal(G.edges(),H.edges())
-
- G2=nx.union_all([G2,G2],rename=('','copy'))
- assert_equal(sorted(G2.nodes()),
- ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4'])
-
- assert_equal(G2.neighbors('copy4'),[])
- assert_equal(sorted(G2.neighbors('copy1')),['copy2', 'copy3', 'copy4'])
- assert_equal(len(G),8)
- assert_equal(nx.number_of_edges(G),6)
-
- E=nx.disjoint_union_all([G,G])
- assert_equal(len(E),16)
- assert_equal(nx.number_of_edges(E),12)
-
- E=nx.disjoint_union_all([G1,G2])
- assert_equal(sorted(E.nodes()),[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
-
- G1=nx.DiGraph()
- G1.add_edge('A','B')
- G2=nx.DiGraph()
- G2.add_edge(1,2)
- G3=nx.DiGraph()
- G3.add_edge(11,22)
- G4=nx.union_all([G1,G2,G3],rename=("G1","G2","G3"))
- assert_equal(sorted(G4.nodes()),
- ['G1A', 'G1B', 'G21', 'G22',
- 'G311', 'G322'])
-
-
-def test_union_all_multigraph():
- G=nx.MultiGraph()
- G.add_edge(1,2,key=0)
- G.add_edge(1,2,key=1)
- H=nx.MultiGraph()
- H.add_edge(3,4,key=0)
- H.add_edge(3,4,key=1)
- GH=nx.union_all([G,H])
- assert_equal( set(GH) , set(G)|set(H))
- assert_equal( set(GH.edges(keys=True)) ,
- set(G.edges(keys=True))|set(H.edges(keys=True)))
-
-
-def test_input_output():
- l = [nx.Graph([(1,2)]),nx.Graph([(3,4)])]
- U = nx.disjoint_union_all(l)
- assert_equal(len(l),2)
- C = nx.compose_all(l)
- assert_equal(len(l),2)
- l = [nx.Graph([(1,2)]),nx.Graph([(1,2)])]
- R = nx.intersection_all(l)
- assert_equal(len(l),2)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_binary.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_binary.py
deleted file mode 100644
index 34b7e6e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_binary.py
+++ /dev/null
@@ -1,270 +0,0 @@
-from nose.tools import *
-import networkx as nx
-from networkx import *
-from networkx.testing import *
-
-def test_union_attributes():
- g = nx.Graph()
- g.add_node(0, x=4)
- g.add_node(1, x=5)
- g.add_edge(0, 1, size=5)
- g.graph['name'] = 'g'
-
- h = g.copy()
- h.graph['name'] = 'h'
- h.graph['attr'] = 'attr'
- h.node[0]['x'] = 7
-
- gh = nx.union(g, h, rename=('g', 'h'))
- assert_equal( set(gh.nodes()) , set(['h0', 'h1', 'g0', 'g1']) )
- for n in gh:
- graph, node = n
- assert_equal( gh.node[n], eval(graph).node[int(node)] )
-
- assert_equal(gh.graph['attr'],'attr')
- assert_equal(gh.graph['name'],'h') # h graph attributes take precendent
-
-def test_intersection():
- G=nx.Graph()
- H=nx.Graph()
- G.add_nodes_from([1,2,3,4])
- G.add_edge(1,2)
- G.add_edge(2,3)
- H.add_nodes_from([1,2,3,4])
- H.add_edge(2,3)
- H.add_edge(3,4)
- I=nx.intersection(G,H)
- assert_equal( set(I.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(I.edges()) , [(2,3)] )
-
-
-def test_intersection_attributes():
- g = nx.Graph()
- g.add_node(0, x=4)
- g.add_node(1, x=5)
- g.add_edge(0, 1, size=5)
- g.graph['name'] = 'g'
-
- h = g.copy()
- h.graph['name'] = 'h'
- h.graph['attr'] = 'attr'
- h.node[0]['x'] = 7
-
- gh = nx.intersection(g, h)
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , sorted(g.edges()) )
-
- h.remove_node(0)
- assert_raises(nx.NetworkXError, nx.intersection, g, h)
-
-
-
-def test_intersection_multigraph_attributes():
- g = nx.MultiGraph()
- g.add_edge(0, 1, key=0)
- g.add_edge(0, 1, key=1)
- g.add_edge(0, 1, key=2)
- h = nx.MultiGraph()
- h.add_edge(0, 1, key=0)
- h.add_edge(0, 1, key=3)
- gh = nx.intersection(g, h)
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , [(0,1)] )
- assert_equal( sorted(gh.edges(keys=True)) , [(0,1,0)] )
-
-
-def test_difference():
- G=nx.Graph()
- H=nx.Graph()
- G.add_nodes_from([1,2,3,4])
- G.add_edge(1,2)
- G.add_edge(2,3)
- H.add_nodes_from([1,2,3,4])
- H.add_edge(2,3)
- H.add_edge(3,4)
- D=nx.difference(G,H)
- assert_equal( set(D.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(D.edges()) , [(1,2)] )
- D=nx.difference(H,G)
- assert_equal( set(D.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(D.edges()) , [(3,4)] )
- D=nx.symmetric_difference(G,H)
- assert_equal( set(D.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(D.edges()) , [(1,2),(3,4)] )
-
-
-def test_difference2():
- G=nx.Graph()
- H=nx.Graph()
- G.add_nodes_from([1,2,3,4])
- H.add_nodes_from([1,2,3,4])
- G.add_edge(1,2)
- H.add_edge(1,2)
- G.add_edge(2,3)
- D=nx.difference(G,H)
- assert_equal( set(D.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(D.edges()) , [(2,3)] )
- D=nx.difference(H,G)
- assert_equal( set(D.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(D.edges()) , [] )
- H.add_edge(3,4)
- D=nx.difference(H,G)
- assert_equal( set(D.nodes()) , set([1,2,3,4]) )
- assert_equal( sorted(D.edges()) , [(3,4)] )
-
-
-def test_difference_attributes():
- g = nx.Graph()
- g.add_node(0, x=4)
- g.add_node(1, x=5)
- g.add_edge(0, 1, size=5)
- g.graph['name'] = 'g'
-
- h = g.copy()
- h.graph['name'] = 'h'
- h.graph['attr'] = 'attr'
- h.node[0]['x'] = 7
-
- gh = nx.difference(g, h)
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , [])
-
- h.remove_node(0)
- assert_raises(nx.NetworkXError, nx.intersection, g, h)
-
-def test_difference_multigraph_attributes():
- g = nx.MultiGraph()
- g.add_edge(0, 1, key=0)
- g.add_edge(0, 1, key=1)
- g.add_edge(0, 1, key=2)
- h = nx.MultiGraph()
- h.add_edge(0, 1, key=0)
- h.add_edge(0, 1, key=3)
- gh = nx.difference(g, h)
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , [(0,1),(0,1)] )
- assert_equal( sorted(gh.edges(keys=True)) , [(0,1,1),(0,1,2)] )
-
-
-@raises(nx.NetworkXError)
-def test_difference_raise():
- G = nx.path_graph(4)
- H = nx.path_graph(3)
- GH = nx.difference(G, H)
-
-def test_symmetric_difference_multigraph():
- g = nx.MultiGraph()
- g.add_edge(0, 1, key=0)
- g.add_edge(0, 1, key=1)
- g.add_edge(0, 1, key=2)
- h = nx.MultiGraph()
- h.add_edge(0, 1, key=0)
- h.add_edge(0, 1, key=3)
- gh = nx.symmetric_difference(g, h)
- assert_equal( set(gh.nodes()) , set(g.nodes()) )
- assert_equal( set(gh.nodes()) , set(h.nodes()) )
- assert_equal( sorted(gh.edges()) , 3*[(0,1)] )
- assert_equal( sorted(sorted(e) for e in gh.edges(keys=True)),
- [[0,1,1],[0,1,2],[0,1,3]] )
-
-@raises(nx.NetworkXError)
-def test_symmetric_difference_raise():
- G = nx.path_graph(4)
- H = nx.path_graph(3)
- GH = nx.symmetric_difference(G, H)
-
-def test_union_and_compose():
- K3=complete_graph(3)
- P3=path_graph(3)
-
- G1=nx.DiGraph()
- G1.add_edge('A','B')
- G1.add_edge('A','C')
- G1.add_edge('A','D')
- G2=nx.DiGraph()
- G2.add_edge('1','2')
- G2.add_edge('1','3')
- G2.add_edge('1','4')
-
- G=union(G1,G2)
- H=compose(G1,G2)
- assert_edges_equal(G.edges(),H.edges())
- assert_false(G.has_edge('A',1))
- assert_raises(nx.NetworkXError, nx.union, K3, P3)
- H1=union(H,G1,rename=('H','G1'))
- assert_equal(sorted(H1.nodes()),
- ['G1A', 'G1B', 'G1C', 'G1D',
- 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])
-
- H2=union(H,G2,rename=("H",""))
- assert_equal(sorted(H2.nodes()),
- ['1', '2', '3', '4',
- 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])
-
- assert_false(H1.has_edge('NB','NA'))
-
- G=compose(G,G)
- assert_edges_equal(G.edges(),H.edges())
-
- G2=union(G2,G2,rename=('','copy'))
- assert_equal(sorted(G2.nodes()),
- ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4'])
-
- assert_equal(G2.neighbors('copy4'),[])
- assert_equal(sorted(G2.neighbors('copy1')),['copy2', 'copy3', 'copy4'])
- assert_equal(len(G),8)
- assert_equal(number_of_edges(G),6)
-
- E=disjoint_union(G,G)
- assert_equal(len(E),16)
- assert_equal(number_of_edges(E),12)
-
- E=disjoint_union(G1,G2)
- assert_equal(sorted(E.nodes()),[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
-
-
-def test_union_multigraph():
- G=nx.MultiGraph()
- G.add_edge(1,2,key=0)
- G.add_edge(1,2,key=1)
- H=nx.MultiGraph()
- H.add_edge(3,4,key=0)
- H.add_edge(3,4,key=1)
- GH=nx.union(G,H)
- assert_equal( set(GH) , set(G)|set(H))
- assert_equal( set(GH.edges(keys=True)) ,
- set(G.edges(keys=True))|set(H.edges(keys=True)))
-
-def test_disjoint_union_multigraph():
- G=nx.MultiGraph()
- G.add_edge(0,1,key=0)
- G.add_edge(0,1,key=1)
- H=nx.MultiGraph()
- H.add_edge(2,3,key=0)
- H.add_edge(2,3,key=1)
- GH=nx.disjoint_union(G,H)
- assert_equal( set(GH) , set(G)|set(H))
- assert_equal( set(GH.edges(keys=True)) ,
- set(G.edges(keys=True))|set(H.edges(keys=True)))
-
-
-def test_compose_multigraph():
- G=nx.MultiGraph()
- G.add_edge(1,2,key=0)
- G.add_edge(1,2,key=1)
- H=nx.MultiGraph()
- H.add_edge(3,4,key=0)
- H.add_edge(3,4,key=1)
- GH=nx.compose(G,H)
- assert_equal( set(GH) , set(G)|set(H))
- assert_equal( set(GH.edges(keys=True)) ,
- set(G.edges(keys=True))|set(H.edges(keys=True)))
- H.add_edge(1,2,key=2)
- GH=nx.compose(G,H)
- assert_equal( set(GH) , set(G)|set(H))
- assert_equal( set(GH.edges(keys=True)) ,
- set(G.edges(keys=True))|set(H.edges(keys=True)))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_product.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_product.py
deleted file mode 100644
index b157aac..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_product.py
+++ /dev/null
@@ -1,334 +0,0 @@
-import networkx as nx
-from networkx import tensor_product,cartesian_product,lexicographic_product,strong_product
-from nose.tools import assert_raises, assert_true, assert_equal, raises
-
-@raises(nx.NetworkXError)
-def test_tensor_product_raises():
- P = tensor_product(nx.DiGraph(),nx.Graph())
-
-def test_tensor_product_null():
- null=nx.null_graph()
- empty10=nx.empty_graph(10)
- K3=nx.complete_graph(3)
- K10=nx.complete_graph(10)
- P3=nx.path_graph(3)
- P10=nx.path_graph(10)
- # null graph
- G=tensor_product(null,null)
- assert_true(nx.is_isomorphic(G,null))
- # null_graph X anything = null_graph and v.v.
- G=tensor_product(null,empty10)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(null,K3)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(null,K10)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(null,P3)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(null,P10)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(empty10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(K3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(K10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(P3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=tensor_product(P10,null)
- assert_true(nx.is_isomorphic(G,null))
-
-def test_tensor_product_size():
- P5 = nx.path_graph(5)
- K3 = nx.complete_graph(3)
- K5 = nx.complete_graph(5)
-
- G=tensor_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=tensor_product(K3,K5)
- assert_equal(nx.number_of_nodes(G),3*5)
-
-
-def test_tensor_product_combinations():
- # basic smoke test, more realistic tests would be usefule
- P5 = nx.path_graph(5)
- K3 = nx.complete_graph(3)
- G=tensor_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=tensor_product(P5,nx.MultiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
- G=tensor_product(nx.MultiGraph(P5),K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=tensor_product(nx.MultiGraph(P5),nx.MultiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
-
- G=tensor_product(nx.DiGraph(P5),nx.DiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
-
-
-def test_tensor_product_classic_result():
- K2 = nx.complete_graph(2)
- G = nx.petersen_graph()
- G = tensor_product(G,K2)
- assert_true(nx.is_isomorphic(G,nx.desargues_graph()))
-
- G = nx.cycle_graph(5)
- G = tensor_product(G,K2)
- assert_true(nx.is_isomorphic(G,nx.cycle_graph(10)))
-
- G = nx.tetrahedral_graph()
- G = tensor_product(G,K2)
- assert_true(nx.is_isomorphic(G,nx.cubical_graph()))
-
-def test_tensor_product_random():
- G = nx.erdos_renyi_graph(10,2/10.)
- H = nx.erdos_renyi_graph(10,2/10.)
- GH = tensor_product(G,H)
-
- for (u_G,u_H) in GH.nodes_iter():
- for (v_G,v_H) in GH.nodes_iter():
- if H.has_edge(u_H,v_H) and G.has_edge(u_G,v_G):
- assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
- else:
- assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))
-
-
-def test_cartesian_product_multigraph():
- G=nx.MultiGraph()
- G.add_edge(1,2,key=0)
- G.add_edge(1,2,key=1)
- H=nx.MultiGraph()
- H.add_edge(3,4,key=0)
- H.add_edge(3,4,key=1)
- GH=cartesian_product(G,H)
- assert_equal( set(GH) , set([(1, 3), (2, 3), (2, 4), (1, 4)]))
- assert_equal( set(GH.edges(keys=True)) ,
- set([((1, 3), (2, 3), 0), ((1, 3), (2, 3), 1),
- ((1, 3), (1, 4), 0), ((1, 3), (1, 4), 1),
- ((2, 3), (2, 4), 0), ((2, 3), (2, 4), 1),
- ((2, 4), (1, 4), 0), ((2, 4), (1, 4), 1)]))
-
-@raises(nx.NetworkXError)
-def test_cartesian_product_raises():
- P = cartesian_product(nx.DiGraph(),nx.Graph())
-
-def test_cartesian_product_null():
- null=nx.null_graph()
- empty10=nx.empty_graph(10)
- K3=nx.complete_graph(3)
- K10=nx.complete_graph(10)
- P3=nx.path_graph(3)
- P10=nx.path_graph(10)
- # null graph
- G=cartesian_product(null,null)
- assert_true(nx.is_isomorphic(G,null))
- # null_graph X anything = null_graph and v.v.
- G=cartesian_product(null,empty10)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(null,K3)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(null,K10)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(null,P3)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(null,P10)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(empty10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(K3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(K10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(P3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=cartesian_product(P10,null)
- assert_true(nx.is_isomorphic(G,null))
-
-def test_cartesian_product_size():
- # order(GXH)=order(G)*order(H)
- K5=nx.complete_graph(5)
- P5=nx.path_graph(5)
- K3=nx.complete_graph(3)
- G=cartesian_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- assert_equal(nx.number_of_edges(G),
- nx.number_of_edges(P5)*nx.number_of_nodes(K3)+
- nx.number_of_edges(K3)*nx.number_of_nodes(P5))
- G=cartesian_product(K3,K5)
- assert_equal(nx.number_of_nodes(G),3*5)
- assert_equal(nx.number_of_edges(G),
- nx.number_of_edges(K5)*nx.number_of_nodes(K3)+
- nx.number_of_edges(K3)*nx.number_of_nodes(K5))
-
-def test_cartesian_product_classic():
- # test some classic product graphs
- P2 = nx.path_graph(2)
- P3 = nx.path_graph(3)
- # cube = 2-path X 2-path
- G=cartesian_product(P2,P2)
- G=cartesian_product(P2,G)
- assert_true(nx.is_isomorphic(G,nx.cubical_graph()))
-
- # 3x3 grid
- G=cartesian_product(P3,P3)
- assert_true(nx.is_isomorphic(G,nx.grid_2d_graph(3,3)))
-
-def test_cartesian_product_random():
- G = nx.erdos_renyi_graph(10,2/10.)
- H = nx.erdos_renyi_graph(10,2/10.)
- GH = cartesian_product(G,H)
-
- for (u_G,u_H) in GH.nodes_iter():
- for (v_G,v_H) in GH.nodes_iter():
- if (u_G==v_G and H.has_edge(u_H,v_H)) or \
- (u_H==v_H and G.has_edge(u_G,v_G)):
- assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
- else:
- assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))
-
-@raises(nx.NetworkXError)
-def test_lexicographic_product_raises():
- P=lexicographic_product(nx.DiGraph(),nx.Graph())
-
-def test_lexicographic_product_null():
- null=nx.null_graph()
- empty10=nx.empty_graph(10)
- K3=nx.complete_graph(3)
- K10=nx.complete_graph(10)
- P3=nx.path_graph(3)
- P10=nx.path_graph(10)
- # null graph
- G=lexicographic_product(null,null)
- assert_true(nx.is_isomorphic(G,null))
- # null_graph X anything = null_graph and v.v.
- G=lexicographic_product(null,empty10)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(null,K3)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(null,K10)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(null,P3)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(null,P10)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(empty10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(K3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(K10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(P3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=lexicographic_product(P10,null)
- assert_true(nx.is_isomorphic(G,null))
-
-def test_lexicographic_product_size():
- K5=nx.complete_graph(5)
- P5=nx.path_graph(5)
- K3=nx.complete_graph(3)
- G=lexicographic_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=lexicographic_product(K3,K5)
- assert_equal(nx.number_of_nodes(G),3*5)
-
-def test_lexicographic_product_combinations():
- P5=nx.path_graph(5)
- K3=nx.complete_graph(3)
- G=lexicographic_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=lexicographic_product(nx.MultiGraph(P5),K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=lexicographic_product(P5,nx.MultiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
- G=lexicographic_product(nx.MultiGraph(P5),nx.MultiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
-
-
-
-
- #No classic easily found classic results for lexicographic product
-def test_lexicographic_product_random():
- G = nx.erdos_renyi_graph(10,2/10.)
- H = nx.erdos_renyi_graph(10,2/10.)
- GH = lexicographic_product(G,H)
-
- for (u_G,u_H) in GH.nodes_iter():
- for (v_G,v_H) in GH.nodes_iter():
- if G.has_edge(u_G,v_G) or (u_G==v_G and H.has_edge(u_H,v_H)):
- assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
- else:
- assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))
-
-@raises(nx.NetworkXError)
-def test_strong_product_raises():
- P = strong_product(nx.DiGraph(),nx.Graph())
-
-def test_strong_product_null():
- null=nx.null_graph()
- empty10=nx.empty_graph(10)
- K3=nx.complete_graph(3)
- K10=nx.complete_graph(10)
- P3=nx.path_graph(3)
- P10=nx.path_graph(10)
- # null graph
- G=strong_product(null,null)
- assert_true(nx.is_isomorphic(G,null))
- # null_graph X anything = null_graph and v.v.
- G=strong_product(null,empty10)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(null,K3)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(null,K10)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(null,P3)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(null,P10)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(empty10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(K3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(K10,null)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(P3,null)
- assert_true(nx.is_isomorphic(G,null))
- G=strong_product(P10,null)
- assert_true(nx.is_isomorphic(G,null))
-
-def test_strong_product_size():
- K5=nx.complete_graph(5)
- P5=nx.path_graph(5)
- K3 = nx.complete_graph(3)
- G=strong_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=strong_product(K3,K5)
- assert_equal(nx.number_of_nodes(G),3*5)
-
-def test_strong_product_combinations():
- P5=nx.path_graph(5)
- K3 = nx.complete_graph(3)
- G=strong_product(P5,K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=strong_product(nx.MultiGraph(P5),K3)
- assert_equal(nx.number_of_nodes(G),5*3)
- G=strong_product(P5,nx.MultiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
- G=strong_product(nx.MultiGraph(P5),nx.MultiGraph(K3))
- assert_equal(nx.number_of_nodes(G),5*3)
-
-
-
- #No classic easily found classic results for strong product
-def test_strong_product_random():
- G = nx.erdos_renyi_graph(10,2/10.)
- H = nx.erdos_renyi_graph(10,2/10.)
- GH = strong_product(G,H)
-
- for (u_G,u_H) in GH.nodes_iter():
- for (v_G,v_H) in GH.nodes_iter():
- if (u_G==v_G and H.has_edge(u_H,v_H)) or \
- (u_H==v_H and G.has_edge(u_G,v_G)) or \
- (G.has_edge(u_G,v_G) and H.has_edge(u_H,v_H)):
- assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
- else:
- assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_unary.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_unary.py
deleted file mode 100644
index ea10d75..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/test_unary.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from nose.tools import *
-import networkx as nx
-from networkx import *
-
-
-def test_complement():
- null=null_graph()
- empty1=empty_graph(1)
- empty10=empty_graph(10)
- K3=complete_graph(3)
- K5=complete_graph(5)
- K10=complete_graph(10)
- P2=path_graph(2)
- P3=path_graph(3)
- P5=path_graph(5)
- P10=path_graph(10)
- #complement of the complete graph is empty
-
- G=complement(K3)
- assert_true(is_isomorphic(G,empty_graph(3)))
- G=complement(K5)
- assert_true(is_isomorphic(G,empty_graph(5)))
- # for any G, G=complement(complement(G))
- P3cc=complement(complement(P3))
- assert_true(is_isomorphic(P3,P3cc))
- nullcc=complement(complement(null))
- assert_true(is_isomorphic(null,nullcc))
- b=bull_graph()
- bcc=complement(complement(b))
- assert_true(is_isomorphic(b,bcc))
-
-def test_complement_2():
- G1=nx.DiGraph()
- G1.add_edge('A','B')
- G1.add_edge('A','C')
- G1.add_edge('A','D')
- G1C=complement(G1)
- assert_equal(sorted(G1C.edges()),
- [('B', 'A'), ('B', 'C'),
- ('B', 'D'), ('C', 'A'), ('C', 'B'),
- ('C', 'D'), ('D', 'A'), ('D', 'B'), ('D', 'C')])
-
-def test_reverse1():
- # Other tests for reverse are done by the DiGraph and MultiDigraph.
- G1=nx.Graph()
- assert_raises(nx.NetworkXError, nx.reverse, G1)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/unary.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/unary.py
deleted file mode 100644
index fbbb31e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/unary.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Unary operations on graphs"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import is_string_like
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-__all__ = ['complement', 'reverse']
-
-def complement(G, name=None):
- """Return the graph complement of G.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- name : string
- Specify name for new graph
-
- Returns
- -------
- GC : A new graph.
-
- Notes
- ------
- Note that complement() does not create self-loops and also
- does not produce parallel edges for MultiGraphs.
-
- Graph, node, and edge data are not propagated to the new graph.
- """
- if name is None:
- name="complement(%s)"%(G.name)
- R=G.__class__()
- R.name=name
- R.add_nodes_from(G)
- R.add_edges_from( ((n,n2)
- for n,nbrs in G.adjacency_iter()
- for n2 in G if n2 not in nbrs
- if n != n2) )
- return R
-
-def reverse(G, copy=True):
- """Return the reverse directed graph of G.
-
- Parameters
- ----------
- G : directed graph
- A NetworkX directed graph
- copy : bool
- If True, then a new graph is returned. If False, then the graph is
- reversed in place.
-
- Returns
- -------
- H : directed graph
- The reversed G.
-
- """
- if not G.is_directed():
- raise nx.NetworkXError("Cannot reverse an undirected graph.")
- else:
- return G.reverse(copy=copy)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/richclub.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/richclub.py
deleted file mode 100644
index 5701806..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/richclub.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# -*- coding: utf-8 -*-
-import networkx as nx
-__author__ = """\n""".join(['Ben Edwards',
- 'Aric Hagberg <hagberg@lanl.gov>'])
-
-__all__ = ['rich_club_coefficient']
-
-def rich_club_coefficient(G, normalized=True, Q=100):
- """Return the rich-club coefficient of the graph G.
-
- The rich-club coefficient is the ratio, for every degree k, of the
- number of actual to the number of potential edges for nodes
- with degree greater than k:
-
- .. math::
-
- \\phi(k) = \\frac{2 Ek}{Nk(Nk-1)}
-
- where Nk is the number of nodes with degree larger than k, and Ek
- be the number of edges among those nodes.
-
- Parameters
- ----------
- G : NetworkX graph
- normalized : bool (optional)
- Normalize using randomized network (see [1]_)
- Q : float (optional, default=100)
- If normalized=True build a random network by performing
- Q*M double-edge swaps, where M is the number of edges in G,
- to use as a null-model for normalization.
-
- Returns
- -------
- rc : dictionary
- A dictionary, keyed by degree, with rich club coefficient values.
-
- Examples
- --------
- >>> G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])
- >>> rc = nx.rich_club_coefficient(G,normalized=False)
- >>> rc[0] # doctest: +SKIP
- 0.4
-
- Notes
- ------
- The rich club definition and algorithm are found in [1]_. This
- algorithm ignores any edge weights and is not defined for directed
- graphs or graphs with parallel edges or self loops.
-
- Estimates for appropriate values of Q are found in [2]_.
-
- References
- ----------
- .. [1] Julian J. McAuley, Luciano da Fontoura Costa, and Tibério S. Caetano,
- "The rich-club phenomenon across complex network hierarchies",
- Applied Physics Letters Vol 91 Issue 8, August 2007.
- http://arxiv.org/abs/physics/0701290
- .. [2] R. Milo, N. Kashtan, S. Itzkovitz, M. E. J. Newman, U. Alon,
- "Uniform generation of random graphs with arbitrary degree
- sequences", 2006. http://arxiv.org/abs/cond-mat/0312028
- """
- if G.is_multigraph() or G.is_directed():
- raise Exception('rich_club_coefficient is not implemented for ',
- 'directed or multiedge graphs.')
- if len(G.selfloop_edges()) > 0:
- raise Exception('rich_club_coefficient is not implemented for ',
- 'graphs with self loops.')
- rc=_compute_rc(G)
- if normalized:
- # make R a copy of G, randomize with Q*|E| double edge swaps
- # and use rich_club coefficient of R to normalize
- R = G.copy()
- E = R.number_of_edges()
- nx.double_edge_swap(R,Q*E,max_tries=Q*E*10)
- rcran=_compute_rc(R)
- for d in rc:
-# if rcran[d] > 0:
- rc[d]/=rcran[d]
- return rc
-
-
-def _compute_rc(G):
- # compute rich club coefficient for all k degrees in G
- deghist = nx.degree_histogram(G)
- total = sum(deghist)
- # number of nodes with degree > k (omit last entry which is zero)
- nks = [total-cs for cs in nx.utils.cumulative_sum(deghist) if total-cs > 1]
- deg=G.degree()
- edge_degrees=sorted(sorted((deg[u],deg[v])) for u,v in G.edges_iter())
- ek=G.number_of_edges()
- k1,k2=edge_degrees.pop(0)
- rc={}
- for d,nk in zip(range(len(nks)),nks):
- while k1 <= d:
- if len(edge_degrees)==0:
- break
- k1,k2=edge_degrees.pop(0)
- ek-=1
- rc[d] = 2.0*ek/(nk*(nk-1))
- return rc
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/__init__.py
deleted file mode 100644
index 64846eb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from networkx.algorithms.shortest_paths.generic import *
-from networkx.algorithms.shortest_paths.unweighted import *
-from networkx.algorithms.shortest_paths.weighted import *
-from networkx.algorithms.shortest_paths.astar import *
-from networkx.algorithms.shortest_paths.dense import *
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/astar.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/astar.py
deleted file mode 100644
index 7b25d64..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/astar.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Shortest paths and path lengths using A* ("A star") algorithm.
-"""
-
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-from heapq import heappush, heappop
-from networkx import NetworkXError
-import networkx as nx
-
-__author__ = "\n".join(["Salim Fadhley <salimfadhley@gmail.com>",
- "Matteo Dell'Amico <matteodellamico@gmail.com>"])
-__all__ = ['astar_path', 'astar_path_length']
-
-
-def astar_path(G, source, target, heuristic=None, weight='weight'):
- """Return a list of nodes in a shortest path between source and target
- using the A* ("A-star") algorithm.
-
- There may be more than one shortest path. This returns only one.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path
-
- target : node
- Ending node for path
-
- heuristic : function
- A function to evaluate the estimate of the distance
- from the a node to the target. The function takes
- two nodes arguments and must return a number.
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> print(nx.astar_path(G,0,4))
- [0, 1, 2, 3, 4]
- >>> G=nx.grid_graph(dim=[3,3]) # nodes are two-tuples (x,y)
- >>> def dist(a, b):
- ... (x1, y1) = a
- ... (x2, y2) = b
- ... return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
- >>> print(nx.astar_path(G,(0,0),(2,2),dist))
- [(0, 0), (0, 1), (1, 1), (1, 2), (2, 2)]
-
-
- See Also
- --------
- shortest_path, dijkstra_path
-
- """
- if G.is_multigraph():
- raise NetworkXError("astar_path() not implemented for Multi(Di)Graphs")
-
- if heuristic is None:
- # The default heuristic is h=0 - same as Dijkstra's algorithm
- def heuristic(u, v):
- return 0
- # The queue stores priority, node, cost to reach, and parent.
- # Uses Python heapq to keep in priority order.
- # Add each node's hash to the queue to prevent the underlying heap from
- # attempting to compare the nodes themselves. The hash breaks ties in the
- # priority and is guarenteed unique for all nodes in the graph.
- queue = [(0, hash(source), source, 0, None)]
-
- # Maps enqueued nodes to distance of discovered paths and the
- # computed heuristics to target. We avoid computing the heuristics
- # more than once and inserting the node into the queue too many times.
- enqueued = {}
- # Maps explored nodes to parent closest to the source.
- explored = {}
-
- while queue:
- # Pop the smallest item from queue.
- _, __, curnode, dist, parent = heappop(queue)
-
- if curnode == target:
- path = [curnode]
- node = parent
- while node is not None:
- path.append(node)
- node = explored[node]
- path.reverse()
- return path
-
- if curnode in explored:
- continue
-
- explored[curnode] = parent
-
- for neighbor, w in G[curnode].items():
- if neighbor in explored:
- continue
- ncost = dist + w.get(weight, 1)
- if neighbor in enqueued:
- qcost, h = enqueued[neighbor]
- # if qcost < ncost, a longer path to neighbor remains
- # enqueued. Removing it would need to filter the whole
- # queue, it's better just to leave it there and ignore
- # it when we visit the node a second time.
- if qcost <= ncost:
- continue
- else:
- h = heuristic(neighbor, target)
- enqueued[neighbor] = ncost, h
- heappush(queue, (ncost + h, hash(neighbor), neighbor,
- ncost, curnode))
-
- raise nx.NetworkXNoPath("Node %s not reachable from %s" % (source, target))
-
-
-def astar_path_length(G, source, target, heuristic=None, weight='weight'):
- """Return the length of the shortest path between source and target using
- the A* ("A-star") algorithm.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path
-
- target : node
- Ending node for path
-
- heuristic : function
- A function to evaluate the estimate of the distance
- from the a node to the target. The function takes
- two nodes arguments and must return a number.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- See Also
- --------
- astar_path
-
- """
- path = astar_path(G, source, target, heuristic, weight)
- return sum(G[u][v].get(weight, 1) for u, v in zip(path[:-1], path[1:]))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/dense.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/dense.py
deleted file mode 100644
index 4dccae6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/dense.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Floyd-Warshall algorithm for shortest paths.
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """Aric Hagberg <aric.hagberg@gmail.com>"""
-__all__ = ['floyd_warshall',
- 'floyd_warshall_predecessor_and_distance',
- 'floyd_warshall_numpy']
-
-def floyd_warshall_numpy(G, nodelist=None, weight='weight'):
- """Find all-pairs shortest path lengths using Floyd's algorithm.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nodelist : list, optional
- The rows and columns are ordered by the nodes in nodelist.
- If nodelist is None then the ordering is produced by G.nodes().
-
- weight: string, optional (default= 'weight')
- Edge data key corresponding to the edge weight.
-
- Returns
- -------
- distance : NumPy matrix
- A matrix of shortest path distances between nodes.
- If there is no path between to nodes the corresponding matrix entry
- will be Inf.
-
- Notes
- ------
- Floyd's algorithm is appropriate for finding shortest paths in
- dense graphs or graphs with negative weights when Dijkstra's
- algorithm fails. This algorithm can still fail if there are
- negative cycles. It has running time O(n^3) with running space of O(n^2).
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(\
- "to_numpy_matrix() requires numpy: http://scipy.org/ ")
- A = nx.to_numpy_matrix(G, nodelist=nodelist, multigraph_weight=min,
- weight=weight)
- n,m = A.shape
- I = np.identity(n)
- A[A==0] = np.inf # set zero entries to inf
- A[I==1] = 0 # except diagonal which should be zero
- for i in range(n):
- A = np.minimum(A, A[i,:] + A[:,i])
- return A
-
-def floyd_warshall_predecessor_and_distance(G, weight='weight'):
- """Find all-pairs shortest path lengths using Floyd's algorithm.
-
- Parameters
- ----------
- G : NetworkX graph
-
- weight: string, optional (default= 'weight')
- Edge data key corresponding to the edge weight.
-
- Returns
- -------
- predecessor,distance : dictionaries
- Dictionaries, keyed by source and target, of predecessors and distances
- in the shortest path.
-
- Notes
- ------
- Floyd's algorithm is appropriate for finding shortest paths
- in dense graphs or graphs with negative weights when Dijkstra's algorithm
- fails. This algorithm can still fail if there are negative cycles.
- It has running time O(n^3) with running space of O(n^2).
-
- See Also
- --------
- floyd_warshall
- floyd_warshall_numpy
- all_pairs_shortest_path
- all_pairs_shortest_path_length
- """
- from collections import defaultdict
- # dictionary-of-dictionaries representation for dist and pred
- # use some defaultdict magick here
- # for dist the default is the floating point inf value
- dist = defaultdict(lambda : defaultdict(lambda: float('inf')))
- for u in G:
- dist[u][u] = 0
- pred = defaultdict(dict)
- # initialize path distance dictionary to be the adjacency matrix
- # also set the distance to self to 0 (zero diagonal)
- undirected = not G.is_directed()
- for u,v,d in G.edges(data=True):
- e_weight = d.get(weight, 1.0)
- dist[u][v] = min(e_weight, dist[u][v])
- pred[u][v] = u
- if undirected:
- dist[v][u] = min(e_weight, dist[v][u])
- pred[v][u] = v
- for w in G:
- for u in G:
- for v in G:
- if dist[u][v] > dist[u][w] + dist[w][v]:
- dist[u][v] = dist[u][w] + dist[w][v]
- pred[u][v] = pred[w][v]
- return dict(pred),dict(dist)
-
-
-def floyd_warshall(G, weight='weight'):
- """Find all-pairs shortest path lengths using Floyd's algorithm.
-
- Parameters
- ----------
- G : NetworkX graph
-
- weight: string, optional (default= 'weight')
- Edge data key corresponding to the edge weight.
-
-
- Returns
- -------
- distance : dict
- A dictionary, keyed by source and target, of shortest paths distances
- between nodes.
-
- Notes
- ------
- Floyd's algorithm is appropriate for finding shortest paths
- in dense graphs or graphs with negative weights when Dijkstra's algorithm
- fails. This algorithm can still fail if there are negative cycles.
- It has running time O(n^3) with running space of O(n^2).
-
- See Also
- --------
- floyd_warshall_predecessor_and_distance
- floyd_warshall_numpy
- all_pairs_shortest_path
- all_pairs_shortest_path_length
- """
- # could make this its own function to reduce memory costs
- return floyd_warshall_predecessor_and_distance(G, weight=weight)[1]
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/generic.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/generic.py
deleted file mode 100644
index a337cbb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/generic.py
+++ /dev/null
@@ -1,392 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Compute the shortest paths and path lengths between nodes in the graph.
-
-These algorithms work with undirected and directed graphs.
-
-For directed graphs the paths can be computed in the reverse
-order by first flipping the edge orientation using R=G.reverse(copy=False).
-
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Sérgio Nery Simões <sergionery@gmail.com>'])
-__all__ = ['shortest_path', 'all_shortest_paths',
- 'shortest_path_length', 'average_shortest_path_length',
- 'has_path']
-
-def has_path(G, source, target):
- """Return True if G has a path from source to target, False otherwise.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path
-
- target : node
- Ending node for path
- """
- try:
- sp = nx.shortest_path(G,source, target)
- except nx.NetworkXNoPath:
- return False
- return True
-
-
-def shortest_path(G, source=None, target=None, weight=None):
- """Compute shortest paths in the graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node, optional
- Starting node for path.
- If not specified, compute shortest paths using all nodes as source nodes.
-
- target : node, optional
- Ending node for path.
- If not specified, compute shortest paths using all nodes as target nodes.
-
- weight : None or string, optional (default = None)
- If None, every edge has weight/distance/cost 1.
- If a string, use this edge attribute as the edge weight.
- Any edge attribute not present defaults to 1.
-
- Returns
- -------
- path: list or dictionary
- All returned paths include both the source and target in the path.
-
- If the source and target are both specified, return a single list
- of nodes in a shortest path from the source to the target.
-
- If only the source is specified, return a dictionary keyed by
- targets with a list of nodes in a shortest path from the source
- to one of the targets.
-
- If only the target is specified, return a dictionary keyed by
- sources with a list of nodes in a shortest path from one of the
- sources to the target.
-
- If neither the source nor target are specified return a dictionary
- of dictionaries with path[source][target]=[list of nodes in path].
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> print(nx.shortest_path(G,source=0,target=4))
- [0, 1, 2, 3, 4]
- >>> p=nx.shortest_path(G,source=0) # target not specified
- >>> p[4]
- [0, 1, 2, 3, 4]
- >>> p=nx.shortest_path(G,target=4) # source not specified
- >>> p[0]
- [0, 1, 2, 3, 4]
- >>> p=nx.shortest_path(G) # source,target not specified
- >>> p[0][4]
- [0, 1, 2, 3, 4]
-
- Notes
- -----
- There may be more than one shortest path between a source and target.
- This returns only one of them.
-
- For digraphs this returns a shortest directed path. To find paths in the
- reverse direction first use G.reverse(copy=False) to flip the edge
- orientation.
-
- See Also
- --------
- all_pairs_shortest_path()
- all_pairs_dijkstra_path()
- single_source_shortest_path()
- single_source_dijkstra_path()
- """
- if source is None:
- if target is None:
- ## Find paths between all pairs.
- if weight is None:
- paths=nx.all_pairs_shortest_path(G)
- else:
- paths=nx.all_pairs_dijkstra_path(G,weight=weight)
- else:
- ## Find paths from all nodes co-accessible to the target.
- directed = G.is_directed()
- if directed:
- G.reverse(copy=False)
-
- if weight is None:
- paths=nx.single_source_shortest_path(G,target)
- else:
- paths=nx.single_source_dijkstra_path(G,target,weight=weight)
-
- # Now flip the paths so they go from a source to the target.
- for target in paths:
- paths[target] = list(reversed(paths[target]))
-
- if directed:
- G.reverse(copy=False)
- else:
- if target is None:
- ## Find paths to all nodes accessible from the source.
- if weight is None:
- paths=nx.single_source_shortest_path(G,source)
- else:
- paths=nx.single_source_dijkstra_path(G,source,weight=weight)
- else:
- ## Find shortest source-target path.
- if weight is None:
- paths=nx.bidirectional_shortest_path(G,source,target)
- else:
- paths=nx.dijkstra_path(G,source,target,weight)
-
- return paths
-
-
-def shortest_path_length(G, source=None, target=None, weight=None):
- """Compute shortest path lengths in the graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node, optional
- Starting node for path.
- If not specified, compute shortest path lengths using all nodes as
- source nodes.
-
- target : node, optional
- Ending node for path.
- If not specified, compute shortest path lengths using all nodes as
- target nodes.
-
- weight : None or string, optional (default = None)
- If None, every edge has weight/distance/cost 1.
- If a string, use this edge attribute as the edge weight.
- Any edge attribute not present defaults to 1.
-
- Returns
- -------
- length: int or dictionary
- If the source and target are both specified, return the length of
- the shortest path from the source to the target.
-
- If only the source is specified, return a dictionary keyed by
- targets whose values are the lengths of the shortest path from the
- source to one of the targets.
-
- If only the target is specified, return a dictionary keyed by
- sources whose values are the lengths of the shortest path from one
- of the sources to the target.
-
- If neither the source nor target are specified return a dictionary
- of dictionaries with path[source][target]=L, where L is the length
- of the shortest path from source to target.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> print(nx.shortest_path_length(G,source=0,target=4))
- 4
- >>> p=nx.shortest_path_length(G,source=0) # target not specified
- >>> p[4]
- 4
- >>> p=nx.shortest_path_length(G,target=4) # source not specified
- >>> p[0]
- 4
- >>> p=nx.shortest_path_length(G) # source,target not specified
- >>> p[0][4]
- 4
-
- Notes
- -----
- The length of the path is always 1 less than the number of nodes involved
- in the path since the length measures the number of edges followed.
-
- For digraphs this returns the shortest directed path length. To find path
- lengths in the reverse direction use G.reverse(copy=False) first to flip
- the edge orientation.
-
- See Also
- --------
- all_pairs_shortest_path_length()
- all_pairs_dijkstra_path_length()
- single_source_shortest_path_length()
- single_source_dijkstra_path_length()
-
- """
- if source is None:
- if target is None:
- ## Find paths between all pairs.
- if weight is None:
- paths=nx.all_pairs_shortest_path_length(G)
- else:
- paths=nx.all_pairs_dijkstra_path_length(G, weight=weight)
- else:
- ## Find paths from all nodes co-accessible to the target.
- directed = G.is_directed()
- if directed:
- G.reverse(copy=False)
-
- if weight is None:
- paths=nx.single_source_shortest_path_length(G,target)
- else:
- paths=nx.single_source_dijkstra_path_length(G,target,
- weight=weight)
-
- if directed:
- G.reverse(copy=False)
- else:
- if target is None:
- ## Find paths to all nodes accessible from the source.
- if weight is None:
- paths=nx.single_source_shortest_path_length(G,source)
- else:
- paths=nx.single_source_dijkstra_path_length(G,source,weight=weight)
- else:
- ## Find shortest source-target path.
- if weight is None:
- p=nx.bidirectional_shortest_path(G,source,target)
- paths=len(p)-1
- else:
- paths=nx.dijkstra_path_length(G,source,target,weight)
- return paths
-
-
-def average_shortest_path_length(G, weight=None):
- r"""Return the average shortest path length.
-
- The average shortest path length is
-
- .. math::
-
- a =\sum_{s,t \in V} \frac{d(s, t)}{n(n-1)}
-
- where `V` is the set of nodes in `G`,
- `d(s, t)` is the shortest path from `s` to `t`,
- and `n` is the number of nodes in `G`.
-
- Parameters
- ----------
- G : NetworkX graph
-
- weight : None or string, optional (default = None)
- If None, every edge has weight/distance/cost 1.
- If a string, use this edge attribute as the edge weight.
- Any edge attribute not present defaults to 1.
-
- Raises
- ------
- NetworkXError:
- if the graph is not connected.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> print(nx.average_shortest_path_length(G))
- 2.0
-
- For disconnected graphs you can compute the average shortest path
- length for each component:
- >>> G=nx.Graph([(1,2),(3,4)])
- >>> for g in nx.connected_component_subgraphs(G):
- ... print(nx.average_shortest_path_length(g))
- 1.0
- 1.0
-
- """
- if G.is_directed():
- if not nx.is_weakly_connected(G):
- raise nx.NetworkXError("Graph is not connected.")
- else:
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph is not connected.")
- avg=0.0
- if weight is None:
- for node in G:
- path_length=nx.single_source_shortest_path_length(G, node)
- avg += sum(path_length.values())
- else:
- for node in G:
- path_length=nx.single_source_dijkstra_path_length(G, node, weight=weight)
- avg += sum(path_length.values())
- n=len(G)
- return avg/(n*(n-1))
-
-
-def all_shortest_paths(G, source, target, weight=None):
- """Compute all shortest paths in the graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path.
-
- target : node
- Ending node for path.
-
- weight : None or string, optional (default = None)
- If None, every edge has weight/distance/cost 1.
- If a string, use this edge attribute as the edge weight.
- Any edge attribute not present defaults to 1.
-
- Returns
- -------
- paths: generator of lists
- A generator of all paths between source and target.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_path([0,1,2])
- >>> G.add_path([0,10,2])
- >>> print([p for p in nx.all_shortest_paths(G,source=0,target=2)])
- [[0, 1, 2], [0, 10, 2]]
-
- Notes
- -----
- There may be many shortest paths between the source and target.
-
- See Also
- --------
- shortest_path()
- single_source_shortest_path()
- all_pairs_shortest_path()
- """
- if weight is not None:
- pred,dist = nx.dijkstra_predecessor_and_distance(G,source,weight=weight)
- else:
- pred = nx.predecessor(G,source)
- if target not in pred:
- raise nx.NetworkXNoPath()
- stack = [[target,0]]
- top = 0
- while top >= 0:
- node,i = stack[top]
- if node == source:
- yield [p for p,n in reversed(stack[:top+1])]
- if len(pred[node]) > i:
- top += 1
- if top == len(stack):
- stack.append([pred[node][i],0])
- else:
- stack[top] = [pred[node][i],0]
- else:
- stack[top-1][1] += 1
- top -= 1
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_astar.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_astar.py
deleted file mode 100644
index 81ba6ab..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_astar.py
+++ /dev/null
@@ -1,137 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from random import random, choice
-
-class TestAStar:
-
- def setUp(self):
- self.XG=nx.DiGraph()
- self.XG.add_edges_from([('s','u',{'weight':10}),
- ('s','x',{'weight':5}),
- ('u','v',{'weight':1}),
- ('u','x',{'weight':2}),
- ('v','y',{'weight':1}),
- ('x','u',{'weight':3}),
- ('x','v',{'weight':5}),
- ('x','y',{'weight':2}),
- ('y','s',{'weight':7}),
- ('y','v',{'weight':6})])
-
- def test_random_graph(self):
-
- def dist(a, b):
- (x1, y1) = a
- (x2, y2) = b
- return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
-
- G = nx.Graph()
-
- points = [(random(), random()) for _ in range(100)]
-
- # Build a path from points[0] to points[-1] to be sure it exists
- for p1, p2 in zip(points[:-1], points[1:]):
- G.add_edge(p1, p2, weight=dist(p1, p2))
-
- # Add other random edges
- for _ in range(100):
- p1, p2 = choice(points), choice(points)
- G.add_edge(p1, p2, weight=dist(p1, p2))
-
- path = nx.astar_path(G, points[0], points[-1], dist)
- assert path == nx.dijkstra_path(G, points[0], points[-1])
-
- def test_astar_directed(self):
- assert nx.astar_path(self.XG,'s','v')==['s', 'x', 'u', 'v']
- assert nx.astar_path_length(self.XG,'s','v')==9
-
- def test_astar_multigraph(self):
- G=nx.MultiDiGraph(self.XG)
- assert_raises((TypeError,nx.NetworkXError),
- nx.astar_path, [G,'s','v'])
- assert_raises((TypeError,nx.NetworkXError),
- nx.astar_path_length, [G,'s','v'])
-
- def test_astar_undirected(self):
- GG=self.XG.to_undirected()
- # make sure we get lower weight
- # to_undirected might choose either edge with weight 2 or weight 3
- GG['u']['x']['weight']=2
- GG['y']['v']['weight'] = 2
- assert_equal(nx.astar_path(GG,'s','v'),['s', 'x', 'u', 'v'])
- assert_equal(nx.astar_path_length(GG,'s','v'),8)
-
- def test_astar_directed2(self):
- XG2=nx.DiGraph()
- XG2.add_edges_from([[1,4,{'weight':1}],
- [4,5,{'weight':1}],
- [5,6,{'weight':1}],
- [6,3,{'weight':1}],
- [1,3,{'weight':50}],
- [1,2,{'weight':100}],
- [2,3,{'weight':100}]])
- assert nx.astar_path(XG2,1,3)==[1, 4, 5, 6, 3]
-
- def test_astar_undirected2(self):
- XG3=nx.Graph()
- XG3.add_edges_from([ [0,1,{'weight':2}],
- [1,2,{'weight':12}],
- [2,3,{'weight':1}],
- [3,4,{'weight':5}],
- [4,5,{'weight':1}],
- [5,0,{'weight':10}] ])
- assert nx.astar_path(XG3,0,3)==[0, 1, 2, 3]
- assert nx.astar_path_length(XG3,0,3)==15
-
-
- def test_astar_undirected3(self):
- XG4=nx.Graph()
- XG4.add_edges_from([ [0,1,{'weight':2}],
- [1,2,{'weight':2}],
- [2,3,{'weight':1}],
- [3,4,{'weight':1}],
- [4,5,{'weight':1}],
- [5,6,{'weight':1}],
- [6,7,{'weight':1}],
- [7,0,{'weight':1}] ])
- assert nx.astar_path(XG4,0,2)==[0, 1, 2]
- assert nx.astar_path_length(XG4,0,2)==4
-
-
-# >>> MXG4=NX.MultiGraph(XG4)
-# >>> MXG4.add_edge(0,1,3)
-# >>> NX.dijkstra_path(MXG4,0,2)
-# [0, 1, 2]
-
- def test_astar_w1(self):
- G=nx.DiGraph()
- G.add_edges_from([('s','u'), ('s','x'), ('u','v'), ('u','x'),
- ('v','y'), ('x','u'), ('x','w'), ('w', 'v'), ('x','y'),
- ('y','s'), ('y','v')])
- assert nx.astar_path(G,'s','v')==['s', 'u', 'v']
- assert nx.astar_path_length(G,'s','v')== 2
-
- @raises(nx.NetworkXNoPath)
- def test_astar_nopath(self):
- p = nx.astar_path(self.XG,'s','moon')
-
- def test_cycle(self):
- C=nx.cycle_graph(7)
- assert nx.astar_path(C,0,3)==[0, 1, 2, 3]
- assert nx.dijkstra_path(C,0,4)==[0, 6, 5, 4]
-
-
- def test_orderable(self):
- class UnorderableClass: pass
- node_1 = UnorderableClass()
- node_2 = UnorderableClass()
- node_3 = UnorderableClass()
- node_4 = UnorderableClass()
- G = nx.Graph()
- G.add_edge(node_1, node_2)
- G.add_edge(node_1, node_3)
- G.add_edge(node_2, node_4)
- G.add_edge(node_3, node_4)
- path=nx.algorithms.shortest_paths.astar.astar_path(G, node_1, node_4)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_dense.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_dense.py
deleted file mode 100644
index 6c170da..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_dense.py
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-
-class TestFloyd:
- def setUp(self):
- pass
-
- def test_floyd_warshall_predecessor_and_distance(self):
- XG=nx.DiGraph()
- XG.add_weighted_edges_from([('s','u',10) ,('s','x',5) ,
- ('u','v',1) ,('u','x',2) ,
- ('v','y',1) ,('x','u',3) ,
- ('x','v',5) ,('x','y',2) ,
- ('y','s',7) ,('y','v',6)])
- path, dist =nx.floyd_warshall_predecessor_and_distance(XG)
- assert_equal(dist['s']['v'],9)
- assert_equal(path['s']['v'],'u')
- assert_equal(dist,
- {'y': {'y': 0, 'x': 12, 's': 7, 'u': 15, 'v': 6},
- 'x': {'y': 2, 'x': 0, 's': 9, 'u': 3, 'v': 4},
- 's': {'y': 7, 'x': 5, 's': 0, 'u': 8, 'v': 9},
- 'u': {'y': 2, 'x': 2, 's': 9, 'u': 0, 'v': 1},
- 'v': {'y': 1, 'x': 13, 's': 8, 'u': 16, 'v': 0}})
-
-
- GG=XG.to_undirected()
- # make sure we get lower weight
- # to_undirected might choose either edge with weight 2 or weight 3
- GG['u']['x']['weight']=2
- path, dist = nx.floyd_warshall_predecessor_and_distance(GG)
- assert_equal(dist['s']['v'],8)
- # skip this test, could be alternate path s-u-v
-# assert_equal(path['s']['v'],'y')
-
- G=nx.DiGraph() # no weights
- G.add_edges_from([('s','u'), ('s','x'),
- ('u','v'), ('u','x'),
- ('v','y'), ('x','u'),
- ('x','v'), ('x','y'),
- ('y','s'), ('y','v')])
- path, dist = nx.floyd_warshall_predecessor_and_distance(G)
- assert_equal(dist['s']['v'],2)
- # skip this test, could be alternate path s-u-v
- # assert_equal(path['s']['v'],'x')
-
- # alternate interface
- dist = nx.floyd_warshall(G)
- assert_equal(dist['s']['v'],2)
-
- def test_cycle(self):
- path, dist = nx.floyd_warshall_predecessor_and_distance(nx.cycle_graph(7))
- assert_equal(dist[0][3],3)
- assert_equal(path[0][3],2)
- assert_equal(dist[0][4],3)
-
- def test_weighted(self):
- XG3=nx.Graph()
- XG3.add_weighted_edges_from([ [0,1,2],[1,2,12],[2,3,1],
- [3,4,5],[4,5,1],[5,0,10] ])
- path, dist = nx.floyd_warshall_predecessor_and_distance(XG3)
- assert_equal(dist[0][3],15)
- assert_equal(path[0][3],2)
-
- def test_weighted2(self):
- XG4=nx.Graph()
- XG4.add_weighted_edges_from([ [0,1,2],[1,2,2],[2,3,1],
- [3,4,1],[4,5,1],[5,6,1],
- [6,7,1],[7,0,1] ])
- path, dist = nx.floyd_warshall_predecessor_and_distance(XG4)
- assert_equal(dist[0][2],4)
- assert_equal(path[0][2],1)
-
- def test_weight_parameter(self):
- XG4 = nx.Graph()
- XG4.add_edges_from([ (0, 1, {'heavy': 2}), (1, 2, {'heavy': 2}),
- (2, 3, {'heavy': 1}), (3, 4, {'heavy': 1}),
- (4, 5, {'heavy': 1}), (5, 6, {'heavy': 1}),
- (6, 7, {'heavy': 1}), (7, 0, {'heavy': 1}) ])
- path, dist = nx.floyd_warshall_predecessor_and_distance(XG4,
- weight='heavy')
- assert_equal(dist[0][2], 4)
- assert_equal(path[0][2], 1)
-
- def test_zero_distance(self):
- XG=nx.DiGraph()
- XG.add_weighted_edges_from([('s','u',10) ,('s','x',5) ,
- ('u','v',1) ,('u','x',2) ,
- ('v','y',1) ,('x','u',3) ,
- ('x','v',5) ,('x','y',2) ,
- ('y','s',7) ,('y','v',6)])
- path, dist =nx.floyd_warshall_predecessor_and_distance(XG)
-
- for u in XG:
- assert_equal(dist[u][u], 0)
-
- GG=XG.to_undirected()
- # make sure we get lower weight
- # to_undirected might choose either edge with weight 2 or weight 3
- GG['u']['x']['weight']=2
- path, dist = nx.floyd_warshall_predecessor_and_distance(GG)
-
- for u in GG:
- dist[u][u] = 0
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_dense_numpy.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_dense_numpy.py
deleted file mode 100644
index 2fa0b67..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_dense_numpy.py
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-
-class TestFloydNumpy(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global numpy
- global assert_equal
- global assert_almost_equal
- try:
- import numpy
- from numpy.testing import assert_equal,assert_almost_equal
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_cycle_numpy(self):
- dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
- assert_equal(dist[0,3],3)
- assert_equal(dist[0,4],3)
-
- def test_weighted_numpy(self):
- XG3=nx.Graph()
- XG3.add_weighted_edges_from([ [0,1,2],[1,2,12],[2,3,1],
- [3,4,5],[4,5,1],[5,0,10] ])
- dist = nx.floyd_warshall_numpy(XG3)
- assert_equal(dist[0,3],15)
-
- def test_weighted_numpy(self):
- XG4=nx.Graph()
- XG4.add_weighted_edges_from([ [0,1,2],[1,2,2],[2,3,1],
- [3,4,1],[4,5,1],[5,6,1],
- [6,7,1],[7,0,1] ])
- dist = nx.floyd_warshall_numpy(XG4)
- assert_equal(dist[0,2],4)
-
- def test_weight_parameter_numpy(self):
- XG4 = nx.Graph()
- XG4.add_edges_from([ (0, 1, {'heavy': 2}), (1, 2, {'heavy': 2}),
- (2, 3, {'heavy': 1}), (3, 4, {'heavy': 1}),
- (4, 5, {'heavy': 1}), (5, 6, {'heavy': 1}),
- (6, 7, {'heavy': 1}), (7, 0, {'heavy': 1}) ])
- dist = nx.floyd_warshall_numpy(XG4, weight='heavy')
- assert_equal(dist[0, 2], 4)
-
- def test_directed_cycle_numpy(self):
- G = nx.DiGraph()
- G.add_cycle([0,1,2,3])
- pred,dist = nx.floyd_warshall_predecessor_and_distance(G)
- D = nx.utils.dict_to_numpy_array(dist)
- assert_equal(nx.floyd_warshall_numpy(G),D)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_generic.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_generic.py
deleted file mode 100644
index edaa9f9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_generic.py
+++ /dev/null
@@ -1,145 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestGenericPath:
-
- def setUp(self):
- from networkx import convert_node_labels_to_integers as cnlti
- self.grid=cnlti(nx.grid_2d_graph(4,4),first_label=1,ordering="sorted")
- self.cycle=nx.cycle_graph(7)
- self.directed_cycle=nx.cycle_graph(7,create_using=nx.DiGraph())
-
-
- def test_shortest_path(self):
- assert_equal(nx.shortest_path(self.cycle,0,3),[0, 1, 2, 3])
- assert_equal(nx.shortest_path(self.cycle,0,4),[0, 6, 5, 4])
- assert_equal(nx.shortest_path(self.grid,1,12),[1, 2, 3, 4, 8, 12])
- assert_equal(nx.shortest_path(self.directed_cycle,0,3),[0, 1, 2, 3])
- # now with weights
- assert_equal(nx.shortest_path(self.cycle,0,3,weight='weight'),[0, 1, 2, 3])
- assert_equal(nx.shortest_path(self.cycle,0,4,weight='weight'),[0, 6, 5, 4])
- assert_equal(nx.shortest_path(self.grid,1,12,weight='weight'),[1, 2, 3, 4, 8, 12])
- assert_equal(nx.shortest_path(self.directed_cycle,0,3,weight='weight'),
- [0, 1, 2, 3])
-
- def test_shortest_path_target(self):
- sp = nx.shortest_path(nx.path_graph(3), target=1)
- assert_equal(sp, {0: [0, 1], 1: [1], 2: [2, 1]})
-
- def test_shortest_path_length(self):
- assert_equal(nx.shortest_path_length(self.cycle,0,3),3)
- assert_equal(nx.shortest_path_length(self.grid,1,12),5)
- assert_equal(nx.shortest_path_length(self.directed_cycle,0,4),4)
- # now with weights
- assert_equal(nx.shortest_path_length(self.cycle,0,3,weight='weight'),3)
- assert_equal(nx.shortest_path_length(self.grid,1,12,weight='weight'),5)
- assert_equal(nx.shortest_path_length(self.directed_cycle,0,4,weight='weight'),4)
-
- def test_shortest_path_length_target(self):
- sp = nx.shortest_path_length(nx.path_graph(3), target=1)
- assert_equal(sp[0], 1)
- assert_equal(sp[1], 0)
- assert_equal(sp[2], 1)
-
- def test_single_source_shortest_path(self):
- p=nx.shortest_path(self.cycle,0)
- assert_equal(p[3],[0,1,2,3])
- assert_equal(p,nx.single_source_shortest_path(self.cycle,0))
- p=nx.shortest_path(self.grid,1)
- assert_equal(p[12],[1, 2, 3, 4, 8, 12])
- # now with weights
- p=nx.shortest_path(self.cycle,0,weight='weight')
- assert_equal(p[3],[0,1,2,3])
- assert_equal(p,nx.single_source_dijkstra_path(self.cycle,0))
- p=nx.shortest_path(self.grid,1,weight='weight')
- assert_equal(p[12],[1, 2, 3, 4, 8, 12])
-
-
- def test_single_source_shortest_path_length(self):
- l=nx.shortest_path_length(self.cycle,0)
- assert_equal(l,{0:0,1:1,2:2,3:3,4:3,5:2,6:1})
- assert_equal(l,nx.single_source_shortest_path_length(self.cycle,0))
- l=nx.shortest_path_length(self.grid,1)
- assert_equal(l[16],6)
- # now with weights
- l=nx.shortest_path_length(self.cycle,0,weight='weight')
- assert_equal(l,{0:0,1:1,2:2,3:3,4:3,5:2,6:1})
- assert_equal(l,nx.single_source_dijkstra_path_length(self.cycle,0))
- l=nx.shortest_path_length(self.grid,1,weight='weight')
- assert_equal(l[16],6)
-
-
- def test_all_pairs_shortest_path(self):
- p=nx.shortest_path(self.cycle)
- assert_equal(p[0][3],[0,1,2,3])
- assert_equal(p,nx.all_pairs_shortest_path(self.cycle))
- p=nx.shortest_path(self.grid)
- assert_equal(p[1][12],[1, 2, 3, 4, 8, 12])
- # now with weights
- p=nx.shortest_path(self.cycle,weight='weight')
- assert_equal(p[0][3],[0,1,2,3])
- assert_equal(p,nx.all_pairs_dijkstra_path(self.cycle))
- p=nx.shortest_path(self.grid,weight='weight')
- assert_equal(p[1][12],[1, 2, 3, 4, 8, 12])
-
-
- def test_all_pairs_shortest_path_length(self):
- l=nx.shortest_path_length(self.cycle)
- assert_equal(l[0],{0:0,1:1,2:2,3:3,4:3,5:2,6:1})
- assert_equal(l,nx.all_pairs_shortest_path_length(self.cycle))
- l=nx.shortest_path_length(self.grid)
- assert_equal(l[1][16],6)
- # now with weights
- l=nx.shortest_path_length(self.cycle,weight='weight')
- assert_equal(l[0],{0:0,1:1,2:2,3:3,4:3,5:2,6:1})
- assert_equal(l,nx.all_pairs_dijkstra_path_length(self.cycle))
- l=nx.shortest_path_length(self.grid,weight='weight')
- assert_equal(l[1][16],6)
-
- def test_average_shortest_path(self):
- l=nx.average_shortest_path_length(self.cycle)
- assert_almost_equal(l,2)
- l=nx.average_shortest_path_length(nx.path_graph(5))
- assert_almost_equal(l,2)
-
-
- def test_weighted_average_shortest_path(self):
- G=nx.Graph()
- G.add_cycle(range(7),weight=2)
- l=nx.average_shortest_path_length(G,weight='weight')
- assert_almost_equal(l,4)
- G=nx.Graph()
- G.add_path(range(5),weight=2)
- l=nx.average_shortest_path_length(G,weight='weight')
- assert_almost_equal(l,4)
-
-
- def test_average_shortest_disconnected(self):
- g = nx.Graph()
- g.add_nodes_from(range(3))
- g.add_edge(0, 1)
- assert_raises(nx.NetworkXError,nx.average_shortest_path_length,g)
- g = g.to_directed()
- assert_raises(nx.NetworkXError,nx.average_shortest_path_length,g)
-
- def test_has_path(self):
- G = nx.Graph()
- G.add_path(range(3))
- G.add_path(range(3,5))
- assert_true(nx.has_path(G,0,2))
- assert_false(nx.has_path(G,0,4))
-
- def test_all_shortest_paths(self):
- G = nx.Graph()
- G.add_path([0,1,2,3])
- G.add_path([0,10,20,3])
- assert_equal([[0,1,2,3],[0,10,20,3]],
- sorted(nx.all_shortest_paths(G,0,3)))
-
- @raises(nx.NetworkXNoPath)
- def test_all_shortest_paths_raise(self):
- G = nx.Graph()
- G.add_path([0,1,2,3])
- G.add_node(4)
- paths = list(nx.all_shortest_paths(G,0,4))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_unweighted.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_unweighted.py
deleted file mode 100644
index fc2abfb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_unweighted.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestUnweightedPath:
-
- def setUp(self):
- from networkx import convert_node_labels_to_integers as cnlti
- self.grid=cnlti(nx.grid_2d_graph(4,4),first_label=1,ordering="sorted")
- self.cycle=nx.cycle_graph(7)
- self.directed_cycle=nx.cycle_graph(7,create_using=nx.DiGraph())
-
-
- def test_bidirectional_shortest_path(self):
- assert_equal(nx.bidirectional_shortest_path(self.cycle,0,3),
- [0, 1, 2, 3])
- assert_equal(nx.bidirectional_shortest_path(self.cycle,0,4),
- [0, 6, 5, 4])
- assert_equal(nx.bidirectional_shortest_path(self.grid,1,12),
- [1, 2, 3, 4, 8, 12])
- assert_equal(nx.bidirectional_shortest_path(self.directed_cycle,0,3),
- [0, 1, 2, 3])
-
- def test_shortest_path_length(self):
- assert_equal(nx.shortest_path_length(self.cycle,0,3),3)
- assert_equal(nx.shortest_path_length(self.grid,1,12),5)
- assert_equal(nx.shortest_path_length(self.directed_cycle,0,4),4)
- # now with weights
- assert_equal(nx.shortest_path_length(self.cycle,0,3,weight=True),3)
- assert_equal(nx.shortest_path_length(self.grid,1,12,weight=True),5)
- assert_equal(nx.shortest_path_length(self.directed_cycle,0,4,weight=True),4)
-
-
- def test_single_source_shortest_path(self):
- p=nx.single_source_shortest_path(self.cycle,0)
- assert_equal(p[3],[0,1,2,3])
- p=nx.single_source_shortest_path(self.cycle,0, cutoff=0)
- assert_equal(p,{0 : [0]})
-
- def test_single_source_shortest_path_length(self):
- assert_equal(nx.single_source_shortest_path_length(self.cycle,0),
- {0:0,1:1,2:2,3:3,4:3,5:2,6:1})
-
- def test_all_pairs_shortest_path(self):
- p=nx.all_pairs_shortest_path(self.cycle)
- assert_equal(p[0][3],[0,1,2,3])
- p=nx.all_pairs_shortest_path(self.grid)
- assert_equal(p[1][12],[1, 2, 3, 4, 8, 12])
-
- def test_all_pairs_shortest_path_length(self):
- l=nx.all_pairs_shortest_path_length(self.cycle)
- assert_equal(l[0],{0:0,1:1,2:2,3:3,4:3,5:2,6:1})
- l=nx.all_pairs_shortest_path_length(self.grid)
- assert_equal(l[1][16],6)
-
- def test_predecessor(self):
- G=nx.path_graph(4)
- assert_equal(nx.predecessor(G,0),{0: [], 1: [0], 2: [1], 3: [2]})
- assert_equal(nx.predecessor(G,0,3),[2])
- G=nx.grid_2d_graph(2,2)
- assert_equal(sorted(nx.predecessor(G,(0,0)).items()),
- [((0, 0), []), ((0, 1), [(0, 0)]),
- ((1, 0), [(0, 0)]), ((1, 1), [(0, 1), (1, 0)])])
-
- def test_predecessor_cutoff(self):
- G=nx.path_graph(4)
- p = nx.predecessor(G,0,3)
- assert_false(4 in p)
-
- def test_predecessor_target(self):
- G=nx.path_graph(4)
- p = nx.predecessor(G,0,3)
- assert_equal(p,[2])
- p = nx.predecessor(G,0,3,cutoff=2)
- assert_equal(p,[])
- p,s = nx.predecessor(G,0,3,return_seen=True)
- assert_equal(p,[2])
- assert_equal(s,3)
- p,s = nx.predecessor(G,0,3,cutoff=2,return_seen=True)
- assert_equal(p,[])
- assert_equal(s,-1)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_weighted.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_weighted.py
deleted file mode 100644
index c3998d4..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/tests/test_weighted.py
+++ /dev/null
@@ -1,246 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestWeightedPath:
-
- def setUp(self):
- from networkx import convert_node_labels_to_integers as cnlti
- self.grid=cnlti(nx.grid_2d_graph(4,4),first_label=1,ordering="sorted")
- self.cycle=nx.cycle_graph(7)
- self.directed_cycle=nx.cycle_graph(7,create_using=nx.DiGraph())
- self.XG=nx.DiGraph()
- self.XG.add_weighted_edges_from([('s','u',10) ,('s','x',5) ,
- ('u','v',1) ,('u','x',2) ,
- ('v','y',1) ,('x','u',3) ,
- ('x','v',5) ,('x','y',2) ,
- ('y','s',7) ,('y','v',6)])
- self.MXG=nx.MultiDiGraph(self.XG)
- self.MXG.add_edge('s','u',weight=15)
- self.XG2=nx.DiGraph()
- self.XG2.add_weighted_edges_from([[1,4,1],[4,5,1],
- [5,6,1],[6,3,1],
- [1,3,50],[1,2,100],[2,3,100]])
-
- self.XG3=nx.Graph()
- self.XG3.add_weighted_edges_from([ [0,1,2],[1,2,12],
- [2,3,1],[3,4,5],
- [4,5,1],[5,0,10] ])
-
- self.XG4=nx.Graph()
- self.XG4.add_weighted_edges_from([ [0,1,2],[1,2,2],
- [2,3,1],[3,4,1],
- [4,5,1],[5,6,1],
- [6,7,1],[7,0,1] ])
- self.MXG4=nx.MultiGraph(self.XG4)
- self.MXG4.add_edge(0,1,weight=3)
- self.G=nx.DiGraph() # no weights
- self.G.add_edges_from([('s','u'), ('s','x'),
- ('u','v'), ('u','x'),
- ('v','y'), ('x','u'),
- ('x','v'), ('x','y'),
- ('y','s'), ('y','v')])
-
- def test_dijkstra(self):
- (D,P)= nx.single_source_dijkstra(self.XG,'s')
- assert_equal(P['v'], ['s', 'x', 'u', 'v'])
- assert_equal(D['v'],9)
-
- assert_equal(nx.single_source_dijkstra_path(self.XG,'s')['v'],
- ['s', 'x', 'u', 'v'])
- assert_equal(nx.single_source_dijkstra_path_length(self.XG,'s')['v'],9)
-
- assert_equal(nx.single_source_dijkstra(self.XG,'s')[1]['v'],
- ['s', 'x', 'u', 'v'])
-
- assert_equal(nx.single_source_dijkstra_path(self.MXG,'s')['v'],
- ['s', 'x', 'u', 'v'])
-
- GG=self.XG.to_undirected()
- # make sure we get lower weight
- # to_undirected might choose either edge with weight 2 or weight 3
- GG['u']['x']['weight']=2
- (D,P)= nx.single_source_dijkstra(GG,'s')
- assert_equal(P['v'] , ['s', 'x', 'u', 'v'])
- assert_equal(D['v'],8) # uses lower weight of 2 on u<->x edge
- assert_equal(nx.dijkstra_path(GG,'s','v'), ['s', 'x', 'u', 'v'])
- assert_equal(nx.dijkstra_path_length(GG,'s','v'),8)
-
- assert_equal(nx.dijkstra_path(self.XG2,1,3), [1, 4, 5, 6, 3])
- assert_equal(nx.dijkstra_path(self.XG3,0,3), [0, 1, 2, 3])
- assert_equal(nx.dijkstra_path_length(self.XG3,0,3),15)
- assert_equal(nx.dijkstra_path(self.XG4,0,2), [0, 1, 2])
- assert_equal(nx.dijkstra_path_length(self.XG4,0,2), 4)
- assert_equal(nx.dijkstra_path(self.MXG4,0,2), [0, 1, 2])
- assert_equal(nx.single_source_dijkstra(self.G,'s','v')[1]['v'],
- ['s', 'u', 'v'])
- assert_equal(nx.single_source_dijkstra(self.G,'s')[1]['v'],
- ['s', 'u', 'v'])
-
- assert_equal(nx.dijkstra_path(self.G,'s','v'), ['s', 'u', 'v'])
- assert_equal(nx.dijkstra_path_length(self.G,'s','v'), 2)
-
- # NetworkXError: node s not reachable from moon
- assert_raises(nx.NetworkXNoPath,nx.dijkstra_path,self.G,'s','moon')
- assert_raises(nx.NetworkXNoPath,nx.dijkstra_path_length,self.G,'s','moon')
-
- assert_equal(nx.dijkstra_path(self.cycle,0,3),[0, 1, 2, 3])
- assert_equal(nx.dijkstra_path(self.cycle,0,4), [0, 6, 5, 4])
-
- assert_equal(nx.single_source_dijkstra(self.cycle,0,0),({0:0}, {0:[0]}) )
-
- def test_bidirectional_dijkstra(self):
- assert_equal(nx.bidirectional_dijkstra(self.XG, 's', 'v'),
- (9, ['s', 'x', 'u', 'v']))
- (dist,path) = nx.bidirectional_dijkstra(self.G,'s','v')
- assert_equal(dist,2)
- # skip this test, correct path could also be ['s','u','v']
-# assert_equal(nx.bidirectional_dijkstra(self.G,'s','v'),
-# (2, ['s', 'x', 'v']))
- assert_equal(nx.bidirectional_dijkstra(self.cycle,0,3),
- (3, [0, 1, 2, 3]))
- assert_equal(nx.bidirectional_dijkstra(self.cycle,0,4),
- (3, [0, 6, 5, 4]))
- assert_equal(nx.bidirectional_dijkstra(self.XG3,0,3),
- (15, [0, 1, 2, 3]))
- assert_equal(nx.bidirectional_dijkstra(self.XG4,0,2),
- (4, [0, 1, 2]))
-
- # need more tests here
- assert_equal(nx.dijkstra_path(self.XG,'s','v'),
- nx.single_source_dijkstra_path(self.XG,'s')['v'])
-
-
- @raises(nx.NetworkXNoPath)
- def test_bidirectional_dijkstra_no_path(self):
- G = nx.Graph()
- G.add_path([1,2,3])
- G.add_path([4,5,6])
- path = nx.bidirectional_dijkstra(G,1,6)
-
- def test_dijkstra_predecessor(self):
- G=nx.path_graph(4)
- assert_equal(nx.dijkstra_predecessor_and_distance(G,0),
- ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3}))
- G=nx.grid_2d_graph(2,2)
- pred,dist=nx.dijkstra_predecessor_and_distance(G,(0,0))
- assert_equal(sorted(pred.items()),
- [((0, 0), []), ((0, 1), [(0, 0)]),
- ((1, 0), [(0, 0)]), ((1, 1), [(0, 1), (1, 0)])])
- assert_equal(sorted(dist.items()),
- [((0, 0), 0), ((0, 1), 1), ((1, 0), 1), ((1, 1), 2)])
-
- XG=nx.DiGraph()
- XG.add_weighted_edges_from([('s','u',10) ,('s','x',5) ,
- ('u','v',1) ,('u','x',2) ,
- ('v','y',1) ,('x','u',3) ,
- ('x','v',5) ,('x','y',2) ,
- ('y','s',7) ,('y','v',6)])
- (P,D)= nx.dijkstra_predecessor_and_distance(XG,'s')
- assert_equal(P['v'],['u'])
- assert_equal(D['v'],9)
- (P,D)= nx.dijkstra_predecessor_and_distance(XG,'s',cutoff=8)
- assert_false('v' in D)
-
- def test_single_source_dijkstra_path_length(self):
- pl = nx.single_source_dijkstra_path_length
- assert_equal(pl(self.MXG4,0)[2], 4)
- spl = pl(self.MXG4,0,cutoff=2)
- assert_false(2 in spl)
-
- def test_bidirectional_dijkstra_multigraph(self):
- G = nx.MultiGraph()
- G.add_edge('a', 'b', weight=10)
- G.add_edge('a', 'b', weight=100)
- dp= nx.bidirectional_dijkstra(G, 'a', 'b')
- assert_equal(dp,(10, ['a', 'b']))
-
-
- def test_dijkstra_pred_distance_multigraph(self):
- G = nx.MultiGraph()
- G.add_edge('a', 'b', key='short',foo=5, weight=100)
- G.add_edge('a', 'b', key='long',bar=1, weight=110)
- p,d= nx.dijkstra_predecessor_and_distance(G, 'a')
- assert_equal(p,{'a': [], 'b': ['a']})
- assert_equal(d,{'a': 0, 'b': 100})
-
- def test_negative_edge_cycle(self):
- G = nx.cycle_graph(5, create_using = nx.DiGraph())
- assert_equal(nx.negative_edge_cycle(G), False)
- G.add_edge(8, 9, weight = -7)
- G.add_edge(9, 8, weight = 3)
- assert_equal(nx.negative_edge_cycle(G), True)
- assert_raises(ValueError,nx.single_source_dijkstra_path_length,G,8)
- assert_raises(ValueError,nx.single_source_dijkstra,G,8)
- assert_raises(ValueError,nx.dijkstra_predecessor_and_distance,G,8)
- G.add_edge(9,10)
- assert_raises(ValueError,nx.bidirectional_dijkstra,G,8,10)
-
- def test_bellman_ford(self):
- # single node graph
- G = nx.DiGraph()
- G.add_node(0)
- assert_equal(nx.bellman_ford(G, 0), ({0: None}, {0: 0}))
- assert_raises(KeyError, nx.bellman_ford, G, 1)
-
- # negative weight cycle
- G = nx.cycle_graph(5, create_using = nx.DiGraph())
- G.add_edge(1, 2, weight = -7)
- for i in range(5):
- assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, i)
- G = nx.cycle_graph(5) # undirected Graph
- G.add_edge(1, 2, weight = -3)
- for i in range(5):
- assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, i)
- # no negative cycle but negative weight
- G = nx.cycle_graph(5, create_using = nx.DiGraph())
- G.add_edge(1, 2, weight = -3)
- assert_equal(nx.bellman_ford(G, 0),
- ({0: None, 1: 0, 2: 1, 3: 2, 4: 3},
- {0: 0, 1: 1, 2: -2, 3: -1, 4: 0}))
-
- # not connected
- G = nx.complete_graph(6)
- G.add_edge(10, 11)
- G.add_edge(10, 12)
- assert_equal(nx.bellman_ford(G, 0),
- ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
- {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}))
-
- # not connected, with a component not containing the source that
- # contains a negative cost cycle.
- G = nx.complete_graph(6)
- G.add_edges_from([('A', 'B', {'load': 3}),
- ('B', 'C', {'load': -10}),
- ('C', 'A', {'load': 2})])
- assert_equal(nx.bellman_ford(G, 0, weight = 'load'),
- ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
- {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}))
-
- # multigraph
- P, D = nx.bellman_ford(self.MXG,'s')
- assert_equal(P['v'], 'u')
- assert_equal(D['v'], 9)
- P, D = nx.bellman_ford(self.MXG4, 0)
- assert_equal(P[2], 1)
- assert_equal(D[2], 4)
-
- # other tests
- (P,D)= nx.bellman_ford(self.XG,'s')
- assert_equal(P['v'], 'u')
- assert_equal(D['v'], 9)
-
- G=nx.path_graph(4)
- assert_equal(nx.bellman_ford(G,0),
- ({0: None, 1: 0, 2: 1, 3: 2}, {0: 0, 1: 1, 2: 2, 3: 3}))
- assert_equal(nx.bellman_ford(G, 3),
- ({0: 1, 1: 2, 2: 3, 3: None}, {0: 3, 1: 2, 2: 1, 3: 0}))
-
- G=nx.grid_2d_graph(2,2)
- pred,dist=nx.bellman_ford(G,(0,0))
- assert_equal(sorted(pred.items()),
- [((0, 0), None), ((0, 1), (0, 0)),
- ((1, 0), (0, 0)), ((1, 1), (0, 1))])
- assert_equal(sorted(dist.items()),
- [((0, 0), 0), ((0, 1), 1), ((1, 0), 1), ((1, 1), 2)])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/unweighted.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/unweighted.py
deleted file mode 100644
index b7ce18a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/unweighted.py
+++ /dev/null
@@ -1,359 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Shortest path algorithms for unweighted graphs.
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['bidirectional_shortest_path',
- 'single_source_shortest_path',
- 'single_source_shortest_path_length',
- 'all_pairs_shortest_path',
- 'all_pairs_shortest_path_length',
- 'predecessor']
-
-
-import networkx as nx
-
-def single_source_shortest_path_length(G,source,cutoff=None):
- """Compute the shortest path lengths from source to all reachable nodes.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path
-
- cutoff : integer, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- lengths : dictionary
- Dictionary of shortest path lengths keyed by target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> length=nx.single_source_shortest_path_length(G,0)
- >>> length[4]
- 4
- >>> print(length)
- {0: 0, 1: 1, 2: 2, 3: 3, 4: 4}
-
- See Also
- --------
- shortest_path_length
- """
- seen={} # level (number of hops) when seen in BFS
- level=0 # the current level
- nextlevel={source:1} # dict of nodes to check at next level
- while nextlevel:
- thislevel=nextlevel # advance to next level
- nextlevel={} # and start a new list (fringe)
- for v in thislevel:
- if v not in seen:
- seen[v]=level # set the level of vertex v
- nextlevel.update(G[v]) # add neighbors of v
- if (cutoff is not None and cutoff <= level): break
- level=level+1
- return seen # return all path lengths as dictionary
-
-
-def all_pairs_shortest_path_length(G,cutoff=None):
- """ Compute the shortest path lengths between all nodes in G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- cutoff : integer, optional
- depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- lengths : dictionary
- Dictionary of shortest path lengths keyed by source and target.
-
- Notes
- -----
- The dictionary returned only has keys for reachable node pairs.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> length=nx.all_pairs_shortest_path_length(G)
- >>> print(length[1][4])
- 3
- >>> length[1]
- {0: 1, 1: 0, 2: 1, 3: 2, 4: 3}
-
- """
- paths={}
- for n in G:
- paths[n]=single_source_shortest_path_length(G,n,cutoff=cutoff)
- return paths
-
-
-
-
-def bidirectional_shortest_path(G,source,target):
- """Return a list of nodes in a shortest path between source and target.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- starting node for path
-
- target : node label
- ending node for path
-
- Returns
- -------
- path: list
- List of nodes in a path from source to target.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- See Also
- --------
- shortest_path
-
- Notes
- -----
- This algorithm is used by shortest_path(G,source,target).
- """
- # call helper to do the real work
- results=_bidirectional_pred_succ(G,source,target)
- pred,succ,w=results
-
- # build path from pred+w+succ
- path=[]
- # from w to target
- while w is not None:
- path.append(w)
- w=succ[w]
- # from source to w
- w=pred[path[0]]
- while w is not None:
- path.insert(0,w)
- w=pred[w]
-
- return path
-
-def _bidirectional_pred_succ(G, source, target):
- """Bidirectional shortest path helper.
-
- Returns (pred,succ,w) where
- pred is a dictionary of predecessors from w to the source, and
- succ is a dictionary of successors from w to the target.
- """
- # does BFS from both source and target and meets in the middle
- if target == source:
- return ({target:None},{source:None},source)
-
- # handle either directed or undirected
- if G.is_directed():
- Gpred=G.predecessors_iter
- Gsucc=G.successors_iter
- else:
- Gpred=G.neighbors_iter
- Gsucc=G.neighbors_iter
-
- # predecesssor and successors in search
- pred={source:None}
- succ={target:None}
-
- # initialize fringes, start with forward
- forward_fringe=[source]
- reverse_fringe=[target]
-
- while forward_fringe and reverse_fringe:
- if len(forward_fringe) <= len(reverse_fringe):
- this_level=forward_fringe
- forward_fringe=[]
- for v in this_level:
- for w in Gsucc(v):
- if w not in pred:
- forward_fringe.append(w)
- pred[w]=v
- if w in succ: return pred,succ,w # found path
- else:
- this_level=reverse_fringe
- reverse_fringe=[]
- for v in this_level:
- for w in Gpred(v):
- if w not in succ:
- succ[w]=v
- reverse_fringe.append(w)
- if w in pred: return pred,succ,w # found path
-
- raise nx.NetworkXNoPath("No path between %s and %s." % (source, target))
-
-
-def single_source_shortest_path(G,source,cutoff=None):
- """Compute shortest path between source
- and all other nodes reachable from source.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- Starting node for path
-
- cutoff : integer, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- lengths : dictionary
- Dictionary, keyed by target, of shortest paths.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> path=nx.single_source_shortest_path(G,0)
- >>> path[4]
- [0, 1, 2, 3, 4]
-
- Notes
- -----
- The shortest path is not necessarily unique. So there can be multiple
- paths between the source and each target node, all of which have the
- same 'shortest' length. For each target node, this function returns
- only one of those paths.
-
- See Also
- --------
- shortest_path
- """
- level=0 # the current level
- nextlevel={source:1} # list of nodes to check at next level
- paths={source:[source]} # paths dictionary (paths to key from source)
- if cutoff==0:
- return paths
- while nextlevel:
- thislevel=nextlevel
- nextlevel={}
- for v in thislevel:
- for w in G[v]:
- if w not in paths:
- paths[w]=paths[v]+[w]
- nextlevel[w]=1
- level=level+1
- if (cutoff is not None and cutoff <= level): break
- return paths
-
-
-def all_pairs_shortest_path(G,cutoff=None):
- """ Compute shortest paths between all nodes.
-
- Parameters
- ----------
- G : NetworkX graph
-
- cutoff : integer, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- lengths : dictionary
- Dictionary, keyed by source and target, of shortest paths.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> path=nx.all_pairs_shortest_path(G)
- >>> print(path[0][4])
- [0, 1, 2, 3, 4]
-
- See Also
- --------
- floyd_warshall()
-
- """
- paths={}
- for n in G:
- paths[n]=single_source_shortest_path(G,n,cutoff=cutoff)
- return paths
-
-
-
-
-def predecessor(G,source,target=None,cutoff=None,return_seen=None):
- """ Returns dictionary of predecessors for the path from source to all nodes in G.
-
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- Starting node for path
-
- target : node label, optional
- Ending node for path. If provided only predecessors between
- source and target are returned
-
- cutoff : integer, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
-
- Returns
- -------
- pred : dictionary
- Dictionary, keyed by node, of predecessors in the shortest path.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> print(G.nodes())
- [0, 1, 2, 3]
- >>> nx.predecessor(G,0)
- {0: [], 1: [0], 2: [1], 3: [2]}
-
- """
- level=0 # the current level
- nextlevel=[source] # list of nodes to check at next level
- seen={source:level} # level (number of hops) when seen in BFS
- pred={source:[]} # predecessor dictionary
- while nextlevel:
- level=level+1
- thislevel=nextlevel
- nextlevel=[]
- for v in thislevel:
- for w in G[v]:
- if w not in seen:
- pred[w]=[v]
- seen[w]=level
- nextlevel.append(w)
- elif (seen[w]==level):# add v to predecessor list if it
- pred[w].append(v) # is at the correct level
- if (cutoff and cutoff <= level):
- break
-
- if target is not None:
- if return_seen:
- if not target in pred: return ([],-1) # No predecessor
- return (pred[target],seen[target])
- else:
- if not target in pred: return [] # No predecessor
- return pred[target]
- else:
- if return_seen:
- return (pred,seen)
- else:
- return pred
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/weighted.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/weighted.py
deleted file mode 100644
index 41757f9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/weighted.py
+++ /dev/null
@@ -1,765 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Shortest path algorithms for weighed graphs.
-"""
-__author__ = """\n""".join(['Aric Hagberg <hagberg@lanl.gov>',
- 'Loïc Séguin-C. <loicseguin@gmail.com>',
- 'Dan Schult <dschult@colgate.edu>'])
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['dijkstra_path',
- 'dijkstra_path_length',
- 'bidirectional_dijkstra',
- 'single_source_dijkstra',
- 'single_source_dijkstra_path',
- 'single_source_dijkstra_path_length',
- 'all_pairs_dijkstra_path',
- 'all_pairs_dijkstra_path_length',
- 'dijkstra_predecessor_and_distance',
- 'bellman_ford','negative_edge_cycle']
-
-import heapq
-import networkx as nx
-from networkx.utils import generate_unique_node
-
-def dijkstra_path(G, source, target, weight='weight'):
- """Returns the shortest path from source to target in a weighted graph G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node
-
- target : node
- Ending node
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- Returns
- -------
- path : list
- List of nodes in a shortest path.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> print(nx.dijkstra_path(G,0,4))
- [0, 1, 2, 3, 4]
-
- Notes
- ------
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- See Also
- --------
- bidirectional_dijkstra()
- """
- (length,path)=single_source_dijkstra(G, source, target=target,
- weight=weight)
- try:
- return path[target]
- except KeyError:
- raise nx.NetworkXNoPath("node %s not reachable from %s"%(source,target))
-
-
-def dijkstra_path_length(G, source, target, weight='weight'):
- """Returns the shortest path length from source to target
- in a weighted graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- starting node for path
-
- target : node label
- ending node for path
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- Returns
- -------
- length : number
- Shortest path length.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> print(nx.dijkstra_path_length(G,0,4))
- 4
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- See Also
- --------
- bidirectional_dijkstra()
- """
- length=single_source_dijkstra_path_length(G, source, weight=weight)
- try:
- return length[target]
- except KeyError:
- raise nx.NetworkXNoPath("node %s not reachable from %s"%(source,target))
-
-
-def single_source_dijkstra_path(G,source, cutoff=None, weight='weight'):
- """Compute shortest path between source and all other reachable
- nodes for a weighted graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path.
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- cutoff : integer or float, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- paths : dictionary
- Dictionary of shortest path lengths keyed by target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> path=nx.single_source_dijkstra_path(G,0)
- >>> path[4]
- [0, 1, 2, 3, 4]
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- See Also
- --------
- single_source_dijkstra()
-
- """
- (length,path)=single_source_dijkstra(G,source, cutoff = cutoff, weight = weight)
- return path
-
-
-def single_source_dijkstra_path_length(G, source, cutoff= None,
- weight= 'weight'):
- """Compute the shortest path length between source and all other
- reachable nodes for a weighted graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- Starting node for path
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight.
-
- cutoff : integer or float, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- length : dictionary
- Dictionary of shortest lengths keyed by target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> length=nx.single_source_dijkstra_path_length(G,0)
- >>> length[4]
- 4
- >>> print(length)
- {0: 0, 1: 1, 2: 2, 3: 3, 4: 4}
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- See Also
- --------
- single_source_dijkstra()
-
- """
- dist = {} # dictionary of final distances
- seen = {source:0}
- fringe=[] # use heapq with (distance,label) tuples
- heapq.heappush(fringe,(0,source))
- while fringe:
- (d,v)=heapq.heappop(fringe)
- if v in dist:
- continue # already searched this node.
- dist[v] = d
- #for ignore,w,edgedata in G.edges_iter(v,data=True):
- #is about 30% slower than the following
- if G.is_multigraph():
- edata=[]
- for w,keydata in G[v].items():
- minweight=min((dd.get(weight,1)
- for k,dd in keydata.items()))
- edata.append((w,{weight:minweight}))
- else:
- edata=iter(G[v].items())
-
- for w,edgedata in edata:
- vw_dist = dist[v] + edgedata.get(weight,1)
- if cutoff is not None:
- if vw_dist>cutoff:
- continue
- if w in dist:
- if vw_dist < dist[w]:
- raise ValueError('Contradictory paths found:',
- 'negative weights?')
- elif w not in seen or vw_dist < seen[w]:
- seen[w] = vw_dist
- heapq.heappush(fringe,(vw_dist,w))
- return dist
-
-
-def single_source_dijkstra(G,source,target=None,cutoff=None,weight='weight'):
- """Compute shortest paths and lengths in a weighted graph G.
-
- Uses Dijkstra's algorithm for shortest paths.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- Starting node for path
-
- target : node label, optional
- Ending node for path
-
- cutoff : integer or float, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- distance,path : dictionaries
- Returns a tuple of two dictionaries keyed by node.
- The first dictionary stores distance from the source.
- The second stores the path from the source to that node.
-
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> length,path=nx.single_source_dijkstra(G,0)
- >>> print(length[4])
- 4
- >>> print(length)
- {0: 0, 1: 1, 2: 2, 3: 3, 4: 4}
- >>> path[4]
- [0, 1, 2, 3, 4]
-
- Notes
- ---------
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- Based on the Python cookbook recipe (119466) at
- http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/119466
-
- This algorithm is not guaranteed to work if edge weights
- are negative or are floating point numbers
- (overflows and roundoff errors can cause problems).
-
- See Also
- --------
- single_source_dijkstra_path()
- single_source_dijkstra_path_length()
- """
- if source==target:
- return ({source:0}, {source:[source]})
- dist = {} # dictionary of final distances
- paths = {source:[source]} # dictionary of paths
- seen = {source:0}
- fringe=[] # use heapq with (distance,label) tuples
- heapq.heappush(fringe,(0,source))
- while fringe:
- (d,v)=heapq.heappop(fringe)
- if v in dist:
- continue # already searched this node.
- dist[v] = d
- if v == target:
- break
- #for ignore,w,edgedata in G.edges_iter(v,data=True):
- #is about 30% slower than the following
- if G.is_multigraph():
- edata=[]
- for w,keydata in G[v].items():
- minweight=min((dd.get(weight,1)
- for k,dd in keydata.items()))
- edata.append((w,{weight:minweight}))
- else:
- edata=iter(G[v].items())
-
- for w,edgedata in edata:
- vw_dist = dist[v] + edgedata.get(weight,1)
- if cutoff is not None:
- if vw_dist>cutoff:
- continue
- if w in dist:
- if vw_dist < dist[w]:
- raise ValueError('Contradictory paths found:',
- 'negative weights?')
- elif w not in seen or vw_dist < seen[w]:
- seen[w] = vw_dist
- heapq.heappush(fringe,(vw_dist,w))
- paths[w] = paths[v]+[w]
- return (dist,paths)
-
-
-def dijkstra_predecessor_and_distance(G,source, cutoff=None, weight='weight'):
- """Compute shortest path length and predecessors on shortest paths
- in weighted graphs.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node label
- Starting node for path
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- cutoff : integer or float, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- pred,distance : dictionaries
- Returns two dictionaries representing a list of predecessors
- of a node and the distance to each node.
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- The list of predecessors contains more than one element only when
- there are more than one shortest paths to the key node.
- """
- push=heapq.heappush
- pop=heapq.heappop
- dist = {} # dictionary of final distances
- pred = {source:[]} # dictionary of predecessors
- seen = {source:0}
- fringe=[] # use heapq with (distance,label) tuples
- push(fringe,(0,source))
- while fringe:
- (d,v)=pop(fringe)
- if v in dist: continue # already searched this node.
- dist[v] = d
- if G.is_multigraph():
- edata=[]
- for w,keydata in G[v].items():
- minweight=min((dd.get(weight,1)
- for k,dd in keydata.items()))
- edata.append((w,{weight:minweight}))
- else:
- edata=iter(G[v].items())
- for w,edgedata in edata:
- vw_dist = dist[v] + edgedata.get(weight,1)
- if cutoff is not None:
- if vw_dist>cutoff:
- continue
- if w in dist:
- if vw_dist < dist[w]:
- raise ValueError('Contradictory paths found:',
- 'negative weights?')
- elif w not in seen or vw_dist < seen[w]:
- seen[w] = vw_dist
- push(fringe,(vw_dist,w))
- pred[w] = [v]
- elif vw_dist==seen[w]:
- pred[w].append(v)
- return (pred,dist)
-
-
-def all_pairs_dijkstra_path_length(G, cutoff=None, weight='weight'):
- """ Compute shortest path lengths between all nodes in a weighted graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- cutoff : integer or float, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- distance : dictionary
- Dictionary, keyed by source and target, of shortest path lengths.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> length=nx.all_pairs_dijkstra_path_length(G)
- >>> print(length[1][4])
- 3
- >>> length[1]
- {0: 1, 1: 0, 2: 1, 3: 2, 4: 3}
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- The dictionary returned only has keys for reachable node pairs.
- """
- paths={}
- for n in G:
- paths[n]=single_source_dijkstra_path_length(G,n, cutoff=cutoff,
- weight=weight)
- return paths
-
-def all_pairs_dijkstra_path(G, cutoff=None, weight='weight'):
- """ Compute shortest paths between all nodes in a weighted graph.
-
- Parameters
- ----------
- G : NetworkX graph
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- cutoff : integer or float, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- distance : dictionary
- Dictionary, keyed by source and target, of shortest paths.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> path=nx.all_pairs_dijkstra_path(G)
- >>> print(path[0][4])
- [0, 1, 2, 3, 4]
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- See Also
- --------
- floyd_warshall()
-
- """
- paths={}
- for n in G:
- paths[n]=single_source_dijkstra_path(G, n, cutoff=cutoff,
- weight=weight)
- return paths
-
-def bellman_ford(G, source, weight = 'weight'):
- """Compute shortest path lengths and predecessors on shortest paths
- in weighted graphs.
-
- The algorithm has a running time of O(mn) where n is the number of
- nodes and m is the number of edges. It is slower than Dijkstra but
- can handle negative edge weights.
-
- Parameters
- ----------
- G : NetworkX graph
- The algorithm works for all types of graphs, including directed
- graphs and multigraphs.
-
- source: node label
- Starting node for path
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- Returns
- -------
- pred, dist : dictionaries
- Returns two dictionaries keyed by node to predecessor in the
- path and to the distance from the source respectively.
-
- Raises
- ------
- NetworkXUnbounded
- If the (di)graph contains a negative cost (di)cycle, the
- algorithm raises an exception to indicate the presence of the
- negative cost (di)cycle. Note: any negative weight edge in an
- undirected graph is a negative cost cycle.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.path_graph(5, create_using = nx.DiGraph())
- >>> pred, dist = nx.bellman_ford(G, 0)
- >>> pred
- {0: None, 1: 0, 2: 1, 3: 2, 4: 3}
- >>> dist
- {0: 0, 1: 1, 2: 2, 3: 3, 4: 4}
-
- >>> from nose.tools import assert_raises
- >>> G = nx.cycle_graph(5, create_using = nx.DiGraph())
- >>> G[1][2]['weight'] = -7
- >>> assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, 0)
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- The dictionaries returned only have keys for nodes reachable from
- the source.
-
- In the case where the (di)graph is not connected, if a component
- not containing the source contains a negative cost (di)cycle, it
- will not be detected.
-
- """
- if source not in G:
- raise KeyError("Node %s is not found in the graph"%source)
- numb_nodes = len(G)
-
- dist = {source: 0}
- pred = {source: None}
-
- if numb_nodes == 1:
- return pred, dist
-
- if G.is_multigraph():
- def get_weight(edge_dict):
- return min([eattr.get(weight,1) for eattr in edge_dict.values()])
- else:
- def get_weight(edge_dict):
- return edge_dict.get(weight,1)
-
- for i in range(numb_nodes):
- no_changes=True
- # Only need edges from nodes in dist b/c all others have dist==inf
- for u, dist_u in list(dist.items()): # get all edges from nodes in dist
- for v, edict in G[u].items(): # double loop handles undirected too
- dist_v = dist_u + get_weight(edict)
- if v not in dist or dist[v] > dist_v:
- dist[v] = dist_v
- pred[v] = u
- no_changes = False
- if no_changes:
- break
- else:
- raise nx.NetworkXUnbounded("Negative cost cycle detected.")
- return pred, dist
-
-def negative_edge_cycle(G, weight = 'weight'):
- """Return True if there exists a negative edge cycle anywhere in G.
-
- Parameters
- ----------
- G : NetworkX graph
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- Returns
- -------
- negative_cycle : bool
- True if a negative edge cycle exists, otherwise False.
-
- Examples
- --------
- >>> import networkx as nx
- >>> G = nx.cycle_graph(5, create_using = nx.DiGraph())
- >>> print(nx.negative_edge_cycle(G))
- False
- >>> G[1][2]['weight'] = -7
- >>> print(nx.negative_edge_cycle(G))
- True
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- This algorithm uses bellman_ford() but finds negative cycles
- on any component by first adding a new node connected to
- every node, and starting bellman_ford on that node. It then
- removes that extra node.
- """
- newnode = generate_unique_node()
- G.add_edges_from([ (newnode,n) for n in G])
-
- try:
- bellman_ford(G, newnode, weight)
- except nx.NetworkXUnbounded:
- G.remove_node(newnode)
- return True
- G.remove_node(newnode)
- return False
-
-
-def bidirectional_dijkstra(G, source, target, weight = 'weight'):
- """Dijkstra's algorithm for shortest paths using bidirectional search.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node.
-
- target : node
- Ending node.
-
- weight: string, optional (default='weight')
- Edge data key corresponding to the edge weight
-
- Returns
- -------
- length : number
- Shortest path length.
-
- Returns a tuple of two dictionaries keyed by node.
- The first dictionary stores distance from the source.
- The second stores the path from the source to that node.
-
- Raises
- ------
- NetworkXNoPath
- If no path exists between source and target.
-
- Examples
- --------
- >>> G=nx.path_graph(5)
- >>> length,path=nx.bidirectional_dijkstra(G,0,4)
- >>> print(length)
- 4
- >>> print(path)
- [0, 1, 2, 3, 4]
-
- Notes
- -----
- Edge weight attributes must be numerical.
- Distances are calculated as sums of weighted edges traversed.
-
- In practice bidirectional Dijkstra is much more than twice as fast as
- ordinary Dijkstra.
-
- Ordinary Dijkstra expands nodes in a sphere-like manner from the
- source. The radius of this sphere will eventually be the length
- of the shortest path. Bidirectional Dijkstra will expand nodes
- from both the source and the target, making two spheres of half
- this radius. Volume of the first sphere is pi*r*r while the
- others are 2*pi*r/2*r/2, making up half the volume.
-
- This algorithm is not guaranteed to work if edge weights
- are negative or are floating point numbers
- (overflows and roundoff errors can cause problems).
-
- See Also
- --------
- shortest_path
- shortest_path_length
- """
- if source == target: return (0, [source])
- #Init: Forward Backward
- dists = [{}, {}]# dictionary of final distances
- paths = [{source:[source]}, {target:[target]}] # dictionary of paths
- fringe = [[], []] #heap of (distance, node) tuples for extracting next node to expand
- seen = [{source:0}, {target:0} ]#dictionary of distances to nodes seen
- #initialize fringe heap
- heapq.heappush(fringe[0], (0, source))
- heapq.heappush(fringe[1], (0, target))
- #neighs for extracting correct neighbor information
- if G.is_directed():
- neighs = [G.successors_iter, G.predecessors_iter]
- else:
- neighs = [G.neighbors_iter, G.neighbors_iter]
- #variables to hold shortest discovered path
- #finaldist = 1e30000
- finalpath = []
- dir = 1
- while fringe[0] and fringe[1]:
- # choose direction
- # dir == 0 is forward direction and dir == 1 is back
- dir = 1-dir
- # extract closest to expand
- (dist, v )= heapq.heappop(fringe[dir])
- if v in dists[dir]:
- # Shortest path to v has already been found
- continue
- # update distance
- dists[dir][v] = dist #equal to seen[dir][v]
- if v in dists[1-dir]:
- # if we have scanned v in both directions we are done
- # we have now discovered the shortest path
- return (finaldist,finalpath)
-
- for w in neighs[dir](v):
- if(dir==0): #forward
- if G.is_multigraph():
- minweight=min((dd.get(weight,1)
- for k,dd in G[v][w].items()))
- else:
- minweight=G[v][w].get(weight,1)
- vwLength = dists[dir][v] + minweight #G[v][w].get(weight,1)
- else: #back, must remember to change v,w->w,v
- if G.is_multigraph():
- minweight=min((dd.get(weight,1)
- for k,dd in G[w][v].items()))
- else:
- minweight=G[w][v].get(weight,1)
- vwLength = dists[dir][v] + minweight #G[w][v].get(weight,1)
-
- if w in dists[dir]:
- if vwLength < dists[dir][w]:
- raise ValueError("Contradictory paths found: negative weights?")
- elif w not in seen[dir] or vwLength < seen[dir][w]:
- # relaxing
- seen[dir][w] = vwLength
- heapq.heappush(fringe[dir], (vwLength,w))
- paths[dir][w] = paths[dir][v]+[w]
- if w in seen[0] and w in seen[1]:
- #see if this path is better than than the already
- #discovered shortest path
- totaldist = seen[0][w] + seen[1][w]
- if finalpath == [] or finaldist > totaldist:
- finaldist = totaldist
- revpath = paths[1][w][:]
- revpath.reverse()
- finalpath = paths[0][w] + revpath[1:]
- raise nx.NetworkXNoPath("No path between %s and %s." % (source, target))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/simple_paths.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/simple_paths.py
deleted file mode 100644
index f72d4d2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/simple_paths.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (C) 2012 by
-# Sergio Nery Simoes <sergionery@gmail.com>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Sérgio Nery Simões <sergionery@gmail.com>',
- 'Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['all_simple_paths']
-
-def all_simple_paths(G, source, target, cutoff=None):
- """Generate all simple paths in the graph G from source to target.
-
- A simple path is a path with no repeated nodes.
-
- Parameters
- ----------
- G : NetworkX graph
-
- source : node
- Starting node for path
-
- target : node
- Ending node for path
-
- cutoff : integer, optional
- Depth to stop the search. Only paths of length <= cutoff are returned.
-
- Returns
- -------
- path_generator: generator
- A generator that produces lists of simple paths. If there are no paths
- between the source and target within the given cutoff the generator
- produces no output.
-
- Examples
- --------
- >>> G = nx.complete_graph(4)
- >>> for path in nx.all_simple_paths(G, source=0, target=3):
- ... print(path)
- ...
- [0, 1, 2, 3]
- [0, 1, 3]
- [0, 2, 1, 3]
- [0, 2, 3]
- [0, 3]
- >>> paths = nx.all_simple_paths(G, source=0, target=3, cutoff=2)
- >>> print(list(paths))
- [[0, 1, 3], [0, 2, 3], [0, 3]]
-
- Notes
- -----
- This algorithm uses a modified depth-first search to generate the
- paths [1]_. A single path can be found in `O(V+E)` time but the
- number of simple paths in a graph can be very large, e.g. `O(n!)` in
- the complete graph of order n.
-
- References
- ----------
- .. [1] R. Sedgewick, "Algorithms in C, Part 5: Graph Algorithms",
- Addison Wesley Professional, 3rd ed., 2001.
-
- See Also
- --------
- all_shortest_paths, shortest_path
- """
- if source not in G:
- raise nx.NetworkXError('source node %s not in graph'%source)
- if target not in G:
- raise nx.NetworkXError('target node %s not in graph'%target)
- if cutoff is None:
- cutoff = len(G)-1
- if G.is_multigraph():
- return _all_simple_paths_multigraph(G, source, target, cutoff=cutoff)
- else:
- return _all_simple_paths_graph(G, source, target, cutoff=cutoff)
-
-def _all_simple_paths_graph(G, source, target, cutoff=None):
- if cutoff < 1:
- return
- visited = [source]
- stack = [iter(G[source])]
- while stack:
- children = stack[-1]
- child = next(children, None)
- if child is None:
- stack.pop()
- visited.pop()
- elif len(visited) < cutoff:
- if child == target:
- yield visited + [target]
- elif child not in visited:
- visited.append(child)
- stack.append(iter(G[child]))
- else: #len(visited) == cutoff:
- if child == target or target in children:
- yield visited + [target]
- stack.pop()
- visited.pop()
-
-
-def _all_simple_paths_multigraph(G, source, target, cutoff=None):
- if cutoff < 1:
- return
- visited = [source]
- stack = [(v for u,v in G.edges(source))]
- while stack:
- children = stack[-1]
- child = next(children, None)
- if child is None:
- stack.pop()
- visited.pop()
- elif len(visited) < cutoff:
- if child == target:
- yield visited + [target]
- elif child not in visited:
- visited.append(child)
- stack.append((v for u,v in G.edges(child)))
- else: #len(visited) == cutoff:
- count = ([child]+list(children)).count(target)
- for i in range(count):
- yield visited + [target]
- stack.pop()
- visited.pop()
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/smetric.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/smetric.py
deleted file mode 100644
index 0e801bf..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/smetric.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import networkx as nx
-#from networkx.generators.smax import li_smax_graph
-
-def s_metric(G, normalized=True):
- """Return the s-metric of graph.
-
- The s-metric is defined as the sum of the products deg(u)*deg(v)
- for every edge (u,v) in G. If norm is provided construct the
- s-max graph and compute it's s_metric, and return the normalized
- s value
-
- Parameters
- ----------
- G : graph
- The graph used to compute the s-metric.
- normalized : bool (optional)
- Normalize the value.
-
- Returns
- -------
- s : float
- The s-metric of the graph.
-
- References
- ----------
- .. [1] Lun Li, David Alderson, John C. Doyle, and Walter Willinger,
- Towards a Theory of Scale-Free Graphs:
- Definition, Properties, and Implications (Extended Version), 2005.
- http://arxiv.org/abs/cond-mat/0501169
- """
- if normalized:
- raise nx.NetworkXError("Normalization not implemented")
-# Gmax = li_smax_graph(list(G.degree().values()))
-# return s_metric(G,normalized=False)/s_metric(Gmax,normalized=False)
-# else:
- return float(sum([G.degree(u)*G.degree(v) for (u,v) in G.edges_iter()]))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/swap.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/swap.py
deleted file mode 100644
index 33d882d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/swap.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Swap edges in a graph.
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import math
-import random
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult (dschult@colgate.edu)'
- 'Joel Miller (joel.c.miller.research@gmail.com)'
- 'Ben Edwards'])
-
-__all__ = ['double_edge_swap',
- 'connected_double_edge_swap']
-
-
-def double_edge_swap(G, nswap=1, max_tries=100):
- """Swap two edges in the graph while keeping the node degrees fixed.
-
- A double-edge swap removes two randomly chosen edges u-v and x-y
- and creates the new edges u-x and v-y::
-
- u--v u v
- becomes | |
- x--y x y
-
- If either the edge u-x or v-y already exist no swap is performed
- and another attempt is made to find a suitable edge pair.
-
- Parameters
- ----------
- G : graph
- An undirected graph
-
- nswap : integer (optional, default=1)
- Number of double-edge swaps to perform
-
- max_tries : integer (optional)
- Maximum number of attempts to swap edges
-
- Returns
- -------
- G : graph
- The graph after double edge swaps.
-
- Notes
- -----
- Does not enforce any connectivity constraints.
-
- The graph G is modified in place.
- """
- if G.is_directed():
- raise nx.NetworkXError(\
- "double_edge_swap() not defined for directed graphs.")
- if nswap>max_tries:
- raise nx.NetworkXError("Number of swaps > number of tries allowed.")
- if len(G) < 4:
- raise nx.NetworkXError("Graph has less than four nodes.")
- # Instead of choosing uniformly at random from a generated edge list,
- # this algorithm chooses nonuniformly from the set of nodes with
- # probability weighted by degree.
- n=0
- swapcount=0
- keys,degrees=zip(*G.degree().items()) # keys, degree
- cdf=nx.utils.cumulative_distribution(degrees) # cdf of degree
- while swapcount < nswap:
-# if random.random() < 0.5: continue # trick to avoid periodicities?
- # pick two random edges without creating edge list
- # choose source node indices from discrete distribution
- (ui,xi)=nx.utils.discrete_sequence(2,cdistribution=cdf)
- if ui==xi:
- continue # same source, skip
- u=keys[ui] # convert index to label
- x=keys[xi]
- # choose target uniformly from neighbors
- v=random.choice(list(G[u]))
- y=random.choice(list(G[x]))
- if v==y:
- continue # same target, skip
- if (x not in G[u]) and (y not in G[v]): # don't create parallel edges
- G.add_edge(u,x)
- G.add_edge(v,y)
- G.remove_edge(u,v)
- G.remove_edge(x,y)
- swapcount+=1
- if n >= max_tries:
- e=('Maximum number of swap attempts (%s) exceeded '%n +
- 'before desired swaps achieved (%s).'%nswap)
- raise nx.NetworkXAlgorithmError(e)
- n+=1
- return G
-
-def connected_double_edge_swap(G, nswap=1):
- """Attempt nswap double-edge swaps in the graph G.
-
- A double-edge swap removes two randomly chosen edges u-v and x-y
- and creates the new edges u-x and v-y::
-
- u--v u v
- becomes | |
- x--y x y
-
- If either the edge u-x or v-y already exist no swap is performed so
- the actual count of swapped edges is always <= nswap
-
- Parameters
- ----------
- G : graph
- An undirected graph
-
- nswap : integer (optional, default=1)
- Number of double-edge swaps to perform
-
- Returns
- -------
- G : int
- The number of successful swaps
-
- Notes
- -----
- The initial graph G must be connected, and the resulting graph is connected.
- The graph G is modified in place.
-
- References
- ----------
- .. [1] C. Gkantsidis and M. Mihail and E. Zegura,
- The Markov chain simulation method for generating connected
- power law random graphs, 2003.
- http://citeseer.ist.psu.edu/gkantsidis03markov.html
- """
- import math
- if not nx.is_connected(G):
- raise nx.NetworkXError("Graph not connected")
- if len(G) < 4:
- raise nx.NetworkXError("Graph has less than four nodes.")
- n=0
- swapcount=0
- deg=G.degree()
- dk=list(deg.keys()) # Label key for nodes
- cdf=nx.utils.cumulative_distribution(list(G.degree().values()))
- window=1
- while n < nswap:
- wcount=0
- swapped=[]
- while wcount < window and n < nswap:
- # Pick two random edges without creating edge list
- # Choose source nodes from discrete degree distribution
- (ui,xi)=nx.utils.discrete_sequence(2,cdistribution=cdf)
- if ui==xi:
- continue # same source, skip
- u=dk[ui] # convert index to label
- x=dk[xi]
- # Choose targets uniformly from neighbors
- v=random.choice(G.neighbors(u))
- y=random.choice(G.neighbors(x)) #
- if v==y: continue # same target, skip
- if (not G.has_edge(u,x)) and (not G.has_edge(v,y)):
- G.remove_edge(u,v)
- G.remove_edge(x,y)
- G.add_edge(u,x)
- G.add_edge(v,y)
- swapped.append((u,v,x,y))
- swapcount+=1
- n+=1
- wcount+=1
- if nx.is_connected(G):
- window+=1
- else:
- # not connected, undo changes from previous window, decrease window
- while swapped:
- (u,v,x,y)=swapped.pop()
- G.add_edge(u,v)
- G.add_edge(x,y)
- G.remove_edge(u,x)
- G.remove_edge(v,y)
- swapcount-=1
- window = int(math.ceil(float(window)/2))
- return swapcount
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_block.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_block.py
deleted file mode 100644
index 281e385..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_block.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-
-class TestBlock:
-
- def test_path(self):
- G=networkx.path_graph(6)
- partition=[[0,1],[2,3],[4,5]]
- M=networkx.blockmodel(G,partition)
- assert_equal(sorted(M.nodes()),[0,1,2])
- assert_equal(sorted(M.edges()),[(0,1),(1,2)])
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],1)
- assert_equal(M.node[n]['nnodes'],2)
- assert_equal(M.node[n]['density'],1.0)
-
- def test_multigraph_path(self):
- G=networkx.MultiGraph(networkx.path_graph(6))
- partition=[[0,1],[2,3],[4,5]]
- M=networkx.blockmodel(G,partition,multigraph=True)
- assert_equal(sorted(M.nodes()),[0,1,2])
- assert_equal(sorted(M.edges()),[(0,1),(1,2)])
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],1)
- assert_equal(M.node[n]['nnodes'],2)
- assert_equal(M.node[n]['density'],1.0)
-
- def test_directed_path(self):
- G = networkx.DiGraph()
- G.add_path(list(range(6)))
- partition=[[0,1],[2,3],[4,5]]
- M=networkx.blockmodel(G,partition)
- assert_equal(sorted(M.nodes()),[0,1,2])
- assert_equal(sorted(M.edges()),[(0,1),(1,2)])
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],1)
- assert_equal(M.node[n]['nnodes'],2)
- assert_equal(M.node[n]['density'],0.5)
-
- def test_directed_multigraph_path(self):
- G = networkx.MultiDiGraph()
- G.add_path(list(range(6)))
- partition=[[0,1],[2,3],[4,5]]
- M=networkx.blockmodel(G,partition,multigraph=True)
- assert_equal(sorted(M.nodes()),[0,1,2])
- assert_equal(sorted(M.edges()),[(0,1),(1,2)])
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],1)
- assert_equal(M.node[n]['nnodes'],2)
- assert_equal(M.node[n]['density'],0.5)
-
- @raises(networkx.NetworkXException)
- def test_overlapping(self):
- G=networkx.path_graph(6)
- partition=[[0,1,2],[2,3],[4,5]]
- M=networkx.blockmodel(G,partition)
-
- def test_weighted_path(self):
- G=networkx.path_graph(6)
- G[0][1]['weight']=1
- G[1][2]['weight']=2
- G[2][3]['weight']=3
- G[3][4]['weight']=4
- G[4][5]['weight']=5
- partition=[[0,1],[2,3],[4,5]]
- M=networkx.blockmodel(G,partition)
- assert_equal(sorted(M.nodes()),[0,1,2])
- assert_equal(sorted(M.edges()),[(0,1),(1,2)])
- assert_equal(M[0][1]['weight'],2)
- assert_equal(M[1][2]['weight'],4)
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],1)
- assert_equal(M.node[n]['nnodes'],2)
- assert_equal(M.node[n]['density'],1.0)
-
-
- def test_barbell(self):
- G=networkx.barbell_graph(3,0)
- partition=[[0,1,2],[3,4,5]]
- M=networkx.blockmodel(G,partition)
- assert_equal(sorted(M.nodes()),[0,1])
- assert_equal(sorted(M.edges()),[(0,1)])
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],3)
- assert_equal(M.node[n]['nnodes'],3)
- assert_equal(M.node[n]['density'],1.0)
-
- def test_barbell_plus(self):
- G=networkx.barbell_graph(3,0)
- G.add_edge(0,5) # add extra edge between bells
- partition=[[0,1,2],[3,4,5]]
- M=networkx.blockmodel(G,partition)
- assert_equal(sorted(M.nodes()),[0,1])
- assert_equal(sorted(M.edges()),[(0,1)])
- assert_equal(M[0][1]['weight'],2)
- for n in M.nodes():
- assert_equal(M.node[n]['nedges'],3)
- assert_equal(M.node[n]['nnodes'],3)
- assert_equal(M.node[n]['density'],1.0)
-
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_boundary.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_boundary.py
deleted file mode 100644
index 2b9e714..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_boundary.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx import convert_node_labels_to_integers as cnlti
-
-class TestBoundary:
-
- def setUp(self):
- self.null=nx.null_graph()
- self.P10=cnlti(nx.path_graph(10),first_label=1)
- self.K10=cnlti(nx.complete_graph(10),first_label=1)
-
- def test_null_node_boundary(self):
- """null graph has empty node boundaries"""
- null=self.null
- assert_equal(nx.node_boundary(null,[]),[])
- assert_equal(nx.node_boundary(null,[],[]),[])
- assert_equal(nx.node_boundary(null,[1,2,3]),[])
- assert_equal(nx.node_boundary(null,[1,2,3],[4,5,6]),[])
- assert_equal(nx.node_boundary(null,[1,2,3],[3,4,5]),[])
-
- def test_null_edge_boundary(self):
- """null graph has empty edge boundaries"""
- null=self.null
- assert_equal(nx.edge_boundary(null,[]),[])
- assert_equal(nx.edge_boundary(null,[],[]),[])
- assert_equal(nx.edge_boundary(null,[1,2,3]),[])
- assert_equal(nx.edge_boundary(null,[1,2,3],[4,5,6]),[])
- assert_equal(nx.edge_boundary(null,[1,2,3],[3,4,5]),[])
-
- def test_path_node_boundary(self):
- """Check node boundaries in path graph."""
- P10=self.P10
- assert_equal(nx.node_boundary(P10,[]),[])
- assert_equal(nx.node_boundary(P10,[],[]),[])
- assert_equal(nx.node_boundary(P10,[1,2,3]),[4])
- assert_equal(sorted(nx.node_boundary(P10,[4,5,6])),[3, 7])
- assert_equal(sorted(nx.node_boundary(P10,[3,4,5,6,7])),[2, 8])
- assert_equal(nx.node_boundary(P10,[8,9,10]),[7])
- assert_equal(sorted(nx.node_boundary(P10,[4,5,6],[9,10])),[])
-
- def test_path_edge_boundary(self):
- """Check edge boundaries in path graph."""
- P10=self.P10
-
- assert_equal(nx.edge_boundary(P10,[]),[])
- assert_equal(nx.edge_boundary(P10,[],[]),[])
- assert_equal(nx.edge_boundary(P10,[1,2,3]),[(3, 4)])
- assert_equal(sorted(nx.edge_boundary(P10,[4,5,6])),[(4, 3), (6, 7)])
- assert_equal(sorted(nx.edge_boundary(P10,[3,4,5,6,7])),[(3, 2), (7, 8)])
- assert_equal(nx.edge_boundary(P10,[8,9,10]),[(8, 7)])
- assert_equal(sorted(nx.edge_boundary(P10,[4,5,6],[9,10])),[])
- assert_equal(nx.edge_boundary(P10,[1,2,3],[3,4,5]) ,[(2, 3), (3, 4)])
-
-
- def test_k10_node_boundary(self):
- """Check node boundaries in K10"""
- K10=self.K10
-
- assert_equal(nx.node_boundary(K10,[]),[])
- assert_equal(nx.node_boundary(K10,[],[]),[])
- assert_equal(sorted(nx.node_boundary(K10,[1,2,3])),
- [4, 5, 6, 7, 8, 9, 10])
- assert_equal(sorted(nx.node_boundary(K10,[4,5,6])),
- [1, 2, 3, 7, 8, 9, 10])
- assert_equal(sorted(nx.node_boundary(K10,[3,4,5,6,7])),
- [1, 2, 8, 9, 10])
- assert_equal(nx.node_boundary(K10,[4,5,6],[]),[])
- assert_equal(nx.node_boundary(K10,K10),[])
- assert_equal(nx.node_boundary(K10,[1,2,3],[3,4,5]),[4, 5])
-
- def test_k10_edge_boundary(self):
- """Check edge boundaries in K10"""
- K10=self.K10
-
- assert_equal(nx.edge_boundary(K10,[]),[])
- assert_equal(nx.edge_boundary(K10,[],[]),[])
- assert_equal(len(nx.edge_boundary(K10,[1,2,3])),21)
- assert_equal(len(nx.edge_boundary(K10,[4,5,6,7])),24)
- assert_equal(len(nx.edge_boundary(K10,[3,4,5,6,7])),25)
- assert_equal(len(nx.edge_boundary(K10,[8,9,10])),21)
- assert_equal(sorted(nx.edge_boundary(K10,[4,5,6],[9,10])),
- [(4, 9), (4, 10), (5, 9), (5, 10), (6, 9), (6, 10)])
- assert_equal(nx.edge_boundary(K10,[1,2,3],[3,4,5]),
- [(1, 3), (1, 4), (1, 5), (2, 3), (2, 4),
- (2, 5), (3, 4), (3, 5)])
-
-
- def test_petersen(self):
- """Check boundaries in the petersen graph
-
- cheeger(G,k)=min(|bdy(S)|/|S| for |S|=k, 0<k<=|V(G)|/2)
- """
- from random import sample
- P=nx.petersen_graph()
- def cheeger(G,k):
- return min([float(len(nx.node_boundary(G,sample(G.nodes(),k))))/k
- for n in range(100)])
-
- assert_almost_equals(cheeger(P,1),3.00,places=2)
- assert_almost_equals(cheeger(P,2),2.00,places=2)
- assert_almost_equals(cheeger(P,3),1.67,places=2)
- assert_almost_equals(cheeger(P,4),1.00,places=2)
- assert_almost_equals(cheeger(P,5),0.80,places=2)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_clique.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_clique.py
deleted file mode 100644
index 892a67d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_clique.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-from networkx import convert_node_labels_to_integers as cnlti
-
-class TestCliques:
-
- def setUp(self):
- z=[3,4,3,4,2,4,2,1,1,1,1]
- self.G=cnlti(nx.generators.havel_hakimi_graph(z),first_label=1)
- self.cl=list(nx.find_cliques(self.G))
- H=nx.complete_graph(6)
- H=nx.relabel_nodes(H,dict( [(i,i+1) for i in range(6)]))
- H.remove_edges_from([(2,6),(2,5),(2,4),(1,3),(5,3)])
- self.H=H
-
- def test_find_cliques1(self):
- cl=list(nx.find_cliques(self.G))
- rcl=nx.find_cliques_recursive(self.G)
- assert_equal(sorted(map(sorted,cl)), sorted(map(sorted,rcl)))
- assert_equal(cl,
- [[2, 6, 1, 3], [2, 6, 4], [5, 4, 7], [8, 9], [10, 11]])
-
- def test_selfloops(self):
- self.G.add_edge(1,1)
- cl=list(nx.find_cliques(self.G))
- rcl=nx.find_cliques_recursive(self.G)
- assert_equal(sorted(map(sorted,cl)), sorted(map(sorted,rcl)))
- assert_equal(cl,
- [[2, 6, 1, 3], [2, 6, 4], [5, 4, 7], [8, 9], [10, 11]])
-
- def test_find_cliques2(self):
- hcl=list(nx.find_cliques(self.H))
- assert_equal(sorted(map(sorted,hcl)),
- [[1, 2], [1, 4, 5, 6], [2, 3], [3, 4, 6]])
-
- def test_clique_number(self):
- G=self.G
- assert_equal(nx.graph_clique_number(G),4)
- assert_equal(nx.graph_clique_number(G,cliques=self.cl),4)
-
- def test_number_of_cliques(self):
- G=self.G
- assert_equal(nx.graph_number_of_cliques(G),5)
- assert_equal(nx.graph_number_of_cliques(G,cliques=self.cl),5)
- assert_equal(nx.number_of_cliques(G,1),1)
- assert_equal(list(nx.number_of_cliques(G,[1]).values()),[1])
- assert_equal(list(nx.number_of_cliques(G,[1,2]).values()),[1, 2])
- assert_equal(nx.number_of_cliques(G,[1,2]),{1: 1, 2: 2})
- assert_equal(nx.number_of_cliques(G,2),2)
- assert_equal(nx.number_of_cliques(G),
- {1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
- 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
- assert_equal(nx.number_of_cliques(G,nodes=G.nodes()),
- {1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
- 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
- assert_equal(nx.number_of_cliques(G,nodes=[2,3,4]),
- {2: 2, 3: 1, 4: 2})
- assert_equal(nx.number_of_cliques(G,cliques=self.cl),
- {1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
- 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
- assert_equal(nx.number_of_cliques(G,G.nodes(),cliques=self.cl),
- {1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
- 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
-
-
-
- def test_node_clique_number(self):
- G=self.G
- assert_equal(nx.node_clique_number(G,1),4)
- assert_equal(list(nx.node_clique_number(G,[1]).values()),[4])
- assert_equal(list(nx.node_clique_number(G,[1,2]).values()),[4, 4])
- assert_equal(nx.node_clique_number(G,[1,2]),{1: 4, 2: 4})
- assert_equal(nx.node_clique_number(G,1),4)
- assert_equal(nx.node_clique_number(G),
- {1: 4, 2: 4, 3: 4, 4: 3, 5: 3, 6: 4,
- 7: 3, 8: 2, 9: 2, 10: 2, 11: 2})
- assert_equal(nx.node_clique_number(G,cliques=self.cl),
- {1: 4, 2: 4, 3: 4, 4: 3, 5: 3, 6: 4,
- 7: 3, 8: 2, 9: 2, 10: 2, 11: 2})
-
- def test_cliques_containing_node(self):
- G=self.G
- assert_equal(nx.cliques_containing_node(G,1),
- [[2, 6, 1, 3]])
- assert_equal(list(nx.cliques_containing_node(G,[1]).values()),
- [[[2, 6, 1, 3]]])
- assert_equal(list(nx.cliques_containing_node(G,[1,2]).values()),
- [[[2, 6, 1, 3]], [[2, 6, 1, 3], [2, 6, 4]]])
- assert_equal(nx.cliques_containing_node(G,[1,2]),
- {1: [[2, 6, 1, 3]], 2: [[2, 6, 1, 3], [2, 6, 4]]})
- assert_equal(nx.cliques_containing_node(G,1),
- [[2, 6, 1, 3]])
- assert_equal(nx.cliques_containing_node(G,2),
- [[2, 6, 1, 3], [2, 6, 4]])
- assert_equal(nx.cliques_containing_node(G,2,cliques=self.cl),
- [[2, 6, 1, 3], [2, 6, 4]])
- assert_equal(len(nx.cliques_containing_node(G)),11)
-
- def test_make_clique_bipartite(self):
- G=self.G
- B=nx.make_clique_bipartite(G)
- assert_equal(sorted(B.nodes()),
- [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
- H=nx.project_down(B)
- assert_equal(H.adj,G.adj)
- H1=nx.project_up(B)
- assert_equal(H1.nodes(),[1, 2, 3, 4, 5])
- H2=nx.make_max_clique_graph(G)
- assert_equal(H1.adj,H2.adj)
-
- @raises(nx.NetworkXNotImplemented)
- def test_directed(self):
- cliques=nx.find_cliques(nx.DiGraph())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_cluster.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_cluster.py
deleted file mode 100644
index 19c00ae..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_cluster.py
+++ /dev/null
@@ -1,195 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestTriangles:
-
- def test_empty(self):
- G = nx.Graph()
- assert_equal(list(nx.triangles(G).values()),[])
-
- def test_path(self):
- G = nx.path_graph(10)
- assert_equal(list(nx.triangles(G).values()),
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
- assert_equal(nx.triangles(G),
- {0: 0, 1: 0, 2: 0, 3: 0, 4: 0,
- 5: 0, 6: 0, 7: 0, 8: 0, 9: 0})
-
- def test_cubical(self):
- G = nx.cubical_graph()
- assert_equal(list(nx.triangles(G).values()),
- [0, 0, 0, 0, 0, 0, 0, 0])
- assert_equal(nx.triangles(G,1),0)
- assert_equal(list(nx.triangles(G,[1,2]).values()),[0, 0])
- assert_equal(nx.triangles(G,1),0)
- assert_equal(nx.triangles(G,[1,2]),{1: 0, 2: 0})
-
- def test_k5(self):
- G = nx.complete_graph(5)
- assert_equal(list(nx.triangles(G).values()),[6, 6, 6, 6, 6])
- assert_equal(sum(nx.triangles(G).values())/3.0,10)
- assert_equal(nx.triangles(G,1),6)
- G.remove_edge(1,2)
- assert_equal(list(nx.triangles(G).values()),[5, 3, 3, 5, 5])
- assert_equal(nx.triangles(G,1),3)
-
-
-class TestWeightedClustering:
-
- def test_clustering(self):
- G = nx.Graph()
- assert_equal(list(nx.clustering(G,weight='weight').values()),[])
- assert_equal(nx.clustering(G),{})
-
- def test_path(self):
- G = nx.path_graph(10)
- assert_equal(list(nx.clustering(G,weight='weight').values()),
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
- assert_equal(nx.clustering(G,weight='weight'),
- {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0,
- 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0})
-
- def test_cubical(self):
- G = nx.cubical_graph()
- assert_equal(list(nx.clustering(G,weight='weight').values()),
- [0, 0, 0, 0, 0, 0, 0, 0])
- assert_equal(nx.clustering(G,1),0)
- assert_equal(list(nx.clustering(G,[1,2],weight='weight').values()),[0, 0])
- assert_equal(nx.clustering(G,1,weight='weight'),0)
- assert_equal(nx.clustering(G,[1,2],weight='weight'),{1: 0, 2: 0})
-
- def test_k5(self):
- G = nx.complete_graph(5)
- assert_equal(list(nx.clustering(G,weight='weight').values()),[1, 1, 1, 1, 1])
- assert_equal(nx.average_clustering(G,weight='weight'),1)
- G.remove_edge(1,2)
- assert_equal(list(nx.clustering(G,weight='weight').values()),
- [5./6., 1.0, 1.0, 5./6., 5./6.])
- assert_equal(nx.clustering(G,[1,4],weight='weight'),{1: 1.0, 4: 0.83333333333333337})
-
-
- def test_triangle_and_edge(self):
- G=nx.Graph()
- G.add_cycle([0,1,2])
- G.add_edge(0,4,weight=2)
- assert_equal(nx.clustering(G)[0],1.0/3.0)
- assert_equal(nx.clustering(G,weight='weight')[0],1.0/6.0)
-
-class TestClustering:
-
- def test_clustering(self):
- G = nx.Graph()
- assert_equal(list(nx.clustering(G).values()),[])
- assert_equal(nx.clustering(G),{})
-
- def test_path(self):
- G = nx.path_graph(10)
- assert_equal(list(nx.clustering(G).values()),
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
- assert_equal(nx.clustering(G),
- {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0,
- 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0})
-
- def test_cubical(self):
- G = nx.cubical_graph()
- assert_equal(list(nx.clustering(G).values()),
- [0, 0, 0, 0, 0, 0, 0, 0])
- assert_equal(nx.clustering(G,1),0)
- assert_equal(list(nx.clustering(G,[1,2]).values()),[0, 0])
- assert_equal(nx.clustering(G,1),0)
- assert_equal(nx.clustering(G,[1,2]),{1: 0, 2: 0})
-
- def test_k5(self):
- G = nx.complete_graph(5)
- assert_equal(list(nx.clustering(G).values()),[1, 1, 1, 1, 1])
- assert_equal(nx.average_clustering(G),1)
- G.remove_edge(1,2)
- assert_equal(list(nx.clustering(G).values()),
- [5./6., 1.0, 1.0, 5./6., 5./6.])
- assert_equal(nx.clustering(G,[1,4]),{1: 1.0, 4: 0.83333333333333337})
-
-
-
-class TestTransitivity:
-
- def test_transitivity(self):
- G = nx.Graph()
- assert_equal(nx.transitivity(G),0.0)
-
- def test_path(self):
- G = nx.path_graph(10)
- assert_equal(nx.transitivity(G),0.0)
-
- def test_cubical(self):
- G = nx.cubical_graph()
- assert_equal(nx.transitivity(G),0.0)
-
- def test_k5(self):
- G = nx.complete_graph(5)
- assert_equal(nx.transitivity(G),1.0)
- G.remove_edge(1,2)
- assert_equal(nx.transitivity(G),0.875)
-
- # def test_clustering_transitivity(self):
- # # check that weighted average of clustering is transitivity
- # G = nx.complete_graph(5)
- # G.remove_edge(1,2)
- # t1=nx.transitivity(G)
- # (cluster_d2,weights)=nx.clustering(G,weights=True)
- # trans=[]
- # for v in G.nodes():
- # trans.append(cluster_d2[v]*weights[v])
- # t2=sum(trans)
- # assert_almost_equal(abs(t1-t2),0)
-
-class TestSquareClustering:
-
- def test_clustering(self):
- G = nx.Graph()
- assert_equal(list(nx.square_clustering(G).values()),[])
- assert_equal(nx.square_clustering(G),{})
-
- def test_path(self):
- G = nx.path_graph(10)
- assert_equal(list(nx.square_clustering(G).values()),
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
- assert_equal(nx.square_clustering(G),
- {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0,
- 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0})
-
- def test_cubical(self):
- G = nx.cubical_graph()
- assert_equal(list(nx.square_clustering(G).values()),
- [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
- assert_equal(list(nx.square_clustering(G,[1,2]).values()),[0.5, 0.5])
- assert_equal(nx.square_clustering(G,[1])[1],0.5)
- assert_equal(nx.square_clustering(G,[1,2]),{1: 0.5, 2: 0.5})
-
- def test_k5(self):
- G = nx.complete_graph(5)
- assert_equal(list(nx.square_clustering(G).values()),[1, 1, 1, 1, 1])
-
- def test_bipartite_k5(self):
- G = nx.complete_bipartite_graph(5,5)
- assert_equal(list(nx.square_clustering(G).values()),
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
-
- def test_lind_square_clustering(self):
- """Test C4 for figure 1 Lind et al (2005)"""
- G = nx.Graph([(1,2),(1,3),(1,6),(1,7),(2,4),(2,5),
- (3,4),(3,5),(6,7),(7,8),(6,8),(7,9),
- (7,10),(6,11),(6,12),(2,13),(2,14),(3,15),(3,16)])
- G1 = G.subgraph([1,2,3,4,5,13,14,15,16])
- G2 = G.subgraph([1,6,7,8,9,10,11,12])
- assert_equal(nx.square_clustering(G, [1])[1], 3/75.0)
- assert_equal(nx.square_clustering(G1, [1])[1], 2/6.0)
- assert_equal(nx.square_clustering(G2, [1])[1], 1/5.0)
-
-
-def test_average_clustering():
- G=nx.cycle_graph(3)
- G.add_edge(2,3)
- assert_equal(nx.average_clustering(G),(1+1+1/3.0)/4.0)
- assert_equal(nx.average_clustering(G,count_zeros=True),(1+1+1/3.0)/4.0)
- assert_equal(nx.average_clustering(G,count_zeros=False),(1+1+1/3.0)/3.0)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_core.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_core.py
deleted file mode 100644
index 48399ae..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_core.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestCore:
-
- def setUp(self):
- # G is the example graph in Figure 1 from Batagelj and
- # Zaversnik's paper titled An O(m) Algorithm for Cores
- # Decomposition of Networks, 2003,
- # http://arXiv.org/abs/cs/0310049. With nodes labeled as
- # shown, the 3-core is given by nodes 1-8, the 2-core by nodes
- # 9-16, the 1-core by nodes 17-20 and node 21 is in the
- # 0-core.
- t1=nx.convert_node_labels_to_integers(nx.tetrahedral_graph(),1)
- t2=nx.convert_node_labels_to_integers(t1,5)
- G=nx.union(t1,t2)
- G.add_edges_from( [(3,7), (2,11), (11,5), (11,12), (5,12), (12,19),
- (12,18), (3,9), (7,9), (7,10), (9,10), (9,20),
- (17,13), (13,14), (14,15), (15,16), (16,13)])
- G.add_node(21)
- self.G=G
-
- # Create the graph H resulting from the degree sequence
- # [0,1,2,2,2,2,3] when using the Havel-Hakimi algorithm.
-
- degseq=[0,1,2,2,2,2,3]
- H = nx.havel_hakimi_graph(degseq)
- mapping = {6:0, 0:1, 4:3, 5:6, 3:4, 1:2, 2:5 }
- self.H = nx.relabel_nodes(H, mapping)
-
- def test_trivial(self):
- """Empty graph"""
- G = nx.Graph()
- assert_equal(nx.find_cores(G),{})
-
- def test_find_cores(self):
- cores=nx.find_cores(self.G)
- nodes_by_core=[]
- for val in [0,1,2,3]:
- nodes_by_core.append( sorted([k for k in cores if cores[k]==val]))
- assert_equal(nodes_by_core[0],[21])
- assert_equal(nodes_by_core[1],[17, 18, 19, 20])
- assert_equal(nodes_by_core[2],[9, 10, 11, 12, 13, 14, 15, 16])
- assert_equal(nodes_by_core[3], [1, 2, 3, 4, 5, 6, 7, 8])
-
- def test_core_number(self):
- # smoke test real name
- cores=nx.core_number(self.G)
-
- def test_find_cores2(self):
- cores=nx.find_cores(self.H)
- nodes_by_core=[]
- for val in [0,1,2]:
- nodes_by_core.append( sorted([k for k in cores if cores[k]==val]))
- assert_equal(nodes_by_core[0],[0])
- assert_equal(nodes_by_core[1],[1, 3])
- assert_equal(nodes_by_core[2],[2, 4, 5, 6])
-
- def test_main_core(self):
- main_core_subgraph=nx.k_core(self.H)
- assert_equal(sorted(main_core_subgraph.nodes()),[2,4,5,6])
-
- def test_k_core(self):
- # k=0
- k_core_subgraph=nx.k_core(self.H,k=0)
- assert_equal(sorted(k_core_subgraph.nodes()),sorted(self.H.nodes()))
- # k=1
- k_core_subgraph=nx.k_core(self.H,k=1)
- assert_equal(sorted(k_core_subgraph.nodes()),[1,2,3,4,5,6])
- # k=2
- k_core_subgraph=nx.k_core(self.H,k=2)
- assert_equal(sorted(k_core_subgraph.nodes()),[2,4,5,6])
-
- def test_main_crust(self):
- main_crust_subgraph=nx.k_crust(self.H)
- assert_equal(sorted(main_crust_subgraph.nodes()),[0,1,3])
-
- def test_k_crust(self):
- # k=0
- k_crust_subgraph=nx.k_crust(self.H,k=2)
- assert_equal(sorted(k_crust_subgraph.nodes()),sorted(self.H.nodes()))
- # k=1
- k_crust_subgraph=nx.k_crust(self.H,k=1)
- assert_equal(sorted(k_crust_subgraph.nodes()),[0,1,3])
- # k=2
- k_crust_subgraph=nx.k_crust(self.H,k=0)
- assert_equal(sorted(k_crust_subgraph.nodes()),[0])
-
- def test_main_shell(self):
- main_shell_subgraph=nx.k_shell(self.H)
- assert_equal(sorted(main_shell_subgraph.nodes()),[2,4,5,6])
-
- def test_k_shell(self):
- # k=0
- k_shell_subgraph=nx.k_shell(self.H,k=2)
- assert_equal(sorted(k_shell_subgraph.nodes()),[2,4,5,6])
- # k=1
- k_shell_subgraph=nx.k_shell(self.H,k=1)
- assert_equal(sorted(k_shell_subgraph.nodes()),[1,3])
- # k=2
- k_shell_subgraph=nx.k_shell(self.H,k=0)
- assert_equal(sorted(k_shell_subgraph.nodes()),[0])
-
- def test_k_corona(self):
- # k=0
- k_corona_subgraph=nx.k_corona(self.H,k=2)
- assert_equal(sorted(k_corona_subgraph.nodes()),[2,4,5,6])
- # k=1
- k_corona_subgraph=nx.k_corona(self.H,k=1)
- assert_equal(sorted(k_corona_subgraph.nodes()),[1])
- # k=2
- k_corona_subgraph=nx.k_corona(self.H,k=0)
- assert_equal(sorted(k_corona_subgraph.nodes()),[0])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_cycles.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_cycles.py
deleted file mode 100644
index e39bb87..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_cycles.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-import networkx as nx
-
-class TestCycles:
- def setUp(self):
- G=networkx.Graph()
- G.add_cycle([0,1,2,3])
- G.add_cycle([0,3,4,5])
- G.add_cycle([0,1,6,7,8])
- G.add_edge(8,9)
- self.G=G
-
- def is_cyclic_permutation(self,a,b):
- n=len(a)
- if len(b)!=n:
- return False
- l=a+a
- return any(l[i:i+n]==b for i in range(2*n-n+1))
-
- def test_cycle_basis(self):
- G=self.G
- cy=networkx.cycle_basis(G,0)
- sort_cy= sorted( sorted(c) for c in cy )
- assert_equal(sort_cy, [[0,1,2,3],[0,1,6,7,8],[0,3,4,5]])
- cy=networkx.cycle_basis(G,1)
- sort_cy= sorted( sorted(c) for c in cy )
- assert_equal(sort_cy, [[0,1,2,3],[0,1,6,7,8],[0,3,4,5]])
- cy=networkx.cycle_basis(G,9)
- sort_cy= sorted( sorted(c) for c in cy )
- assert_equal(sort_cy, [[0,1,2,3],[0,1,6,7,8],[0,3,4,5]])
- # test disconnected graphs
- G.add_cycle(list("ABC"))
- cy=networkx.cycle_basis(G,9)
- sort_cy= sorted(sorted(c) for c in cy[:-1]) + [sorted(cy[-1])]
- assert_equal(sort_cy, [[0,1,2,3],[0,1,6,7,8],[0,3,4,5],['A','B','C']])
-
- @raises(nx.NetworkXNotImplemented)
- def test_cycle_basis(self):
- G=nx.DiGraph()
- cy=networkx.cycle_basis(G,0)
-
- @raises(nx.NetworkXNotImplemented)
- def test_cycle_basis(self):
- G=nx.MultiGraph()
- cy=networkx.cycle_basis(G,0)
-
- def test_simple_cycles(self):
- G = nx.DiGraph([(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)])
- cc=sorted(nx.simple_cycles(G))
- ca=[[0], [0, 1, 2], [0, 2], [1, 2], [2]]
- for c in cc:
- assert_true(any(self.is_cyclic_permutation(c,rc) for rc in ca))
-
- @raises(nx.NetworkXNotImplemented)
- def test_simple_cycles_graph(self):
- G = nx.Graph()
- c = sorted(nx.simple_cycles(G))
-
- def test_unsortable(self):
- # TODO What does this test do? das 6/2013
- G=nx.DiGraph()
- G.add_cycle(['a',1])
- c=list(nx.simple_cycles(G))
-
- def test_simple_cycles_small(self):
- G = nx.DiGraph()
- G.add_cycle([1,2,3])
- c=sorted(nx.simple_cycles(G))
- assert_equal(len(c),1)
- assert_true(self.is_cyclic_permutation(c[0],[1,2,3]))
- G.add_cycle([10,20,30])
- cc=sorted(nx.simple_cycles(G))
- ca=[[1,2,3],[10,20,30]]
- for c in cc:
- assert_true(any(self.is_cyclic_permutation(c,rc) for rc in ca))
-
- def test_simple_cycles_empty(self):
- G = nx.DiGraph()
- assert_equal(list(nx.simple_cycles(G)),[])
-
- def test_complete_directed_graph(self):
- # see table 2 in Johnson's paper
- ncircuits=[1,5,20,84,409,2365,16064]
- for n,c in zip(range(2,9),ncircuits):
- G=nx.DiGraph(nx.complete_graph(n))
- assert_equal(len(list(nx.simple_cycles(G))),c)
-
- def worst_case_graph(self,k):
- # see figure 1 in Johnson's paper
- # this graph has excactly 3k simple cycles
- G=nx.DiGraph()
- for n in range(2,k+2):
- G.add_edge(1,n)
- G.add_edge(n,k+2)
- G.add_edge(2*k+1,1)
- for n in range(k+2,2*k+2):
- G.add_edge(n,2*k+2)
- G.add_edge(n,n+1)
- G.add_edge(2*k+3,k+2)
- for n in range(2*k+3,3*k+3):
- G.add_edge(2*k+2,n)
- G.add_edge(n,3*k+3)
- G.add_edge(3*k+3,2*k+2)
- return G
-
- def test_worst_case_graph(self):
- # see figure 1 in Johnson's paper
- for k in range(3,10):
- G=self.worst_case_graph(k)
- l=len(list(nx.simple_cycles(G)))
- assert_equal(l,3*k)
-
- def test_recursive_simple_and_not(self):
- for k in range(2,10):
- G=self.worst_case_graph(k)
- cc=sorted(nx.simple_cycles(G))
- rcc=sorted(nx.recursive_simple_cycles(G))
- assert_equal(len(cc),len(rcc))
- for c in cc:
- assert_true(any(self.is_cyclic_permutation(c,rc) for rc in rcc))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_dag.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_dag.py
deleted file mode 100644
index eb368af..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_dag.py
+++ /dev/null
@@ -1,163 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestDAG:
-
- def setUp(self):
- pass
-
- def test_topological_sort1(self):
- DG=nx.DiGraph()
- DG.add_edges_from([(1,2),(1,3),(2,3)])
- assert_equal(nx.topological_sort(DG),[1, 2, 3])
- assert_equal(nx.topological_sort_recursive(DG),[1, 2, 3])
-
- DG.add_edge(3,2)
- assert_raises(nx.NetworkXUnfeasible, nx.topological_sort, DG)
- assert_raises(nx.NetworkXUnfeasible, nx.topological_sort_recursive, DG)
-
- DG.remove_edge(2,3)
- assert_equal(nx.topological_sort(DG),[1, 3, 2])
- assert_equal(nx.topological_sort_recursive(DG),[1, 3, 2])
-
- def test_is_directed_acyclic_graph(self):
- G = nx.generators.complete_graph(2)
- assert_false(nx.is_directed_acyclic_graph(G))
- assert_false(nx.is_directed_acyclic_graph(G.to_directed()))
- assert_false(nx.is_directed_acyclic_graph(nx.Graph([(3, 4), (4, 5)])))
- assert_true(nx.is_directed_acyclic_graph(nx.DiGraph([(3, 4), (4, 5)])))
-
- def test_topological_sort2(self):
- DG=nx.DiGraph({1:[2],2:[3],3:[4],
- 4:[5],5:[1],11:[12],
- 12:[13],13:[14],14:[15]})
- assert_raises(nx.NetworkXUnfeasible, nx.topological_sort, DG)
- assert_raises(nx.NetworkXUnfeasible, nx.topological_sort_recursive, DG)
-
- assert_false(nx.is_directed_acyclic_graph(DG))
-
- DG.remove_edge(1,2)
- assert_equal(nx.topological_sort_recursive(DG),
- [11, 12, 13, 14, 15, 2, 3, 4, 5, 1])
- assert_equal(nx.topological_sort(DG),
- [11, 12, 13, 14, 15, 2, 3, 4, 5, 1])
- assert_true(nx.is_directed_acyclic_graph(DG))
-
- def test_topological_sort3(self):
- DG=nx.DiGraph()
- DG.add_edges_from([(1,i) for i in range(2,5)])
- DG.add_edges_from([(2,i) for i in range(5,9)])
- DG.add_edges_from([(6,i) for i in range(9,12)])
- DG.add_edges_from([(4,i) for i in range(12,15)])
- assert_equal(nx.topological_sort_recursive(DG),
- [1, 4, 14, 13, 12, 3, 2, 7, 6, 11, 10, 9, 5, 8])
- assert_equal(nx.topological_sort(DG),
- [1, 2, 8, 5, 6, 9, 10, 11, 7, 3, 4, 12, 13, 14])
-
- DG.add_edge(14,1)
- assert_raises(nx.NetworkXUnfeasible, nx.topological_sort, DG)
- assert_raises(nx.NetworkXUnfeasible, nx.topological_sort_recursive, DG)
-
- def test_topological_sort4(self):
- G=nx.Graph()
- G.add_edge(1,2)
- assert_raises(nx.NetworkXError, nx.topological_sort, G)
- assert_raises(nx.NetworkXError, nx.topological_sort_recursive, G)
-
- def test_topological_sort5(self):
- G=nx.DiGraph()
- G.add_edge(0,1)
- assert_equal(nx.topological_sort_recursive(G), [0,1])
- assert_equal(nx.topological_sort(G), [0,1])
-
- def test_nbunch_argument(self):
- G=nx.DiGraph()
- G.add_edges_from([(1,2), (2,3), (1,4), (1,5), (2,6)])
- assert_equal(nx.topological_sort(G), [1, 2, 3, 6, 4, 5])
- assert_equal(nx.topological_sort_recursive(G), [1, 5, 4, 2, 6, 3])
- assert_equal(nx.topological_sort(G,[1]), [1, 2, 3, 6, 4, 5])
- assert_equal(nx.topological_sort_recursive(G,[1]), [1, 5, 4, 2, 6, 3])
- assert_equal(nx.topological_sort(G,[5]), [5])
- assert_equal(nx.topological_sort_recursive(G,[5]), [5])
-
- def test_ancestors(self):
- G=nx.DiGraph()
- ancestors = nx.algorithms.dag.ancestors
- G.add_edges_from([
- (1, 2), (1, 3), (4, 2), (4, 3), (4, 5), (2, 6), (5, 6)])
- assert_equal(ancestors(G, 6), set([1, 2, 4, 5]))
- assert_equal(ancestors(G, 3), set([1, 4]))
- assert_equal(ancestors(G, 1), set())
- assert_raises(nx.NetworkXError, ancestors, G, 8)
-
- def test_descendants(self):
- G=nx.DiGraph()
- descendants = nx.algorithms.dag.descendants
- G.add_edges_from([
- (1, 2), (1, 3), (4, 2), (4, 3), (4, 5), (2, 6), (5, 6)])
- assert_equal(descendants(G, 1), set([2, 3, 6]))
- assert_equal(descendants(G, 4), set([2, 3, 5, 6]))
- assert_equal(descendants(G, 3), set())
- assert_raises(nx.NetworkXError, descendants, G, 8)
-
-
-def test_is_aperiodic_cycle():
- G=nx.DiGraph()
- G.add_cycle([1,2,3,4])
- assert_false(nx.is_aperiodic(G))
-
-def test_is_aperiodic_cycle2():
- G=nx.DiGraph()
- G.add_cycle([1,2,3,4])
- G.add_cycle([3,4,5,6,7])
- assert_true(nx.is_aperiodic(G))
-
-def test_is_aperiodic_cycle3():
- G=nx.DiGraph()
- G.add_cycle([1,2,3,4])
- G.add_cycle([3,4,5,6])
- assert_false(nx.is_aperiodic(G))
-
-def test_is_aperiodic_cycle4():
- G = nx.DiGraph()
- G.add_cycle([1,2,3,4])
- G.add_edge(1,3)
- assert_true(nx.is_aperiodic(G))
-
-def test_is_aperiodic_selfloop():
- G = nx.DiGraph()
- G.add_cycle([1,2,3,4])
- G.add_edge(1,1)
- assert_true(nx.is_aperiodic(G))
-
-def test_is_aperiodic_raise():
- G = nx.Graph()
- assert_raises(nx.NetworkXError,
- nx.is_aperiodic,
- G)
-
-def test_is_aperiodic_bipartite():
- #Bipartite graph
- G = nx.DiGraph(nx.davis_southern_women_graph())
- assert_false(nx.is_aperiodic(G))
-
-def test_is_aperiodic_rary_tree():
- G = nx.full_rary_tree(3,27,create_using=nx.DiGraph())
- assert_false(nx.is_aperiodic(G))
-
-def test_is_aperiodic_disconnected():
- #disconnected graph
- G = nx.DiGraph()
- G.add_cycle([1,2,3,4])
- G.add_cycle([5,6,7,8])
- assert_false(nx.is_aperiodic(G))
- G.add_edge(1,3)
- G.add_edge(5,7)
- assert_true(nx.is_aperiodic(G))
-
-def test_is_aperiodic_disconnected2():
- G = nx.DiGraph()
- G.add_cycle([0,1,2])
- G.add_edge(3,3)
- assert_false(nx.is_aperiodic(G))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_distance_measures.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_distance_measures.py
deleted file mode 100644
index ff3530c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_distance_measures.py
+++ /dev/null
@@ -1,69 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-
-class TestDistance:
-
- def setUp(self):
- G=networkx.Graph()
- from networkx import convert_node_labels_to_integers as cnlti
- G=cnlti(networkx.grid_2d_graph(4,4),first_label=1,ordering="sorted")
- self.G=G
-
- def test_eccentricity(self):
- assert_equal(networkx.eccentricity(self.G,1),6)
- e=networkx.eccentricity(self.G)
- assert_equal(e[1],6)
- sp=networkx.shortest_path_length(self.G)
- e=networkx.eccentricity(self.G,sp=sp)
- assert_equal(e[1],6)
- e=networkx.eccentricity(self.G,v=1)
- assert_equal(e,6)
- e=networkx.eccentricity(self.G,v=[1,1]) #This behavior changed in version 1.8 (ticket #739)
- assert_equal(e[1],6)
- e=networkx.eccentricity(self.G,v=[1,2])
- assert_equal(e[1],6)
- # test against graph with one node
- G=networkx.path_graph(1)
- e=networkx.eccentricity(G)
- assert_equal(e[0],0)
- e=networkx.eccentricity(G,v=0)
- assert_equal(e,0)
- assert_raises(networkx.NetworkXError, networkx.eccentricity, G, 1)
- # test against empty graph
- G=networkx.empty_graph()
- e=networkx.eccentricity(G)
- assert_equal(e,{})
-
-
-
-
- def test_diameter(self):
- assert_equal(networkx.diameter(self.G),6)
-
- def test_radius(self):
- assert_equal(networkx.radius(self.G),4)
-
- def test_periphery(self):
- assert_equal(set(networkx.periphery(self.G)),set([1, 4, 13, 16]))
-
- def test_center(self):
- assert_equal(set(networkx.center(self.G)),set([6, 7, 10, 11]))
-
- def test_radius_exception(self):
- G=networkx.Graph()
- G.add_edge(1,2)
- G.add_edge(3,4)
- assert_raises(networkx.NetworkXError, networkx.diameter, G)
-
- @raises(networkx.NetworkXError)
- def test_eccentricity_infinite(self):
- G=networkx.Graph([(1,2),(3,4)])
- e = networkx.eccentricity(G)
-
- @raises(networkx.NetworkXError)
- def test_eccentricity_invalid(self):
- G=networkx.Graph([(1,2),(3,4)])
- e = networkx.eccentricity(G,sp=1)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_distance_regular.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_distance_regular.py
deleted file mode 100644
index 068a81f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_distance_regular.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestDistanceRegular:
-
- def test_is_distance_regular(self):
- assert_true(nx.is_distance_regular(nx.icosahedral_graph()))
- assert_true(nx.is_distance_regular(nx.petersen_graph()))
- assert_true(nx.is_distance_regular(nx.cubical_graph()))
- assert_true(nx.is_distance_regular(nx.complete_bipartite_graph(3,3)))
- assert_true(nx.is_distance_regular(nx.tetrahedral_graph()))
- assert_true(nx.is_distance_regular(nx.dodecahedral_graph()))
- assert_true(nx.is_distance_regular(nx.pappus_graph()))
- assert_true(nx.is_distance_regular(nx.heawood_graph()))
- assert_true(nx.is_distance_regular(nx.cycle_graph(3)))
- # no distance regular
- assert_false(nx.is_distance_regular(nx.path_graph(4)))
-
- def test_not_connected(self):
- G=nx.cycle_graph(4)
- G.add_cycle([5,6,7])
- assert_false(nx.is_distance_regular(G))
-
-
- def test_global_parameters(self):
- b,c=nx.intersection_array(nx.cycle_graph(5))
- g=nx.global_parameters(b,c)
- assert_equal(list(g),[(0, 0, 2), (1, 0, 1), (1, 1, 0)])
- b,c=nx.intersection_array(nx.cycle_graph(3))
- g=nx.global_parameters(b,c)
- assert_equal(list(g),[(0, 0, 2), (1, 1, 0)])
-
-
- def test_intersection_array(self):
- b,c=nx.intersection_array(nx.cycle_graph(5))
- assert_equal(b,[2, 1])
- assert_equal(c,[1, 1])
- b,c=nx.intersection_array(nx.dodecahedral_graph())
- assert_equal(b,[3, 2, 1, 1, 1])
- assert_equal(c,[1, 1, 1, 2, 3])
- b,c=nx.intersection_array(nx.icosahedral_graph())
- assert_equal(b,[5, 2, 1])
- assert_equal(c,[1, 2, 5])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_euler.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_euler.py
deleted file mode 100644
index 0b55f1f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_euler.py
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/usr/bin/env python
-# run with nose: nosetests -v test_euler.py
-
-from nose.tools import *
-import networkx as nx
-from networkx import is_eulerian,eulerian_circuit
-
-class TestEuler:
-
- def test_is_eulerian(self):
- assert_true(is_eulerian(nx.complete_graph(5)))
- assert_true(is_eulerian(nx.complete_graph(7)))
- assert_true(is_eulerian(nx.hypercube_graph(4)))
- assert_true(is_eulerian(nx.hypercube_graph(6)))
-
- assert_false(is_eulerian(nx.complete_graph(4)))
- assert_false(is_eulerian(nx.complete_graph(6)))
- assert_false(is_eulerian(nx.hypercube_graph(3)))
- assert_false(is_eulerian(nx.hypercube_graph(5)))
-
- assert_false(is_eulerian(nx.petersen_graph()))
- assert_false(is_eulerian(nx.path_graph(4)))
-
- def test_is_eulerian2(self):
- # not connected
- G = nx.Graph()
- G.add_nodes_from([1,2,3])
- assert_false(is_eulerian(G))
- # not strongly connected
- G = nx.DiGraph()
- G.add_nodes_from([1,2,3])
- assert_false(is_eulerian(G))
- G = nx.MultiDiGraph()
- G.add_edge(1,2)
- G.add_edge(2,3)
- G.add_edge(2,3)
- G.add_edge(3,1)
- assert_false(is_eulerian(G))
-
-
-
- def test_eulerian_circuit_cycle(self):
- G=nx.cycle_graph(4)
-
- edges=list(eulerian_circuit(G,source=0))
- nodes=[u for u,v in edges]
- assert_equal(nodes,[0,1,2,3])
- assert_equal(edges,[(0,1),(1,2),(2,3),(3,0)])
-
- edges=list(eulerian_circuit(G,source=1))
- nodes=[u for u,v in edges]
- assert_equal(nodes,[1,0,3,2])
- assert_equal(edges,[(1,0),(0,3),(3,2),(2,1)])
-
-
- def test_eulerian_circuit_digraph(self):
- G=nx.DiGraph()
- G.add_cycle([0,1,2,3])
-
- edges=list(eulerian_circuit(G,source=0))
- nodes=[u for u,v in edges]
- assert_equal(nodes,[0,1,2,3])
- assert_equal(edges,[(0,1),(1,2),(2,3),(3,0)])
-
- edges=list(eulerian_circuit(G,source=1))
- nodes=[u for u,v in edges]
- assert_equal(nodes,[1,2,3,0])
- assert_equal(edges,[(1,2),(2,3),(3,0),(0,1)])
-
-
- def test_eulerian_circuit_multigraph(self):
- G=nx.MultiGraph()
- G.add_cycle([0,1,2,3])
- G.add_edge(1,2)
- G.add_edge(1,2)
- edges=list(eulerian_circuit(G,source=0))
- nodes=[u for u,v in edges]
- assert_equal(nodes,[0,1,2,1,2,3])
- assert_equal(edges,[(0,1),(1,2),(2,1),(1,2),(2,3),(3,0)])
-
-
- @raises(nx.NetworkXError)
- def test_not_eulerian(self):
- f=list(eulerian_circuit(nx.complete_graph(4)))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_graphical.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_graphical.py
deleted file mode 100644
index 9609d25..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_graphical.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-def test_valid_degree_sequence1():
- n = 100
- p = .3
- for i in range(10):
- G = nx.erdos_renyi_graph(n,p)
- deg = list(G.degree().values())
- assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
- assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
-
-def test_valid_degree_sequence2():
- n = 100
- for i in range(10):
- G = nx.barabasi_albert_graph(n,1)
- deg = list(G.degree().values())
- assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
- assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
-
-@raises(nx.NetworkXException)
-def test_string_input():
- a = nx.is_valid_degree_sequence([],'foo')
-
-def test_negative_input():
- assert_false(nx.is_valid_degree_sequence([-1],'hh'))
- assert_false(nx.is_valid_degree_sequence([-1],'eg'))
- assert_false(nx.is_valid_degree_sequence([72.5],'eg'))
-
-
-def test_atlas():
- for graph in nx.graph_atlas_g():
- deg = list(graph.degree().values())
- assert_true( nx.is_valid_degree_sequence(deg, method='eg') )
- assert_true( nx.is_valid_degree_sequence(deg, method='hh') )
-
-def test_small_graph_true():
- z=[5,3,3,3,3,2,2,2,1,1,1]
- assert_true(nx.is_valid_degree_sequence(z, method='hh'))
- assert_true(nx.is_valid_degree_sequence(z, method='eg'))
- z=[10,3,3,3,3,2,2,2,2,2,2]
- assert_true(nx.is_valid_degree_sequence(z, method='hh'))
- assert_true(nx.is_valid_degree_sequence(z, method='eg'))
- z=[1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- assert_true(nx.is_valid_degree_sequence(z, method='hh'))
- assert_true(nx.is_valid_degree_sequence(z, method='eg'))
-
-
-
-def test_small_graph_false():
- z=[1000,3,3,3,3,2,2,2,1,1,1]
- assert_false(nx.is_valid_degree_sequence(z, method='hh'))
- assert_false(nx.is_valid_degree_sequence(z, method='eg'))
- z=[6,5,4,4,2,1,1,1]
- assert_false(nx.is_valid_degree_sequence(z, method='hh'))
- assert_false(nx.is_valid_degree_sequence(z, method='eg'))
- z=[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- assert_false(nx.is_valid_degree_sequence(z, method='hh'))
- assert_false(nx.is_valid_degree_sequence(z, method='eg'))
-
-def test_directed_degree_sequence():
- # Test a range of valid directed degree sequences
- n, r = 100, 10
- p = 1.0 / r
- for i in range(r):
- G = nx.erdos_renyi_graph(n,p*(i+1),None,True)
- din = list(G.in_degree().values())
- dout = list(G.out_degree().values())
- assert_true(nx.is_digraphical(din, dout))
-
-def test_small_directed_sequences():
- dout=[5,3,3,3,3,2,2,2,1,1,1]
- din=[3,3,3,3,3,2,2,2,2,2,1]
- assert_true(nx.is_digraphical(din, dout))
- # Test nongraphical directed sequence
- dout = [1000,3,3,3,3,2,2,2,1,1,1]
- din=[103,102,102,102,102,102,102,102,102,102]
- assert_false(nx.is_digraphical(din, dout))
- # Test digraphical small sequence
- dout=[1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- din=[2, 2, 2, 2, 2, 2, 2, 2, 1, 1]
- assert_true(nx.is_digraphical(din, dout))
- # Test nonmatching sum
- din=[2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1]
- assert_false(nx.is_digraphical(din, dout))
- # Test for negative integer in sequence
- din=[2, 2, 2, -2, 2, 2, 2, 2, 1, 1, 4]
- assert_false(nx.is_digraphical(din, dout))
-
-def test_multi_sequence():
- # Test nongraphical multi sequence
- seq=[1000,3,3,3,3,2,2,2,1,1]
- assert_false(nx.is_multigraphical(seq))
- # Test small graphical multi sequence
- seq=[6,5,4,4,2,1,1,1]
- assert_true(nx.is_multigraphical(seq))
- # Test for negative integer in sequence
- seq=[6,5,4,-4,2,1,1,1]
- assert_false(nx.is_multigraphical(seq))
- # Test for sequence with odd sum
- seq=[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- assert_false(nx.is_multigraphical(seq))
-
-def test_pseudo_sequence():
- # Test small valid pseudo sequence
- seq=[1000,3,3,3,3,2,2,2,1,1]
- assert_true(nx.is_pseudographical(seq))
- # Test for sequence with odd sum
- seq=[1000,3,3,3,3,2,2,2,1,1,1]
- assert_false(nx.is_pseudographical(seq))
- # Test for negative integer in sequence
- seq=[1000,3,3,3,3,2,2,-2,1,1]
- assert_false(nx.is_pseudographical(seq))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_hierarchy.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_hierarchy.py
deleted file mode 100644
index 6f2907e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_hierarchy.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-def test_hierarchy_exception():
- G = nx.cycle_graph(5)
- assert_raises(nx.NetworkXError,nx.flow_hierarchy,G)
-
-def test_hierarchy_cycle():
- G = nx.cycle_graph(5,create_using=nx.DiGraph())
- assert_equal(nx.flow_hierarchy(G),0.0)
-
-def test_hierarchy_tree():
- G = nx.full_rary_tree(2,16,create_using=nx.DiGraph())
- assert_equal(nx.flow_hierarchy(G),1.0)
-
-def test_hierarchy_1():
- G = nx.DiGraph()
- G.add_edges_from([(0,1),(1,2),(2,3),(3,1),(3,4),(0,4)])
- assert_equal(nx.flow_hierarchy(G),0.5)
-
-def test_hierarchy_weight():
- G = nx.DiGraph()
- G.add_edges_from([(0,1,{'weight':.3}),
- (1,2,{'weight':.1}),
- (2,3,{'weight':.1}),
- (3,1,{'weight':.1}),
- (3,4,{'weight':.3}),
- (0,4,{'weight':.3})])
- assert_equal(nx.flow_hierarchy(G,weight='weight'),.75)
\ No newline at end of file
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_matching.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_matching.py
deleted file mode 100644
index 5509e2d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_matching.py
+++ /dev/null
@@ -1,247 +0,0 @@
-#!/usr/bin/env python
-import math
-from nose.tools import *
-import networkx as nx
-
-class TestMatching:
-
- def setUp(self):
- pass
-
- def test_trivial1(self):
- """Empty graph"""
- G = nx.Graph()
- assert_equal(nx.max_weight_matching(G),{})
-
- def test_trivial2(self):
- """Self loop"""
- G = nx.Graph()
- G.add_edge(0, 0, weight=100)
- assert_equal(nx.max_weight_matching(G),{})
-
- def test_trivial3(self):
- """Single edge"""
- G = nx.Graph()
- G.add_edge(0, 1)
- assert_equal(nx.max_weight_matching(G),
- {0: 1, 1: 0})
-
- def test_trivial4(self):
- """Small graph"""
- G = nx.Graph()
- G.add_edge('one', 'two', weight=10)
- G.add_edge('two', 'three', weight=11)
- assert_equal(nx.max_weight_matching(G),
- {'three': 'two', 'two': 'three'})
-
- def test_trivial5(self):
- """Path"""
- G = nx.Graph()
- G.add_edge(1, 2, weight=5)
- G.add_edge(2, 3, weight=11)
- G.add_edge(3, 4, weight=5)
- assert_equal(nx.max_weight_matching(G),
- {2: 3, 3: 2})
- assert_equal(nx.max_weight_matching(G, 1),
- {1: 2, 2: 1, 3: 4, 4: 3})
-
-
- def test_floating_point_weights(self):
- """Floating point weights"""
- G = nx.Graph()
- G.add_edge(1, 2, weight=math.pi)
- G.add_edge(2, 3, weight=math.exp(1))
- G.add_edge(1, 3, weight=3.0)
- G.add_edge(1, 4, weight=math.sqrt(2.0))
- assert_equal(nx.max_weight_matching(G),
- {1: 4, 2: 3, 3: 2, 4: 1})
-
- def test_negative_weights(self):
- """Negative weights"""
- G = nx.Graph()
- G.add_edge(1, 2, weight=2)
- G.add_edge(1, 3, weight=-2)
- G.add_edge(2, 3, weight=1)
- G.add_edge(2, 4, weight=-1)
- G.add_edge(3, 4, weight=-6)
- assert_equal(nx.max_weight_matching(G),
- {1: 2, 2: 1})
- assert_equal(nx.max_weight_matching(G, 1),
- {1: 3, 2: 4, 3: 1, 4: 2})
-
- def test_s_blossom(self):
- """Create S-blossom and use it for augmentation:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 8), (1, 3, 9),
- (2, 3, 10), (3, 4, 7) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 2, 2: 1, 3: 4, 4: 3})
-
- G.add_weighted_edges_from([ (1, 6, 5), (4, 5, 6) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
-
- def test_s_t_blossom(self):
- """Create S-blossom, relabel as T-blossom, use for augmentation:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 9), (1, 3, 8), (2, 3, 10),
- (1, 4, 5), (4, 5, 4), (1, 6, 3) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
- G.add_edge(4, 5, weight=3)
- G.add_edge(1, 6, weight=4)
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
- G.remove_edge(1, 6)
- G.add_edge(3, 6, weight=4)
- assert_equal(nx.max_weight_matching(G),
- {1: 2, 2: 1, 3: 6, 4: 5, 5: 4, 6: 3})
-
- def test_nested_s_blossom(self):
- """Create nested S-blossom, use for augmentation:"""
-
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 9), (1, 3, 9), (2, 3, 10),
- (2, 4, 8), (3, 5, 8), (4, 5, 10),
- (5, 6, 6) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 3, 2: 4, 3: 1, 4: 2, 5: 6, 6: 5})
-
- def test_nested_s_blossom_relabel(self):
- """Create S-blossom, relabel as S, include in nested S-blossom:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 10), (1, 7, 10), (2, 3, 12),
- (3, 4, 20), (3, 5, 20), (4, 5, 25),
- (5, 6, 10), (6, 7, 10), (7, 8, 8) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 2, 2: 1, 3: 4, 4: 3, 5: 6, 6: 5, 7: 8, 8: 7})
-
- def test_nested_s_blossom_expand(self):
- """Create nested S-blossom, augment, expand recursively:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 8), (1, 3, 8), (2, 3, 10),
- (2, 4, 12),(3, 5, 12), (4, 5, 14),
- (4, 6, 12), (5, 7, 12), (6, 7, 14),
- (7, 8, 12) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 2, 2: 1, 3: 5, 4: 6, 5: 3, 6: 4, 7: 8, 8: 7})
-
-
- def test_s_blossom_relabel_expand(self):
- """Create S-blossom, relabel as T, expand:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 23), (1, 5, 22), (1, 6, 15),
- (2, 3, 25), (3, 4, 22), (4, 5, 25),
- (4, 8, 14), (5, 7, 13) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 8, 5: 7, 6: 1, 7: 5, 8: 4})
-
- def test_nested_s_blossom_relabel_expand(self):
- """Create nested S-blossom, relabel as T, expand:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 19), (1, 3, 20), (1, 8, 8),
- (2, 3, 25), (2, 4, 18), (3, 5, 18),
- (4, 5, 13), (4, 7, 7), (5, 6, 7) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 8, 2: 3, 3: 2, 4: 7, 5: 6, 6: 5, 7: 4, 8: 1})
-
-
- def test_nasty_blossom1(self):
- """Create blossom, relabel as T in more than one way, expand,
- augment:
- """
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 45), (1, 5, 45), (2, 3, 50),
- (3, 4, 45), (4, 5, 50), (1, 6, 30),
- (3, 9, 35), (4, 8, 35), (5, 7, 26),
- (9, 10, 5) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 8, 5: 7,
- 6: 1, 7: 5, 8: 4, 9: 10, 10: 9})
-
- def test_nasty_blossom2(self):
- """Again but slightly different:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 45), (1, 5, 45), (2, 3, 50),
- (3, 4, 45), (4, 5, 50), (1, 6, 30),
- (3, 9, 35), (4, 8, 26), (5, 7, 40),
- (9, 10, 5) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 8, 5: 7,
- 6: 1, 7: 5, 8: 4, 9: 10, 10: 9})
-
- def test_nasty_blossom_least_slack(self):
- """Create blossom, relabel as T, expand such that a new
- least-slack S-to-free dge is produced, augment:
- """
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 45), (1, 5, 45), (2, 3, 50),
- (3, 4, 45), (4, 5, 50), (1, 6, 30),
- (3, 9, 35), (4, 8, 28), (5, 7, 26),
- (9, 10, 5) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 6, 2: 3, 3: 2, 4: 8, 5: 7,
- 6: 1, 7: 5, 8: 4, 9: 10, 10: 9})
-
- def test_nasty_blossom_augmenting(self):
- """Create nested blossom, relabel as T in more than one way"""
- # expand outer blossom such that inner blossom ends up on an
- # augmenting path:
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 45), (1, 7, 45), (2, 3, 50),
- (3, 4, 45), (4, 5, 95), (4, 6, 94),
- (5, 6, 94), (6, 7, 50), (1, 8, 30),
- (3, 11, 35), (5, 9, 36), (7, 10, 26),
- (11, 12, 5) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 8, 2: 3, 3: 2, 4: 6, 5: 9, 6: 4,
- 7: 10, 8: 1, 9: 5, 10: 7, 11: 12, 12: 11})
-
- def test_nasty_blossom_expand_recursively(self):
- """Create nested S-blossom, relabel as S, expand recursively:"""
- G = nx.Graph()
- G.add_weighted_edges_from([ (1, 2, 40), (1, 3, 40), (2, 3, 60),
- (2, 4, 55), (3, 5, 55), (4, 5, 50),
- (1, 8, 15), (5, 7, 30), (7, 6, 10),
- (8, 10, 10), (4, 9, 30) ])
- assert_equal(nx.max_weight_matching(G),
- {1: 2, 2: 1, 3: 5, 4: 9, 5: 3,
- 6: 7, 7: 6, 8: 10, 9: 4, 10: 8})
-
-def test_maximal_matching():
- graph = nx.Graph()
- graph.add_edge(0, 1)
- graph.add_edge(0, 2)
- graph.add_edge(0, 3)
- graph.add_edge(0, 4)
- graph.add_edge(0, 5)
- graph.add_edge(1, 2)
- matching = nx.maximal_matching(graph)
-
- vset = set(u for u, v in matching)
- vset = vset | set(v for u, v in matching)
-
- for edge in graph.edges_iter():
- u, v = edge
- ok_(len(set([v]) & vset) > 0 or len(set([u]) & vset) > 0, \
- "not a proper matching!")
-
- eq_(1, len(matching), "matching not length 1!")
- graph = nx.Graph()
- graph.add_edge(1, 2)
- graph.add_edge(1, 5)
- graph.add_edge(2, 3)
- graph.add_edge(2, 5)
- graph.add_edge(3, 4)
- graph.add_edge(3, 6)
- graph.add_edge(5, 6)
-
- matching = nx.maximal_matching(graph)
- vset = set(u for u, v in matching)
- vset = vset | set(v for u, v in matching)
-
- for edge in graph.edges_iter():
- u, v = edge
- ok_(len(set([v]) & vset) > 0 or len(set([u]) & vset) > 0, \
- "not a proper matching!")
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_mis.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_mis.py
deleted file mode 100644
index 01092ef..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_mis.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# $Id: test_maximal_independent_set.py 577 2011-03-01 06:07:53Z lleeoo $
-"""
-Tests for maximal (not maximum) independent sets.
-
-"""
-# Copyright (C) 2004-2010 by
-# Leo Lopes <leo.lopes@monash.edu>
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__author__ = """Leo Lopes (leo.lopes@monash.edu)"""
-
-from nose.tools import *
-import networkx as nx
-import random
-
-class TestMaximalIndependantSet(object):
- def setup(self):
- self.florentine = nx.Graph()
- self.florentine.add_edge('Acciaiuoli','Medici')
- self.florentine.add_edge('Castellani','Peruzzi')
- self.florentine.add_edge('Castellani','Strozzi')
- self.florentine.add_edge('Castellani','Barbadori')
- self.florentine.add_edge('Medici','Barbadori')
- self.florentine.add_edge('Medici','Ridolfi')
- self.florentine.add_edge('Medici','Tornabuoni')
- self.florentine.add_edge('Medici','Albizzi')
- self.florentine.add_edge('Medici','Salviati')
- self.florentine.add_edge('Salviati','Pazzi')
- self.florentine.add_edge('Peruzzi','Strozzi')
- self.florentine.add_edge('Peruzzi','Bischeri')
- self.florentine.add_edge('Strozzi','Ridolfi')
- self.florentine.add_edge('Strozzi','Bischeri')
- self.florentine.add_edge('Ridolfi','Tornabuoni')
- self.florentine.add_edge('Tornabuoni','Guadagni')
- self.florentine.add_edge('Albizzi','Ginori')
- self.florentine.add_edge('Albizzi','Guadagni')
- self.florentine.add_edge('Bischeri','Guadagni')
- self.florentine.add_edge('Guadagni','Lamberteschi')
-
- def test_K5(self):
- """Maximal independent set: K5"""
- G = nx.complete_graph(5)
- for node in G:
- assert_equal(nx.maximal_independent_set(G, [node]), [node])
-
- def test_K55(self):
- """Maximal independent set: K55"""
- G = nx.complete_graph(55)
- for node in G:
- assert_equal(nx.maximal_independent_set(G, [node]), [node])
-
- def test_exception(self):
- """Bad input should raise exception."""
- G = self.florentine
- assert_raises(nx.NetworkXUnfeasible,
- nx.maximal_independent_set, G, ["Smith"])
- assert_raises(nx.NetworkXUnfeasible,
- nx.maximal_independent_set, G, ["Salviati", "Pazzi"])
-
- def test_florentine_family(self):
- G = self.florentine
- indep = nx.maximal_independent_set(G, ["Medici", "Bischeri"])
- assert_equal(sorted(indep),
- sorted(["Medici", "Bischeri", "Castellani", "Pazzi",
- "Ginori", "Lamberteschi"]))
-
- def test_bipartite(self):
- G = nx.complete_bipartite_graph(12, 34)
- indep = nx.maximal_independent_set(G, [4, 5, 9, 10])
- assert_equal(sorted(indep), list(range(12)))
-
-
- def test_random_graphs(self):
- """Generate 50 random graphs of different types and sizes and
- make sure that all sets are independent and maximal."""
- for i in range(0, 50, 10):
- G = nx.random_graphs.erdos_renyi_graph(i*10+1, random.random())
- IS = nx.maximal_independent_set(G)
- assert_false(G.subgraph(IS).edges())
- neighbors_of_MIS = set.union(*(set(G.neighbors(v)) for v in IS))
- for v in set(G.nodes()).difference(IS):
- assert_true(v in neighbors_of_MIS)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_mst.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_mst.py
deleted file mode 100644
index 5716506..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_mst.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestMST:
-
- def setUp(self):
- # example from Wikipedia: http://en.wikipedia.org/wiki/Kruskal's_algorithm
- G=nx.Graph()
- edgelist = [(0,3,[('weight',5)]),
- (0,1,[('weight',7)]),
- (1,3,[('weight',9)]),
- (1,2,[('weight',8)]),
- (1,4,[('weight',7)]),
- (3,4,[('weight',15)]),
- (3,5,[('weight',6)]),
- (2,4,[('weight',5)]),
- (4,5,[('weight',8)]),
- (4,6,[('weight',9)]),
- (5,6,[('weight',11)])]
-
-
- G.add_edges_from(edgelist)
- self.G=G
- tree_edgelist = [(0,1,{'weight':7}),
- (0,3,{'weight':5}),
- (3,5,{'weight':6}),
- (1,4,{'weight':7}),
- (4,2,{'weight':5}),
- (4,6,{'weight':9})]
- self.tree_edgelist=sorted((sorted((u, v))[0], sorted((u, v))[1], d)
- for u,v,d in tree_edgelist)
-
- def test_mst(self):
- T=nx.minimum_spanning_tree(self.G)
- assert_equal(T.edges(data=True),self.tree_edgelist)
-
- def test_mst_edges(self):
- edgelist=sorted(nx.minimum_spanning_edges(self.G))
- assert_equal(edgelist,self.tree_edgelist)
-
- def test_mst_disconnected(self):
- G=nx.Graph()
- G.add_path([1,2])
- G.add_path([10,20])
- T=nx.minimum_spanning_tree(G)
- assert_equal(sorted(T.edges()),[(1, 2), (20, 10)])
- assert_equal(sorted(T.nodes()),[1, 2, 10, 20])
-
- def test_mst_isolate(self):
- G=nx.Graph()
- G.add_nodes_from([1,2])
- T=nx.minimum_spanning_tree(G)
- assert_equal(sorted(T.nodes()),[1, 2])
- assert_equal(sorted(T.edges()),[])
-
- def test_mst_attributes(self):
- G=nx.Graph()
- G.add_edge(1,2,weight=1,color='red',distance=7)
- G.add_edge(2,3,weight=1,color='green',distance=2)
- G.add_edge(1,3,weight=10,color='blue',distance=1)
- G.add_node(13,color='purple')
- G.graph['foo']='bar'
- T=nx.minimum_spanning_tree(G)
- assert_equal(T.graph,G.graph)
- assert_equal(T.node[13],G.node[13])
- assert_equal(T.edge[1][2],G.edge[1][2])
-
- def test_mst_edges_specify_weight(self):
- G=nx.Graph()
- G.add_edge(1,2,weight=1,color='red',distance=7)
- G.add_edge(1,3,weight=30,color='blue',distance=1)
- G.add_edge(2,3,weight=1,color='green',distance=1)
- G.add_node(13,color='purple')
- G.graph['foo']='bar'
- T=nx.minimum_spanning_tree(G)
- assert_equal(sorted(T.nodes()),[1,2,3,13])
- assert_equal(sorted(T.edges()),[(1,2),(2,3)])
- T=nx.minimum_spanning_tree(G,weight='distance')
- assert_equal(sorted(T.edges()),[(1,3),(2,3)])
- assert_equal(sorted(T.nodes()),[1,2,3,13])
-
- def test_prim_mst(self):
- T=nx.prim_mst(self.G)
- assert_equal(T.edges(data=True),self.tree_edgelist)
-
- def test_prim_mst_edges(self):
- edgelist=sorted(nx.prim_mst_edges(self.G))
- edgelist=sorted((sorted((u, v))[0], sorted((u, v))[1], d)
- for u,v,d in edgelist)
- assert_equal(edgelist,self.tree_edgelist)
-
- def test_prim_mst_disconnected(self):
- G=nx.Graph()
- G.add_path([1,2])
- G.add_path([10,20])
- T=nx.prim_mst(G)
- assert_equal(sorted(T.edges()),[(1, 2), (20, 10)])
- assert_equal(sorted(T.nodes()),[1, 2, 10, 20])
-
- def test_prim_mst_isolate(self):
- G=nx.Graph()
- G.add_nodes_from([1,2])
- T=nx.prim_mst(G)
- assert_equal(sorted(T.nodes()),[1, 2])
- assert_equal(sorted(T.edges()),[])
-
- def test_prim_mst_attributes(self):
- G=nx.Graph()
- G.add_edge(1,2,weight=1,color='red',distance=7)
- G.add_edge(2,3,weight=1,color='green',distance=2)
- G.add_edge(1,3,weight=10,color='blue',distance=1)
- G.add_node(13,color='purple')
- G.graph['foo']='bar'
- T=nx.prim_mst(G)
- assert_equal(T.graph,G.graph)
- assert_equal(T.node[13],G.node[13])
- assert_equal(T.edge[1][2],G.edge[1][2])
-
- def test_prim_mst_edges_specify_weight(self):
- G=nx.Graph()
- G.add_edge(1,2,weight=1,color='red',distance=7)
- G.add_edge(1,3,weight=30,color='blue',distance=1)
- G.add_edge(2,3,weight=1,color='green',distance=1)
- G.add_node(13,color='purple')
- G.graph['foo']='bar'
- T=nx.prim_mst(G)
- assert_equal(sorted(T.nodes()),[1,2,3,13])
- assert_equal(sorted(T.edges()),[(1,2),(2,3)])
- T=nx.prim_mst(G,weight='distance')
- assert_equal(sorted(T.edges()),[(1,3),(2,3)])
- assert_equal(sorted(T.nodes()),[1,2,3,13])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_richclub.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_richclub.py
deleted file mode 100644
index 8114d91..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_richclub.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import networkx as nx
-from nose.tools import *
-
-
-def test_richclub():
- G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])
- rc = nx.richclub.rich_club_coefficient(G,normalized=False)
- assert_equal(rc,{0: 12.0/30,1:8.0/12})
-
- # test single value
- rc0 = nx.richclub.rich_club_coefficient(G,normalized=False)[0]
- assert_equal(rc0,12.0/30.0)
-
-def test_richclub_normalized():
- G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])
- rcNorm = nx.richclub.rich_club_coefficient(G,Q=2)
- assert_equal(rcNorm,{0:1.0,1:1.0})
-
-
-def test_richclub2():
- T = nx.balanced_tree(2,10)
- rc = nx.richclub.rich_club_coefficient(T,normalized=False)
- assert_equal(rc,{0:4092/(2047*2046.0),
- 1:(2044.0/(1023*1022)),
- 2:(2040.0/(1022*1021))})
-
-#def test_richclub2_normalized():
-# T = nx.balanced_tree(2,10)
-# rcNorm = nx.richclub.rich_club_coefficient(T,Q=2)
-# assert_true(rcNorm[0] ==1.0 and rcNorm[1] < 0.9 and rcNorm[2] < 0.9)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_simple_paths.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_simple_paths.py
deleted file mode 100644
index 268a890..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_simple_paths.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-def test_all_simple_paths():
- G = nx.path_graph(4)
- paths = nx.all_simple_paths(G,0,3)
- assert_equal(list(list(p) for p in paths),[[0,1,2,3]])
-
-def test_all_simple_paths_cutoff():
- G = nx.complete_graph(4)
- paths = nx.all_simple_paths(G,0,1,cutoff=1)
- assert_equal(list(list(p) for p in paths),[[0,1]])
- paths = nx.all_simple_paths(G,0,1,cutoff=2)
- assert_equal(list(list(p) for p in paths),[[0,1],[0,2,1],[0,3,1]])
-
-def test_all_simple_paths_multigraph():
- G = nx.MultiGraph([(1,2),(1,2)])
- paths = nx.all_simple_paths(G,1,2)
- assert_equal(list(list(p) for p in paths),[[1,2],[1,2]])
-
-def test_all_simple_paths_multigraph_with_cutoff():
- G = nx.MultiGraph([(1,2),(1,2),(1,10),(10,2)])
- paths = nx.all_simple_paths(G,1,2, cutoff=1)
- assert_equal(list(list(p) for p in paths),[[1,2],[1,2]])
-
-
-def test_all_simple_paths_directed():
- G = nx.DiGraph()
- G.add_path([1,2,3])
- G.add_path([3,2,1])
- paths = nx.all_simple_paths(G,1,3)
- assert_equal(list(list(p) for p in paths),[[1,2,3]])
-
-def test_all_simple_paths_empty():
- G = nx.path_graph(4)
- paths = nx.all_simple_paths(G,0,3,cutoff=2)
- assert_equal(list(list(p) for p in paths),[])
-
-def hamiltonian_path(G,source):
- source = next(G.nodes_iter())
- neighbors = set(G[source])-set([source])
- n = len(G)
- for target in neighbors:
- for path in nx.all_simple_paths(G,source,target):
- if len(path) == n:
- yield path
-
-def test_hamiltonian_path():
- from itertools import permutations
- G=nx.complete_graph(4)
- paths = [list(p) for p in hamiltonian_path(G,0)]
- exact = [[0]+list(p) for p in permutations([1,2,3],3) ]
- assert_equal(sorted(paths),sorted(exact))
-
-def test_cutoff_zero():
- G = nx.complete_graph(4)
- paths = nx.all_simple_paths(G,0,3,cutoff=0)
- assert_equal(list(list(p) for p in paths),[])
- paths = nx.all_simple_paths(nx.MultiGraph(G),0,3,cutoff=0)
- assert_equal(list(list(p) for p in paths),[])
-
-@raises(nx.NetworkXError)
-def test_source_missing():
- G = nx.Graph()
- G.add_path([1,2,3])
- paths = list(nx.all_simple_paths(nx.MultiGraph(G),0,3))
-
-@raises(nx.NetworkXError)
-def test_target_missing():
- G = nx.Graph()
- G.add_path([1,2,3])
- paths = list(nx.all_simple_paths(nx.MultiGraph(G),1,4))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_smetric.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_smetric.py
deleted file mode 100644
index 3fb288b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_smetric.py
+++ /dev/null
@@ -1,19 +0,0 @@
-
-from nose.tools import assert_equal,raises
-
-import networkx as nx
-
-def test_smetric():
- g = nx.Graph()
- g.add_edge(1,2)
- g.add_edge(2,3)
- g.add_edge(2,4)
- g.add_edge(1,4)
- sm = nx.s_metric(g,normalized=False)
- assert_equal(sm, 19.0)
-# smNorm = nx.s_metric(g,normalized=True)
-# assert_equal(smNorm, 0.95)
-
-@raises(nx.NetworkXError)
-def test_normalized():
- sm = nx.s_metric(nx.Graph(),normalized=True)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_swap.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_swap.py
deleted file mode 100644
index afa2355..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_swap.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from networkx import *
-
-def test_double_edge_swap():
- graph = barabasi_albert_graph(200,1)
- degrees = sorted(graph.degree().values())
- G = double_edge_swap(graph, 40)
- assert_equal(degrees, sorted(graph.degree().values()))
-
-def test_connected_double_edge_swap():
- graph = barabasi_albert_graph(200,1)
- degrees = sorted(graph.degree().values())
- G = connected_double_edge_swap(graph, 40)
- assert_true(is_connected(graph))
- assert_equal(degrees, sorted(graph.degree().values()))
-
-@raises(NetworkXError)
-def test_double_edge_swap_small():
- G = nx.double_edge_swap(nx.path_graph(3))
-
-@raises(NetworkXError)
-def test_double_edge_swap_tries():
- G = nx.double_edge_swap(nx.path_graph(10),nswap=1,max_tries=0)
-
-@raises(NetworkXError)
-def test_connected_double_edge_swap_small():
- G = nx.connected_double_edge_swap(nx.path_graph(3))
-
-@raises(NetworkXError)
-def test_connected_double_edge_swap_not_connected():
- G = nx.path_graph(3)
- G.add_path([10,11,12])
- G = nx.connected_double_edge_swap(G)
-
-
-def test_degree_seq_c4():
- G = cycle_graph(4)
- degrees = sorted(G.degree().values())
- G = double_edge_swap(G,1,100)
- assert_equal(degrees, sorted(G.degree().values()))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_vitality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_vitality.py
deleted file mode 100644
index 06f09fe..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/test_vitality.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestVitality:
-
- def test_closeness_vitality_unweighted(self):
- G=nx.cycle_graph(3)
- v=nx.closeness_vitality(G)
- assert_equal(v,{0:4.0, 1:4.0, 2:4.0})
- assert_equal(v[0],4.0)
-
- def test_closeness_vitality_weighted(self):
- G=nx.Graph()
- G.add_cycle([0,1,2],weight=2)
- v=nx.closeness_vitality(G,weight='weight')
- assert_equal(v,{0:8.0, 1:8.0, 2:8.0})
-
- def test_closeness_vitality_unweighted_digraph(self):
- G=nx.DiGraph()
- G.add_cycle([0,1,2])
- v=nx.closeness_vitality(G)
- assert_equal(v,{0:8.0, 1:8.0, 2:8.0})
-
- def test_closeness_vitality_weighted_digraph(self):
- G=nx.DiGraph()
- G.add_cycle([0,1,2],weight=2)
- v=nx.closeness_vitality(G,weight='weight')
- assert_equal(v,{0:16.0, 1:16.0, 2:16.0})
-
- def test_closeness_vitality_weighted_multidigraph(self):
- G=nx.MultiDiGraph()
- G.add_cycle([0,1,2],weight=2)
- v=nx.closeness_vitality(G,weight='weight')
- assert_equal(v,{0:16.0, 1:16.0, 2:16.0})
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/__init__.py
deleted file mode 100644
index de558ba..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import networkx.algorithms.traversal.depth_first_search
-from networkx.algorithms.traversal.depth_first_search import *
-import networkx.algorithms.traversal.breadth_first_search
-from networkx.algorithms.traversal.breadth_first_search import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/breadth_first_search.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/breadth_first_search.py
deleted file mode 100644
index 6ef2985..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/breadth_first_search.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-====================
-Breadth-first search
-====================
-
-Basic algorithms for breadth-first searching.
-"""
-__author__ = """\n""".join(['Aric Hagberg <hagberg@lanl.gov>'])
-
-__all__ = ['bfs_edges', 'bfs_tree',
- 'bfs_predecessors', 'bfs_successors']
-
-import networkx as nx
-from collections import defaultdict, deque
-
-def bfs_edges(G, source, reverse=False):
- """Produce edges in a breadth-first-search starting at source."""
- # Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py
- # by D. Eppstein, July 2004.
- if reverse and isinstance(G, nx.DiGraph):
- neighbors = G.predecessors_iter
- else:
- neighbors = G.neighbors_iter
- visited=set([source])
- queue = deque([(source, neighbors(source))])
- while queue:
- parent, children = queue[0]
- try:
- child = next(children)
- if child not in visited:
- yield parent, child
- visited.add(child)
- queue.append((child, neighbors(child)))
- except StopIteration:
- queue.popleft()
-
-def bfs_tree(G, source, reverse=False):
- """Return directed tree of breadth-first-search from source."""
- T = nx.DiGraph()
- T.add_node(source)
- T.add_edges_from(bfs_edges(G,source,reverse=reverse))
- return T
-
-def bfs_predecessors(G, source):
- """Return dictionary of predecessors in breadth-first-search from source."""
- return dict((t,s) for s,t in bfs_edges(G,source))
-
-def bfs_successors(G, source):
- """Return dictionary of successors in breadth-first-search from source."""
- d=defaultdict(list)
- for s,t in bfs_edges(G,source):
- d[s].append(t)
- return dict(d)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/depth_first_search.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/depth_first_search.py
deleted file mode 100644
index feb6713..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/depth_first_search.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""
-==================
-Depth-first search
-==================
-
-Basic algorithms for depth-first searching.
-
-Based on http://www.ics.uci.edu/~eppstein/PADS/DFS.py
-by D. Eppstein, July 2004.
-"""
-__author__ = """\n""".join(['Aric Hagberg <hagberg@lanl.gov>'])
-
-__all__ = ['dfs_edges', 'dfs_tree',
- 'dfs_predecessors', 'dfs_successors',
- 'dfs_preorder_nodes','dfs_postorder_nodes',
- 'dfs_labeled_edges']
-
-import networkx as nx
-from collections import defaultdict
-
-def dfs_edges(G,source=None):
- """Produce edges in a depth-first-search starting at source."""
- # Based on http://www.ics.uci.edu/~eppstein/PADS/DFS.py
- # by D. Eppstein, July 2004.
- if source is None:
- # produce edges for all components
- nodes=G
- else:
- # produce edges for components with source
- nodes=[source]
- visited=set()
- for start in nodes:
- if start in visited:
- continue
- visited.add(start)
- stack = [(start,iter(G[start]))]
- while stack:
- parent,children = stack[-1]
- try:
- child = next(children)
- if child not in visited:
- yield parent,child
- visited.add(child)
- stack.append((child,iter(G[child])))
- except StopIteration:
- stack.pop()
-
-def dfs_tree(G, source):
- """Return directed tree of depth-first-search from source."""
- T = nx.DiGraph()
- if source is None:
- T.add_nodes_from(G)
- else:
- T.add_node(source)
- T.add_edges_from(dfs_edges(G,source))
- return T
-
-def dfs_predecessors(G, source=None):
- """Return dictionary of predecessors in depth-first-search from source."""
- return dict((t,s) for s,t in dfs_edges(G,source=source))
-
-
-def dfs_successors(G, source=None):
- """Return dictionary of successors in depth-first-search from source."""
- d=defaultdict(list)
- for s,t in dfs_edges(G,source=source):
- d[s].append(t)
- return dict(d)
-
-
-def dfs_postorder_nodes(G,source=None):
- """Produce nodes in a depth-first-search post-ordering starting
- from source.
- """
- post=(v for u,v,d in nx.dfs_labeled_edges(G,source=source)
- if d['dir']=='reverse')
- # chain source to end of pre-ordering
-# return chain(post,[source])
- return post
-
-
-def dfs_preorder_nodes(G,source=None):
- """Produce nodes in a depth-first-search pre-ordering starting at source."""
- pre=(v for u,v,d in nx.dfs_labeled_edges(G,source=source)
- if d['dir']=='forward')
- # chain source to beginning of pre-ordering
-# return chain([source],pre)
- return pre
-
-
-def dfs_labeled_edges(G,source=None):
- """Produce edges in a depth-first-search starting at source and
- labeled by direction type (forward, reverse, nontree).
- """
- # Based on http://www.ics.uci.edu/~eppstein/PADS/DFS.py
- # by D. Eppstein, July 2004.
- if source is None:
- # produce edges for all components
- nodes=G
- else:
- # produce edges for components with source
- nodes=[source]
- visited=set()
- for start in nodes:
- if start in visited:
- continue
- yield start,start,{'dir':'forward'}
- visited.add(start)
- stack = [(start,iter(G[start]))]
- while stack:
- parent,children = stack[-1]
- try:
- child = next(children)
- if child in visited:
- yield parent,child,{'dir':'nontree'}
- else:
- yield parent,child,{'dir':'forward'}
- visited.add(child)
- stack.append((child,iter(G[child])))
- except StopIteration:
- stack.pop()
- if stack:
- yield stack[-1][0],parent,{'dir':'reverse'}
- yield start,start,{'dir':'reverse'}
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/tests/test_bfs.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/tests/test_bfs.py
deleted file mode 100644
index bae2fac..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/tests/test_bfs.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestBFS:
-
- def setUp(self):
- # simple graph
- G=nx.Graph()
- G.add_edges_from([(0,1),(1,2),(1,3),(2,4),(3,4)])
- self.G=G
-
- def test_successor(self):
- assert_equal(nx.bfs_successors(self.G,source=0),
- {0: [1], 1: [2,3], 2:[4]})
-
- def test_predecessor(self):
- assert_equal(nx.bfs_predecessors(self.G,source=0),
- {1: 0, 2: 1, 3: 1, 4: 2})
-
- def test_bfs_tree(self):
- T=nx.bfs_tree(self.G,source=0)
- assert_equal(sorted(T.nodes()),sorted(self.G.nodes()))
- assert_equal(sorted(T.edges()),[(0, 1), (1, 2), (1, 3), (2, 4)])
-
- def test_bfs_edges(self):
- edges=nx.bfs_edges(self.G,source=0)
- assert_equal(list(edges),[(0, 1), (1, 2), (1, 3), (2, 4)])
-
- def test_bfs_tree_isolates(self):
- G = nx.Graph()
- G.add_node(1)
- G.add_node(2)
- T=nx.bfs_tree(G,source=1)
- assert_equal(sorted(T.nodes()),[1])
- assert_equal(sorted(T.edges()),[])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/tests/test_dfs.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/tests/test_dfs.py
deleted file mode 100644
index 9fad985..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/traversal/tests/test_dfs.py
+++ /dev/null
@@ -1,68 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestDFS:
-
- def setUp(self):
- # simple graph
- G=nx.Graph()
- G.add_edges_from([(0,1),(1,2),(1,3),(2,4),(3,4)])
- self.G=G
- # simple graph, disconnected
- D=nx.Graph()
- D.add_edges_from([(0,1),(2,3)])
- self.D=D
-
-
- def test_preorder_nodes(self):
- assert_equal(list(nx.dfs_preorder_nodes(self.G,source=0)),
- [0, 1, 2, 4, 3])
- assert_equal(list(nx.dfs_preorder_nodes(self.D)),[0, 1, 2, 3])
-
- def test_postorder_nodes(self):
- assert_equal(list(nx.dfs_postorder_nodes(self.G,source=0)),
- [3, 4, 2, 1, 0])
- assert_equal(list(nx.dfs_postorder_nodes(self.D)),[1, 0, 3, 2])
-
- def test_successor(self):
- assert_equal(nx.dfs_successors(self.G,source=0),
- {0: [1], 1: [2], 2: [4], 4: [3]})
- assert_equal(nx.dfs_successors(self.D), {0: [1], 2: [3]})
-
- def test_predecessor(self):
- assert_equal(nx.dfs_predecessors(self.G,source=0),
- {1: 0, 2: 1, 3: 4, 4: 2})
- assert_equal(nx.dfs_predecessors(self.D), {1: 0, 3: 2})
-
- def test_dfs_tree(self):
- T=nx.dfs_tree(self.G,source=0)
- assert_equal(sorted(T.nodes()),sorted(self.G.nodes()))
- assert_equal(sorted(T.edges()),[(0, 1), (1, 2), (2, 4), (4, 3)])
-
- def test_dfs_edges(self):
- edges=nx.dfs_edges(self.G,source=0)
- assert_equal(list(edges),[(0, 1), (1, 2), (2, 4), (4, 3)])
- edges=nx.dfs_edges(self.D)
- assert_equal(list(edges),[(0, 1), (2, 3)])
-
- def test_dfs_labeled_edges(self):
- edges=list(nx.dfs_labeled_edges(self.G,source=0))
- forward=[(u,v) for (u,v,d) in edges if d['dir']=='forward']
- assert_equal(forward,[(0,0), (0, 1), (1, 2), (2, 4), (4, 3)])
-
- def test_dfs_labeled_disconnected_edges(self):
- edges=list(nx.dfs_labeled_edges(self.D))
- forward=[(u,v) for (u,v,d) in edges if d['dir']=='forward']
- assert_equal(forward,[(0, 0), (0, 1), (2, 2), (2, 3)])
-
- def test_dfs_tree_isolates(self):
- G = nx.Graph()
- G.add_node(1)
- G.add_node(2)
- T=nx.dfs_tree(G,source=1)
- assert_equal(sorted(T.nodes()),[1])
- assert_equal(sorted(T.edges()),[])
- T=nx.dfs_tree(G,source=None)
- assert_equal(sorted(T.nodes()),[1, 2])
- assert_equal(sorted(T.edges()),[])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/vitality.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/vitality.py
deleted file mode 100644
index c4db32e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/vitality.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""
-Vitality measures.
-"""
-# Copyright (C) 2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Renato Fabbri'])
-__all__ = ['closeness_vitality']
-
-def weiner_index(G, weight=None):
- # compute sum of distances between all node pairs
- # (with optional weights)
- weiner=0.0
- if weight is None:
- for n in G:
- path_length=nx.single_source_shortest_path_length(G,n)
- weiner+=sum(path_length.values())
- else:
- for n in G:
- path_length=nx.single_source_dijkstra_path_length(G,
- n,weight=weight)
- weiner+=sum(path_length.values())
- return weiner
-
-
-def closeness_vitality(G, weight=None):
- """Compute closeness vitality for nodes.
-
- Closeness vitality of a node is the change in the sum of distances
- between all node pairs when excluding that node.
-
- Parameters
- ----------
- G : graph
-
- weight : None or string (optional)
- The name of the edge attribute used as weight. If None the edge
- weights are ignored.
-
- Returns
- -------
- nodes : dictionary
- Dictionary with nodes as keys and closeness vitality as the value.
-
- Examples
- --------
- >>> G=nx.cycle_graph(3)
- >>> nx.closeness_vitality(G)
- {0: 4.0, 1: 4.0, 2: 4.0}
-
- See Also
- --------
- closeness_centrality()
-
- References
- ----------
- .. [1] Ulrik Brandes, Sec. 3.6.2 in
- Network Analysis: Methodological Foundations, Springer, 2005.
- http://books.google.com/books?id=TTNhSm7HYrIC
- """
- multigraph = G.is_multigraph()
- wig = weiner_index(G,weight)
- closeness_vitality = {}
- for n in G:
- # remove edges connected to node n and keep list of edges with data
- # could remove node n but it doesn't count anyway
- if multigraph:
- edges = G.edges(n,data=True,keys=True)
- if G.is_directed():
- edges += G.in_edges(n,data=True,keys=True)
- else:
- edges = G.edges(n,data=True)
- if G.is_directed():
- edges += G.in_edges(n,data=True)
- G.remove_edges_from(edges)
- closeness_vitality[n] = wig - weiner_index(G,weight)
- # add edges and data back to graph
- G.add_edges_from(edges)
- return closeness_vitality
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/__init__.py
deleted file mode 100644
index fa97851..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from networkx.classes.graph import Graph
-from networkx.classes.digraph import DiGraph
-from networkx.classes.multigraph import MultiGraph
-from networkx.classes.multidigraph import MultiDiGraph
-from networkx.classes.function import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/digraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/digraph.py
deleted file mode 100644
index 37b9f9d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/digraph.py
+++ /dev/null
@@ -1,1236 +0,0 @@
-"""Base class for directed graphs."""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from copy import deepcopy
-import networkx as nx
-from networkx.classes.graph import Graph
-from networkx.exception import NetworkXError
-import networkx.convert as convert
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-
-class DiGraph(Graph):
- """
- Base class for directed graphs.
-
- A DiGraph stores nodes and edges with optional data, or attributes.
-
- DiGraphs hold directed edges. Self loops are allowed but multiple
- (parallel) edges are not.
-
- Nodes can be arbitrary (hashable) Python objects with optional
- key/value attributes.
-
- Edges are represented as links between nodes with optional
- key/value attributes.
-
- Parameters
- ----------
- data : input graph
- Data to initialize graph. If data=None (default) an empty
- graph is created. The data can be an edge list, or any
- NetworkX graph object. If the corresponding optional Python
- packages are installed the data can also be a NumPy matrix
- or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to graph as key=value pairs.
-
- See Also
- --------
- Graph
- MultiGraph
- MultiDiGraph
-
- Examples
- --------
- Create an empty graph structure (a "null graph") with no nodes and
- no edges.
-
- >>> G = nx.DiGraph()
-
- G can be grown in several ways.
-
- **Nodes:**
-
- Add one node at a time:
-
- >>> G.add_node(1)
-
- Add the nodes from any container (a list, dict, set or
- even the lines from a file or the nodes from another graph).
-
- >>> G.add_nodes_from([2,3])
- >>> G.add_nodes_from(range(100,110))
- >>> H=nx.Graph()
- >>> H.add_path([0,1,2,3,4,5,6,7,8,9])
- >>> G.add_nodes_from(H)
-
- In addition to strings and integers any hashable Python object
- (except None) can represent a node, e.g. a customized node object,
- or even another Graph.
-
- >>> G.add_node(H)
-
- **Edges:**
-
- G can also be grown by adding edges.
-
- Add one edge,
-
- >>> G.add_edge(1, 2)
-
- a list of edges,
-
- >>> G.add_edges_from([(1,2),(1,3)])
-
- or a collection of edges,
-
- >>> G.add_edges_from(H.edges())
-
- If some edges connect nodes not yet in the graph, the nodes
- are added automatically. There are no errors when adding
- nodes or edges that already exist.
-
- **Attributes:**
-
- Each graph, node, and edge can hold key/value attribute pairs
- in an associated attribute dictionary (the keys must be hashable).
- By default these are empty, but can be added or changed using
- add_edge, add_node or direct manipulation of the attribute
- dictionaries named graph, node and edge respectively.
-
- >>> G = nx.DiGraph(day="Friday")
- >>> G.graph
- {'day': 'Friday'}
-
- Add node attributes using add_node(), add_nodes_from() or G.node
-
- >>> G.add_node(1, time='5pm')
- >>> G.add_nodes_from([3], time='2pm')
- >>> G.node[1]
- {'time': '5pm'}
- >>> G.node[1]['room'] = 714
- >>> del G.node[1]['room'] # remove attribute
- >>> G.nodes(data=True)
- [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
-
- Warning: adding a node to G.node does not add it to the graph.
-
- Add edge attributes using add_edge(), add_edges_from(), subscript
- notation, or G.edge.
-
- >>> G.add_edge(1, 2, weight=4.7 )
- >>> G.add_edges_from([(3,4),(4,5)], color='red')
- >>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
- >>> G[1][2]['weight'] = 4.7
- >>> G.edge[1][2]['weight'] = 4
-
- **Shortcuts:**
-
- Many common graph features allow python syntax to speed reporting.
-
- >>> 1 in G # check if node in graph
- True
- >>> [n for n in G if n<3] # iterate through nodes
- [1, 2]
- >>> len(G) # number of nodes in graph
- 5
-
- The fastest way to traverse all edges of a graph is via
- adjacency_iter(), but the edges() method is often more convenient.
-
- >>> for n,nbrsdict in G.adjacency_iter():
- ... for nbr,eattr in nbrsdict.items():
- ... if 'weight' in eattr:
- ... (n,nbr,eattr['weight'])
- (1, 2, 4)
- (2, 3, 8)
- >>> [ (u,v,edata['weight']) for u,v,edata in G.edges(data=True) if 'weight' in edata ]
- [(1, 2, 4), (2, 3, 8)]
-
- **Reporting:**
-
- Simple graph information is obtained using methods.
- Iterator versions of many reporting methods exist for efficiency.
- Methods exist for reporting nodes(), edges(), neighbors() and degree()
- as well as the number of nodes and edges.
-
- For details on these and other miscellaneous methods, see below.
- """
- def __init__(self, data=None, **attr):
- """Initialize a graph with edges, name, graph attributes.
-
- Parameters
- ----------
- data : input graph
- Data to initialize graph. If data=None (default) an empty
- graph is created. The data can be an edge list, or any
- NetworkX graph object. If the corresponding optional Python
- packages are installed the data can also be a NumPy matrix
- or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- name : string, optional (default='')
- An optional name for the graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to graph as key=value pairs.
-
- See Also
- --------
- convert
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G = nx.Graph(name='my graph')
- >>> e = [(1,2),(2,3),(3,4)] # list of edges
- >>> G = nx.Graph(e)
-
- Arbitrary graph attribute pairs (key=value) may be assigned
-
- >>> G=nx.Graph(e, day="Friday")
- >>> G.graph
- {'day': 'Friday'}
-
- """
- self.graph = {} # dictionary for graph attributes
- self.node = {} # dictionary for node attributes
- # We store two adjacency lists:
- # the predecessors of node n are stored in the dict self.pred
- # the successors of node n are stored in the dict self.succ=self.adj
- self.adj = {} # empty adjacency dictionary
- self.pred = {} # predecessor
- self.succ = self.adj # successor
-
- # attempt to load graph with data
- if data is not None:
- convert.to_networkx_graph(data,create_using=self)
- # load graph attributes (must be after convert)
- self.graph.update(attr)
- self.edge=self.adj
-
-
- def add_node(self, n, attr_dict=None, **attr):
- """Add a single node n and update node attributes.
-
- Parameters
- ----------
- n : node
- A node can be any hashable Python object except None.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of node attributes. Key/value pairs will
- update existing data associated with the node.
- attr : keyword arguments, optional
- Set or change attributes using key=value.
-
- See Also
- --------
- add_nodes_from
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_node(1)
- >>> G.add_node('Hello')
- >>> K3 = nx.Graph([(0,1),(1,2),(2,0)])
- >>> G.add_node(K3)
- >>> G.number_of_nodes()
- 3
-
- Use keywords set/change node attributes:
-
- >>> G.add_node(1,size=10)
- >>> G.add_node(3,weight=0.4,UTM=('13S',382871,3972649))
-
- Notes
- -----
- A hashable object is one that can be used as a key in a Python
- dictionary. This includes strings, numbers, tuples of strings
- and numbers, etc.
-
- On many platforms hashable items also include mutables such as
- NetworkX Graphs, though one should be careful that the hash
- doesn't change on mutables.
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- if n not in self.succ:
- self.succ[n] = {}
- self.pred[n] = {}
- self.node[n] = attr_dict
- else: # update attr even if node already exists
- self.node[n].update(attr_dict)
-
-
- def add_nodes_from(self, nodes, **attr):
- """Add multiple nodes.
-
- Parameters
- ----------
- nodes : iterable container
- A container of nodes (list, dict, set, etc.).
- OR
- A container of (node, attribute dict) tuples.
- Node attributes are updated using the attribute dict.
- attr : keyword arguments, optional (default= no attributes)
- Update attributes for all nodes in nodes.
- Node attributes specified in nodes as a tuple
- take precedence over attributes specified generally.
-
- See Also
- --------
- add_node
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_nodes_from('Hello')
- >>> K3 = nx.Graph([(0,1),(1,2),(2,0)])
- >>> G.add_nodes_from(K3)
- >>> sorted(G.nodes(),key=str)
- [0, 1, 2, 'H', 'e', 'l', 'o']
-
- Use keywords to update specific node attributes for every node.
-
- >>> G.add_nodes_from([1,2], size=10)
- >>> G.add_nodes_from([3,4], weight=0.4)
-
- Use (node, attrdict) tuples to update attributes for specific
- nodes.
-
- >>> G.add_nodes_from([(1,dict(size=11)), (2,{'color':'blue'})])
- >>> G.node[1]['size']
- 11
- >>> H = nx.Graph()
- >>> H.add_nodes_from(G.nodes(data=True))
- >>> H.node[1]['size']
- 11
-
- """
- for n in nodes:
- try:
- newnode=n not in self.succ
- except TypeError:
- nn,ndict = n
- if nn not in self.succ:
- self.succ[nn] = {}
- self.pred[nn] = {}
- newdict = attr.copy()
- newdict.update(ndict)
- self.node[nn] = newdict
- else:
- olddict = self.node[nn]
- olddict.update(attr)
- olddict.update(ndict)
- continue
- if newnode:
- self.succ[n] = {}
- self.pred[n] = {}
- self.node[n] = attr.copy()
- else:
- self.node[n].update(attr)
-
- def remove_node(self, n):
- """Remove node n.
-
- Removes the node n and all adjacent edges.
- Attempting to remove a non-existent node will raise an exception.
-
- Parameters
- ----------
- n : node
- A node in the graph
-
- Raises
- -------
- NetworkXError
- If n is not in the graph.
-
- See Also
- --------
- remove_nodes_from
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> G.edges()
- [(0, 1), (1, 2)]
- >>> G.remove_node(1)
- >>> G.edges()
- []
-
- """
- try:
- nbrs=self.succ[n]
- del self.node[n]
- except KeyError: # NetworkXError if n not in self
- raise NetworkXError("The node %s is not in the digraph."%(n,))
- for u in nbrs:
- del self.pred[u][n] # remove all edges n-u in digraph
- del self.succ[n] # remove node from succ
- for u in self.pred[n]:
- del self.succ[u][n] # remove all edges n-u in digraph
- del self.pred[n] # remove node from pred
-
-
- def remove_nodes_from(self, nbunch):
- """Remove multiple nodes.
-
- Parameters
- ----------
- nodes : iterable container
- A container of nodes (list, dict, set, etc.). If a node
- in the container is not in the graph it is silently
- ignored.
-
- See Also
- --------
- remove_node
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> e = G.nodes()
- >>> e
- [0, 1, 2]
- >>> G.remove_nodes_from(e)
- >>> G.nodes()
- []
-
- """
- for n in nbunch:
- try:
- succs=self.succ[n]
- del self.node[n]
- for u in succs:
- del self.pred[u][n] # remove all edges n-u in digraph
- del self.succ[n] # now remove node
- for u in self.pred[n]:
- del self.succ[u][n] # remove all edges n-u in digraph
- del self.pred[n] # now remove node
- except KeyError:
- pass # silent failure on remove
-
-
- def add_edge(self, u, v, attr_dict=None, **attr):
- """Add an edge between u and v.
-
- The nodes u and v will be automatically added if they are
- not already in the graph.
-
- Edge attributes can be specified with keywords or by providing
- a dictionary with key/value pairs. See examples below.
-
- Parameters
- ----------
- u,v : nodes
- Nodes can be, for example, strings or numbers.
- Nodes must be hashable (and not None) Python objects.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with the edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
- See Also
- --------
- add_edges_from : add a collection of edges
-
- Notes
- -----
- Adding an edge that already exists updates the edge data.
-
- Many NetworkX algorithms designed for weighted graphs use as
- the edge weight a numerical value assigned to a keyword
- which by default is 'weight'.
-
- Examples
- --------
- The following all add the edge e=(1,2) to graph G:
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> e = (1,2)
- >>> G.add_edge(1, 2) # explicit two-node form
- >>> G.add_edge(*e) # single edge as tuple of two nodes
- >>> G.add_edges_from( [(1,2)] ) # add edges from iterable container
-
- Associate data to edges using keywords:
-
- >>> G.add_edge(1, 2, weight=3)
- >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- # add nodes
- if u not in self.succ:
- self.succ[u]={}
- self.pred[u]={}
- self.node[u] = {}
- if v not in self.succ:
- self.succ[v]={}
- self.pred[v]={}
- self.node[v] = {}
- # add the edge
- datadict=self.adj[u].get(v,{})
- datadict.update(attr_dict)
- self.succ[u][v]=datadict
- self.pred[v][u]=datadict
-
- def add_edges_from(self, ebunch, attr_dict=None, **attr):
- """Add all the edges in ebunch.
-
- Parameters
- ----------
- ebunch : container of edges
- Each edge given in the container will be added to the
- graph. The edges must be given as as 2-tuples (u,v) or
- 3-tuples (u,v,d) where d is a dictionary containing edge
- data.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with each edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
-
- See Also
- --------
- add_edge : add a single edge
- add_weighted_edges_from : convenient way to add weighted edges
-
- Notes
- -----
- Adding the same edge twice has no effect but any edge data
- will be updated when each duplicate edge is added.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edges_from([(0,1),(1,2)]) # using a list of edge tuples
- >>> e = zip(range(0,3),range(1,4))
- >>> G.add_edges_from(e) # Add the path graph 0-1-2-3
-
- Associate data to edges
-
- >>> G.add_edges_from([(1,2),(2,3)], weight=3)
- >>> G.add_edges_from([(3,4),(1,4)], label='WN2898')
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dict.")
- # process ebunch
- for e in ebunch:
- ne = len(e)
- if ne==3:
- u,v,dd = e
- assert hasattr(dd,"update")
- elif ne==2:
- u,v = e
- dd = {}
- else:
- raise NetworkXError(\
- "Edge tuple %s must be a 2-tuple or 3-tuple."%(e,))
- if u not in self.succ:
- self.succ[u] = {}
- self.pred[u] = {}
- self.node[u] = {}
- if v not in self.succ:
- self.succ[v] = {}
- self.pred[v] = {}
- self.node[v] = {}
- datadict=self.adj[u].get(v,{})
- datadict.update(attr_dict)
- datadict.update(dd)
- self.succ[u][v] = datadict
- self.pred[v][u] = datadict
-
-
- def remove_edge(self, u, v):
- """Remove the edge between u and v.
-
- Parameters
- ----------
- u,v: nodes
- Remove the edge between nodes u and v.
-
- Raises
- ------
- NetworkXError
- If there is not an edge between u and v.
-
- See Also
- --------
- remove_edges_from : remove a collection of edges
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.remove_edge(0,1)
- >>> e = (1,2)
- >>> G.remove_edge(*e) # unpacks e from an edge tuple
- >>> e = (2,3,{'weight':7}) # an edge with attribute data
- >>> G.remove_edge(*e[:2]) # select first part of edge tuple
- """
- try:
- del self.succ[u][v]
- del self.pred[v][u]
- except KeyError:
- raise NetworkXError("The edge %s-%s not in graph."%(u,v))
-
-
- def remove_edges_from(self, ebunch):
- """Remove all edges specified in ebunch.
-
- Parameters
- ----------
- ebunch: list or container of edge tuples
- Each edge given in the list or container will be removed
- from the graph. The edges can be:
-
- - 2-tuples (u,v) edge between u and v.
- - 3-tuples (u,v,k) where k is ignored.
-
- See Also
- --------
- remove_edge : remove a single edge
-
- Notes
- -----
- Will fail silently if an edge in ebunch is not in the graph.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> ebunch=[(1,2),(2,3)]
- >>> G.remove_edges_from(ebunch)
- """
- for e in ebunch:
- (u,v)=e[:2] # ignore edge data
- if u in self.succ and v in self.succ[u]:
- del self.succ[u][v]
- del self.pred[v][u]
-
-
- def has_successor(self, u, v):
- """Return True if node u has successor v.
-
- This is true if graph has the edge u->v.
- """
- return (u in self.succ and v in self.succ[u])
-
- def has_predecessor(self, u, v):
- """Return True if node u has predecessor v.
-
- This is true if graph has the edge u<-v.
- """
- return (u in self.pred and v in self.pred[u])
-
- def successors_iter(self,n):
- """Return an iterator over successor nodes of n.
-
- neighbors_iter() and successors_iter() are the same.
- """
- try:
- return iter(self.succ[n])
- except KeyError:
- raise NetworkXError("The node %s is not in the digraph."%(n,))
-
- def predecessors_iter(self,n):
- """Return an iterator over predecessor nodes of n."""
- try:
- return iter(self.pred[n])
- except KeyError:
- raise NetworkXError("The node %s is not in the digraph."%(n,))
-
- def successors(self, n):
- """Return a list of successor nodes of n.
-
- neighbors() and successors() are the same function.
- """
- return list(self.successors_iter(n))
-
- def predecessors(self, n):
- """Return a list of predecessor nodes of n."""
- return list(self.predecessors_iter(n))
-
-
- # digraph definitions
- neighbors = successors
- neighbors_iter = successors_iter
-
- def edges_iter(self, nbunch=None, data=False):
- """Return an iterator over the edges.
-
- Edges are returned as tuples with optional data
- in the order (node, neighbor, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict in 3-tuple (u,v,data).
-
- Returns
- -------
- edge_iter : iterator
- An iterator of (u,v) or (u,v,d) tuples of edges.
-
- See Also
- --------
- edges : return a list of edges
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs this returns the out-edges.
-
- Examples
- --------
- >>> G = nx.DiGraph() # or MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> [e for e in G.edges_iter()]
- [(0, 1), (1, 2), (2, 3)]
- >>> list(G.edges_iter(data=True)) # default data is {} (empty dict)
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
- >>> list(G.edges_iter([0,2]))
- [(0, 1), (2, 3)]
- >>> list(G.edges_iter(0))
- [(0, 1)]
-
- """
- if nbunch is None:
- nodes_nbrs=self.adj.items()
- else:
- nodes_nbrs=((n,self.adj[n]) for n in self.nbunch_iter(nbunch))
- if data:
- for n,nbrs in nodes_nbrs:
- for nbr,data in nbrs.items():
- yield (n,nbr,data)
- else:
- for n,nbrs in nodes_nbrs:
- for nbr in nbrs:
- yield (n,nbr)
-
- # alias out_edges to edges
- out_edges_iter=edges_iter
- out_edges=Graph.edges
-
- def in_edges_iter(self, nbunch=None, data=False):
- """Return an iterator over the incoming edges.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict in 3-tuple (u,v,data).
-
- Returns
- -------
- in_edge_iter : iterator
- An iterator of (u,v) or (u,v,d) tuples of incoming edges.
-
- See Also
- --------
- edges_iter : return an iterator of edges
- """
- if nbunch is None:
- nodes_nbrs=self.pred.items()
- else:
- nodes_nbrs=((n,self.pred[n]) for n in self.nbunch_iter(nbunch))
- if data:
- for n,nbrs in nodes_nbrs:
- for nbr,data in nbrs.items():
- yield (nbr,n,data)
- else:
- for n,nbrs in nodes_nbrs:
- for nbr in nbrs:
- yield (nbr,n)
-
- def in_edges(self, nbunch=None, data=False):
- """Return a list of the incoming edges.
-
- See Also
- --------
- edges : return a list of edges
- """
- return list(self.in_edges_iter(nbunch, data))
-
- def degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, degree).
-
- The node degree is the number of edges adjacent to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, degree).
-
- See Also
- --------
- degree, in_degree, out_degree, in_degree_iter, out_degree_iter
-
- Examples
- --------
- >>> G = nx.DiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> list(G.degree_iter(0)) # node 0 with degree 1
- [(0, 1)]
- >>> list(G.degree_iter([0,1]))
- [(0, 1), (1, 2)]
-
- """
- if nbunch is None:
- nodes_nbrs=zip(iter(self.succ.items()),iter(self.pred.items()))
- else:
- nodes_nbrs=zip(
- ((n,self.succ[n]) for n in self.nbunch_iter(nbunch)),
- ((n,self.pred[n]) for n in self.nbunch_iter(nbunch)))
-
- if weight is None:
- for (n,succ),(n2,pred) in nodes_nbrs:
- yield (n,len(succ)+len(pred))
- else:
- # edge weighted graph - degree is sum of edge weights
- for (n,succ),(n2,pred) in nodes_nbrs:
- yield (n,
- sum((succ[nbr].get(weight,1) for nbr in succ))+
- sum((pred[nbr].get(weight,1) for nbr in pred)))
-
-
- def in_degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, in-degree).
-
- The node in-degree is the number of edges pointing in to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, in-degree).
-
- See Also
- --------
- degree, in_degree, out_degree, out_degree_iter
-
- Examples
- --------
- >>> G = nx.DiGraph()
- >>> G.add_path([0,1,2,3])
- >>> list(G.in_degree_iter(0)) # node 0 with degree 0
- [(0, 0)]
- >>> list(G.in_degree_iter([0,1]))
- [(0, 0), (1, 1)]
-
- """
- if nbunch is None:
- nodes_nbrs=self.pred.items()
- else:
- nodes_nbrs=((n,self.pred[n]) for n in self.nbunch_iter(nbunch))
-
- if weight is None:
- for n,nbrs in nodes_nbrs:
- yield (n,len(nbrs))
- else:
- # edge weighted graph - degree is sum of edge weights
- for n,nbrs in nodes_nbrs:
- yield (n, sum(data.get(weight,1) for data in nbrs.values()))
-
-
- def out_degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, out-degree).
-
- The node out-degree is the number of edges pointing out of the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, out-degree).
-
- See Also
- --------
- degree, in_degree, out_degree, in_degree_iter
-
- Examples
- --------
- >>> G = nx.DiGraph()
- >>> G.add_path([0,1,2,3])
- >>> list(G.out_degree_iter(0)) # node 0 with degree 1
- [(0, 1)]
- >>> list(G.out_degree_iter([0,1]))
- [(0, 1), (1, 1)]
-
- """
- if nbunch is None:
- nodes_nbrs=self.succ.items()
- else:
- nodes_nbrs=((n,self.succ[n]) for n in self.nbunch_iter(nbunch))
-
- if weight is None:
- for n,nbrs in nodes_nbrs:
- yield (n,len(nbrs))
- else:
- # edge weighted graph - degree is sum of edge weights
- for n,nbrs in nodes_nbrs:
- yield (n, sum(data.get(weight,1) for data in nbrs.values()))
-
-
- def in_degree(self, nbunch=None, weight=None):
- """Return the in-degree of a node or nodes.
-
- The node in-degree is the number of edges pointing in to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd : dictionary, or number
- A dictionary with nodes as keys and in-degree as values or
- a number if a single node is specified.
-
- See Also
- --------
- degree, out_degree, in_degree_iter
-
- Examples
- --------
- >>> G = nx.DiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> G.in_degree(0)
- 0
- >>> G.in_degree([0,1])
- {0: 0, 1: 1}
- >>> list(G.in_degree([0,1]).values())
- [0, 1]
- """
- if nbunch in self: # return a single node
- return next(self.in_degree_iter(nbunch,weight))[1]
- else: # return a dict
- return dict(self.in_degree_iter(nbunch,weight))
-
- def out_degree(self, nbunch=None, weight=None):
- """Return the out-degree of a node or nodes.
-
- The node out-degree is the number of edges pointing out of the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd : dictionary, or number
- A dictionary with nodes as keys and out-degree as values or
- a number if a single node is specified.
-
- Examples
- --------
- >>> G = nx.DiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> G.out_degree(0)
- 1
- >>> G.out_degree([0,1])
- {0: 1, 1: 1}
- >>> list(G.out_degree([0,1]).values())
- [1, 1]
-
-
- """
- if nbunch in self: # return a single node
- return next(self.out_degree_iter(nbunch,weight))[1]
- else: # return a dict
- return dict(self.out_degree_iter(nbunch,weight))
-
- def clear(self):
- """Remove all nodes and edges from the graph.
-
- This also removes the name, and all graph, node, and edge attributes.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.clear()
- >>> G.nodes()
- []
- >>> G.edges()
- []
-
- """
- self.succ.clear()
- self.pred.clear()
- self.node.clear()
- self.graph.clear()
-
-
- def is_multigraph(self):
- """Return True if graph is a multigraph, False otherwise."""
- return False
-
-
- def is_directed(self):
- """Return True if graph is directed, False otherwise."""
- return True
-
- def to_directed(self):
- """Return a directed copy of the graph.
-
- Returns
- -------
- G : DiGraph
- A deepcopy of the graph.
-
- Notes
- -----
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar D=DiGraph(G) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
-
- Examples
- --------
- >>> G = nx.Graph() # or MultiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1), (1, 0)]
-
- If already directed, return a (deep) copy
-
- >>> G = nx.DiGraph() # or MultiDiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1)]
- """
- return deepcopy(self)
-
- def to_undirected(self, reciprocal=False):
- """Return an undirected representation of the digraph.
-
- Parameters
- ----------
- reciprocal : bool (optional)
- If True only keep edges that appear in both directions
- in the original digraph.
-
- Returns
- -------
- G : Graph
- An undirected graph with the same name and nodes and
- with edge (u,v,data) if either (u,v,data) or (v,u,data)
- is in the digraph. If both edges exist in digraph and
- their edge data is different, only one edge is created
- with an arbitrary choice of which edge data to use.
- You must check and correct for this manually if desired.
-
- Notes
- -----
- If edges in both directions (u,v) and (v,u) exist in the
- graph, attributes for the new undirected edge will be a combination of
- the attributes of the directed edges. The edge data is updated
- in the (arbitrary) order that the edges are encountered. For
- more customized control of the edge attributes use add_edge().
-
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar G=DiGraph(D) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
- """
- H=Graph()
- H.name=self.name
- H.add_nodes_from(self)
- if reciprocal is True:
- H.add_edges_from( (u,v,deepcopy(d))
- for u,nbrs in self.adjacency_iter()
- for v,d in nbrs.items()
- if v in self.pred[u])
- else:
- H.add_edges_from( (u,v,deepcopy(d))
- for u,nbrs in self.adjacency_iter()
- for v,d in nbrs.items() )
- H.graph=deepcopy(self.graph)
- H.node=deepcopy(self.node)
- return H
-
-
- def reverse(self, copy=True):
- """Return the reverse of the graph.
-
- The reverse is a graph with the same nodes and edges
- but with the directions of the edges reversed.
-
- Parameters
- ----------
- copy : bool optional (default=True)
- If True, return a new DiGraph holding the reversed edges.
- If False, reverse the reverse graph is created using
- the original graph (this changes the original graph).
- """
- if copy:
- H = self.__class__(name="Reverse of (%s)"%self.name)
- H.add_nodes_from(self)
- H.add_edges_from( (v,u,deepcopy(d)) for u,v,d
- in self.edges(data=True) )
- H.graph=deepcopy(self.graph)
- H.node=deepcopy(self.node)
- else:
- self.pred,self.succ=self.succ,self.pred
- self.adj=self.succ
- H=self
- return H
-
-
- def subgraph(self, nbunch):
- """Return the subgraph induced on nodes in nbunch.
-
- The induced subgraph of the graph contains the nodes in nbunch
- and the edges between those nodes.
-
- Parameters
- ----------
- nbunch : list, iterable
- A container of nodes which will be iterated through once.
-
- Returns
- -------
- G : Graph
- A subgraph of the graph with the same edge attributes.
-
- Notes
- -----
- The graph, edge or node attributes just point to the original graph.
- So changes to the node or edge structure will not be reflected in
- the original graph while changes to the attributes will.
-
- To create a subgraph with its own copy of the edge/node attributes use:
- nx.Graph(G.subgraph(nbunch))
-
- If edge attributes are containers, a deep copy can be obtained using:
- G.subgraph(nbunch).copy()
-
- For an inplace reduction of a graph to a subgraph you can remove nodes:
- G.remove_nodes_from([ n in G if n not in set(nbunch)])
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> H = G.subgraph([0,1,2])
- >>> H.edges()
- [(0, 1), (1, 2)]
- """
- bunch = self.nbunch_iter(nbunch)
- # create new graph and copy subgraph into it
- H = self.__class__()
- # copy node and attribute dictionaries
- for n in bunch:
- H.node[n]=self.node[n]
- # namespace shortcuts for speed
- H_succ=H.succ
- H_pred=H.pred
- self_succ=self.succ
- # add nodes
- for n in H:
- H_succ[n]={}
- H_pred[n]={}
- # add edges
- for u in H_succ:
- Hnbrs=H_succ[u]
- for v,datadict in self_succ[u].items():
- if v in H_succ:
- # add both representations of edge: u-v and v-u
- Hnbrs[v]=datadict
- H_pred[v][u]=datadict
- H.graph=self.graph
- return H
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/function.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/function.py
deleted file mode 100644
index 0c5e208..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/function.py
+++ /dev/null
@@ -1,423 +0,0 @@
-"""Functional interface to graph methods and assorted utilities.
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-#
-import networkx as nx
-import itertools
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-__all__ = ['nodes', 'edges', 'degree', 'degree_histogram', 'neighbors',
- 'number_of_nodes', 'number_of_edges', 'density',
- 'nodes_iter', 'edges_iter', 'is_directed','info',
- 'freeze','is_frozen','subgraph','create_empty_copy',
- 'set_node_attributes','get_node_attributes',
- 'set_edge_attributes','get_edge_attributes',
- 'all_neighbors','non_neighbors']
-
-def nodes(G):
- """Return a copy of the graph nodes in a list."""
- return G.nodes()
-
-def nodes_iter(G):
- """Return an iterator over the graph nodes."""
- return G.nodes_iter()
-
-def edges(G,nbunch=None):
- """Return list of edges adjacent to nodes in nbunch.
-
- Return all edges if nbunch is unspecified or nbunch=None.
-
- For digraphs, edges=out_edges
- """
- return G.edges(nbunch)
-
-def edges_iter(G,nbunch=None):
- """Return iterator over edges adjacent to nodes in nbunch.
-
- Return all edges if nbunch is unspecified or nbunch=None.
-
- For digraphs, edges=out_edges
- """
- return G.edges_iter(nbunch)
-
-def degree(G,nbunch=None,weight=None):
- """Return degree of single node or of nbunch of nodes.
- If nbunch is ommitted, then return degrees of *all* nodes.
- """
- return G.degree(nbunch,weight)
-
-def neighbors(G,n):
- """Return a list of nodes connected to node n. """
- return G.neighbors(n)
-
-def number_of_nodes(G):
- """Return the number of nodes in the graph."""
- return G.number_of_nodes()
-
-def number_of_edges(G):
- """Return the number of edges in the graph. """
- return G.number_of_edges()
-
-def density(G):
- r"""Return the density of a graph.
-
- The density for undirected graphs is
-
- .. math::
-
- d = \frac{2m}{n(n-1)},
-
- and for directed graphs is
-
- .. math::
-
- d = \frac{m}{n(n-1)},
-
- where `n` is the number of nodes and `m` is the number of edges in `G`.
-
- Notes
- -----
- The density is 0 for a graph without edges and 1 for a complete graph.
- The density of multigraphs can be higher than 1.
-
- Self loops are counted in the total number of edges so graphs with self
- loops can have density higher than 1.
- """
- n=number_of_nodes(G)
- m=number_of_edges(G)
- if m==0 or n <= 1:
- d=0.0
- else:
- if G.is_directed():
- d=m/float(n*(n-1))
- else:
- d= m*2.0/float(n*(n-1))
- return d
-
-def degree_histogram(G):
- """Return a list of the frequency of each degree value.
-
- Parameters
- ----------
- G : Networkx graph
- A graph
-
- Returns
- -------
- hist : list
- A list of frequencies of degrees.
- The degree values are the index in the list.
-
- Notes
- -----
- Note: the bins are width one, hence len(list) can be large
- (Order(number_of_edges))
- """
- degseq=list(G.degree().values())
- dmax=max(degseq)+1
- freq= [ 0 for d in range(dmax) ]
- for d in degseq:
- freq[d] += 1
- return freq
-
-def is_directed(G):
- """ Return True if graph is directed."""
- return G.is_directed()
-
-
-def freeze(G):
- """Modify graph to prevent further change by adding or removing
- nodes or edges.
-
- Node and edge data can still be modified.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_path([0,1,2,3])
- >>> G=nx.freeze(G)
- >>> try:
- ... G.add_edge(4,5)
- ... except nx.NetworkXError as e:
- ... print(str(e))
- Frozen graph can't be modified
-
- Notes
- -----
- To "unfreeze" a graph you must make a copy by creating a new graph object:
-
- >>> graph = nx.path_graph(4)
- >>> frozen_graph = nx.freeze(graph)
- >>> unfrozen_graph = nx.Graph(frozen_graph)
- >>> nx.is_frozen(unfrozen_graph)
- False
-
- See Also
- --------
- is_frozen
- """
- def frozen(*args):
- raise nx.NetworkXError("Frozen graph can't be modified")
- G.add_node=frozen
- G.add_nodes_from=frozen
- G.remove_node=frozen
- G.remove_nodes_from=frozen
- G.add_edge=frozen
- G.add_edges_from=frozen
- G.remove_edge=frozen
- G.remove_edges_from=frozen
- G.clear=frozen
- G.frozen=True
- return G
-
-def is_frozen(G):
- """Return True if graph is frozen.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- See Also
- --------
- freeze
- """
- try:
- return G.frozen
- except AttributeError:
- return False
-
-def subgraph(G, nbunch):
- """Return the subgraph induced on nodes in nbunch.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nbunch : list, iterable
- A container of nodes that will be iterated through once (thus
- it should be an iterator or be iterable). Each element of the
- container should be a valid node type: any hashable type except
- None. If nbunch is None, return all edges data in the graph.
- Nodes in nbunch that are not in the graph will be (quietly)
- ignored.
-
- Notes
- -----
- subgraph(G) calls G.subgraph()
- """
- return G.subgraph(nbunch)
-
-def create_empty_copy(G,with_nodes=True):
- """Return a copy of the graph G with all of the edges removed.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- with_nodes : bool (default=True)
- Include nodes.
-
- Notes
- -----
- Graph, node, and edge data is not propagated to the new graph.
- """
- H=G.__class__()
- if with_nodes:
- H.add_nodes_from(G)
- return H
-
-
-def info(G, n=None):
- """Print short summary of information for the graph G or the node n.
-
- Parameters
- ----------
- G : Networkx graph
- A graph
- n : node (any hashable)
- A node in the graph G
- """
- info='' # append this all to a string
- if n is None:
- info+="Name: %s\n"%G.name
- type_name = [type(G).__name__]
- info+="Type: %s\n"%",".join(type_name)
- info+="Number of nodes: %d\n"%G.number_of_nodes()
- info+="Number of edges: %d\n"%G.number_of_edges()
- nnodes=G.number_of_nodes()
- if len(G) > 0:
- if G.is_directed():
- info+="Average in degree: %8.4f\n"%\
- (sum(G.in_degree().values())/float(nnodes))
- info+="Average out degree: %8.4f"%\
- (sum(G.out_degree().values())/float(nnodes))
- else:
- s=sum(G.degree().values())
- info+="Average degree: %8.4f"%\
- (float(s)/float(nnodes))
-
- else:
- if n not in G:
- raise nx.NetworkXError("node %s not in graph"%(n,))
- info+="Node % s has the following properties:\n"%n
- info+="Degree: %d\n"%G.degree(n)
- info+="Neighbors: "
- info+=' '.join(str(nbr) for nbr in G.neighbors(n))
- return info
-
-def set_node_attributes(G,name,attributes):
- """Set node attributes from dictionary of nodes and values
-
- Parameters
- ----------
- G : NetworkX Graph
-
- name : string
- Attribute name
-
- attributes: dict
- Dictionary of attributes keyed by node.
-
- Examples
- --------
- >>> G=nx.path_graph(3)
- >>> bb=nx.betweenness_centrality(G)
- >>> nx.set_node_attributes(G,'betweenness',bb)
- >>> G.node[1]['betweenness']
- 1.0
- """
- for node,value in attributes.items():
- G.node[node][name]=value
-
-def get_node_attributes(G,name):
- """Get node attributes from graph
-
- Parameters
- ----------
- G : NetworkX Graph
-
- name : string
- Attribute name
-
- Returns
- -------
- Dictionary of attributes keyed by node.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_nodes_from([1,2,3],color='red')
- >>> color=nx.get_node_attributes(G,'color')
- >>> color[1]
- 'red'
- """
- return dict( (n,d[name]) for n,d in G.node.items() if name in d)
-
-
-def set_edge_attributes(G,name,attributes):
- """Set edge attributes from dictionary of edge tuples and values
-
- Parameters
- ----------
- G : NetworkX Graph
-
- name : string
- Attribute name
-
- attributes: dict
- Dictionary of attributes keyed by edge (tuple).
-
- Examples
- --------
- >>> G=nx.path_graph(3)
- >>> bb=nx.edge_betweenness_centrality(G, normalized=False)
- >>> nx.set_edge_attributes(G,'betweenness',bb)
- >>> G[1][2]['betweenness']
- 2.0
- """
- for (u,v),value in attributes.items():
- G[u][v][name]=value
-
-def get_edge_attributes(G,name):
- """Get edge attributes from graph
-
- Parameters
- ----------
- G : NetworkX Graph
-
- name : string
- Attribute name
-
- Returns
- -------
- Dictionary of attributes keyed by node.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_path([1,2,3],color='red')
- >>> color=nx.get_edge_attributes(G,'color')
- >>> color[(1,2)]
- 'red'
- """
- return dict( ((u,v),d[name]) for u,v,d in G.edges(data=True) if name in d)
-
-
-def all_neighbors(graph, node):
- """ Returns all of the neighbors of a node in the graph.
-
- If the graph is directed returns predecessors as well as successors.
-
- Parameters
- ----------
- graph : NetworkX graph
- Graph to find neighbors.
-
- node : node
- The node whose neighbors will be returned.
-
- Returns
- -------
- neighbors : iterator
- Iterator of neighbors
- """
- if graph.is_directed():
- values = itertools.chain.from_iterable([graph.predecessors_iter(node),
- graph.successors_iter(node)])
- else:
- values = graph.neighbors_iter(node)
-
- return values
-
-def non_neighbors(graph, node):
- """Returns the non-neighbors of the node in the graph.
-
- Parameters
- ----------
- graph : NetworkX graph
- Graph to find neighbors.
-
- node : node
- The node whose neighbors will be returned.
-
- Returns
- -------
- non_neighbors : iterator
- Iterator of nodes in the graph that are not neighbors of the node.
- """
- nbors = set(neighbors(graph, node)) | set([node])
- return (nnode for nnode in graph if nnode not in nbors)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/graph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/graph.py
deleted file mode 100644
index 9ef7c23..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/graph.py
+++ /dev/null
@@ -1,1816 +0,0 @@
-"""Base class for undirected graphs.
-
-The Graph class allows any hashable object as a node
-and can associate key/value attribute pairs with each undirected edge.
-
-Self-loops are allowed but multiple edges are not (see MultiGraph).
-
-For directed graphs see DiGraph and MultiDiGraph.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from copy import deepcopy
-import networkx as nx
-from networkx.exception import NetworkXError
-import networkx.convert as convert
-
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-
-class Graph(object):
- """
- Base class for undirected graphs.
-
- A Graph stores nodes and edges with optional data, or attributes.
-
- Graphs hold undirected edges. Self loops are allowed but multiple
- (parallel) edges are not.
-
- Nodes can be arbitrary (hashable) Python objects with optional
- key/value attributes.
-
- Edges are represented as links between nodes with optional
- key/value attributes.
-
- Parameters
- ----------
- data : input graph
- Data to initialize graph. If data=None (default) an empty
- graph is created. The data can be an edge list, or any
- NetworkX graph object. If the corresponding optional Python
- packages are installed the data can also be a NumPy matrix
- or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to graph as key=value pairs.
-
- See Also
- --------
- DiGraph
- MultiGraph
- MultiDiGraph
-
- Examples
- --------
- Create an empty graph structure (a "null graph") with no nodes and
- no edges.
-
- >>> G = nx.Graph()
-
- G can be grown in several ways.
-
- **Nodes:**
-
- Add one node at a time:
-
- >>> G.add_node(1)
-
- Add the nodes from any container (a list, dict, set or
- even the lines from a file or the nodes from another graph).
-
- >>> G.add_nodes_from([2,3])
- >>> G.add_nodes_from(range(100,110))
- >>> H=nx.Graph()
- >>> H.add_path([0,1,2,3,4,5,6,7,8,9])
- >>> G.add_nodes_from(H)
-
- In addition to strings and integers any hashable Python object
- (except None) can represent a node, e.g. a customized node object,
- or even another Graph.
-
- >>> G.add_node(H)
-
- **Edges:**
-
- G can also be grown by adding edges.
-
- Add one edge,
-
- >>> G.add_edge(1, 2)
-
- a list of edges,
-
- >>> G.add_edges_from([(1,2),(1,3)])
-
- or a collection of edges,
-
- >>> G.add_edges_from(H.edges())
-
- If some edges connect nodes not yet in the graph, the nodes
- are added automatically. There are no errors when adding
- nodes or edges that already exist.
-
- **Attributes:**
-
- Each graph, node, and edge can hold key/value attribute pairs
- in an associated attribute dictionary (the keys must be hashable).
- By default these are empty, but can be added or changed using
- add_edge, add_node or direct manipulation of the attribute
- dictionaries named graph, node and edge respectively.
-
- >>> G = nx.Graph(day="Friday")
- >>> G.graph
- {'day': 'Friday'}
-
- Add node attributes using add_node(), add_nodes_from() or G.node
-
- >>> G.add_node(1, time='5pm')
- >>> G.add_nodes_from([3], time='2pm')
- >>> G.node[1]
- {'time': '5pm'}
- >>> G.node[1]['room'] = 714
- >>> del G.node[1]['room'] # remove attribute
- >>> G.nodes(data=True)
- [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
-
- Warning: adding a node to G.node does not add it to the graph.
-
- Add edge attributes using add_edge(), add_edges_from(), subscript
- notation, or G.edge.
-
- >>> G.add_edge(1, 2, weight=4.7 )
- >>> G.add_edges_from([(3,4),(4,5)], color='red')
- >>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
- >>> G[1][2]['weight'] = 4.7
- >>> G.edge[1][2]['weight'] = 4
-
- **Shortcuts:**
-
- Many common graph features allow python syntax to speed reporting.
-
- >>> 1 in G # check if node in graph
- True
- >>> [n for n in G if n<3] # iterate through nodes
- [1, 2]
- >>> len(G) # number of nodes in graph
- 5
-
- The fastest way to traverse all edges of a graph is via
- adjacency_iter(), but the edges() method is often more convenient.
-
- >>> for n,nbrsdict in G.adjacency_iter():
- ... for nbr,eattr in nbrsdict.items():
- ... if 'weight' in eattr:
- ... (n,nbr,eattr['weight'])
- (1, 2, 4)
- (2, 1, 4)
- (2, 3, 8)
- (3, 2, 8)
- >>> [ (u,v,edata['weight']) for u,v,edata in G.edges(data=True) if 'weight' in edata ]
- [(1, 2, 4), (2, 3, 8)]
-
- **Reporting:**
-
- Simple graph information is obtained using methods.
- Iterator versions of many reporting methods exist for efficiency.
- Methods exist for reporting nodes(), edges(), neighbors() and degree()
- as well as the number of nodes and edges.
-
- For details on these and other miscellaneous methods, see below.
- """
- def __init__(self, data=None, **attr):
- """Initialize a graph with edges, name, graph attributes.
-
- Parameters
- ----------
- data : input graph
- Data to initialize graph. If data=None (default) an empty
- graph is created. The data can be an edge list, or any
- NetworkX graph object. If the corresponding optional Python
- packages are installed the data can also be a NumPy matrix
- or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- name : string, optional (default='')
- An optional name for the graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to graph as key=value pairs.
-
- See Also
- --------
- convert
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G = nx.Graph(name='my graph')
- >>> e = [(1,2),(2,3),(3,4)] # list of edges
- >>> G = nx.Graph(e)
-
- Arbitrary graph attribute pairs (key=value) may be assigned
-
- >>> G=nx.Graph(e, day="Friday")
- >>> G.graph
- {'day': 'Friday'}
-
- """
- self.graph = {} # dictionary for graph attributes
- self.node = {} # empty node dict (created before convert)
- self.adj = {} # empty adjacency dict
- # attempt to load graph with data
- if data is not None:
- convert.to_networkx_graph(data,create_using=self)
- # load graph attributes (must be after convert)
- self.graph.update(attr)
- self.edge = self.adj
-
- @property
- def name(self):
- return self.graph.get('name','')
- @name.setter
- def name(self, s):
- self.graph['name']=s
-
- def __str__(self):
- """Return the graph name.
-
- Returns
- -------
- name : string
- The name of the graph.
-
- Examples
- --------
- >>> G = nx.Graph(name='foo')
- >>> str(G)
- 'foo'
- """
- return self.name
-
- def __iter__(self):
- """Iterate over the nodes. Use the expression 'for n in G'.
-
- Returns
- -------
- niter : iterator
- An iterator over all nodes in the graph.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- """
- return iter(self.node)
-
- def __contains__(self,n):
- """Return True if n is a node, False otherwise. Use the expression
- 'n in G'.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> 1 in G
- True
- """
- try:
- return n in self.node
- except TypeError:
- return False
-
- def __len__(self):
- """Return the number of nodes. Use the expression 'len(G)'.
-
- Returns
- -------
- nnodes : int
- The number of nodes in the graph.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> len(G)
- 4
-
- """
- return len(self.node)
-
- def __getitem__(self, n):
- """Return a dict of neighbors of node n. Use the expression 'G[n]'.
-
- Parameters
- ----------
- n : node
- A node in the graph.
-
- Returns
- -------
- adj_dict : dictionary
- The adjacency dictionary for nodes connected to n.
-
- Notes
- -----
- G[n] is similar to G.neighbors(n) but the internal data dictionary
- is returned instead of a list.
-
- Assigning G[n] will corrupt the internal graph data structure.
- Use G[n] for reading data only.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G[0]
- {1: {}}
- """
- return self.adj[n]
-
-
- def add_node(self, n, attr_dict=None, **attr):
- """Add a single node n and update node attributes.
-
- Parameters
- ----------
- n : node
- A node can be any hashable Python object except None.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of node attributes. Key/value pairs will
- update existing data associated with the node.
- attr : keyword arguments, optional
- Set or change attributes using key=value.
-
- See Also
- --------
- add_nodes_from
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_node(1)
- >>> G.add_node('Hello')
- >>> K3 = nx.Graph([(0,1),(1,2),(2,0)])
- >>> G.add_node(K3)
- >>> G.number_of_nodes()
- 3
-
- Use keywords set/change node attributes:
-
- >>> G.add_node(1,size=10)
- >>> G.add_node(3,weight=0.4,UTM=('13S',382871,3972649))
-
- Notes
- -----
- A hashable object is one that can be used as a key in a Python
- dictionary. This includes strings, numbers, tuples of strings
- and numbers, etc.
-
- On many platforms hashable items also include mutables such as
- NetworkX Graphs, though one should be careful that the hash
- doesn't change on mutables.
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- if n not in self.node:
- self.adj[n] = {}
- self.node[n] = attr_dict
- else: # update attr even if node already exists
- self.node[n].update(attr_dict)
-
-
- def add_nodes_from(self, nodes, **attr):
- """Add multiple nodes.
-
- Parameters
- ----------
- nodes : iterable container
- A container of nodes (list, dict, set, etc.).
- OR
- A container of (node, attribute dict) tuples.
- Node attributes are updated using the attribute dict.
- attr : keyword arguments, optional (default= no attributes)
- Update attributes for all nodes in nodes.
- Node attributes specified in nodes as a tuple
- take precedence over attributes specified generally.
-
- See Also
- --------
- add_node
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_nodes_from('Hello')
- >>> K3 = nx.Graph([(0,1),(1,2),(2,0)])
- >>> G.add_nodes_from(K3)
- >>> sorted(G.nodes(),key=str)
- [0, 1, 2, 'H', 'e', 'l', 'o']
-
- Use keywords to update specific node attributes for every node.
-
- >>> G.add_nodes_from([1,2], size=10)
- >>> G.add_nodes_from([3,4], weight=0.4)
-
- Use (node, attrdict) tuples to update attributes for specific
- nodes.
-
- >>> G.add_nodes_from([(1,dict(size=11)), (2,{'color':'blue'})])
- >>> G.node[1]['size']
- 11
- >>> H = nx.Graph()
- >>> H.add_nodes_from(G.nodes(data=True))
- >>> H.node[1]['size']
- 11
-
- """
- for n in nodes:
- try:
- newnode=n not in self.node
- except TypeError:
- nn,ndict = n
- if nn not in self.node:
- self.adj[nn] = {}
- newdict = attr.copy()
- newdict.update(ndict)
- self.node[nn] = newdict
- else:
- olddict = self.node[nn]
- olddict.update(attr)
- olddict.update(ndict)
- continue
- if newnode:
- self.adj[n] = {}
- self.node[n] = attr.copy()
- else:
- self.node[n].update(attr)
-
- def remove_node(self,n):
- """Remove node n.
-
- Removes the node n and all adjacent edges.
- Attempting to remove a non-existent node will raise an exception.
-
- Parameters
- ----------
- n : node
- A node in the graph
-
- Raises
- -------
- NetworkXError
- If n is not in the graph.
-
- See Also
- --------
- remove_nodes_from
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> G.edges()
- [(0, 1), (1, 2)]
- >>> G.remove_node(1)
- >>> G.edges()
- []
-
- """
- adj = self.adj
- try:
- nbrs = list(adj[n].keys()) # keys handles self-loops (allow mutation later)
- del self.node[n]
- except KeyError: # NetworkXError if n not in self
- raise NetworkXError("The node %s is not in the graph."%(n,))
- for u in nbrs:
- del adj[u][n] # remove all edges n-u in graph
- del adj[n] # now remove node
-
-
- def remove_nodes_from(self, nodes):
- """Remove multiple nodes.
-
- Parameters
- ----------
- nodes : iterable container
- A container of nodes (list, dict, set, etc.). If a node
- in the container is not in the graph it is silently
- ignored.
-
- See Also
- --------
- remove_node
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> e = G.nodes()
- >>> e
- [0, 1, 2]
- >>> G.remove_nodes_from(e)
- >>> G.nodes()
- []
-
- """
- adj = self.adj
- for n in nodes:
- try:
- del self.node[n]
- for u in list(adj[n].keys()): # keys() handles self-loops
- del adj[u][n] #(allows mutation of dict in loop)
- del adj[n]
- except KeyError:
- pass
-
-
- def nodes_iter(self, data=False):
- """Return an iterator over the nodes.
-
- Parameters
- ----------
- data : boolean, optional (default=False)
- If False the iterator returns nodes. If True
- return a two-tuple of node and node data dictionary
-
- Returns
- -------
- niter : iterator
- An iterator over nodes. If data=True the iterator gives
- two-tuples containing (node, node data, dictionary)
-
- Notes
- -----
- If the node data is not required it is simpler and equivalent
- to use the expression 'for n in G'.
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
-
- >>> [d for n,d in G.nodes_iter(data=True)]
- [{}, {}, {}]
- """
- if data:
- return iter(self.node.items())
- return iter(self.node)
-
- def nodes(self, data=False):
- """Return a list of the nodes in the graph.
-
- Parameters
- ----------
- data : boolean, optional (default=False)
- If False return a list of nodes. If True return a
- two-tuple of node and node data dictionary
-
- Returns
- -------
- nlist : list
- A list of nodes. If data=True a list of two-tuples containing
- (node, node data dictionary).
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> G.nodes()
- [0, 1, 2]
- >>> G.add_node(1, time='5pm')
- >>> G.nodes(data=True)
- [(0, {}), (1, {'time': '5pm'}), (2, {})]
- """
- return list(self.nodes_iter(data=data))
-
- def number_of_nodes(self):
- """Return the number of nodes in the graph.
-
- Returns
- -------
- nnodes : int
- The number of nodes in the graph.
-
- See Also
- --------
- order, __len__ which are identical
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> len(G)
- 3
- """
- return len(self.node)
-
- def order(self):
- """Return the number of nodes in the graph.
-
- Returns
- -------
- nnodes : int
- The number of nodes in the graph.
-
- See Also
- --------
- number_of_nodes, __len__ which are identical
-
- """
- return len(self.node)
-
- def has_node(self, n):
- """Return True if the graph contains the node n.
-
- Parameters
- ----------
- n : node
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2])
- >>> G.has_node(0)
- True
-
- It is more readable and simpler to use
-
- >>> 0 in G
- True
-
- """
- try:
- return n in self.node
- except TypeError:
- return False
-
- def add_edge(self, u, v, attr_dict=None, **attr):
- """Add an edge between u and v.
-
- The nodes u and v will be automatically added if they are
- not already in the graph.
-
- Edge attributes can be specified with keywords or by providing
- a dictionary with key/value pairs. See examples below.
-
- Parameters
- ----------
- u,v : nodes
- Nodes can be, for example, strings or numbers.
- Nodes must be hashable (and not None) Python objects.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with the edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
- See Also
- --------
- add_edges_from : add a collection of edges
-
- Notes
- -----
- Adding an edge that already exists updates the edge data.
-
- Many NetworkX algorithms designed for weighted graphs use as
- the edge weight a numerical value assigned to a keyword
- which by default is 'weight'.
-
- Examples
- --------
- The following all add the edge e=(1,2) to graph G:
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> e = (1,2)
- >>> G.add_edge(1, 2) # explicit two-node form
- >>> G.add_edge(*e) # single edge as tuple of two nodes
- >>> G.add_edges_from( [(1,2)] ) # add edges from iterable container
-
- Associate data to edges using keywords:
-
- >>> G.add_edge(1, 2, weight=3)
- >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
- """
- # set up attribute dictionary
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- # add nodes
- if u not in self.node:
- self.adj[u] = {}
- self.node[u] = {}
- if v not in self.node:
- self.adj[v] = {}
- self.node[v] = {}
- # add the edge
- datadict=self.adj[u].get(v,{})
- datadict.update(attr_dict)
- self.adj[u][v] = datadict
- self.adj[v][u] = datadict
-
-
- def add_edges_from(self, ebunch, attr_dict=None, **attr):
- """Add all the edges in ebunch.
-
- Parameters
- ----------
- ebunch : container of edges
- Each edge given in the container will be added to the
- graph. The edges must be given as as 2-tuples (u,v) or
- 3-tuples (u,v,d) where d is a dictionary containing edge
- data.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with each edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
-
- See Also
- --------
- add_edge : add a single edge
- add_weighted_edges_from : convenient way to add weighted edges
-
- Notes
- -----
- Adding the same edge twice has no effect but any edge data
- will be updated when each duplicate edge is added.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edges_from([(0,1),(1,2)]) # using a list of edge tuples
- >>> e = zip(range(0,3),range(1,4))
- >>> G.add_edges_from(e) # Add the path graph 0-1-2-3
-
- Associate data to edges
-
- >>> G.add_edges_from([(1,2),(2,3)], weight=3)
- >>> G.add_edges_from([(3,4),(1,4)], label='WN2898')
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- # process ebunch
- for e in ebunch:
- ne=len(e)
- if ne==3:
- u,v,dd = e
- elif ne==2:
- u,v = e
- dd = {}
- else:
- raise NetworkXError(\
- "Edge tuple %s must be a 2-tuple or 3-tuple."%(e,))
- if u not in self.node:
- self.adj[u] = {}
- self.node[u] = {}
- if v not in self.node:
- self.adj[v] = {}
- self.node[v] = {}
- datadict=self.adj[u].get(v,{})
- datadict.update(attr_dict)
- datadict.update(dd)
- self.adj[u][v] = datadict
- self.adj[v][u] = datadict
-
-
- def add_weighted_edges_from(self, ebunch, weight='weight', **attr):
- """Add all the edges in ebunch as weighted edges with specified
- weights.
-
- Parameters
- ----------
- ebunch : container of edges
- Each edge given in the list or container will be added
- to the graph. The edges must be given as 3-tuples (u,v,w)
- where w is a number.
- weight : string, optional (default= 'weight')
- The attribute name for the edge weights to be added.
- attr : keyword arguments, optional (default= no attributes)
- Edge attributes to add/update for all edges.
-
- See Also
- --------
- add_edge : add a single edge
- add_edges_from : add multiple edges
-
- Notes
- -----
- Adding the same edge twice for Graph/DiGraph simply updates
- the edge data. For MultiGraph/MultiDiGraph, duplicate edges
- are stored.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_weighted_edges_from([(0,1,3.0),(1,2,7.5)])
- """
- self.add_edges_from(((u,v,{weight:d}) for u,v,d in ebunch),**attr)
-
- def remove_edge(self, u, v):
- """Remove the edge between u and v.
-
- Parameters
- ----------
- u,v: nodes
- Remove the edge between nodes u and v.
-
- Raises
- ------
- NetworkXError
- If there is not an edge between u and v.
-
- See Also
- --------
- remove_edges_from : remove a collection of edges
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.remove_edge(0,1)
- >>> e = (1,2)
- >>> G.remove_edge(*e) # unpacks e from an edge tuple
- >>> e = (2,3,{'weight':7}) # an edge with attribute data
- >>> G.remove_edge(*e[:2]) # select first part of edge tuple
- """
- try:
- del self.adj[u][v]
- if u != v: # self-loop needs only one entry removed
- del self.adj[v][u]
- except KeyError:
- raise NetworkXError("The edge %s-%s is not in the graph"%(u,v))
-
-
-
- def remove_edges_from(self, ebunch):
- """Remove all edges specified in ebunch.
-
- Parameters
- ----------
- ebunch: list or container of edge tuples
- Each edge given in the list or container will be removed
- from the graph. The edges can be:
-
- - 2-tuples (u,v) edge between u and v.
- - 3-tuples (u,v,k) where k is ignored.
-
- See Also
- --------
- remove_edge : remove a single edge
-
- Notes
- -----
- Will fail silently if an edge in ebunch is not in the graph.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> ebunch=[(1,2),(2,3)]
- >>> G.remove_edges_from(ebunch)
- """
- adj=self.adj
- for e in ebunch:
- u,v = e[:2] # ignore edge data if present
- if u in adj and v in adj[u]:
- del adj[u][v]
- if u != v: # self loop needs only one entry removed
- del adj[v][u]
-
-
- def has_edge(self, u, v):
- """Return True if the edge (u,v) is in the graph.
-
- Parameters
- ----------
- u,v : nodes
- Nodes can be, for example, strings or numbers.
- Nodes must be hashable (and not None) Python objects.
-
- Returns
- -------
- edge_ind : bool
- True if edge is in the graph, False otherwise.
-
- Examples
- --------
- Can be called either using two nodes u,v or edge tuple (u,v)
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.has_edge(0,1) # using two nodes
- True
- >>> e = (0,1)
- >>> G.has_edge(*e) # e is a 2-tuple (u,v)
- True
- >>> e = (0,1,{'weight':7})
- >>> G.has_edge(*e[:2]) # e is a 3-tuple (u,v,data_dictionary)
- True
-
- The following syntax are all equivalent:
-
- >>> G.has_edge(0,1)
- True
- >>> 1 in G[0] # though this gives KeyError if 0 not in G
- True
-
- """
- try:
- return v in self.adj[u]
- except KeyError:
- return False
-
-
- def neighbors(self, n):
- """Return a list of the nodes connected to the node n.
-
- Parameters
- ----------
- n : node
- A node in the graph
-
- Returns
- -------
- nlist : list
- A list of nodes that are adjacent to n.
-
- Raises
- ------
- NetworkXError
- If the node n is not in the graph.
-
- Notes
- -----
- It is usually more convenient (and faster) to access the
- adjacency dictionary as G[n]:
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edge('a','b',weight=7)
- >>> G['a']
- {'b': {'weight': 7}}
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.neighbors(0)
- [1]
-
- """
- try:
- return list(self.adj[n])
- except KeyError:
- raise NetworkXError("The node %s is not in the graph."%(n,))
-
- def neighbors_iter(self, n):
- """Return an iterator over all neighbors of node n.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> [n for n in G.neighbors_iter(0)]
- [1]
-
- Notes
- -----
- It is faster to use the idiom "in G[0]", e.g.
-
- >>> G = nx.path_graph(4)
- >>> [n for n in G[0]]
- [1]
- """
- try:
- return iter(self.adj[n])
- except KeyError:
- raise NetworkXError("The node %s is not in the graph."%(n,))
-
- def edges(self, nbunch=None, data=False):
- """Return a list of edges.
-
- Edges are returned as tuples with optional data
- in the order (node, neighbor, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- Return two tuples (u,v) (False) or three-tuples (u,v,data) (True).
-
- Returns
- --------
- edge_list: list of edge tuples
- Edges that are adjacent to any node in nbunch, or a list
- of all edges if nbunch is not specified.
-
- See Also
- --------
- edges_iter : return an iterator over the edges
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs this returns the out-edges.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.edges()
- [(0, 1), (1, 2), (2, 3)]
- >>> G.edges(data=True) # default edge data is {} (empty dictionary)
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
- >>> G.edges([0,3])
- [(0, 1), (3, 2)]
- >>> G.edges(0)
- [(0, 1)]
-
- """
- return list(self.edges_iter(nbunch, data))
-
- def edges_iter(self, nbunch=None, data=False):
- """Return an iterator over the edges.
-
- Edges are returned as tuples with optional data
- in the order (node, neighbor, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict in 3-tuple (u,v,data).
-
- Returns
- -------
- edge_iter : iterator
- An iterator of (u,v) or (u,v,d) tuples of edges.
-
- See Also
- --------
- edges : return a list of edges
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs this returns the out-edges.
-
- Examples
- --------
- >>> G = nx.Graph() # or MultiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> [e for e in G.edges_iter()]
- [(0, 1), (1, 2), (2, 3)]
- >>> list(G.edges_iter(data=True)) # default data is {} (empty dict)
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
- >>> list(G.edges_iter([0,3]))
- [(0, 1), (3, 2)]
- >>> list(G.edges_iter(0))
- [(0, 1)]
-
- """
- seen={} # helper dict to keep track of multiply stored edges
- if nbunch is None:
- nodes_nbrs = self.adj.items()
- else:
- nodes_nbrs=((n,self.adj[n]) for n in self.nbunch_iter(nbunch))
- if data:
- for n,nbrs in nodes_nbrs:
- for nbr,data in nbrs.items():
- if nbr not in seen:
- yield (n,nbr,data)
- seen[n]=1
- else:
- for n,nbrs in nodes_nbrs:
- for nbr in nbrs:
- if nbr not in seen:
- yield (n,nbr)
- seen[n] = 1
- del seen
-
-
- def get_edge_data(self, u, v, default=None):
- """Return the attribute dictionary associated with edge (u,v).
-
- Parameters
- ----------
- u,v : nodes
- default: any Python object (default=None)
- Value to return if the edge (u,v) is not found.
-
- Returns
- -------
- edge_dict : dictionary
- The edge attribute dictionary.
-
- Notes
- -----
- It is faster to use G[u][v].
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G[0][1]
- {}
-
- Warning: Assigning G[u][v] corrupts the graph data structure.
- But it is safe to assign attributes to that dictionary,
-
- >>> G[0][1]['weight'] = 7
- >>> G[0][1]['weight']
- 7
- >>> G[1][0]['weight']
- 7
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.get_edge_data(0,1) # default edge data is {}
- {}
- >>> e = (0,1)
- >>> G.get_edge_data(*e) # tuple form
- {}
- >>> G.get_edge_data('a','b',default=0) # edge not in graph, return 0
- 0
- """
- try:
- return self.adj[u][v]
- except KeyError:
- return default
-
- def adjacency_list(self):
- """Return an adjacency list representation of the graph.
-
- The output adjacency list is in the order of G.nodes().
- For directed graphs, only outgoing adjacencies are included.
-
- Returns
- -------
- adj_list : lists of lists
- The adjacency structure of the graph as a list of lists.
-
- See Also
- --------
- adjacency_iter
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.adjacency_list() # in order given by G.nodes()
- [[1], [0, 2], [1, 3], [2]]
-
- """
- return list(map(list,iter(self.adj.values())))
-
- def adjacency_iter(self):
- """Return an iterator of (node, adjacency dict) tuples for all nodes.
-
- This is the fastest way to look at every edge.
- For directed graphs, only outgoing adjacencies are included.
-
- Returns
- -------
- adj_iter : iterator
- An iterator of (node, adjacency dictionary) for all nodes in
- the graph.
-
- See Also
- --------
- adjacency_list
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> [(n,nbrdict) for n,nbrdict in G.adjacency_iter()]
- [(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
-
- """
- return iter(self.adj.items())
-
- def degree(self, nbunch=None, weight=None):
- """Return the degree of a node or nodes.
-
- The node degree is the number of edges adjacent to that node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd : dictionary, or number
- A dictionary with nodes as keys and degree as values or
- a number if a single node is specified.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.degree(0)
- 1
- >>> G.degree([0,1])
- {0: 1, 1: 2}
- >>> list(G.degree([0,1]).values())
- [1, 2]
-
- """
- if nbunch in self: # return a single node
- return next(self.degree_iter(nbunch,weight))[1]
- else: # return a dict
- return dict(self.degree_iter(nbunch,weight))
-
- def degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, degree).
-
- The node degree is the number of edges adjacent to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, degree).
-
- See Also
- --------
- degree
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> list(G.degree_iter(0)) # node 0 with degree 1
- [(0, 1)]
- >>> list(G.degree_iter([0,1]))
- [(0, 1), (1, 2)]
-
- """
- if nbunch is None:
- nodes_nbrs = self.adj.items()
- else:
- nodes_nbrs=((n,self.adj[n]) for n in self.nbunch_iter(nbunch))
-
- if weight is None:
- for n,nbrs in nodes_nbrs:
- yield (n,len(nbrs)+(n in nbrs)) # return tuple (n,degree)
- else:
- # edge weighted graph - degree is sum of nbr edge weights
- for n,nbrs in nodes_nbrs:
- yield (n, sum((nbrs[nbr].get(weight,1) for nbr in nbrs)) +
- (n in nbrs and nbrs[n].get(weight,1)))
-
-
- def clear(self):
- """Remove all nodes and edges from the graph.
-
- This also removes the name, and all graph, node, and edge attributes.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.clear()
- >>> G.nodes()
- []
- >>> G.edges()
- []
-
- """
- self.name = ''
- self.adj.clear()
- self.node.clear()
- self.graph.clear()
-
- def copy(self):
- """Return a copy of the graph.
-
- Returns
- -------
- G : Graph
- A copy of the graph.
-
- See Also
- --------
- to_directed: return a directed copy of the graph.
-
- Notes
- -----
- This makes a complete copy of the graph including all of the
- node or edge attributes.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> H = G.copy()
-
- """
- return deepcopy(self)
-
- def is_multigraph(self):
- """Return True if graph is a multigraph, False otherwise."""
- return False
-
-
- def is_directed(self):
- """Return True if graph is directed, False otherwise."""
- return False
-
- def to_directed(self):
- """Return a directed representation of the graph.
-
- Returns
- -------
- G : DiGraph
- A directed graph with the same name, same nodes, and with
- each edge (u,v,data) replaced by two directed edges
- (u,v,data) and (v,u,data).
-
- Notes
- -----
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar D=DiGraph(G) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
-
- Examples
- --------
- >>> G = nx.Graph() # or MultiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1), (1, 0)]
-
- If already directed, return a (deep) copy
-
- >>> G = nx.DiGraph() # or MultiDiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1)]
- """
- from networkx import DiGraph
- G=DiGraph()
- G.name=self.name
- G.add_nodes_from(self)
- G.add_edges_from( ((u,v,deepcopy(data))
- for u,nbrs in self.adjacency_iter()
- for v,data in nbrs.items()) )
- G.graph=deepcopy(self.graph)
- G.node=deepcopy(self.node)
- return G
-
- def to_undirected(self):
- """Return an undirected copy of the graph.
-
- Returns
- -------
- G : Graph/MultiGraph
- A deepcopy of the graph.
-
- See Also
- --------
- copy, add_edge, add_edges_from
-
- Notes
- -----
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar G=DiGraph(D) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
-
- Examples
- --------
- >>> G = nx.Graph() # or MultiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1), (1, 0)]
- >>> G2 = H.to_undirected()
- >>> G2.edges()
- [(0, 1)]
- """
- return deepcopy(self)
-
- def subgraph(self, nbunch):
- """Return the subgraph induced on nodes in nbunch.
-
- The induced subgraph of the graph contains the nodes in nbunch
- and the edges between those nodes.
-
- Parameters
- ----------
- nbunch : list, iterable
- A container of nodes which will be iterated through once.
-
- Returns
- -------
- G : Graph
- A subgraph of the graph with the same edge attributes.
-
- Notes
- -----
- The graph, edge or node attributes just point to the original graph.
- So changes to the node or edge structure will not be reflected in
- the original graph while changes to the attributes will.
-
- To create a subgraph with its own copy of the edge/node attributes use:
- nx.Graph(G.subgraph(nbunch))
-
- If edge attributes are containers, a deep copy can be obtained using:
- G.subgraph(nbunch).copy()
-
- For an inplace reduction of a graph to a subgraph you can remove nodes:
- G.remove_nodes_from([ n in G if n not in set(nbunch)])
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> H = G.subgraph([0,1,2])
- >>> H.edges()
- [(0, 1), (1, 2)]
- """
- bunch =self.nbunch_iter(nbunch)
- # create new graph and copy subgraph into it
- H = self.__class__()
- # copy node and attribute dictionaries
- for n in bunch:
- H.node[n]=self.node[n]
- # namespace shortcuts for speed
- H_adj=H.adj
- self_adj=self.adj
- # add nodes and edges (undirected method)
- for n in H.node:
- Hnbrs={}
- H_adj[n]=Hnbrs
- for nbr,d in self_adj[n].items():
- if nbr in H_adj:
- # add both representations of edge: n-nbr and nbr-n
- Hnbrs[nbr]=d
- H_adj[nbr][n]=d
- H.graph=self.graph
- return H
-
-
- def nodes_with_selfloops(self):
- """Return a list of nodes with self loops.
-
- A node with a self loop has an edge with both ends adjacent
- to that node.
-
- Returns
- -------
- nodelist : list
- A list of nodes with self loops.
-
- See Also
- --------
- selfloop_edges, number_of_selfloops
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edge(1,1)
- >>> G.add_edge(1,2)
- >>> G.nodes_with_selfloops()
- [1]
- """
- return [ n for n,nbrs in self.adj.items() if n in nbrs ]
-
- def selfloop_edges(self, data=False):
- """Return a list of selfloop edges.
-
- A selfloop edge has the same node at both ends.
-
- Parameters
- -----------
- data : bool, optional (default=False)
- Return selfloop edges as two tuples (u,v) (data=False)
- or three-tuples (u,v,data) (data=True)
-
- Returns
- -------
- edgelist : list of edge tuples
- A list of all selfloop edges.
-
- See Also
- --------
- nodes_with_selfloops, number_of_selfloops
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edge(1,1)
- >>> G.add_edge(1,2)
- >>> G.selfloop_edges()
- [(1, 1)]
- >>> G.selfloop_edges(data=True)
- [(1, 1, {})]
- """
- if data:
- return [ (n,n,nbrs[n])
- for n,nbrs in self.adj.items() if n in nbrs ]
- else:
- return [ (n,n)
- for n,nbrs in self.adj.items() if n in nbrs ]
-
-
- def number_of_selfloops(self):
- """Return the number of selfloop edges.
-
- A selfloop edge has the same node at both ends.
-
- Returns
- -------
- nloops : int
- The number of selfloops.
-
- See Also
- --------
- nodes_with_selfloops, selfloop_edges
-
- Examples
- --------
- >>> G=nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edge(1,1)
- >>> G.add_edge(1,2)
- >>> G.number_of_selfloops()
- 1
- """
- return len(self.selfloop_edges())
-
-
- def size(self, weight=None):
- """Return the number of edges.
-
- Parameters
- ----------
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
-
- Returns
- -------
- nedges : int
- The number of edges of sum of edge weights in the graph.
-
- See Also
- --------
- number_of_edges
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.size()
- 3
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edge('a','b',weight=2)
- >>> G.add_edge('b','c',weight=4)
- >>> G.size()
- 2
- >>> G.size(weight='weight')
- 6.0
- """
- s=sum(self.degree(weight=weight).values())/2
- if weight is None:
- return int(s)
- else:
- return float(s)
-
- def number_of_edges(self, u=None, v=None):
- """Return the number of edges between two nodes.
-
- Parameters
- ----------
- u,v : nodes, optional (default=all edges)
- If u and v are specified, return the number of edges between
- u and v. Otherwise return the total number of all edges.
-
- Returns
- -------
- nedges : int
- The number of edges in the graph. If nodes u and v are specified
- return the number of edges between those nodes.
-
- See Also
- --------
- size
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.number_of_edges()
- 3
- >>> G.number_of_edges(0,1)
- 1
- >>> e = (0,1)
- >>> G.number_of_edges(*e)
- 1
- """
- if u is None: return int(self.size())
- if v in self.adj[u]:
- return 1
- else:
- return 0
-
-
- def add_star(self, nodes, **attr):
- """Add a star.
-
- The first node in nodes is the middle of the star. It is connected
- to all other nodes.
-
- Parameters
- ----------
- nodes : iterable container
- A container of nodes.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to every edge in star.
-
- See Also
- --------
- add_path, add_cycle
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_star([0,1,2,3])
- >>> G.add_star([10,11,12],weight=2)
-
- """
- nlist = list(nodes)
- v=nlist[0]
- edges=((v,n) for n in nlist[1:])
- self.add_edges_from(edges, **attr)
-
- def add_path(self, nodes, **attr):
- """Add a path.
-
- Parameters
- ----------
- nodes : iterable container
- A container of nodes. A path will be constructed from
- the nodes (in order) and added to the graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to every edge in path.
-
- See Also
- --------
- add_star, add_cycle
-
- Examples
- --------
- >>> G=nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.add_path([10,11,12],weight=7)
-
- """
- nlist = list(nodes)
- edges=zip(nlist[:-1],nlist[1:])
- self.add_edges_from(edges, **attr)
-
- def add_cycle(self, nodes, **attr):
- """Add a cycle.
-
- Parameters
- ----------
- nodes: iterable container
- A container of nodes. A cycle will be constructed from
- the nodes (in order) and added to the graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to every edge in cycle.
-
- See Also
- --------
- add_path, add_star
-
- Examples
- --------
- >>> G=nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_cycle([0,1,2,3])
- >>> G.add_cycle([10,11,12],weight=7)
-
- """
- nlist = list(nodes)
- edges=zip(nlist,nlist[1:]+[nlist[0]])
- self.add_edges_from(edges, **attr)
-
-
- def nbunch_iter(self, nbunch=None):
- """Return an iterator of nodes contained in nbunch that are
- also in the graph.
-
- The nodes in nbunch are checked for membership in the graph
- and if not are silently ignored.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- Returns
- -------
- niter : iterator
- An iterator over nodes in nbunch that are also in the graph.
- If nbunch is None, iterate over all nodes in the graph.
-
- Raises
- ------
- NetworkXError
- If nbunch is not a node or or sequence of nodes.
- If a node in nbunch is not hashable.
-
- See Also
- --------
- Graph.__iter__
-
- Notes
- -----
- When nbunch is an iterator, the returned iterator yields values
- directly from nbunch, becoming exhausted when nbunch is exhausted.
-
- To test whether nbunch is a single node, one can use
- "if nbunch in self:", even after processing with this routine.
-
- If nbunch is not a node or a (possibly empty) sequence/iterator
- or None, a NetworkXError is raised. Also, if any object in
- nbunch is not hashable, a NetworkXError is raised.
- """
- if nbunch is None: # include all nodes via iterator
- bunch=iter(self.adj.keys())
- elif nbunch in self: # if nbunch is a single node
- bunch=iter([nbunch])
- else: # if nbunch is a sequence of nodes
- def bunch_iter(nlist,adj):
- try:
- for n in nlist:
- if n in adj:
- yield n
- except TypeError as e:
- message=e.args[0]
- import sys
- sys.stdout.write(message)
- # capture error for non-sequence/iterator nbunch.
- if 'iter' in message:
- raise NetworkXError(\
- "nbunch is not a node or a sequence of nodes.")
- # capture error for unhashable node.
- elif 'hashable' in message:
- raise NetworkXError(\
- "Node %s in the sequence nbunch is not a valid node."%n)
- else:
- raise
- bunch=bunch_iter(nbunch,self.adj)
- return bunch
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/multidigraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/multidigraph.py
deleted file mode 100644
index 392db89..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/multidigraph.py
+++ /dev/null
@@ -1,851 +0,0 @@
-"""Base class for MultiDiGraph."""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from copy import deepcopy
-import networkx as nx
-from networkx.classes.graph import Graph # for doctests
-from networkx.classes.digraph import DiGraph
-from networkx.classes.multigraph import MultiGraph
-from networkx.exception import NetworkXError
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-
-class MultiDiGraph(MultiGraph,DiGraph):
- """A directed graph class that can store multiedges.
-
- Multiedges are multiple edges between two nodes. Each edge
- can hold optional data or attributes.
-
- A MultiDiGraph holds directed edges. Self loops are allowed.
-
- Nodes can be arbitrary (hashable) Python objects with optional
- key/value attributes.
-
- Edges are represented as links between nodes with optional
- key/value attributes.
-
- Parameters
- ----------
- data : input graph
- Data to initialize graph. If data=None (default) an empty
- graph is created. The data can be an edge list, or any
- NetworkX graph object. If the corresponding optional Python
- packages are installed the data can also be a NumPy matrix
- or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to graph as key=value pairs.
-
- See Also
- --------
- Graph
- DiGraph
- MultiGraph
-
- Examples
- --------
- Create an empty graph structure (a "null graph") with no nodes and
- no edges.
-
- >>> G = nx.MultiDiGraph()
-
- G can be grown in several ways.
-
- **Nodes:**
-
- Add one node at a time:
-
- >>> G.add_node(1)
-
- Add the nodes from any container (a list, dict, set or
- even the lines from a file or the nodes from another graph).
-
- >>> G.add_nodes_from([2,3])
- >>> G.add_nodes_from(range(100,110))
- >>> H=nx.Graph()
- >>> H.add_path([0,1,2,3,4,5,6,7,8,9])
- >>> G.add_nodes_from(H)
-
- In addition to strings and integers any hashable Python object
- (except None) can represent a node, e.g. a customized node object,
- or even another Graph.
-
- >>> G.add_node(H)
-
- **Edges:**
-
- G can also be grown by adding edges.
-
- Add one edge,
-
- >>> G.add_edge(1, 2)
-
- a list of edges,
-
- >>> G.add_edges_from([(1,2),(1,3)])
-
- or a collection of edges,
-
- >>> G.add_edges_from(H.edges())
-
- If some edges connect nodes not yet in the graph, the nodes
- are added automatically. If an edge already exists, an additional
- edge is created and stored using a key to identify the edge.
- By default the key is the lowest unused integer.
-
- >>> G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))])
- >>> G[4]
- {5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}}
-
- **Attributes:**
-
- Each graph, node, and edge can hold key/value attribute pairs
- in an associated attribute dictionary (the keys must be hashable).
- By default these are empty, but can be added or changed using
- add_edge, add_node or direct manipulation of the attribute
- dictionaries named graph, node and edge respectively.
-
- >>> G = nx.MultiDiGraph(day="Friday")
- >>> G.graph
- {'day': 'Friday'}
-
- Add node attributes using add_node(), add_nodes_from() or G.node
-
- >>> G.add_node(1, time='5pm')
- >>> G.add_nodes_from([3], time='2pm')
- >>> G.node[1]
- {'time': '5pm'}
- >>> G.node[1]['room'] = 714
- >>> del G.node[1]['room'] # remove attribute
- >>> G.nodes(data=True)
- [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
-
- Warning: adding a node to G.node does not add it to the graph.
-
- Add edge attributes using add_edge(), add_edges_from(), subscript
- notation, or G.edge.
-
- >>> G.add_edge(1, 2, weight=4.7 )
- >>> G.add_edges_from([(3,4),(4,5)], color='red')
- >>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
- >>> G[1][2][0]['weight'] = 4.7
- >>> G.edge[1][2][0]['weight'] = 4
-
- **Shortcuts:**
-
- Many common graph features allow python syntax to speed reporting.
-
- >>> 1 in G # check if node in graph
- True
- >>> [n for n in G if n<3] # iterate through nodes
- [1, 2]
- >>> len(G) # number of nodes in graph
- 5
- >>> G[1] # adjacency dict keyed by neighbor to edge attributes
- ... # Note: you should not change this dict manually!
- {2: {0: {'weight': 4}, 1: {'color': 'blue'}}}
-
- The fastest way to traverse all edges of a graph is via
- adjacency_iter(), but the edges() method is often more convenient.
-
- >>> for n,nbrsdict in G.adjacency_iter():
- ... for nbr,keydict in nbrsdict.items():
- ... for key,eattr in keydict.items():
- ... if 'weight' in eattr:
- ... (n,nbr,eattr['weight'])
- (1, 2, 4)
- (2, 3, 8)
- >>> [ (u,v,edata['weight']) for u,v,edata in G.edges(data=True) if 'weight' in edata ]
- [(1, 2, 4), (2, 3, 8)]
-
- **Reporting:**
-
- Simple graph information is obtained using methods.
- Iterator versions of many reporting methods exist for efficiency.
- Methods exist for reporting nodes(), edges(), neighbors() and degree()
- as well as the number of nodes and edges.
-
- For details on these and other miscellaneous methods, see below.
- """
- def add_edge(self, u, v, key=None, attr_dict=None, **attr):
- """Add an edge between u and v.
-
- The nodes u and v will be automatically added if they are
- not already in the graph.
-
- Edge attributes can be specified with keywords or by providing
- a dictionary with key/value pairs. See examples below.
-
- Parameters
- ----------
- u,v : nodes
- Nodes can be, for example, strings or numbers.
- Nodes must be hashable (and not None) Python objects.
- key : hashable identifier, optional (default=lowest unused integer)
- Used to distinguish multiedges between a pair of nodes.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with the edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
- See Also
- --------
- add_edges_from : add a collection of edges
-
- Notes
- -----
- To replace/update edge data, use the optional key argument
- to identify a unique edge. Otherwise a new edge will be created.
-
- NetworkX algorithms designed for weighted graphs cannot use
- multigraphs directly because it is not clear how to handle
- multiedge weights. Convert to Graph using edge attribute
- 'weight' to enable weighted graph algorithms.
-
- Examples
- --------
- The following all add the edge e=(1,2) to graph G:
-
- >>> G = nx.MultiDiGraph()
- >>> e = (1,2)
- >>> G.add_edge(1, 2) # explicit two-node form
- >>> G.add_edge(*e) # single edge as tuple of two nodes
- >>> G.add_edges_from( [(1,2)] ) # add edges from iterable container
-
- Associate data to edges using keywords:
-
- >>> G.add_edge(1, 2, weight=3)
- >>> G.add_edge(1, 2, key=0, weight=4) # update data for key=0
- >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- # add nodes
- if u not in self.succ:
- self.succ[u] = {}
- self.pred[u] = {}
- self.node[u] = {}
- if v not in self.succ:
- self.succ[v] = {}
- self.pred[v] = {}
- self.node[v] = {}
- if v in self.succ[u]:
- keydict=self.adj[u][v]
- if key is None:
- # find a unique integer key
- # other methods might be better here?
- key=len(keydict)
- while key in keydict:
- key+=1
- datadict=keydict.get(key,{})
- datadict.update(attr_dict)
- keydict[key]=datadict
- else:
- # selfloops work this way without special treatment
- if key is None:
- key=0
- datadict={}
- datadict.update(attr_dict)
- keydict={key:datadict}
- self.succ[u][v] = keydict
- self.pred[v][u] = keydict
-
- def remove_edge(self, u, v, key=None):
- """Remove an edge between u and v.
-
- Parameters
- ----------
- u,v: nodes
- Remove an edge between nodes u and v.
- key : hashable identifier, optional (default=None)
- Used to distinguish multiple edges between a pair of nodes.
- If None remove a single (abritrary) edge between u and v.
-
- Raises
- ------
- NetworkXError
- If there is not an edge between u and v, or
- if there is no edge with the specified key.
-
- See Also
- --------
- remove_edges_from : remove a collection of edges
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_path([0,1,2,3])
- >>> G.remove_edge(0,1)
- >>> e = (1,2)
- >>> G.remove_edge(*e) # unpacks e from an edge tuple
-
- For multiple edges
-
- >>> G = nx.MultiDiGraph()
- >>> G.add_edges_from([(1,2),(1,2),(1,2)])
- >>> G.remove_edge(1,2) # remove a single (arbitrary) edge
-
- For edges with keys
-
- >>> G = nx.MultiDiGraph()
- >>> G.add_edge(1,2,key='first')
- >>> G.add_edge(1,2,key='second')
- >>> G.remove_edge(1,2,key='second')
-
- """
- try:
- d=self.adj[u][v]
- except (KeyError):
- raise NetworkXError(
- "The edge %s-%s is not in the graph."%(u,v))
- # remove the edge with specified data
- if key is None:
- d.popitem()
- else:
- try:
- del d[key]
- except (KeyError):
- raise NetworkXError(
- "The edge %s-%s with key %s is not in the graph."%(u,v,key))
- if len(d)==0:
- # remove the key entries if last edge
- del self.succ[u][v]
- del self.pred[v][u]
-
-
- def edges_iter(self, nbunch=None, data=False, keys=False):
- """Return an iterator over the edges.
-
- Edges are returned as tuples with optional data and keys
- in the order (node, neighbor, key, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict with each edge.
- keys : bool, optional (default=False)
- If True, return edge keys with each edge.
-
- Returns
- -------
- edge_iter : iterator
- An iterator of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
-
- See Also
- --------
- edges : return a list of edges
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs this returns the out-edges.
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_path([0,1,2,3])
- >>> [e for e in G.edges_iter()]
- [(0, 1), (1, 2), (2, 3)]
- >>> list(G.edges_iter(data=True)) # default data is {} (empty dict)
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
- >>> list(G.edges_iter([0,2]))
- [(0, 1), (2, 3)]
- >>> list(G.edges_iter(0))
- [(0, 1)]
-
- """
- if nbunch is None:
- nodes_nbrs = self.adj.items()
- else:
- nodes_nbrs=((n,self.adj[n]) for n in self.nbunch_iter(nbunch))
- if data:
- for n,nbrs in nodes_nbrs:
- for nbr,keydict in nbrs.items():
- for key,data in keydict.items():
- if keys:
- yield (n,nbr,key,data)
- else:
- yield (n,nbr,data)
- else:
- for n,nbrs in nodes_nbrs:
- for nbr,keydict in nbrs.items():
- for key,data in keydict.items():
- if keys:
- yield (n,nbr,key)
- else:
- yield (n,nbr)
-
- # alias out_edges to edges
- out_edges_iter=edges_iter
-
- def out_edges(self, nbunch=None, keys=False, data=False):
- """Return a list of the outgoing edges.
-
- Edges are returned as tuples with optional data and keys
- in the order (node, neighbor, key, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict with each edge.
- keys : bool, optional (default=False)
- If True, return edge keys with each edge.
-
- Returns
- -------
- out_edges : list
- An listr of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs edges() is the same as out_edges().
-
- See Also
- --------
- in_edges: return a list of incoming edges
- """
- return list(self.out_edges_iter(nbunch, keys=keys, data=data))
-
-
- def in_edges_iter(self, nbunch=None, data=False, keys=False):
- """Return an iterator over the incoming edges.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict with each edge.
- keys : bool, optional (default=False)
- If True, return edge keys with each edge.
-
- Returns
- -------
- in_edge_iter : iterator
- An iterator of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
-
- See Also
- --------
- edges_iter : return an iterator of edges
- """
- if nbunch is None:
- nodes_nbrs=self.pred.items()
- else:
- nodes_nbrs=((n,self.pred[n]) for n in self.nbunch_iter(nbunch))
- if data:
- for n,nbrs in nodes_nbrs:
- for nbr,keydict in nbrs.items():
- for key,data in keydict.items():
- if keys:
- yield (nbr,n,key,data)
- else:
- yield (nbr,n,data)
- else:
- for n,nbrs in nodes_nbrs:
- for nbr,keydict in nbrs.items():
- for key,data in keydict.items():
- if keys:
- yield (nbr,n,key)
- else:
- yield (nbr,n)
-
- def in_edges(self, nbunch=None, keys=False, data=False):
- """Return a list of the incoming edges.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict with each edge.
- keys : bool, optional (default=False)
- If True, return edge keys with each edge.
-
- Returns
- -------
- in_edges : list
- A list of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
-
- See Also
- --------
- out_edges: return a list of outgoing edges
- """
- return list(self.in_edges_iter(nbunch, keys=keys, data=data))
-
-
- def degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, degree).
-
- The node degree is the number of edges adjacent to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, degree).
-
- See Also
- --------
- degree
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_path([0,1,2,3])
- >>> list(G.degree_iter(0)) # node 0 with degree 1
- [(0, 1)]
- >>> list(G.degree_iter([0,1]))
- [(0, 1), (1, 2)]
-
- """
- if nbunch is None:
- nodes_nbrs=zip(iter(self.succ.items()),iter(self.pred.items()))
- else:
- nodes_nbrs=zip(
- ((n,self.succ[n]) for n in self.nbunch_iter(nbunch)),
- ((n,self.pred[n]) for n in self.nbunch_iter(nbunch)))
-
- if weight is None:
- for (n,succ),(n2,pred) in nodes_nbrs:
- indeg = sum([len(data) for data in pred.values()])
- outdeg = sum([len(data) for data in succ.values()])
- yield (n, indeg + outdeg)
- else:
- # edge weighted graph - degree is sum of nbr edge weights
- for (n,succ),(n2,pred) in nodes_nbrs:
- deg = sum([d.get(weight,1)
- for data in pred.values()
- for d in data.values()])
- deg += sum([d.get(weight,1)
- for data in succ.values()
- for d in data.values()])
- yield (n, deg)
-
-
- def in_degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, in-degree).
-
- The node in-degree is the number of edges pointing in to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, in-degree).
-
- See Also
- --------
- degree, in_degree, out_degree, out_degree_iter
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_path([0,1,2,3])
- >>> list(G.in_degree_iter(0)) # node 0 with degree 0
- [(0, 0)]
- >>> list(G.in_degree_iter([0,1]))
- [(0, 0), (1, 1)]
-
- """
- if nbunch is None:
- nodes_nbrs=self.pred.items()
- else:
- nodes_nbrs=((n,self.pred[n]) for n in self.nbunch_iter(nbunch))
-
- if weight is None:
- for n,nbrs in nodes_nbrs:
- yield (n, sum([len(data) for data in nbrs.values()]) )
- else:
- # edge weighted graph - degree is sum of nbr edge weights
- for n,pred in nodes_nbrs:
- deg = sum([d.get(weight,1)
- for data in pred.values()
- for d in data.values()])
- yield (n, deg)
-
-
- def out_degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, out-degree).
-
- The node out-degree is the number of edges pointing out of the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, out-degree).
-
- See Also
- --------
- degree, in_degree, out_degree, in_degree_iter
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_path([0,1,2,3])
- >>> list(G.out_degree_iter(0)) # node 0 with degree 1
- [(0, 1)]
- >>> list(G.out_degree_iter([0,1]))
- [(0, 1), (1, 1)]
-
- """
- if nbunch is None:
- nodes_nbrs=self.succ.items()
- else:
- nodes_nbrs=((n,self.succ[n]) for n in self.nbunch_iter(nbunch))
-
- if weight is None:
- for n,nbrs in nodes_nbrs:
- yield (n, sum([len(data) for data in nbrs.values()]) )
- else:
- for n,succ in nodes_nbrs:
- deg = sum([d.get(weight,1)
- for data in succ.values()
- for d in data.values()])
- yield (n, deg)
-
- def is_multigraph(self):
- """Return True if graph is a multigraph, False otherwise."""
- return True
-
- def is_directed(self):
- """Return True if graph is directed, False otherwise."""
- return True
-
- def to_directed(self):
- """Return a directed copy of the graph.
-
- Returns
- -------
- G : MultiDiGraph
- A deepcopy of the graph.
-
- Notes
- -----
- If edges in both directions (u,v) and (v,u) exist in the
- graph, attributes for the new undirected edge will be a combination of
- the attributes of the directed edges. The edge data is updated
- in the (arbitrary) order that the edges are encountered. For
- more customized control of the edge attributes use add_edge().
-
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar G=DiGraph(D) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
-
- Examples
- --------
- >>> G = nx.Graph() # or MultiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1), (1, 0)]
-
- If already directed, return a (deep) copy
-
- >>> G = nx.MultiDiGraph()
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1)]
- """
- return deepcopy(self)
-
- def to_undirected(self, reciprocal=False):
- """Return an undirected representation of the digraph.
-
- Parameters
- ----------
- reciprocal : bool (optional)
- If True only keep edges that appear in both directions
- in the original digraph.
-
- Returns
- -------
- G : MultiGraph
- An undirected graph with the same name and nodes and
- with edge (u,v,data) if either (u,v,data) or (v,u,data)
- is in the digraph. If both edges exist in digraph and
- their edge data is different, only one edge is created
- with an arbitrary choice of which edge data to use.
- You must check and correct for this manually if desired.
-
- Notes
- -----
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar D=DiGraph(G) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
- """
- H=MultiGraph()
- H.name=self.name
- H.add_nodes_from(self)
- if reciprocal is True:
- H.add_edges_from( (u,v,key,deepcopy(data))
- for u,nbrs in self.adjacency_iter()
- for v,keydict in nbrs.items()
- for key,data in keydict.items()
- if self.has_edge(v,u,key))
- else:
- H.add_edges_from( (u,v,key,deepcopy(data))
- for u,nbrs in self.adjacency_iter()
- for v,keydict in nbrs.items()
- for key,data in keydict.items())
- H.graph=deepcopy(self.graph)
- H.node=deepcopy(self.node)
- return H
-
- def subgraph(self, nbunch):
- """Return the subgraph induced on nodes in nbunch.
-
- The induced subgraph of the graph contains the nodes in nbunch
- and the edges between those nodes.
-
- Parameters
- ----------
- nbunch : list, iterable
- A container of nodes which will be iterated through once.
-
- Returns
- -------
- G : Graph
- A subgraph of the graph with the same edge attributes.
-
- Notes
- -----
- The graph, edge or node attributes just point to the original graph.
- So changes to the node or edge structure will not be reflected in
- the original graph while changes to the attributes will.
-
- To create a subgraph with its own copy of the edge/node attributes use:
- nx.Graph(G.subgraph(nbunch))
-
- If edge attributes are containers, a deep copy can be obtained using:
- G.subgraph(nbunch).copy()
-
- For an inplace reduction of a graph to a subgraph you can remove nodes:
- G.remove_nodes_from([ n in G if n not in set(nbunch)])
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> H = G.subgraph([0,1,2])
- >>> H.edges()
- [(0, 1), (1, 2)]
- """
- bunch = self.nbunch_iter(nbunch)
- # create new graph and copy subgraph into it
- H = self.__class__()
- # copy node and attribute dictionaries
- for n in bunch:
- H.node[n]=self.node[n]
- # namespace shortcuts for speed
- H_succ=H.succ
- H_pred=H.pred
- self_succ=self.succ
- self_pred=self.pred
- # add nodes
- for n in H:
- H_succ[n]={}
- H_pred[n]={}
- # add edges
- for u in H_succ:
- Hnbrs=H_succ[u]
- for v,edgedict in self_succ[u].items():
- if v in H_succ:
- # add both representations of edge: u-v and v-u
- # they share the same edgedict
- ed=edgedict.copy()
- Hnbrs[v]=ed
- H_pred[v][u]=ed
- H.graph=self.graph
- return H
-
- def reverse(self, copy=True):
- """Return the reverse of the graph.
-
- The reverse is a graph with the same nodes and edges
- but with the directions of the edges reversed.
-
- Parameters
- ----------
- copy : bool optional (default=True)
- If True, return a new DiGraph holding the reversed edges.
- If False, reverse the reverse graph is created using
- the original graph (this changes the original graph).
- """
- if copy:
- H = self.__class__(name="Reverse of (%s)"%self.name)
- H.add_nodes_from(self)
- H.add_edges_from( (v,u,k,deepcopy(d)) for u,v,k,d
- in self.edges(keys=True, data=True) )
- H.graph=deepcopy(self.graph)
- H.node=deepcopy(self.node)
- else:
- self.pred,self.succ=self.succ,self.pred
- self.adj=self.succ
- H=self
- return H
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/multigraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/multigraph.py
deleted file mode 100644
index 63bdf0f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/multigraph.py
+++ /dev/null
@@ -1,966 +0,0 @@
-"""Base class for MultiGraph."""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from copy import deepcopy
-import networkx as nx
-from networkx.classes.graph import Graph
-from networkx import NetworkXError
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-
-class MultiGraph(Graph):
- """
- An undirected graph class that can store multiedges.
-
- Multiedges are multiple edges between two nodes. Each edge
- can hold optional data or attributes.
-
- A MultiGraph holds undirected edges. Self loops are allowed.
-
- Nodes can be arbitrary (hashable) Python objects with optional
- key/value attributes.
-
- Edges are represented as links between nodes with optional
- key/value attributes.
-
- Parameters
- ----------
- data : input graph
- Data to initialize graph. If data=None (default) an empty
- graph is created. The data can be an edge list, or any
- NetworkX graph object. If the corresponding optional Python
- packages are installed the data can also be a NumPy matrix
- or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- attr : keyword arguments, optional (default= no attributes)
- Attributes to add to graph as key=value pairs.
-
- See Also
- --------
- Graph
- DiGraph
- MultiDiGraph
-
- Examples
- --------
- Create an empty graph structure (a "null graph") with no nodes and
- no edges.
-
- >>> G = nx.MultiGraph()
-
- G can be grown in several ways.
-
- **Nodes:**
-
- Add one node at a time:
-
- >>> G.add_node(1)
-
- Add the nodes from any container (a list, dict, set or
- even the lines from a file or the nodes from another graph).
-
- >>> G.add_nodes_from([2,3])
- >>> G.add_nodes_from(range(100,110))
- >>> H=nx.Graph()
- >>> H.add_path([0,1,2,3,4,5,6,7,8,9])
- >>> G.add_nodes_from(H)
-
- In addition to strings and integers any hashable Python object
- (except None) can represent a node, e.g. a customized node object,
- or even another Graph.
-
- >>> G.add_node(H)
-
- **Edges:**
-
- G can also be grown by adding edges.
-
- Add one edge,
-
- >>> G.add_edge(1, 2)
-
- a list of edges,
-
- >>> G.add_edges_from([(1,2),(1,3)])
-
- or a collection of edges,
-
- >>> G.add_edges_from(H.edges())
-
- If some edges connect nodes not yet in the graph, the nodes
- are added automatically. If an edge already exists, an additional
- edge is created and stored using a key to identify the edge.
- By default the key is the lowest unused integer.
-
- >>> G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))])
- >>> G[4]
- {3: {0: {}}, 5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}}
-
- **Attributes:**
-
- Each graph, node, and edge can hold key/value attribute pairs
- in an associated attribute dictionary (the keys must be hashable).
- By default these are empty, but can be added or changed using
- add_edge, add_node or direct manipulation of the attribute
- dictionaries named graph, node and edge respectively.
-
- >>> G = nx.MultiGraph(day="Friday")
- >>> G.graph
- {'day': 'Friday'}
-
- Add node attributes using add_node(), add_nodes_from() or G.node
-
- >>> G.add_node(1, time='5pm')
- >>> G.add_nodes_from([3], time='2pm')
- >>> G.node[1]
- {'time': '5pm'}
- >>> G.node[1]['room'] = 714
- >>> del G.node[1]['room'] # remove attribute
- >>> G.nodes(data=True)
- [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
-
- Warning: adding a node to G.node does not add it to the graph.
-
- Add edge attributes using add_edge(), add_edges_from(), subscript
- notation, or G.edge.
-
- >>> G.add_edge(1, 2, weight=4.7 )
- >>> G.add_edges_from([(3,4),(4,5)], color='red')
- >>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
- >>> G[1][2][0]['weight'] = 4.7
- >>> G.edge[1][2][0]['weight'] = 4
-
- **Shortcuts:**
-
- Many common graph features allow python syntax to speed reporting.
-
- >>> 1 in G # check if node in graph
- True
- >>> [n for n in G if n<3] # iterate through nodes
- [1, 2]
- >>> len(G) # number of nodes in graph
- 5
- >>> G[1] # adjacency dict keyed by neighbor to edge attributes
- ... # Note: you should not change this dict manually!
- {2: {0: {'weight': 4}, 1: {'color': 'blue'}}}
-
- The fastest way to traverse all edges of a graph is via
- adjacency_iter(), but the edges() method is often more convenient.
-
- >>> for n,nbrsdict in G.adjacency_iter():
- ... for nbr,keydict in nbrsdict.items():
- ... for key,eattr in keydict.items():
- ... if 'weight' in eattr:
- ... (n,nbr,eattr['weight'])
- (1, 2, 4)
- (2, 1, 4)
- (2, 3, 8)
- (3, 2, 8)
- >>> [ (u,v,edata['weight']) for u,v,edata in G.edges(data=True) if 'weight' in edata ]
- [(1, 2, 4), (2, 3, 8)]
-
- **Reporting:**
-
- Simple graph information is obtained using methods.
- Iterator versions of many reporting methods exist for efficiency.
- Methods exist for reporting nodes(), edges(), neighbors() and degree()
- as well as the number of nodes and edges.
-
- For details on these and other miscellaneous methods, see below.
- """
- def add_edge(self, u, v, key=None, attr_dict=None, **attr):
- """Add an edge between u and v.
-
- The nodes u and v will be automatically added if they are
- not already in the graph.
-
- Edge attributes can be specified with keywords or by providing
- a dictionary with key/value pairs. See examples below.
-
- Parameters
- ----------
- u,v : nodes
- Nodes can be, for example, strings or numbers.
- Nodes must be hashable (and not None) Python objects.
- key : hashable identifier, optional (default=lowest unused integer)
- Used to distinguish multiedges between a pair of nodes.
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with the edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
- See Also
- --------
- add_edges_from : add a collection of edges
-
- Notes
- -----
- To replace/update edge data, use the optional key argument
- to identify a unique edge. Otherwise a new edge will be created.
-
- NetworkX algorithms designed for weighted graphs cannot use
- multigraphs directly because it is not clear how to handle
- multiedge weights. Convert to Graph using edge attribute
- 'weight' to enable weighted graph algorithms.
-
- Examples
- --------
- The following all add the edge e=(1,2) to graph G:
-
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> e = (1,2)
- >>> G.add_edge(1, 2) # explicit two-node form
- >>> G.add_edge(*e) # single edge as tuple of two nodes
- >>> G.add_edges_from( [(1,2)] ) # add edges from iterable container
-
- Associate data to edges using keywords:
-
- >>> G.add_edge(1, 2, weight=3)
- >>> G.add_edge(1, 2, key=0, weight=4) # update data for key=0
- >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- # add nodes
- if u not in self.adj:
- self.adj[u] = {}
- self.node[u] = {}
- if v not in self.adj:
- self.adj[v] = {}
- self.node[v] = {}
- if v in self.adj[u]:
- keydict=self.adj[u][v]
- if key is None:
- # find a unique integer key
- # other methods might be better here?
- key=len(keydict)
- while key in keydict:
- key+=1
- datadict=keydict.get(key,{})
- datadict.update(attr_dict)
- keydict[key]=datadict
- else:
- # selfloops work this way without special treatment
- if key is None:
- key=0
- datadict={}
- datadict.update(attr_dict)
- keydict={key:datadict}
- self.adj[u][v] = keydict
- self.adj[v][u] = keydict
-
-
- def add_edges_from(self, ebunch, attr_dict=None, **attr):
- """Add all the edges in ebunch.
-
- Parameters
- ----------
- ebunch : container of edges
- Each edge given in the container will be added to the
- graph. The edges can be:
-
- - 2-tuples (u,v) or
- - 3-tuples (u,v,d) for an edge attribute dict d, or
- - 4-tuples (u,v,k,d) for an edge identified by key k
-
- attr_dict : dictionary, optional (default= no attributes)
- Dictionary of edge attributes. Key/value pairs will
- update existing data associated with each edge.
- attr : keyword arguments, optional
- Edge data (or labels or objects) can be assigned using
- keyword arguments.
-
-
- See Also
- --------
- add_edge : add a single edge
- add_weighted_edges_from : convenient way to add weighted edges
-
- Notes
- -----
- Adding the same edge twice has no effect but any edge data
- will be updated when each duplicate edge is added.
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_edges_from([(0,1),(1,2)]) # using a list of edge tuples
- >>> e = zip(range(0,3),range(1,4))
- >>> G.add_edges_from(e) # Add the path graph 0-1-2-3
-
- Associate data to edges
-
- >>> G.add_edges_from([(1,2),(2,3)], weight=3)
- >>> G.add_edges_from([(3,4),(1,4)], label='WN2898')
- """
- # set up attribute dict
- if attr_dict is None:
- attr_dict=attr
- else:
- try:
- attr_dict.update(attr)
- except AttributeError:
- raise NetworkXError(\
- "The attr_dict argument must be a dictionary.")
- # process ebunch
- for e in ebunch:
- ne=len(e)
- if ne==4:
- u,v,key,dd = e
- elif ne==3:
- u,v,dd = e
- key=None
- elif ne==2:
- u,v = e
- dd = {}
- key=None
- else:
- raise NetworkXError(\
- "Edge tuple %s must be a 2-tuple, 3-tuple or 4-tuple."%(e,))
- if u in self.adj:
- keydict=self.adj[u].get(v,{})
- else:
- keydict={}
- if key is None:
- # find a unique integer key
- # other methods might be better here?
- key=len(keydict)
- while key in keydict:
- key+=1
- datadict=keydict.get(key,{})
- datadict.update(attr_dict)
- datadict.update(dd)
- self.add_edge(u,v,key=key,attr_dict=datadict)
-
-
- def remove_edge(self, u, v, key=None):
- """Remove an edge between u and v.
-
- Parameters
- ----------
- u,v: nodes
- Remove an edge between nodes u and v.
- key : hashable identifier, optional (default=None)
- Used to distinguish multiple edges between a pair of nodes.
- If None remove a single (abritrary) edge between u and v.
-
- Raises
- ------
- NetworkXError
- If there is not an edge between u and v, or
- if there is no edge with the specified key.
-
- See Also
- --------
- remove_edges_from : remove a collection of edges
-
- Examples
- --------
- >>> G = nx.MultiGraph()
- >>> G.add_path([0,1,2,3])
- >>> G.remove_edge(0,1)
- >>> e = (1,2)
- >>> G.remove_edge(*e) # unpacks e from an edge tuple
-
- For multiple edges
-
- >>> G = nx.MultiGraph() # or MultiDiGraph, etc
- >>> G.add_edges_from([(1,2),(1,2),(1,2)])
- >>> G.remove_edge(1,2) # remove a single (arbitrary) edge
-
- For edges with keys
-
- >>> G = nx.MultiGraph() # or MultiDiGraph, etc
- >>> G.add_edge(1,2,key='first')
- >>> G.add_edge(1,2,key='second')
- >>> G.remove_edge(1,2,key='second')
-
- """
- try:
- d=self.adj[u][v]
- except (KeyError):
- raise NetworkXError(
- "The edge %s-%s is not in the graph."%(u,v))
- # remove the edge with specified data
- if key is None:
- d.popitem()
- else:
- try:
- del d[key]
- except (KeyError):
- raise NetworkXError(
- "The edge %s-%s with key %s is not in the graph."%(u,v,key))
- if len(d)==0:
- # remove the key entries if last edge
- del self.adj[u][v]
- if u!=v: # check for selfloop
- del self.adj[v][u]
-
-
- def remove_edges_from(self, ebunch):
- """Remove all edges specified in ebunch.
-
- Parameters
- ----------
- ebunch: list or container of edge tuples
- Each edge given in the list or container will be removed
- from the graph. The edges can be:
-
- - 2-tuples (u,v) All edges between u and v are removed.
- - 3-tuples (u,v,key) The edge identified by key is removed.
- - 4-tuples (u,v,key,data) where data is ignored.
-
- See Also
- --------
- remove_edge : remove a single edge
-
- Notes
- -----
- Will fail silently if an edge in ebunch is not in the graph.
-
- Examples
- --------
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> ebunch=[(1,2),(2,3)]
- >>> G.remove_edges_from(ebunch)
-
- Removing multiple copies of edges
-
- >>> G = nx.MultiGraph()
- >>> G.add_edges_from([(1,2),(1,2),(1,2)])
- >>> G.remove_edges_from([(1,2),(1,2)])
- >>> G.edges()
- [(1, 2)]
- >>> G.remove_edges_from([(1,2),(1,2)]) # silently ignore extra copy
- >>> G.edges() # now empty graph
- []
- """
- for e in ebunch:
- try:
- self.remove_edge(*e[:3])
- except NetworkXError:
- pass
-
-
- def has_edge(self, u, v, key=None):
- """Return True if the graph has an edge between nodes u and v.
-
- Parameters
- ----------
- u,v : nodes
- Nodes can be, for example, strings or numbers.
-
- key : hashable identifier, optional (default=None)
- If specified return True only if the edge with
- key is found.
-
- Returns
- -------
- edge_ind : bool
- True if edge is in the graph, False otherwise.
-
- Examples
- --------
- Can be called either using two nodes u,v, an edge tuple (u,v),
- or an edge tuple (u,v,key).
-
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> G.has_edge(0,1) # using two nodes
- True
- >>> e = (0,1)
- >>> G.has_edge(*e) # e is a 2-tuple (u,v)
- True
- >>> G.add_edge(0,1,key='a')
- >>> G.has_edge(0,1,key='a') # specify key
- True
- >>> e=(0,1,'a')
- >>> G.has_edge(*e) # e is a 3-tuple (u,v,'a')
- True
-
- The following syntax are equivalent:
-
- >>> G.has_edge(0,1)
- True
- >>> 1 in G[0] # though this gives KeyError if 0 not in G
- True
-
-
-
- """
- try:
- if key is None:
- return v in self.adj[u]
- else:
- return key in self.adj[u][v]
- except KeyError:
- return False
-
- def edges(self, nbunch=None, data=False, keys=False):
- """Return a list of edges.
-
- Edges are returned as tuples with optional data and keys
- in the order (node, neighbor, key, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- Return two tuples (u,v) (False) or three-tuples (u,v,data) (True).
- keys : bool, optional (default=False)
- Return two tuples (u,v) (False) or three-tuples (u,v,key) (True).
-
- Returns
- --------
- edge_list: list of edge tuples
- Edges that are adjacent to any node in nbunch, or a list
- of all edges if nbunch is not specified.
-
- See Also
- --------
- edges_iter : return an iterator over the edges
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs this returns the out-edges.
-
- Examples
- --------
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> G.edges()
- [(0, 1), (1, 2), (2, 3)]
- >>> G.edges(data=True) # default edge data is {} (empty dictionary)
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
- >>> G.edges(keys=True) # default keys are integers
- [(0, 1, 0), (1, 2, 0), (2, 3, 0)]
- >>> G.edges(data=True,keys=True) # default keys are integers
- [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {})]
- >>> G.edges([0,3])
- [(0, 1), (3, 2)]
- >>> G.edges(0)
- [(0, 1)]
-
- """
- return list(self.edges_iter(nbunch, data=data,keys=keys))
-
- def edges_iter(self, nbunch=None, data=False, keys=False):
- """Return an iterator over the edges.
-
- Edges are returned as tuples with optional data and keys
- in the order (node, neighbor, key, data).
-
- Parameters
- ----------
- nbunch : iterable container, optional (default= all nodes)
- A container of nodes. The container will be iterated
- through once.
- data : bool, optional (default=False)
- If True, return edge attribute dict with each edge.
- keys : bool, optional (default=False)
- If True, return edge keys with each edge.
-
- Returns
- -------
- edge_iter : iterator
- An iterator of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
-
- See Also
- --------
- edges : return a list of edges
-
- Notes
- -----
- Nodes in nbunch that are not in the graph will be (quietly) ignored.
- For directed graphs this returns the out-edges.
-
- Examples
- --------
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> [e for e in G.edges_iter()]
- [(0, 1), (1, 2), (2, 3)]
- >>> list(G.edges_iter(data=True)) # default data is {} (empty dict)
- [(0, 1, {}), (1, 2, {}), (2, 3, {})]
- >>> list(G.edges(keys=True)) # default keys are integers
- [(0, 1, 0), (1, 2, 0), (2, 3, 0)]
- >>> list(G.edges(data=True,keys=True)) # default keys are integers
- [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {})]
- >>> list(G.edges_iter([0,3]))
- [(0, 1), (3, 2)]
- >>> list(G.edges_iter(0))
- [(0, 1)]
-
- """
- seen={} # helper dict to keep track of multiply stored edges
- if nbunch is None:
- nodes_nbrs = self.adj.items()
- else:
- nodes_nbrs=((n,self.adj[n]) for n in self.nbunch_iter(nbunch))
- if data:
- for n,nbrs in nodes_nbrs:
- for nbr,keydict in nbrs.items():
- if nbr not in seen:
- for key,data in keydict.items():
- if keys:
- yield (n,nbr,key,data)
- else:
- yield (n,nbr,data)
- seen[n]=1
- else:
- for n,nbrs in nodes_nbrs:
- for nbr,keydict in nbrs.items():
- if nbr not in seen:
- for key,data in keydict.items():
- if keys:
- yield (n,nbr,key)
- else:
- yield (n,nbr)
-
- seen[n] = 1
- del seen
-
-
- def get_edge_data(self, u, v, key=None, default=None):
- """Return the attribute dictionary associated with edge (u,v).
-
- Parameters
- ----------
- u,v : nodes
- default: any Python object (default=None)
- Value to return if the edge (u,v) is not found.
- key : hashable identifier, optional (default=None)
- Return data only for the edge with specified key.
-
- Returns
- -------
- edge_dict : dictionary
- The edge attribute dictionary.
-
- Notes
- -----
- It is faster to use G[u][v][key].
-
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_edge(0,1,key='a',weight=7)
- >>> G[0][1]['a'] # key='a'
- {'weight': 7}
-
- Warning: Assigning G[u][v][key] corrupts the graph data structure.
- But it is safe to assign attributes to that dictionary,
-
- >>> G[0][1]['a']['weight'] = 10
- >>> G[0][1]['a']['weight']
- 10
- >>> G[1][0]['a']['weight']
- 10
-
- Examples
- --------
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_path([0,1,2,3])
- >>> G.get_edge_data(0,1)
- {0: {}}
- >>> e = (0,1)
- >>> G.get_edge_data(*e) # tuple form
- {0: {}}
- >>> G.get_edge_data('a','b',default=0) # edge not in graph, return 0
- 0
- """
- try:
- if key is None:
- return self.adj[u][v]
- else:
- return self.adj[u][v][key]
- except KeyError:
- return default
-
- def degree_iter(self, nbunch=None, weight=None):
- """Return an iterator for (node, degree).
-
- The node degree is the number of edges adjacent to the node.
-
- Parameters
- ----------
- nbunch : iterable container, optional (default=all nodes)
- A container of nodes. The container will be iterated
- through once.
-
- weight : string or None, optional (default=None)
- The edge attribute that holds the numerical value used
- as a weight. If None, then each edge has weight 1.
- The degree is the sum of the edge weights adjacent to the node.
-
- Returns
- -------
- nd_iter : an iterator
- The iterator returns two-tuples of (node, degree).
-
- See Also
- --------
- degree
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> list(G.degree_iter(0)) # node 0 with degree 1
- [(0, 1)]
- >>> list(G.degree_iter([0,1]))
- [(0, 1), (1, 2)]
-
- """
- if nbunch is None:
- nodes_nbrs = self.adj.items()
- else:
- nodes_nbrs=((n,self.adj[n]) for n in self.nbunch_iter(nbunch))
-
- if weight is None:
- for n,nbrs in nodes_nbrs:
- deg = sum([len(data) for data in nbrs.values()])
- yield (n, deg+(n in nbrs and len(nbrs[n])))
- else:
- # edge weighted graph - degree is sum of nbr edge weights
- for n,nbrs in nodes_nbrs:
- deg = sum([d.get(weight,1)
- for data in nbrs.values()
- for d in data.values()])
- if n in nbrs:
- deg += sum([d.get(weight,1)
- for key,d in nbrs[n].items()])
- yield (n, deg)
-
-
- def is_multigraph(self):
- """Return True if graph is a multigraph, False otherwise."""
- return True
-
- def is_directed(self):
- """Return True if graph is directed, False otherwise."""
- return False
-
- def to_directed(self):
- """Return a directed representation of the graph.
-
- Returns
- -------
- G : MultiDiGraph
- A directed graph with the same name, same nodes, and with
- each edge (u,v,data) replaced by two directed edges
- (u,v,data) and (v,u,data).
-
- Notes
- -----
- This returns a "deepcopy" of the edge, node, and
- graph attributes which attempts to completely copy
- all of the data and references.
-
- This is in contrast to the similar D=DiGraph(G) which returns a
- shallow copy of the data.
-
- See the Python copy module for more information on shallow
- and deep copies, http://docs.python.org/library/copy.html.
-
-
- Examples
- --------
- >>> G = nx.Graph() # or MultiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1), (1, 0)]
-
- If already directed, return a (deep) copy
-
- >>> G = nx.DiGraph() # or MultiDiGraph, etc
- >>> G.add_path([0,1])
- >>> H = G.to_directed()
- >>> H.edges()
- [(0, 1)]
- """
- from networkx.classes.multidigraph import MultiDiGraph
- G=MultiDiGraph()
- G.add_nodes_from(self)
- G.add_edges_from( (u,v,key,deepcopy(datadict))
- for u,nbrs in self.adjacency_iter()
- for v,keydict in nbrs.items()
- for key,datadict in keydict.items() )
- G.graph=deepcopy(self.graph)
- G.node=deepcopy(self.node)
- return G
-
-
- def selfloop_edges(self, data=False, keys=False):
- """Return a list of selfloop edges.
-
- A selfloop edge has the same node at both ends.
-
- Parameters
- -----------
- data : bool, optional (default=False)
- Return selfloop edges as two tuples (u,v) (data=False)
- or three-tuples (u,v,data) (data=True)
- keys : bool, optional (default=False)
- If True, return edge keys with each edge.
-
- Returns
- -------
- edgelist : list of edge tuples
- A list of all selfloop edges.
-
- See Also
- --------
- nodes_with_selfloops, number_of_selfloops
-
- Examples
- --------
- >>> G = nx.MultiGraph() # or MultiDiGraph
- >>> G.add_edge(1,1)
- >>> G.add_edge(1,2)
- >>> G.selfloop_edges()
- [(1, 1)]
- >>> G.selfloop_edges(data=True)
- [(1, 1, {})]
- >>> G.selfloop_edges(keys=True)
- [(1, 1, 0)]
- >>> G.selfloop_edges(keys=True, data=True)
- [(1, 1, 0, {})]
- """
- if data:
- if keys:
- return [ (n,n,k,d)
- for n,nbrs in self.adj.items()
- if n in nbrs for k,d in nbrs[n].items()]
- else:
- return [ (n,n,d)
- for n,nbrs in self.adj.items()
- if n in nbrs for d in nbrs[n].values()]
- else:
- if keys:
- return [ (n,n,k)
- for n,nbrs in self.adj.items()
- if n in nbrs for k in nbrs[n].keys()]
-
- else:
- return [ (n,n)
- for n,nbrs in self.adj.items()
- if n in nbrs for d in nbrs[n].values()]
-
-
- def number_of_edges(self, u=None, v=None):
- """Return the number of edges between two nodes.
-
- Parameters
- ----------
- u,v : nodes, optional (default=all edges)
- If u and v are specified, return the number of edges between
- u and v. Otherwise return the total number of all edges.
-
- Returns
- -------
- nedges : int
- The number of edges in the graph. If nodes u and v are specified
- return the number of edges between those nodes.
-
- See Also
- --------
- size
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> G.number_of_edges()
- 3
- >>> G.number_of_edges(0,1)
- 1
- >>> e = (0,1)
- >>> G.number_of_edges(*e)
- 1
- """
- if u is None: return self.size()
- try:
- edgedata=self.adj[u][v]
- except KeyError:
- return 0 # no such edge
- return len(edgedata)
-
-
- def subgraph(self, nbunch):
- """Return the subgraph induced on nodes in nbunch.
-
- The induced subgraph of the graph contains the nodes in nbunch
- and the edges between those nodes.
-
- Parameters
- ----------
- nbunch : list, iterable
- A container of nodes which will be iterated through once.
-
- Returns
- -------
- G : Graph
- A subgraph of the graph with the same edge attributes.
-
- Notes
- -----
- The graph, edge or node attributes just point to the original graph.
- So changes to the node or edge structure will not be reflected in
- the original graph while changes to the attributes will.
-
- To create a subgraph with its own copy of the edge/node attributes use:
- nx.Graph(G.subgraph(nbunch))
-
- If edge attributes are containers, a deep copy can be obtained using:
- G.subgraph(nbunch).copy()
-
- For an inplace reduction of a graph to a subgraph you can remove nodes:
- G.remove_nodes_from([ n in G if n not in set(nbunch)])
-
- Examples
- --------
- >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
- >>> G.add_path([0,1,2,3])
- >>> H = G.subgraph([0,1,2])
- >>> H.edges()
- [(0, 1), (1, 2)]
- """
- bunch =self.nbunch_iter(nbunch)
- # create new graph and copy subgraph into it
- H = self.__class__()
- # copy node and attribute dictionaries
- for n in bunch:
- H.node[n]=self.node[n]
- # namespace shortcuts for speed
- H_adj=H.adj
- self_adj=self.adj
- # add nodes and edges (undirected method)
- for n in H:
- Hnbrs={}
- H_adj[n]=Hnbrs
- for nbr,edgedict in self_adj[n].items():
- if nbr in H_adj:
- # add both representations of edge: n-nbr and nbr-n
- # they share the same edgedict
- ed=edgedict.copy()
- Hnbrs[nbr]=ed
- H_adj[nbr][n]=ed
- H.graph=self.graph
- return H
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/historical_tests.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/historical_tests.py
deleted file mode 100644
index 5dd398c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/historical_tests.py
+++ /dev/null
@@ -1,477 +0,0 @@
-#!/usr/bin/env python
-"""Original NetworkX graph tests"""
-from nose.tools import *
-import networkx
-import networkx as nx
-from networkx import convert_node_labels_to_integers as cnlti
-from networkx.testing import *
-
-class HistoricalTests(object):
-
- def setUp(self):
- self.null=nx.null_graph()
- self.P1=cnlti(nx.path_graph(1),first_label=1)
- self.P3=cnlti(nx.path_graph(3),first_label=1)
- self.P10=cnlti(nx.path_graph(10),first_label=1)
- self.K1=cnlti(nx.complete_graph(1),first_label=1)
- self.K3=cnlti(nx.complete_graph(3),first_label=1)
- self.K4=cnlti(nx.complete_graph(4),first_label=1)
- self.K5=cnlti(nx.complete_graph(5),first_label=1)
- self.K10=cnlti(nx.complete_graph(10),first_label=1)
- self.G=nx.Graph
-
- def test_name(self):
- G=self.G(name="test")
- assert_equal(str(G),'test')
- assert_equal(G.name,'test')
- H=self.G()
- assert_equal(H.name,'')
-
- # Nodes
-
- def test_add_remove_node(self):
- G=self.G()
- G.add_node('A')
- assert_true(G.has_node('A'))
- G.remove_node('A')
- assert_false(G.has_node('A'))
-
- def test_nonhashable_node(self):
- # Test if a non-hashable object is in the Graph. A python dict will
- # raise a TypeError, but for a Graph class a simple False should be
- # returned (see Graph __contains__). If it cannot be a node then it is
- # not a node.
- G=self.G()
- assert_false(G.has_node(['A']))
- assert_false(G.has_node({'A':1}))
-
- def test_add_nodes_from(self):
- G=self.G()
- G.add_nodes_from(list("ABCDEFGHIJKL"))
- assert_true(G.has_node("L"))
- G.remove_nodes_from(['H','I','J','K','L'])
- G.add_nodes_from([1,2,3,4])
- assert_equal(sorted(G.nodes(),key=str),
- [1, 2, 3, 4, 'A', 'B', 'C', 'D', 'E', 'F', 'G'])
- # test __iter__
- assert_equal(sorted(G,key=str),
- [1, 2, 3, 4, 'A', 'B', 'C', 'D', 'E', 'F', 'G'])
-
-
- def test_contains(self):
- G=self.G()
- G.add_node('A')
- assert_true('A' in G)
- assert_false([] in G) # never raise a Key or TypeError in this test
- assert_false({1:1} in G)
-
- def test_add_remove(self):
- # Test add_node and remove_node acting for various nbunch
- G=self.G()
- G.add_node('m')
- assert_true(G.has_node('m'))
- G.add_node('m') # no complaints
- assert_raises(nx.NetworkXError,G.remove_node,'j')
- G.remove_node('m')
- assert_equal(G.nodes(),[])
-
- def test_nbunch_is_list(self):
- G=self.G()
- G.add_nodes_from(list("ABCD"))
- G.add_nodes_from(self.P3) # add nbunch of nodes (nbunch=Graph)
- assert_equal(sorted(G.nodes(),key=str),
- [1, 2, 3, 'A', 'B', 'C', 'D'])
- G.remove_nodes_from(self.P3) # remove nbunch of nodes (nbunch=Graph)
- assert_equal(sorted(G.nodes(),key=str),
- ['A', 'B', 'C', 'D'])
-
- def test_nbunch_is_set(self):
- G=self.G()
- nbunch=set("ABCDEFGHIJKL")
- G.add_nodes_from(nbunch)
- assert_true(G.has_node("L"))
-
- def test_nbunch_dict(self):
- # nbunch is a dict with nodes as keys
- G=self.G()
- nbunch=set("ABCDEFGHIJKL")
- G.add_nodes_from(nbunch)
- nbunch={'I':"foo",'J':2,'K':True,'L':"spam"}
- G.remove_nodes_from(nbunch)
- assert_true(sorted(G.nodes(),key=str),
- ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
-
- def test_nbunch_iterator(self):
- G=self.G()
- G.add_nodes_from(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
- n_iter=self.P3.nodes_iter()
- G.add_nodes_from(n_iter)
- assert_equal(sorted(G.nodes(),key=str),
- [1, 2, 3, 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
- n_iter=self.P3.nodes_iter() # rebuild same iterator
- G.remove_nodes_from(n_iter) # remove nbunch of nodes (nbunch=iterator)
- assert_equal(sorted(G.nodes(),key=str),
- ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
-
- def test_nbunch_graph(self):
- G=self.G()
- G.add_nodes_from(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
- nbunch=self.K3
- G.add_nodes_from(nbunch)
- assert_true(sorted(G.nodes(),key=str),
- [1, 2, 3, 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
-
-
-
- # Edges
-
- def test_add_edge(self):
- G=self.G()
- assert_raises(TypeError,G.add_edge,'A')
-
- G.add_edge('A','B') # testing add_edge()
- G.add_edge('A','B') # should fail silently
- assert_true(G.has_edge('A','B'))
- assert_false(G.has_edge('A','C'))
- assert_true(G.has_edge( *('A','B') ))
- if G.is_directed():
- assert_false(G.has_edge('B','A'))
- else:
- # G is undirected, so B->A is an edge
- assert_true(G.has_edge('B','A'))
-
-
- G.add_edge('A','C') # test directedness
- G.add_edge('C','A')
- G.remove_edge('C','A')
- if G.is_directed():
- assert_true(G.has_edge('A','C'))
- else:
- assert_false(G.has_edge('A','C'))
- assert_false(G.has_edge('C','A'))
-
-
- def test_self_loop(self):
- G=self.G()
- G.add_edge('A','A') # test self loops
- assert_true(G.has_edge('A','A'))
- G.remove_edge('A','A')
- G.add_edge('X','X')
- assert_true(G.has_node('X'))
- G.remove_node('X')
- G.add_edge('A','Z') # should add the node silently
- assert_true(G.has_node('Z'))
-
- def test_add_edges_from(self):
- G=self.G()
- G.add_edges_from([('B','C')]) # test add_edges_from()
- assert_true(G.has_edge('B','C'))
- if G.is_directed():
- assert_false(G.has_edge('C','B'))
- else:
- assert_true(G.has_edge('C','B')) # undirected
-
- G.add_edges_from([('D','F'),('B','D')])
- assert_true(G.has_edge('D','F'))
- assert_true(G.has_edge('B','D'))
-
- if G.is_directed():
- assert_false(G.has_edge('D','B'))
- else:
- assert_true(G.has_edge('D','B')) # undirected
-
- def test_add_edges_from2(self):
- G=self.G()
- # after failing silently, should add 2nd edge
- G.add_edges_from([tuple('IJ'),list('KK'),tuple('JK')])
- assert_true(G.has_edge(*('I','J')))
- assert_true(G.has_edge(*('K','K')))
- assert_true(G.has_edge(*('J','K')))
- if G.is_directed():
- assert_false(G.has_edge(*('K','J')))
- else:
- assert_true(G.has_edge(*('K','J')))
-
- def test_add_edges_from3(self):
- G=self.G()
- G.add_edges_from(zip(list('ACD'),list('CDE')))
- assert_true(G.has_edge('D','E'))
- assert_false(G.has_edge('E','C'))
-
- def test_remove_edge(self):
- G=self.G()
- G.add_nodes_from([1, 2, 3, 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])
-
- G.add_edges_from(zip(list('MNOP'),list('NOPM')))
- assert_true(G.has_edge('O','P'))
- assert_true( G.has_edge('P','M'))
- G.remove_node('P') # tests remove_node()'s handling of edges.
- assert_false(G.has_edge('P','M'))
- assert_raises(TypeError,G.remove_edge,'M')
-
- G.add_edge('N','M')
- assert_true(G.has_edge('M','N'))
- G.remove_edge('M','N')
- assert_false(G.has_edge('M','N'))
-
- # self loop fails silently
- G.remove_edges_from([list('HI'),list('DF'),
- tuple('KK'),tuple('JK')])
- assert_false(G.has_edge('H','I'))
- assert_false(G.has_edge('J','K'))
- G.remove_edges_from([list('IJ'),list('KK'),list('JK')])
- assert_false(G.has_edge('I','J'))
- G.remove_nodes_from(set('ZEFHIMNO'))
- G.add_edge('J','K')
-
-
- def test_edges_nbunch(self):
- # Test G.edges(nbunch) with various forms of nbunch
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- # node not in nbunch should be quietly ignored
- assert_raises(nx.NetworkXError,G.edges,6)
- assert_equals(G.edges('Z'),[]) # iterable non-node
- # nbunch can be an empty list
- assert_equals(G.edges([]),[])
- if G.is_directed():
- elist=[('A', 'B'), ('A', 'C'), ('B', 'D')]
- else:
- elist=[('A', 'B'), ('A', 'C'), ('B', 'C'), ('B', 'D')]
- # nbunch can be a list
- assert_edges_equal(G.edges(['A','B']),elist)
- # nbunch can be a set
- assert_edges_equal(G.edges(set(['A','B'])),elist)
- # nbunch can be a graph
- G1=self.G()
- G1.add_nodes_from('AB')
- assert_edges_equal(G.edges(G1),elist)
- # nbunch can be a dict with nodes as keys
- ndict={'A': "thing1", 'B': "thing2"}
- assert_edges_equal(G.edges(ndict),elist)
- # nbunch can be a single node
- assert_edges_equal(G.edges('A'), [('A', 'B'), ('A', 'C')])
-
- assert_edges_equal(G.nodes_iter(), ['A', 'B', 'C', 'D'])
-
- def test_edges_iter_nbunch(self):
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- # Test G.edges_iter(nbunch) with various forms of nbunch
- # node not in nbunch should be quietly ignored
- assert_equals(list(G.edges_iter('Z')),[])
- # nbunch can be an empty list
- assert_equals(sorted(G.edges_iter([])),[])
- if G.is_directed():
- elist=[('A', 'B'), ('A', 'C'), ('B', 'D')]
- else:
- elist=[('A', 'B'), ('A', 'C'), ('B', 'C'), ('B', 'D')]
- # nbunch can be a list
- assert_edges_equal(G.edges_iter(['A','B']),elist)
- # nbunch can be a set
- assert_edges_equal(G.edges_iter(set(['A','B'])),elist)
- # nbunch can be a graph
- G1=self.G()
- G1.add_nodes_from(['A','B'])
- assert_edges_equal(G.edges_iter(G1),elist)
- # nbunch can be a dict with nodes as keys
- ndict={'A': "thing1", 'B': "thing2"}
- assert_edges_equal(G.edges_iter(ndict),elist)
- # nbunch can be a single node
- assert_edges_equal(G.edges_iter('A'), [('A', 'B'), ('A', 'C')])
-
- # nbunch can be nothing (whole graph)
- assert_edges_equal(G.edges_iter(), [('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
-
-
- def test_degree(self):
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- assert_equal(G.degree('A'),2)
-
- # degree of single node in iterable container must return dict
- assert_equal(list(G.degree(['A']).values()),[2])
- assert_equal(G.degree(['A']),{'A': 2})
- assert_equal(sorted(G.degree(['A','B']).values()),[2, 3])
- assert_equal(G.degree(['A','B']),{'A': 2, 'B': 3})
- assert_equal(sorted(G.degree().values()),[2, 2, 3, 3])
- assert_equal(sorted([v for k,v in G.degree_iter()]),
- [2, 2, 3, 3])
-
- def test_degree2(self):
- H=self.G()
- H.add_edges_from([(1,24),(1,2)])
- assert_equal(sorted(H.degree([1,24]).values()),[1, 2])
-
- def test_degree_graph(self):
- P3=nx.path_graph(3)
- P5=nx.path_graph(5)
- # silently ignore nodes not in P3
- assert_equal(P3.degree(['A','B']),{})
- # nbunch can be a graph
- assert_equal(sorted(P5.degree(P3).values()),[1, 2, 2])
- # nbunch can be a graph thats way to big
- assert_equal(sorted(P3.degree(P5).values()),[1, 1, 2])
- assert_equal(P5.degree([]),{})
- assert_equal(list(P5.degree_iter([])),[])
- assert_equal(dict(P5.degree_iter([])),{})
-
- def test_null(self):
- null=nx.null_graph()
- assert_equal(null.degree(),{})
- assert_equal(list(null.degree_iter()),[])
- assert_equal(dict(null.degree_iter()),{})
-
- def test_order_size(self):
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- assert_equal(G.order(),4)
- assert_equal(G.size(),5)
- assert_equal(G.number_of_edges(),5)
- assert_equal(G.number_of_edges('A','B'),1)
- assert_equal(G.number_of_edges('A','D'),0)
-
- def test_copy(self):
- G=self.G()
- H=G.copy() # copy
- assert_equal(H.adj,G.adj)
- assert_equal(H.name,G.name)
- assert_not_equal(H,G)
-
- def test_subgraph(self):
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- SG=G.subgraph(['A','B','D'])
- assert_nodes_equal(SG.nodes(),['A', 'B', 'D'])
- assert_edges_equal(SG.edges(),[('A', 'B'), ('B', 'D')])
-
- def test_to_directed(self):
- G=self.G()
- if not G.is_directed():
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
-
- DG=G.to_directed()
- assert_not_equal(DG,G) # directed copy or copy
-
- assert_true(DG.is_directed())
- assert_equal(DG.name,G.name)
- assert_equal(DG.adj,G.adj)
- assert_equal(sorted(DG.out_edges(list('AB'))),
- [('A', 'B'), ('A', 'C'), ('B', 'A'),
- ('B', 'C'), ('B', 'D')])
- DG.remove_edge('A','B')
- assert_true(DG.has_edge('B','A')) # this removes B-A but not A-B
- assert_false(DG.has_edge('A','B'))
-
- def test_to_undirected(self):
- G=self.G()
- if G.is_directed():
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- UG=G.to_undirected() # to_undirected
- assert_not_equal(UG,G)
- assert_false(UG.is_directed())
- assert_true(G.is_directed())
- assert_equal(UG.name,G.name)
- assert_not_equal(UG.adj,G.adj)
- assert_equal(sorted(UG.edges(list('AB'))),
- [('A', 'B'), ('A', 'C'), ('B', 'C'), ('B', 'D')])
- assert_equal(sorted(UG.edges(['A','B'])),
- [('A', 'B'), ('A', 'C'), ('B', 'C'), ('B', 'D')])
- UG.remove_edge('A','B')
- assert_false(UG.has_edge('B','A'))
- assert_false( UG.has_edge('A','B'))
-
-
-
- def test_neighbors(self):
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- G.add_nodes_from('GJK')
- assert_equal(sorted(G['A']),['B', 'C'])
- assert_equal(sorted(G.neighbors('A')),['B', 'C'])
- assert_equal(sorted(G.neighbors_iter('A')),['B', 'C'])
- assert_equal(sorted(G.neighbors('G')),[])
- assert_raises(nx.NetworkXError,G.neighbors,'j')
-
- def test_iterators(self):
- G=self.G()
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('C', 'B'), ('C', 'D')])
- G.add_nodes_from('GJK')
- assert_equal(sorted(G.nodes_iter()),
- ['A', 'B', 'C', 'D', 'G', 'J', 'K'])
- assert_edges_equal(G.edges_iter(),
- [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'B'), ('C', 'D')])
-
- assert_equal(sorted([v for k,v in G.degree_iter()]),
- [0, 0, 0, 2, 2, 3, 3])
- assert_equal(sorted(G.degree_iter(),key=str),
- [('A', 2), ('B', 3), ('C', 3), ('D', 2),
- ('G', 0), ('J', 0), ('K', 0)])
- assert_equal(sorted(G.neighbors_iter('A')),['B', 'C'])
- assert_raises(nx.NetworkXError,G.neighbors_iter,'X')
- G.clear()
- assert_equal(nx.number_of_nodes(G),0)
- assert_equal(nx.number_of_edges(G),0)
-
-
- def test_null_subgraph(self):
- # Subgraph of a null graph is a null graph
- nullgraph=nx.null_graph()
- G=nx.null_graph()
- H=G.subgraph([])
- assert_true(nx.is_isomorphic(H,nullgraph))
-
- def test_empty_subgraph(self):
- # Subgraph of an empty graph is an empty graph. test 1
- nullgraph=nx.null_graph()
- E5=nx.empty_graph(5)
- E10=nx.empty_graph(10)
- H=E10.subgraph([])
- assert_true(nx.is_isomorphic(H,nullgraph))
- H=E10.subgraph([1,2,3,4,5])
- assert_true(nx.is_isomorphic(H,E5))
-
- def test_complete_subgraph(self):
- # Subgraph of a complete graph is a complete graph
- K1=nx.complete_graph(1)
- K3=nx.complete_graph(3)
- K5=nx.complete_graph(5)
- H=K5.subgraph([1,2,3])
- assert_true(nx.is_isomorphic(H,K3))
-
- def test_subgraph_nbunch(self):
- nullgraph=nx.null_graph()
- K1=nx.complete_graph(1)
- K3=nx.complete_graph(3)
- K5=nx.complete_graph(5)
- # Test G.subgraph(nbunch), where nbunch is a single node
- H=K5.subgraph(1)
- assert_true(nx.is_isomorphic(H,K1))
- # Test G.subgraph(nbunch), where nbunch is a set
- H=K5.subgraph(set([1]))
- assert_true(nx.is_isomorphic(H,K1))
- # Test G.subgraph(nbunch), where nbunch is an iterator
- H=K5.subgraph(iter(K3))
- assert_true(nx.is_isomorphic(H,K3))
- # Test G.subgraph(nbunch), where nbunch is another graph
- H=K5.subgraph(K3)
- assert_true(nx.is_isomorphic(H,K3))
- H=K5.subgraph([9])
- assert_true(nx.is_isomorphic(H,nullgraph))
-
- def test_node_tuple_error(self):
- H=self.G()
- # Test error handling of tuple as a node
- assert_raises(nx.NetworkXError,H.remove_node,(1,2))
- H.remove_nodes_from([(1,2)]) # no error
- assert_raises(nx.NetworkXError,H.neighbors,(1,2))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_digraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_digraph.py
deleted file mode 100644
index 9ac3d3e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_digraph.py
+++ /dev/null
@@ -1,257 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-from test_graph import BaseGraphTester, BaseAttrGraphTester, TestGraph
-
-class BaseDiGraphTester(BaseGraphTester):
- def test_has_successor(self):
- G=self.K3
- assert_equal(G.has_successor(0,1),True)
- assert_equal(G.has_successor(0,-1),False)
-
- def test_successors(self):
- G=self.K3
- assert_equal(sorted(G.successors(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.successors,-1)
-
- def test_successors_iter(self):
- G=self.K3
- assert_equal(sorted(G.successors_iter(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.successors_iter,-1)
-
- def test_has_predecessor(self):
- G=self.K3
- assert_equal(G.has_predecessor(0,1),True)
- assert_equal(G.has_predecessor(0,-1),False)
-
- def test_predecessors(self):
- G=self.K3
- assert_equal(sorted(G.predecessors(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.predecessors,-1)
-
- def test_predecessors_iter(self):
- G=self.K3
- assert_equal(sorted(G.predecessors_iter(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.predecessors_iter,-1)
-
- def test_edges(self):
- G=self.K3
- assert_equal(sorted(G.edges()),[(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.edges(0)),[(0,1),(0,2)])
- assert_raises((KeyError,networkx.NetworkXError), G.edges,-1)
-
- def test_edges_iter(self):
- G=self.K3
- assert_equal(sorted(G.edges_iter()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.edges_iter(0)),[(0,1),(0,2)])
-
- def test_edges_data(self):
- G=self.K3
- assert_equal(sorted(G.edges(data=True)),
- [(0,1,{}),(0,2,{}),(1,0,{}),(1,2,{}),(2,0,{}),(2,1,{})])
- assert_equal(sorted(G.edges(0,data=True)),[(0,1,{}),(0,2,{})])
- assert_raises((KeyError,networkx.NetworkXError), G.edges,-1)
-
- def test_out_edges(self):
- G=self.K3
- assert_equal(sorted(G.out_edges()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.out_edges(0)),[(0,1),(0,2)])
- assert_raises((KeyError,networkx.NetworkXError), G.out_edges,-1)
-
- def test_out_edges_iter(self):
- G=self.K3
- assert_equal(sorted(G.out_edges_iter()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.edges_iter(0)),[(0,1),(0,2)])
-
- def test_out_edges_dir(self):
- G=self.P3
- assert_equal(sorted(G.out_edges()),[(0, 1), (1, 2)])
- assert_equal(sorted(G.out_edges(0)),[(0, 1)])
- assert_equal(sorted(G.out_edges(2)),[])
-
- def test_out_edges_iter_dir(self):
- G=self.P3
- assert_equal(sorted(G.out_edges_iter()),[(0, 1), (1, 2)])
- assert_equal(sorted(G.out_edges_iter(0)),[(0, 1)])
- assert_equal(sorted(G.out_edges_iter(2)),[])
-
- def test_in_edges_dir(self):
- G=self.P3
- assert_equal(sorted(G.in_edges()),[(0, 1), (1, 2)])
- assert_equal(sorted(G.in_edges(0)),[])
- assert_equal(sorted(G.in_edges(2)),[(1,2)])
-
- def test_in_edges_iter_dir(self):
- G=self.P3
- assert_equal(sorted(G.in_edges_iter()),[(0, 1), (1, 2)])
- assert_equal(sorted(G.in_edges_iter(0)),[])
- assert_equal(sorted(G.in_edges_iter(2)),[(1,2)])
-
- def test_degree(self):
- G=self.K3
- assert_equal(list(G.degree().values()),[4,4,4])
- assert_equal(G.degree(),{0:4,1:4,2:4})
- assert_equal(G.degree(0),4)
- assert_equal(G.degree([0]),{0:4})
- assert_raises((KeyError,networkx.NetworkXError), G.degree,-1)
-
- def test_degree_iter(self):
- G=self.K3
- assert_equal(list(G.degree_iter()),[(0,4),(1,4),(2,4)])
- assert_equal(dict(G.degree_iter()),{0:4,1:4,2:4})
- assert_equal(list(G.degree_iter(0)),[(0,4)])
-
- def test_in_degree(self):
- G=self.K3
- assert_equal(list(G.in_degree().values()),[2,2,2])
- assert_equal(G.in_degree(),{0:2,1:2,2:2})
- assert_equal(G.in_degree(0),2)
- assert_equal(G.in_degree([0]),{0:2})
- assert_raises((KeyError,networkx.NetworkXError), G.in_degree,-1)
-
- def test_in_degree_iter(self):
- G=self.K3
- assert_equal(list(G.in_degree_iter()),[(0,2),(1,2),(2,2)])
- assert_equal(dict(G.in_degree_iter()),{0:2,1:2,2:2})
- assert_equal(list(G.in_degree_iter(0)),[(0,2)])
-
- def test_in_degree_iter_weighted(self):
- G=self.K3
- G.add_edge(0,1,weight=0.3,other=1.2)
- assert_equal(list(G.in_degree_iter(weight='weight')),[(0,2),(1,1.3),(2,2)])
- assert_equal(dict(G.in_degree_iter(weight='weight')),{0:2,1:1.3,2:2})
- assert_equal(list(G.in_degree_iter(1,weight='weight')),[(1,1.3)])
- assert_equal(list(G.in_degree_iter(weight='other')),[(0,2),(1,2.2),(2,2)])
- assert_equal(dict(G.in_degree_iter(weight='other')),{0:2,1:2.2,2:2})
- assert_equal(list(G.in_degree_iter(1,weight='other')),[(1,2.2)])
-
- def test_out_degree(self):
- G=self.K3
- assert_equal(list(G.out_degree().values()),[2,2,2])
- assert_equal(G.out_degree(),{0:2,1:2,2:2})
- assert_equal(G.out_degree(0),2)
- assert_equal(G.out_degree([0]),{0:2})
- assert_raises((KeyError,networkx.NetworkXError), G.out_degree,-1)
-
- def test_out_degree_iter_weighted(self):
- G=self.K3
- G.add_edge(0,1,weight=0.3,other=1.2)
- assert_equal(list(G.out_degree_iter(weight='weight')),[(0,1.3),(1,2),(2,2)])
- assert_equal(dict(G.out_degree_iter(weight='weight')),{0:1.3,1:2,2:2})
- assert_equal(list(G.out_degree_iter(0,weight='weight')),[(0,1.3)])
- assert_equal(list(G.out_degree_iter(weight='other')),[(0,2.2),(1,2),(2,2)])
- assert_equal(dict(G.out_degree_iter(weight='other')),{0:2.2,1:2,2:2})
- assert_equal(list(G.out_degree_iter(0,weight='other')),[(0,2.2)])
-
- def test_out_degree_iter(self):
- G=self.K3
- assert_equal(list(G.out_degree_iter()),[(0,2),(1,2),(2,2)])
- assert_equal(dict(G.out_degree_iter()),{0:2,1:2,2:2})
- assert_equal(list(G.out_degree_iter(0)),[(0,2)])
-
- def test_size(self):
- G=self.K3
- assert_equal(G.size(),6)
- assert_equal(G.number_of_edges(),6)
-
- def test_to_undirected_reciprocal(self):
- G=self.Graph()
- G.add_edge(1,2)
- assert_true(G.to_undirected().has_edge(1,2))
- assert_false(G.to_undirected(reciprocal=True).has_edge(1,2))
- G.add_edge(2,1)
- assert_true(G.to_undirected(reciprocal=True).has_edge(1,2))
-
- def test_reverse_copy(self):
- G=networkx.DiGraph([(0,1),(1,2)])
- R=G.reverse()
- assert_equal(sorted(R.edges()),[(1,0),(2,1)])
- R.remove_edge(1,0)
- assert_equal(sorted(R.edges()),[(2,1)])
- assert_equal(sorted(G.edges()),[(0,1),(1,2)])
-
- def test_reverse_nocopy(self):
- G=networkx.DiGraph([(0,1),(1,2)])
- R=G.reverse(copy=False)
- assert_equal(sorted(R.edges()),[(1,0),(2,1)])
- R.remove_edge(1,0)
- assert_equal(sorted(R.edges()),[(2,1)])
- assert_equal(sorted(G.edges()),[(2,1)])
-
-class BaseAttrDiGraphTester(BaseDiGraphTester,BaseAttrGraphTester):
- pass
-
-
-class TestDiGraph(BaseAttrDiGraphTester,TestGraph):
- """Tests specific to dict-of-dict-of-dict digraph data structure"""
- def setUp(self):
- self.Graph=networkx.DiGraph
- # build dict-of-dict-of-dict K3
- ed1,ed2,ed3,ed4,ed5,ed6 = ({},{},{},{},{},{})
- self.k3adj={0: {1: ed1, 2: ed2}, 1: {0: ed3, 2: ed4}, 2: {0: ed5, 1:ed6}}
- self.k3edges=[(0, 1), (0, 2), (1, 2)]
- self.k3nodes=[0, 1, 2]
- self.K3=self.Graph()
- self.K3.adj = self.K3.succ = self.K3.edge = self.k3adj
- self.K3.pred={0: {1: ed3, 2: ed5}, 1: {0: ed1, 2: ed6}, 2: {0: ed2, 1:ed4}}
-
- ed1,ed2 = ({},{})
- self.P3=self.Graph()
- self.P3.adj={0: {1: ed1}, 1: {2: ed2}, 2: {}}
- self.P3.succ=self.P3.adj
- self.P3.pred={0: {}, 1: {0: ed1}, 2: {1: ed2}}
- self.K3.node={}
- self.K3.node[0]={}
- self.K3.node[1]={}
- self.K3.node[2]={}
- self.P3.node={}
- self.P3.node[0]={}
- self.P3.node[1]={}
- self.P3.node[2]={}
-
- def test_data_input(self):
- G=self.Graph(data={1:[2],2:[1]}, name="test")
- assert_equal(G.name,"test")
- assert_equal(sorted(G.adj.items()),[(1, {2: {}}), (2, {1: {}})])
- assert_equal(sorted(G.succ.items()),[(1, {2: {}}), (2, {1: {}})])
- assert_equal(sorted(G.pred.items()),[(1, {2: {}}), (2, {1: {}})])
-
- def test_add_edge(self):
- G=self.Graph()
- G.add_edge(0,1)
- assert_equal(G.adj,{0: {1: {}}, 1: {}})
- assert_equal(G.succ,{0: {1: {}}, 1: {}})
- assert_equal(G.pred,{0: {}, 1: {0:{}}})
- G=self.Graph()
- G.add_edge(*(0,1))
- assert_equal(G.adj,{0: {1: {}}, 1: {}})
- assert_equal(G.succ,{0: {1: {}}, 1: {}})
- assert_equal(G.pred,{0: {}, 1: {0:{}}})
-
- def test_add_edges_from(self):
- G=self.Graph()
- G.add_edges_from([(0,1),(0,2,{'data':3})],data=2)
- assert_equal(G.adj,{0: {1: {'data':2}, 2: {'data':3}}, 1: {}, 2: {}})
- assert_equal(G.succ,{0: {1: {'data':2}, 2: {'data':3}}, 1: {}, 2: {}})
- assert_equal(G.pred,{0: {}, 1: {0: {'data':2}}, 2: {0: {'data':3}}})
-
- assert_raises(networkx.NetworkXError, G.add_edges_from,[(0,)]) # too few in tuple
- assert_raises(networkx.NetworkXError, G.add_edges_from,[(0,1,2,3)]) # too many in tuple
- assert_raises(TypeError, G.add_edges_from,[0]) # not a tuple
-
- def test_remove_edge(self):
- G=self.K3
- G.remove_edge(0,1)
- assert_equal(G.succ,{0:{2:{}},1:{0:{},2:{}},2:{0:{},1:{}}})
- assert_equal(G.pred,{0:{1:{}, 2:{}}, 1:{2:{}}, 2:{0:{},1:{}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,-1,0)
-
- def test_remove_edges_from(self):
- G=self.K3
- G.remove_edges_from([(0,1)])
- assert_equal(G.succ,{0:{2:{}},1:{0:{},2:{}},2:{0:{},1:{}}})
- assert_equal(G.pred,{0:{1:{}, 2:{}}, 1:{2:{}}, 2:{0:{},1: {}}})
- G.remove_edges_from([(0,0)]) # silent fail
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_digraph_historical.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_digraph_historical.py
deleted file mode 100644
index 6bf295c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_digraph_historical.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python
-"""Original NetworkX graph tests"""
-from nose.tools import *
-import networkx
-import networkx as nx
-
-from historical_tests import HistoricalTests
-
-class TestDiGraphHistorical(HistoricalTests):
-
- def setUp(self):
- HistoricalTests.setUp(self)
- self.G=nx.DiGraph
-
-
- def test_in_degree(self):
- G=self.G()
- G.add_nodes_from('GJK')
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('B', 'C'), ('C', 'D')])
-
- assert_equal(sorted(G.in_degree().values()),[0, 0, 0, 0, 1, 2, 2])
- assert_equal(G.in_degree(),
- {'A': 0, 'C': 2, 'B': 1, 'D': 2, 'G': 0, 'K': 0, 'J': 0})
- assert_equal(sorted([v for k,v in G.in_degree_iter()]),
- [0, 0, 0, 0, 1, 2, 2])
- assert_equal(dict(G.in_degree_iter()),
- {'A': 0, 'C': 2, 'B': 1, 'D': 2, 'G': 0, 'K': 0, 'J': 0})
-
-
- def test_out_degree(self):
- G=self.G()
- G.add_nodes_from('GJK')
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('B', 'C'), ('C', 'D')])
- assert_equal(sorted(G.out_degree().values()),[0, 0, 0, 0, 1, 2, 2])
- assert_equal(G.out_degree(),
- {'A': 2, 'C': 1, 'B': 2, 'D': 0, 'G': 0, 'K': 0, 'J': 0})
- assert_equal(sorted([v for k,v in G.in_degree_iter()]),
- [0, 0, 0, 0, 1, 2, 2])
- assert_equal(dict(G.out_degree_iter()),
- {'A': 2, 'C': 1, 'B': 2, 'D': 0, 'G': 0, 'K': 0, 'J': 0})
-
-
- def test_degree_digraph(self):
- H=nx.DiGraph()
- H.add_edges_from([(1,24),(1,2)])
- assert_equal(sorted(H.in_degree([1,24]).values()),[0, 1])
- assert_equal(sorted(H.out_degree([1,24]).values()),[0, 2])
- assert_equal(sorted(H.degree([1,24]).values()),[1, 2])
-
-
- def test_neighbors(self):
- G=self.G()
- G.add_nodes_from('GJK')
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('B', 'C'), ('C', 'D')])
-
- assert_equal(sorted(G.neighbors('C')),['D'])
- assert_equal(sorted(G['C']),['D'])
- assert_equal(sorted(G.neighbors('A')),['B', 'C'])
- assert_equal(sorted(G.neighbors_iter('A')),['B', 'C'])
- assert_equal(sorted(G.neighbors_iter('C')),['D'])
- assert_equal(sorted(G.neighbors('A')),['B', 'C'])
- assert_raises(nx.NetworkXError,G.neighbors,'j')
- assert_raises(nx.NetworkXError,G.neighbors_iter,'j')
-
- def test_successors(self):
- G=self.G()
- G.add_nodes_from('GJK')
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('B', 'C'), ('C', 'D')])
- assert_equal(sorted(G.successors('A')),['B', 'C'])
- assert_equal(sorted(G.successors_iter('A')),['B', 'C'])
- assert_equal(sorted(G.successors('G')),[])
- assert_equal(sorted(G.successors('D')),[])
- assert_equal(sorted(G.successors_iter('G')),[])
- assert_raises(nx.NetworkXError,G.successors,'j')
- assert_raises(nx.NetworkXError,G.successors_iter,'j')
-
-
- def test_predecessors(self):
- G=self.G()
- G.add_nodes_from('GJK')
- G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
- ('B', 'C'), ('C', 'D')])
- assert_equal(sorted(G.predecessors('C')),['A', 'B'])
- assert_equal(sorted(G.predecessors_iter('C')),['A', 'B'])
- assert_equal(sorted(G.predecessors('G')),[])
- assert_equal(sorted(G.predecessors('A')),[])
- assert_equal(sorted(G.predecessors_iter('G')),[])
- assert_equal(sorted(G.predecessors_iter('A')),[])
- assert_equal(sorted(G.successors_iter('D')),[])
-
- assert_raises(nx.NetworkXError,G.predecessors,'j')
- assert_raises(nx.NetworkXError,G.predecessors,'j')
-
-
-
- def test_reverse(self):
- G=nx.complete_graph(10)
- H=G.to_directed()
- HR=H.reverse()
- assert_true(nx.is_isomorphic(H,HR))
- assert_equal(sorted(H.edges()),sorted(HR.edges()))
-
- def test_reverse2(self):
- H=nx.DiGraph()
- foo=[H.add_edge(u,u+1) for u in range(0,5)]
- HR=H.reverse()
- for u in range(0,5):
- assert_true(HR.has_edge(u+1,u))
-
- def test_reverse3(self):
- H=nx.DiGraph()
- H.add_nodes_from([1,2,3,4])
- HR=H.reverse()
- assert_equal(sorted(HR.nodes()),[1, 2, 3, 4])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_function.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_function.py
deleted file mode 100644
index 2c12178..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_function.py
+++ /dev/null
@@ -1,190 +0,0 @@
-#!/usr/bin/env python
-import random
-from nose.tools import *
-import networkx
-import networkx as nx
-
-class TestFunction(object):
- def setUp(self):
- self.G=networkx.Graph({0:[1,2,3], 1:[1,2,0], 4:[]}, name='Test')
- self.Gdegree={0:3, 1:2, 2:2, 3:1, 4:0}
- self.Gnodes=list(range(5))
- self.Gedges=[(0,1),(0,2),(0,3),(1,0),(1,1),(1,2)]
- self.DG=networkx.DiGraph({0:[1,2,3], 1:[1,2,0], 4:[]})
- self.DGin_degree={0:1, 1:2, 2:2, 3:1, 4:0}
- self.DGout_degree={0:3, 1:3, 2:0, 3:0, 4:0}
- self.DGnodes=list(range(5))
- self.DGedges=[(0,1),(0,2),(0,3),(1,0),(1,1),(1,2)]
-
- def test_nodes(self):
- assert_equal(self.G.nodes(),networkx.nodes(self.G))
- assert_equal(self.DG.nodes(),networkx.nodes(self.DG))
- def test_edges(self):
- assert_equal(self.G.edges(),networkx.edges(self.G))
- assert_equal(self.DG.edges(),networkx.edges(self.DG))
- assert_equal(self.G.edges(nbunch=[0,1,3]),networkx.edges(self.G,nbunch=[0,1,3]))
- assert_equal(self.DG.edges(nbunch=[0,1,3]),networkx.edges(self.DG,nbunch=[0,1,3]))
- def test_nodes_iter(self):
- assert_equal(list(self.G.nodes_iter()),list(networkx.nodes_iter(self.G)))
- assert_equal(list(self.DG.nodes_iter()),list(networkx.nodes_iter(self.DG)))
- def test_edges_iter(self):
- assert_equal(list(self.G.edges_iter()),list(networkx.edges_iter(self.G)))
- assert_equal(list(self.DG.edges_iter()),list(networkx.edges_iter(self.DG)))
- assert_equal(list(self.G.edges_iter(nbunch=[0,1,3])),list(networkx.edges_iter(self.G,nbunch=[0,1,3])))
- assert_equal(list(self.DG.edges_iter(nbunch=[0,1,3])),list(networkx.edges_iter(self.DG,nbunch=[0,1,3])))
- def test_degree(self):
- assert_equal(self.G.degree(),networkx.degree(self.G))
- assert_equal(self.DG.degree(),networkx.degree(self.DG))
- assert_equal(self.G.degree(nbunch=[0,1]),networkx.degree(self.G,nbunch=[0,1]))
- assert_equal(self.DG.degree(nbunch=[0,1]),networkx.degree(self.DG,nbunch=[0,1]))
- assert_equal(self.G.degree(weight='weight'),networkx.degree(self.G,weight='weight'))
- assert_equal(self.DG.degree(weight='weight'),networkx.degree(self.DG,weight='weight'))
- def test_neighbors(self):
- assert_equal(self.G.neighbors(1),networkx.neighbors(self.G,1))
- assert_equal(self.DG.neighbors(1),networkx.neighbors(self.DG,1))
- def test_number_of_nodes(self):
- assert_equal(self.G.number_of_nodes(),networkx.number_of_nodes(self.G))
- assert_equal(self.DG.number_of_nodes(),networkx.number_of_nodes(self.DG))
- def test_number_of_edges(self):
- assert_equal(self.G.number_of_edges(),networkx.number_of_edges(self.G))
- assert_equal(self.DG.number_of_edges(),networkx.number_of_edges(self.DG))
- def test_is_directed(self):
- assert_equal(self.G.is_directed(),networkx.is_directed(self.G))
- assert_equal(self.DG.is_directed(),networkx.is_directed(self.DG))
- def test_subgraph(self):
- assert_equal(self.G.subgraph([0,1,2,4]).adj,networkx.subgraph(self.G,[0,1,2,4]).adj)
- assert_equal(self.DG.subgraph([0,1,2,4]).adj,networkx.subgraph(self.DG,[0,1,2,4]).adj)
-
- def test_create_empty_copy(self):
- G=networkx.create_empty_copy(self.G, with_nodes=False)
- assert_equal(G.nodes(),[])
- assert_equal(G.graph,{})
- assert_equal(G.node,{})
- assert_equal(G.edge,{})
- G=networkx.create_empty_copy(self.G)
- assert_equal(G.nodes(),self.G.nodes())
- assert_equal(G.graph,{})
- assert_equal(G.node,{}.fromkeys(self.G.nodes(),{}))
- assert_equal(G.edge,{}.fromkeys(self.G.nodes(),{}))
-
- def test_degree_histogram(self):
- assert_equal(networkx.degree_histogram(self.G), [1,1,1,1,1])
-
- def test_density(self):
- assert_equal(networkx.density(self.G), 0.5)
- assert_equal(networkx.density(self.DG), 0.3)
- G=networkx.Graph()
- G.add_node(1)
- assert_equal(networkx.density(G), 0.0)
-
- def test_density_selfloop(self):
- G = nx.Graph()
- G.add_edge(1,1)
- assert_equal(networkx.density(G), 0.0)
- G.add_edge(1,2)
- assert_equal(networkx.density(G), 2.0)
-
- def test_freeze(self):
- G=networkx.freeze(self.G)
- assert_equal(G.frozen,True)
- assert_raises(networkx.NetworkXError, G.add_node, 1)
- assert_raises(networkx.NetworkXError, G.add_nodes_from, [1])
- assert_raises(networkx.NetworkXError, G.remove_node, 1)
- assert_raises(networkx.NetworkXError, G.remove_nodes_from, [1])
- assert_raises(networkx.NetworkXError, G.add_edge, 1,2)
- assert_raises(networkx.NetworkXError, G.add_edges_from, [(1,2)])
- assert_raises(networkx.NetworkXError, G.remove_edge, 1,2)
- assert_raises(networkx.NetworkXError, G.remove_edges_from, [(1,2)])
- assert_raises(networkx.NetworkXError, G.clear)
-
- def test_is_frozen(self):
- assert_equal(networkx.is_frozen(self.G), False)
- G=networkx.freeze(self.G)
- assert_equal(G.frozen, networkx.is_frozen(self.G))
- assert_equal(G.frozen,True)
-
- def test_info(self):
- G=networkx.path_graph(5)
- info=networkx.info(G)
- expected_graph_info='\n'.join(['Name: path_graph(5)',
- 'Type: Graph',
- 'Number of nodes: 5',
- 'Number of edges: 4',
- 'Average degree: 1.6000'])
- assert_equal(info,expected_graph_info)
-
- info=networkx.info(G,n=1)
- expected_node_info='\n'.join(
- ['Node 1 has the following properties:',
- 'Degree: 2',
- 'Neighbors: 0 2'])
- assert_equal(info,expected_node_info)
-
- def test_info_digraph(self):
- G=networkx.DiGraph(name='path_graph(5)')
- G.add_path([0,1,2,3,4])
- info=networkx.info(G)
- expected_graph_info='\n'.join(['Name: path_graph(5)',
- 'Type: DiGraph',
- 'Number of nodes: 5',
- 'Number of edges: 4',
- 'Average in degree: 0.8000',
- 'Average out degree: 0.8000'])
- assert_equal(info,expected_graph_info)
-
- info=networkx.info(G,n=1)
- expected_node_info='\n'.join(
- ['Node 1 has the following properties:',
- 'Degree: 2',
- 'Neighbors: 2'])
- assert_equal(info,expected_node_info)
-
- assert_raises(networkx.NetworkXError,networkx.info,G,n=-1)
-
- def test_neighbors(self):
- graph = nx.complete_graph(100)
- pop = random.sample(graph.nodes(), 1)
- nbors = list(nx.neighbors(graph, pop[0]))
- # should be all the other vertices in the graph
- assert_equal(len(nbors), len(graph) - 1)
-
- graph = nx.path_graph(100)
- node = random.sample(graph.nodes(), 1)[0]
- nbors = list(nx.neighbors(graph, node))
- # should be all the other vertices in the graph
- if node != 0 and node != 99:
- assert_equal(len(nbors), 2)
- else:
- assert_equal(len(nbors), 1)
-
- # create a star graph with 99 outer nodes
- graph = nx.star_graph(99)
- nbors = list(nx.neighbors(graph, 0))
- assert_equal(len(nbors), 99)
-
- def test_non_neighbors(self):
- graph = nx.complete_graph(100)
- pop = random.sample(graph.nodes(), 1)
- nbors = list(nx.non_neighbors(graph, pop[0]))
- # should be all the other vertices in the graph
- assert_equal(len(nbors), 0)
-
- graph = nx.path_graph(100)
- node = random.sample(graph.nodes(), 1)[0]
- nbors = list(nx.non_neighbors(graph, node))
- # should be all the other vertices in the graph
- if node != 0 and node != 99:
- assert_equal(len(nbors), 97)
- else:
- assert_equal(len(nbors), 98)
-
- # create a star graph with 99 outer nodes
- graph = nx.star_graph(99)
- nbors = list(nx.non_neighbors(graph, 0))
- assert_equal(len(nbors), 0)
-
- # disconnected graph
- graph = nx.Graph()
- graph.add_nodes_from(range(10))
- nbors = list(nx.non_neighbors(graph, 0))
- assert_equal(len(nbors), 9)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_graph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_graph.py
deleted file mode 100644
index 077dead..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_graph.py
+++ /dev/null
@@ -1,602 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-
-class BaseGraphTester(object):
- """ Tests for data-structure independent graph class features."""
- def test_contains(self):
- G=self.K3
- assert(1 in G )
- assert(4 not in G )
- assert('b' not in G )
- assert([] not in G ) # no exception for nonhashable
- assert({1:1} not in G) # no exception for nonhashable
-
- def test_order(self):
- G=self.K3
- assert_equal(len(G),3)
- assert_equal(G.order(),3)
- assert_equal(G.number_of_nodes(),3)
-
- def test_nodes_iter(self):
- G=self.K3
- assert_equal(sorted(G.nodes_iter()),self.k3nodes)
- assert_equal(sorted(G.nodes_iter(data=True)),[(0,{}),(1,{}),(2,{})])
-
- def test_nodes(self):
- G=self.K3
- assert_equal(sorted(G.nodes()),self.k3nodes)
- assert_equal(sorted(G.nodes(data=True)),[(0,{}),(1,{}),(2,{})])
-
- def test_has_node(self):
- G=self.K3
- assert(G.has_node(1))
- assert(not G.has_node(4))
- assert(not G.has_node([])) # no exception for nonhashable
- assert(not G.has_node({1:1})) # no exception for nonhashable
-
- def test_has_edge(self):
- G=self.K3
- assert_equal(G.has_edge(0,1),True)
- assert_equal(G.has_edge(0,-1),False)
-
- def test_neighbors(self):
- G=self.K3
- assert_equal(sorted(G.neighbors(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.neighbors,-1)
-
- def test_neighbors_iter(self):
- G=self.K3
- assert_equal(sorted(G.neighbors_iter(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.neighbors_iter,-1)
-
- def test_edges(self):
- G=self.K3
- assert_equal(sorted(G.edges()),[(0,1),(0,2),(1,2)])
- assert_equal(sorted(G.edges(0)),[(0,1),(0,2)])
- assert_raises((KeyError,networkx.NetworkXError), G.edges,-1)
-
- def test_edges_iter(self):
- G=self.K3
- assert_equal(sorted(G.edges_iter()),[(0,1),(0,2),(1,2)])
- assert_equal(sorted(G.edges_iter(0)),[(0,1),(0,2)])
- f=lambda x:list(G.edges_iter(x))
- assert_raises((KeyError,networkx.NetworkXError), f, -1)
-
- def test_adjacency_list(self):
- G=self.K3
- assert_equal(G.adjacency_list(),[[1,2],[0,2],[0,1]])
-
- def test_degree(self):
- G=self.K3
- assert_equal(list(G.degree().values()),[2,2,2])
- assert_equal(G.degree(),{0:2,1:2,2:2})
- assert_equal(G.degree(0),2)
- assert_equal(G.degree([0]),{0:2})
- assert_raises((KeyError,networkx.NetworkXError), G.degree,-1)
-
- def test_weighted_degree(self):
- G=self.Graph()
- G.add_edge(1,2,weight=2)
- G.add_edge(2,3,weight=3)
- assert_equal(list(G.degree(weight='weight').values()),[2,5,3])
- assert_equal(G.degree(weight='weight'),{1:2,2:5,3:3})
- assert_equal(G.degree(1,weight='weight'),2)
- assert_equal(G.degree([1],weight='weight'),{1:2})
-
- def test_degree_iter(self):
- G=self.K3
- assert_equal(list(G.degree_iter()),[(0,2),(1,2),(2,2)])
- assert_equal(dict(G.degree_iter()),{0:2,1:2,2:2})
- assert_equal(list(G.degree_iter(0)),[(0,2)])
-
- def test_size(self):
- G=self.K3
- assert_equal(G.size(),3)
- assert_equal(G.number_of_edges(),3)
-
- def test_add_star(self):
- G=self.K3.copy()
- nlist=[12,13,14,15]
- G.add_star(nlist)
- assert_equal(sorted(G.edges(nlist)),[(12,13),(12,14),(12,15)])
- G=self.K3.copy()
- G.add_star(nlist,weight=2.0)
- assert_equal(sorted(G.edges(nlist,data=True)),\
- [(12,13,{'weight':2.}),
- (12,14,{'weight':2.}),
- (12,15,{'weight':2.})])
-
- def test_add_path(self):
- G=self.K3.copy()
- nlist=[12,13,14,15]
- G.add_path(nlist)
- assert_equal(sorted(G.edges(nlist)),[(12,13),(13,14),(14,15)])
- G=self.K3.copy()
- G.add_path(nlist,weight=2.0)
- assert_equal(sorted(G.edges(nlist,data=True)),\
- [(12,13,{'weight':2.}),
- (13,14,{'weight':2.}),
- (14,15,{'weight':2.})])
-
- def test_add_cycle(self):
- G=self.K3.copy()
- nlist=[12,13,14,15]
- oklists=[ [(12,13),(12,15),(13,14),(14,15)], \
- [(12,13),(13,14),(14,15),(15,12)] ]
- G.add_cycle(nlist)
- assert_true(sorted(G.edges(nlist)) in oklists)
- G=self.K3.copy()
- oklists=[ [(12,13,{'weight':1.}),\
- (12,15,{'weight':1.}),\
- (13,14,{'weight':1.}),\
- (14,15,{'weight':1.})], \
- \
- [(12,13,{'weight':1.}),\
- (13,14,{'weight':1.}),\
- (14,15,{'weight':1.}),\
- (15,12,{'weight':1.})] \
- ]
-
- G.add_cycle(nlist,weight=1.0)
- assert_true(sorted(G.edges(nlist,data=True)) in oklists)
-
- def test_nbunch_iter(self):
- G=self.K3
- assert_equal(list(G.nbunch_iter()),self.k3nodes) # all nodes
- assert_equal(list(G.nbunch_iter(0)),[0]) # single node
- assert_equal(list(G.nbunch_iter([0,1])),[0,1]) # sequence
- # sequence with none in graph
- assert_equal(list(G.nbunch_iter([-1])),[])
- # string sequence with none in graph
- assert_equal(list(G.nbunch_iter("foo")),[])
- # node not in graph doesn't get caught upon creation of iterator
- bunch=G.nbunch_iter(-1)
- # but gets caught when iterator used
- assert_raises(networkx.NetworkXError,list,bunch)
- # unhashable doesn't get caught upon creation of iterator
- bunch=G.nbunch_iter([0,1,2,{}])
- # but gets caught when iterator hits the unhashable
- assert_raises(networkx.NetworkXError,list,bunch)
-
- def test_selfloop_degree(self):
- G=self.Graph()
- G.add_edge(1,1)
- assert_equal(list(G.degree().values()),[2])
- assert_equal(G.degree(),{1:2})
- assert_equal(G.degree(1),2)
- assert_equal(G.degree([1]),{1:2})
- assert_equal(G.degree([1],weight='weight'),{1:2})
-
- def test_selfloops(self):
- G=self.K3.copy()
- G.add_edge(0,0)
- assert_equal(G.nodes_with_selfloops(),[0])
- assert_equal(G.selfloop_edges(),[(0,0)])
- assert_equal(G.number_of_selfloops(),1)
- G.remove_edge(0,0)
- G.add_edge(0,0)
- G.remove_edges_from([(0,0)])
- G.add_edge(1,1)
- G.remove_node(1)
- G.add_edge(0,0)
- G.add_edge(1,1)
- G.remove_nodes_from([0,1])
-
-
-class BaseAttrGraphTester(BaseGraphTester):
- """ Tests of graph class attribute features."""
- def test_weighted_degree(self):
- G=self.Graph()
- G.add_edge(1,2,weight=2,other=3)
- G.add_edge(2,3,weight=3,other=4)
- assert_equal(list(G.degree(weight='weight').values()),[2,5,3])
- assert_equal(G.degree(weight='weight'),{1:2,2:5,3:3})
- assert_equal(G.degree(1,weight='weight'),2)
- assert_equal(G.degree([1],weight='weight'),{1:2})
-
- assert_equal(list(G.degree(weight='other').values()),[3,7,4])
- assert_equal(G.degree(weight='other'),{1:3,2:7,3:4})
- assert_equal(G.degree(1,weight='other'),3)
- assert_equal(G.degree([1],weight='other'),{1:3})
-
- def add_attributes(self,G):
- G.graph['foo']=[]
- G.node[0]['foo']=[]
- G.remove_edge(1,2)
- ll=[]
- G.add_edge(1,2,foo=ll)
- G.add_edge(2,1,foo=ll)
- # attr_dict must be dict
- assert_raises(networkx.NetworkXError,G.add_edge,0,1,attr_dict=[])
-
- def test_name(self):
- G=self.Graph(name='')
- assert_equal(G.name,"")
- G=self.Graph(name='test')
- assert_equal(G.__str__(),"test")
- assert_equal(G.name,"test")
-
- def test_copy(self):
- G=self.K3
- self.add_attributes(G)
- H=G.copy()
- self.is_deepcopy(H,G)
- H=G.__class__(G)
- self.is_shallow_copy(H,G)
-
- def test_copy_attr(self):
- G=self.Graph(foo=[])
- G.add_node(0,foo=[])
- G.add_edge(1,2,foo=[])
- H=G.copy()
- self.is_deepcopy(H,G)
- H=G.__class__(G) # just copy
- self.is_shallow_copy(H,G)
-
- def is_deepcopy(self,H,G):
- self.graphs_equal(H,G)
- self.different_attrdict(H,G)
- self.deep_copy_attrdict(H,G)
-
- def deep_copy_attrdict(self,H,G):
- self.deepcopy_graph_attr(H,G)
- self.deepcopy_node_attr(H,G)
- self.deepcopy_edge_attr(H,G)
-
- def deepcopy_graph_attr(self,H,G):
- assert_equal(G.graph['foo'],H.graph['foo'])
- G.graph['foo'].append(1)
- assert_not_equal(G.graph['foo'],H.graph['foo'])
-
- def deepcopy_node_attr(self,H,G):
- assert_equal(G.node[0]['foo'],H.node[0]['foo'])
- G.node[0]['foo'].append(1)
- assert_not_equal(G.node[0]['foo'],H.node[0]['foo'])
-
- def deepcopy_edge_attr(self,H,G):
- assert_equal(G[1][2]['foo'],H[1][2]['foo'])
- G[1][2]['foo'].append(1)
- assert_not_equal(G[1][2]['foo'],H[1][2]['foo'])
-
- def is_shallow_copy(self,H,G):
- self.graphs_equal(H,G)
- self.different_attrdict(H,G)
- self.shallow_copy_attrdict(H,G)
-
- def shallow_copy_attrdict(self,H,G):
- self.shallow_copy_graph_attr(H,G)
- self.shallow_copy_node_attr(H,G)
- self.shallow_copy_edge_attr(H,G)
-
- def shallow_copy_graph_attr(self,H,G):
- assert_equal(G.graph['foo'],H.graph['foo'])
- G.graph['foo'].append(1)
- assert_equal(G.graph['foo'],H.graph['foo'])
-
- def shallow_copy_node_attr(self,H,G):
- assert_equal(G.node[0]['foo'],H.node[0]['foo'])
- G.node[0]['foo'].append(1)
- assert_equal(G.node[0]['foo'],H.node[0]['foo'])
-
- def shallow_copy_edge_attr(self,H,G):
- assert_equal(G[1][2]['foo'],H[1][2]['foo'])
- G[1][2]['foo'].append(1)
- assert_equal(G[1][2]['foo'],H[1][2]['foo'])
-
- def same_attrdict(self, H, G):
- old_foo=H[1][2]['foo']
- H.add_edge(1,2,foo='baz')
- assert_equal(G.edge,H.edge)
- H.add_edge(1,2,foo=old_foo)
- assert_equal(G.edge,H.edge)
- old_foo=H.node[0]['foo']
- H.node[0]['foo']='baz'
- assert_equal(G.node,H.node)
- H.node[0]['foo']=old_foo
- assert_equal(G.node,H.node)
-
- def different_attrdict(self, H, G):
- old_foo=H[1][2]['foo']
- H.add_edge(1,2,foo='baz')
- assert_not_equal(G.edge,H.edge)
- H.add_edge(1,2,foo=old_foo)
- assert_equal(G.edge,H.edge)
- old_foo=H.node[0]['foo']
- H.node[0]['foo']='baz'
- assert_not_equal(G.node,H.node)
- H.node[0]['foo']=old_foo
- assert_equal(G.node,H.node)
-
- def graphs_equal(self,H,G):
- assert_equal(G.adj,H.adj)
- assert_equal(G.edge,H.edge)
- assert_equal(G.node,H.node)
- assert_equal(G.graph,H.graph)
- assert_equal(G.name,H.name)
- if not G.is_directed() and not H.is_directed():
- assert_true(H.adj[1][2] is H.adj[2][1])
- assert_true(G.adj[1][2] is G.adj[2][1])
- else: # at least one is directed
- if not G.is_directed():
- G.pred=G.adj
- G.succ=G.adj
- if not H.is_directed():
- H.pred=H.adj
- H.succ=H.adj
- assert_equal(G.pred,H.pred)
- assert_equal(G.succ,H.succ)
- assert_true(H.succ[1][2] is H.pred[2][1])
- assert_true(G.succ[1][2] is G.pred[2][1])
-
- def test_graph_attr(self):
- G=self.K3
- G.graph['foo']='bar'
- assert_equal(G.graph['foo'], 'bar')
- del G.graph['foo']
- assert_equal(G.graph, {})
- H=self.Graph(foo='bar')
- assert_equal(H.graph['foo'], 'bar')
-
- def test_node_attr(self):
- G=self.K3
- G.add_node(1,foo='bar')
- assert_equal(G.nodes(), [0,1,2])
- assert_equal(G.nodes(data=True), [(0,{}),(1,{'foo':'bar'}),(2,{})])
- G.node[1]['foo']='baz'
- assert_equal(G.nodes(data=True), [(0,{}),(1,{'foo':'baz'}),(2,{})])
-
- def test_node_attr2(self):
- G=self.K3
- a={'foo':'bar'}
- G.add_node(3,attr_dict=a)
- assert_equal(G.nodes(), [0,1,2,3])
- assert_equal(G.nodes(data=True),
- [(0,{}),(1,{}),(2,{}),(3,{'foo':'bar'})])
-
- def test_edge_attr(self):
- G=self.Graph()
- G.add_edge(1,2,foo='bar')
- assert_equal(G.edges(data=True), [(1,2,{'foo':'bar'})])
-
- def test_edge_attr2(self):
- G=self.Graph()
- G.add_edges_from([(1,2),(3,4)],foo='foo')
- assert_equal(sorted(G.edges(data=True)),
- [(1,2,{'foo':'foo'}),(3,4,{'foo':'foo'})])
-
- def test_edge_attr3(self):
- G=self.Graph()
- G.add_edges_from([(1,2,{'weight':32}),(3,4,{'weight':64})],foo='foo')
- assert_equal(G.edges(data=True),
- [(1,2,{'foo':'foo','weight':32}),\
- (3,4,{'foo':'foo','weight':64})])
-
- G.remove_edges_from([(1,2),(3,4)])
- G.add_edge(1,2,data=7,spam='bar',bar='foo')
- assert_equal(G.edges(data=True),
- [(1,2,{'data':7,'spam':'bar','bar':'foo'})])
-
- def test_edge_attr4(self):
- G=self.Graph()
- G.add_edge(1,2,data=7,spam='bar',bar='foo')
- assert_equal(G.edges(data=True),
- [(1,2,{'data':7,'spam':'bar','bar':'foo'})])
- G[1][2]['data']=10 # OK to set data like this
- assert_equal(G.edges(data=True),
- [(1,2,{'data':10,'spam':'bar','bar':'foo'})])
-
- G.edge[1][2]['data']=20 # another spelling, "edge"
- assert_equal(G.edges(data=True),
- [(1,2,{'data':20,'spam':'bar','bar':'foo'})])
- G.edge[1][2]['listdata']=[20,200]
- G.edge[1][2]['weight']=20
- assert_equal(G.edges(data=True),
- [(1,2,{'data':20,'spam':'bar',
- 'bar':'foo','listdata':[20,200],'weight':20})])
-
- def test_attr_dict_not_dict(self):
- # attr_dict must be dict
- G=self.Graph()
- edges=[(1,2)]
- assert_raises(networkx.NetworkXError,G.add_edges_from,edges,
- attr_dict=[])
-
- def test_to_undirected(self):
- G=self.K3
- self.add_attributes(G)
- H=networkx.Graph(G)
- self.is_shallow_copy(H,G)
- H=G.to_undirected()
- self.is_deepcopy(H,G)
-
- def test_to_directed(self):
- G=self.K3
- self.add_attributes(G)
- H=networkx.DiGraph(G)
- self.is_shallow_copy(H,G)
- H=G.to_directed()
- self.is_deepcopy(H,G)
-
- def test_subgraph(self):
- G=self.K3
- self.add_attributes(G)
- H=G.subgraph([0,1,2,5])
-# assert_equal(H.name, 'Subgraph of ('+G.name+')')
- H.name=G.name
- self.graphs_equal(H,G)
- self.same_attrdict(H,G)
- self.shallow_copy_attrdict(H,G)
-
- H=G.subgraph(0)
- assert_equal(H.adj,{0:{}})
- H=G.subgraph([])
- assert_equal(H.adj,{})
- assert_not_equal(G.adj,{})
-
- def test_selfloops_attr(self):
- G=self.K3.copy()
- G.add_edge(0,0)
- G.add_edge(1,1,weight=2)
- assert_equal(G.selfloop_edges(data=True),
- [(0,0,{}),(1,1,{'weight':2})])
-
-
-class TestGraph(BaseAttrGraphTester):
- """Tests specific to dict-of-dict-of-dict graph data structure"""
- def setUp(self):
- self.Graph=networkx.Graph
- # build dict-of-dict-of-dict K3
- ed1,ed2,ed3 = ({},{},{})
- self.k3adj={0: {1: ed1, 2: ed2},
- 1: {0: ed1, 2: ed3},
- 2: {0: ed2, 1: ed3}}
- self.k3edges=[(0, 1), (0, 2), (1, 2)]
- self.k3nodes=[0, 1, 2]
- self.K3=self.Graph()
- self.K3.adj=self.K3.edge=self.k3adj
- self.K3.node={}
- self.K3.node[0]={}
- self.K3.node[1]={}
- self.K3.node[2]={}
-
- def test_data_input(self):
- G=self.Graph(data={1:[2],2:[1]}, name="test")
- assert_equal(G.name,"test")
- assert_equal(sorted(G.adj.items()),[(1, {2: {}}), (2, {1: {}})])
- G=self.Graph({1:[2],2:[1]}, name="test")
- assert_equal(G.name,"test")
- assert_equal(sorted(G.adj.items()),[(1, {2: {}}), (2, {1: {}})])
-
- def test_adjacency_iter(self):
- G=self.K3
- assert_equal(dict(G.adjacency_iter()),
- {0: {1: {}, 2: {}}, 1: {0: {}, 2: {}}, 2: {0: {}, 1: {}}})
-
- def test_getitem(self):
- G=self.K3
- assert_equal(G[0],{1: {}, 2: {}})
- assert_raises(KeyError, G.__getitem__, 'j')
- assert_raises((TypeError,networkx.NetworkXError), G.__getitem__, ['A'])
-
- def test_add_node(self):
- G=self.Graph()
- G.add_node(0)
- assert_equal(G.adj,{0:{}})
- # test add attributes
- G.add_node(1,c='red')
- G.add_node(2,{'c':'blue'})
- G.add_node(3,{'c':'blue'},c='red')
- assert_raises(networkx.NetworkXError, G.add_node, 4, [])
- assert_raises(networkx.NetworkXError, G.add_node, 4, 4)
- assert_equal(G.node[1]['c'],'red')
- assert_equal(G.node[2]['c'],'blue')
- assert_equal(G.node[3]['c'],'red')
- # test updating attributes
- G.add_node(1,c='blue')
- G.add_node(2,{'c':'red'})
- G.add_node(3,{'c':'red'},c='blue')
- assert_equal(G.node[1]['c'],'blue')
- assert_equal(G.node[2]['c'],'red')
- assert_equal(G.node[3]['c'],'blue')
-
- def test_add_nodes_from(self):
- G=self.Graph()
- G.add_nodes_from([0,1,2])
- assert_equal(G.adj,{0:{},1:{},2:{}})
- # test add attributes
- G.add_nodes_from([0,1,2],c='red')
- assert_equal(G.node[0]['c'],'red')
- assert_equal(G.node[2]['c'],'red')
- # test that attribute dicts are not the same
- assert(G.node[0] is not G.node[1])
- # test updating attributes
- G.add_nodes_from([0,1,2],c='blue')
- assert_equal(G.node[0]['c'],'blue')
- assert_equal(G.node[2]['c'],'blue')
- assert(G.node[0] is not G.node[1])
- # test tuple input
- H=self.Graph()
- H.add_nodes_from(G.nodes(data=True))
- assert_equal(H.node[0]['c'],'blue')
- assert_equal(H.node[2]['c'],'blue')
- assert(H.node[0] is not H.node[1])
- # specific overrides general
- H.add_nodes_from([0,(1,{'c':'green'}),(3,{'c':'cyan'})],c='red')
- assert_equal(H.node[0]['c'],'red')
- assert_equal(H.node[1]['c'],'green')
- assert_equal(H.node[2]['c'],'blue')
- assert_equal(H.node[3]['c'],'cyan')
-
- def test_remove_node(self):
- G=self.K3
- G.remove_node(0)
- assert_equal(G.adj,{1:{2:{}},2:{1:{}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_node,-1)
-
- # generator here to implement list,set,string...
- def test_remove_nodes_from(self):
- G=self.K3
- G.remove_nodes_from([0,1])
- assert_equal(G.adj,{2:{}})
- G.remove_nodes_from([-1]) # silent fail
-
- def test_add_edge(self):
- G=self.Graph()
- G.add_edge(0,1)
- assert_equal(G.adj,{0: {1: {}}, 1: {0: {}}})
- G=self.Graph()
- G.add_edge(*(0,1))
- assert_equal(G.adj,{0: {1: {}}, 1: {0: {}}})
-
- def test_add_edges_from(self):
- G=self.Graph()
- G.add_edges_from([(0,1),(0,2,{'weight':3})])
- assert_equal(G.adj,{0: {1:{}, 2:{'weight':3}}, 1: {0:{}}, \
- 2:{0:{'weight':3}}})
- G=self.Graph()
- G.add_edges_from([(0,1),(0,2,{'weight':3}),(1,2,{'data':4})],data=2)
- assert_equal(G.adj,{\
- 0: {1:{'data':2}, 2:{'weight':3,'data':2}}, \
- 1: {0:{'data':2}, 2:{'data':4}}, \
- 2: {0:{'weight':3,'data':2}, 1:{'data':4}} \
- })
-
- assert_raises(networkx.NetworkXError,
- G.add_edges_from,[(0,)]) # too few in tuple
- assert_raises(networkx.NetworkXError,
- G.add_edges_from,[(0,1,2,3)]) # too many in tuple
- assert_raises(TypeError, G.add_edges_from,[0]) # not a tuple
-
-
- def test_remove_edge(self):
- G=self.K3
- G.remove_edge(0,1)
- assert_equal(G.adj,{0:{2:{}},1:{2:{}},2:{0:{},1:{}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,-1,0)
-
- def test_remove_edges_from(self):
- G=self.K3
- G.remove_edges_from([(0,1)])
- assert_equal(G.adj,{0:{2:{}},1:{2:{}},2:{0:{},1:{}}})
- G.remove_edges_from([(0,0)]) # silent fail
-
- def test_clear(self):
- G=self.K3
- G.clear()
- assert_equal(G.adj,{})
-
- def test_edges_data(self):
- G=self.K3
- assert_equal(sorted(G.edges(data=True)),[(0,1,{}),(0,2,{}),(1,2,{})])
- assert_equal(sorted(G.edges(0,data=True)),[(0,1,{}),(0,2,{})])
- assert_raises((KeyError,networkx.NetworkXError), G.edges,-1)
-
-
- def test_get_edge_data(self):
- G=self.K3
- assert_equal(G.get_edge_data(0,1),{})
- assert_equal(G[0][1],{})
- assert_equal(G.get_edge_data(10,20),None)
- assert_equal(G.get_edge_data(-1,0),None)
- assert_equal(G.get_edge_data(-1,0,default=1),1)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_graph_historical.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_graph_historical.py
deleted file mode 100644
index 0ade19a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_graph_historical.py
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env python
-"""Original NetworkX graph tests"""
-from nose.tools import *
-import networkx
-import networkx as nx
-
-from historical_tests import HistoricalTests
-
-class TestGraphHistorical(HistoricalTests):
-
- def setUp(self):
- HistoricalTests.setUp(self)
- self.G=nx.Graph
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_multidigraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_multidigraph.py
deleted file mode 100644
index e8c2543..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_multidigraph.py
+++ /dev/null
@@ -1,327 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-from test_multigraph import BaseMultiGraphTester, TestMultiGraph
-
-class BaseMultiDiGraphTester(BaseMultiGraphTester):
- def test_edges(self):
- G=self.K3
- assert_equal(sorted(G.edges()),[(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.edges(0)),[(0,1),(0,2)])
- assert_raises((KeyError,networkx.NetworkXError), G.edges,-1)
-
- def test_edges_data(self):
- G=self.K3
- assert_equal(sorted(G.edges(data=True)),
- [(0,1,{}),(0,2,{}),(1,0,{}),(1,2,{}),(2,0,{}),(2,1,{})])
- assert_equal(sorted(G.edges(0,data=True)),[(0,1,{}),(0,2,{})])
- assert_raises((KeyError,networkx.NetworkXError), G.neighbors,-1)
-
-
- def test_edges_iter(self):
- G=self.K3
- assert_equal(sorted(G.edges_iter()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.edges_iter(0)),[(0,1),(0,2)])
- G.add_edge(0,1)
- assert_equal(sorted(G.edges_iter()),
- [(0,1),(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
-
- def test_out_edges(self):
- G=self.K3
- assert_equal(sorted(G.out_edges()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.out_edges(0)),[(0,1),(0,2)])
- assert_raises((KeyError,networkx.NetworkXError), G.out_edges,-1)
- assert_equal(sorted(G.out_edges(0,keys=True)),[(0,1,0),(0,2,0)])
-
- def test_out_edges_iter(self):
- G=self.K3
- assert_equal(sorted(G.out_edges_iter()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.out_edges_iter(0)),[(0,1),(0,2)])
- G.add_edge(0,1,2)
- assert_equal(sorted(G.out_edges_iter()),
- [(0,1),(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
-
- def test_in_edges(self):
- G=self.K3
- assert_equal(sorted(G.in_edges()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.in_edges(0)),[(1,0),(2,0)])
- assert_raises((KeyError,networkx.NetworkXError), G.in_edges,-1)
- G.add_edge(0,1,2)
- assert_equal(sorted(G.in_edges()),
- [(0,1),(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.in_edges(0,keys=True)),[(1,0,0),(2,0,0)])
-
- def test_in_edges_iter(self):
- G=self.K3
- assert_equal(sorted(G.in_edges_iter()),
- [(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
- assert_equal(sorted(G.in_edges_iter(0)),[(1,0),(2,0)])
- G.add_edge(0,1,2)
- assert_equal(sorted(G.in_edges_iter()),
- [(0,1),(0,1),(0,2),(1,0),(1,2),(2,0),(2,1)])
-
- assert_equal(sorted(G.in_edges_iter(data=True,keys=False)),
- [(0,1,{}),(0,1,{}),(0,2,{}),(1,0,{}),(1,2,{}),
- (2,0,{}),(2,1,{})])
-
-
- def is_shallow(self,H,G):
- # graph
- assert_equal(G.graph['foo'],H.graph['foo'])
- G.graph['foo'].append(1)
- assert_equal(G.graph['foo'],H.graph['foo'])
- # node
- assert_equal(G.node[0]['foo'],H.node[0]['foo'])
- G.node[0]['foo'].append(1)
- assert_equal(G.node[0]['foo'],H.node[0]['foo'])
- # edge
- assert_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
- G[1][2][0]['foo'].append(1)
- assert_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
-
- def is_deep(self,H,G):
- # graph
- assert_equal(G.graph['foo'],H.graph['foo'])
- G.graph['foo'].append(1)
- assert_not_equal(G.graph['foo'],H.graph['foo'])
- # node
- assert_equal(G.node[0]['foo'],H.node[0]['foo'])
- G.node[0]['foo'].append(1)
- assert_not_equal(G.node[0]['foo'],H.node[0]['foo'])
- # edge
- assert_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
- G[1][2][0]['foo'].append(1)
- assert_not_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
-
- def test_to_undirected(self):
- # MultiDiGraph -> MultiGraph changes number of edges so it is
- # not a copy operation... use is_shallow, not is_shallow_copy
- G=self.K3
- self.add_attributes(G)
- H=networkx.MultiGraph(G)
- self.is_shallow(H,G)
- H=G.to_undirected()
- self.is_deep(H,G)
-
- def test_has_successor(self):
- G=self.K3
- assert_equal(G.has_successor(0,1),True)
- assert_equal(G.has_successor(0,-1),False)
-
- def test_successors(self):
- G=self.K3
- assert_equal(sorted(G.successors(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.successors,-1)
-
- def test_successors_iter(self):
- G=self.K3
- assert_equal(sorted(G.successors_iter(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.successors_iter,-1)
-
- def test_has_predecessor(self):
- G=self.K3
- assert_equal(G.has_predecessor(0,1),True)
- assert_equal(G.has_predecessor(0,-1),False)
-
- def test_predecessors(self):
- G=self.K3
- assert_equal(sorted(G.predecessors(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.predecessors,-1)
-
- def test_predecessors_iter(self):
- G=self.K3
- assert_equal(sorted(G.predecessors_iter(0)),[1,2])
- assert_raises((KeyError,networkx.NetworkXError), G.predecessors_iter,-1)
-
-
- def test_degree(self):
- G=self.K3
- assert_equal(list(G.degree().values()),[4,4,4])
- assert_equal(G.degree(),{0:4,1:4,2:4})
- assert_equal(G.degree(0),4)
- assert_equal(G.degree([0]),{0:4})
- assert_raises((KeyError,networkx.NetworkXError), G.degree,-1)
-
- def test_degree_iter(self):
- G=self.K3
- assert_equal(list(G.degree_iter()),[(0,4),(1,4),(2,4)])
- assert_equal(dict(G.degree_iter()),{0:4,1:4,2:4})
- assert_equal(list(G.degree_iter(0)),[(0,4)])
- G.add_edge(0,1,weight=0.3,other=1.2)
- assert_equal(list(G.degree_iter(weight='weight')),[(0,4.3),(1,4.3),(2,4)])
- assert_equal(list(G.degree_iter(weight='other')),[(0,5.2),(1,5.2),(2,4)])
-
-
- def test_in_degree(self):
- G=self.K3
- assert_equal(list(G.in_degree().values()),[2,2,2])
- assert_equal(G.in_degree(),{0:2,1:2,2:2})
- assert_equal(G.in_degree(0),2)
- assert_equal(G.in_degree([0]),{0:2})
- assert_raises((KeyError,networkx.NetworkXError), G.in_degree,-1)
-
- def test_in_degree_iter(self):
- G=self.K3
- assert_equal(list(G.in_degree_iter()),[(0,2),(1,2),(2,2)])
- assert_equal(dict(G.in_degree_iter()),{0:2,1:2,2:2})
- assert_equal(list(G.in_degree_iter(0)),[(0,2)])
- assert_equal(list(G.in_degree_iter(0,weight='weight')),[(0,2)])
-
- def test_out_degree(self):
- G=self.K3
- assert_equal(list(G.out_degree().values()),[2,2,2])
- assert_equal(G.out_degree(),{0:2,1:2,2:2})
- assert_equal(G.out_degree(0),2)
- assert_equal(G.out_degree([0]),{0:2})
- assert_raises((KeyError,networkx.NetworkXError), G.out_degree,-1)
-
- def test_out_degree_iter(self):
- G=self.K3
- assert_equal(list(G.out_degree_iter()),[(0,2),(1,2),(2,2)])
- assert_equal(dict(G.out_degree_iter()),{0:2,1:2,2:2})
- assert_equal(list(G.out_degree_iter(0)),[(0,2)])
- assert_equal(list(G.out_degree_iter(0,weight='weight')),[(0,2)])
-
-
- def test_size(self):
- G=self.K3
- assert_equal(G.size(),6)
- assert_equal(G.number_of_edges(),6)
- G.add_edge(0,1,weight=0.3,other=1.2)
- assert_equal(G.size(weight='weight'),6.3)
- assert_equal(G.size(weight='other'),7.2)
-
- def test_to_undirected_reciprocal(self):
- G=self.Graph()
- G.add_edge(1,2)
- assert_true(G.to_undirected().has_edge(1,2))
- assert_false(G.to_undirected(reciprocal=True).has_edge(1,2))
- G.add_edge(2,1)
- assert_true(G.to_undirected(reciprocal=True).has_edge(1,2))
-
- def test_reverse_copy(self):
- G=networkx.MultiDiGraph([(0,1),(0,1)])
- R=G.reverse()
- assert_equal(sorted(R.edges()),[(1,0),(1,0)])
- R.remove_edge(1,0)
- assert_equal(sorted(R.edges()),[(1,0)])
- assert_equal(sorted(G.edges()),[(0,1),(0,1)])
-
- def test_reverse_nocopy(self):
- G=networkx.MultiDiGraph([(0,1),(0,1)])
- R=G.reverse(copy=False)
- assert_equal(sorted(R.edges()),[(1,0),(1,0)])
- R.remove_edge(1,0)
- assert_equal(sorted(R.edges()),[(1,0)])
- assert_equal(sorted(G.edges()),[(1,0)])
-
-
-class TestMultiDiGraph(BaseMultiDiGraphTester,TestMultiGraph):
- def setUp(self):
- self.Graph=networkx.MultiDiGraph
- # build K3
- self.k3edges=[(0, 1), (0, 2), (1, 2)]
- self.k3nodes=[0, 1, 2]
- self.K3=self.Graph()
- self.K3.adj={0:{},1:{},2:{}}
- self.K3.succ=self.K3.adj
- self.K3.pred={0:{},1:{},2:{}}
- for u in self.k3nodes:
- for v in self.k3nodes:
- if u==v: continue
- d={0:{}}
- self.K3.succ[u][v]=d
- self.K3.pred[v][u]=d
- self.K3.adj=self.K3.succ
- self.K3.edge=self.K3.adj
- self.K3.node={}
- self.K3.node[0]={}
- self.K3.node[1]={}
- self.K3.node[2]={}
-
-
- def test_add_edge(self):
- G=self.Graph()
- G.add_edge(0,1)
- assert_equal(G.adj,{0: {1: {0:{}}}, 1: {}})
- assert_equal(G.succ,{0: {1: {0:{}}}, 1: {}})
- assert_equal(G.pred,{0: {}, 1: {0:{0:{}}}})
- G=self.Graph()
- G.add_edge(*(0,1))
- assert_equal(G.adj,{0: {1: {0:{}}}, 1: {}})
- assert_equal(G.succ,{0: {1: {0:{}}}, 1: {}})
- assert_equal(G.pred,{0: {}, 1: {0:{0:{}}}})
-
- def test_add_edges_from(self):
- G=self.Graph()
- G.add_edges_from([(0,1),(0,1,{'weight':3})])
- assert_equal(G.adj,{0: {1: {0:{},1:{'weight':3}}}, 1: {}})
- assert_equal(G.succ,{0: {1: {0:{},1:{'weight':3}}}, 1: {}})
- assert_equal(G.pred,{0: {}, 1: {0:{0:{},1:{'weight':3}}}})
-
- G.add_edges_from([(0,1),(0,1,{'weight':3})],weight=2)
- assert_equal(G.succ,{0: {1: {0:{},
- 1:{'weight':3},
- 2:{'weight':2},
- 3:{'weight':3}}},
- 1: {}})
- assert_equal(G.pred,{0: {}, 1: {0:{0:{},1:{'weight':3},
- 2:{'weight':2},
- 3:{'weight':3}}}})
-
- assert_raises(networkx.NetworkXError, G.add_edges_from,[(0,)]) # too few in tuple
- assert_raises(networkx.NetworkXError, G.add_edges_from,[(0,1,2,3,4)]) # too many in tuple
- assert_raises(TypeError, G.add_edges_from,[0]) # not a tuple
-
- def test_remove_edge(self):
- G=self.K3
- G.remove_edge(0,1)
- assert_equal(G.succ,{0:{2:{0:{}}},
- 1:{0:{0:{}},2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- assert_equal(G.pred,{0:{1:{0:{}}, 2:{0:{}}},
- 1:{2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,-1,0)
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,0,2,
- key=1)
-
-
- def test_remove_multiedge(self):
- G=self.K3
- G.add_edge(0,1,key='parallel edge')
- G.remove_edge(0,1,key='parallel edge')
- assert_equal(G.adj,{0: {1: {0:{}}, 2: {0:{}}},
- 1: {0: {0:{}}, 2: {0:{}}},
- 2: {0: {0:{}}, 1: {0:{}}}})
-
- assert_equal(G.succ,{0: {1: {0:{}}, 2: {0:{}}},
- 1: {0: {0:{}}, 2: {0:{}}},
- 2: {0: {0:{}}, 1: {0:{}}}})
-
- assert_equal(G.pred,{0:{1: {0:{}},2:{0:{}}},
- 1:{0:{0:{}},2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- G.remove_edge(0,1)
- assert_equal(G.succ,{0:{2:{0:{}}},
- 1:{0:{0:{}},2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- assert_equal(G.pred,{0:{1:{0:{}}, 2:{0:{}}},
- 1:{2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,-1,0)
-
- def test_remove_edges_from(self):
- G=self.K3
- G.remove_edges_from([(0,1)])
- assert_equal(G.succ,{0:{2:{0:{}}},
- 1:{0:{0:{}},2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- assert_equal(G.pred,{0:{1:{0:{}}, 2:{0:{}}},
- 1:{2:{0:{}}},
- 2:{0:{0:{}},1:{0:{}}}})
- G.remove_edges_from([(0,0)]) # silent fail
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_multigraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_multigraph.py
deleted file mode 100644
index 6c9adbc..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/test_multigraph.py
+++ /dev/null
@@ -1,244 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-from test_graph import BaseAttrGraphTester, TestGraph
-
-class BaseMultiGraphTester(BaseAttrGraphTester):
- def test_has_edge(self):
- G=self.K3
- assert_equal(G.has_edge(0,1),True)
- assert_equal(G.has_edge(0,-1),False)
- assert_equal(G.has_edge(0,1,0),True)
- assert_equal(G.has_edge(0,1,1),False)
-
- def test_get_edge_data(self):
- G=self.K3
- assert_equal(G.get_edge_data(0,1),{0:{}})
- assert_equal(G[0][1],{0:{}})
- assert_equal(G[0][1][0],{})
- assert_equal(G.get_edge_data(10,20),None)
- assert_equal(G.get_edge_data(0,1,0),{})
-
-
- def test_adjacency_iter(self):
- G=self.K3
- assert_equal(dict(G.adjacency_iter()),
- {0: {1: {0:{}}, 2: {0:{}}},
- 1: {0: {0:{}}, 2: {0:{}}},
- 2: {0: {0:{}}, 1: {0:{}}}})
-
- def deepcopy_edge_attr(self,H,G):
- assert_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
- G[1][2][0]['foo'].append(1)
- assert_not_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
-
- def shallow_copy_edge_attr(self,H,G):
- assert_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
- G[1][2][0]['foo'].append(1)
- assert_equal(G[1][2][0]['foo'],H[1][2][0]['foo'])
-
- def same_attrdict(self, H, G):
- # same attrdict in the edgedata
- old_foo=H[1][2][0]['foo']
- H.add_edge(1,2,0,foo='baz')
- assert_equal(G.edge,H.edge)
- H.add_edge(1,2,0,foo=old_foo)
- assert_equal(G.edge,H.edge)
- # but not same edgedata dict
- H.add_edge(1,2,foo='baz')
- assert_not_equal(G.edge,H.edge)
-
- old_foo=H.node[0]['foo']
- H.node[0]['foo']='baz'
- assert_equal(G.node,H.node)
- H.node[0]['foo']=old_foo
- assert_equal(G.node,H.node)
-
- def different_attrdict(self, H, G):
- # used by graph_equal_but_different
- old_foo=H[1][2][0]['foo']
- H.add_edge(1,2,0,foo='baz')
- assert_not_equal(G.edge,H.edge)
- H.add_edge(1,2,0,foo=old_foo)
- assert_equal(G.edge,H.edge)
- HH=H.copy()
- H.add_edge(1,2,foo='baz')
- assert_not_equal(G.edge,H.edge)
- H=HH
- old_foo=H.node[0]['foo']
- H.node[0]['foo']='baz'
- assert_not_equal(G.node,H.node)
- H.node[0]['foo']=old_foo
- assert_equal(G.node,H.node)
-
- def test_to_undirected(self):
- G=self.K3
- self.add_attributes(G)
- H=networkx.MultiGraph(G)
- self.is_shallow_copy(H,G)
- H=G.to_undirected()
- self.is_deepcopy(H,G)
-
- def test_to_directed(self):
- G=self.K3
- self.add_attributes(G)
- H=networkx.MultiDiGraph(G)
- self.is_shallow_copy(H,G)
- H=G.to_directed()
- self.is_deepcopy(H,G)
-
- def test_selfloops(self):
- G=self.K3
- G.add_edge(0,0)
- assert_equal(G.nodes_with_selfloops(),[0])
- assert_equal(G.selfloop_edges(),[(0,0)])
- assert_equal(G.selfloop_edges(data=True),[(0,0,{})])
- assert_equal(G.number_of_selfloops(),1)
-
- def test_selfloops2(self):
- G=self.K3
- G.add_edge(0,0)
- G.add_edge(0,0)
- G.add_edge(0,0,key='parallel edge')
- G.remove_edge(0,0,key='parallel edge')
- assert_equal(G.number_of_edges(0,0),2)
- G.remove_edge(0,0)
- assert_equal(G.number_of_edges(0,0),1)
-
- def test_edge_attr4(self):
- G=self.Graph()
- G.add_edge(1,2,key=0,data=7,spam='bar',bar='foo')
- assert_equal(G.edges(data=True),
- [(1,2,{'data':7,'spam':'bar','bar':'foo'})])
- G[1][2][0]['data']=10 # OK to set data like this
- assert_equal(G.edges(data=True),
- [(1,2,{'data':10,'spam':'bar','bar':'foo'})])
-
- G.edge[1][2][0]['data']=20 # another spelling, "edge"
- assert_equal(G.edges(data=True),
- [(1,2,{'data':20,'spam':'bar','bar':'foo'})])
- G.edge[1][2][0]['listdata']=[20,200]
- G.edge[1][2][0]['weight']=20
- assert_equal(G.edges(data=True),
- [(1,2,{'data':20,'spam':'bar',
- 'bar':'foo','listdata':[20,200],'weight':20})])
-
-
-class TestMultiGraph(BaseMultiGraphTester,TestGraph):
- def setUp(self):
- self.Graph=networkx.MultiGraph
- # build K3
- ed1,ed2,ed3 = ({0:{}},{0:{}},{0:{}})
- self.k3adj={0: {1: ed1, 2: ed2},
- 1: {0: ed1, 2: ed3},
- 2: {0: ed2, 1: ed3}}
- self.k3edges=[(0, 1), (0, 2), (1, 2)]
- self.k3nodes=[0, 1, 2]
- self.K3=self.Graph()
- self.K3.adj = self.K3.edge = self.k3adj
- self.K3.node={}
- self.K3.node[0]={}
- self.K3.node[1]={}
- self.K3.node[2]={}
-
- def test_data_input(self):
- G=self.Graph(data={1:[2],2:[1]}, name="test")
- assert_equal(G.name,"test")
- assert_equal(sorted(G.adj.items()),[(1, {2: {0:{}}}), (2, {1: {0:{}}})])
-
- def test_getitem(self):
- G=self.K3
- assert_equal(G[0],{1: {0:{}}, 2: {0:{}}})
- assert_raises(KeyError, G.__getitem__, 'j')
- assert_raises((TypeError,networkx.NetworkXError), G.__getitem__, ['A'])
-
- def test_remove_node(self):
- G=self.K3
- G.remove_node(0)
- assert_equal(G.adj,{1:{2:{0:{}}},2:{1:{0:{}}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_node,-1)
-
- def test_add_edge(self):
- G=self.Graph()
- G.add_edge(0,1)
- assert_equal(G.adj,{0: {1: {0:{}}}, 1: {0: {0:{}}}})
- G=self.Graph()
- G.add_edge(*(0,1))
- assert_equal(G.adj,{0: {1: {0:{}}}, 1: {0: {0:{}}}})
-
- def test_add_edge_conflicting_key(self):
- G=self.Graph()
- G.add_edge(0,1,key=1)
- G.add_edge(0,1)
- assert_equal(G.number_of_edges(),2)
- G=self.Graph()
- G.add_edges_from([(0,1,1,{})])
- G.add_edges_from([(0,1)])
- assert_equal(G.number_of_edges(),2)
-
-
-
- def test_add_edges_from(self):
- G=self.Graph()
- G.add_edges_from([(0,1),(0,1,{'weight':3})])
- assert_equal(G.adj,{0: {1: {0:{},1:{'weight':3}}},
- 1: {0: {0:{},1:{'weight':3}}}})
- G.add_edges_from([(0,1),(0,1,{'weight':3})],weight=2)
- assert_equal(G.adj,{0: {1: {0:{},1:{'weight':3},
- 2:{'weight':2},3:{'weight':3}}},
- 1: {0: {0:{},1:{'weight':3},
- 2:{'weight':2},3:{'weight':3}}}})
-
- # too few in tuple
- assert_raises(networkx.NetworkXError, G.add_edges_from,[(0,)])
- # too many in tuple
- assert_raises(networkx.NetworkXError, G.add_edges_from,[(0,1,2,3,4)])
- assert_raises(TypeError, G.add_edges_from,[0]) # not a tuple
-
-
- def test_remove_edge(self):
- G=self.K3
- G.remove_edge(0,1)
- assert_equal(G.adj,{0: {2: {0: {}}},
- 1: {2: {0: {}}},
- 2: {0: {0: {}},
- 1: {0: {}}}})
-
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,-1,0)
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,0,2,
- key=1)
-
-
-
- def test_remove_edges_from(self):
- G=self.K3.copy()
- G.remove_edges_from([(0,1)])
- assert_equal(G.adj,{0:{2:{0:{}}},1:{2:{0:{}}},2:{0:{0:{}},1:{0:{}}}})
- G.remove_edges_from([(0,0)]) # silent fail
- self.K3.add_edge(0,1)
- G=self.K3.copy()
- G.remove_edges_from(G.edges(data=True,keys=True))
- assert_equal(G.adj,{0:{},1:{},2:{}})
- G=self.K3.copy()
- G.remove_edges_from(G.edges(data=False,keys=True))
- assert_equal(G.adj,{0:{},1:{},2:{}})
- G=self.K3.copy()
- G.remove_edges_from(G.edges(data=False,keys=False))
- assert_equal(G.adj,{0:{},1:{},2:{}})
- G=self.K3.copy()
- G.remove_edges_from([(0,1,0),(0,2,0,{}),(1,2)])
- assert_equal(G.adj,{0:{1:{1:{}}},1:{0:{1:{}}},2:{}})
-
-
-
- def test_remove_multiedge(self):
- G=self.K3
- G.add_edge(0,1,key='parallel edge')
- G.remove_edge(0,1,key='parallel edge')
- assert_equal(G.adj,{0: {1: {0:{}}, 2: {0:{}}},
- 1: {0: {0:{}}, 2: {0:{}}},
- 2: {0: {0:{}}, 1: {0:{}}}})
- G.remove_edge(0,1)
- assert_equal(G.adj,{0:{2:{0:{}}},1:{2:{0:{}}},2:{0:{0:{}},1:{0:{}}}})
- assert_raises((KeyError,networkx.NetworkXError), G.remove_edge,-1,0)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/convert.py b/lib/python2.7/site-packages/setoolsgui/networkx/convert.py
deleted file mode 100644
index 6333b9f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/convert.py
+++ /dev/null
@@ -1,847 +0,0 @@
-"""
-This module provides functions to convert
-NetworkX graphs to and from other formats.
-
-The preferred way of converting data to a NetworkX graph
-is through the graph constuctor. The constructor calls
-the to_networkx_graph() function which attempts to guess the
-input type and convert it automatically.
-
-Examples
---------
-
-Create a 10 node random graph from a numpy matrix
-
->>> import numpy
->>> a=numpy.reshape(numpy.random.random_integers(0,1,size=100),(10,10))
->>> D=nx.DiGraph(a)
-
-or equivalently
-
->>> D=nx.to_networkx_graph(a,create_using=nx.DiGraph())
-
-Create a graph with a single edge from a dictionary of dictionaries
-
->>> d={0: {1: 1}} # dict-of-dicts single edge (0,1)
->>> G=nx.Graph(d)
-
-
-See Also
---------
-nx_pygraphviz, nx_pydot
-
-"""
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-# Copyright (C) 2006-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-import warnings
-import networkx as nx
-
-__all__ = ['to_networkx_graph',
- 'from_dict_of_dicts', 'to_dict_of_dicts',
- 'from_dict_of_lists', 'to_dict_of_lists',
- 'from_edgelist', 'to_edgelist',
- 'from_numpy_matrix', 'to_numpy_matrix',
- 'to_numpy_recarray',
- 'from_scipy_sparse_matrix', 'to_scipy_sparse_matrix']
-
-def _prep_create_using(create_using):
- """Return a graph object ready to be populated.
-
- If create_using is None return the default (just networkx.Graph())
- If create_using.clear() works, assume it returns a graph object.
- Otherwise raise an exception because create_using is not a networkx graph.
-
- """
- if create_using is None:
- G=nx.Graph()
- else:
- G=create_using
- try:
- G.clear()
- except:
- raise TypeError("Input graph is not a networkx graph type")
- return G
-
-def to_networkx_graph(data,create_using=None,multigraph_input=False):
- """Make a NetworkX graph from a known data structure.
-
- The preferred way to call this is automatically
- from the class constructor
-
- >>> d={0: {1: {'weight':1}}} # dict-of-dicts single edge (0,1)
- >>> G=nx.Graph(d)
-
- instead of the equivalent
-
- >>> G=nx.from_dict_of_dicts(d)
-
- Parameters
- ----------
- data : a object to be converted
- Current known types are:
- any NetworkX graph
- dict-of-dicts
- dist-of-lists
- list of edges
- numpy matrix
- numpy ndarray
- scipy sparse matrix
- pygraphviz agraph
-
- create_using : NetworkX graph
- Use specified graph for result. Otherwise a new graph is created.
-
- multigraph_input : bool (default False)
- If True and data is a dict_of_dicts,
- try to create a multigraph assuming dict_of_dict_of_lists.
- If data and create_using are both multigraphs then create
- a multigraph from a multigraph.
-
- """
- # NX graph
- if hasattr(data,"adj"):
- try:
- result= from_dict_of_dicts(data.adj,\
- create_using=create_using,\
- multigraph_input=data.is_multigraph())
- if hasattr(data,'graph') and isinstance(data.graph,dict):
- result.graph=data.graph.copy()
- if hasattr(data,'node') and isinstance(data.node,dict):
- result.node=dict( (n,dd.copy()) for n,dd in data.node.items() )
- return result
- except:
- raise nx.NetworkXError("Input is not a correct NetworkX graph.")
-
- # pygraphviz agraph
- if hasattr(data,"is_strict"):
- try:
- return nx.from_agraph(data,create_using=create_using)
- except:
- raise nx.NetworkXError("Input is not a correct pygraphviz graph.")
-
- # dict of dicts/lists
- if isinstance(data,dict):
- try:
- return from_dict_of_dicts(data,create_using=create_using,\
- multigraph_input=multigraph_input)
- except:
- try:
- return from_dict_of_lists(data,create_using=create_using)
- except:
- raise TypeError("Input is not known type.")
-
- # list or generator of edges
- if (isinstance(data,list)
- or hasattr(data,'next')
- or hasattr(data, '__next__')):
- try:
- return from_edgelist(data,create_using=create_using)
- except:
- raise nx.NetworkXError("Input is not a valid edge list")
-
- # numpy matrix or ndarray
- try:
- import numpy
- if isinstance(data,numpy.matrix) or \
- isinstance(data,numpy.ndarray):
- try:
- return from_numpy_matrix(data,create_using=create_using)
- except:
- raise nx.NetworkXError(\
- "Input is not a correct numpy matrix or array.")
- except ImportError:
- warnings.warn('numpy not found, skipping conversion test.',
- ImportWarning)
-
- # scipy sparse matrix - any format
- try:
- import scipy
- if hasattr(data,"format"):
- try:
- return from_scipy_sparse_matrix(data,create_using=create_using)
- except:
- raise nx.NetworkXError(\
- "Input is not a correct scipy sparse matrix type.")
- except ImportError:
- warnings.warn('scipy not found, skipping conversion test.',
- ImportWarning)
-
-
- raise nx.NetworkXError(\
- "Input is not a known data type for conversion.")
-
- return
-
-
-def convert_to_undirected(G):
- """Return a new undirected representation of the graph G."""
- return G.to_undirected()
-
-
-def convert_to_directed(G):
- """Return a new directed representation of the graph G."""
- return G.to_directed()
-
-
-def to_dict_of_lists(G,nodelist=None):
- """Return adjacency representation of graph as a dictionary of lists.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list
- Use only nodes specified in nodelist
-
- Notes
- -----
- Completely ignores edge data for MultiGraph and MultiDiGraph.
-
- """
- if nodelist is None:
- nodelist=G
-
- d = {}
- for n in nodelist:
- d[n]=[nbr for nbr in G.neighbors(n) if nbr in nodelist]
- return d
-
-def from_dict_of_lists(d,create_using=None):
- """Return a graph from a dictionary of lists.
-
- Parameters
- ----------
- d : dictionary of lists
- A dictionary of lists adjacency representation.
-
- create_using : NetworkX graph
- Use specified graph for result. Otherwise a new graph is created.
-
- Examples
- --------
- >>> dol= {0:[1]} # single edge (0,1)
- >>> G=nx.from_dict_of_lists(dol)
-
- or
- >>> G=nx.Graph(dol) # use Graph constructor
-
- """
- G=_prep_create_using(create_using)
- G.add_nodes_from(d)
- if G.is_multigraph() and not G.is_directed():
- # a dict_of_lists can't show multiedges. BUT for undirected graphs,
- # each edge shows up twice in the dict_of_lists.
- # So we need to treat this case separately.
- seen={}
- for node,nbrlist in d.items():
- for nbr in nbrlist:
- if nbr not in seen:
- G.add_edge(node,nbr)
- seen[node]=1 # don't allow reverse edge to show up
- else:
- G.add_edges_from( ((node,nbr) for node,nbrlist in d.items()
- for nbr in nbrlist) )
- return G
-
-
-def to_dict_of_dicts(G,nodelist=None,edge_data=None):
- """Return adjacency representation of graph as a dictionary of dictionaries.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list
- Use only nodes specified in nodelist
-
- edge_data : list, optional
- If provided, the value of the dictionary will be
- set to edge_data for all edges. This is useful to make
- an adjacency matrix type representation with 1 as the edge data.
- If edgedata is None, the edgedata in G is used to fill the values.
- If G is a multigraph, the edgedata is a dict for each pair (u,v).
- """
- dod={}
- if nodelist is None:
- if edge_data is None:
- for u,nbrdict in G.adjacency_iter():
- dod[u]=nbrdict.copy()
- else: # edge_data is not None
- for u,nbrdict in G.adjacency_iter():
- dod[u]=dod.fromkeys(nbrdict, edge_data)
- else: # nodelist is not None
- if edge_data is None:
- for u in nodelist:
- dod[u]={}
- for v,data in ((v,data) for v,data in G[u].items() if v in nodelist):
- dod[u][v]=data
- else: # nodelist and edge_data are not None
- for u in nodelist:
- dod[u]={}
- for v in ( v for v in G[u] if v in nodelist):
- dod[u][v]=edge_data
- return dod
-
-def from_dict_of_dicts(d,create_using=None,multigraph_input=False):
- """Return a graph from a dictionary of dictionaries.
-
- Parameters
- ----------
- d : dictionary of dictionaries
- A dictionary of dictionaries adjacency representation.
-
- create_using : NetworkX graph
- Use specified graph for result. Otherwise a new graph is created.
-
- multigraph_input : bool (default False)
- When True, the values of the inner dict are assumed
- to be containers of edge data for multiple edges.
- Otherwise this routine assumes the edge data are singletons.
-
- Examples
- --------
- >>> dod= {0: {1:{'weight':1}}} # single edge (0,1)
- >>> G=nx.from_dict_of_dicts(dod)
-
- or
- >>> G=nx.Graph(dod) # use Graph constructor
-
- """
- G=_prep_create_using(create_using)
- G.add_nodes_from(d)
- # is dict a MultiGraph or MultiDiGraph?
- if multigraph_input:
- # make a copy of the list of edge data (but not the edge data)
- if G.is_directed():
- if G.is_multigraph():
- G.add_edges_from( (u,v,key,data)
- for u,nbrs in d.items()
- for v,datadict in nbrs.items()
- for key,data in datadict.items()
- )
- else:
- G.add_edges_from( (u,v,data)
- for u,nbrs in d.items()
- for v,datadict in nbrs.items()
- for key,data in datadict.items()
- )
- else: # Undirected
- if G.is_multigraph():
- seen=set() # don't add both directions of undirected graph
- for u,nbrs in d.items():
- for v,datadict in nbrs.items():
- if (u,v) not in seen:
- G.add_edges_from( (u,v,key,data)
- for key,data in datadict.items()
- )
- seen.add((v,u))
- else:
- seen=set() # don't add both directions of undirected graph
- for u,nbrs in d.items():
- for v,datadict in nbrs.items():
- if (u,v) not in seen:
- G.add_edges_from( (u,v,data)
- for key,data in datadict.items() )
- seen.add((v,u))
-
- else: # not a multigraph to multigraph transfer
- if G.is_multigraph() and not G.is_directed():
- # d can have both representations u-v, v-u in dict. Only add one.
- # We don't need this check for digraphs since we add both directions,
- # or for Graph() since it is done implicitly (parallel edges not allowed)
- seen=set()
- for u,nbrs in d.items():
- for v,data in nbrs.items():
- if (u,v) not in seen:
- G.add_edge(u,v,attr_dict=data)
- seen.add((v,u))
- else:
- G.add_edges_from( ( (u,v,data)
- for u,nbrs in d.items()
- for v,data in nbrs.items()) )
- return G
-
-def to_edgelist(G,nodelist=None):
- """Return a list of edges in the graph.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list
- Use only nodes specified in nodelist
-
- """
- if nodelist is None:
- return G.edges(data=True)
- else:
- return G.edges(nodelist,data=True)
-
-def from_edgelist(edgelist,create_using=None):
- """Return a graph from a list of edges.
-
- Parameters
- ----------
- edgelist : list or iterator
- Edge tuples
-
- create_using : NetworkX graph
- Use specified graph for result. Otherwise a new graph is created.
-
- Examples
- --------
- >>> edgelist= [(0,1)] # single edge (0,1)
- >>> G=nx.from_edgelist(edgelist)
-
- or
- >>> G=nx.Graph(edgelist) # use Graph constructor
-
- """
- G=_prep_create_using(create_using)
- G.add_edges_from(edgelist)
- return G
-
-def to_numpy_matrix(G, nodelist=None, dtype=None, order=None,
- multigraph_weight=sum, weight='weight'):
- """Return the graph adjacency matrix as a NumPy matrix.
-
- Parameters
- ----------
- G : graph
- The NetworkX graph used to construct the NumPy matrix.
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in `nodelist`.
- If `nodelist` is None, then the ordering is produced by G.nodes().
-
- dtype : NumPy data type, optional
- A valid single NumPy data type used to initialize the array.
- This must be a simple type such as int or numpy.float64 and
- not a compound data type (see to_numpy_recarray)
- If None, then the NumPy default is used.
-
- order : {'C', 'F'}, optional
- Whether to store multidimensional data in C- or Fortran-contiguous
- (row- or column-wise) order in memory. If None, then the NumPy default
- is used.
-
- multigraph_weight : {sum, min, max}, optional
- An operator that determines how weights in multigraphs are handled.
- The default is to sum the weights of the multiple edges.
-
- weight : string or None optional (default='weight')
- The edge attribute that holds the numerical value used for
- the edge weight. If None then all edge weights are 1.
-
-
- Returns
- -------
- M : NumPy matrix
- Graph adjacency matrix.
-
- See Also
- --------
- to_numpy_recarray, from_numpy_matrix
-
- Notes
- -----
- The matrix entries are assigned with weight edge attribute. When
- an edge does not have the weight attribute, the value of the entry is 1.
- For multiple edges, the values of the entries are the sums of the edge
- attributes for each edge.
-
- When `nodelist` does not contain every node in `G`, the matrix is built
- from the subgraph of `G` that is induced by the nodes in `nodelist`.
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_edge(0,1,weight=2)
- >>> G.add_edge(1,0)
- >>> G.add_edge(2,2,weight=3)
- >>> G.add_edge(2,2)
- >>> nx.to_numpy_matrix(G, nodelist=[0,1,2])
- matrix([[ 0., 2., 0.],
- [ 1., 0., 0.],
- [ 0., 0., 4.]])
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(\
- "to_numpy_matrix() requires numpy: http://scipy.org/ ")
-
- if nodelist is None:
- nodelist = G.nodes()
-
- nodeset = set(nodelist)
- if len(nodelist) != len(nodeset):
- msg = "Ambiguous ordering: `nodelist` contained duplicates."
- raise nx.NetworkXError(msg)
-
- nlen=len(nodelist)
- undirected = not G.is_directed()
- index=dict(zip(nodelist,range(nlen)))
-
- if G.is_multigraph():
- # Handle MultiGraphs and MultiDiGraphs
- # array of nan' to start with, any leftover nans will be converted to 0
- # nans are used so we can use sum, min, max for multigraphs
- M = np.zeros((nlen,nlen), dtype=dtype, order=order)+np.nan
- # use numpy nan-aware operations
- operator={sum:np.nansum, min:np.nanmin, max:np.nanmax}
- try:
- op=operator[multigraph_weight]
- except:
- raise ValueError('multigraph_weight must be sum, min, or max')
-
- for u,v,attrs in G.edges_iter(data=True):
- if (u in nodeset) and (v in nodeset):
- i,j = index[u],index[v]
- e_weight = attrs.get(weight, 1)
- M[i,j] = op([e_weight,M[i,j]])
- if undirected:
- M[j,i] = M[i,j]
- # convert any nans to zeros
- M = np.asmatrix(np.nan_to_num(M))
- else:
- # Graph or DiGraph, this is much faster than above
- M = np.zeros((nlen,nlen), dtype=dtype, order=order)
- for u,nbrdict in G.adjacency_iter():
- for v,d in nbrdict.items():
- try:
- M[index[u],index[v]]=d.get(weight,1)
- except KeyError:
- pass
- M = np.asmatrix(M)
- return M
-
-
-def from_numpy_matrix(A,create_using=None):
- """Return a graph from numpy matrix.
-
- The numpy matrix is interpreted as an adjacency matrix for the graph.
-
- Parameters
- ----------
- A : numpy matrix
- An adjacency matrix representation of a graph
-
- create_using : NetworkX graph
- Use specified graph for result. The default is Graph()
-
- Notes
- -----
- If the numpy matrix has a single data type for each matrix entry it
- will be converted to an appropriate Python data type.
-
- If the numpy matrix has a user-specified compound data type the names
- of the data fields will be used as attribute keys in the resulting
- NetworkX graph.
-
- See Also
- --------
- to_numpy_matrix, to_numpy_recarray
-
- Examples
- --------
- Simple integer weights on edges:
-
- >>> import numpy
- >>> A=numpy.matrix([[1,1],[2,1]])
- >>> G=nx.from_numpy_matrix(A)
-
- User defined compound data type on edges:
-
- >>> import numpy
- >>> dt=[('weight',float),('cost',int)]
- >>> A=numpy.matrix([[(1.0,2)]],dtype=dt)
- >>> G=nx.from_numpy_matrix(A)
- >>> G.edges(data=True)
- [(0, 0, {'cost': 2, 'weight': 1.0})]
- """
- kind_to_python_type={'f':float,
- 'i':int,
- 'u':int,
- 'b':bool,
- 'c':complex,
- 'S':str,
- 'V':'void'}
-
- try: # Python 3.x
- blurb = chr(1245) # just to trigger the exception
- kind_to_python_type['U']=str
- except ValueError: # Python 2.6+
- kind_to_python_type['U']=unicode
-
- # This should never fail if you have created a numpy matrix with numpy...
- try:
- import numpy as np
- except ImportError:
- raise ImportError(\
- "from_numpy_matrix() requires numpy: http://scipy.org/ ")
-
- G=_prep_create_using(create_using)
- n,m=A.shape
- if n!=m:
- raise nx.NetworkXError("Adjacency matrix is not square.",
- "nx,ny=%s"%(A.shape,))
- dt=A.dtype
- try:
- python_type=kind_to_python_type[dt.kind]
- except:
- raise TypeError("Unknown numpy data type: %s"%dt)
-
- # make sure we get isolated nodes
- G.add_nodes_from(range(n))
- # get a list of edges
- x,y=np.asarray(A).nonzero()
-
- # handle numpy constructed data type
- if python_type is 'void':
- fields=sorted([(offset,dtype,name) for name,(dtype,offset) in
- A.dtype.fields.items()])
- for (u,v) in zip(x,y):
- attr={}
- for (offset,dtype,name),val in zip(fields,A[u,v]):
- attr[name]=kind_to_python_type[dtype.kind](val)
- G.add_edge(u,v,attr)
- else: # basic data type
- G.add_edges_from( ((u,v,{'weight':python_type(A[u,v])})
- for (u,v) in zip(x,y)) )
- return G
-
-
-def to_numpy_recarray(G,nodelist=None,
- dtype=[('weight',float)],
- order=None):
- """Return the graph adjacency matrix as a NumPy recarray.
-
- Parameters
- ----------
- G : graph
- The NetworkX graph used to construct the NumPy matrix.
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in `nodelist`.
- If `nodelist` is None, then the ordering is produced by G.nodes().
-
- dtype : NumPy data-type, optional
- A valid NumPy named dtype used to initialize the NumPy recarray.
- The data type names are assumed to be keys in the graph edge attribute
- dictionary.
-
- order : {'C', 'F'}, optional
- Whether to store multidimensional data in C- or Fortran-contiguous
- (row- or column-wise) order in memory. If None, then the NumPy default
- is used.
-
- Returns
- -------
- M : NumPy recarray
- The graph with specified edge data as a Numpy recarray
-
- Notes
- -----
- When `nodelist` does not contain every node in `G`, the matrix is built
- from the subgraph of `G` that is induced by the nodes in `nodelist`.
-
- Examples
- --------
- >>> G = nx.Graph()
- >>> G.add_edge(1,2,weight=7.0,cost=5)
- >>> A=nx.to_numpy_recarray(G,dtype=[('weight',float),('cost',int)])
- >>> print(A.weight)
- [[ 0. 7.]
- [ 7. 0.]]
- >>> print(A.cost)
- [[0 5]
- [5 0]]
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(\
- "to_numpy_matrix() requires numpy: http://scipy.org/ ")
-
- if G.is_multigraph():
- raise nx.NetworkXError("Not implemented for multigraphs.")
-
- if nodelist is None:
- nodelist = G.nodes()
-
- nodeset = set(nodelist)
- if len(nodelist) != len(nodeset):
- msg = "Ambiguous ordering: `nodelist` contained duplicates."
- raise nx.NetworkXError(msg)
-
- nlen=len(nodelist)
- undirected = not G.is_directed()
- index=dict(zip(nodelist,range(nlen)))
- M = np.zeros((nlen,nlen), dtype=dtype, order=order)
-
- names=M.dtype.names
- for u,v,attrs in G.edges_iter(data=True):
- if (u in nodeset) and (v in nodeset):
- i,j = index[u],index[v]
- values=tuple([attrs[n] for n in names])
- M[i,j] = values
- if undirected:
- M[j,i] = M[i,j]
-
- return M.view(np.recarray)
-
-
-def to_scipy_sparse_matrix(G, nodelist=None, dtype=None,
- weight='weight', format='csr'):
- """Return the graph adjacency matrix as a SciPy sparse matrix.
-
- Parameters
- ----------
- G : graph
- The NetworkX graph used to construct the NumPy matrix.
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in `nodelist`.
- If `nodelist` is None, then the ordering is produced by G.nodes().
-
- dtype : NumPy data-type, optional
- A valid NumPy dtype used to initialize the array. If None, then the
- NumPy default is used.
-
- weight : string or None optional (default='weight')
- The edge attribute that holds the numerical value used for
- the edge weight. If None then all edge weights are 1.
-
- format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
- The type of the matrix to be returned (default 'csr'). For
- some algorithms different implementations of sparse matrices
- can perform better. See [1]_ for details.
-
- Returns
- -------
- M : SciPy sparse matrix
- Graph adjacency matrix.
-
- Notes
- -----
- The matrix entries are populated using the edge attribute held in
- parameter weight. When an edge does not have that attribute, the
- value of the entry is 1.
-
- For multiple edges the matrix values are the sums of the edge weights.
-
- When `nodelist` does not contain every node in `G`, the matrix is built
- from the subgraph of `G` that is induced by the nodes in `nodelist`.
-
- Uses coo_matrix format. To convert to other formats specify the
- format= keyword.
-
- Examples
- --------
- >>> G = nx.MultiDiGraph()
- >>> G.add_edge(0,1,weight=2)
- >>> G.add_edge(1,0)
- >>> G.add_edge(2,2,weight=3)
- >>> G.add_edge(2,2)
- >>> S = nx.to_scipy_sparse_matrix(G, nodelist=[0,1,2])
- >>> print(S.todense())
- [[0 2 0]
- [1 0 0]
- [0 0 4]]
-
- References
- ----------
- .. [1] Scipy Dev. References, "Sparse Matrices",
- http://docs.scipy.org/doc/scipy/reference/sparse.html
- """
- try:
- from scipy import sparse
- except ImportError:
- raise ImportError(\
- "to_scipy_sparse_matrix() requires scipy: http://scipy.org/ ")
-
- if nodelist is None:
- nodelist = G
- nlen = len(nodelist)
- if nlen == 0:
- raise nx.NetworkXError("Graph has no nodes or edges")
-
- if len(nodelist) != len(set(nodelist)):
- msg = "Ambiguous ordering: `nodelist` contained duplicates."
- raise nx.NetworkXError(msg)
-
- index = dict(zip(nodelist,range(nlen)))
- if G.number_of_edges() == 0:
- row,col,data=[],[],[]
- else:
- row,col,data=zip(*((index[u],index[v],d.get(weight,1))
- for u,v,d in G.edges_iter(nodelist, data=True)
- if u in index and v in index))
- if G.is_directed():
- M = sparse.coo_matrix((data,(row,col)),shape=(nlen,nlen), dtype=dtype)
- else:
- # symmetrize matrix
- M = sparse.coo_matrix((data+data,(row+col,col+row)),shape=(nlen,nlen),
- dtype=dtype)
- try:
- return M.asformat(format)
- except AttributeError:
- raise nx.NetworkXError("Unknown sparse matrix format: %s"%format)
-
-def from_scipy_sparse_matrix(A,create_using=None):
- """Return a graph from scipy sparse matrix adjacency list.
-
- Parameters
- ----------
- A : scipy sparse matrix
- An adjacency matrix representation of a graph
-
- create_using : NetworkX graph
- Use specified graph for result. The default is Graph()
-
- Examples
- --------
- >>> import scipy.sparse
- >>> A=scipy.sparse.eye(2,2,1)
- >>> G=nx.from_scipy_sparse_matrix(A)
-
- """
- G=_prep_create_using(create_using)
-
- # convert all formats to lil - not the most efficient way
- AA=A.tolil()
- n,m=AA.shape
-
- if n!=m:
- raise nx.NetworkXError(\
- "Adjacency matrix is not square. nx,ny=%s"%(A.shape,))
- G.add_nodes_from(range(n)) # make sure we get isolated nodes
-
- for i,row in enumerate(AA.rows):
- for pos,j in enumerate(row):
- G.add_edge(i,j,**{'weight':AA.data[i][pos]})
- return G
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/__init__.py
deleted file mode 100644
index 211457e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# graph drawing and interface to graphviz
-import sys
-from networkx.drawing.layout import *
-from networkx.drawing.nx_pylab import *
-
-# graphviz interface
-# prefer pygraphviz/agraph (it's faster)
-from networkx.drawing.nx_agraph import *
-try:
- import pydot
- import networkx.drawing.nx_pydot
- from networkx.drawing.nx_pydot import *
-except ImportError:
- pass
-try:
- import pygraphviz
- from networkx.drawing.nx_agraph import *
-except ImportError:
- pass
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/layout.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/layout.py
deleted file mode 100644
index 671d3b6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/layout.py
+++ /dev/null
@@ -1,540 +0,0 @@
-"""
-******
-Layout
-******
-
-Node positioning algorithms for graph drawing.
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nDan Schult(dschult@colgate.edu)"""
-__all__ = ['circular_layout',
- 'random_layout',
- 'shell_layout',
- 'spring_layout',
- 'spectral_layout',
- 'fruchterman_reingold_layout']
-
-def random_layout(G,dim=2):
- """Position nodes uniformly at random in the unit square.
-
- For every node, a position is generated by choosing each of dim
- coordinates uniformly at random on the interval [0.0, 1.0).
-
- NumPy (http://scipy.org) is required for this function.
-
- Parameters
- ----------
- G : NetworkX graph
- A position will be assigned to every node in G.
-
- dim : int
- Dimension of layout.
-
- Returns
- -------
- dict :
- A dictionary of positions keyed by node
-
- Examples
- --------
- >>> G = nx.lollipop_graph(4, 3)
- >>> pos = nx.random_layout(G)
-
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError("random_layout() requires numpy: http://scipy.org/ ")
- n=len(G)
- pos=np.asarray(np.random.random((n,dim)),dtype=np.float32)
- return dict(zip(G,pos))
-
-
-def circular_layout(G, dim=2, scale=1):
- # dim=2 only
- """Position nodes on a circle.
-
- Parameters
- ----------
- G : NetworkX graph
-
- dim : int
- Dimension of layout, currently only dim=2 is supported
-
- scale : float
- Scale factor for positions
-
- Returns
- -------
- dict :
- A dictionary of positions keyed by node
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> pos=nx.circular_layout(G)
-
- Notes
- ------
- This algorithm currently only works in two dimensions and does not
- try to minimize edge crossings.
-
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError("circular_layout() requires numpy: http://scipy.org/ ")
- if len(G)==0:
- return {}
- if len(G)==1:
- return {G.nodes()[0]:(1,)*dim}
- t=np.arange(0,2.0*np.pi,2.0*np.pi/len(G),dtype=np.float32)
- pos=np.transpose(np.array([np.cos(t),np.sin(t)]))
- pos=_rescale_layout(pos,scale=scale)
- return dict(zip(G,pos))
-
-def shell_layout(G,nlist=None,dim=2,scale=1):
- """Position nodes in concentric circles.
-
- Parameters
- ----------
- G : NetworkX graph
-
- nlist : list of lists
- List of node lists for each shell.
-
- dim : int
- Dimension of layout, currently only dim=2 is supported
-
- scale : float
- Scale factor for positions
-
- Returns
- -------
- dict :
- A dictionary of positions keyed by node
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> shells=[[0],[1,2,3]]
- >>> pos=nx.shell_layout(G,shells)
-
- Notes
- ------
- This algorithm currently only works in two dimensions and does not
- try to minimize edge crossings.
-
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError("shell_layout() requires numpy: http://scipy.org/ ")
- if len(G)==0:
- return {}
- if len(G)==1:
- return {G.nodes()[0]:(1,)*dim}
- if nlist==None:
- nlist=[G.nodes()] # draw the whole graph in one shell
-
- if len(nlist[0])==1:
- radius=0.0 # single node at center
- else:
- radius=1.0 # else start at r=1
-
- npos={}
- for nodes in nlist:
- t=np.arange(0,2.0*np.pi,2.0*np.pi/len(nodes),dtype=np.float32)
- pos=np.transpose(np.array([radius*np.cos(t),radius*np.sin(t)]))
- npos.update(zip(nodes,pos))
- radius+=1.0
-
- # FIXME: rescale
- return npos
-
-
-def fruchterman_reingold_layout(G,dim=2,k=None,
- pos=None,
- fixed=None,
- iterations=50,
- weight='weight',
- scale=1.0):
- """Position nodes using Fruchterman-Reingold force-directed algorithm.
-
- Parameters
- ----------
- G : NetworkX graph
-
- dim : int
- Dimension of layout
-
- k : float (default=None)
- Optimal distance between nodes. If None the distance is set to
- 1/sqrt(n) where n is the number of nodes. Increase this value
- to move nodes farther apart.
-
-
- pos : dict or None optional (default=None)
- Initial positions for nodes as a dictionary with node as keys
- and values as a list or tuple. If None, then nuse random initial
- positions.
-
- fixed : list or None optional (default=None)
- Nodes to keep fixed at initial position.
-
- iterations : int optional (default=50)
- Number of iterations of spring-force relaxation
-
- weight : string or None optional (default='weight')
- The edge attribute that holds the numerical value used for
- the edge weight. If None, then all edge weights are 1.
-
- scale : float (default=1.0)
- Scale factor for positions. The nodes are positioned
- in a box of size [0,scale] x [0,scale].
-
-
- Returns
- -------
- dict :
- A dictionary of positions keyed by node
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> pos=nx.spring_layout(G)
-
- # The same using longer function name
- >>> pos=nx.fruchterman_reingold_layout(G)
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError("fruchterman_reingold_layout() requires numpy: http://scipy.org/ ")
- if fixed is not None:
- nfixed=dict(zip(G,range(len(G))))
- fixed=np.asarray([nfixed[v] for v in fixed])
-
- if pos is not None:
- pos_arr=np.asarray(np.random.random((len(G),dim)))
- for i,n in enumerate(G):
- if n in pos:
- pos_arr[i]=np.asarray(pos[n])
- else:
- pos_arr=None
-
- if len(G)==0:
- return {}
- if len(G)==1:
- return {G.nodes()[0]:(1,)*dim}
-
- try:
- # Sparse matrix
- if len(G) < 500: # sparse solver for large graphs
- raise ValueError
- A=nx.to_scipy_sparse_matrix(G,weight=weight,dtype='f')
- pos=_sparse_fruchterman_reingold(A,dim,k,pos_arr,fixed,iterations)
- except:
- A=nx.to_numpy_matrix(G,weight=weight)
- pos=_fruchterman_reingold(A,dim,k,pos_arr,fixed,iterations)
- if fixed is None:
- pos=_rescale_layout(pos,scale=scale)
- return dict(zip(G,pos))
-
-spring_layout=fruchterman_reingold_layout
-
-def _fruchterman_reingold(A, dim=2, k=None, pos=None, fixed=None,
- iterations=50):
- # Position nodes in adjacency matrix A using Fruchterman-Reingold
- # Entry point for NetworkX graph is fruchterman_reingold_layout()
- try:
- import numpy as np
- except ImportError:
- raise ImportError("_fruchterman_reingold() requires numpy: http://scipy.org/ ")
-
- try:
- nnodes,_=A.shape
- except AttributeError:
- raise nx.NetworkXError(
- "fruchterman_reingold() takes an adjacency matrix as input")
-
- A=np.asarray(A) # make sure we have an array instead of a matrix
-
- if pos==None:
- # random initial positions
- pos=np.asarray(np.random.random((nnodes,dim)),dtype=A.dtype)
- else:
- # make sure positions are of same type as matrix
- pos=pos.astype(A.dtype)
-
- # optimal distance between nodes
- if k is None:
- k=np.sqrt(1.0/nnodes)
- # the initial "temperature" is about .1 of domain area (=1x1)
- # this is the largest step allowed in the dynamics.
- t=0.1
- # simple cooling scheme.
- # linearly step down by dt on each iteration so last iteration is size dt.
- dt=t/float(iterations+1)
- delta = np.zeros((pos.shape[0],pos.shape[0],pos.shape[1]),dtype=A.dtype)
- # the inscrutable (but fast) version
- # this is still O(V^2)
- # could use multilevel methods to speed this up significantly
- for iteration in range(iterations):
- # matrix of difference between points
- for i in range(pos.shape[1]):
- delta[:,:,i]= pos[:,i,None]-pos[:,i]
- # distance between points
- distance=np.sqrt((delta**2).sum(axis=-1))
- # enforce minimum distance of 0.01
- distance=np.where(distance<0.01,0.01,distance)
- # displacement "force"
- displacement=np.transpose(np.transpose(delta)*\
- (k*k/distance**2-A*distance/k))\
- .sum(axis=1)
- # update positions
- length=np.sqrt((displacement**2).sum(axis=1))
- length=np.where(length<0.01,0.1,length)
- delta_pos=np.transpose(np.transpose(displacement)*t/length)
- if fixed is not None:
- # don't change positions of fixed nodes
- delta_pos[fixed]=0.0
- pos+=delta_pos
- # cool temperature
- t-=dt
- pos=_rescale_layout(pos)
- return pos
-
-
-def _sparse_fruchterman_reingold(A, dim=2, k=None, pos=None, fixed=None,
- iterations=50):
- # Position nodes in adjacency matrix A using Fruchterman-Reingold
- # Entry point for NetworkX graph is fruchterman_reingold_layout()
- # Sparse version
- try:
- import numpy as np
- except ImportError:
- raise ImportError("_sparse_fruchterman_reingold() requires numpy: http://scipy.org/ ")
- try:
- nnodes,_=A.shape
- except AttributeError:
- raise nx.NetworkXError(
- "fruchterman_reingold() takes an adjacency matrix as input")
- try:
- from scipy.sparse import spdiags,coo_matrix
- except ImportError:
- raise ImportError("_sparse_fruchterman_reingold() scipy numpy: http://scipy.org/ ")
- # make sure we have a LIst of Lists representation
- try:
- A=A.tolil()
- except:
- A=(coo_matrix(A)).tolil()
-
- if pos==None:
- # random initial positions
- pos=np.asarray(np.random.random((nnodes,dim)),dtype=A.dtype)
- else:
- # make sure positions are of same type as matrix
- pos=pos.astype(A.dtype)
-
- # no fixed nodes
- if fixed==None:
- fixed=[]
-
- # optimal distance between nodes
- if k is None:
- k=np.sqrt(1.0/nnodes)
- # the initial "temperature" is about .1 of domain area (=1x1)
- # this is the largest step allowed in the dynamics.
- t=0.1
- # simple cooling scheme.
- # linearly step down by dt on each iteration so last iteration is size dt.
- dt=t/float(iterations+1)
-
- displacement=np.zeros((dim,nnodes))
- for iteration in range(iterations):
- displacement*=0
- # loop over rows
- for i in range(A.shape[0]):
- if i in fixed:
- continue
- # difference between this row's node position and all others
- delta=(pos[i]-pos).T
- # distance between points
- distance=np.sqrt((delta**2).sum(axis=0))
- # enforce minimum distance of 0.01
- distance=np.where(distance<0.01,0.01,distance)
- # the adjacency matrix row
- Ai=np.asarray(A.getrowview(i).toarray())
- # displacement "force"
- displacement[:,i]+=\
- (delta*(k*k/distance**2-Ai*distance/k)).sum(axis=1)
- # update positions
- length=np.sqrt((displacement**2).sum(axis=0))
- length=np.where(length<0.01,0.1,length)
- pos+=(displacement*t/length).T
- # cool temperature
- t-=dt
- pos=_rescale_layout(pos)
- return pos
-
-
-def spectral_layout(G, dim=2, weight='weight', scale=1):
- """Position nodes using the eigenvectors of the graph Laplacian.
-
- Parameters
- ----------
- G : NetworkX graph
-
- dim : int
- Dimension of layout
-
- weight : string or None optional (default='weight')
- The edge attribute that holds the numerical value used for
- the edge weight. If None, then all edge weights are 1.
-
- scale : float
- Scale factor for positions
-
- Returns
- -------
- dict :
- A dictionary of positions keyed by node
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> pos=nx.spectral_layout(G)
-
- Notes
- -----
- Directed graphs will be considered as unidrected graphs when
- positioning the nodes.
-
- For larger graphs (>500 nodes) this will use the SciPy sparse
- eigenvalue solver (ARPACK).
- """
- # handle some special cases that break the eigensolvers
- try:
- import numpy as np
- except ImportError:
- raise ImportError("spectral_layout() requires numpy: http://scipy.org/ ")
- if len(G)<=2:
- if len(G)==0:
- pos=np.array([])
- elif len(G)==1:
- pos=np.array([[1,1]])
- else:
- pos=np.array([[0,0.5],[1,0.5]])
- return dict(zip(G,pos))
- try:
- # Sparse matrix
- if len(G)< 500: # dense solver is faster for small graphs
- raise ValueError
- A=nx.to_scipy_sparse_matrix(G, weight=weight,dtype='f')
- # Symmetrize directed graphs
- if G.is_directed():
- A=A+np.transpose(A)
- pos=_sparse_spectral(A,dim)
- except (ImportError,ValueError):
- # Dense matrix
- A=nx.to_numpy_matrix(G, weight=weight)
- # Symmetrize directed graphs
- if G.is_directed():
- A=A+np.transpose(A)
- pos=_spectral(A,dim)
-
- pos=_rescale_layout(pos,scale)
- return dict(zip(G,pos))
-
-
-def _spectral(A, dim=2):
- # Input adjacency matrix A
- # Uses dense eigenvalue solver from numpy
- try:
- import numpy as np
- except ImportError:
- raise ImportError("spectral_layout() requires numpy: http://scipy.org/ ")
- try:
- nnodes,_=A.shape
- except AttributeError:
- raise nx.NetworkXError(\
- "spectral() takes an adjacency matrix as input")
-
- # form Laplacian matrix
- # make sure we have an array instead of a matrix
- A=np.asarray(A)
- I=np.identity(nnodes,dtype=A.dtype)
- D=I*np.sum(A,axis=1) # diagonal of degrees
- L=D-A
-
- eigenvalues,eigenvectors=np.linalg.eig(L)
- # sort and keep smallest nonzero
- index=np.argsort(eigenvalues)[1:dim+1] # 0 index is zero eigenvalue
- return np.real(eigenvectors[:,index])
-
-def _sparse_spectral(A,dim=2):
- # Input adjacency matrix A
- # Uses sparse eigenvalue solver from scipy
- # Could use multilevel methods here, see Koren "On spectral graph drawing"
- try:
- import numpy as np
- from scipy.sparse import spdiags
- except ImportError:
- raise ImportError("_sparse_spectral() requires scipy & numpy: http://scipy.org/ ")
- try:
- from scipy.sparse.linalg.eigen import eigsh
- except ImportError:
- # scipy <0.9.0 names eigsh differently
- from scipy.sparse.linalg import eigen_symmetric as eigsh
- try:
- nnodes,_=A.shape
- except AttributeError:
- raise nx.NetworkXError(\
- "sparse_spectral() takes an adjacency matrix as input")
-
- # form Laplacian matrix
- data=np.asarray(A.sum(axis=1).T)
- D=spdiags(data,0,nnodes,nnodes)
- L=D-A
-
- k=dim+1
- # number of Lanczos vectors for ARPACK solver.What is the right scaling?
- ncv=max(2*k+1,int(np.sqrt(nnodes)))
- # return smallest k eigenvalues and eigenvectors
- eigenvalues,eigenvectors=eigsh(L,k,which='SM',ncv=ncv)
- index=np.argsort(eigenvalues)[1:k] # 0 index is zero eigenvalue
- return np.real(eigenvectors[:,index])
-
-
-def _rescale_layout(pos,scale=1):
- # rescale to (0,pscale) in all axes
-
- # shift origin to (0,0)
- lim=0 # max coordinate for all axes
- for i in range(pos.shape[1]):
- pos[:,i]-=pos[:,i].min()
- lim=max(pos[:,i].max(),lim)
- # rescale to (0,scale) in all directions, preserves aspect
- for i in range(pos.shape[1]):
- pos[:,i]*=scale/lim
- return pos
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_agraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_agraph.py
deleted file mode 100644
index ff61253..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_agraph.py
+++ /dev/null
@@ -1,447 +0,0 @@
-"""
-***************
-Graphviz AGraph
-***************
-
-Interface to pygraphviz AGraph class.
-
-Examples
---------
->>> G=nx.complete_graph(5)
->>> A=nx.to_agraph(G)
->>> H=nx.from_agraph(A)
-
-See Also
---------
-Pygraphviz: http://networkx.lanl.gov/pygraphviz
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import os
-import sys
-import tempfile
-import networkx as nx
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-__all__ = ['from_agraph', 'to_agraph',
- 'write_dot', 'read_dot',
- 'graphviz_layout',
- 'pygraphviz_layout',
- 'view_pygraphviz']
-
-def from_agraph(A,create_using=None):
- """Return a NetworkX Graph or DiGraph from a PyGraphviz graph.
-
- Parameters
- ----------
- A : PyGraphviz AGraph
- A graph created with PyGraphviz
-
- create_using : NetworkX graph class instance
- The output is created using the given graph class instance
-
- Examples
- --------
- >>> K5=nx.complete_graph(5)
- >>> A=nx.to_agraph(K5)
- >>> G=nx.from_agraph(A)
- >>> G=nx.from_agraph(A)
-
-
- Notes
- -----
- The Graph G will have a dictionary G.graph_attr containing
- the default graphviz attributes for graphs, nodes and edges.
-
- Default node attributes will be in the dictionary G.node_attr
- which is keyed by node.
-
- Edge attributes will be returned as edge data in G. With
- edge_attr=False the edge data will be the Graphviz edge weight
- attribute or the value 1 if no edge weight attribute is found.
-
- """
- if create_using is None:
- if A.is_directed():
- if A.is_strict():
- create_using=nx.DiGraph()
- else:
- create_using=nx.MultiDiGraph()
- else:
- if A.is_strict():
- create_using=nx.Graph()
- else:
- create_using=nx.MultiGraph()
-
- # assign defaults
- N=nx.empty_graph(0,create_using)
- N.name=''
- if A.name is not None:
- N.name=A.name
-
- # add nodes, attributes to N.node_attr
- for n in A.nodes():
- str_attr=dict((str(k),v) for k,v in n.attr.items())
- N.add_node(str(n),**str_attr)
-
- # add edges, assign edge data as dictionary of attributes
- for e in A.edges():
- u,v=str(e[0]),str(e[1])
- attr=dict(e.attr)
- str_attr=dict((str(k),v) for k,v in attr.items())
- if not N.is_multigraph():
- if e.name is not None:
- str_attr['key']=e.name
- N.add_edge(u,v,**str_attr)
- else:
- N.add_edge(u,v,key=e.name,**str_attr)
-
- # add default attributes for graph, nodes, and edges
- # hang them on N.graph_attr
- N.graph['graph']=dict(A.graph_attr)
- N.graph['node']=dict(A.node_attr)
- N.graph['edge']=dict(A.edge_attr)
- return N
-
-def to_agraph(N):
- """Return a pygraphviz graph from a NetworkX graph N.
-
- Parameters
- ----------
- N : NetworkX graph
- A graph created with NetworkX
-
- Examples
- --------
- >>> K5=nx.complete_graph(5)
- >>> A=nx.to_agraph(K5)
-
- Notes
- -----
- If N has an dict N.graph_attr an attempt will be made first
- to copy properties attached to the graph (see from_agraph)
- and then updated with the calling arguments if any.
-
- """
- try:
- import pygraphviz
- except ImportError:
- raise ImportError('requires pygraphviz ',
- 'http://networkx.lanl.gov/pygraphviz ',
- '(not available for Python3)')
- directed=N.is_directed()
- strict=N.number_of_selfloops()==0 and not N.is_multigraph()
- A=pygraphviz.AGraph(name=N.name,strict=strict,directed=directed)
-
- # default graph attributes
- A.graph_attr.update(N.graph.get('graph',{}))
- A.node_attr.update(N.graph.get('node',{}))
- A.edge_attr.update(N.graph.get('edge',{}))
-
- # add nodes
- for n,nodedata in N.nodes(data=True):
- A.add_node(n,**nodedata)
-
- # loop over edges
-
- if N.is_multigraph():
- for u,v,key,edgedata in N.edges_iter(data=True,keys=True):
- str_edgedata=dict((k,str(v)) for k,v in edgedata.items())
- A.add_edge(u,v,key=str(key),**str_edgedata)
- else:
- for u,v,edgedata in N.edges_iter(data=True):
- str_edgedata=dict((k,str(v)) for k,v in edgedata.items())
- A.add_edge(u,v,**str_edgedata)
-
-
- return A
-
-def write_dot(G,path):
- """Write NetworkX graph G to Graphviz dot format on path.
-
- Parameters
- ----------
- G : graph
- A networkx graph
- path : filename
- Filename or file handle to write
- """
- try:
- import pygraphviz
- except ImportError:
- raise ImportError('requires pygraphviz ',
- 'http://networkx.lanl.gov/pygraphviz ',
- '(not available for Python3)')
- A=to_agraph(G)
- A.write(path)
- A.clear()
- return
-
-def read_dot(path):
- """Return a NetworkX graph from a dot file on path.
-
- Parameters
- ----------
- path : file or string
- File name or file handle to read.
- """
- try:
- import pygraphviz
- except ImportError:
- raise ImportError('read_dot() requires pygraphviz ',
- 'http://networkx.lanl.gov/pygraphviz ',
- '(not available for Python3)')
- A=pygraphviz.AGraph(file=path)
- return from_agraph(A)
-
-
-def graphviz_layout(G,prog='neato',root=None, args=''):
- """Create node positions for G using Graphviz.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph created with NetworkX
- prog : string
- Name of Graphviz layout program
- root : string, optional
- Root node for twopi layout
- args : string, optional
- Extra arguments to Graphviz layout program
-
- Returns : dictionary
- Dictionary of x,y, positions keyed by node.
-
- Examples
- --------
- >>> G=nx.petersen_graph()
- >>> pos=nx.graphviz_layout(G)
- >>> pos=nx.graphviz_layout(G,prog='dot')
-
- Notes
- -----
- This is a wrapper for pygraphviz_layout.
-
- """
- return pygraphviz_layout(G,prog=prog,root=root,args=args)
-
-def pygraphviz_layout(G,prog='neato',root=None, args=''):
- """Create node positions for G using Graphviz.
-
- Parameters
- ----------
- G : NetworkX graph
- A graph created with NetworkX
- prog : string
- Name of Graphviz layout program
- root : string, optional
- Root node for twopi layout
- args : string, optional
- Extra arguments to Graphviz layout program
-
- Returns : dictionary
- Dictionary of x,y, positions keyed by node.
-
- Examples
- --------
- >>> G=nx.petersen_graph()
- >>> pos=nx.graphviz_layout(G)
- >>> pos=nx.graphviz_layout(G,prog='dot')
-
- """
- try:
- import pygraphviz
- except ImportError:
- raise ImportError('requires pygraphviz ',
- 'http://networkx.lanl.gov/pygraphviz ',
- '(not available for Python3)')
- if root is not None:
- args+="-Groot=%s"%root
- A=to_agraph(G)
- A.layout(prog=prog,args=args)
- node_pos={}
- for n in G:
- node=pygraphviz.Node(A,n)
- try:
- xx,yy=node.attr["pos"].split(',')
- node_pos[n]=(float(xx),float(yy))
- except:
- print("no position for node",n)
- node_pos[n]=(0.0,0.0)
- return node_pos
-
-@nx.utils.open_file(5, 'w')
-def view_pygraphviz(G, edgelabel=None, prog='dot', args='',
- suffix='', path=None):
- """Views the graph G using the specified layout algorithm.
-
- Parameters
- ----------
- G : NetworkX graph
- The machine to draw.
- edgelabel : str, callable, None
- If a string, then it specifes the edge attribute to be displayed
- on the edge labels. If a callable, then it is called for each
- edge and it should return the string to be displayed on the edges.
- The function signature of `edgelabel` should be edgelabel(data),
- where `data` is the edge attribute dictionary.
- prog : string
- Name of Graphviz layout program.
- args : str
- Additional arguments to pass to the Graphviz layout program.
- suffix : str
- If `filename` is None, we save to a temporary file. The value of
- `suffix` will appear at the tail end of the temporary filename.
- path : str, None
- The filename used to save the image. If None, save to a temporary
- file. File formats are the same as those from pygraphviz.agraph.draw.
-
- Returns
- -------
- path : str
- The filename of the generated image.
- A : PyGraphviz graph
- The PyGraphviz graph instance used to generate the image.
-
- Notes
- -----
- If this function is called in succession too quickly, sometimes the
- image is not displayed. So you might consider time.sleep(.5) between
- calls if you experience problems.
-
- """
- if not len(G):
- raise nx.NetworkXException("An empty graph cannot be drawn.")
-
- import pygraphviz
-
- # If we are providing default values for graphviz, these must be set
- # before any nodes or edges are added to the PyGraphviz graph object.
- # The reason for this is that default values only affect incoming objects.
- # If you change the default values after the objects have been added,
- # then they inherit no value and are set only if explicitly set.
-
- # to_agraph() uses these values.
- attrs = ['edge', 'node', 'graph']
- for attr in attrs:
- if attr not in G.graph:
- G.graph[attr] = {}
-
- # These are the default values.
- edge_attrs = {'fontsize': '10'}
- node_attrs = {'style': 'filled',
- 'fillcolor': '#0000FF40',
- 'height': '0.75',
- 'width': '0.75',
- 'shape': 'circle'}
- graph_attrs = {}
-
- def update_attrs(which, attrs):
- # Update graph attributes. Return list of those which were added.
- added = []
- for k,v in attrs.items():
- if k not in G.graph[which]:
- G.graph[which][k] = v
- added.append(k)
-
- def clean_attrs(which, added):
- # Remove added attributes
- for attr in added:
- del G.graph[which][attr]
- if not G.graph[which]:
- del G.graph[which]
-
- # Update all default values
- update_attrs('edge', edge_attrs)
- update_attrs('node', node_attrs)
- update_attrs('graph', graph_attrs)
-
- # Convert to agraph, so we inherit default values
- A = to_agraph(G)
-
- # Remove the default values we added to the original graph.
- clean_attrs('edge', edge_attrs)
- clean_attrs('node', node_attrs)
- clean_attrs('graph', graph_attrs)
-
- # If the user passed in an edgelabel, we update the labels for all edges.
- if edgelabel is not None:
- if not hasattr(edgelabel, '__call__'):
- def func(data):
- return ''.join([" ", str(data[edgelabel]), " "])
- else:
- func = edgelabel
-
- # update all the edge labels
- if G.is_multigraph():
- for u,v,key,data in G.edges_iter(keys=True, data=True):
- # PyGraphviz doesn't convert the key to a string. See #339
- edge = A.get_edge(u,v,str(key))
- edge.attr['label'] = str(func(data))
- else:
- for u,v,data in G.edges_iter(data=True):
- edge = A.get_edge(u,v)
- edge.attr['label'] = str(func(data))
-
- if path is None:
- ext = 'png'
- if suffix:
- suffix = '_%s.%s' % (suffix, ext)
- else:
- suffix = '.%s' % (ext,)
- path = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
- else:
- # Assume the decorator worked and it is a file-object.
- pass
-
- display_pygraphviz(A, path=path, prog=prog, args=args)
-
- return path.name, A
-
-def display_pygraphviz(graph, path, format=None, prog=None, args=''):
- """Internal function to display a graph in OS dependent manner.
-
- Parameters
- ----------
- graph : PyGraphviz graph
- A PyGraphviz AGraph instance.
- path : file object
- An already opened file object that will be closed.
- format : str, None
- An attempt is made to guess the output format based on the extension
- of the filename. If that fails, the value of `format` is used.
- prog : string
- Name of Graphviz layout program.
- args : str
- Additional arguments to pass to the Graphviz layout program.
-
- Notes
- -----
- If this function is called in succession too quickly, sometimes the
- image is not displayed. So you might consider time.sleep(.5) between
- calls if you experience problems.
-
- """
- if format is None:
- filename = path.name
- format = os.path.splitext(filename)[1].lower()[1:]
- if not format:
- # Let the draw() function use its default
- format = None
-
- # Save to a file and display in the default viewer.
- # We must close the file before viewing it.
- graph.draw(path, format, prog, args)
- path.close()
- nx.utils.default_opener(filename)
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import pygraphviz
- except:
- raise SkipTest("pygraphviz not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_pydot.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_pydot.py
deleted file mode 100644
index 183f6ab..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_pydot.py
+++ /dev/null
@@ -1,287 +0,0 @@
-"""
-*****
-Pydot
-*****
-
-Import and export NetworkX graphs in Graphviz dot format using pydot.
-
-Either this module or nx_pygraphviz can be used to interface with graphviz.
-
-See Also
---------
-Pydot: http://code.google.com/p/pydot/
-Graphviz: http://www.research.att.com/sw/tools/graphviz/
-DOT Language: http://www.graphviz.org/doc/info/lang.html
-"""
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from networkx.utils import open_file, make_str
-import networkx as nx
-__author__ = """Aric Hagberg (aric.hagberg@gmail.com)"""
-__all__ = ['write_dot', 'read_dot', 'graphviz_layout', 'pydot_layout',
- 'to_pydot', 'from_pydot']
-
-@open_file(1,mode='w')
-def write_dot(G,path):
- """Write NetworkX graph G to Graphviz dot format on path.
-
- Path can be a string or a file handle.
- """
- try:
- import pydot
- except ImportError:
- raise ImportError("write_dot() requires pydot",
- "http://code.google.com/p/pydot/")
- P=to_pydot(G)
- path.write(P.to_string())
- return
-
-@open_file(0,mode='r')
-def read_dot(path):
- """Return a NetworkX MultiGraph or MultiDiGraph from a dot file on path.
-
- Parameters
- ----------
- path : filename or file handle
-
- Returns
- -------
- G : NetworkX multigraph
- A MultiGraph or MultiDiGraph.
-
- Notes
- -----
- Use G=nx.Graph(nx.read_dot(path)) to return a Graph instead of a MultiGraph.
- """
- try:
- import pydot
- except ImportError:
- raise ImportError("read_dot() requires pydot",
- "http://code.google.com/p/pydot/")
-
- data=path.read()
- P=pydot.graph_from_dot_data(data)
- return from_pydot(P)
-
-def from_pydot(P):
- """Return a NetworkX graph from a Pydot graph.
-
- Parameters
- ----------
- P : Pydot graph
- A graph created with Pydot
-
- Returns
- -------
- G : NetworkX multigraph
- A MultiGraph or MultiDiGraph.
-
- Examples
- --------
- >>> K5=nx.complete_graph(5)
- >>> A=nx.to_pydot(K5)
- >>> G=nx.from_pydot(A) # return MultiGraph
- >>> G=nx.Graph(nx.from_pydot(A)) # make a Graph instead of MultiGraph
-
- """
- if P.get_strict(None): # pydot bug: get_strict() shouldn't take argument
- multiedges=False
- else:
- multiedges=True
-
- if P.get_type()=='graph': # undirected
- if multiedges:
- create_using=nx.MultiGraph()
- else:
- create_using=nx.Graph()
- else:
- if multiedges:
- create_using=nx.MultiDiGraph()
- else:
- create_using=nx.DiGraph()
-
- # assign defaults
- N=nx.empty_graph(0,create_using)
- N.name=P.get_name()
-
- # add nodes, attributes to N.node_attr
- for p in P.get_node_list():
- n=p.get_name().strip('"')
- if n in ('node','graph','edge'):
- continue
- N.add_node(n,**p.get_attributes())
-
- # add edges
- for e in P.get_edge_list():
- u=e.get_source().strip('"')
- v=e.get_destination().strip('"')
- attr=e.get_attributes()
- N.add_edge(u,v,**attr)
-
- # add default attributes for graph, nodes, edges
- N.graph['graph']=P.get_attributes()
- try:
- N.graph['node']=P.get_node_defaults()[0]
- except:# IndexError,TypeError:
- N.graph['node']={}
- try:
- N.graph['edge']=P.get_edge_defaults()[0]
- except:# IndexError,TypeError:
- N.graph['edge']={}
- return N
-
-def to_pydot(N, strict=True):
- """Return a pydot graph from a NetworkX graph N.
-
- Parameters
- ----------
- N : NetworkX graph
- A graph created with NetworkX
-
- Examples
- --------
- >>> K5=nx.complete_graph(5)
- >>> P=nx.to_pydot(K5)
-
- Notes
- -----
-
- """
- try:
- import pydot
- except ImportError:
- raise ImportError('to_pydot() requires pydot: '
- 'http://code.google.com/p/pydot/')
-
- # set Graphviz graph type
- if N.is_directed():
- graph_type='digraph'
- else:
- graph_type='graph'
- strict=N.number_of_selfloops()==0 and not N.is_multigraph()
-
- name = N.graph.get('name')
- graph_defaults=N.graph.get('graph',{})
- if name is None:
- P = pydot.Dot(graph_type=graph_type,strict=strict,**graph_defaults)
- else:
- P = pydot.Dot('"%s"'%name,graph_type=graph_type,strict=strict,
- **graph_defaults)
- try:
- P.set_node_defaults(**N.graph['node'])
- except KeyError:
- pass
- try:
- P.set_edge_defaults(**N.graph['edge'])
- except KeyError:
- pass
-
- for n,nodedata in N.nodes_iter(data=True):
- str_nodedata=dict((k,make_str(v)) for k,v in nodedata.items())
- p=pydot.Node(make_str(n),**str_nodedata)
- P.add_node(p)
-
- if N.is_multigraph():
- for u,v,key,edgedata in N.edges_iter(data=True,keys=True):
- str_edgedata=dict((k,make_str(v)) for k,v in edgedata.items())
- edge=pydot.Edge(make_str(u),make_str(v),key=make_str(key),**str_edgedata)
- P.add_edge(edge)
-
- else:
- for u,v,edgedata in N.edges_iter(data=True):
- str_edgedata=dict((k,make_str(v)) for k,v in edgedata.items())
- edge=pydot.Edge(make_str(u),make_str(v),**str_edgedata)
- P.add_edge(edge)
- return P
-
-
-def pydot_from_networkx(N):
- """Create a Pydot graph from a NetworkX graph."""
- from warnings import warn
- warn('pydot_from_networkx is replaced by to_pydot', DeprecationWarning)
- return to_pydot(N)
-
-def networkx_from_pydot(D, create_using=None):
- """Create a NetworkX graph from a Pydot graph."""
- from warnings import warn
- warn('networkx_from_pydot is replaced by from_pydot',
- DeprecationWarning)
- return from_pydot(D)
-
-def graphviz_layout(G,prog='neato',root=None, **kwds):
- """Create node positions using Pydot and Graphviz.
-
- Returns a dictionary of positions keyed by node.
-
- Examples
- --------
- >>> G=nx.complete_graph(4)
- >>> pos=nx.graphviz_layout(G)
- >>> pos=nx.graphviz_layout(G,prog='dot')
-
- Notes
- -----
- This is a wrapper for pydot_layout.
- """
- return pydot_layout(G=G,prog=prog,root=root,**kwds)
-
-
-def pydot_layout(G,prog='neato',root=None, **kwds):
- """Create node positions using Pydot and Graphviz.
-
- Returns a dictionary of positions keyed by node.
-
- Examples
- --------
- >>> G=nx.complete_graph(4)
- >>> pos=nx.pydot_layout(G)
- >>> pos=nx.pydot_layout(G,prog='dot')
- """
- try:
- import pydot
- except ImportError:
- raise ImportError('pydot_layout() requires pydot ',
- 'http://code.google.com/p/pydot/')
-
- P=to_pydot(G)
- if root is not None :
- P.set("root",make_str(root))
-
- D=P.create_dot(prog=prog)
-
- if D=="": # no data returned
- print("Graphviz layout with %s failed"%(prog))
- print()
- print("To debug what happened try:")
- print("P=pydot_from_networkx(G)")
- print("P.write_dot(\"file.dot\")")
- print("And then run %s on file.dot"%(prog))
- return
-
- Q=pydot.graph_from_dot_data(D)
-
- node_pos={}
- for n in G.nodes():
- pydot_node = pydot.Node(make_str(n)).get_name().encode('utf-8')
- node=Q.get_node(pydot_node)
-
- if isinstance(node,list):
- node=node[0]
- pos=node.get_pos()[1:-1] # strip leading and trailing double quotes
- if pos != None:
- xx,yy=pos.split(",")
- node_pos[n]=(float(xx),float(yy))
- return node_pos
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import pydot
- import dot_parser
- except:
- raise SkipTest("pydot not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_pylab.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_pylab.py
deleted file mode 100644
index c7d0cf6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/nx_pylab.py
+++ /dev/null
@@ -1,896 +0,0 @@
-"""
-**********
-Matplotlib
-**********
-
-Draw networks with matplotlib.
-
-See Also
---------
-
-matplotlib: http://matplotlib.sourceforge.net/
-
-pygraphviz: http://networkx.lanl.gov/pygraphviz/
-
-"""
-# Copyright (C) 2004-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.drawing.layout import shell_layout,\
- circular_layout,spectral_layout,spring_layout,random_layout
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-__all__ = ['draw',
- 'draw_networkx',
- 'draw_networkx_nodes',
- 'draw_networkx_edges',
- 'draw_networkx_labels',
- 'draw_networkx_edge_labels',
- 'draw_circular',
- 'draw_random',
- 'draw_spectral',
- 'draw_spring',
- 'draw_shell',
- 'draw_graphviz']
-
-def draw(G, pos=None, ax=None, hold=None, **kwds):
- """Draw the graph G with Matplotlib.
-
- Draw the graph as a simple representation with no node
- labels or edge labels and using the full Matplotlib figure area
- and no axis labels by default. See draw_networkx() for more
- full-featured drawing that allows title, axis labels etc.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- pos : dictionary, optional
- A dictionary with nodes as keys and positions as values.
- If not specified a spring layout positioning will be computed.
- See networkx.layout for functions that compute node positions.
-
- ax : Matplotlib Axes object, optional
- Draw the graph in specified Matplotlib axes.
-
- hold : bool, optional
- Set the Matplotlib hold state. If True subsequent draw
- commands will be added to the current axes.
-
- **kwds : optional keywords
- See networkx.draw_networkx() for a description of optional keywords.
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> nx.draw(G)
- >>> nx.draw(G,pos=nx.spring_layout(G)) # use spring layout
-
- See Also
- --------
- draw_networkx()
- draw_networkx_nodes()
- draw_networkx_edges()
- draw_networkx_labels()
- draw_networkx_edge_labels()
-
- Notes
- -----
- This function has the same name as pylab.draw and pyplot.draw
- so beware when using
-
- >>> from networkx import *
-
- since you might overwrite the pylab.draw function.
-
- With pyplot use
-
- >>> import matplotlib.pyplot as plt
- >>> import networkx as nx
- >>> G=nx.dodecahedral_graph()
- >>> nx.draw(G) # networkx draw()
- >>> plt.draw() # pyplot draw()
-
- Also see the NetworkX drawing examples at
- http://networkx.lanl.gov/gallery.html
- """
- try:
- import matplotlib.pyplot as plt
- except ImportError:
- raise ImportError("Matplotlib required for draw()")
- except RuntimeError:
- print("Matplotlib unable to open display")
- raise
-
- if ax is None:
- cf = plt.gcf()
- else:
- cf = ax.get_figure()
- cf.set_facecolor('w')
- if ax is None:
- if cf._axstack() is None:
- ax=cf.add_axes((0,0,1,1))
- else:
- ax=cf.gca()
-
- # allow callers to override the hold state by passing hold=True|False
- b = plt.ishold()
- h = kwds.pop('hold', None)
- if h is not None:
- plt.hold(h)
- try:
- draw_networkx(G,pos=pos,ax=ax,**kwds)
- ax.set_axis_off()
- plt.draw_if_interactive()
- except:
- plt.hold(b)
- raise
- plt.hold(b)
- return
-
-
-def draw_networkx(G, pos=None, with_labels=True, **kwds):
- """Draw the graph G using Matplotlib.
-
- Draw the graph with Matplotlib with options for node positions,
- labeling, titles, and many other drawing features.
- See draw() for simple drawing without labels or axes.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- pos : dictionary, optional
- A dictionary with nodes as keys and positions as values.
- If not specified a spring layout positioning will be computed.
- See networkx.layout for functions that compute node positions.
-
- with_labels : bool, optional (default=True)
- Set to True to draw labels on the nodes.
-
- ax : Matplotlib Axes object, optional
- Draw the graph in the specified Matplotlib axes.
-
- nodelist : list, optional (default G.nodes())
- Draw only specified nodes
-
- edgelist : list, optional (default=G.edges())
- Draw only specified edges
-
- node_size : scalar or array, optional (default=300)
- Size of nodes. If an array is specified it must be the
- same length as nodelist.
-
- node_color : color string, or array of floats, (default='r')
- Node color. Can be a single color format string,
- or a sequence of colors with the same length as nodelist.
- If numeric values are specified they will be mapped to
- colors using the cmap and vmin,vmax parameters. See
- matplotlib.scatter for more details.
-
- node_shape : string, optional (default='o')
- The shape of the node. Specification is as matplotlib.scatter
- marker, one of 'so^>v<dph8'.
-
- alpha : float, optional (default=1.0)
- The node transparency
-
- cmap : Matplotlib colormap, optional (default=None)
- Colormap for mapping intensities of nodes
-
- vmin,vmax : float, optional (default=None)
- Minimum and maximum for node colormap scaling
-
- linewidths : [None | scalar | sequence]
- Line width of symbol border (default =1.0)
-
- width : float, optional (default=1.0)
- Line width of edges
-
- edge_color : color string, or array of floats (default='r')
- Edge color. Can be a single color format string,
- or a sequence of colors with the same length as edgelist.
- If numeric values are specified they will be mapped to
- colors using the edge_cmap and edge_vmin,edge_vmax parameters.
-
- edge_ cmap : Matplotlib colormap, optional (default=None)
- Colormap for mapping intensities of edges
-
- edge_vmin,edge_vmax : floats, optional (default=None)
- Minimum and maximum for edge colormap scaling
-
- style : string, optional (default='solid')
- Edge line style (solid|dashed|dotted,dashdot)
-
- labels : dictionary, optional (default=None)
- Node labels in a dictionary keyed by node of text labels
-
- font_size : int, optional (default=12)
- Font size for text labels
-
- font_color : string, optional (default='k' black)
- Font color string
-
- font_weight : string, optional (default='normal')
- Font weight
-
- font_family : string, optional (default='sans-serif')
- Font family
-
- label : string, optional
- Label for graph legend
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> nx.draw(G)
- >>> nx.draw(G,pos=nx.spring_layout(G)) # use spring layout
-
- >>> import matplotlib.pyplot as plt
- >>> limits=plt.axis('off') # turn of axis
-
- Also see the NetworkX drawing examples at
- http://networkx.lanl.gov/gallery.html
-
- See Also
- --------
- draw()
- draw_networkx_nodes()
- draw_networkx_edges()
- draw_networkx_labels()
- draw_networkx_edge_labels()
- """
- try:
- import matplotlib.pyplot as plt
- except ImportError:
- raise ImportError("Matplotlib required for draw()")
- except RuntimeError:
- print("Matplotlib unable to open display")
- raise
-
- if pos is None:
- pos=nx.drawing.spring_layout(G) # default to spring layout
-
- node_collection=draw_networkx_nodes(G, pos, **kwds)
- edge_collection=draw_networkx_edges(G, pos, **kwds)
- if with_labels:
- draw_networkx_labels(G, pos, **kwds)
- plt.draw_if_interactive()
-
-def draw_networkx_nodes(G, pos,
- nodelist=None,
- node_size=300,
- node_color='r',
- node_shape='o',
- alpha=1.0,
- cmap=None,
- vmin=None,
- vmax=None,
- ax=None,
- linewidths=None,
- label = None,
- **kwds):
- """Draw the nodes of the graph G.
-
- This draws only the nodes of the graph G.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- pos : dictionary
- A dictionary with nodes as keys and positions as values.
- If not specified a spring layout positioning will be computed.
- See networkx.layout for functions that compute node positions.
-
- ax : Matplotlib Axes object, optional
- Draw the graph in the specified Matplotlib axes.
-
- nodelist : list, optional
- Draw only specified nodes (default G.nodes())
-
- node_size : scalar or array
- Size of nodes (default=300). If an array is specified it must be the
- same length as nodelist.
-
- node_color : color string, or array of floats
- Node color. Can be a single color format string (default='r'),
- or a sequence of colors with the same length as nodelist.
- If numeric values are specified they will be mapped to
- colors using the cmap and vmin,vmax parameters. See
- matplotlib.scatter for more details.
-
- node_shape : string
- The shape of the node. Specification is as matplotlib.scatter
- marker, one of 'so^>v<dph8' (default='o').
-
- alpha : float
- The node transparency (default=1.0)
-
- cmap : Matplotlib colormap
- Colormap for mapping intensities of nodes (default=None)
-
- vmin,vmax : floats
- Minimum and maximum for node colormap scaling (default=None)
-
- linewidths : [None | scalar | sequence]
- Line width of symbol border (default =1.0)
-
- label : [None| string]
- Label for legend
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> nodes=nx.draw_networkx_nodes(G,pos=nx.spring_layout(G))
-
- Also see the NetworkX drawing examples at
- http://networkx.lanl.gov/gallery.html
-
- See Also
- --------
- draw()
- draw_networkx()
- draw_networkx_edges()
- draw_networkx_labels()
- draw_networkx_edge_labels()
- """
- try:
- import matplotlib.pyplot as plt
- import numpy
- except ImportError:
- raise ImportError("Matplotlib required for draw()")
- except RuntimeError:
- print("Matplotlib unable to open display")
- raise
-
-
- if ax is None:
- ax=plt.gca()
-
- if nodelist is None:
- nodelist=G.nodes()
-
- if not nodelist or len(nodelist)==0: # empty nodelist, no drawing
- return None
-
- try:
- xy=numpy.asarray([pos[v] for v in nodelist])
- except KeyError as e:
- raise nx.NetworkXError('Node %s has no position.'%e)
- except ValueError:
- raise nx.NetworkXError('Bad value in node positions.')
-
-
-
- node_collection=ax.scatter(xy[:,0], xy[:,1],
- s=node_size,
- c=node_color,
- marker=node_shape,
- cmap=cmap,
- vmin=vmin,
- vmax=vmax,
- alpha=alpha,
- linewidths=linewidths,
- label=label)
-
- node_collection.set_zorder(2)
- return node_collection
-
-
-def draw_networkx_edges(G, pos,
- edgelist=None,
- width=1.0,
- edge_color='k',
- style='solid',
- alpha=None,
- edge_cmap=None,
- edge_vmin=None,
- edge_vmax=None,
- ax=None,
- arrows=True,
- label=None,
- **kwds):
- """Draw the edges of the graph G.
-
- This draws only the edges of the graph G.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- pos : dictionary
- A dictionary with nodes as keys and positions as values.
- If not specified a spring layout positioning will be computed.
- See networkx.layout for functions that compute node positions.
-
- edgelist : collection of edge tuples
- Draw only specified edges(default=G.edges())
-
- width : float
- Line width of edges (default =1.0)
-
- edge_color : color string, or array of floats
- Edge color. Can be a single color format string (default='r'),
- or a sequence of colors with the same length as edgelist.
- If numeric values are specified they will be mapped to
- colors using the edge_cmap and edge_vmin,edge_vmax parameters.
-
- style : string
- Edge line style (default='solid') (solid|dashed|dotted,dashdot)
-
- alpha : float
- The edge transparency (default=1.0)
-
- edge_ cmap : Matplotlib colormap
- Colormap for mapping intensities of edges (default=None)
-
- edge_vmin,edge_vmax : floats
- Minimum and maximum for edge colormap scaling (default=None)
-
- ax : Matplotlib Axes object, optional
- Draw the graph in the specified Matplotlib axes.
-
- arrows : bool, optional (default=True)
- For directed graphs, if True draw arrowheads.
-
- label : [None| string]
- Label for legend
-
- Notes
- -----
- For directed graphs, "arrows" (actually just thicker stubs) are drawn
- at the head end. Arrows can be turned off with keyword arrows=False.
- Yes, it is ugly but drawing proper arrows with Matplotlib this
- way is tricky.
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> edges=nx.draw_networkx_edges(G,pos=nx.spring_layout(G))
-
- Also see the NetworkX drawing examples at
- http://networkx.lanl.gov/gallery.html
-
- See Also
- --------
- draw()
- draw_networkx()
- draw_networkx_nodes()
- draw_networkx_labels()
- draw_networkx_edge_labels()
- """
- try:
- import matplotlib
- import matplotlib.pyplot as plt
- import matplotlib.cbook as cb
- from matplotlib.colors import colorConverter,Colormap
- from matplotlib.collections import LineCollection
- import numpy
- except ImportError:
- raise ImportError("Matplotlib required for draw()")
- except RuntimeError:
- print("Matplotlib unable to open display")
- raise
-
- if ax is None:
- ax=plt.gca()
-
- if edgelist is None:
- edgelist=G.edges()
-
- if not edgelist or len(edgelist)==0: # no edges!
- return None
-
- # set edge positions
- edge_pos=numpy.asarray([(pos[e[0]],pos[e[1]]) for e in edgelist])
-
- if not cb.iterable(width):
- lw = (width,)
- else:
- lw = width
-
- if not cb.is_string_like(edge_color) \
- and cb.iterable(edge_color) \
- and len(edge_color)==len(edge_pos):
- if numpy.alltrue([cb.is_string_like(c)
- for c in edge_color]):
- # (should check ALL elements)
- # list of color letters such as ['k','r','k',...]
- edge_colors = tuple([colorConverter.to_rgba(c,alpha)
- for c in edge_color])
- elif numpy.alltrue([not cb.is_string_like(c)
- for c in edge_color]):
- # If color specs are given as (rgb) or (rgba) tuples, we're OK
- if numpy.alltrue([cb.iterable(c) and len(c) in (3,4)
- for c in edge_color]):
- edge_colors = tuple(edge_color)
- else:
- # numbers (which are going to be mapped with a colormap)
- edge_colors = None
- else:
- raise ValueError('edge_color must consist of either color names or numbers')
- else:
- if cb.is_string_like(edge_color) or len(edge_color)==1:
- edge_colors = ( colorConverter.to_rgba(edge_color, alpha), )
- else:
- raise ValueError('edge_color must be a single color or list of exactly m colors where m is the number or edges')
-
- edge_collection = LineCollection(edge_pos,
- colors = edge_colors,
- linewidths = lw,
- antialiaseds = (1,),
- linestyle = style,
- transOffset = ax.transData,
- )
-
-
- edge_collection.set_zorder(1) # edges go behind nodes
- edge_collection.set_label(label)
- ax.add_collection(edge_collection)
-
- # Note: there was a bug in mpl regarding the handling of alpha values for
- # each line in a LineCollection. It was fixed in matplotlib in r7184 and
- # r7189 (June 6 2009). We should then not set the alpha value globally,
- # since the user can instead provide per-edge alphas now. Only set it
- # globally if provided as a scalar.
- if cb.is_numlike(alpha):
- edge_collection.set_alpha(alpha)
-
- if edge_colors is None:
- if edge_cmap is not None:
- assert(isinstance(edge_cmap, Colormap))
- edge_collection.set_array(numpy.asarray(edge_color))
- edge_collection.set_cmap(edge_cmap)
- if edge_vmin is not None or edge_vmax is not None:
- edge_collection.set_clim(edge_vmin, edge_vmax)
- else:
- edge_collection.autoscale()
-
- arrow_collection=None
-
- if G.is_directed() and arrows:
-
- # a directed graph hack
- # draw thick line segments at head end of edge
- # waiting for someone else to implement arrows that will work
- arrow_colors = edge_colors
- a_pos=[]
- p=1.0-0.25 # make head segment 25 percent of edge length
- for src,dst in edge_pos:
- x1,y1=src
- x2,y2=dst
- dx=x2-x1 # x offset
- dy=y2-y1 # y offset
- d=numpy.sqrt(float(dx**2+dy**2)) # length of edge
- if d==0: # source and target at same position
- continue
- if dx==0: # vertical edge
- xa=x2
- ya=dy*p+y1
- if dy==0: # horizontal edge
- ya=y2
- xa=dx*p+x1
- else:
- theta=numpy.arctan2(dy,dx)
- xa=p*d*numpy.cos(theta)+x1
- ya=p*d*numpy.sin(theta)+y1
-
- a_pos.append(((xa,ya),(x2,y2)))
-
- arrow_collection = LineCollection(a_pos,
- colors = arrow_colors,
- linewidths = [4*ww for ww in lw],
- antialiaseds = (1,),
- transOffset = ax.transData,
- )
-
- arrow_collection.set_zorder(1) # edges go behind nodes
- arrow_collection.set_label(label)
- ax.add_collection(arrow_collection)
-
-
- # update view
- minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
- maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
- miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
- maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
-
- w = maxx-minx
- h = maxy-miny
- padx, pady = 0.05*w, 0.05*h
- corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
- ax.update_datalim( corners)
- ax.autoscale_view()
-
-# if arrow_collection:
-
- return edge_collection
-
-
-def draw_networkx_labels(G, pos,
- labels=None,
- font_size=12,
- font_color='k',
- font_family='sans-serif',
- font_weight='normal',
- alpha=1.0,
- ax=None,
- **kwds):
- """Draw node labels on the graph G.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- pos : dictionary, optional
- A dictionary with nodes as keys and positions as values.
- If not specified a spring layout positioning will be computed.
- See networkx.layout for functions that compute node positions.
-
- labels : dictionary, optional (default=None)
- Node labels in a dictionary keyed by node of text labels
-
- font_size : int
- Font size for text labels (default=12)
-
- font_color : string
- Font color string (default='k' black)
-
- font_family : string
- Font family (default='sans-serif')
-
- font_weight : string
- Font weight (default='normal')
-
- alpha : float
- The text transparency (default=1.0)
-
- ax : Matplotlib Axes object, optional
- Draw the graph in the specified Matplotlib axes.
-
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> labels=nx.draw_networkx_labels(G,pos=nx.spring_layout(G))
-
- Also see the NetworkX drawing examples at
- http://networkx.lanl.gov/gallery.html
-
-
- See Also
- --------
- draw()
- draw_networkx()
- draw_networkx_nodes()
- draw_networkx_edges()
- draw_networkx_edge_labels()
- """
- try:
- import matplotlib.pyplot as plt
- import matplotlib.cbook as cb
- except ImportError:
- raise ImportError("Matplotlib required for draw()")
- except RuntimeError:
- print("Matplotlib unable to open display")
- raise
-
- if ax is None:
- ax=plt.gca()
-
- if labels is None:
- labels=dict( (n,n) for n in G.nodes())
-
- # set optional alignment
- horizontalalignment=kwds.get('horizontalalignment','center')
- verticalalignment=kwds.get('verticalalignment','center')
-
- text_items={} # there is no text collection so we'll fake one
- for n, label in labels.items():
- (x,y)=pos[n]
- if not cb.is_string_like(label):
- label=str(label) # this will cause "1" and 1 to be labeled the same
- t=ax.text(x, y,
- label,
- size=font_size,
- color=font_color,
- family=font_family,
- weight=font_weight,
- horizontalalignment=horizontalalignment,
- verticalalignment=verticalalignment,
- transform = ax.transData,
- clip_on=True,
- )
- text_items[n]=t
-
- return text_items
-
-def draw_networkx_edge_labels(G, pos,
- edge_labels=None,
- label_pos=0.5,
- font_size=10,
- font_color='k',
- font_family='sans-serif',
- font_weight='normal',
- alpha=1.0,
- bbox=None,
- ax=None,
- rotate=True,
- **kwds):
- """Draw edge labels.
-
- Parameters
- ----------
- G : graph
- A networkx graph
-
- pos : dictionary, optional
- A dictionary with nodes as keys and positions as values.
- If not specified a spring layout positioning will be computed.
- See networkx.layout for functions that compute node positions.
-
- ax : Matplotlib Axes object, optional
- Draw the graph in the specified Matplotlib axes.
-
- alpha : float
- The text transparency (default=1.0)
-
- edge_labels : dictionary
- Edge labels in a dictionary keyed by edge two-tuple of text
- labels (default=None). Only labels for the keys in the dictionary
- are drawn.
-
- label_pos : float
- Position of edge label along edge (0=head, 0.5=center, 1=tail)
-
- font_size : int
- Font size for text labels (default=12)
-
- font_color : string
- Font color string (default='k' black)
-
- font_weight : string
- Font weight (default='normal')
-
- font_family : string
- Font family (default='sans-serif')
-
- bbox : Matplotlib bbox
- Specify text box shape and colors.
-
- clip_on : bool
- Turn on clipping at axis boundaries (default=True)
-
- Examples
- --------
- >>> G=nx.dodecahedral_graph()
- >>> edge_labels=nx.draw_networkx_edge_labels(G,pos=nx.spring_layout(G))
-
- Also see the NetworkX drawing examples at
- http://networkx.lanl.gov/gallery.html
-
- See Also
- --------
- draw()
- draw_networkx()
- draw_networkx_nodes()
- draw_networkx_edges()
- draw_networkx_labels()
- """
- try:
- import matplotlib.pyplot as plt
- import matplotlib.cbook as cb
- import numpy
- except ImportError:
- raise ImportError("Matplotlib required for draw()")
- except RuntimeError:
- print("Matplotlib unable to open display")
- raise
-
- if ax is None:
- ax=plt.gca()
- if edge_labels is None:
- labels=dict( ((u,v), d) for u,v,d in G.edges(data=True) )
- else:
- labels = edge_labels
- text_items={}
- for (n1,n2), label in labels.items():
- (x1,y1)=pos[n1]
- (x2,y2)=pos[n2]
- (x,y) = (x1 * label_pos + x2 * (1.0 - label_pos),
- y1 * label_pos + y2 * (1.0 - label_pos))
-
- if rotate:
- angle=numpy.arctan2(y2-y1,x2-x1)/(2.0*numpy.pi)*360 # degrees
- # make label orientation "right-side-up"
- if angle > 90:
- angle-=180
- if angle < - 90:
- angle+=180
- # transform data coordinate angle to screen coordinate angle
- xy=numpy.array((x,y))
- trans_angle=ax.transData.transform_angles(numpy.array((angle,)),
- xy.reshape((1,2)))[0]
- else:
- trans_angle=0.0
- # use default box of white with white border
- if bbox is None:
- bbox = dict(boxstyle='round',
- ec=(1.0, 1.0, 1.0),
- fc=(1.0, 1.0, 1.0),
- )
- if not cb.is_string_like(label):
- label=str(label) # this will cause "1" and 1 to be labeled the same
-
- # set optional alignment
- horizontalalignment=kwds.get('horizontalalignment','center')
- verticalalignment=kwds.get('verticalalignment','center')
-
- t=ax.text(x, y,
- label,
- size=font_size,
- color=font_color,
- family=font_family,
- weight=font_weight,
- horizontalalignment=horizontalalignment,
- verticalalignment=verticalalignment,
- rotation=trans_angle,
- transform = ax.transData,
- bbox = bbox,
- zorder = 1,
- clip_on=True,
- )
- text_items[(n1,n2)]=t
-
- return text_items
-
-def draw_circular(G, **kwargs):
- """Draw the graph G with a circular layout."""
- draw(G,circular_layout(G),**kwargs)
-
-def draw_random(G, **kwargs):
- """Draw the graph G with a random layout."""
- draw(G,random_layout(G),**kwargs)
-
-def draw_spectral(G, **kwargs):
- """Draw the graph G with a spectral layout."""
- draw(G,spectral_layout(G),**kwargs)
-
-def draw_spring(G, **kwargs):
- """Draw the graph G with a spring layout."""
- draw(G,spring_layout(G),**kwargs)
-
-def draw_shell(G, **kwargs):
- """Draw networkx graph with shell layout."""
- nlist = kwargs.get('nlist', None)
- if nlist != None:
- del(kwargs['nlist'])
- draw(G,shell_layout(G,nlist=nlist),**kwargs)
-
-def draw_graphviz(G, prog="neato", **kwargs):
- """Draw networkx graph with graphviz layout."""
- pos=nx.drawing.graphviz_layout(G,prog)
- draw(G,pos,**kwargs)
-
-def draw_nx(G,pos,**kwds):
- """For backward compatibility; use draw or draw_networkx."""
- draw(G,pos,**kwds)
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import matplotlib as mpl
- mpl.use('PS',warn=False)
- import matplotlib.pyplot as plt
- except:
- raise SkipTest("matplotlib not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_agraph.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_agraph.py
deleted file mode 100644
index b2f28a3..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_agraph.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Unit tests for PyGraphviz intefaace.
-"""
-import os
-import tempfile
-
-from nose import SkipTest
-from nose.tools import assert_true,assert_equal
-
-import networkx as nx
-
-class TestAGraph(object):
- @classmethod
- def setupClass(cls):
- global pygraphviz
- try:
- import pygraphviz
- except ImportError:
- raise SkipTest('PyGraphviz not available.')
-
- def build_graph(self, G):
- G.add_edge('A','B')
- G.add_edge('A','C')
- G.add_edge('A','C')
- G.add_edge('B','C')
- G.add_edge('A','D')
- G.add_node('E')
- return G
-
- def assert_equal(self, G1, G2):
- assert_true( sorted(G1.nodes())==sorted(G2.nodes()) )
- assert_true( sorted(G1.edges())==sorted(G2.edges()) )
-
-
- def agraph_checks(self, G):
- G = self.build_graph(G)
- A=nx.to_agraph(G)
- H=nx.from_agraph(A)
- self.assert_equal(G, H)
-
- fname=tempfile.mktemp()
- nx.drawing.nx_agraph.write_dot(H,fname)
- Hin=nx.drawing.nx_agraph.read_dot(fname)
- os.unlink(fname)
- self.assert_equal(H,Hin)
-
-
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- nx.drawing.nx_agraph.write_dot(H,fh)
- fh.close()
-
- fh=open(fname,'r')
- Hin=nx.drawing.nx_agraph.read_dot(fh)
- fh.close()
- os.unlink(fname)
- self.assert_equal(H,Hin)
-
- def test_from_agraph_name(self):
- G=nx.Graph(name='test')
- A=nx.to_agraph(G)
- H=nx.from_agraph(A)
- assert_equal(G.name,'test')
-
-
- def testUndirected(self):
- self.agraph_checks(nx.Graph())
-
- def testDirected(self):
- self.agraph_checks(nx.DiGraph())
-
- def testMultiUndirected(self):
- self.agraph_checks(nx.MultiGraph())
-
- def testMultiDirected(self):
- self.agraph_checks(nx.MultiDiGraph())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_layout.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_layout.py
deleted file mode 100644
index 0327782..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_layout.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Unit tests for layout functions."""
-import sys
-from nose import SkipTest
-from nose.tools import assert_equal
-import networkx as nx
-
-class TestLayout(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global numpy
- try:
- import numpy
- except ImportError:
- raise SkipTest('numpy not available.')
-
-
- def setUp(self):
- self.Gi=nx.grid_2d_graph(5,5)
- self.Gs=nx.Graph()
- self.Gs.add_path('abcdef')
- self.bigG=nx.grid_2d_graph(25,25) #bigger than 500 nodes for sparse
-
- def test_smoke_int(self):
- G=self.Gi
- vpos=nx.random_layout(G)
- vpos=nx.circular_layout(G)
- vpos=nx.spring_layout(G)
- vpos=nx.fruchterman_reingold_layout(G)
- vpos=nx.spectral_layout(G)
- vpos=nx.spectral_layout(self.bigG)
- vpos=nx.shell_layout(G)
-
- def test_smoke_string(self):
- G=self.Gs
- vpos=nx.random_layout(G)
- vpos=nx.circular_layout(G)
- vpos=nx.spring_layout(G)
- vpos=nx.fruchterman_reingold_layout(G)
- vpos=nx.spectral_layout(G)
- vpos=nx.shell_layout(G)
-
-
- def test_adjacency_interface_numpy(self):
- A=nx.to_numpy_matrix(self.Gs)
- pos=nx.drawing.layout._fruchterman_reingold(A)
- pos=nx.drawing.layout._fruchterman_reingold(A,dim=3)
- assert_equal(pos.shape,(6,3))
-
- def test_adjacency_interface_scipy(self):
- try:
- import scipy
- except ImportError:
- raise SkipTest('scipy not available.')
-
- A=nx.to_scipy_sparse_matrix(self.Gs,dtype='f')
- pos=nx.drawing.layout._sparse_fruchterman_reingold(A)
- pos=nx.drawing.layout._sparse_spectral(A)
-
- pos=nx.drawing.layout._sparse_fruchterman_reingold(A,dim=3)
- assert_equal(pos.shape,(6,3))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_pydot.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_pydot.py
deleted file mode 100644
index 9cdffee..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_pydot.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""
- Unit tests for pydot drawing functions.
-"""
-import os
-import tempfile
-
-from nose import SkipTest
-from nose.tools import assert_true
-
-import networkx as nx
-
-class TestPydot(object):
- @classmethod
- def setupClass(cls):
- global pydot
- try:
- import pydot
- import dot_parser
- except ImportError:
- raise SkipTest('pydot not available.')
-
- def build_graph(self, G):
- G.add_edge('A','B')
- G.add_edge('A','C')
- G.add_edge('B','C')
- G.add_edge('A','D')
- G.add_node('E')
- return G, nx.to_pydot(G)
-
- def assert_equal(self, G1, G2):
- assert_true( sorted(G1.nodes())==sorted(G2.nodes()) )
- assert_true( sorted(G1.edges())==sorted(G2.edges()) )
-
- def pydot_checks(self, G):
- H, P = self.build_graph(G)
- G2 = H.__class__(nx.from_pydot(P))
- self.assert_equal(H, G2)
-
- fname = tempfile.mktemp()
- assert_true( P.write_raw(fname) )
-
- Pin = pydot.graph_from_dot_file(fname)
-
- n1 = sorted([p.get_name() for p in P.get_node_list()])
- n2 = sorted([p.get_name() for p in Pin.get_node_list()])
- assert_true( n1 == n2 )
-
- e1=[(e.get_source(),e.get_destination()) for e in P.get_edge_list()]
- e2=[(e.get_source(),e.get_destination()) for e in Pin.get_edge_list()]
- assert_true( sorted(e1)==sorted(e2) )
-
- Hin = nx.drawing.nx_pydot.read_dot(fname)
- Hin = H.__class__(Hin)
- self.assert_equal(H, Hin)
-# os.unlink(fname)
-
-
- def testUndirected(self):
- self.pydot_checks(nx.Graph())
-
- def testDirected(self):
- self.pydot_checks(nx.DiGraph())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_pylab.py b/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_pylab.py
deleted file mode 100644
index 9c55590..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/drawing/tests/test_pylab.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""
- Unit tests for matplotlib drawing functions.
-"""
-
-import os
-
-from nose import SkipTest
-
-import networkx as nx
-
-class TestPylab(object):
- @classmethod
- def setupClass(cls):
- global plt
- try:
- import matplotlib as mpl
- mpl.use('PS',warn=False)
- import matplotlib.pyplot as plt
- except ImportError:
- raise SkipTest('matplotlib not available.')
- except RuntimeError:
- raise SkipTest('matplotlib not available.')
-
- def setUp(self):
- self.G=nx.barbell_graph(5,10)
-
-
- def test_draw(self):
- N=self.G
- nx.draw_spring(N)
- plt.savefig("test.ps")
- nx.draw_random(N)
- plt.savefig("test.ps")
- nx.draw_circular(N)
- plt.savefig("test.ps")
- nx.draw_spectral(N)
- plt.savefig("test.ps")
- nx.draw_spring(N.to_directed())
- plt.savefig("test.ps")
- os.unlink('test.ps')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/exception.py b/lib/python2.7/site-packages/setoolsgui/networkx/exception.py
deleted file mode 100644
index 0267038..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/exception.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-**********
-Exceptions
-**********
-
-Base exceptions and errors for NetworkX.
-
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)\nDan Schult(dschult@colgate.edu)\nLoïc Séguin-C. <loicseguin@gmail.com>"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-#
-
-# Exception handling
-
-# the root of all Exceptions
-class NetworkXException(Exception):
- """Base class for exceptions in NetworkX."""
-
-class NetworkXError(NetworkXException):
- """Exception for a serious error in NetworkX"""
-
-class NetworkXPointlessConcept(NetworkXException):
- """Harary, F. and Read, R. "Is the Null Graph a Pointless Concept?"
-In Graphs and Combinatorics Conference, George Washington University.
-New York: Springer-Verlag, 1973.
-"""
-
-class NetworkXAlgorithmError(NetworkXException):
- """Exception for unexpected termination of algorithms."""
-
-class NetworkXUnfeasible(NetworkXAlgorithmError):
- """Exception raised by algorithms trying to solve a problem
- instance that has no feasible solution."""
-
-class NetworkXNoPath(NetworkXUnfeasible):
- """Exception for algorithms that should return a path when running
- on graphs where such a path does not exist."""
-
-class NetworkXUnbounded(NetworkXAlgorithmError):
- """Exception raised by algorithms trying to solve a maximization
- or a minimization problem instance that is unbounded."""
-
-class NetworkXNotImplemented(NetworkXException):
- """Exception raised by algorithms not implemented for a type of graph."""
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/external/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/external/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/external/__init__.py
+++ /dev/null
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/__init__.py
deleted file mode 100644
index 154bfd6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""
- Hack for including decorator-3.3.1 in NetworkX.
-"""
-import sys
-if sys.version >= '3':
- from .decorator3._decorator3 import *
-else:
- from .decorator2._decorator2 import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/decorator2/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/decorator2/__init__.py
deleted file mode 100644
index 792d600..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/decorator2/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/decorator2/_decorator2.py b/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/decorator2/_decorator2.py
deleted file mode 100644
index 2e8c123..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/external/decorator/decorator2/_decorator2.py
+++ /dev/null
@@ -1,210 +0,0 @@
-########################## LICENCE ###############################
-##
-## Copyright (c) 2005-2011, Michele Simionato
-## All rights reserved.
-##
-## Redistributions of source code must retain the above copyright
-## notice, this list of conditions and the following disclaimer.
-## Redistributions in bytecode form must reproduce the above copyright
-## notice, this list of conditions and the following disclaimer in
-## the documentation and/or other materials provided with the
-## distribution.
-
-## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-## "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-## LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-## A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-## HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-## INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-## BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
-## OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
-## TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
-## USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
-## DAMAGE.
-
-"""
-Decorator module, see http://pypi.python.org/pypi/decorator
-for the documentation.
-"""
-
-__version__ = '3.3.2'
-
-__all__ = ["decorator", "FunctionMaker", "partial"]
-
-import sys, re, inspect
-
-try:
- from functools import partial
-except ImportError: # for Python version < 2.5
- class partial(object):
- "A simple replacement of functools.partial"
- def __init__(self, func, *args, **kw):
- self.func = func
- self.args = args
- self.keywords = kw
- def __call__(self, *otherargs, **otherkw):
- kw = self.keywords.copy()
- kw.update(otherkw)
- return self.func(*(self.args + otherargs), **kw)
-
-if sys.version >= '3':
- from inspect import getfullargspec
-else:
- class getfullargspec(object):
- "A quick and dirty replacement for getfullargspec for Python 2.X"
- def __init__(self, f):
- self.args, self.varargs, self.varkw, self.defaults = \
- inspect.getargspec(f)
- self.kwonlyargs = []
- self.kwonlydefaults = None
- self.annotations = getattr(f, '__annotations__', {})
- def __iter__(self):
- yield self.args
- yield self.varargs
- yield self.varkw
- yield self.defaults
-
-DEF = re.compile('\s*def\s*([_\w][_\w\d]*)\s*\(')
-
-# basic functionality
-class FunctionMaker(object):
- """
- An object with the ability to create functions with a given signature.
- It has attributes name, doc, module, signature, defaults, dict and
- methods update and make.
- """
- def __init__(self, func=None, name=None, signature=None,
- defaults=None, doc=None, module=None, funcdict=None):
- self.shortsignature = signature
- if func:
- # func can be a class or a callable, but not an instance method
- self.name = func.__name__
- if self.name == '<lambda>': # small hack for lambda functions
- self.name = '_lambda_'
- self.doc = func.__doc__
- self.module = func.__module__
- if inspect.isfunction(func):
- argspec = getfullargspec(func)
- for a in ('args', 'varargs', 'varkw', 'defaults', 'kwonlyargs',
- 'kwonlydefaults', 'annotations'):
- setattr(self, a, getattr(argspec, a))
- for i, arg in enumerate(self.args):
- setattr(self, 'arg%d' % i, arg)
- self.signature = inspect.formatargspec(
- formatvalue=lambda val: "", *argspec)[1:-1]
- allargs = list(self.args)
- if self.varargs:
- allargs.append('*' + self.varargs)
- if self.varkw:
- allargs.append('**' + self.varkw)
- try:
- self.shortsignature = ', '.join(allargs)
- except TypeError: # exotic signature, valid only in Python 2.X
- self.shortsignature = self.signature
- self.dict = func.__dict__.copy()
- # func=None happens when decorating a caller
- if name:
- self.name = name
- if signature is not None:
- self.signature = signature
- if defaults:
- self.defaults = defaults
- if doc:
- self.doc = doc
- if module:
- self.module = module
- if funcdict:
- self.dict = funcdict
- # check existence required attributes
- assert hasattr(self, 'name')
- if not hasattr(self, 'signature'):
- raise TypeError('You are decorating a non function: %s' % func)
-
- def update(self, func, **kw):
- "Update the signature of func with the data in self"
- func.__name__ = self.name
- func.__doc__ = getattr(self, 'doc', None)
- func.__dict__ = getattr(self, 'dict', {})
- func.func_defaults = getattr(self, 'defaults', ())
- func.__kwdefaults__ = getattr(self, 'kwonlydefaults', None)
- callermodule = sys._getframe(3).f_globals.get('__name__', '?')
- func.__module__ = getattr(self, 'module', callermodule)
- func.__dict__.update(kw)
-
- def make(self, src_templ, evaldict=None, addsource=False, **attrs):
- "Make a new function from a given template and update the signature"
- src = src_templ % vars(self) # expand name and signature
- evaldict = evaldict or {}
- mo = DEF.match(src)
- if mo is None:
- raise SyntaxError('not a valid function template\n%s' % src)
- name = mo.group(1) # extract the function name
- names = set([name] + [arg.strip(' *') for arg in
- self.shortsignature.split(',')])
- for n in names:
- if n in ('_func_', '_call_'):
- raise NameError('%s is overridden in\n%s' % (n, src))
- if not src.endswith('\n'): # add a newline just for safety
- src += '\n' # this is needed in old versions of Python
- try:
- code = compile(src, '<string>', 'single')
- # print >> sys.stderr, 'Compiling %s' % src
- exec code in evaldict
- except:
- print >> sys.stderr, 'Error in generated code:'
- print >> sys.stderr, src
- raise
- func = evaldict[name]
- if addsource:
- attrs['__source__'] = src
- self.update(func, **attrs)
- return func
-
- @classmethod
- def create(cls, obj, body, evaldict, defaults=None,
- doc=None, module=None, addsource=True, **attrs):
- """
- Create a function from the strings name, signature and body.
- evaldict is the evaluation dictionary. If addsource is true an attribute
- __source__ is added to the result. The attributes attrs are added,
- if any.
- """
- if isinstance(obj, str): # "name(signature)"
- name, rest = obj.strip().split('(', 1)
- signature = rest[:-1] #strip a right parens
- func = None
- else: # a function
- name = None
- signature = None
- func = obj
- self = cls(func, name, signature, defaults, doc, module)
- ibody = '\n'.join(' ' + line for line in body.splitlines())
- return self.make('def %(name)s(%(signature)s):\n' + ibody,
- evaldict, addsource, **attrs)
-
-def decorator(caller, func=None):
- """
- decorator(caller) converts a caller function into a decorator;
- decorator(caller, func) decorates a function using a caller.
- """
- if func is not None: # returns a decorated function
- evaldict = func.func_globals.copy()
- evaldict['_call_'] = caller
- evaldict['_func_'] = func
- return FunctionMaker.create(
- func, "return _call_(_func_, %(shortsignature)s)",
- evaldict, undecorated=func, __wrapped__=func)
- else: # returns a decorator
- if isinstance(caller, partial):
- return partial(decorator, caller)
- # otherwise assume caller is a function
- first = inspect.getargspec(caller)[0][0] # first arg
- evaldict = caller.func_globals.copy()
- evaldict['_call_'] = caller
- evaldict['decorator'] = decorator
- return FunctionMaker.create(
- '%s(%s)' % (caller.__name__, first),
- 'return decorator(_call_, %s)' % first,
- evaldict, undecorated=caller, __wrapped__=caller,
- doc=caller.__doc__, module=caller.__module__)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/__init__.py
deleted file mode 100644
index 92edd41..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""
-A package for generating various graphs in networkx.
-
-"""
-from networkx.generators.atlas import *
-from networkx.generators.bipartite import *
-from networkx.generators.classic import *
-from networkx.generators.degree_seq import *
-from networkx.generators.directed import *
-from networkx.generators.ego import *
-from networkx.generators.geometric import *
-from networkx.generators.hybrid import *
-from networkx.generators.line import *
-from networkx.generators.random_graphs import *
-from networkx.generators.small import *
-from networkx.generators.stochastic import *
-from networkx.generators.social import *
-from networkx.generators.threshold import *
-from networkx.generators.intersection import *
-from networkx.generators.random_clustered import *
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/atlas.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/atlas.py
deleted file mode 100644
index f3d9c57..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/atlas.py
+++ /dev/null
@@ -1,12336 +0,0 @@
-"""
-Generators for the small graph atlas.
-
-
-See
-"An Atlas of Graphs" by Ronald C. Read and Robin J. Wilson,
-Oxford University Press, 1998.
-
-Because of its size, this module is not imported by default.
-
-"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """Pieter Swart (swart@lanl.gov)"""
-
-__all__ = ['graph_atlas_g']
-
-from networkx.generators.small import make_small_graph
-
-def graph_atlas_g():
- """
- Return the list [G0,G1,...,G1252] of graphs as named in the Graph Atlas.
- G0,G1,...,G1252 are all graphs with up to 7 nodes.
-
- The graphs are listed:
- 1. in increasing order of number of nodes;
- 2. for a fixed number of nodes,
- in increasing order of the number of edges;
- 3. for fixed numbers of nodes and edges,
- in increasing order of the degree sequence,
- for example 111223 < 112222;
- 4. for fixed degree sequence, in increasing number of automorphisms.
-
- Note that indexing is set up so that for
- GAG=graph_atlas_g(), then
- G123=GAG[123] and G[0]=empty_graph(0)
-
- """
-
- descr_list=[
- ['edgelist', 'G0', 0, []],
- ['edgelist', 'G1', 1, []],
- ['edgelist', 'G2', 2, []],
- ['edgelist', 'G3', 2, [[1, 2]]],
- ['edgelist', 'G4', 3, []],
- ['edgelist', 'G5', 3, [[2, 3]]],
- ['edgelist', 'G6', 3, [[1, 2], [1, 3]]],
- ['edgelist', 'G7', 3, [[1, 2], [1, 3], [2, 3]]],
- ['edgelist', 'G8', 4, []],
- ['edgelist', 'G9', 4, [[4, 3]]],
- ['edgelist', 'G10', 4, [[4, 3], [4, 2]]],
- ['edgelist', 'G11', 4, [[1, 2], [4, 3]]],
- ['edgelist', 'G12', 4, [[4, 3], [2, 3], [4, 2]]],
- ['edgelist', 'G13', 4, [[4, 1], [4, 2], [4, 3]]],
- ['edgelist', 'G14', 4, [[1, 2], [2, 3], [1, 4]]],
- ['edgelist', 'G15', 4, [[4, 3], [2, 3], [4, 2], [4, 1]]],
- ['edgelist', 'G16', 4, [[1, 2], [2, 3], [3, 4], [1, 4]]],
- ['edgelist', 'G17', 4, [[1, 2], [1, 3], [1, 4], [2, 3], [3, 4]]],
- ['edgelist', 'G18', 4, [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3]]],
- ['edgelist', 'G19', 5, []],
- ['edgelist', 'G20', 5, [[5, 4]]],
- ['edgelist', 'G21', 5, [[2, 3], [1, 2]]],
- ['edgelist', 'G22', 5, [[1, 3], [5, 4]]],
- ['edgelist', 'G23', 5, [[2, 3], [1, 2], [3, 1]]],
- ['edgelist', 'G24', 5, [[5, 4], [4, 3], [4, 2]]],
- ['edgelist', 'G25', 5, [[4, 3], [5, 4], [1, 5]]],
- ['edgelist', 'G26', 5, [[2, 3], [1, 2], [5, 4]]],
- ['edgelist', 'G27', 5, [[5, 4], [2, 3], [4, 2], [4, 3]]],
- ['edgelist', 'G28', 5, [[1, 4], [2, 1], [3, 2], [4, 3]]],
- ['edgelist', 'G29', 5, [[5, 4], [5, 1], [5, 2], [5, 3]]],
- ['edgelist', 'G30', 5, [[5, 1], [4, 2], [5, 4], [4, 3]]],
- ['edgelist', 'G31', 5, [[3, 4], [2, 3], [1, 2], [5, 1]]],
- ['edgelist', 'G32', 5, [[2, 3], [1, 2], [3, 1], [5, 4]]],
- ['edgelist', 'G33', 5, [[1, 4], [3, 1], [4, 3], [2, 1], [3, 2]]],
- ['edgelist', 'G34', 5, [[5, 3], [5, 4], [3, 4], [5, 2], [5, 1]]],
- ['edgelist', 'G35', 5, [[1, 2], [2, 3], [3, 4], [1, 5], [1, 3]]],
- ['edgelist', 'G36', 5, [[5, 1], [2, 3], [5, 4], [4, 3], [4, 2]]],
- ['edgelist', 'G37', 5, [[2, 1], [5, 2], [3, 5], [4, 3], [2, 4]]],
- ['edgelist', 'G38', 5, [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5]]],
- ['edgelist', 'G39', 5, [[2, 1], [5, 2], [5, 1], [1, 4], [2, 4], [4, 5]]],
- ['edgelist', 'G40', 5, [[2, 1], [5, 2], [3, 5], [4, 3], [2, 4], [3, 2]]],
- ['edgelist', 'G41', 5, [[2, 1], [5, 2], [3, 5], [4, 3], [2, 4], [4, 5]]],
- ['edgelist', 'G42', 5, [[1, 2], [5, 4], [3, 4], [5, 3], [5, 1], [5, 2]]],
- ['edgelist', 'G43', 5, [[1, 5], [4, 1], [5, 4], [3, 4], [2, 3], [1, 2]]],
- ['edgelist', 'G44', 5, [[3, 2], [1, 3], [4, 1], [2, 4], [5, 2], [1, 5]]],
- ['edgelist',
- 'G45',
- 5,
- [[5, 1], [2, 3], [5, 4], [4, 3], [4, 2], [5, 2], [3, 5]]],
- ['edgelist',
- 'G46',
- 5,
- [[5, 2], [3, 5], [4, 3], [2, 4], [4, 5], [1, 4], [5, 1]]],
- ['edgelist',
- 'G47',
- 5,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2]]],
- ['edgelist',
- 'G48',
- 5,
- [[3, 2], [1, 3], [4, 1], [2, 4], [5, 2], [1, 5], [3, 5]]],
- ['edgelist',
- 'G49',
- 5,
- [[2, 1], [5, 2], [3, 5], [4, 3], [2, 4], [5, 1], [4, 5], [1, 4]]],
- ['edgelist',
- 'G50',
- 5,
- [[1, 2], [2, 3], [3, 4], [1, 4], [5, 1], [5, 2], [5, 3], [5, 4]]],
- ['edgelist',
- 'G51',
- 5,
- [[1, 2], [4, 5], [1, 4], [1, 5], [2, 3], [2, 4], [2, 5], [3, 4], [3, 5]]],
- ['edgelist',
- 'G52',
- 5,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [2, 3],
- [2, 4],
- [2, 5],
- [3, 4],
- [3, 5],
- [4, 5]]],
- ['edgelist', 'G53', 6, []],
- ['edgelist', 'G54', 6, [[6, 5]]],
- ['edgelist', 'G55', 6, [[1, 4], [6, 5]]],
- ['edgelist', 'G56', 6, [[2, 4], [2, 3]]],
- ['edgelist', 'G57', 6, [[2, 4], [3, 2], [4, 3]]],
- ['edgelist', 'G58', 6, [[1, 4], [6, 1], [5, 1]]],
- ['edgelist', 'G59', 6, [[5, 4], [6, 5], [1, 6]]],
- ['edgelist', 'G60', 6, [[5, 4], [6, 2], [6, 3]]],
- ['edgelist', 'G61', 6, [[2, 3], [4, 1], [6, 5]]],
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- ['edgelist', 'G63', 6, [[4, 1], [6, 4], [5, 6], [1, 5]]],
- ['edgelist', 'G64', 6, [[6, 2], [6, 4], [6, 3], [1, 6]]],
- ['edgelist', 'G65', 6, [[5, 4], [4, 2], [5, 1], [4, 3]]],
- ['edgelist', 'G66', 6, [[1, 3], [2, 4], [3, 2], [6, 4]]],
- ['edgelist', 'G67', 6, [[2, 4], [3, 2], [4, 3], [1, 6]]],
- ['edgelist', 'G68', 6, [[2, 3], [1, 4], [6, 1], [5, 1]]],
- ['edgelist', 'G69', 6, [[5, 6], [2, 3], [1, 6], [4, 5]]],
- ['edgelist', 'G70', 6, [[1, 3], [5, 1], [4, 2], [6, 4]]],
- ['edgelist', 'G71', 6, [[4, 1], [6, 4], [5, 6], [1, 5], [6, 1]]],
- ['edgelist', 'G72', 6, [[6, 4], [4, 2], [4, 3], [5, 4], [5, 6]]],
- ['edgelist', 'G73', 6, [[6, 4], [6, 5], [3, 4], [4, 5], [1, 5]]],
- ['edgelist', 'G74', 6, [[5, 4], [2, 3], [5, 1], [4, 3], [4, 2]]],
- ['edgelist', 'G75', 6, [[2, 5], [4, 5], [5, 1], [3, 2], [4, 3]]],
- ['edgelist', 'G76', 6, [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5]]],
- ['edgelist', 'G77', 6, [[6, 4], [6, 5], [6, 1], [6, 2], [6, 3]]],
- ['edgelist', 'G78', 6, [[2, 5], [6, 2], [2, 1], [3, 2], [3, 4]]],
- ['edgelist', 'G79', 6, [[1, 2], [4, 5], [1, 3], [4, 1], [6, 4]]],
- ['edgelist', 'G80', 6, [[2, 1], [3, 2], [3, 5], [2, 4], [6, 4]]],
- ['edgelist', 'G81', 6, [[5, 4], [1, 6], [5, 1], [4, 3], [4, 2]]],
- ['edgelist', 'G82', 6, [[2, 3], [1, 2], [5, 6], [2, 4], [3, 4]]],
- ['edgelist', 'G83', 6, [[1, 2], [1, 6], [3, 4], [4, 5], [5, 6]]],
- ['edgelist', 'G84', 6, [[5, 4], [6, 2], [6, 3], [1, 4], [5, 1]]],
- ['edgelist', 'G85', 6, [[2, 3], [4, 1], [6, 4], [5, 6], [1, 5]]],
- ['edgelist', 'G86', 6, [[1, 4], [6, 1], [5, 6], [4, 5], [6, 4], [5, 1]]],
- ['edgelist', 'G87', 6, [[2, 5], [3, 5], [5, 1], [3, 4], [4, 2], [4, 5]]],
- ['edgelist', 'G88', 6, [[2, 5], [3, 5], [5, 1], [3, 2], [4, 2], [3, 4]]],
- ['edgelist', 'G89', 6, [[3, 1], [6, 5], [5, 4], [6, 4], [5, 1], [3, 5]]],
- ['edgelist', 'G90', 6, [[4, 3], [5, 4], [1, 5], [2, 1], [3, 2], [1, 4]]],
- ['edgelist', 'G91', 6, [[5, 2], [4, 2], [5, 3], [4, 3], [3, 1], [2, 1]]],
- ['edgelist', 'G92', 6, [[6, 3], [6, 4], [6, 5], [4, 5], [6, 2], [6, 1]]],
- ['edgelist', 'G93', 6, [[5, 4], [5, 3], [5, 1], [2, 5], [4, 1], [6, 4]]],
- ['edgelist', 'G94', 6, [[5, 4], [4, 6], [6, 5], [6, 2], [4, 3], [5, 1]]],
- ['edgelist', 'G95', 6, [[5, 3], [2, 3], [5, 4], [5, 2], [5, 1], [1, 6]]],
- ['edgelist', 'G96', 6, [[2, 3], [4, 2], [1, 4], [3, 1], [5, 1], [6, 1]]],
- ['edgelist', 'G97', 6, [[3, 1], [5, 3], [2, 5], [3, 2], [4, 2], [6, 4]]],
- ['edgelist', 'G98', 6, [[2, 3], [4, 2], [1, 4], [3, 1], [5, 1], [6, 4]]],
- ['edgelist', 'G99', 6, [[6, 4], [3, 6], [3, 1], [5, 3], [5, 4], [4, 2]]],
- ['edgelist', 'G100', 6, [[1, 3], [4, 5], [2, 1], [6, 4], [5, 6], [4, 1]]],
- ['edgelist', 'G101', 6, [[2, 3], [4, 1], [6, 4], [5, 6], [1, 5], [6, 1]]],
- ['edgelist', 'G102', 6, [[5, 4], [2, 3], [5, 1], [4, 3], [4, 2], [6, 1]]],
- ['edgelist', 'G103', 6, [[2, 5], [3, 5], [5, 1], [1, 6], [4, 2], [3, 4]]],
- ['edgelist', 'G104', 6, [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 6]]],
- ['edgelist', 'G105', 6, [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6]]],
- ['edgelist', 'G106', 6, [[2, 4], [3, 2], [4, 3], [1, 5], [6, 1], [5, 6]]],
- ['edgelist',
- 'G107',
- 6,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [1, 6]]],
- ['edgelist',
- 'G108',
- 6,
- [[2, 5], [3, 5], [3, 2], [4, 2], [3, 4], [3, 1], [1, 2]]],
- ['edgelist',
- 'G109',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2]]],
- ['edgelist',
- 'G110',
- 6,
- [[1, 2], [4, 3], [1, 3], [4, 1], [4, 2], [6, 2], [6, 3]]],
- ['edgelist',
- 'G111',
- 6,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [4, 5]]],
- ['edgelist',
- 'G112',
- 6,
- [[2, 1], [5, 2], [3, 5], [4, 3], [6, 2], [3, 6], [2, 3]]],
- ['edgelist',
- 'G113',
- 6,
- [[1, 5], [3, 1], [2, 3], [4, 2], [6, 4], [4, 1], [3, 4]]],
- ['edgelist',
- 'G114',
- 6,
- [[2, 5], [3, 5], [3, 4], [3, 2], [4, 2], [5, 6], [1, 5]]],
- ['edgelist',
- 'G115',
- 6,
- [[2, 1], [5, 2], [3, 5], [4, 3], [6, 2], [3, 6], [5, 6]]],
- ['edgelist',
- 'G116',
- 6,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 5]]],
- ['edgelist',
- 'G117',
- 6,
- [[1, 6], [5, 1], [6, 5], [1, 3], [4, 1], [4, 3], [1, 2]]],
- ['edgelist',
- 'G118',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 6], [5, 2]]],
- ['edgelist',
- 'G119',
- 6,
- [[1, 2], [5, 1], [2, 5], [1, 3], [4, 1], [4, 3], [4, 6]]],
- ['edgelist',
- 'G120',
- 6,
- [[2, 5], [3, 5], [5, 1], [1, 6], [4, 2], [3, 4], [4, 5]]],
- ['edgelist',
- 'G121',
- 6,
- [[3, 1], [4, 3], [5, 4], [6, 5], [3, 6], [2, 3], [5, 2]]],
- ['edgelist',
- 'G122',
- 6,
- [[2, 6], [1, 2], [5, 1], [4, 5], [3, 4], [2, 3], [1, 4]]],
- ['edgelist',
- 'G123',
- 6,
- [[2, 5], [3, 5], [5, 1], [1, 6], [4, 2], [3, 4], [3, 2]]],
- ['edgelist',
- 'G124',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [1, 3], [6, 2]]],
- ['edgelist',
- 'G125',
- 6,
- [[3, 1], [5, 2], [2, 3], [6, 5], [3, 6], [4, 2], [6, 4]]],
- ['edgelist',
- 'G126',
- 6,
- [[6, 1], [4, 6], [3, 4], [1, 3], [2, 4], [5, 2], [4, 5]]],
- ['edgelist',
- 'G127',
- 6,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [3, 4]]],
- ['edgelist',
- 'G128',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [1, 4]]],
- ['edgelist',
- 'G129',
- 6,
- [[5, 4], [1, 5], [2, 1], [3, 2], [4, 3], [1, 6], [6, 4]]],
- ['edgelist',
- 'G130',
- 6,
- [[2, 3], [1, 2], [3, 1], [4, 1], [5, 4], [6, 5], [4, 6]]],
- ['edgelist',
- 'G131',
- 6,
- [[1, 2], [3, 6], [1, 3], [4, 1], [4, 2], [4, 3], [3, 2], [6, 2]]],
- ['edgelist',
- 'G132',
- 6,
- [[1, 2], [2, 3], [3, 4], [1, 4], [5, 1], [5, 2], [5, 3], [5, 4]]],
- ['edgelist',
- 'G133',
- 6,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 1], [1, 5]]],
- ['edgelist',
- 'G134',
- 6,
- [[2, 3], [4, 2], [1, 4], [2, 1], [3, 1], [4, 3], [6, 4], [5, 1]]],
- ['edgelist',
- 'G135',
- 6,
- [[1, 2], [3, 5], [1, 3], [6, 3], [4, 2], [4, 3], [3, 2], [5, 2]]],
- ['edgelist',
- 'G136',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [2, 6]]],
- ['edgelist',
- 'G137',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 5]]],
- ['edgelist',
- 'G138',
- 6,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [3, 2], [6, 2]]],
- ['edgelist',
- 'G139',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 1]]],
- ['edgelist',
- 'G140',
- 6,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [4, 1], [6, 2]]],
- ['edgelist',
- 'G141',
- 6,
- [[3, 1], [4, 3], [5, 4], [6, 5], [3, 6], [2, 3], [5, 2], [6, 4]]],
- ['edgelist',
- 'G142',
- 6,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [1, 6], [6, 5]]],
- ['edgelist',
- 'G143',
- 6,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [6, 2], [6, 4]]],
- ['edgelist',
- 'G144',
- 6,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [4, 5]]],
- ['edgelist',
- 'G145',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 1], [6, 3], [1, 3]]],
- ['edgelist',
- 'G146',
- 6,
- [[2, 6], [5, 2], [1, 5], [6, 1], [3, 6], [5, 3], [4, 5], [6, 4]]],
- ['edgelist',
- 'G147',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3]]],
- ['edgelist',
- 'G148',
- 6,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [2, 5], [1, 2]]],
- ['edgelist',
- 'G149',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1]]],
- ['edgelist',
- 'G150',
- 6,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [3, 2]]],
- ['edgelist',
- 'G151',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [5, 6], [6, 4], [2, 6]]],
- ['edgelist',
- 'G152',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 2]]],
- ['edgelist',
- 'G153',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 6], [6, 3], [6, 1]]],
- ['edgelist',
- 'G154',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [5, 2], [6, 3]]],
- ['edgelist',
- 'G155',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [3, 5], [1, 4]]],
- ['edgelist',
- 'G156',
- 6,
- [[1, 2], [3, 6], [1, 3], [4, 1], [4, 2], [4, 3], [3, 2], [6, 2], [5, 3]]],
- ['edgelist',
- 'G157',
- 6,
- [[1, 2], [3, 6], [1, 3], [4, 1], [4, 2], [4, 3], [3, 2], [6, 2], [1, 5]]],
- ['edgelist',
- 'G158',
- 6,
- [[1, 2], [2, 3], [3, 4], [1, 4], [5, 1], [5, 2], [5, 3], [5, 4], [5, 6]]],
- ['edgelist',
- 'G159',
- 6,
- [[3, 1], [5, 2], [2, 3], [6, 5], [3, 6], [4, 2], [6, 4], [4, 3], [5, 4]]],
- ['edgelist',
- 'G160',
- 6,
- [[1, 2], [3, 6], [1, 3], [4, 1], [4, 2], [4, 3], [3, 2], [6, 2], [5, 6]]],
- ['edgelist',
- 'G161',
- 6,
- [[2, 6], [5, 2], [1, 5], [6, 1], [3, 6], [5, 3], [4, 5], [6, 4], [5, 6]]],
- ['edgelist',
- 'G162',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [3, 6], [1, 6], [3, 1], [4, 1]]],
- ['edgelist',
- 'G163',
- 6,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [1, 5], [2, 1], [5, 2]]],
- ['edgelist',
- 'G164',
- 6,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [5, 2], [2, 1], [6, 2]]],
- ['edgelist',
- 'G165',
- 6,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 5], [5, 1], [6, 1]]],
- ['edgelist',
- 'G166',
- 6,
- [[5, 4], [1, 5], [2, 1], [3, 2], [4, 3], [1, 6], [6, 4], [1, 4], [2, 6]]],
- ['edgelist',
- 'G167',
- 6,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [4, 3], [1, 4], [5, 1]]],
- ['edgelist',
- 'G168',
- 6,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [4, 3], [1, 4], [3, 5]]],
- ['edgelist',
- 'G169',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [3, 6], [1, 6], [3, 1], [6, 2]]],
- ['edgelist',
- 'G170',
- 6,
- [[2, 6], [5, 2], [1, 5], [6, 1], [3, 6], [5, 3], [4, 5], [6, 4], [3, 1]]],
- ['edgelist',
- 'G171',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 1], [6, 5], [6, 3], [6, 4]]],
- ['edgelist',
- 'G172',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [6, 2]]],
- ['edgelist',
- 'G173',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [1, 4], [5, 3], [6, 3]]],
- ['edgelist',
- 'G174',
- 6,
- [[3, 4], [1, 3], [4, 1], [5, 4], [2, 5], [6, 2], [5, 6], [2, 1], [6, 3]]],
- ['edgelist',
- 'G175',
- 6,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [1, 4], [6, 3], [5, 2]]],
- ['edgelist',
- 'G176',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [1, 3]]],
- ['edgelist',
- 'G177',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [5, 6]]],
- ['edgelist',
- 'G178',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [1, 6]]],
- ['edgelist',
- 'G179',
- 6,
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- [5, 2],
- [1, 5],
- [6, 1],
- [3, 6],
- [5, 3],
- [4, 5],
- [6, 4],
- [5, 6],
- [2, 1]]],
- ['edgelist',
- 'G180',
- 6,
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- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [2, 4],
- [5, 2],
- [6, 5],
- [4, 6],
- [2, 6]]],
- ['edgelist',
- 'G181',
- 6,
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- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [4, 5],
- [4, 3],
- [1, 4],
- [5, 1],
- [3, 5]]],
- ['edgelist',
- 'G182',
- 6,
- [[2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [4, 5],
- [4, 3],
- [1, 4],
- [3, 5],
- [6, 3]]],
- ['edgelist',
- 'G183',
- 6,
- [[2, 1],
- [5, 2],
- [1, 5],
- [6, 1],
- [5, 6],
- [4, 5],
- [2, 4],
- [6, 2],
- [3, 4],
- [2, 3]]],
- ['edgelist',
- 'G184',
- 6,
- [[5, 4],
- [1, 5],
- [2, 1],
- [3, 2],
- [4, 3],
- [1, 6],
- [6, 4],
- [1, 4],
- [2, 6],
- [6, 3]]],
- ['edgelist',
- 'G185',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [3, 6],
- [1, 6],
- [3, 1],
- [6, 2],
- [5, 2]]],
- ['edgelist',
- 'G186',
- 6,
- [[1, 2],
- [3, 5],
- [1, 3],
- [5, 6],
- [4, 2],
- [4, 3],
- [5, 2],
- [6, 2],
- [6, 3],
- [6, 4]]],
- ['edgelist',
- 'G187',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [6, 1],
- [6, 2],
- [6, 3],
- [6, 4],
- [6, 5]]],
- ['edgelist',
- 'G188',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [5, 6],
- [1, 6],
- [3, 5],
- [1, 3],
- [2, 4],
- [6, 2]]],
- ['edgelist',
- 'G189',
- 6,
- [[4, 5],
- [2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [3, 5],
- [6, 2],
- [4, 3],
- [1, 4]]],
- ['edgelist',
- 'G190',
- 6,
- [[2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [4, 5],
- [5, 2],
- [6, 4],
- [3, 6],
- [2, 1]]],
- ['edgelist',
- 'G191',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [1, 3],
- [2, 6]]],
- ['edgelist',
- 'G192',
- 6,
- [[1, 2],
- [3, 5],
- [1, 3],
- [3, 2],
- [4, 2],
- [4, 3],
- [5, 2],
- [6, 2],
- [6, 3],
- [6, 4],
- [1, 4]]],
- ['edgelist',
- 'G193',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [6, 1],
- [5, 6]]],
- ['edgelist',
- 'G194',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [5, 6],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [6, 4],
- [1, 3]]],
- ['edgelist',
- 'G195',
- 6,
- [[2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [4, 5],
- [5, 2],
- [6, 4],
- [3, 6],
- [2, 1],
- [6, 2]]],
- ['edgelist',
- 'G196',
- 6,
- [[2, 4],
- [5, 2],
- [4, 5],
- [3, 4],
- [1, 3],
- [5, 1],
- [6, 5],
- [3, 6],
- [5, 3],
- [1, 6],
- [2, 6]]],
- ['edgelist',
- 'G197',
- 6,
- [[4, 5],
- [2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [3, 5],
- [6, 2],
- [1, 4],
- [2, 5],
- [1, 2]]],
- ['edgelist',
- 'G198',
- 6,
- [[2, 6],
- [5, 2],
- [1, 5],
- [6, 1],
- [3, 6],
- [5, 3],
- [4, 5],
- [6, 4],
- [1, 2],
- [3, 1],
- [4, 3]]],
- ['edgelist',
- 'G199',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [5, 6],
- [1, 6],
- [3, 5],
- [6, 3],
- [2, 6],
- [2, 5],
- [1, 4]]],
- ['edgelist',
- 'G200',
- 6,
- [[1, 2],
- [2, 3],
- [1, 3],
- [3, 4],
- [5, 6],
- [1, 5],
- [2, 4],
- [5, 2],
- [3, 5],
- [1, 4],
- [6, 4],
- [5, 4]]],
- ['edgelist',
- 'G201',
- 6,
- [[4, 3],
- [2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [3, 6],
- [3, 5],
- [6, 2],
- [1, 4],
- [2, 5],
- [1, 2],
- [1, 5]]],
- ['edgelist',
- 'G202',
- 6,
- [[2, 6],
- [5, 2],
- [1, 5],
- [6, 1],
- [3, 6],
- [5, 3],
- [4, 5],
- [6, 4],
- [1, 2],
- [3, 1],
- [4, 3],
- [5, 6]]],
- ['edgelist',
- 'G203',
- 6,
- [[4, 5],
- [2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [3, 5],
- [6, 2],
- [1, 4],
- [2, 5],
- [1, 2],
- [3, 4]]],
- ['edgelist',
- 'G204',
- 6,
- [[1, 2],
- [2, 3],
- [1, 3],
- [4, 3],
- [4, 2],
- [5, 1],
- [3, 5],
- [6, 2],
- [1, 6],
- [5, 6],
- [4, 5],
- [6, 4]]],
- ['edgelist',
- 'G205',
- 6,
- [[4, 5],
- [2, 4],
- [3, 2],
- [1, 3],
- [6, 1],
- [5, 6],
- [3, 5],
- [6, 2],
- [1, 4],
- [2, 5],
- [1, 2],
- [3, 4],
- [1, 5]]],
- ['edgelist',
- 'G206',
- 6,
- [[1, 2],
- [2, 3],
- [1, 3],
- [4, 3],
- [4, 2],
- [5, 1],
- [3, 5],
- [6, 2],
- [1, 6],
- [5, 6],
- [4, 5],
- [6, 4],
- [4, 1]]],
- ['edgelist',
- 'G207',
- 6,
- [[1, 2],
- [2, 3],
- [3, 4],
- [4, 5],
- [5, 6],
- [1, 6],
- [3, 5],
- [6, 3],
- [2, 6],
- [2, 5],
- [2, 4],
- [3, 1],
- [5, 1],
- [6, 4]]],
- ['edgelist',
- 'G208',
- 6,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [3, 4],
- [3, 5],
- [3, 6],
- [4, 5],
- [4, 6],
- [5, 6]]],
- ['edgelist', 'G209', 7, []],
- ['edgelist', 'G210', 7, [[7, 6]]],
- ['edgelist', 'G211', 7, [[3, 4], [2, 3]]],
- ['edgelist', 'G212', 7, [[6, 5], [7, 1]]],
- ['edgelist', 'G213', 7, [[1, 5], [5, 3], [3, 1]]],
- ['edgelist', 'G214', 7, [[1, 2], [1, 7], [1, 6]]],
- ['edgelist', 'G215', 7, [[6, 5], [7, 1], [6, 7]]],
- ['edgelist', 'G216', 7, [[4, 3], [2, 3], [6, 7]]],
- ['edgelist', 'G217', 7, [[4, 2], [6, 7], [1, 5]]],
- ['edgelist', 'G218', 7, [[3, 6], [7, 3], [6, 7], [2, 3]]],
- ['edgelist', 'G219', 7, [[2, 3], [5, 2], [6, 5], [3, 6]]],
- ['edgelist', 'G220', 7, [[2, 1], [6, 2], [2, 3], [5, 2]]],
- ['edgelist', 'G221', 7, [[2, 1], [3, 2], [6, 3], [7, 3]]],
- ['edgelist', 'G222', 7, [[4, 5], [3, 4], [2, 3], [1, 2]]],
- ['edgelist', 'G223', 7, [[5, 3], [1, 5], [3, 1], [6, 7]]],
- ['edgelist', 'G224', 7, [[1, 2], [7, 1], [1, 6], [5, 3]]],
- ['edgelist', 'G225', 7, [[4, 2], [6, 5], [7, 6], [1, 7]]],
- ['edgelist', 'G226', 7, [[1, 5], [4, 1], [3, 6], [7, 3]]],
- ['edgelist', 'G227', 7, [[3, 4], [2, 3], [7, 1], [6, 5]]],
- ['edgelist', 'G228', 7, [[1, 5], [4, 1], [2, 4], [5, 2], [2, 1]]],
- ['edgelist', 'G229', 7, [[3, 6], [7, 3], [6, 7], [5, 3], [4, 3]]],
- ['edgelist', 'G230', 7, [[5, 3], [5, 1], [3, 1], [6, 5], [7, 1]]],
- ['edgelist', 'G231', 7, [[3, 6], [7, 3], [6, 7], [2, 3], [1, 2]]],
- ['edgelist', 'G232', 7, [[5, 2], [1, 5], [4, 1], [2, 4], [3, 2]]],
- ['edgelist', 'G233', 7, [[2, 3], [1, 2], [5, 1], [4, 5], [3, 4]]],
- ['edgelist', 'G234', 7, [[6, 2], [6, 1], [3, 6], [4, 6], [5, 6]]],
- ['edgelist', 'G235', 7, [[2, 6], [7, 2], [2, 1], [3, 2], [4, 3]]],
- ['edgelist', 'G236', 7, [[2, 6], [5, 2], [3, 4], [7, 3], [3, 2]]],
- ['edgelist', 'G237', 7, [[2, 6], [7, 2], [2, 3], [3, 4], [5, 4]]],
- ['edgelist', 'G238', 7, [[3, 2], [4, 3], [5, 4], [6, 5], [4, 7]]],
- ['edgelist', 'G239', 7, [[7, 6], [3, 7], [2, 3], [6, 3], [4, 5]]],
- ['edgelist', 'G240', 7, [[5, 4], [6, 5], [7, 6], [1, 7], [2, 1]]],
- ['edgelist', 'G241', 7, [[1, 5], [4, 1], [3, 6], [7, 3], [6, 7]]],
- ['edgelist', 'G242', 7, [[5, 2], [6, 3], [7, 6], [4, 7], [3, 4]]],
- ['edgelist', 'G243', 7, [[2, 5], [4, 2], [2, 1], [3, 2], [7, 6]]],
- ['edgelist', 'G244', 7, [[1, 5], [4, 1], [2, 1], [3, 2], [7, 6]]],
- ['edgelist', 'G245', 7, [[1, 5], [4, 1], [3, 2], [6, 3], [7, 3]]],
- ['edgelist', 'G246', 7, [[7, 6], [4, 5], [3, 4], [2, 3], [1, 2]]],
- ['edgelist', 'G247', 7, [[3, 4], [2, 3], [7, 1], [6, 7], [6, 5]]],
- ['edgelist', 'G248', 7, [[1, 2], [5, 7], [6, 5], [4, 3], [7, 6]]],
- ['edgelist', 'G249', 7, [[2, 6], [7, 2], [6, 7], [3, 6], [2, 3], [7, 3]]],
- ['edgelist', 'G250', 7, [[2, 5], [4, 2], [3, 4], [5, 3], [2, 1], [3, 2]]],
- ['edgelist', 'G251', 7, [[1, 5], [4, 1], [2, 4], [3, 2], [2, 5], [4, 5]]],
- ['edgelist', 'G252', 7, [[6, 3], [5, 6], [3, 5], [4, 3], [7, 4], [3, 7]]],
- ['edgelist', 'G253', 7, [[2, 3], [5, 2], [6, 5], [3, 6], [1, 2], [5, 1]]],
- ['edgelist', 'G254', 7, [[2, 3], [6, 2], [5, 6], [3, 5], [1, 3], [6, 1]]],
- ['edgelist', 'G255', 7, [[3, 6], [7, 3], [6, 7], [3, 5], [2, 3], [4, 3]]],
- ['edgelist', 'G256', 7, [[2, 5], [4, 2], [3, 4], [2, 3], [3, 6], [7, 3]]],
- ['edgelist', 'G257', 7, [[6, 5], [7, 6], [2, 7], [6, 2], [4, 7], [1, 2]]],
- ['edgelist', 'G258', 7, [[7, 6], [2, 7], [6, 2], [4, 2], [1, 4], [2, 5]]],
- ['edgelist', 'G259', 7, [[1, 5], [4, 1], [3, 4], [5, 3], [3, 6], [7, 3]]],
- ['edgelist', 'G260', 7, [[2, 5], [4, 2], [3, 4], [2, 3], [3, 6], [7, 6]]],
- ['edgelist', 'G261', 7, [[3, 4], [2, 3], [4, 7], [6, 5], [7, 6], [6, 3]]],
- ['edgelist', 'G262', 7, [[3, 6], [7, 3], [6, 7], [2, 5], [4, 2], [3, 2]]],
- ['edgelist', 'G263', 7, [[5, 6], [1, 5], [4, 1], [3, 4], [5, 3], [7, 4]]],
- ['edgelist', 'G264', 7, [[1, 5], [4, 1], [2, 4], [7, 6], [2, 5], [2, 1]]],
- ['edgelist', 'G265', 7, [[2, 5], [4, 2], [3, 4], [6, 3], [7, 6], [3, 7]]],
- ['edgelist', 'G266', 7, [[7, 4], [6, 7], [5, 6], [2, 5], [3, 2], [6, 3]]],
- ['edgelist', 'G267', 7, [[2, 1], [4, 2], [7, 4], [6, 7], [5, 6], [2, 5]]],
- ['edgelist', 'G268', 7, [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6]]],
- ['edgelist', 'G269', 7, [[1, 5], [4, 1], [5, 4], [3, 6], [7, 3], [6, 7]]],
- ['edgelist', 'G270', 7, [[7, 4], [1, 7], [7, 3], [6, 7], [7, 2], [5, 7]]],
- ['edgelist', 'G271', 7, [[3, 5], [6, 3], [3, 4], [7, 3], [2, 3], [2, 1]]],
- ['edgelist', 'G272', 7, [[2, 1], [3, 2], [6, 3], [2, 5], [4, 2], [7, 3]]],
- ['edgelist', 'G273', 7, [[2, 1], [3, 2], [4, 7], [2, 4], [5, 2], [6, 5]]],
- ['edgelist', 'G274', 7, [[2, 1], [3, 2], [6, 3], [7, 6], [2, 5], [4, 2]]],
- ['edgelist', 'G275', 7, [[2, 1], [3, 5], [6, 3], [7, 6], [3, 7], [4, 3]]],
- ['edgelist', 'G276', 7, [[5, 1], [2, 5], [4, 2], [3, 2], [6, 3], [7, 3]]],
- ['edgelist', 'G277', 7, [[7, 6], [2, 3], [1, 2], [3, 1], [4, 3], [1, 5]]],
- ['edgelist', 'G278', 7, [[1, 5], [4, 1], [2, 1], [3, 2], [6, 3], [7, 3]]],
- ['edgelist', 'G279', 7, [[2, 1], [4, 2], [7, 4], [3, 7], [5, 2], [6, 5]]],
- ['edgelist', 'G280', 7, [[3, 6], [7, 3], [5, 3], [2, 5], [4, 2], [1, 4]]],
- ['edgelist', 'G281', 7, [[1, 5], [4, 1], [3, 4], [5, 3], [2, 3], [7, 6]]],
- ['edgelist', 'G282', 7, [[1, 5], [4, 1], [3, 2], [6, 3], [7, 6], [3, 7]]],
- ['edgelist', 'G283', 7, [[4, 5], [2, 1], [3, 2], [6, 3], [7, 6], [3, 7]]],
- ['edgelist', 'G284', 7, [[5, 6], [1, 5], [4, 1], [7, 4], [2, 1], [3, 2]]],
- ['edgelist', 'G285', 7, [[3, 6], [7, 3], [6, 7], [2, 5], [4, 2], [2, 1]]],
- ['edgelist', 'G286', 7, [[5, 6], [4, 5], [3, 4], [2, 3], [1, 2], [7, 1]]],
- ['edgelist', 'G287', 7, [[7, 5], [6, 7], [5, 6], [3, 4], [2, 3], [1, 2]]],
- ['edgelist', 'G288', 7, [[1, 2], [5, 1], [3, 4], [6, 3], [7, 6], [4, 7]]],
- ['edgelist', 'G289', 7, [[2, 3], [1, 2], [5, 1], [4, 5], [3, 4], [7, 6]]],
- ['edgelist',
- 'G290',
- 7,
- [[2, 5], [4, 2], [3, 4], [5, 3], [2, 1], [3, 2], [4, 5]]],
- ['edgelist',
- 'G291',
- 7,
- [[2, 3], [6, 2], [5, 6], [3, 5], [1, 3], [6, 1], [6, 3]]],
- ['edgelist',
- 'G292',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2]]],
- ['edgelist',
- 'G293',
- 7,
- [[2, 3], [6, 2], [5, 6], [3, 5], [1, 3], [6, 1], [2, 1]]],
- ['edgelist',
- 'G294',
- 7,
- [[1, 5], [4, 1], [3, 4], [5, 3], [3, 6], [7, 3], [3, 1]]],
- ['edgelist',
- 'G295',
- 7,
- [[2, 5], [4, 2], [3, 4], [5, 3], [2, 1], [3, 2], [3, 7]]],
- ['edgelist',
- 'G296',
- 7,
- [[2, 5], [4, 2], [3, 4], [5, 3], [2, 1], [4, 5], [7, 4]]],
- ['edgelist',
- 'G297',
- 7,
- [[1, 5], [4, 1], [3, 4], [5, 3], [3, 6], [7, 3], [4, 5]]],
- ['edgelist',
- 'G298',
- 7,
- [[1, 5], [4, 1], [2, 4], [4, 7], [2, 5], [2, 1], [6, 5]]],
- ['edgelist',
- 'G299',
- 7,
- [[1, 5], [4, 1], [2, 4], [7, 6], [2, 5], [2, 1], [4, 5]]],
- ['edgelist',
- 'G300',
- 7,
- [[6, 3], [5, 6], [3, 5], [4, 3], [7, 4], [3, 7], [3, 2]]],
- ['edgelist',
- 'G301',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [1, 3], [3, 6]]],
- ['edgelist',
- 'G302',
- 7,
- [[6, 3], [5, 6], [3, 5], [4, 3], [7, 4], [3, 7], [4, 2]]],
- ['edgelist',
- 'G303',
- 7,
- [[2, 5], [4, 2], [3, 4], [5, 3], [3, 1], [3, 2], [7, 1]]],
- ['edgelist',
- 'G304',
- 7,
- [[2, 3], [6, 2], [5, 6], [3, 5], [1, 3], [6, 1], [4, 6]]],
- ['edgelist',
- 'G305',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [1, 3], [4, 6]]],
- ['edgelist',
- 'G306',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [1, 3], [2, 6]]],
- ['edgelist',
- 'G307',
- 7,
- [[4, 3], [5, 4], [4, 6], [3, 5], [6, 3], [7, 2], [7, 5]]],
- ['edgelist',
- 'G308',
- 7,
- [[2, 3], [6, 2], [5, 6], [3, 5], [1, 3], [6, 1], [1, 4]]],
- ['edgelist',
- 'G309',
- 7,
- [[4, 5], [2, 4], [3, 2], [7, 3], [6, 7], [2, 6], [5, 2]]],
- ['edgelist',
- 'G310',
- 7,
- [[1, 2], [5, 1], [2, 5], [3, 2], [4, 3], [6, 4], [5, 6]]],
- ['edgelist',
- 'G311',
- 7,
- [[7, 4], [6, 7], [2, 6], [3, 2], [4, 3], [5, 3], [6, 5]]],
- ['edgelist',
- 'G312',
- 7,
- [[2, 3], [5, 2], [6, 5], [7, 6], [4, 7], [3, 4], [6, 3]]],
- ['edgelist',
- 'G313',
- 7,
- [[5, 2], [4, 5], [2, 4], [3, 2], [7, 3], [6, 7], [3, 6]]],
- ['edgelist',
- 'G314',
- 7,
- [[4, 1], [7, 4], [1, 7], [2, 1], [1, 3], [6, 1], [1, 5]]],
- ['edgelist',
- 'G315',
- 7,
- [[2, 6], [7, 2], [2, 3], [4, 2], [5, 4], [2, 5], [5, 1]]],
- ['edgelist',
- 'G316',
- 7,
- [[6, 1], [7, 6], [1, 7], [6, 3], [2, 6], [7, 4], [5, 7]]],
- ['edgelist',
- 'G317',
- 7,
- [[5, 2], [1, 5], [2, 1], [3, 2], [1, 4], [7, 1], [5, 6]]],
- ['edgelist',
- 'G318',
- 7,
- [[6, 3], [7, 6], [3, 7], [3, 5], [4, 3], [2, 1], [3, 2]]],
- ['edgelist',
- 'G319',
- 7,
- [[5, 2], [1, 5], [4, 1], [2, 4], [3, 2], [2, 6], [7, 2]]],
- ['edgelist',
- 'G320',
- 7,
- [[2, 1], [5, 2], [1, 5], [6, 5], [3, 2], [4, 3], [7, 2]]],
- ['edgelist',
- 'G321',
- 7,
- [[1, 2], [5, 1], [2, 5], [3, 2], [4, 3], [6, 5], [7, 5]]],
- ['edgelist',
- 'G322',
- 7,
- [[3, 4], [6, 3], [7, 6], [4, 7], [2, 3], [5, 6], [1, 6]]],
- ['edgelist',
- 'G323',
- 7,
- [[1, 5], [4, 1], [2, 4], [5, 2], [2, 1], [3, 2], [7, 6]]],
- ['edgelist',
- 'G324',
- 7,
- [[3, 6], [7, 3], [6, 7], [5, 3], [2, 3], [1, 2], [4, 2]]],
- ['edgelist',
- 'G325',
- 7,
- [[3, 6], [7, 3], [5, 3], [2, 5], [4, 2], [3, 4], [1, 2]]],
- ['edgelist',
- 'G326',
- 7,
- [[7, 3], [6, 7], [3, 6], [2, 3], [1, 2], [5, 2], [4, 2]]],
- ['edgelist',
- 'G327',
- 7,
- [[1, 5], [4, 1], [2, 4], [5, 2], [6, 5], [3, 2], [7, 4]]],
- ['edgelist',
- 'G328',
- 7,
- [[3, 6], [7, 3], [6, 7], [5, 6], [4, 7], [2, 3], [1, 2]]],
- ['edgelist',
- 'G329',
- 7,
- [[3, 6], [7, 3], [2, 5], [2, 3], [1, 2], [5, 1], [1, 4]]],
- ['edgelist',
- 'G330',
- 7,
- [[7, 6], [2, 3], [5, 2], [1, 5], [4, 1], [2, 4], [4, 5]]],
- ['edgelist',
- 'G331',
- 7,
- [[5, 2], [1, 5], [2, 1], [4, 7], [3, 4], [1, 3], [6, 1]]],
- ['edgelist',
- 'G332',
- 7,
- [[5, 2], [1, 5], [4, 1], [2, 4], [3, 2], [6, 3], [7, 2]]],
- ['edgelist',
- 'G333',
- 7,
- [[5, 2], [1, 5], [2, 1], [3, 4], [1, 3], [6, 1], [7, 6]]],
- ['edgelist',
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- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 5], [5, 1], [6, 1]]],
- ['edgelist',
- 'G463',
- 7,
- [[5, 4], [1, 5], [2, 1], [3, 2], [4, 3], [1, 6], [6, 4], [1, 4], [2, 6]]],
- ['edgelist',
- 'G464',
- 7,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [4, 3], [1, 4], [5, 1]]],
- ['edgelist',
- 'G465',
- 7,
- [[2, 4], [3, 2], [1, 3], [6, 1], [5, 6], [4, 5], [4, 3], [1, 4], [3, 5]]],
- ['edgelist',
- 'G466',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [3, 6], [1, 6], [3, 1], [6, 2]]],
- ['edgelist',
- 'G467',
- 7,
- [[2, 6], [5, 2], [1, 5], [6, 1], [3, 6], [5, 3], [4, 5], [6, 4], [3, 1]]],
- ['edgelist',
- 'G468',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 1], [6, 5], [6, 3], [6, 4]]],
- ['edgelist',
- 'G469',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [6, 2]]],
- ['edgelist',
- 'G470',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [1, 4], [5, 3], [6, 3]]],
- ['edgelist',
- 'G471',
- 7,
- [[3, 4], [1, 3], [4, 1], [5, 4], [2, 5], [6, 2], [5, 6], [2, 1], [6, 3]]],
- ['edgelist',
- 'G472',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [1, 4], [6, 3], [5, 2]]],
- ['edgelist',
- 'G473',
- 7,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 1], [1, 5], [1, 7]]],
- ['edgelist',
- 'G474',
- 7,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 1], [1, 5], [3, 7]]],
- ['edgelist',
- 'G475',
- 7,
- [[2, 3], [4, 2], [1, 4], [2, 1], [3, 1], [4, 3], [6, 4], [5, 1], [2, 7]]],
- ['edgelist',
- 'G476',
- 7,
- [[1, 2], [3, 5], [1, 3], [4, 2], [4, 3], [3, 2], [5, 2], [6, 3], [3, 7]]],
- ['edgelist',
- 'G477',
- 7,
- [[1, 2], [3, 5], [1, 3], [6, 3], [4, 2], [4, 3], [3, 2], [5, 2], [2, 7]]],
- ['edgelist',
- 'G478',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [2, 6], [2, 7]]],
- ['edgelist',
- 'G479',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [2, 6], [5, 7]]],
- ['edgelist',
- 'G480',
- 7,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [3, 2], [6, 2], [2, 7]]],
- ['edgelist',
- 'G481',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 5], [5, 7]]],
- ['edgelist',
- 'G482',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 5], [4, 7]]],
- ['edgelist',
- 'G483',
- 7,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [3, 2], [6, 2], [1, 7]]],
- ['edgelist',
- 'G484',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 1], [2, 7]]],
- ['edgelist',
- 'G485',
- 7,
- [[3, 1], [4, 3], [5, 4], [6, 5], [3, 6], [2, 3], [5, 2], [6, 4], [3, 7]]],
- ['edgelist',
- 'G486',
- 7,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [4, 1], [6, 2], [1, 7]]],
- ['edgelist',
- 'G487',
- 7,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [6, 1], [1, 5], [6, 7]]],
- ['edgelist',
- 'G488',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 1], [5, 7]]],
- ['edgelist',
- 'G489',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 5], [3, 7]]],
- ['edgelist',
- 'G490',
- 7,
- [[3, 1], [4, 3], [5, 4], [6, 5], [3, 6], [2, 3], [5, 2], [6, 4], [6, 7]]],
- ['edgelist',
- 'G491',
- 7,
- [[2, 3], [4, 2], [1, 4], [2, 1], [3, 1], [4, 3], [5, 1], [7, 6], [7, 4]]],
- ['edgelist',
- 'G492',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 1], [1, 7]]],
- ['edgelist',
- 'G493',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 7], [6, 5], [1, 4], [3, 5]]],
- ['edgelist',
- 'G494',
- 7,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [3, 2], [6, 2], [6, 7]]],
- ['edgelist',
- 'G495',
- 7,
- [[3, 1], [4, 3], [5, 4], [6, 5], [3, 6], [2, 3], [5, 2], [6, 4], [5, 7]]],
- ['edgelist',
- 'G496',
- 7,
- [[1, 2], [3, 6], [1, 3], [4, 1], [4, 2], [4, 3], [3, 2], [6, 2], [5, 7]]],
- ['edgelist',
- 'G497',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 1], [3, 7]]],
- ['edgelist',
- 'G498',
- 7,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [4, 1], [6, 2], [6, 7]]],
- ['edgelist',
- 'G499',
- 7,
- [[1, 2], [3, 6], [1, 3], [6, 5], [4, 2], [4, 3], [4, 1], [6, 2], [3, 7]]],
- ['edgelist',
- 'G500',
- 7,
- [[1, 2], [3, 6], [1, 3], [5, 1], [4, 2], [4, 3], [6, 2], [6, 4], [1, 7]]],
- ['edgelist',
- 'G501',
- 7,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [1, 6], [6, 5], [6, 7]]],
- ['edgelist',
- 'G502',
- 7,
- [[1, 2], [2, 3], [3, 4], [1, 4], [5, 1], [5, 2], [5, 3], [5, 4], [6, 7]]],
- ['edgelist',
- 'G503',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [4, 5], [5, 7]]],
- ['edgelist',
- 'G504',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 1], [6, 3], [1, 3], [1, 7]]],
- ['edgelist',
- 'G505',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [4, 5], [4, 7]]],
- ['edgelist',
- 'G506',
- 7,
- [[1, 2], [3, 5], [1, 3], [6, 3], [4, 2], [4, 3], [3, 2], [5, 2], [6, 7]]],
- ['edgelist',
- 'G507',
- 7,
- [[2, 6], [5, 2], [1, 5], [6, 1], [3, 6], [5, 3], [4, 5], [6, 4], [5, 7]]],
- ['edgelist',
- 'G508',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3], [3, 7]]],
- ['edgelist',
- 'G509',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [7, 6], [7, 2]]],
- ['edgelist',
- 'G510',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [4, 5], [3, 7]]],
- ['edgelist',
- 'G511',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [4, 5], [1, 7]]],
- ['edgelist',
- 'G512',
- 7,
- [[2, 4], [3, 2], [1, 3], [6, 1], [7, 6], [4, 7], [2, 7], [1, 2], [2, 5]]],
- ['edgelist',
- 'G513',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [1, 7]]],
- ['edgelist',
- 'G514',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [3, 2], [5, 7]]],
- ['edgelist',
- 'G515',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 5], [6, 7]]],
- ['edgelist',
- 'G516',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3], [5, 7]]],
- ['edgelist',
- 'G517',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3], [6, 7]]],
- ['edgelist',
- 'G518',
- 7,
- [[2, 4], [3, 2], [1, 3], [6, 1], [7, 6], [4, 7], [2, 7], [1, 2], [1, 5]]],
- ['edgelist',
- 'G519',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 1], [6, 3], [1, 3], [2, 7]]],
- ['edgelist',
- 'G520',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 1], [6, 3], [1, 3], [5, 7]]],
- ['edgelist',
- 'G521',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [3, 2], [3, 7]]],
- ['edgelist',
- 'G522',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [4, 7]]],
- ['edgelist',
- 'G523',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [3, 7]]],
- ['edgelist',
- 'G524',
- 7,
- [[1, 2], [3, 6], [1, 3], [6, 2], [4, 2], [4, 3], [3, 2], [7, 1], [7, 5]]],
- ['edgelist',
- 'G525',
- 7,
- [[2, 6], [5, 2], [1, 5], [6, 1], [3, 6], [5, 3], [4, 5], [6, 4], [2, 7]]],
- ['edgelist',
- 'G526',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 4], [5, 2], [6, 1], [6, 7]]],
- ['edgelist',
- 'G527',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3], [2, 7]]],
- ['edgelist',
- 'G528',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3], [1, 7]]],
- ['edgelist',
- 'G529',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 3], [4, 7]]],
- ['edgelist',
- 'G530',
- 7,
- [[2, 4], [3, 2], [1, 3], [6, 1], [7, 6], [4, 7], [2, 7], [1, 2], [3, 5]]],
- ['edgelist',
- 'G531',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [5, 6], [6, 4], [2, 6], [4, 7]]],
- ['edgelist',
- 'G532',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [2, 7]]],
- ['edgelist',
- 'G533',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 2], [5, 7]]],
- ['edgelist',
- 'G534',
- 7,
- [[1, 2], [3, 6], [1, 3], [6, 2], [4, 2], [4, 3], [4, 1], [7, 5], [7, 1]]],
- ['edgelist',
- 'G535',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 6], [6, 3], [6, 1], [2, 7]]],
- ['edgelist',
- 'G536',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [3, 2], [7, 1]]],
- ['edgelist',
- 'G537',
- 7,
- [[6, 4], [4, 3], [5, 4], [6, 5], [3, 6], [2, 3], [5, 2], [7, 1], [7, 3]]],
- ['edgelist',
- 'G538',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 6], [6, 3], [6, 1], [1, 7]]],
- ['edgelist',
- 'G539',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [5, 6], [6, 4], [2, 6], [6, 7]]],
- ['edgelist',
- 'G540',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [4, 1], [6, 3], [6, 1], [5, 7]]],
- ['edgelist',
- 'G541',
- 7,
- [[2, 4], [3, 2], [1, 3], [6, 1], [7, 6], [4, 7], [2, 7], [1, 2], [6, 5]]],
- ['edgelist',
- 'G542',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [5, 2], [6, 3], [6, 7]]],
- ['edgelist',
- 'G543',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [5, 6], [6, 4], [2, 6], [2, 7]]],
- ['edgelist',
- 'G544',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [3, 2], [4, 7]]],
- ['edgelist',
- 'G545',
- 7,
- [[1, 2], [2, 3], [1, 3], [4, 1], [4, 2], [4, 3], [1, 6], [6, 5], [5, 7]]],
- ['edgelist',
- 'G546',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [5, 6], [6, 4], [2, 6], [1, 7]]],
- ['edgelist',
- 'G547',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [2, 6], [6, 3], [6, 1], [5, 7]]],
- ['edgelist',
- 'G548',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [3, 5], [6, 2], [1, 7]]],
- ['edgelist',
- 'G549',
- 7,
- [[1, 2], [3, 6], [1, 3], [6, 4], [4, 2], [4, 3], [6, 2], [7, 5], [7, 1]]],
- ['edgelist',
- 'G550',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [5, 2], [6, 3], [1, 7]]],
- ['edgelist',
- 'G551',
- 7,
- [[7, 4], [2, 3], [7, 6], [4, 5], [7, 5], [1, 6], [7, 1], [7, 2], [7, 3]]],
- ['edgelist',
- 'G552',
- 7,
- [[1, 2], [3, 1], [4, 3], [5, 4], [2, 5], [3, 2], [7, 3], [6, 7], [3, 6]]],
- ['edgelist',
- 'G553',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [4, 5], [7, 6], [7, 1]]],
- ['edgelist',
- 'G554',
- 7,
- [[2, 5], [3, 5], [3, 4], [1, 5], [4, 2], [5, 6], [1, 6], [7, 5], [7, 4]]],
- ['edgelist',
- 'G555',
- 7,
- [[5, 2], [6, 5], [7, 6], [4, 7], [3, 4], [2, 3], [6, 3], [1, 6], [3, 1]]],
- ['edgelist',
- 'G556',
- 7,
- [[5, 2], [4, 2], [3, 4], [5, 1], [6, 1], [6, 3], [6, 5], [7, 5], [6, 7]]],
- ['edgelist',
- 'G557',
- 7,
- [[2, 1], [3, 2], [7, 3], [4, 7], [6, 4], [5, 6], [4, 5], [3, 4], [1, 3]]],
- ['edgelist',
- 'G558',
- 7,
- [[1, 3], [6, 1], [2, 6], [3, 2], [5, 3], [6, 5], [7, 6], [4, 7], [3, 4]]],
- ['edgelist',
- 'G559',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [1, 7], [2, 4], [5, 2]]],
- ['edgelist',
- 'G560',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 5], [6, 2], [7, 2], [1, 7]]],
- ['edgelist',
- 'G561',
- 7,
- [[1, 5], [2, 1], [5, 2], [4, 5], [3, 4], [7, 3], [6, 7], [2, 6], [3, 2]]],
- ['edgelist',
- 'G562',
- 7,
- [[1, 2], [3, 1], [4, 3], [5, 4], [2, 5], [3, 2], [6, 4], [7, 6], [4, 7]]],
- ['edgelist',
- 'G563',
- 7,
- [[7, 6], [4, 7], [3, 4], [1, 5], [1, 6], [2, 1], [3, 1], [2, 3], [6, 5]]],
- ['edgelist',
- 'G564',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [1, 5], [6, 4], [6, 2], [7, 2], [1, 7]]],
- ['edgelist',
- 'G565',
- 7,
- [[6, 3], [7, 6], [4, 7], [3, 4], [1, 3], [5, 1], [6, 5], [2, 6], [1, 2]]],
- ['edgelist',
- 'G566',
- 7,
- [[3, 5], [2, 3], [5, 2], [6, 5], [1, 6], [2, 1], [7, 5], [4, 7], [3, 4]]],
- ['edgelist',
- 'G567',
- 7,
- [[7, 3], [6, 7], [3, 6], [2, 3], [1, 2], [5, 1], [2, 5], [4, 2], [1, 4]]],
- ['edgelist',
- 'G568',
- 7,
- [[1, 6], [7, 1], [2, 7], [5, 2], [3, 5], [4, 3], [2, 4], [6, 2], [7, 6]]],
- ['edgelist',
- 'G569',
- 7,
- [[7, 6], [4, 7], [3, 4], [6, 3], [1, 6], [2, 1], [5, 2], [1, 5], [3, 1]]],
- ['edgelist',
- 'G570',
- 7,
- [[1, 5], [4, 1], [2, 4], [5, 2], [3, 5], [7, 3], [6, 7], [3, 6], [4, 3]]],
- ['edgelist',
- 'G571',
- 7,
- [[2, 1], [5, 2], [6, 5], [1, 6], [7, 1], [4, 7], [3, 4], [1, 3], [4, 5]]],
- ['edgelist',
- 'G572',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [7, 1], [7, 2], [7, 4]]],
- ['edgelist',
- 'G573',
- 7,
- [[1, 2], [2, 3], [3, 4], [1, 4], [5, 2], [6, 5], [6, 4], [7, 1], [7, 5]]],
- ['edgelist',
- 'G574',
- 7,
- [[1, 2], [5, 1], [2, 5], [3, 2], [6, 3], [5, 6], [7, 6], [4, 7], [3, 4]]],
- ['edgelist',
- 'G575',
- 7,
- [[2, 1], [7, 4], [1, 5], [6, 1], [4, 6], [6, 7], [2, 3], [2, 5], [7, 3]]],
- ['edgelist',
- 'G576',
- 7,
- [[7, 3], [6, 7], [3, 6], [2, 3], [1, 4], [5, 1], [2, 5], [4, 2], [4, 5]]],
- ['edgelist',
- 'G577',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [7, 2], [6, 7], [7, 1]]],
- ['edgelist',
- 'G578',
- 7,
- [[1, 5], [2, 1], [3, 2], [4, 3], [1, 4], [3, 5], [6, 5], [7, 6], [4, 7]]],
- ['edgelist',
- 'G579',
- 7,
- [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [1, 6], [5, 3], [7, 2], [6, 7]]],
- ['edgelist',
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- [2, 6],
- [3, 4],
- [3, 5],
- [3, 6],
- [4, 5],
- [4, 6],
- [5, 6],
- [3, 7]]],
- ['edgelist',
- 'G1214',
- 7,
- [[4, 1],
- [5, 2],
- [5, 4],
- [2, 4],
- [5, 1],
- [3, 6],
- [7, 3],
- [6, 7],
- [2, 6],
- [5, 6],
- [4, 6],
- [1, 6],
- [1, 7],
- [4, 7],
- [5, 7],
- [7, 2]]],
- ['edgelist',
- 'G1215',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1216',
- 7,
- [[1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7]]],
- ['edgelist',
- 'G1217',
- 7,
- [[4, 5],
- [6, 2],
- [1, 4],
- [1, 5],
- [6, 1],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [7, 1],
- [4, 6],
- [3, 4],
- [3, 5],
- [6, 7],
- [5, 6],
- [3, 6]]],
- ['edgelist',
- 'G1218',
- 7,
- [[3, 5],
- [4, 2],
- [4, 1],
- [5, 4],
- [5, 1],
- [6, 3],
- [5, 6],
- [6, 1],
- [4, 6],
- [6, 2],
- [7, 6],
- [2, 7],
- [4, 7],
- [7, 1],
- [5, 7],
- [7, 3]]],
- ['edgelist',
- 'G1219',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1220',
- 7,
- [[3, 5],
- [5, 2],
- [4, 1],
- [4, 2],
- [5, 1],
- [6, 3],
- [5, 6],
- [6, 1],
- [4, 6],
- [7, 6],
- [2, 6],
- [7, 2],
- [4, 7],
- [5, 7],
- [7, 3],
- [7, 1]]],
- ['edgelist',
- 'G1221',
- 7,
- [[1, 2],
- [1, 4],
- [1, 6],
- [1, 7],
- [2, 4],
- [2, 6],
- [2, 7],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1222',
- 7,
- [[3, 6],
- [1, 2],
- [5, 6],
- [2, 4],
- [6, 1],
- [5, 4],
- [6, 4],
- [3, 5],
- [2, 5],
- [4, 1],
- [7, 4],
- [3, 7],
- [7, 5],
- [6, 7],
- [7, 2],
- [1, 7]]],
- ['edgelist',
- 'G1223',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 4],
- [2, 7],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1224',
- 7,
- [[3, 6],
- [6, 2],
- [4, 2],
- [1, 5],
- [6, 1],
- [5, 4],
- [6, 4],
- [3, 5],
- [2, 5],
- [4, 1],
- [7, 3],
- [5, 7],
- [6, 7],
- [7, 2],
- [1, 7],
- [4, 7]]],
- ['edgelist',
- 'G1225',
- 7,
- [[2, 7],
- [1, 2],
- [1, 4],
- [1, 5],
- [6, 1],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [7, 1],
- [4, 6],
- [3, 4],
- [3, 5],
- [6, 7],
- [5, 6],
- [3, 6]]],
- ['edgelist',
- 'G1226',
- 7,
- [[4, 5],
- [6, 2],
- [1, 4],
- [1, 5],
- [6, 1],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [7, 1],
- [2, 7],
- [3, 4],
- [3, 5],
- [6, 7],
- [1, 2],
- [3, 6]]],
- ['edgelist',
- 'G1227',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 7],
- [5, 6]]],
- ['edgelist',
- 'G1228',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 7],
- [5, 6],
- [6, 7]]],
- ['edgelist',
- 'G1229',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [4, 7],
- [6, 7]]],
- ['edgelist',
- 'G1230',
- 7,
- [[3, 6],
- [6, 2],
- [4, 6],
- [1, 5],
- [1, 2],
- [5, 4],
- [4, 3],
- [3, 5],
- [2, 5],
- [1, 6],
- [7, 5],
- [3, 7],
- [7, 4],
- [6, 7],
- [7, 2],
- [1, 7]]],
- ['edgelist',
- 'G1231',
- 7,
- [[6, 7],
- [6, 2],
- [1, 4],
- [1, 5],
- [1, 2],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [7, 1],
- [4, 6],
- [3, 4],
- [3, 5],
- [7, 3],
- [5, 6],
- [3, 6]]],
- ['edgelist',
- 'G1232',
- 7,
- [[4, 5],
- [6, 2],
- [1, 4],
- [1, 5],
- [1, 2],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [7, 1],
- [6, 1],
- [3, 4],
- [3, 5],
- [7, 3],
- [7, 6],
- [3, 6]]],
- ['edgelist',
- 'G1233',
- 7,
- [[6, 1],
- [6, 2],
- [1, 4],
- [1, 5],
- [7, 2],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [7, 1],
- [4, 6],
- [3, 4],
- [3, 5],
- [7, 3],
- [5, 6],
- [3, 6]]],
- ['edgelist',
- 'G1234',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [3, 4],
- [3, 5],
- [3, 6],
- [4, 5],
- [4, 6],
- [5, 6],
- [7, 3],
- [2, 7]]],
- ['edgelist',
- 'G1235',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [5, 6],
- [5, 7]]],
- ['edgelist',
- 'G1236',
- 7,
- [[5, 1],
- [5, 4],
- [1, 2],
- [4, 1],
- [3, 5],
- [4, 2],
- [6, 4],
- [5, 6],
- [6, 3],
- [7, 6],
- [6, 1],
- [2, 6],
- [7, 2],
- [1, 7],
- [7, 5],
- [3, 7],
- [4, 7]]],
- ['edgelist',
- 'G1237',
- 7,
- [[1, 2],
- [6, 2],
- [6, 4],
- [1, 5],
- [6, 1],
- [5, 4],
- [4, 2],
- [3, 6],
- [2, 5],
- [4, 1],
- [3, 5],
- [7, 3],
- [6, 7],
- [7, 4],
- [2, 7],
- [7, 1],
- [5, 7]]],
- ['edgelist',
- 'G1238',
- 7,
- [[4, 5],
- [6, 2],
- [1, 4],
- [1, 5],
- [5, 6],
- [5, 7],
- [4, 7],
- [2, 4],
- [2, 5],
- [1, 2],
- [4, 6],
- [3, 4],
- [3, 5],
- [3, 6],
- [6, 1],
- [6, 7],
- [7, 3]]],
- ['edgelist',
- 'G1239',
- 7,
- [[4, 3],
- [5, 2],
- [1, 2],
- [4, 1],
- [3, 5],
- [5, 4],
- [6, 2],
- [5, 6],
- [6, 3],
- [1, 6],
- [6, 4],
- [7, 6],
- [3, 7],
- [7, 4],
- [1, 7],
- [2, 7],
- [5, 7]]],
- ['edgelist',
- 'G1240',
- 7,
- [[4, 3],
- [5, 2],
- [5, 1],
- [4, 1],
- [3, 5],
- [4, 2],
- [6, 3],
- [5, 6],
- [6, 1],
- [4, 6],
- [6, 2],
- [7, 6],
- [3, 7],
- [7, 1],
- [4, 7],
- [7, 5],
- [2, 7]]],
- ['edgelist',
- 'G1241',
- 7,
- [[4, 3],
- [6, 2],
- [6, 1],
- [1, 5],
- [1, 2],
- [5, 4],
- [6, 4],
- [3, 6],
- [2, 5],
- [4, 1],
- [3, 5],
- [7, 5],
- [6, 7],
- [7, 3],
- [4, 7],
- [7, 1],
- [2, 7]]],
- ['edgelist',
- 'G1242',
- 7,
- [[4, 3],
- [6, 2],
- [6, 1],
- [1, 5],
- [5, 6],
- [1, 2],
- [4, 2],
- [3, 6],
- [2, 5],
- [4, 1],
- [3, 5],
- [7, 1],
- [4, 7],
- [7, 2],
- [6, 7],
- [7, 3],
- [5, 7]]],
- ['edgelist',
- 'G1243',
- 7,
- [[1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7]]],
- ['edgelist',
- 'G1244',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [3, 4],
- [3, 5],
- [3, 6],
- [4, 5],
- [4, 6],
- [5, 6],
- [7, 2],
- [1, 7],
- [6, 7]]],
- ['edgelist',
- 'G1245',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [4, 7]]],
- ['edgelist',
- 'G1246',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 5],
- [2, 6],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1247',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1248',
- 7,
- [[5, 1],
- [5, 6],
- [4, 1],
- [4, 6],
- [3, 1],
- [3, 6],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 1],
- [3, 4],
- [3, 5],
- [7, 1],
- [6, 7],
- [7, 2],
- [3, 7],
- [7, 5],
- [4, 7]]],
- ['edgelist',
- 'G1249',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1250',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1251',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]],
- ['edgelist',
- 'G1252',
- 7,
- [[1, 2],
- [1, 3],
- [1, 4],
- [1, 5],
- [1, 6],
- [1, 7],
- [2, 3],
- [2, 4],
- [2, 5],
- [2, 6],
- [2, 7],
- [3, 4],
- [3, 5],
- [3, 6],
- [3, 7],
- [4, 5],
- [4, 6],
- [4, 7],
- [5, 6],
- [5, 7],
- [6, 7]]]]
-
- GAG=[]
-
- for i in range(1253):
- g=make_small_graph(descr_list[i])
- GAG.append(g)
-
- return GAG
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/bipartite.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/bipartite.py
deleted file mode 100644
index 435ed0d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/bipartite.py
+++ /dev/null
@@ -1,529 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Generators and functions for bipartite graphs.
-
-"""
-# Copyright (C) 2006-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import math
-import random
-import networkx
-from functools import reduce
-import networkx as nx
-__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-__all__=['bipartite_configuration_model',
- 'bipartite_havel_hakimi_graph',
- 'bipartite_reverse_havel_hakimi_graph',
- 'bipartite_alternating_havel_hakimi_graph',
- 'bipartite_preferential_attachment_graph',
- 'bipartite_random_graph',
- 'bipartite_gnmk_random_graph',
- ]
-
-
-def bipartite_configuration_model(aseq, bseq, create_using=None, seed=None):
- """Return a random bipartite graph from two given degree sequences.
-
- Parameters
- ----------
- aseq : list or iterator
- Degree sequence for node set A.
- bseq : list or iterator
- Degree sequence for node set B.
- create_using : NetworkX graph instance, optional
- Return graph of this type.
- seed : integer, optional
- Seed for random number generator.
-
- Nodes from the set A are connected to nodes in the set B by
- choosing randomly from the possible free stubs, one in A and
- one in B.
-
- Notes
- -----
- The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
- If no graph type is specified use MultiGraph with parallel edges.
- If you want a graph with no parallel edges use create_using=Graph()
- but then the resulting degree sequences might not be exact.
-
- The nodes are assigned the attribute 'bipartite' with the value 0 or 1
- to indicate which bipartite set the node belongs to.
- """
- if create_using is None:
- create_using=networkx.MultiGraph()
- elif create_using.is_directed():
- raise networkx.NetworkXError(\
- "Directed Graph not supported")
-
-
- G=networkx.empty_graph(0,create_using)
-
- if not seed is None:
- random.seed(seed)
-
- # length and sum of each sequence
- lena=len(aseq)
- lenb=len(bseq)
- suma=sum(aseq)
- sumb=sum(bseq)
-
- if not suma==sumb:
- raise networkx.NetworkXError(\
- 'invalid degree sequences, sum(aseq)!=sum(bseq),%s,%s'\
- %(suma,sumb))
-
- G=_add_nodes_with_bipartite_label(G,lena,lenb)
-
- if max(aseq)==0: return G # done if no edges
-
- # build lists of degree-repeated vertex numbers
- stubs=[]
- stubs.extend([[v]*aseq[v] for v in range(0,lena)])
- astubs=[]
- astubs=[x for subseq in stubs for x in subseq]
-
- stubs=[]
- stubs.extend([[v]*bseq[v-lena] for v in range(lena,lena+lenb)])
- bstubs=[]
- bstubs=[x for subseq in stubs for x in subseq]
-
- # shuffle lists
- random.shuffle(astubs)
- random.shuffle(bstubs)
-
- G.add_edges_from([[astubs[i],bstubs[i]] for i in range(suma)])
-
- G.name="bipartite_configuration_model"
- return G
-
-
-def bipartite_havel_hakimi_graph(aseq, bseq, create_using=None):
- """Return a bipartite graph from two given degree sequences using a
- Havel-Hakimi style construction.
-
- Nodes from the set A are connected to nodes in the set B by
- connecting the highest degree nodes in set A to the highest degree
- nodes in set B until all stubs are connected.
-
- Parameters
- ----------
- aseq : list or iterator
- Degree sequence for node set A.
- bseq : list or iterator
- Degree sequence for node set B.
- create_using : NetworkX graph instance, optional
- Return graph of this type.
-
- Notes
- -----
- The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
- If no graph type is specified use MultiGraph with parallel edges.
- If you want a graph with no parallel edges use create_using=Graph()
- but then the resulting degree sequences might not be exact.
-
- The nodes are assigned the attribute 'bipartite' with the value 0 or 1
- to indicate which bipartite set the node belongs to.
- """
- if create_using is None:
- create_using=networkx.MultiGraph()
- elif create_using.is_directed():
- raise networkx.NetworkXError(\
- "Directed Graph not supported")
-
- G=networkx.empty_graph(0,create_using)
-
- # length of the each sequence
- naseq=len(aseq)
- nbseq=len(bseq)
-
- suma=sum(aseq)
- sumb=sum(bseq)
-
- if not suma==sumb:
- raise networkx.NetworkXError(\
- 'invalid degree sequences, sum(aseq)!=sum(bseq),%s,%s'\
- %(suma,sumb))
-
- G=_add_nodes_with_bipartite_label(G,naseq,nbseq)
-
- if max(aseq)==0: return G # done if no edges
-
- # build list of degree-repeated vertex numbers
- astubs=[[aseq[v],v] for v in range(0,naseq)]
- bstubs=[[bseq[v-naseq],v] for v in range(naseq,naseq+nbseq)]
- astubs.sort()
- while astubs:
- (degree,u)=astubs.pop() # take of largest degree node in the a set
- if degree==0: break # done, all are zero
- # connect the source to largest degree nodes in the b set
- bstubs.sort()
- for target in bstubs[-degree:]:
- v=target[1]
- G.add_edge(u,v)
- target[0] -= 1 # note this updates bstubs too.
- if target[0]==0:
- bstubs.remove(target)
-
- G.name="bipartite_havel_hakimi_graph"
- return G
-
-def bipartite_reverse_havel_hakimi_graph(aseq, bseq, create_using=None):
- """Return a bipartite graph from two given degree sequences using a
- Havel-Hakimi style construction.
-
- Nodes from set A are connected to nodes in the set B by connecting
- the highest degree nodes in set A to the lowest degree nodes in
- set B until all stubs are connected.
-
- Parameters
- ----------
- aseq : list or iterator
- Degree sequence for node set A.
- bseq : list or iterator
- Degree sequence for node set B.
- create_using : NetworkX graph instance, optional
- Return graph of this type.
-
-
- Notes
- -----
- The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
- If no graph type is specified use MultiGraph with parallel edges.
- If you want a graph with no parallel edges use create_using=Graph()
- but then the resulting degree sequences might not be exact.
-
- The nodes are assigned the attribute 'bipartite' with the value 0 or 1
- to indicate which bipartite set the node belongs to.
- """
- if create_using is None:
- create_using=networkx.MultiGraph()
- elif create_using.is_directed():
- raise networkx.NetworkXError(\
- "Directed Graph not supported")
-
- G=networkx.empty_graph(0,create_using)
-
-
- # length of the each sequence
- lena=len(aseq)
- lenb=len(bseq)
- suma=sum(aseq)
- sumb=sum(bseq)
-
- if not suma==sumb:
- raise networkx.NetworkXError(\
- 'invalid degree sequences, sum(aseq)!=sum(bseq),%s,%s'\
- %(suma,sumb))
-
- G=_add_nodes_with_bipartite_label(G,lena,lenb)
-
- if max(aseq)==0: return G # done if no edges
-
- # build list of degree-repeated vertex numbers
- astubs=[[aseq[v],v] for v in range(0,lena)]
- bstubs=[[bseq[v-lena],v] for v in range(lena,lena+lenb)]
- astubs.sort()
- bstubs.sort()
- while astubs:
- (degree,u)=astubs.pop() # take of largest degree node in the a set
- if degree==0: break # done, all are zero
- # connect the source to the smallest degree nodes in the b set
- for target in bstubs[0:degree]:
- v=target[1]
- G.add_edge(u,v)
- target[0] -= 1 # note this updates bstubs too.
- if target[0]==0:
- bstubs.remove(target)
-
- G.name="bipartite_reverse_havel_hakimi_graph"
- return G
-
-
-def bipartite_alternating_havel_hakimi_graph(aseq, bseq,create_using=None):
- """Return a bipartite graph from two given degree sequences using
- an alternating Havel-Hakimi style construction.
-
- Nodes from the set A are connected to nodes in the set B by
- connecting the highest degree nodes in set A to alternatively the
- highest and the lowest degree nodes in set B until all stubs are
- connected.
-
- Parameters
- ----------
- aseq : list or iterator
- Degree sequence for node set A.
- bseq : list or iterator
- Degree sequence for node set B.
- create_using : NetworkX graph instance, optional
- Return graph of this type.
-
-
- Notes
- -----
- The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
- If no graph type is specified use MultiGraph with parallel edges.
- If you want a graph with no parallel edges use create_using=Graph()
- but then the resulting degree sequences might not be exact.
-
- The nodes are assigned the attribute 'bipartite' with the value 0 or 1
- to indicate which bipartite set the node belongs to.
- """
- if create_using is None:
- create_using=networkx.MultiGraph()
- elif create_using.is_directed():
- raise networkx.NetworkXError(\
- "Directed Graph not supported")
-
- G=networkx.empty_graph(0,create_using)
-
- # length of the each sequence
- naseq=len(aseq)
- nbseq=len(bseq)
- suma=sum(aseq)
- sumb=sum(bseq)
-
- if not suma==sumb:
- raise networkx.NetworkXError(\
- 'invalid degree sequences, sum(aseq)!=sum(bseq),%s,%s'\
- %(suma,sumb))
-
- G=_add_nodes_with_bipartite_label(G,naseq,nbseq)
-
- if max(aseq)==0: return G # done if no edges
- # build list of degree-repeated vertex numbers
- astubs=[[aseq[v],v] for v in range(0,naseq)]
- bstubs=[[bseq[v-naseq],v] for v in range(naseq,naseq+nbseq)]
- while astubs:
- astubs.sort()
- (degree,u)=astubs.pop() # take of largest degree node in the a set
- if degree==0: break # done, all are zero
- bstubs.sort()
- small=bstubs[0:degree // 2] # add these low degree targets
- large=bstubs[(-degree+degree // 2):] # and these high degree targets
- stubs=[x for z in zip(large,small) for x in z] # combine, sorry
- if len(stubs)<len(small)+len(large): # check for zip truncation
- stubs.append(large.pop())
- for target in stubs:
- v=target[1]
- G.add_edge(u,v)
- target[0] -= 1 # note this updates bstubs too.
- if target[0]==0:
- bstubs.remove(target)
-
- G.name="bipartite_alternating_havel_hakimi_graph"
- return G
-
-def bipartite_preferential_attachment_graph(aseq,p,create_using=None,seed=None):
- """Create a bipartite graph with a preferential attachment model from
- a given single degree sequence.
-
- Parameters
- ----------
- aseq : list or iterator
- Degree sequence for node set A.
- p : float
- Probability that a new bottom node is added.
- create_using : NetworkX graph instance, optional
- Return graph of this type.
- seed : integer, optional
- Seed for random number generator.
-
- References
- ----------
- .. [1] Jean-Loup Guillaume and Matthieu Latapy,
- Bipartite structure of all complex networks,
- Inf. Process. Lett. 90, 2004, pg. 215-221
- http://dx.doi.org/10.1016/j.ipl.2004.03.007
- """
- if create_using is None:
- create_using=networkx.MultiGraph()
- elif create_using.is_directed():
- raise networkx.NetworkXError(\
- "Directed Graph not supported")
-
- if p > 1:
- raise networkx.NetworkXError("probability %s > 1"%(p))
-
- G=networkx.empty_graph(0,create_using)
-
- if not seed is None:
- random.seed(seed)
-
- naseq=len(aseq)
- G=_add_nodes_with_bipartite_label(G,naseq,0)
- vv=[ [v]*aseq[v] for v in range(0,naseq)]
- while vv:
- while vv[0]:
- source=vv[0][0]
- vv[0].remove(source)
- if random.random() < p or G.number_of_nodes() == naseq:
- target=G.number_of_nodes()
- G.add_node(target,bipartite=1)
- G.add_edge(source,target)
- else:
- bb=[ [b]*G.degree(b) for b in range(naseq,G.number_of_nodes())]
- # flatten the list of lists into a list.
- bbstubs=reduce(lambda x,y: x+y, bb)
- # choose preferentially a bottom node.
- target=random.choice(bbstubs)
- G.add_node(target,bipartite=1)
- G.add_edge(source,target)
- vv.remove(vv[0])
- G.name="bipartite_preferential_attachment_model"
- return G
-
-
-
-def bipartite_random_graph(n, m, p, seed=None, directed=False):
- """Return a bipartite random graph.
-
- This is a bipartite version of the binomial (Erdős-Rényi) graph.
-
- Parameters
- ----------
- n : int
- The number of nodes in the first bipartite set.
- m : int
- The number of nodes in the second bipartite set.
- p : float
- Probability for edge creation.
- seed : int, optional
- Seed for random number generator (default=None).
- directed : bool, optional (default=False)
- If True return a directed graph
-
- Notes
- -----
- The bipartite random graph algorithm chooses each of the n*m (undirected)
- or 2*nm (directed) possible edges with probability p.
-
- This algorithm is O(n+m) where m is the expected number of edges.
-
- The nodes are assigned the attribute 'bipartite' with the value 0 or 1
- to indicate which bipartite set the node belongs to.
-
- See Also
- --------
- gnp_random_graph, bipartite_configuration_model
-
- References
- ----------
- .. [1] Vladimir Batagelj and Ulrik Brandes,
- "Efficient generation of large random networks",
- Phys. Rev. E, 71, 036113, 2005.
- """
- G=nx.Graph()
- G=_add_nodes_with_bipartite_label(G,n,m)
- if directed:
- G=nx.DiGraph(G)
- G.name="fast_gnp_random_graph(%s,%s,%s)"%(n,m,p)
-
- if not seed is None:
- random.seed(seed)
-
- if p <= 0:
- return G
- if p >= 1:
- return nx.complete_bipartite_graph(n,m)
-
- lp = math.log(1.0 - p)
-
- v = 0
- w = -1
- while v < n:
- lr = math.log(1.0 - random.random())
- w = w + 1 + int(lr/lp)
- while w >= m and v < n:
- w = w - m
- v = v + 1
- if v < n:
- G.add_edge(v, n+w)
-
- if directed:
- # use the same algorithm to
- # add edges from the "m" to "n" set
- v = 0
- w = -1
- while v < n:
- lr = math.log(1.0 - random.random())
- w = w + 1 + int(lr/lp)
- while w>= m and v < n:
- w = w - m
- v = v + 1
- if v < n:
- G.add_edge(n+w, v)
-
- return G
-
-def bipartite_gnmk_random_graph(n, m, k, seed=None, directed=False):
- """Return a random bipartite graph G_{n,m,k}.
-
- Produces a bipartite graph chosen randomly out of the set of all graphs
- with n top nodes, m bottom nodes, and k edges.
-
- Parameters
- ----------
- n : int
- The number of nodes in the first bipartite set.
- m : int
- The number of nodes in the second bipartite set.
- k : int
- The number of edges
- seed : int, optional
- Seed for random number generator (default=None).
- directed : bool, optional (default=False)
- If True return a directed graph
-
- Examples
- --------
- G = nx.bipartite_gnmk_random_graph(10,20,50)
-
- See Also
- --------
- gnm_random_graph
-
- Notes
- -----
- If k > m * n then a complete bipartite graph is returned.
-
- This graph is a bipartite version of the `G_{nm}` random graph model.
- """
- G = networkx.Graph()
- G=_add_nodes_with_bipartite_label(G,n,m)
- if directed:
- G=nx.DiGraph(G)
- G.name="bipartite_gnm_random_graph(%s,%s,%s)"%(n,m,k)
- if seed is not None:
- random.seed(seed)
- if n == 1 or m == 1:
- return G
- max_edges = n*m # max_edges for bipartite networks
- if k >= max_edges: # Maybe we should raise an exception here
- return networkx.complete_bipartite_graph(n, m, create_using=G)
-
- top = [n for n,d in G.nodes(data=True) if d['bipartite']==0]
- bottom = list(set(G) - set(top))
- edge_count = 0
- while edge_count < k:
- # generate random edge,u,v
- u = random.choice(top)
- v = random.choice(bottom)
- if v in G[u]:
- continue
- else:
- G.add_edge(u,v)
- edge_count += 1
- return G
-
-def _add_nodes_with_bipartite_label(G, lena, lenb):
- G.add_nodes_from(range(0,lena+lenb))
- b=dict(zip(range(0,lena),[0]*lena))
- b.update(dict(zip(range(lena,lena+lenb),[1]*lenb)))
- nx.set_node_attributes(G,'bipartite',b)
- return G
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/classic.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/classic.py
deleted file mode 100644
index f8ca43b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/classic.py
+++ /dev/null
@@ -1,508 +0,0 @@
-"""
-Generators for some classic graphs.
-
-The typical graph generator is called as follows:
-
->>> G=nx.complete_graph(100)
-
-returning the complete graph on n nodes labeled 0,..,99
-as a simple graph. Except for empty_graph, all the generators
-in this module return a Graph class (i.e. a simple, undirected graph).
-
-"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import itertools
-__author__ ="""Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)"""
-
-__all__ = [ 'balanced_tree',
- 'barbell_graph',
- 'complete_graph',
- 'complete_bipartite_graph',
- 'circular_ladder_graph',
- 'cycle_graph',
- 'dorogovtsev_goltsev_mendes_graph',
- 'empty_graph',
- 'full_rary_tree',
- 'grid_graph',
- 'grid_2d_graph',
- 'hypercube_graph',
- 'ladder_graph',
- 'lollipop_graph',
- 'null_graph',
- 'path_graph',
- 'star_graph',
- 'trivial_graph',
- 'wheel_graph']
-
-
-#-------------------------------------------------------------------
-# Some Classic Graphs
-#-------------------------------------------------------------------
-import networkx as nx
-from networkx.utils import is_list_of_ints, flatten
-
-def _tree_edges(n,r):
- # helper function for trees
- # yields edges in rooted tree at 0 with n nodes and branching ratio r
- nodes=iter(range(n))
- parents=[next(nodes)] # stack of max length r
- while parents:
- source=parents.pop(0)
- for i in range(r):
- try:
- target=next(nodes)
- parents.append(target)
- yield source,target
- except StopIteration:
- break
-
-def full_rary_tree(r, n, create_using=None):
- """Creates a full r-ary tree of n vertices.
-
- Sometimes called a k-ary, n-ary, or m-ary tree. "... all non-leaf
- vertices have exactly r children and all levels are full except
- for some rightmost position of the bottom level (if a leaf at the
- bottom level is missing, then so are all of the leaves to its
- right." [1]_
-
- Parameters
- ----------
- r : int
- branching factor of the tree
- n : int
- Number of nodes in the tree
- create_using : NetworkX graph type, optional
- Use specified type to construct graph (default = networkx.Graph)
-
- Returns
- -------
- G : networkx Graph
- An r-ary tree with n nodes
-
- References
- ----------
- .. [1] An introduction to data structures and algorithms,
- James Andrew Storer, Birkhauser Boston 2001, (page 225).
- """
- G=nx.empty_graph(n,create_using)
- G.add_edges_from(_tree_edges(n,r))
- return G
-
-def balanced_tree(r, h, create_using=None):
- """Return the perfectly balanced r-tree of height h.
-
- Parameters
- ----------
- r : int
- Branching factor of the tree
- h : int
- Height of the tree
- create_using : NetworkX graph type, optional
- Use specified type to construct graph (default = networkx.Graph)
-
- Returns
- -------
- G : networkx Graph
- A tree with n nodes
-
- Notes
- -----
- This is the rooted tree where all leaves are at distance h from
- the root. The root has degree r and all other internal nodes have
- degree r+1.
-
- Node labels are the integers 0 (the root) up to number_of_nodes - 1.
-
- Also refered to as a complete r-ary tree.
- """
- # number of nodes is n=1+r+..+r^h
- if r==1:
- n=2
- else:
- n = int((1-r**(h+1))/(1-r)) # sum of geometric series r!=1
- G=nx.empty_graph(n,create_using)
- G.add_edges_from(_tree_edges(n,r))
- return G
-
- return nx.full_rary_tree(r,n,create_using)
-
-def barbell_graph(m1,m2,create_using=None):
- """Return the Barbell Graph: two complete graphs connected by a path.
-
- For m1 > 1 and m2 >= 0.
-
- Two identical complete graphs K_{m1} form the left and right bells,
- and are connected by a path P_{m2}.
-
- The 2*m1+m2 nodes are numbered
- 0,...,m1-1 for the left barbell,
- m1,...,m1+m2-1 for the path,
- and m1+m2,...,2*m1+m2-1 for the right barbell.
-
- The 3 subgraphs are joined via the edges (m1-1,m1) and (m1+m2-1,m1+m2).
- If m2=0, this is merely two complete graphs joined together.
-
- This graph is an extremal example in David Aldous
- and Jim Fill's etext on Random Walks on Graphs.
-
- """
- if create_using is not None and create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
- if m1<2:
- raise nx.NetworkXError(\
- "Invalid graph description, m1 should be >=2")
- if m2<0:
- raise nx.NetworkXError(\
- "Invalid graph description, m2 should be >=0")
-
- # left barbell
- G=complete_graph(m1,create_using)
- G.name="barbell_graph(%d,%d)"%(m1,m2)
-
- # connecting path
- G.add_nodes_from([v for v in range(m1,m1+m2-1)])
- if m2>1:
- G.add_edges_from([(v,v+1) for v in range(m1,m1+m2-1)])
- # right barbell
- G.add_edges_from( (u,v) for u in range(m1+m2,2*m1+m2) for v in range(u+1,2*m1+m2))
- # connect it up
- G.add_edge(m1-1,m1)
- if m2>0:
- G.add_edge(m1+m2-1,m1+m2)
- return G
-
-def complete_graph(n,create_using=None):
- """ Return the complete graph K_n with n nodes.
-
- Node labels are the integers 0 to n-1.
- """
- G=empty_graph(n,create_using)
- G.name="complete_graph(%d)"%(n)
- if n>1:
- if G.is_directed():
- edges=itertools.permutations(range(n),2)
- else:
- edges=itertools.combinations(range(n),2)
- G.add_edges_from(edges)
- return G
-
-
-def complete_bipartite_graph(n1,n2,create_using=None):
- """Return the complete bipartite graph K_{n1_n2}.
-
- Composed of two partitions with n1 nodes in the first
- and n2 nodes in the second. Each node in the first is
- connected to each node in the second.
-
- Node labels are the integers 0 to n1+n2-1
-
- """
- if create_using is not None and create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
- G=empty_graph(n1+n2,create_using)
- G.name="complete_bipartite_graph(%d,%d)"%(n1,n2)
- for v1 in range(n1):
- for v2 in range(n2):
- G.add_edge(v1,n1+v2)
- return G
-
-def circular_ladder_graph(n,create_using=None):
- """Return the circular ladder graph CL_n of length n.
-
- CL_n consists of two concentric n-cycles in which
- each of the n pairs of concentric nodes are joined by an edge.
-
- Node labels are the integers 0 to n-1
-
- """
- G=ladder_graph(n,create_using)
- G.name="circular_ladder_graph(%d)"%n
- G.add_edge(0,n-1)
- G.add_edge(n,2*n-1)
- return G
-
-def cycle_graph(n,create_using=None):
- """Return the cycle graph C_n over n nodes.
-
- C_n is the n-path with two end-nodes connected.
-
- Node labels are the integers 0 to n-1
- If create_using is a DiGraph, the direction is in increasing order.
-
- """
- G=path_graph(n,create_using)
- G.name="cycle_graph(%d)"%n
- if n>1: G.add_edge(n-1,0)
- return G
-
-def dorogovtsev_goltsev_mendes_graph(n,create_using=None):
- """Return the hierarchically constructed Dorogovtsev-Goltsev-Mendes graph.
-
- n is the generation.
- See: arXiv:/cond-mat/0112143 by Dorogovtsev, Goltsev and Mendes.
-
- """
- if create_using is not None:
- if create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
- if create_using.is_multigraph():
- raise nx.NetworkXError("Multigraph not supported")
- G=empty_graph(0,create_using)
- G.name="Dorogovtsev-Goltsev-Mendes Graph"
- G.add_edge(0,1)
- if n==0:
- return G
- new_node = 2 # next node to be added
- for i in range(1,n+1): #iterate over number of generations.
- last_generation_edges = G.edges()
- number_of_edges_in_last_generation = len(last_generation_edges)
- for j in range(0,number_of_edges_in_last_generation):
- G.add_edge(new_node,last_generation_edges[j][0])
- G.add_edge(new_node,last_generation_edges[j][1])
- new_node += 1
- return G
-
-def empty_graph(n=0,create_using=None):
- """Return the empty graph with n nodes and zero edges.
-
- Node labels are the integers 0 to n-1
-
- For example:
- >>> G=nx.empty_graph(10)
- >>> G.number_of_nodes()
- 10
- >>> G.number_of_edges()
- 0
-
- The variable create_using should point to a "graph"-like object that
- will be cleaned (nodes and edges will be removed) and refitted as
- an empty "graph" with n nodes with integer labels. This capability
- is useful for specifying the class-nature of the resulting empty
- "graph" (i.e. Graph, DiGraph, MyWeirdGraphClass, etc.).
-
- The variable create_using has two main uses:
- Firstly, the variable create_using can be used to create an
- empty digraph, network,etc. For example,
-
- >>> n=10
- >>> G=nx.empty_graph(n,create_using=nx.DiGraph())
-
- will create an empty digraph on n nodes.
-
- Secondly, one can pass an existing graph (digraph, pseudograph,
- etc.) via create_using. For example, if G is an existing graph
- (resp. digraph, pseudograph, etc.), then empty_graph(n,create_using=G)
- will empty G (i.e. delete all nodes and edges using G.clear() in
- base) and then add n nodes and zero edges, and return the modified
- graph (resp. digraph, pseudograph, etc.).
-
- See also create_empty_copy(G).
-
- """
- if create_using is None:
- # default empty graph is a simple graph
- G=nx.Graph()
- else:
- G=create_using
- G.clear()
-
- G.add_nodes_from(range(n))
- G.name="empty_graph(%d)"%n
- return G
-
-def grid_2d_graph(m,n,periodic=False,create_using=None):
- """ Return the 2d grid graph of mxn nodes,
- each connected to its nearest neighbors.
- Optional argument periodic=True will connect
- boundary nodes via periodic boundary conditions.
- """
- G=empty_graph(0,create_using)
- G.name="grid_2d_graph"
- rows=range(m)
- columns=range(n)
- G.add_nodes_from( (i,j) for i in rows for j in columns )
- G.add_edges_from( ((i,j),(i-1,j)) for i in rows for j in columns if i>0 )
- G.add_edges_from( ((i,j),(i,j-1)) for i in rows for j in columns if j>0 )
- if G.is_directed():
- G.add_edges_from( ((i,j),(i+1,j)) for i in rows for j in columns if i<m-1 )
- G.add_edges_from( ((i,j),(i,j+1)) for i in rows for j in columns if j<n-1 )
- if periodic:
- if n>2:
- G.add_edges_from( ((i,0),(i,n-1)) for i in rows )
- if G.is_directed():
- G.add_edges_from( ((i,n-1),(i,0)) for i in rows )
- if m>2:
- G.add_edges_from( ((0,j),(m-1,j)) for j in columns )
- if G.is_directed():
- G.add_edges_from( ((m-1,j),(0,j)) for j in columns )
- G.name="periodic_grid_2d_graph(%d,%d)"%(m,n)
- return G
-
-
-def grid_graph(dim,periodic=False):
- """ Return the n-dimensional grid graph.
-
- The dimension is the length of the list 'dim' and the
- size in each dimension is the value of the list element.
-
- E.g. G=grid_graph(dim=[2,3]) produces a 2x3 grid graph.
-
- If periodic=True then join grid edges with periodic boundary conditions.
-
- """
- dlabel="%s"%dim
- if dim==[]:
- G=empty_graph(0)
- G.name="grid_graph(%s)"%dim
- return G
- if not is_list_of_ints(dim):
- raise nx.NetworkXError("dim is not a list of integers")
- if min(dim)<=0:
- raise nx.NetworkXError(\
- "dim is not a list of strictly positive integers")
- if periodic:
- func=cycle_graph
- else:
- func=path_graph
-
- dim=list(dim)
- current_dim=dim.pop()
- G=func(current_dim)
- while len(dim)>0:
- current_dim=dim.pop()
- # order matters: copy before it is cleared during the creation of Gnew
- Gold=G.copy()
- Gnew=func(current_dim)
- # explicit: create_using=None
- # This is so that we get a new graph of Gnew's class.
- G=nx.cartesian_product(Gnew,Gold)
- # graph G is done but has labels of the form (1,(2,(3,1)))
- # so relabel
- H=nx.relabel_nodes(G, flatten)
- H.name="grid_graph(%s)"%dlabel
- return H
-
-def hypercube_graph(n):
- """Return the n-dimensional hypercube.
-
- Node labels are the integers 0 to 2**n - 1.
-
- """
- dim=n*[2]
- G=grid_graph(dim)
- G.name="hypercube_graph_(%d)"%n
- return G
-
-def ladder_graph(n,create_using=None):
- """Return the Ladder graph of length n.
-
- This is two rows of n nodes, with
- each pair connected by a single edge.
-
- Node labels are the integers 0 to 2*n - 1.
-
- """
- if create_using is not None and create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
- G=empty_graph(2*n,create_using)
- G.name="ladder_graph_(%d)"%n
- G.add_edges_from([(v,v+1) for v in range(n-1)])
- G.add_edges_from([(v,v+1) for v in range(n,2*n-1)])
- G.add_edges_from([(v,v+n) for v in range(n)])
- return G
-
-def lollipop_graph(m,n,create_using=None):
- """Return the Lollipop Graph; K_m connected to P_n.
-
- This is the Barbell Graph without the right barbell.
-
- For m>1 and n>=0, the complete graph K_m is connected to the
- path P_n. The resulting m+n nodes are labelled 0,...,m-1 for the
- complete graph and m,...,m+n-1 for the path. The 2 subgraphs
- are joined via the edge (m-1,m). If n=0, this is merely a complete
- graph.
-
- Node labels are the integers 0 to number_of_nodes - 1.
-
- (This graph is an extremal example in David Aldous and Jim
- Fill's etext on Random Walks on Graphs.)
-
- """
- if create_using is not None and create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
- if m<2:
- raise nx.NetworkXError(\
- "Invalid graph description, m should be >=2")
- if n<0:
- raise nx.NetworkXError(\
- "Invalid graph description, n should be >=0")
- # the ball
- G=complete_graph(m,create_using)
- # the stick
- G.add_nodes_from([v for v in range(m,m+n)])
- if n>1:
- G.add_edges_from([(v,v+1) for v in range(m,m+n-1)])
- # connect ball to stick
- if m>0: G.add_edge(m-1,m)
- G.name="lollipop_graph(%d,%d)"%(m,n)
- return G
-
-def null_graph(create_using=None):
- """ Return the Null graph with no nodes or edges.
-
- See empty_graph for the use of create_using.
-
- """
- G=empty_graph(0,create_using)
- G.name="null_graph()"
- return G
-
-def path_graph(n,create_using=None):
- """Return the Path graph P_n of n nodes linearly connected by n-1 edges.
-
- Node labels are the integers 0 to n - 1.
- If create_using is a DiGraph then the edges are directed in
- increasing order.
-
- """
- G=empty_graph(n,create_using)
- G.name="path_graph(%d)"%n
- G.add_edges_from([(v,v+1) for v in range(n-1)])
- return G
-
-def star_graph(n,create_using=None):
- """ Return the Star graph with n+1 nodes: one center node, connected to n outer nodes.
-
- Node labels are the integers 0 to n.
-
- """
- G=complete_bipartite_graph(1,n,create_using)
- G.name="star_graph(%d)"%n
- return G
-
-def trivial_graph(create_using=None):
- """ Return the Trivial graph with one node (with integer label 0) and no edges.
-
- """
- G=empty_graph(1,create_using)
- G.name="trivial_graph()"
- return G
-
-def wheel_graph(n,create_using=None):
- """ Return the wheel graph: a single hub node connected to each node of the (n-1)-node cycle graph.
-
- Node labels are the integers 0 to n - 1.
-
- """
- G=star_graph(n-1,create_using)
- G.name="wheel_graph(%d)"%n
- G.add_edges_from([(v,v+1) for v in range(1,n-1)])
- if n>2:
- G.add_edge(1,n-1)
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/degree_seq.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/degree_seq.py
deleted file mode 100644
index c5b8f95..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/degree_seq.py
+++ /dev/null
@@ -1,793 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Generate graphs with a given degree sequence or expected degree sequence.
-"""
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import heapq
-from itertools import combinations, permutations
-import math
-from operator import itemgetter
-import random
-import networkx as nx
-from networkx.utils import random_weighted_sample
-
-__author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Pieter Swart <swart@lanl.gov>',
- 'Dan Schult <dschult@colgate.edu>'
- 'Joel Miller <joel.c.miller.research@gmail.com>',
- 'Nathan Lemons <nlemons@gmail.com>'
- 'Brian Cloteaux <brian.cloteaux@nist.gov>'])
-
-__all__ = ['configuration_model',
- 'directed_configuration_model',
- 'expected_degree_graph',
- 'havel_hakimi_graph',
- 'directed_havel_hakimi_graph',
- 'degree_sequence_tree',
- 'random_degree_sequence_graph']
-
-
-def configuration_model(deg_sequence,create_using=None,seed=None):
- """Return a random graph with the given degree sequence.
-
- The configuration model generates a random pseudograph (graph with
- parallel edges and self loops) by randomly assigning edges to
- match the given degree sequence.
-
- Parameters
- ----------
- deg_sequence : list of integers
- Each list entry corresponds to the degree of a node.
- create_using : graph, optional (default MultiGraph)
- Return graph of this type. The instance will be cleared.
- seed : hashable object, optional
- Seed for random number generator.
-
- Returns
- -------
- G : MultiGraph
- A graph with the specified degree sequence.
- Nodes are labeled starting at 0 with an index
- corresponding to the position in deg_sequence.
-
- Raises
- ------
- NetworkXError
- If the degree sequence does not have an even sum.
-
- See Also
- --------
- is_valid_degree_sequence
-
- Notes
- -----
- As described by Newman [1]_.
-
- A non-graphical degree sequence (not realizable by some simple
- graph) is allowed since this function returns graphs with self
- loops and parallel edges. An exception is raised if the degree
- sequence does not have an even sum.
-
- This configuration model construction process can lead to
- duplicate edges and loops. You can remove the self-loops and
- parallel edges (see below) which will likely result in a graph
- that doesn't have the exact degree sequence specified. This
- "finite-size effect" decreases as the size of the graph increases.
-
- References
- ----------
- .. [1] M.E.J. Newman, "The structure and function of complex networks",
- SIAM REVIEW 45-2, pp 167-256, 2003.
-
- Examples
- --------
- >>> from networkx.utils import powerlaw_sequence
- >>> z=nx.utils.create_degree_sequence(100,powerlaw_sequence)
- >>> G=nx.configuration_model(z)
-
- To remove parallel edges:
-
- >>> G=nx.Graph(G)
-
- To remove self loops:
-
- >>> G.remove_edges_from(G.selfloop_edges())
- """
- if not sum(deg_sequence)%2 ==0:
- raise nx.NetworkXError('Invalid degree sequence')
-
- if create_using is None:
- create_using = nx.MultiGraph()
- elif create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
-
- if not seed is None:
- random.seed(seed)
-
- # start with empty N-node graph
- N=len(deg_sequence)
-
- # allow multiedges and selfloops
- G=nx.empty_graph(N,create_using)
-
- if N==0 or max(deg_sequence)==0: # done if no edges
- return G
-
- # build stublist, a list of available degree-repeated stubs
- # e.g. for deg_sequence=[3,2,1,1,1]
- # initially, stublist=[1,1,1,2,2,3,4,5]
- # i.e., node 1 has degree=3 and is repeated 3 times, etc.
- stublist=[]
- for n in G:
- for i in range(deg_sequence[n]):
- stublist.append(n)
-
- # shuffle stublist and assign pairs by removing 2 elements at a time
- random.shuffle(stublist)
- while stublist:
- n1 = stublist.pop()
- n2 = stublist.pop()
- G.add_edge(n1,n2)
-
- G.name="configuration_model %d nodes %d edges"%(G.order(),G.size())
- return G
-
-
-def directed_configuration_model(in_degree_sequence,
- out_degree_sequence,
- create_using=None,seed=None):
- """Return a directed_random graph with the given degree sequences.
-
- The configuration model generates a random directed pseudograph
- (graph with parallel edges and self loops) by randomly assigning
- edges to match the given degree sequences.
-
- Parameters
- ----------
- in_degree_sequence : list of integers
- Each list entry corresponds to the in-degree of a node.
- out_degree_sequence : list of integers
- Each list entry corresponds to the out-degree of a node.
- create_using : graph, optional (default MultiDiGraph)
- Return graph of this type. The instance will be cleared.
- seed : hashable object, optional
- Seed for random number generator.
-
- Returns
- -------
- G : MultiDiGraph
- A graph with the specified degree sequences.
- Nodes are labeled starting at 0 with an index
- corresponding to the position in deg_sequence.
-
- Raises
- ------
- NetworkXError
- If the degree sequences do not have the same sum.
-
- See Also
- --------
- configuration_model
-
- Notes
- -----
- Algorithm as described by Newman [1]_.
-
- A non-graphical degree sequence (not realizable by some simple
- graph) is allowed since this function returns graphs with self
- loops and parallel edges. An exception is raised if the degree
- sequences does not have the same sum.
-
- This configuration model construction process can lead to
- duplicate edges and loops. You can remove the self-loops and
- parallel edges (see below) which will likely result in a graph
- that doesn't have the exact degree sequence specified. This
- "finite-size effect" decreases as the size of the graph increases.
-
- References
- ----------
- .. [1] Newman, M. E. J. and Strogatz, S. H. and Watts, D. J.
- Random graphs with arbitrary degree distributions and their applications
- Phys. Rev. E, 64, 026118 (2001)
-
- Examples
- --------
- >>> D=nx.DiGraph([(0,1),(1,2),(2,3)]) # directed path graph
- >>> din=list(D.in_degree().values())
- >>> dout=list(D.out_degree().values())
- >>> din.append(1)
- >>> dout[0]=2
- >>> D=nx.directed_configuration_model(din,dout)
-
- To remove parallel edges:
-
- >>> D=nx.DiGraph(D)
-
- To remove self loops:
-
- >>> D.remove_edges_from(D.selfloop_edges())
- """
- if not sum(in_degree_sequence) == sum(out_degree_sequence):
- raise nx.NetworkXError('Invalid degree sequences. '
- 'Sequences must have equal sums.')
-
- if create_using is None:
- create_using = nx.MultiDiGraph()
-
- if not seed is None:
- random.seed(seed)
-
- nin=len(in_degree_sequence)
- nout=len(out_degree_sequence)
-
- # pad in- or out-degree sequence with zeros to match lengths
- if nin>nout:
- out_degree_sequence.extend((nin-nout)*[0])
- else:
- in_degree_sequence.extend((nout-nin)*[0])
-
- # start with empty N-node graph
- N=len(in_degree_sequence)
-
- # allow multiedges and selfloops
- G=nx.empty_graph(N,create_using)
-
- if N==0 or max(in_degree_sequence)==0: # done if no edges
- return G
-
- # build stublists of available degree-repeated stubs
- # e.g. for degree_sequence=[3,2,1,1,1]
- # initially, stublist=[1,1,1,2,2,3,4,5]
- # i.e., node 1 has degree=3 and is repeated 3 times, etc.
- in_stublist=[]
- for n in G:
- for i in range(in_degree_sequence[n]):
- in_stublist.append(n)
-
- out_stublist=[]
- for n in G:
- for i in range(out_degree_sequence[n]):
- out_stublist.append(n)
-
- # shuffle stublists and assign pairs by removing 2 elements at a time
- random.shuffle(in_stublist)
- random.shuffle(out_stublist)
- while in_stublist and out_stublist:
- source = out_stublist.pop()
- target = in_stublist.pop()
- G.add_edge(source,target)
-
- G.name="directed configuration_model %d nodes %d edges"%(G.order(),G.size())
- return G
-
-
-def expected_degree_graph(w, seed=None, selfloops=True):
- r"""Return a random graph with given expected degrees.
-
- Given a sequence of expected degrees `W=(w_0,w_1,\ldots,w_{n-1}`)
- of length `n` this algorithm assigns an edge between node `u` and
- node `v` with probability
-
- .. math::
-
- p_{uv} = \frac{w_u w_v}{\sum_k w_k} .
-
- Parameters
- ----------
- w : list
- The list of expected degrees.
- selfloops: bool (default=True)
- Set to False to remove the possibility of self-loop edges.
- seed : hashable object, optional
- The seed for the random number generator.
-
- Returns
- -------
- Graph
-
- Examples
- --------
- >>> z=[10 for i in range(100)]
- >>> G=nx.expected_degree_graph(z)
-
- Notes
- -----
- The nodes have integer labels corresponding to index of expected degrees
- input sequence.
-
- The complexity of this algorithm is `\mathcal{O}(n+m)` where `n` is the
- number of nodes and `m` is the expected number of edges.
-
- The model in [1]_ includes the possibility of self-loop edges.
- Set selfloops=False to produce a graph without self loops.
-
- For finite graphs this model doesn't produce exactly the given
- expected degree sequence. Instead the expected degrees are as
- follows.
-
- For the case without self loops (selfloops=False),
-
- .. math::
-
- E[deg(u)] = \sum_{v \ne u} p_{uv}
- = w_u \left( 1 - \frac{w_u}{\sum_k w_k} \right) .
-
-
- NetworkX uses the standard convention that a self-loop edge counts 2
- in the degree of a node, so with self loops (selfloops=True),
-
- .. math::
-
- E[deg(u)] = \sum_{v \ne u} p_{uv} + 2 p_{uu}
- = w_u \left( 1 + \frac{w_u}{\sum_k w_k} \right) .
-
- References
- ----------
- .. [1] Fan Chung and L. Lu, Connected components in random graphs with
- given expected degree sequences, Ann. Combinatorics, 6,
- pp. 125-145, 2002.
- .. [2] Joel Miller and Aric Hagberg,
- Efficient generation of networks with given expected degrees,
- in Algorithms and Models for the Web-Graph (WAW 2011),
- Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,
- pp. 115-126, 2011.
- """
- n = len(w)
- G=nx.empty_graph(n)
- if n==0 or max(w)==0: # done if no edges
- return G
- if seed is not None:
- random.seed(seed)
- rho = 1/float(sum(w))
- # sort weights, largest first
- # preserve order of weights for integer node label mapping
- order = sorted(enumerate(w),key=itemgetter(1),reverse=True)
- mapping = dict((c,uv[0]) for c,uv in enumerate(order))
- seq = [v for u,v in order]
- last=n
- if not selfloops:
- last-=1
- for u in range(last):
- v = u
- if not selfloops:
- v += 1
- factor = seq[u] * rho
- p = seq[v]*factor
- if p>1:
- p = 1
- while v<n and p>0:
- if p != 1:
- r = random.random()
- v += int(math.floor(math.log(r)/math.log(1-p)))
- if v < n:
- q = seq[v]*factor
- if q>1:
- q = 1
- if random.random() < q/p:
- G.add_edge(mapping[u],mapping[v])
- v += 1
- p = q
- return G
-
-def havel_hakimi_graph(deg_sequence,create_using=None):
- """Return a simple graph with given degree sequence constructed
- using the Havel-Hakimi algorithm.
-
- Parameters
- ----------
- deg_sequence: list of integers
- Each integer corresponds to the degree of a node (need not be sorted).
- create_using : graph, optional (default Graph)
- Return graph of this type. The instance will be cleared.
- Directed graphs are not allowed.
-
- Raises
- ------
- NetworkXException
- For a non-graphical degree sequence (i.e. one
- not realizable by some simple graph).
-
- Notes
- -----
- The Havel-Hakimi algorithm constructs a simple graph by
- successively connecting the node of highest degree to other nodes
- of highest degree, resorting remaining nodes by degree, and
- repeating the process. The resulting graph has a high
- degree-associativity. Nodes are labeled 1,.., len(deg_sequence),
- corresponding to their position in deg_sequence.
-
- The basic algorithm is from Hakimi [1]_ and was generalized by
- Kleitman and Wang [2]_.
-
- References
- ----------
- .. [1] Hakimi S., On Realizability of a Set of Integers as
- Degrees of the Vertices of a Linear Graph. I,
- Journal of SIAM, 10(3), pp. 496-506 (1962)
- .. [2] Kleitman D.J. and Wang D.L.
- Algorithms for Constructing Graphs and Digraphs with Given Valences
- and Factors Discrete Mathematics, 6(1), pp. 79-88 (1973)
- """
- if not nx.is_valid_degree_sequence(deg_sequence):
- raise nx.NetworkXError('Invalid degree sequence')
- if create_using is not None:
- if create_using.is_directed():
- raise nx.NetworkXError("Directed graphs are not supported")
-
- p = len(deg_sequence)
- G=nx.empty_graph(p,create_using)
- num_degs = []
- for i in range(p):
- num_degs.append([])
- dmax, dsum, n = 0, 0, 0
- for d in deg_sequence:
- # Process only the non-zero integers
- if d>0:
- num_degs[d].append(n)
- dmax, dsum, n = max(dmax,d), dsum+d, n+1
- # Return graph if no edges
- if n==0:
- return G
-
- modstubs = [(0,0)]*(dmax+1)
- # Successively reduce degree sequence by removing the maximum degree
- while n > 0:
- # Retrieve the maximum degree in the sequence
- while len(num_degs[dmax]) == 0:
- dmax -= 1;
- # If there are not enough stubs to connect to, then the sequence is
- # not graphical
- if dmax > n-1:
- raise nx.NetworkXError('Non-graphical integer sequence')
-
- # Remove largest stub in list
- source = num_degs[dmax].pop()
- n -= 1
- # Reduce the next dmax largest stubs
- mslen = 0
- k = dmax
- for i in range(dmax):
- while len(num_degs[k]) == 0:
- k -= 1
- target = num_degs[k].pop()
- G.add_edge(source, target)
- n -= 1
- if k > 1:
- modstubs[mslen] = (k-1,target)
- mslen += 1
- # Add back to the list any nonzero stubs that were removed
- for i in range(mslen):
- (stubval, stubtarget) = modstubs[i]
- num_degs[stubval].append(stubtarget)
- n += 1
-
- G.name="havel_hakimi_graph %d nodes %d edges"%(G.order(),G.size())
- return G
-
-def directed_havel_hakimi_graph(in_deg_sequence,
- out_deg_sequence,
- create_using=None):
- """Return a directed graph with the given degree sequences.
-
- Parameters
- ----------
- in_deg_sequence : list of integers
- Each list entry corresponds to the in-degree of a node.
- out_deg_sequence : list of integers
- Each list entry corresponds to the out-degree of a node.
- create_using : graph, optional (default DiGraph)
- Return graph of this type. The instance will be cleared.
-
- Returns
- -------
- G : DiGraph
- A graph with the specified degree sequences.
- Nodes are labeled starting at 0 with an index
- corresponding to the position in deg_sequence
-
- Raises
- ------
- NetworkXError
- If the degree sequences are not digraphical.
-
- See Also
- --------
- configuration_model
-
- Notes
- -----
- Algorithm as described by Kleitman and Wang [1]_.
-
- References
- ----------
- .. [1] D.J. Kleitman and D.L. Wang
- Algorithms for Constructing Graphs and Digraphs with Given Valences
- and Factors Discrete Mathematics, 6(1), pp. 79-88 (1973)
- """
- assert(nx.utils.is_list_of_ints(in_deg_sequence))
- assert(nx.utils.is_list_of_ints(out_deg_sequence))
-
- if create_using is None:
- create_using = nx.DiGraph()
-
- # Process the sequences and form two heaps to store degree pairs with
- # either zero or nonzero out degrees
- sumin, sumout, nin, nout = 0, 0, len(in_deg_sequence), len(out_deg_sequence)
- maxn = max(nin, nout)
- G = nx.empty_graph(maxn,create_using)
- if maxn==0:
- return G
- maxin = 0
- stubheap, zeroheap = [ ], [ ]
- for n in range(maxn):
- in_deg, out_deg = 0, 0
- if n<nout:
- out_deg = out_deg_sequence[n]
- if n<nin:
- in_deg = in_deg_sequence[n]
- if in_deg<0 or out_deg<0:
- raise nx.NetworkXError(
- 'Invalid degree sequences. Sequence values must be positive.')
- sumin, sumout, maxin = sumin+in_deg, sumout+out_deg, max(maxin, in_deg)
- if in_deg > 0:
- stubheap.append((-1*out_deg, -1*in_deg,n))
- elif out_deg > 0:
- zeroheap.append((-1*out_deg,n))
- if sumin != sumout:
- raise nx.NetworkXError(
- 'Invalid degree sequences. Sequences must have equal sums.')
- heapq.heapify(stubheap)
- heapq.heapify(zeroheap)
-
- modstubs = [(0,0,0)]*(maxin+1)
- # Successively reduce degree sequence by removing the maximum
- while stubheap:
- # Remove first value in the sequence with a non-zero in degree
- (freeout, freein, target) = heapq.heappop(stubheap)
- freein *= -1
- if freein > len(stubheap)+len(zeroheap):
- raise nx.NetworkXError('Non-digraphical integer sequence')
-
- # Attach arcs from the nodes with the most stubs
- mslen = 0
- for i in range(freein):
- if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0][0]):
- (stubout, stubsource) = heapq.heappop(zeroheap)
- stubin = 0
- else:
- (stubout, stubin, stubsource) = heapq.heappop(stubheap)
- if stubout == 0:
- raise nx.NetworkXError('Non-digraphical integer sequence')
- G.add_edge(stubsource, target)
- # Check if source is now totally connected
- if stubout+1<0 or stubin<0:
- modstubs[mslen] = (stubout+1, stubin, stubsource)
- mslen += 1
-
- # Add the nodes back to the heaps that still have available stubs
- for i in range(mslen):
- stub = modstubs[i]
- if stub[1] < 0:
- heapq.heappush(stubheap, stub)
- else:
- heapq.heappush(zeroheap, (stub[0], stub[2]))
- if freeout<0:
- heapq.heappush(zeroheap, (freeout, target))
-
- G.name="directed_havel_hakimi_graph %d nodes %d edges"%(G.order(),G.size())
- return G
-
-def degree_sequence_tree(deg_sequence,create_using=None):
- """Make a tree for the given degree sequence.
-
- A tree has #nodes-#edges=1 so
- the degree sequence must have
- len(deg_sequence)-sum(deg_sequence)/2=1
- """
-
- if not len(deg_sequence)-sum(deg_sequence)/2.0 == 1.0:
- raise nx.NetworkXError("Degree sequence invalid")
- if create_using is not None and create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
-
- # single node tree
- if len(deg_sequence)==1:
- G=nx.empty_graph(0,create_using)
- return G
-
- # all degrees greater than 1
- deg=[s for s in deg_sequence if s>1]
- deg.sort(reverse=True)
-
- # make path graph as backbone
- n=len(deg)+2
- G=nx.path_graph(n,create_using)
- last=n
-
- # add the leaves
- for source in range(1,n-1):
- nedges=deg.pop()-2
- for target in range(last,last+nedges):
- G.add_edge(source, target)
- last+=nedges
-
- # in case we added one too many
- if len(G.degree())>len(deg_sequence):
- G.remove_node(0)
- return G
-
-def random_degree_sequence_graph(sequence, seed=None, tries=10):
- r"""Return a simple random graph with the given degree sequence.
-
- If the maximum degree `d_m` in the sequence is `O(m^{1/4})` then the
- algorithm produces almost uniform random graphs in `O(m d_m)` time
- where `m` is the number of edges.
-
- Parameters
- ----------
- sequence : list of integers
- Sequence of degrees
- seed : hashable object, optional
- Seed for random number generator
- tries : int, optional
- Maximum number of tries to create a graph
-
- Returns
- -------
- G : Graph
- A graph with the specified degree sequence.
- Nodes are labeled starting at 0 with an index
- corresponding to the position in the sequence.
-
- Raises
- ------
- NetworkXUnfeasible
- If the degree sequence is not graphical.
- NetworkXError
- If a graph is not produced in specified number of tries
-
- See Also
- --------
- is_valid_degree_sequence, configuration_model
-
- Notes
- -----
- The generator algorithm [1]_ is not guaranteed to produce a graph.
-
- References
- ----------
- .. [1] Moshen Bayati, Jeong Han Kim, and Amin Saberi,
- A sequential algorithm for generating random graphs.
- Algorithmica, Volume 58, Number 4, 860-910,
- DOI: 10.1007/s00453-009-9340-1
-
- Examples
- --------
- >>> sequence = [1, 2, 2, 3]
- >>> G = nx.random_degree_sequence_graph(sequence)
- >>> sorted(G.degree().values())
- [1, 2, 2, 3]
- """
- DSRG = DegreeSequenceRandomGraph(sequence, seed=seed)
- for try_n in range(tries):
- try:
- return DSRG.generate()
- except nx.NetworkXUnfeasible:
- pass
- raise nx.NetworkXError('failed to generate graph in %d tries'%tries)
-
-class DegreeSequenceRandomGraph(object):
- # class to generate random graphs with a given degree sequence
- # use random_degree_sequence_graph()
- def __init__(self, degree, seed=None):
- if not nx.is_valid_degree_sequence(degree):
- raise nx.NetworkXUnfeasible('degree sequence is not graphical')
- if seed is not None:
- random.seed(seed)
- self.degree = list(degree)
- # node labels are integers 0,...,n-1
- self.m = sum(self.degree)/2.0 # number of edges
- try:
- self.dmax = max(self.degree) # maximum degree
- except ValueError:
- self.dmax = 0
-
- def generate(self):
- # remaining_degree is mapping from int->remaining degree
- self.remaining_degree = dict(enumerate(self.degree))
- # add all nodes to make sure we get isolated nodes
- self.graph = nx.Graph()
- self.graph.add_nodes_from(self.remaining_degree)
- # remove zero degree nodes
- for n,d in list(self.remaining_degree.items()):
- if d == 0:
- del self.remaining_degree[n]
- if len(self.remaining_degree) > 0:
- # build graph in three phases according to how many unmatched edges
- self.phase1()
- self.phase2()
- self.phase3()
- return self.graph
-
- def update_remaining(self, u, v, aux_graph=None):
- # decrement remaining nodes, modify auxilliary graph if in phase3
- if aux_graph is not None:
- # remove edges from auxilliary graph
- aux_graph.remove_edge(u,v)
- if self.remaining_degree[u] == 1:
- del self.remaining_degree[u]
- if aux_graph is not None:
- aux_graph.remove_node(u)
- else:
- self.remaining_degree[u] -= 1
- if self.remaining_degree[v] == 1:
- del self.remaining_degree[v]
- if aux_graph is not None:
- aux_graph.remove_node(v)
- else:
- self.remaining_degree[v] -= 1
-
- def p(self,u,v):
- # degree probability
- return 1 - self.degree[u]*self.degree[v]/(4.0*self.m)
-
- def q(self,u,v):
- # remaining degree probability
- norm = float(max(self.remaining_degree.values()))**2
- return self.remaining_degree[u]*self.remaining_degree[v]/norm
-
- def suitable_edge(self):
- # Check if there is a suitable edge that is not in the graph
- # True if an (arbitrary) remaining node has at least one possible
- # connection to another remaining node
- nodes = iter(self.remaining_degree)
- u = next(nodes) # one arbitrary node
- for v in nodes: # loop over all other remaining nodes
- if not self.graph.has_edge(u, v):
- return True
- return False
-
- def phase1(self):
- # choose node pairs from (degree) weighted distribution
- while sum(self.remaining_degree.values()) >= 2 * self.dmax**2:
- u,v = sorted(random_weighted_sample(self.remaining_degree, 2))
- if self.graph.has_edge(u,v):
- continue
- if random.random() < self.p(u,v): # accept edge
- self.graph.add_edge(u,v)
- self.update_remaining(u,v)
-
- def phase2(self):
- # choose remaining nodes uniformly at random and use rejection sampling
- while len(self.remaining_degree) >= 2 * self.dmax:
- norm = float(max(self.remaining_degree.values()))**2
- while True:
- u,v = sorted(random.sample(self.remaining_degree.keys(), 2))
- if self.graph.has_edge(u,v):
- continue
- if random.random() < self.q(u,v):
- break
- if random.random() < self.p(u,v): # accept edge
- self.graph.add_edge(u,v)
- self.update_remaining(u,v)
-
- def phase3(self):
- # build potential remaining edges and choose with rejection sampling
- potential_edges = combinations(self.remaining_degree, 2)
- # build auxilliary graph of potential edges not already in graph
- H = nx.Graph([(u,v) for (u,v) in potential_edges
- if not self.graph.has_edge(u,v)])
- while self.remaining_degree:
- if not self.suitable_edge():
- raise nx.NetworkXUnfeasible('no suitable edges left')
- while True:
- u,v = sorted(random.choice(H.edges()))
- if random.random() < self.q(u,v):
- break
- if random.random() < self.p(u,v): # accept edge
- self.graph.add_edge(u,v)
- self.update_remaining(u,v, aux_graph=H)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/directed.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/directed.py
deleted file mode 100644
index d1dc712..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/directed.py
+++ /dev/null
@@ -1,304 +0,0 @@
-"""
-Generators for some directed graphs.
-
-gn_graph: growing network
-gnc_graph: growing network with copying
-gnr_graph: growing network with redirection
-scale_free_graph: scale free directed graph
-
-"""
-# Copyright (C) 2006-2009 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ ="""Aric Hagberg (hagberg@lanl.gov)\nWillem Ligtenberg (W.P.A.Ligtenberg@tue.nl)"""
-
-__all__ = ['gn_graph', 'gnc_graph', 'gnr_graph','scale_free_graph']
-
-import random
-
-import networkx as nx
-from networkx.generators.classic import empty_graph
-from networkx.utils import discrete_sequence
-
-
-def gn_graph(n,kernel=None,create_using=None,seed=None):
- """Return the GN digraph with n nodes.
-
- The GN (growing network) graph is built by adding nodes one at a time with
- a link to one previously added node. The target node for the link is
- chosen with probability based on degree. The default attachment kernel is
- a linear function of degree.
-
- The graph is always a (directed) tree.
-
- Parameters
- ----------
- n : int
- The number of nodes for the generated graph.
- kernel : function
- The attachment kernel.
- create_using : graph, optional (default DiGraph)
- Return graph of this type. The instance will be cleared.
- seed : hashable object, optional
- The seed for the random number generator.
-
- Examples
- --------
- >>> D=nx.gn_graph(10) # the GN graph
- >>> G=D.to_undirected() # the undirected version
-
- To specify an attachment kernel use the kernel keyword
-
- >>> D=nx.gn_graph(10,kernel=lambda x:x**1.5) # A_k=k^1.5
-
- References
- ----------
- .. [1] P. L. Krapivsky and S. Redner,
- Organization of Growing Random Networks,
- Phys. Rev. E, 63, 066123, 2001.
- """
- if create_using is None:
- create_using = nx.DiGraph()
- elif not create_using.is_directed():
- raise nx.NetworkXError("Directed Graph required in create_using")
-
- if kernel is None:
- kernel = lambda x: x
-
- if seed is not None:
- random.seed(seed)
-
- G=empty_graph(1,create_using)
- G.name="gn_graph(%s)"%(n)
-
- if n==1:
- return G
-
- G.add_edge(1,0) # get started
- ds=[1,1] # degree sequence
-
- for source in range(2,n):
- # compute distribution from kernel and degree
- dist=[kernel(d) for d in ds]
- # choose target from discrete distribution
- target=discrete_sequence(1,distribution=dist)[0]
- G.add_edge(source,target)
- ds.append(1) # the source has only one link (degree one)
- ds[target]+=1 # add one to the target link degree
- return G
-
-
-def gnr_graph(n,p,create_using=None,seed=None):
- """Return the GNR digraph with n nodes and redirection probability p.
-
- The GNR (growing network with redirection) graph is built by adding nodes
- one at a time with a link to one previously added node. The previous
- target node is chosen uniformly at random. With probabiliy p the link is
- instead "redirected" to the successor node of the target. The graph is
- always a (directed) tree.
-
- Parameters
- ----------
- n : int
- The number of nodes for the generated graph.
- p : float
- The redirection probability.
- create_using : graph, optional (default DiGraph)
- Return graph of this type. The instance will be cleared.
- seed : hashable object, optional
- The seed for the random number generator.
-
- Examples
- --------
- >>> D=nx.gnr_graph(10,0.5) # the GNR graph
- >>> G=D.to_undirected() # the undirected version
-
- References
- ----------
- .. [1] P. L. Krapivsky and S. Redner,
- Organization of Growing Random Networks,
- Phys. Rev. E, 63, 066123, 2001.
- """
- if create_using is None:
- create_using = nx.DiGraph()
- elif not create_using.is_directed():
- raise nx.NetworkXError("Directed Graph required in create_using")
-
- if not seed is None:
- random.seed(seed)
-
- G=empty_graph(1,create_using)
- G.name="gnr_graph(%s,%s)"%(n,p)
-
- if n==1:
- return G
-
- for source in range(1,n):
- target=random.randrange(0,source)
- if random.random() < p and target !=0:
- target=G.successors(target)[0]
- G.add_edge(source,target)
-
- return G
-
-
-def gnc_graph(n,create_using=None,seed=None):
- """Return the GNC digraph with n nodes.
-
- The GNC (growing network with copying) graph is built by adding nodes one
- at a time with a links to one previously added node (chosen uniformly at
- random) and to all of that node's successors.
-
- Parameters
- ----------
- n : int
- The number of nodes for the generated graph.
- create_using : graph, optional (default DiGraph)
- Return graph of this type. The instance will be cleared.
- seed : hashable object, optional
- The seed for the random number generator.
-
- References
- ----------
- .. [1] P. L. Krapivsky and S. Redner,
- Network Growth by Copying,
- Phys. Rev. E, 71, 036118, 2005k.},
- """
- if create_using is None:
- create_using = nx.DiGraph()
- elif not create_using.is_directed():
- raise nx.NetworkXError("Directed Graph required in create_using")
-
- if not seed is None:
- random.seed(seed)
-
- G=empty_graph(1,create_using)
- G.name="gnc_graph(%s)"%(n)
-
- if n==1:
- return G
-
- for source in range(1,n):
- target=random.randrange(0,source)
- for succ in G.successors(target):
- G.add_edge(source,succ)
- G.add_edge(source,target)
-
- return G
-
-
-def scale_free_graph(n,
- alpha=0.41,
- beta=0.54,
- gamma=0.05,
- delta_in=0.2,
- delta_out=0,
- create_using=None,
- seed=None):
- """Return a scale free directed graph.
-
- Parameters
- ----------
- n : integer
- Number of nodes in graph
- alpha : float
- Probability for adding a new node connected to an existing node
- chosen randomly according to the in-degree distribution.
- beta : float
- Probability for adding an edge between two existing nodes.
- One existing node is chosen randomly according the in-degree
- distribution and the other chosen randomly according to the out-degree
- distribution.
- gamma : float
- Probability for adding a new node conecgted to an existing node
- chosen randomly according to the out-degree distribution.
- delta_in : float
- Bias for choosing ndoes from in-degree distribution.
- delta_out : float
- Bias for choosing ndoes from out-degree distribution.
- create_using : graph, optional (default MultiDiGraph)
- Use this graph instance to start the process (default=3-cycle).
- seed : integer, optional
- Seed for random number generator
-
- Examples
- --------
- >>> G=nx.scale_free_graph(100)
-
- Notes
- -----
- The sum of alpha, beta, and gamma must be 1.
-
- References
- ----------
- .. [1] B. Bollob{\'a}s, C. Borgs, J. Chayes, and O. Riordan,
- Directed scale-free graphs,
- Proceedings of the fourteenth annual ACM-SIAM symposium on
- Discrete algorithms, 132--139, 2003.
- """
-
- def _choose_node(G,distribution,delta):
- cumsum=0.0
- # normalization
- psum=float(sum(distribution.values()))+float(delta)*len(distribution)
- r=random.random()
- for i in range(0,len(distribution)):
- cumsum+=(distribution[i]+delta)/psum
- if r < cumsum:
- break
- return i
-
- if create_using is None:
- # start with 3-cycle
- G = nx.MultiDiGraph()
- G.add_edges_from([(0,1),(1,2),(2,0)])
- else:
- # keep existing graph structure?
- G = create_using
- if not (G.is_directed() and G.is_multigraph()):
- raise nx.NetworkXError(\
- "MultiDiGraph required in create_using")
-
- if alpha <= 0:
- raise ValueError('alpha must be >= 0.')
- if beta <= 0:
- raise ValueError('beta must be >= 0.')
- if gamma <= 0:
- raise ValueError('beta must be >= 0.')
-
- if alpha+beta+gamma !=1.0:
- raise ValueError('alpha+beta+gamma must equal 1.')
-
- G.name="directed_scale_free_graph(%s,alpha=%s,beta=%s,gamma=%s,delta_in=%s,delta_out=%s)"%(n,alpha,beta,gamma,delta_in,delta_out)
-
- # seed random number generated (uses None as default)
- random.seed(seed)
-
- while len(G)<n:
- r = random.random()
- # random choice in alpha,beta,gamma ranges
- if r<alpha:
- # alpha
- # add new node v
- v = len(G)
- # choose w according to in-degree and delta_in
- w = _choose_node(G, G.in_degree(),delta_in)
- elif r < alpha+beta:
- # beta
- # choose v according to out-degree and delta_out
- v = _choose_node(G, G.out_degree(),delta_out)
- # choose w according to in-degree and delta_in
- w = _choose_node(G, G.in_degree(),delta_in)
- else:
- # gamma
- # choose v according to out-degree and delta_out
- v = _choose_node(G, G.out_degree(),delta_out)
- # add new node w
- w = len(G)
- G.add_edge(v,w)
-
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/ego.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/ego.py
deleted file mode 100644
index 15e5bc2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/ego.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""
-Ego graph.
-"""
-# Copyright (C) 2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """\n""".join(['Drew Conway <drew.conway@nyu.edu>',
- 'Aric Hagberg <hagberg@lanl.gov>'])
-__all__ = ['ego_graph']
-
-import networkx as nx
-
-def ego_graph(G,n,radius=1,center=True,undirected=False,distance=None):
- """Returns induced subgraph of neighbors centered at node n within
- a given radius.
-
- Parameters
- ----------
- G : graph
- A NetworkX Graph or DiGraph
-
- n : node
- A single node
-
- radius : number, optional
- Include all neighbors of distance<=radius from n.
-
- center : bool, optional
- If False, do not include center node in graph
-
- undirected : bool, optional
- If True use both in- and out-neighbors of directed graphs.
-
- distance : key, optional
- Use specified edge data key as distance. For example, setting
- distance='weight' will use the edge weight to measure the
- distance from the node n.
-
- Notes
- -----
- For directed graphs D this produces the "out" neighborhood
- or successors. If you want the neighborhood of predecessors
- first reverse the graph with D.reverse(). If you want both
- directions use the keyword argument undirected=True.
-
- Node, edge, and graph attributes are copied to the returned subgraph.
- """
- if undirected:
- if distance is not None:
- sp,_=nx.single_source_dijkstra(G.to_undirected(),
- n,cutoff=radius,
- weight=distance)
- else:
- sp=nx.single_source_shortest_path_length(G.to_undirected(),
- n,cutoff=radius)
- else:
- if distance is not None:
- sp,_=nx.single_source_dijkstra(G,
- n,cutoff=radius,
- weight=distance)
- else:
- sp=nx.single_source_shortest_path_length(G,n,cutoff=radius)
-
- H=G.subgraph(sp).copy()
- if not center:
- H.remove_node(n)
- return H
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/geometric.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/geometric.py
deleted file mode 100644
index b64e0c2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/geometric.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Generators for geometric graphs.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-from __future__ import print_function
-
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Dan Schult (dschult@colgate.edu)',
- 'Ben Edwards (BJEdwards@gmail.com)'])
-
-__all__ = ['random_geometric_graph',
- 'waxman_graph',
- 'geographical_threshold_graph',
- 'navigable_small_world_graph']
-
-from bisect import bisect_left
-from functools import reduce
-from itertools import product
-import math, random, sys
-import networkx as nx
-
-#---------------------------------------------------------------------------
-# Random Geometric Graphs
-#---------------------------------------------------------------------------
-
-def random_geometric_graph(n, radius, dim=2, pos=None):
- r"""Return the random geometric graph in the unit cube.
-
- The random geometric graph model places n nodes uniformly at random
- in the unit cube Two nodes `u,v` are connected with an edge if
- `d(u,v)<=r` where `d` is the Euclidean distance and `r` is a radius
- threshold.
-
- Parameters
- ----------
- n : int
- Number of nodes
- radius: float
- Distance threshold value
- dim : int, optional
- Dimension of graph
- pos : dict, optional
- A dictionary keyed by node with node positions as values.
-
- Returns
- -------
- Graph
-
- Examples
- --------
- >>> G = nx.random_geometric_graph(20,0.1)
-
- Notes
- -----
- This uses an `n^2` algorithm to build the graph. A faster algorithm
- is possible using k-d trees.
-
- The pos keyword can be used to specify node positions so you can create
- an arbitrary distribution and domain for positions. If you need a distance
- function other than Euclidean you'll have to hack the algorithm.
-
- E.g to use a 2d Gaussian distribution of node positions with mean (0,0)
- and std. dev. 2
-
- >>> import random
- >>> n=20
- >>> p=dict((i,(random.gauss(0,2),random.gauss(0,2))) for i in range(n))
- >>> G = nx.random_geometric_graph(n,0.2,pos=p)
-
- References
- ----------
- .. [1] Penrose, Mathew, Random Geometric Graphs,
- Oxford Studies in Probability, 5, 2003.
- """
- G=nx.Graph()
- G.name="Random Geometric Graph"
- G.add_nodes_from(range(n))
- if pos is None:
- # random positions
- for n in G:
- G.node[n]['pos']=[random.random() for i in range(0,dim)]
- else:
- nx.set_node_attributes(G,'pos',pos)
- # connect nodes within "radius" of each other
- # n^2 algorithm, could use a k-d tree implementation
- nodes = G.nodes(data=True)
- while nodes:
- u,du = nodes.pop()
- pu = du['pos']
- for v,dv in nodes:
- pv = dv['pos']
- d = sum(((a-b)**2 for a,b in zip(pu,pv)))
- if d <= radius**2:
- G.add_edge(u,v)
- return G
-
-def geographical_threshold_graph(n, theta, alpha=2, dim=2,
- pos=None, weight=None):
- r"""Return a geographical threshold graph.
-
- The geographical threshold graph model places n nodes uniformly at random
- in a rectangular domain. Each node `u` is assigned a weight `w_u`.
- Two nodes `u,v` are connected with an edge if
-
- .. math::
-
- w_u + w_v \ge \theta r^{\alpha}
-
- where `r` is the Euclidean distance between `u` and `v`,
- and `\theta`, `\alpha` are parameters.
-
- Parameters
- ----------
- n : int
- Number of nodes
- theta: float
- Threshold value
- alpha: float, optional
- Exponent of distance function
- dim : int, optional
- Dimension of graph
- pos : dict
- Node positions as a dictionary of tuples keyed by node.
- weight : dict
- Node weights as a dictionary of numbers keyed by node.
-
- Returns
- -------
- Graph
-
- Examples
- --------
- >>> G = nx.geographical_threshold_graph(20,50)
-
- Notes
- -----
- If weights are not specified they are assigned to nodes by drawing randomly
- from an the exponential distribution with rate parameter `\lambda=1`.
- To specify a weights from a different distribution assign them to a
- dictionary and pass it as the weight= keyword
-
- >>> import random
- >>> n = 20
- >>> w=dict((i,random.expovariate(5.0)) for i in range(n))
- >>> G = nx.geographical_threshold_graph(20,50,weight=w)
-
- If node positions are not specified they are randomly assigned from the
- uniform distribution.
-
- References
- ----------
- .. [1] Masuda, N., Miwa, H., Konno, N.:
- Geographical threshold graphs with small-world and scale-free properties.
- Physical Review E 71, 036108 (2005)
- .. [2] Milan Bradonjić, Aric Hagberg and Allon G. Percus,
- Giant component and connectivity in geographical threshold graphs,
- in Algorithms and Models for the Web-Graph (WAW 2007),
- Antony Bonato and Fan Chung (Eds), pp. 209--216, 2007
- """
- G=nx.Graph()
- # add n nodes
- G.add_nodes_from([v for v in range(n)])
- if weight is None:
- # choose weights from exponential distribution
- for n in G:
- G.node[n]['weight'] = random.expovariate(1.0)
- else:
- nx.set_node_attributes(G,'weight',weight)
- if pos is None:
- # random positions
- for n in G:
- G.node[n]['pos']=[random.random() for i in range(0,dim)]
- else:
- nx.set_node_attributes(G,'pos',pos)
- G.add_edges_from(geographical_threshold_edges(G, theta, alpha))
- return G
-
-def geographical_threshold_edges(G, theta, alpha=2):
- # generate edges for a geographical threshold graph given a graph
- # with positions and weights assigned as node attributes 'pos' and 'weight'.
- nodes = G.nodes(data=True)
- while nodes:
- u,du = nodes.pop()
- wu = du['weight']
- pu = du['pos']
- for v,dv in nodes:
- wv = dv['weight']
- pv = dv['pos']
- r = math.sqrt(sum(((a-b)**2 for a,b in zip(pu,pv))))
- if wu+wv >= theta*r**alpha:
- yield(u,v)
-
-def waxman_graph(n, alpha=0.4, beta=0.1, L=None, domain=(0,0,1,1)):
- r"""Return a Waxman random graph.
-
- The Waxman random graph models place n nodes uniformly at random
- in a rectangular domain. Two nodes u,v are connected with an edge
- with probability
-
- .. math::
- p = \alpha*exp(-d/(\beta*L)).
-
- This function implements both Waxman models.
-
- Waxman-1: `L` not specified
- The distance `d` is the Euclidean distance between the nodes u and v.
- `L` is the maximum distance between all nodes in the graph.
-
- Waxman-2: `L` specified
- The distance `d` is chosen randomly in `[0,L]`.
-
- Parameters
- ----------
- n : int
- Number of nodes
- alpha: float
- Model parameter
- beta: float
- Model parameter
- L : float, optional
- Maximum distance between nodes. If not specified the actual distance
- is calculated.
- domain : tuple of numbers, optional
- Domain size (xmin, ymin, xmax, ymax)
-
- Returns
- -------
- G: Graph
-
- References
- ----------
- .. [1] B. M. Waxman, Routing of multipoint connections.
- IEEE J. Select. Areas Commun. 6(9),(1988) 1617-1622.
- """
- # build graph of n nodes with random positions in the unit square
- G = nx.Graph()
- G.add_nodes_from(range(n))
- (xmin,ymin,xmax,ymax)=domain
- for n in G:
- G.node[n]['pos']=((xmin + (xmax-xmin))*random.random(),
- (ymin + (ymax-ymin))*random.random())
- if L is None:
- # find maximum distance L between two nodes
- l = 0
- pos = list(nx.get_node_attributes(G,'pos').values())
- while pos:
- x1,y1 = pos.pop()
- for x2,y2 in pos:
- r2 = (x1-x2)**2 + (y1-y2)**2
- if r2 > l:
- l = r2
- l=math.sqrt(l)
- else:
- # user specified maximum distance
- l = L
-
- nodes=G.nodes()
- if L is None:
- # Waxman-1 model
- # try all pairs, connect randomly based on euclidean distance
- while nodes:
- u = nodes.pop()
- x1,y1 = G.node[u]['pos']
- for v in nodes:
- x2,y2 = G.node[v]['pos']
- r = math.sqrt((x1-x2)**2 + (y1-y2)**2)
- if random.random() < alpha*math.exp(-r/(beta*l)):
- G.add_edge(u,v)
- else:
- # Waxman-2 model
- # try all pairs, connect randomly based on randomly chosen l
- while nodes:
- u = nodes.pop()
- for v in nodes:
- r = random.random()*l
- if random.random() < alpha*math.exp(-r/(beta*l)):
- G.add_edge(u,v)
- return G
-
-
-def navigable_small_world_graph(n, p=1, q=1, r=2, dim=2, seed=None):
- r"""Return a navigable small-world graph.
-
- A navigable small-world graph is a directed grid with additional
- long-range connections that are chosen randomly. From [1]_:
-
- Begin with a set of nodes that are identified with the set of lattice
- points in an `n \times n` square, `{(i,j): i\in {1,2,\ldots,n}, j\in {1,2,\ldots,n}}`
- and define the lattice distance between two nodes `(i,j)` and `(k,l)`
- to be the number of "lattice steps" separating them: `d((i,j),(k,l)) = |k-i|+|l-j|`.
-
- For a universal constant `p`, the node `u` has a directed edge to every other
- node within lattice distance `p` (local contacts) .
-
- For universal constants `q\ge 0` and `r\ge 0` construct directed edges from `u` to `q`
- other nodes (long-range contacts) using independent random trials; the i'th
- directed edge from `u` has endpoint `v` with probability proportional to `d(u,v)^{-r}`.
-
- Parameters
- ----------
- n : int
- The number of nodes.
- p : int
- The diameter of short range connections. Each node is connected
- to every other node within lattice distance p.
- q : int
- The number of long-range connections for each node.
- r : float
- Exponent for decaying probability of connections. The probability of
- connecting to a node at lattice distance d is 1/d^r.
- dim : int
- Dimension of grid
- seed : int, optional
- Seed for random number generator (default=None).
-
- References
- ----------
- .. [1] J. Kleinberg. The small-world phenomenon: An algorithmic
- perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000.
- """
- if (p < 1):
- raise nx.NetworkXException("p must be >= 1")
- if (q < 0):
- raise nx.NetworkXException("q must be >= 0")
- if (r < 0):
- raise nx.NetworkXException("r must be >= 1")
- if not seed is None:
- random.seed(seed)
- G = nx.DiGraph()
- nodes = list(product(range(n),repeat=dim))
- for p1 in nodes:
- probs = [0]
- for p2 in nodes:
- if p1==p2:
- continue
- d = sum((abs(b-a) for a,b in zip(p1,p2)))
- if d <= p:
- G.add_edge(p1,p2)
- probs.append(d**-r)
- cdf = list(nx.utils.cumulative_sum(probs))
- for _ in range(q):
- target = nodes[bisect_left(cdf,random.uniform(0, cdf[-1]))]
- G.add_edge(p1,target)
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/hybrid.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/hybrid.py
deleted file mode 100644
index b4936fa..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/hybrid.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""
-Hybrid
-
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nDan Schult (dschult@colgate.edu)"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-_all__ = ['kl_connected_subgraph', 'is_kl_connected']
-
-import copy
-import networkx as nx
-
-def kl_connected_subgraph(G,k,l,low_memory=False,same_as_graph=False):
- """ Returns the maximum locally (k,l) connected subgraph of G.
-
- (k,l)-connected subgraphs are presented by Fan Chung and Li
- in "The Small World Phenomenon in hybrid power law graphs"
- to appear in "Complex Networks" (Ed. E. Ben-Naim) Lecture
- Notes in Physics, Springer (2004)
-
- low_memory=True then use a slightly slower, but lower memory version
- same_as_graph=True then return a tuple with subgraph and
- pflag for if G is kl-connected
- """
- H=copy.deepcopy(G) # subgraph we construct by removing from G
-
- graphOK=True
- deleted_some=True # hack to start off the while loop
- while deleted_some:
- deleted_some=False
- for edge in H.edges():
- (u,v)=edge
- ### Get copy of graph needed for this search
- if low_memory:
- verts=set([u,v])
- for i in range(k):
- [verts.update(G.neighbors(w)) for w in verts.copy()]
- G2=G.subgraph(list(verts))
- else:
- G2=copy.deepcopy(G)
- ###
- path=[u,v]
- cnt=0
- accept=0
- while path:
- cnt += 1 # Found a path
- if cnt>=l:
- accept=1
- break
- # record edges along this graph
- prev=u
- for w in path:
- if prev!=w:
- G2.remove_edge(prev,w)
- prev=w
-# path=shortest_path(G2,u,v,k) # ??? should "Cutoff" be k+1?
- try:
- path=nx.shortest_path(G2,u,v) # ??? should "Cutoff" be k+1?
- except nx.NetworkXNoPath:
- path = False
- # No Other Paths
- if accept==0:
- H.remove_edge(u,v)
- deleted_some=True
- if graphOK: graphOK=False
- # We looked through all edges and removed none of them.
- # So, H is the maximal (k,l)-connected subgraph of G
- if same_as_graph:
- return (H,graphOK)
- return H
-
-def is_kl_connected(G,k,l,low_memory=False):
- """Returns True if G is kl connected."""
- graphOK=True
- for edge in G.edges():
- (u,v)=edge
- ### Get copy of graph needed for this search
- if low_memory:
- verts=set([u,v])
- for i in range(k):
- [verts.update(G.neighbors(w)) for w in verts.copy()]
- G2=G.subgraph(verts)
- else:
- G2=copy.deepcopy(G)
- ###
- path=[u,v]
- cnt=0
- accept=0
- while path:
- cnt += 1 # Found a path
- if cnt>=l:
- accept=1
- break
- # record edges along this graph
- prev=u
- for w in path:
- if w!=prev:
- G2.remove_edge(prev,w)
- prev=w
-# path=shortest_path(G2,u,v,k) # ??? should "Cutoff" be k+1?
- try:
- path=nx.shortest_path(G2,u,v) # ??? should "Cutoff" be k+1?
- except nx.NetworkXNoPath:
- path = False
- # No Other Paths
- if accept==0:
- graphOK=False
- break
- # return status
- return graphOK
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/intersection.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/intersection.py
deleted file mode 100644
index cc7903d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/intersection.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Generators for random intersection graphs.
-"""
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import random
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)'])
-
-__all__ = ['uniform_random_intersection_graph',
- 'k_random_intersection_graph',
- 'general_random_intersection_graph',
- ]
-
-def uniform_random_intersection_graph(n, m, p, seed=None):
- """Return a uniform random intersection graph.
-
- Parameters
- ----------
- n : int
- The number of nodes in the first bipartite set (nodes)
- m : int
- The number of nodes in the second bipartite set (attributes)
- p : float
- Probability of connecting nodes between bipartite sets
- seed : int, optional
- Seed for random number generator (default=None).
-
- See Also
- --------
- gnp_random_graph
-
- References
- ----------
- .. [1] K.B. Singer-Cohen, Random Intersection Graphs, 1995,
- PhD thesis, Johns Hopkins University
- .. [2] Fill, J. A., Scheinerman, E. R., and Singer-Cohen, K. B.,
- Random intersection graphs when m = !(n):
- An equivalence theorem relating the evolution of the g(n, m, p)
- and g(n, p) models. Random Struct. Algorithms 16, 2 (2000), 156–176.
- """
- G=nx.bipartite_random_graph(n, m, p, seed=seed)
- return nx.projected_graph(G, range(n))
-
-def k_random_intersection_graph(n,m,k):
- """Return a intersection graph with randomly chosen attribute sets for
- each node that are of equal size (k).
-
- Parameters
- ----------
- n : int
- The number of nodes in the first bipartite set (nodes)
- m : int
- The number of nodes in the second bipartite set (attributes)
- k : float
- Size of attribute set to assign to each node.
- seed : int, optional
- Seed for random number generator (default=None).
-
- See Also
- --------
- gnp_random_graph, uniform_random_intersection_graph
-
- References
- ----------
- .. [1] Godehardt, E., and Jaworski, J.
- Two models of random intersection graphs and their applications.
- Electronic Notes in Discrete Mathematics 10 (2001), 129--132.
- """
- G = nx.empty_graph(n + m)
- mset = range(n,n+m)
- for v in range(n):
- targets = random.sample(mset, k)
- G.add_edges_from(zip([v]*len(targets), targets))
- return nx.projected_graph(G, range(n))
-
-def general_random_intersection_graph(n,m,p):
- """Return a random intersection graph with independent probabilities
- for connections between node and attribute sets.
-
- Parameters
- ----------
- n : int
- The number of nodes in the first bipartite set (nodes)
- m : int
- The number of nodes in the second bipartite set (attributes)
- p : list of floats of length m
- Probabilities for connecting nodes to each attribute
- seed : int, optional
- Seed for random number generator (default=None).
-
- See Also
- --------
- gnp_random_graph, uniform_random_intersection_graph
-
- References
- ----------
- .. [1] Nikoletseas, S. E., Raptopoulos, C., and Spirakis, P. G.
- The existence and efficient construction of large independent sets
- in general random intersection graphs. In ICALP (2004), J. D´ıaz,
- J. Karhum¨aki, A. Lepist¨o, and D. Sannella, Eds., vol. 3142
- of Lecture Notes in Computer Science, Springer, pp. 1029–1040.
- """
- if len(p)!=m:
- raise ValueError("Probability list p must have m elements.")
- G = nx.empty_graph(n + m)
- mset = range(n,n+m)
- for u in range(n):
- for v,q in zip(mset,p):
- if random.random()<q:
- G.add_edge(u,v)
- return nx.projected_graph(G, range(n))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/line.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/line.py
deleted file mode 100644
index 4d6c14d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/line.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""
-Line graphs.
-
-"""
-# Copyright (C) 2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)\nDan Schult(dschult@colgate.edu)"""
-
-__all__ = ['line_graph']
-
-import networkx as nx
-
-def line_graph(G):
- """Return the line graph of the graph or digraph G.
-
- The line graph of a graph G has a node for each edge
- in G and an edge between those nodes if the two edges
- in G share a common node.
-
- For DiGraphs an edge an edge represents a directed path of length 2.
-
- The original node labels are kept as two-tuple node labels
- in the line graph.
-
- Parameters
- ----------
- G : graph
- A NetworkX Graph or DiGraph
-
- Examples
- --------
- >>> G=nx.star_graph(3)
- >>> L=nx.line_graph(G)
- >>> print(sorted(L.edges())) # makes a clique, K3
- [((0, 1), (0, 2)), ((0, 1), (0, 3)), ((0, 3), (0, 2))]
-
- Notes
- -----
- Not implemented for MultiGraph or MultiDiGraph classes.
-
- Graph, node, and edge data are not propagated to the new graph.
-
- """
- if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
- raise Exception("Line graph not implemented for Multi(Di)Graphs")
- L=G.__class__()
- if G.is_directed():
- for u,nlist in G.adjacency_iter(): # same as successors for digraph
- # look for directed path of length two
- for n in nlist:
- nbrs=G[n] # successors
- for nbr in nbrs:
- if nbr!=u:
- L.add_edge((u,n),(n,nbr))
- else:
- for u,nlist in G.adjacency_iter():
- # label nodes as tuple of edge endpoints in original graph
- # "node tuple" must be in lexigraphical order
- nodes=[tuple(sorted(n)) for n in zip([u]*len(nlist),nlist)]
- # add clique of nodes to graph
- while nodes:
- u=nodes.pop()
- L.add_edges_from((u,v) for v in nodes)
- return L
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/random_clustered.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/random_clustered.py
deleted file mode 100644
index fe294e6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/random_clustered.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Generate graphs with given degree and triangle sequence.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import random
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Joel Miller (joel.c.miller.research@gmail.com)'])
-
-__all__ = ['random_clustered_graph']
-
-
-def random_clustered_graph(joint_degree_sequence, create_using=None, seed=None):
- """Generate a random graph with the given joint degree and triangle
- degree sequence.
-
- This uses a configuration model-like approach to generate a
- random pseudograph (graph with parallel edges and self loops) by
- randomly assigning edges to match the given indepdenent edge
- and triangle degree sequence.
-
- Parameters
- ----------
- joint_degree_sequence : list of integer pairs
- Each list entry corresponds to the independent edge degree and
- triangle degree of a node.
- create_using : graph, optional (default MultiGraph)
- Return graph of this type. The instance will be cleared.
- seed : hashable object, optional
- The seed for the random number generator.
-
- Returns
- -------
- G : MultiGraph
- A graph with the specified degree sequence. Nodes are labeled
- starting at 0 with an index corresponding to the position in
- deg_sequence.
-
- Raises
- ------
- NetworkXError
- If the independent edge degree sequence sum is not even
- or the triangle degree sequence sum is not divisible by 3.
-
- Notes
- -----
- As described by Miller [1]_ (see also Newman [2]_ for an equivalent
- description).
-
- A non-graphical degree sequence (not realizable by some simple
- graph) is allowed since this function returns graphs with self
- loops and parallel edges. An exception is raised if the
- independent degree sequence does not have an even sum or the
- triangle degree sequence sum is not divisible by 3.
-
- This configuration model-like construction process can lead to
- duplicate edges and loops. You can remove the self-loops and
- parallel edges (see below) which will likely result in a graph
- that doesn't have the exact degree sequence specified. This
- "finite-size effect" decreases as the size of the graph increases.
-
- References
- ----------
- .. [1] J. C. Miller "Percolation and Epidemics on Random Clustered Graphs."
- Physical Review E, Rapid Communication (to appear).
- .. [2] M.E.J. Newman, "Random clustered networks".
- Physical Review Letters (to appear).
-
- Examples
- --------
- >>> deg_tri=[[1,0],[1,0],[1,0],[2,0],[1,0],[2,1],[0,1],[0,1]]
- >>> G = nx.random_clustered_graph(deg_tri)
-
- To remove parallel edges:
-
- >>> G=nx.Graph(G)
-
- To remove self loops:
-
- >>> G.remove_edges_from(G.selfloop_edges())
-
- """
- if create_using is None:
- create_using = nx.MultiGraph()
- elif create_using.is_directed():
- raise nx.NetworkXError("Directed Graph not supported")
-
- if not seed is None:
- random.seed(seed)
-
- # In Python 3, zip() returns an iterator. Make this into a list.
- joint_degree_sequence = list(joint_degree_sequence)
-
- N = len(joint_degree_sequence)
- G = nx.empty_graph(N,create_using)
-
- ilist = []
- tlist = []
- for n in G:
- degrees = joint_degree_sequence[n]
- for icount in range(degrees[0]):
- ilist.append(n)
- for tcount in range(degrees[1]):
- tlist.append(n)
-
- if len(ilist)%2 != 0 or len(tlist)%3 != 0:
- raise nx.NetworkXError('Invalid degree sequence')
-
- random.shuffle(ilist)
- random.shuffle(tlist)
- while ilist:
- G.add_edge(ilist.pop(),ilist.pop())
- while tlist:
- n1 = tlist.pop()
- n2 = tlist.pop()
- n3 = tlist.pop()
- G.add_edges_from([(n1,n2),(n1,n3),(n2,n3)])
- G.name = "random_clustered %d nodes %d edges"%(G.order(),G.size())
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/random_graphs.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/random_graphs.py
deleted file mode 100644
index 81c20ab..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/random_graphs.py
+++ /dev/null
@@ -1,890 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Generators for random graphs.
-
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult (dschult@colgate.edu)'])
-import itertools
-import random
-import math
-import networkx as nx
-from networkx.generators.classic import empty_graph, path_graph, complete_graph
-
-from collections import defaultdict
-
-__all__ = ['fast_gnp_random_graph',
- 'gnp_random_graph',
- 'dense_gnm_random_graph',
- 'gnm_random_graph',
- 'erdos_renyi_graph',
- 'binomial_graph',
- 'newman_watts_strogatz_graph',
- 'watts_strogatz_graph',
- 'connected_watts_strogatz_graph',
- 'random_regular_graph',
- 'barabasi_albert_graph',
- 'powerlaw_cluster_graph',
- 'random_lobster',
- 'random_shell_graph',
- 'random_powerlaw_tree',
- 'random_powerlaw_tree_sequence']
-
-
-#-------------------------------------------------------------------------
-# Some Famous Random Graphs
-#-------------------------------------------------------------------------
-
-
-def fast_gnp_random_graph(n, p, seed=None, directed=False):
- """Return a random graph G_{n,p} (Erdős-Rényi graph, binomial graph).
-
- Parameters
- ----------
- n : int
- The number of nodes.
- p : float
- Probability for edge creation.
- seed : int, optional
- Seed for random number generator (default=None).
- directed : bool, optional (default=False)
- If True return a directed graph
-
- Notes
- -----
- The G_{n,p} graph algorithm chooses each of the [n(n-1)]/2
- (undirected) or n(n-1) (directed) possible edges with probability p.
-
- This algorithm is O(n+m) where m is the expected number of
- edges m=p*n*(n-1)/2.
-
- It should be faster than gnp_random_graph when p is small and
- the expected number of edges is small (sparse graph).
-
- See Also
- --------
- gnp_random_graph
-
- References
- ----------
- .. [1] Vladimir Batagelj and Ulrik Brandes,
- "Efficient generation of large random networks",
- Phys. Rev. E, 71, 036113, 2005.
- """
- G = empty_graph(n)
- G.name="fast_gnp_random_graph(%s,%s)"%(n,p)
-
- if not seed is None:
- random.seed(seed)
-
- if p <= 0 or p >= 1:
- return nx.gnp_random_graph(n,p,directed=directed)
-
- v = 1 # Nodes in graph are from 0,n-1 (this is the second node index).
- w = -1
- lp = math.log(1.0 - p)
-
- if directed:
- G=nx.DiGraph(G)
- while v < n:
- lr = math.log(1.0 - random.random())
- w = w + 1 + int(lr/lp)
- if v == w: # avoid self loops
- w = w + 1
- while w >= n and v < n:
- w = w - n
- v = v + 1
- if v == w: # avoid self loops
- w = w + 1
- if v < n:
- G.add_edge(v, w)
- else:
- while v < n:
- lr = math.log(1.0 - random.random())
- w = w + 1 + int(lr/lp)
- while w >= v and v < n:
- w = w - v
- v = v + 1
- if v < n:
- G.add_edge(v, w)
- return G
-
-
-def gnp_random_graph(n, p, seed=None, directed=False):
- """Return a random graph G_{n,p} (Erdős-Rényi graph, binomial graph).
-
- Chooses each of the possible edges with probability p.
-
- This is also called binomial_graph and erdos_renyi_graph.
-
- Parameters
- ----------
- n : int
- The number of nodes.
- p : float
- Probability for edge creation.
- seed : int, optional
- Seed for random number generator (default=None).
- directed : bool, optional (default=False)
- If True return a directed graph
-
- See Also
- --------
- fast_gnp_random_graph
-
- Notes
- -----
- This is an O(n^2) algorithm. For sparse graphs (small p) see
- fast_gnp_random_graph for a faster algorithm.
-
- References
- ----------
- .. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
- .. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
- """
- if directed:
- G=nx.DiGraph()
- else:
- G=nx.Graph()
- G.add_nodes_from(range(n))
- G.name="gnp_random_graph(%s,%s)"%(n,p)
- if p<=0:
- return G
- if p>=1:
- return complete_graph(n,create_using=G)
-
- if not seed is None:
- random.seed(seed)
-
- if G.is_directed():
- edges=itertools.permutations(range(n),2)
- else:
- edges=itertools.combinations(range(n),2)
-
- for e in edges:
- if random.random() < p:
- G.add_edge(*e)
- return G
-
-
-# add some aliases to common names
-binomial_graph=gnp_random_graph
-erdos_renyi_graph=gnp_random_graph
-
-def dense_gnm_random_graph(n, m, seed=None):
- """Return the random graph G_{n,m}.
-
- Gives a graph picked randomly out of the set of all graphs
- with n nodes and m edges.
- This algorithm should be faster than gnm_random_graph for dense graphs.
-
- Parameters
- ----------
- n : int
- The number of nodes.
- m : int
- The number of edges.
- seed : int, optional
- Seed for random number generator (default=None).
-
- See Also
- --------
- gnm_random_graph()
-
- Notes
- -----
- Algorithm by Keith M. Briggs Mar 31, 2006.
- Inspired by Knuth's Algorithm S (Selection sampling technique),
- in section 3.4.2 of [1]_.
-
- References
- ----------
- .. [1] Donald E. Knuth, The Art of Computer Programming,
- Volume 2/Seminumerical algorithms, Third Edition, Addison-Wesley, 1997.
- """
- mmax=n*(n-1)/2
- if m>=mmax:
- G=complete_graph(n)
- else:
- G=empty_graph(n)
- G.name="dense_gnm_random_graph(%s,%s)"%(n,m)
-
- if n==1 or m>=mmax:
- return G
-
- if seed is not None:
- random.seed(seed)
-
- u=0
- v=1
- t=0
- k=0
- while True:
- if random.randrange(mmax-t)<m-k:
- G.add_edge(u,v)
- k+=1
- if k==m: return G
- t+=1
- v+=1
- if v==n: # go to next row of adjacency matrix
- u+=1
- v=u+1
-
-def gnm_random_graph(n, m, seed=None, directed=False):
- """Return the random graph G_{n,m}.
-
- Produces a graph picked randomly out of the set of all graphs
- with n nodes and m edges.
-
- Parameters
- ----------
- n : int
- The number of nodes.
- m : int
- The number of edges.
- seed : int, optional
- Seed for random number generator (default=None).
- directed : bool, optional (default=False)
- If True return a directed graph
- """
- if directed:
- G=nx.DiGraph()
- else:
- G=nx.Graph()
- G.add_nodes_from(range(n))
- G.name="gnm_random_graph(%s,%s)"%(n,m)
-
- if seed is not None:
- random.seed(seed)
-
- if n==1:
- return G
- max_edges=n*(n-1)
- if not directed:
- max_edges/=2.0
- if m>=max_edges:
- return complete_graph(n,create_using=G)
-
- nlist=G.nodes()
- edge_count=0
- while edge_count < m:
- # generate random edge,u,v
- u = random.choice(nlist)
- v = random.choice(nlist)
- if u==v or G.has_edge(u,v):
- continue
- else:
- G.add_edge(u,v)
- edge_count=edge_count+1
- return G
-
-
-def newman_watts_strogatz_graph(n, k, p, seed=None):
- """Return a Newman-Watts-Strogatz small world graph.
-
- Parameters
- ----------
- n : int
- The number of nodes
- k : int
- Each node is connected to k nearest neighbors in ring topology
- p : float
- The probability of adding a new edge for each edge
- seed : int, optional
- seed for random number generator (default=None)
-
- Notes
- -----
- First create a ring over n nodes. Then each node in the ring is
- connected with its k nearest neighbors (k-1 neighbors if k is odd).
- Then shortcuts are created by adding new edges as follows:
- for each edge u-v in the underlying "n-ring with k nearest neighbors"
- with probability p add a new edge u-w with randomly-chosen existing
- node w. In contrast with watts_strogatz_graph(), no edges are removed.
-
- See Also
- --------
- watts_strogatz_graph()
-
- References
- ----------
- .. [1] M. E. J. Newman and D. J. Watts,
- Renormalization group analysis of the small-world network model,
- Physics Letters A, 263, 341, 1999.
- http://dx.doi.org/10.1016/S0375-9601(99)00757-4
- """
- if seed is not None:
- random.seed(seed)
- if k>=n:
- raise nx.NetworkXError("k>=n, choose smaller k or larger n")
- G=empty_graph(n)
- G.name="newman_watts_strogatz_graph(%s,%s,%s)"%(n,k,p)
- nlist = G.nodes()
- fromv = nlist
- # connect the k/2 neighbors
- for j in range(1, k // 2+1):
- tov = fromv[j:] + fromv[0:j] # the first j are now last
- for i in range(len(fromv)):
- G.add_edge(fromv[i], tov[i])
- # for each edge u-v, with probability p, randomly select existing
- # node w and add new edge u-w
- e = G.edges()
- for (u, v) in e:
- if random.random() < p:
- w = random.choice(nlist)
- # no self-loops and reject if edge u-w exists
- # is that the correct NWS model?
- while w == u or G.has_edge(u, w):
- w = random.choice(nlist)
- if G.degree(u) >= n-1:
- break # skip this rewiring
- else:
- G.add_edge(u,w)
- return G
-
-
-def watts_strogatz_graph(n, k, p, seed=None):
- """Return a Watts-Strogatz small-world graph.
-
-
- Parameters
- ----------
- n : int
- The number of nodes
- k : int
- Each node is connected to k nearest neighbors in ring topology
- p : float
- The probability of rewiring each edge
- seed : int, optional
- Seed for random number generator (default=None)
-
- See Also
- --------
- newman_watts_strogatz_graph()
- connected_watts_strogatz_graph()
-
- Notes
- -----
- First create a ring over n nodes. Then each node in the ring is
- connected with its k nearest neighbors (k-1 neighbors if k is odd).
- Then shortcuts are created by replacing some edges as follows:
- for each edge u-v in the underlying "n-ring with k nearest neighbors"
- with probability p replace it with a new edge u-w with uniformly
- random choice of existing node w.
-
- In contrast with newman_watts_strogatz_graph(), the random
- rewiring does not increase the number of edges. The rewired graph
- is not guaranteed to be connected as in connected_watts_strogatz_graph().
-
- References
- ----------
- .. [1] Duncan J. Watts and Steven H. Strogatz,
- Collective dynamics of small-world networks,
- Nature, 393, pp. 440--442, 1998.
- """
- if k>=n:
- raise nx.NetworkXError("k>=n, choose smaller k or larger n")
- if seed is not None:
- random.seed(seed)
-
- G = nx.Graph()
- G.name="watts_strogatz_graph(%s,%s,%s)"%(n,k,p)
- nodes = list(range(n)) # nodes are labeled 0 to n-1
- # connect each node to k/2 neighbors
- for j in range(1, k // 2+1):
- targets = nodes[j:] + nodes[0:j] # first j nodes are now last in list
- G.add_edges_from(zip(nodes,targets))
- # rewire edges from each node
- # loop over all nodes in order (label) and neighbors in order (distance)
- # no self loops or multiple edges allowed
- for j in range(1, k // 2+1): # outer loop is neighbors
- targets = nodes[j:] + nodes[0:j] # first j nodes are now last in list
- # inner loop in node order
- for u,v in zip(nodes,targets):
- if random.random() < p:
- w = random.choice(nodes)
- # Enforce no self-loops or multiple edges
- while w == u or G.has_edge(u, w):
- w = random.choice(nodes)
- if G.degree(u) >= n-1:
- break # skip this rewiring
- else:
- G.remove_edge(u,v)
- G.add_edge(u,w)
- return G
-
-def connected_watts_strogatz_graph(n, k, p, tries=100, seed=None):
- """Return a connected Watts-Strogatz small-world graph.
-
- Attempt to generate a connected realization by repeated
- generation of Watts-Strogatz small-world graphs.
- An exception is raised if the maximum number of tries is exceeded.
-
- Parameters
- ----------
- n : int
- The number of nodes
- k : int
- Each node is connected to k nearest neighbors in ring topology
- p : float
- The probability of rewiring each edge
- tries : int
- Number of attempts to generate a connected graph.
- seed : int, optional
- The seed for random number generator.
-
- See Also
- --------
- newman_watts_strogatz_graph()
- watts_strogatz_graph()
-
- """
- G = watts_strogatz_graph(n, k, p, seed)
- t=1
- while not nx.is_connected(G):
- G = watts_strogatz_graph(n, k, p, seed)
- t=t+1
- if t>tries:
- raise nx.NetworkXError("Maximum number of tries exceeded")
- return G
-
-
-def random_regular_graph(d, n, seed=None):
- """Return a random regular graph of n nodes each with degree d.
-
- The resulting graph G has no self-loops or parallel edges.
-
- Parameters
- ----------
- d : int
- Degree
- n : integer
- Number of nodes. The value of n*d must be even.
- seed : hashable object
- The seed for random number generator.
-
- Notes
- -----
- The nodes are numbered form 0 to n-1.
-
- Kim and Vu's paper [2]_ shows that this algorithm samples in an
- asymptotically uniform way from the space of random graphs when
- d = O(n**(1/3-epsilon)).
-
- References
- ----------
- .. [1] A. Steger and N. Wormald,
- Generating random regular graphs quickly,
- Probability and Computing 8 (1999), 377-396, 1999.
- http://citeseer.ist.psu.edu/steger99generating.html
-
- .. [2] Jeong Han Kim and Van H. Vu,
- Generating random regular graphs,
- Proceedings of the thirty-fifth ACM symposium on Theory of computing,
- San Diego, CA, USA, pp 213--222, 2003.
- http://portal.acm.org/citation.cfm?id=780542.780576
- """
- if (n * d) % 2 != 0:
- raise nx.NetworkXError("n * d must be even")
-
- if not 0 <= d < n:
- raise nx.NetworkXError("the 0 <= d < n inequality must be satisfied")
-
- if seed is not None:
- random.seed(seed)
-
- def _suitable(edges, potential_edges):
- # Helper subroutine to check if there are suitable edges remaining
- # If False, the generation of the graph has failed
- if not potential_edges:
- return True
- for s1 in potential_edges:
- for s2 in potential_edges:
- # Two iterators on the same dictionary are guaranteed
- # to visit it in the same order if there are no
- # intervening modifications.
- if s1 == s2:
- # Only need to consider s1-s2 pair one time
- break
- if s1 > s2:
- s1, s2 = s2, s1
- if (s1, s2) not in edges:
- return True
- return False
-
- def _try_creation():
- # Attempt to create an edge set
-
- edges = set()
- stubs = list(range(n)) * d
-
- while stubs:
- potential_edges = defaultdict(lambda: 0)
- random.shuffle(stubs)
- stubiter = iter(stubs)
- for s1, s2 in zip(stubiter, stubiter):
- if s1 > s2:
- s1, s2 = s2, s1
- if s1 != s2 and ((s1, s2) not in edges):
- edges.add((s1, s2))
- else:
- potential_edges[s1] += 1
- potential_edges[s2] += 1
-
- if not _suitable(edges, potential_edges):
- return None # failed to find suitable edge set
-
- stubs = [node for node, potential in potential_edges.items()
- for _ in range(potential)]
- return edges
-
- # Even though a suitable edge set exists,
- # the generation of such a set is not guaranteed.
- # Try repeatedly to find one.
- edges = _try_creation()
- while edges is None:
- edges = _try_creation()
-
- G = nx.Graph()
- G.name = "random_regular_graph(%s, %s)" % (d, n)
- G.add_edges_from(edges)
-
- return G
-
-def _random_subset(seq,m):
- """ Return m unique elements from seq.
-
- This differs from random.sample which can return repeated
- elements if seq holds repeated elements.
- """
- targets=set()
- while len(targets)<m:
- x=random.choice(seq)
- targets.add(x)
- return targets
-
-def barabasi_albert_graph(n, m, seed=None):
- """Return random graph using Barabási-Albert preferential attachment model.
-
- A graph of n nodes is grown by attaching new nodes each with m
- edges that are preferentially attached to existing nodes with high
- degree.
-
- Parameters
- ----------
- n : int
- Number of nodes
- m : int
- Number of edges to attach from a new node to existing nodes
- seed : int, optional
- Seed for random number generator (default=None).
-
- Returns
- -------
- G : Graph
-
- Notes
- -----
- The initialization is a graph with with m nodes and no edges.
-
- References
- ----------
- .. [1] A. L. Barabási and R. Albert "Emergence of scaling in
- random networks", Science 286, pp 509-512, 1999.
- """
-
- if m < 1 or m >=n:
- raise nx.NetworkXError(\
- "Barabási-Albert network must have m>=1 and m<n, m=%d,n=%d"%(m,n))
- if seed is not None:
- random.seed(seed)
-
- # Add m initial nodes (m0 in barabasi-speak)
- G=empty_graph(m)
- G.name="barabasi_albert_graph(%s,%s)"%(n,m)
- # Target nodes for new edges
- targets=list(range(m))
- # List of existing nodes, with nodes repeated once for each adjacent edge
- repeated_nodes=[]
- # Start adding the other n-m nodes. The first node is m.
- source=m
- while source<n:
- # Add edges to m nodes from the source.
- G.add_edges_from(zip([source]*m,targets))
- # Add one node to the list for each new edge just created.
- repeated_nodes.extend(targets)
- # And the new node "source" has m edges to add to the list.
- repeated_nodes.extend([source]*m)
- # Now choose m unique nodes from the existing nodes
- # Pick uniformly from repeated_nodes (preferential attachement)
- targets = _random_subset(repeated_nodes,m)
- source += 1
- return G
-
-def powerlaw_cluster_graph(n, m, p, seed=None):
- """Holme and Kim algorithm for growing graphs with powerlaw
- degree distribution and approximate average clustering.
-
- Parameters
- ----------
- n : int
- the number of nodes
- m : int
- the number of random edges to add for each new node
- p : float,
- Probability of adding a triangle after adding a random edge
- seed : int, optional
- Seed for random number generator (default=None).
-
- Notes
- -----
- The average clustering has a hard time getting above
- a certain cutoff that depends on m. This cutoff is often quite low.
- Note that the transitivity (fraction of triangles to possible
- triangles) seems to go down with network size.
-
- It is essentially the Barabási-Albert (B-A) growth model with an
- extra step that each random edge is followed by a chance of
- making an edge to one of its neighbors too (and thus a triangle).
-
- This algorithm improves on B-A in the sense that it enables a
- higher average clustering to be attained if desired.
-
- It seems possible to have a disconnected graph with this algorithm
- since the initial m nodes may not be all linked to a new node
- on the first iteration like the B-A model.
-
- References
- ----------
- .. [1] P. Holme and B. J. Kim,
- "Growing scale-free networks with tunable clustering",
- Phys. Rev. E, 65, 026107, 2002.
- """
-
- if m < 1 or n < m:
- raise nx.NetworkXError(\
- "NetworkXError must have m>1 and m<n, m=%d,n=%d"%(m,n))
-
- if p > 1 or p < 0:
- raise nx.NetworkXError(\
- "NetworkXError p must be in [0,1], p=%f"%(p))
- if seed is not None:
- random.seed(seed)
-
- G=empty_graph(m) # add m initial nodes (m0 in barabasi-speak)
- G.name="Powerlaw-Cluster Graph"
- repeated_nodes=G.nodes() # list of existing nodes to sample from
- # with nodes repeated once for each adjacent edge
- source=m # next node is m
- while source<n: # Now add the other n-1 nodes
- possible_targets = _random_subset(repeated_nodes,m)
- # do one preferential attachment for new node
- target=possible_targets.pop()
- G.add_edge(source,target)
- repeated_nodes.append(target) # add one node to list for each new link
- count=1
- while count<m: # add m-1 more new links
- if random.random()<p: # clustering step: add triangle
- neighborhood=[nbr for nbr in G.neighbors(target) \
- if not G.has_edge(source,nbr) \
- and not nbr==source]
- if neighborhood: # if there is a neighbor without a link
- nbr=random.choice(neighborhood)
- G.add_edge(source,nbr) # add triangle
- repeated_nodes.append(nbr)
- count=count+1
- continue # go to top of while loop
- # else do preferential attachment step if above fails
- target=possible_targets.pop()
- G.add_edge(source,target)
- repeated_nodes.append(target)
- count=count+1
-
- repeated_nodes.extend([source]*m) # add source node to list m times
- source += 1
- return G
-
-def random_lobster(n, p1, p2, seed=None):
- """Return a random lobster.
-
- A lobster is a tree that reduces to a caterpillar when pruning all
- leaf nodes.
-
- A caterpillar is a tree that reduces to a path graph when pruning
- all leaf nodes (p2=0).
-
- Parameters
- ----------
- n : int
- The expected number of nodes in the backbone
- p1 : float
- Probability of adding an edge to the backbone
- p2 : float
- Probability of adding an edge one level beyond backbone
- seed : int, optional
- Seed for random number generator (default=None).
- """
- # a necessary ingredient in any self-respecting graph library
- if seed is not None:
- random.seed(seed)
- llen=int(2*random.random()*n + 0.5)
- L=path_graph(llen)
- L.name="random_lobster(%d,%s,%s)"%(n,p1,p2)
- # build caterpillar: add edges to path graph with probability p1
- current_node=llen-1
- for n in range(llen):
- if random.random()<p1: # add fuzzy caterpillar parts
- current_node+=1
- L.add_edge(n,current_node)
- if random.random()<p2: # add crunchy lobster bits
- current_node+=1
- L.add_edge(current_node-1,current_node)
- return L # voila, un lobster!
-
-def random_shell_graph(constructor, seed=None):
- """Return a random shell graph for the constructor given.
-
- Parameters
- ----------
- constructor: a list of three-tuples
- (n,m,d) for each shell starting at the center shell.
- n : int
- The number of nodes in the shell
- m : int
- The number or edges in the shell
- d : float
- The ratio of inter-shell (next) edges to intra-shell edges.
- d=0 means no intra shell edges, d=1 for the last shell
- seed : int, optional
- Seed for random number generator (default=None).
-
- Examples
- --------
- >>> constructor=[(10,20,0.8),(20,40,0.8)]
- >>> G=nx.random_shell_graph(constructor)
-
- """
- G=empty_graph(0)
- G.name="random_shell_graph(constructor)"
-
- if seed is not None:
- random.seed(seed)
-
- glist=[]
- intra_edges=[]
- nnodes=0
- # create gnm graphs for each shell
- for (n,m,d) in constructor:
- inter_edges=int(m*d)
- intra_edges.append(m-inter_edges)
- g=nx.convert_node_labels_to_integers(
- gnm_random_graph(n,inter_edges),
- first_label=nnodes)
- glist.append(g)
- nnodes+=n
- G=nx.operators.union(G,g)
-
- # connect the shells randomly
- for gi in range(len(glist)-1):
- nlist1=glist[gi].nodes()
- nlist2=glist[gi+1].nodes()
- total_edges=intra_edges[gi]
- edge_count=0
- while edge_count < total_edges:
- u = random.choice(nlist1)
- v = random.choice(nlist2)
- if u==v or G.has_edge(u,v):
- continue
- else:
- G.add_edge(u,v)
- edge_count=edge_count+1
- return G
-
-
-def random_powerlaw_tree(n, gamma=3, seed=None, tries=100):
- """Return a tree with a powerlaw degree distribution.
-
- Parameters
- ----------
- n : int,
- The number of nodes
- gamma : float
- Exponent of the power-law
- seed : int, optional
- Seed for random number generator (default=None).
- tries : int
- Number of attempts to adjust sequence to make a tree
-
- Notes
- -----
- A trial powerlaw degree sequence is chosen and then elements are
- swapped with new elements from a powerlaw distribution until
- the sequence makes a tree (#edges=#nodes-1).
-
- """
- from networkx.generators.degree_seq import degree_sequence_tree
- try:
- s=random_powerlaw_tree_sequence(n,
- gamma=gamma,
- seed=seed,
- tries=tries)
- except:
- raise nx.NetworkXError(\
- "Exceeded max (%d) attempts for a valid tree sequence."%tries)
- G=degree_sequence_tree(s)
- G.name="random_powerlaw_tree(%s,%s)"%(n,gamma)
- return G
-
-
-def random_powerlaw_tree_sequence(n, gamma=3, seed=None, tries=100):
- """ Return a degree sequence for a tree with a powerlaw distribution.
-
- Parameters
- ----------
- n : int,
- The number of nodes
- gamma : float
- Exponent of the power-law
- seed : int, optional
- Seed for random number generator (default=None).
- tries : int
- Number of attempts to adjust sequence to make a tree
-
- Notes
- -----
- A trial powerlaw degree sequence is chosen and then elements are
- swapped with new elements from a powerlaw distribution until
- the sequence makes a tree (#edges=#nodes-1).
-
-
- """
- if seed is not None:
- random.seed(seed)
-
- # get trial sequence
- z=nx.utils.powerlaw_sequence(n,exponent=gamma)
- # round to integer values in the range [0,n]
- zseq=[min(n, max( int(round(s)),0 )) for s in z]
-
- # another sequence to swap values from
- z=nx.utils.powerlaw_sequence(tries,exponent=gamma)
- # round to integer values in the range [0,n]
- swap=[min(n, max( int(round(s)),0 )) for s in z]
-
- for deg in swap:
- if n-sum(zseq)/2.0 == 1.0: # is a tree, return sequence
- return zseq
- index=random.randint(0,n-1)
- zseq[index]=swap.pop()
-
- raise nx.NetworkXError(\
- "Exceeded max (%d) attempts for a valid tree sequence."%tries)
- return False
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/small.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/small.py
deleted file mode 100644
index f41f8d0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/small.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Various small and named graphs, together with some compact generators.
-
-"""
-__author__ ="""Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['make_small_graph',
- 'LCF_graph',
- 'bull_graph',
- 'chvatal_graph',
- 'cubical_graph',
- 'desargues_graph',
- 'diamond_graph',
- 'dodecahedral_graph',
- 'frucht_graph',
- 'heawood_graph',
- 'house_graph',
- 'house_x_graph',
- 'icosahedral_graph',
- 'krackhardt_kite_graph',
- 'moebius_kantor_graph',
- 'octahedral_graph',
- 'pappus_graph',
- 'petersen_graph',
- 'sedgewick_maze_graph',
- 'tetrahedral_graph',
- 'truncated_cube_graph',
- 'truncated_tetrahedron_graph',
- 'tutte_graph']
-
-import networkx as nx
-from networkx.generators.classic import empty_graph, cycle_graph, path_graph, complete_graph
-from networkx.exception import NetworkXError
-
-#------------------------------------------------------------------------------
-# Tools for creating small graphs
-#------------------------------------------------------------------------------
-def make_small_undirected_graph(graph_description, create_using=None):
- """
- Return a small undirected graph described by graph_description.
-
- See make_small_graph.
- """
- if create_using is not None and create_using.is_directed():
- raise NetworkXError("Directed Graph not supported")
- return make_small_graph(graph_description, create_using)
-
-def make_small_graph(graph_description, create_using=None):
- """
- Return the small graph described by graph_description.
-
- graph_description is a list of the form [ltype,name,n,xlist]
-
- Here ltype is one of "adjacencylist" or "edgelist",
- name is the name of the graph and n the number of nodes.
- This constructs a graph of n nodes with integer labels 0,..,n-1.
-
- If ltype="adjacencylist" then xlist is an adjacency list
- with exactly n entries, in with the j'th entry (which can be empty)
- specifies the nodes connected to vertex j.
- e.g. the "square" graph C_4 can be obtained by
-
- >>> G=nx.make_small_graph(["adjacencylist","C_4",4,[[2,4],[1,3],[2,4],[1,3]]])
-
- or, since we do not need to add edges twice,
-
- >>> G=nx.make_small_graph(["adjacencylist","C_4",4,[[2,4],[3],[4],[]]])
-
- If ltype="edgelist" then xlist is an edge list
- written as [[v1,w2],[v2,w2],...,[vk,wk]],
- where vj and wj integers in the range 1,..,n
- e.g. the "square" graph C_4 can be obtained by
-
- >>> G=nx.make_small_graph(["edgelist","C_4",4,[[1,2],[3,4],[2,3],[4,1]]])
-
- Use the create_using argument to choose the graph class/type.
- """
- ltype=graph_description[0]
- name=graph_description[1]
- n=graph_description[2]
-
- G=empty_graph(n, create_using)
- nodes=G.nodes()
-
- if ltype=="adjacencylist":
- adjlist=graph_description[3]
- if len(adjlist) != n:
- raise NetworkXError("invalid graph_description")
- G.add_edges_from([(u-1,v) for v in nodes for u in adjlist[v]])
- elif ltype=="edgelist":
- edgelist=graph_description[3]
- for e in edgelist:
- v1=e[0]-1
- v2=e[1]-1
- if v1<0 or v1>n-1 or v2<0 or v2>n-1:
- raise NetworkXError("invalid graph_description")
- else:
- G.add_edge(v1,v2)
- G.name=name
- return G
-
-
-def LCF_graph(n,shift_list,repeats,create_using=None):
- """
- Return the cubic graph specified in LCF notation.
-
- LCF notation (LCF=Lederberg-Coxeter-Fruchte) is a compressed
- notation used in the generation of various cubic Hamiltonian
- graphs of high symmetry. See, for example, dodecahedral_graph,
- desargues_graph, heawood_graph and pappus_graph below.
-
- n (number of nodes)
- The starting graph is the n-cycle with nodes 0,...,n-1.
- (The null graph is returned if n < 0.)
-
- shift_list = [s1,s2,..,sk], a list of integer shifts mod n,
-
- repeats
- integer specifying the number of times that shifts in shift_list
- are successively applied to each v_current in the n-cycle
- to generate an edge between v_current and v_current+shift mod n.
-
- For v1 cycling through the n-cycle a total of k*repeats
- with shift cycling through shiftlist repeats times connect
- v1 with v1+shift mod n
-
- The utility graph K_{3,3}
-
- >>> G=nx.LCF_graph(6,[3,-3],3)
-
- The Heawood graph
-
- >>> G=nx.LCF_graph(14,[5,-5],7)
-
- See http://mathworld.wolfram.com/LCFNotation.html for a description
- and references.
-
- """
- if create_using is not None and create_using.is_directed():
- raise NetworkXError("Directed Graph not supported")
-
- if n <= 0:
- return empty_graph(0, create_using)
-
- # start with the n-cycle
- G=cycle_graph(n, create_using)
- G.name="LCF_graph"
- nodes=G.nodes()
-
- n_extra_edges=repeats*len(shift_list)
- # edges are added n_extra_edges times
- # (not all of these need be new)
- if n_extra_edges < 1:
- return G
-
- for i in range(n_extra_edges):
- shift=shift_list[i%len(shift_list)] #cycle through shift_list
- v1=nodes[i%n] # cycle repeatedly through nodes
- v2=nodes[(i + shift)%n]
- G.add_edge(v1, v2)
- return G
-
-
-#-------------------------------------------------------------------------------
-# Various small and named graphs
-#-------------------------------------------------------------------------------
-
-def bull_graph(create_using=None):
- """Return the Bull graph. """
- description=[
- "adjacencylist",
- "Bull Graph",
- 5,
- [[2,3],[1,3,4],[1,2,5],[2],[3]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def chvatal_graph(create_using=None):
- """Return the Chvátal graph."""
- description=[
- "adjacencylist",
- "Chvatal Graph",
- 12,
- [[2,5,7,10],[3,6,8],[4,7,9],[5,8,10],
- [6,9],[11,12],[11,12],[9,12],
- [11],[11,12],[],[]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def cubical_graph(create_using=None):
- """Return the 3-regular Platonic Cubical graph."""
- description=[
- "adjacencylist",
- "Platonic Cubical Graph",
- 8,
- [[2,4,5],[1,3,8],[2,4,7],[1,3,6],
- [1,6,8],[4,5,7],[3,6,8],[2,5,7]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def desargues_graph(create_using=None):
- """ Return the Desargues graph."""
- G=LCF_graph(20, [5,-5,9,-9], 5, create_using)
- G.name="Desargues Graph"
- return G
-
-def diamond_graph(create_using=None):
- """Return the Diamond graph. """
- description=[
- "adjacencylist",
- "Diamond Graph",
- 4,
- [[2,3],[1,3,4],[1,2,4],[2,3]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def dodecahedral_graph(create_using=None):
- """ Return the Platonic Dodecahedral graph. """
- G=LCF_graph(20, [10,7,4,-4,-7,10,-4,7,-7,4], 2, create_using)
- G.name="Dodecahedral Graph"
- return G
-
-def frucht_graph(create_using=None):
- """Return the Frucht Graph.
-
- The Frucht Graph is the smallest cubical graph whose
- automorphism group consists only of the identity element.
-
- """
- G=cycle_graph(7, create_using)
- G.add_edges_from([[0,7],[1,7],[2,8],[3,9],[4,9],[5,10],[6,10],
- [7,11],[8,11],[8,9],[10,11]])
-
- G.name="Frucht Graph"
- return G
-
-def heawood_graph(create_using=None):
- """ Return the Heawood graph, a (3,6) cage. """
- G=LCF_graph(14, [5,-5], 7, create_using)
- G.name="Heawood Graph"
- return G
-
-def house_graph(create_using=None):
- """Return the House graph (square with triangle on top)."""
- description=[
- "adjacencylist",
- "House Graph",
- 5,
- [[2,3],[1,4],[1,4,5],[2,3,5],[3,4]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def house_x_graph(create_using=None):
- """Return the House graph with a cross inside the house square."""
- description=[
- "adjacencylist",
- "House-with-X-inside Graph",
- 5,
- [[2,3,4],[1,3,4],[1,2,4,5],[1,2,3,5],[3,4]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def icosahedral_graph(create_using=None):
- """Return the Platonic Icosahedral graph."""
- description=[
- "adjacencylist",
- "Platonic Icosahedral Graph",
- 12,
- [[2,6,8,9,12],[3,6,7,9],[4,7,9,10],[5,7,10,11],
- [6,7,11,12],[7,12],[],[9,10,11,12],
- [10],[11],[12],[]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-
-def krackhardt_kite_graph(create_using=None):
- """
- Return the Krackhardt Kite Social Network.
-
- A 10 actor social network introduced by David Krackhardt
- to illustrate: degree, betweenness, centrality, closeness, etc.
- The traditional labeling is:
- Andre=1, Beverley=2, Carol=3, Diane=4,
- Ed=5, Fernando=6, Garth=7, Heather=8, Ike=9, Jane=10.
-
- """
- description=[
- "adjacencylist",
- "Krackhardt Kite Social Network",
- 10,
- [[2,3,4,6],[1,4,5,7],[1,4,6],[1,2,3,5,6,7],[2,4,7],
- [1,3,4,7,8],[2,4,5,6,8],[6,7,9],[8,10],[9]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def moebius_kantor_graph(create_using=None):
- """Return the Moebius-Kantor graph."""
- G=LCF_graph(16, [5,-5], 8, create_using)
- G.name="Moebius-Kantor Graph"
- return G
-
-def octahedral_graph(create_using=None):
- """Return the Platonic Octahedral graph."""
- description=[
- "adjacencylist",
- "Platonic Octahedral Graph",
- 6,
- [[2,3,4,5],[3,4,6],[5,6],[5,6],[6],[]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def pappus_graph():
- """ Return the Pappus graph."""
- G=LCF_graph(18,[5,7,-7,7,-7,-5],3)
- G.name="Pappus Graph"
- return G
-
-def petersen_graph(create_using=None):
- """Return the Petersen graph."""
- description=[
- "adjacencylist",
- "Petersen Graph",
- 10,
- [[2,5,6],[1,3,7],[2,4,8],[3,5,9],[4,1,10],[1,8,9],[2,9,10],
- [3,6,10],[4,6,7],[5,7,8]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-
-def sedgewick_maze_graph(create_using=None):
- """
- Return a small maze with a cycle.
-
- This is the maze used in Sedgewick,3rd Edition, Part 5, Graph
- Algorithms, Chapter 18, e.g. Figure 18.2 and following.
- Nodes are numbered 0,..,7
- """
- G=empty_graph(0, create_using)
- G.add_nodes_from(range(8))
- G.add_edges_from([[0,2],[0,7],[0,5]])
- G.add_edges_from([[1,7],[2,6]])
- G.add_edges_from([[3,4],[3,5]])
- G.add_edges_from([[4,5],[4,7],[4,6]])
- G.name="Sedgewick Maze"
- return G
-
-def tetrahedral_graph(create_using=None):
- """ Return the 3-regular Platonic Tetrahedral graph."""
- G=complete_graph(4, create_using)
- G.name="Platonic Tetrahedral graph"
- return G
-
-def truncated_cube_graph(create_using=None):
- """Return the skeleton of the truncated cube."""
- description=[
- "adjacencylist",
- "Truncated Cube Graph",
- 24,
- [[2,3,5],[12,15],[4,5],[7,9],
- [6],[17,19],[8,9],[11,13],
- [10],[18,21],[12,13],[15],
- [14],[22,23],[16],[20,24],
- [18,19],[21],[20],[24],
- [22],[23],[24],[]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
-def truncated_tetrahedron_graph(create_using=None):
- """Return the skeleton of the truncated Platonic tetrahedron."""
- G=path_graph(12, create_using)
-# G.add_edges_from([(1,3),(1,10),(2,7),(4,12),(5,12),(6,8),(9,11)])
- G.add_edges_from([(0,2),(0,9),(1,6),(3,11),(4,11),(5,7),(8,10)])
- G.name="Truncated Tetrahedron Graph"
- return G
-
-def tutte_graph(create_using=None):
- """Return the Tutte graph."""
- description=[
- "adjacencylist",
- "Tutte's Graph",
- 46,
- [[2,3,4],[5,27],[11,12],[19,20],[6,34],
- [7,30],[8,28],[9,15],[10,39],[11,38],
- [40],[13,40],[14,36],[15,16],[35],
- [17,23],[18,45],[19,44],[46],[21,46],
- [22,42],[23,24],[41],[25,28],[26,33],
- [27,32],[34],[29],[30,33],[31],
- [32,34],[33],[],[],[36,39],
- [37],[38,40],[39],[],[],
- [42,45],[43],[44,46],[45],[],[]]
- ]
- G=make_small_undirected_graph(description, create_using)
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/social.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/social.py
deleted file mode 100644
index 212dd9c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/social.py
+++ /dev/null
@@ -1,280 +0,0 @@
-"""
-Famous social networks.
-"""
-import networkx as nx
-__author__ = """\n""".join(['Jordi Torrents <jtorrents@milnou.net>',
- 'Katy Bold <kbold@princeton.edu>',
- 'Aric Hagberg <aric.hagberg@gmail.com)'])
-
-__all__ = ['karate_club_graph','davis_southern_women_graph',
- 'florentine_families_graph']
-
-def karate_club_graph():
- """Return Zachary's Karate club graph.
-
- References
- ----------
- .. [1] Zachary W.
- An information flow model for conflict and fission in small groups.
- Journal of Anthropological Research, 33, 452-473, (1977).
-
- .. [2] Data file from:
- http://vlado.fmf.uni-lj.si/pub/networks/data/Ucinet/UciData.htm
- """
- G=nx.Graph()
- G.add_nodes_from(range(34))
- G.name="Zachary's Karate Club"
-
- zacharydat="""\
-0 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0
-1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0
-1 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0
-1 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1
-0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
-1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
-0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
-1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
-1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
-0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1
-0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
-0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1
-0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
-1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1
-0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 0 0 1 1 1 0 1
-0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 0"""
- row=0
- for line in zacharydat.split('\n'):
- thisrow=list(map(int,line.split(' ')))
- for col in range(0,len(thisrow)):
- if thisrow[col]==1:
- G.add_edge(row,col) # col goes from 0,33
- row+=1
- club1 = 'Mr. Hi'
- club2 = 'Officer'
- G.node[0]['club'] = club1
- G.node[1]['club'] = club1
- G.node[2]['club'] = club1
- G.node[3]['club'] = club1
- G.node[4]['club'] = club1
- G.node[5]['club'] = club1
- G.node[6]['club'] = club1
- G.node[7]['club'] = club1
- G.node[8]['club'] = club1
- G.node[9]['club'] = club2
- G.node[10]['club'] = club1
- G.node[11]['club'] = club1
- G.node[12]['club'] = club1
- G.node[13]['club'] = club1
- G.node[14]['club'] = club2
- G.node[15]['club'] = club2
- G.node[16]['club'] = club1
- G.node[17]['club'] = club1
- G.node[18]['club'] = club2
- G.node[19]['club'] = club1
- G.node[20]['club'] = club2
- G.node[21]['club'] = club1
- G.node[22]['club'] = club2
- G.node[23]['club'] = club2
- G.node[24]['club'] = club2
- G.node[25]['club'] = club2
- G.node[26]['club'] = club2
- G.node[27]['club'] = club2
- G.node[28]['club'] = club2
- G.node[29]['club'] = club2
- G.node[30]['club'] = club2
- G.node[31]['club'] = club2
- G.node[32]['club'] = club2
- G.node[33]['club'] = club2
- return G
-
-
-
-def davis_southern_women_graph():
- """Return Davis Southern women social network.
-
- This is a bipartite graph.
-
- References
- ----------
- .. [1] A. Davis, Gardner, B. B., Gardner, M. R., 1941. Deep South.
- University of Chicago Press, Chicago, IL.
- """
- G = nx.Graph()
- # Top nodes
- G.add_nodes_from(["Evelyn Jefferson",
- "Laura Mandeville",
- "Theresa Anderson",
- "Brenda Rogers",
- "Charlotte McDowd",
- "Frances Anderson",
- "Eleanor Nye",
- "Pearl Oglethorpe",
- "Ruth DeSand",
- "Verne Sanderson",
- "Myra Liddel",
- "Katherina Rogers",
- "Sylvia Avondale",
- "Nora Fayette",
- "Helen Lloyd",
- "Dorothy Murchison",
- "Olivia Carleton",
- "Flora Price"],
- bipartite=0)
- # Bottom nodes
- G.add_nodes_from(["E1",
- "E2",
- "E3",
- "E4",
- "E5",
- "E6",
- "E7",
- "E8",
- "E9",
- "E10",
- "E11",
- "E12",
- "E13",
- "E14"],
- bipartite=1)
-
- G.add_edges_from([("Evelyn Jefferson","E1"),
- ("Evelyn Jefferson","E2"),
- ("Evelyn Jefferson","E3"),
- ("Evelyn Jefferson","E4"),
- ("Evelyn Jefferson","E5"),
- ("Evelyn Jefferson","E6"),
- ("Evelyn Jefferson","E8"),
- ("Evelyn Jefferson","E9"),
- ("Laura Mandeville","E1"),
- ("Laura Mandeville","E2"),
- ("Laura Mandeville","E3"),
- ("Laura Mandeville","E5"),
- ("Laura Mandeville","E6"),
- ("Laura Mandeville","E7"),
- ("Laura Mandeville","E8"),
- ("Theresa Anderson","E2"),
- ("Theresa Anderson","E3"),
- ("Theresa Anderson","E4"),
- ("Theresa Anderson","E5"),
- ("Theresa Anderson","E6"),
- ("Theresa Anderson","E7"),
- ("Theresa Anderson","E8"),
- ("Theresa Anderson","E9"),
- ("Brenda Rogers","E1"),
- ("Brenda Rogers","E3"),
- ("Brenda Rogers","E4"),
- ("Brenda Rogers","E5"),
- ("Brenda Rogers","E6"),
- ("Brenda Rogers","E7"),
- ("Brenda Rogers","E8"),
- ("Charlotte McDowd","E3"),
- ("Charlotte McDowd","E4"),
- ("Charlotte McDowd","E5"),
- ("Charlotte McDowd","E7"),
- ("Frances Anderson","E3"),
- ("Frances Anderson","E5"),
- ("Frances Anderson","E6"),
- ("Frances Anderson","E8"),
- ("Eleanor Nye","E5"),
- ("Eleanor Nye","E6"),
- ("Eleanor Nye","E7"),
- ("Eleanor Nye","E8"),
- ("Pearl Oglethorpe","E6"),
- ("Pearl Oglethorpe","E8"),
- ("Pearl Oglethorpe","E9"),
- ("Ruth DeSand","E5"),
- ("Ruth DeSand","E7"),
- ("Ruth DeSand","E8"),
- ("Ruth DeSand","E9"),
- ("Verne Sanderson","E7"),
- ("Verne Sanderson","E8"),
- ("Verne Sanderson","E9"),
- ("Verne Sanderson","E12"),
- ("Myra Liddel","E8"),
- ("Myra Liddel","E9"),
- ("Myra Liddel","E10"),
- ("Myra Liddel","E12"),
- ("Katherina Rogers","E8"),
- ("Katherina Rogers","E9"),
- ("Katherina Rogers","E10"),
- ("Katherina Rogers","E12"),
- ("Katherina Rogers","E13"),
- ("Katherina Rogers","E14"),
- ("Sylvia Avondale","E7"),
- ("Sylvia Avondale","E8"),
- ("Sylvia Avondale","E9"),
- ("Sylvia Avondale","E10"),
- ("Sylvia Avondale","E12"),
- ("Sylvia Avondale","E13"),
- ("Sylvia Avondale","E14"),
- ("Nora Fayette","E6"),
- ("Nora Fayette","E7"),
- ("Nora Fayette","E9"),
- ("Nora Fayette","E10"),
- ("Nora Fayette","E11"),
- ("Nora Fayette","E12"),
- ("Nora Fayette","E13"),
- ("Nora Fayette","E14"),
- ("Helen Lloyd","E7"),
- ("Helen Lloyd","E8"),
- ("Helen Lloyd","E10"),
- ("Helen Lloyd","E11"),
- ("Helen Lloyd","E12"),
- ("Dorothy Murchison","E8"),
- ("Dorothy Murchison","E9"),
- ("Olivia Carleton","E9"),
- ("Olivia Carleton","E11"),
- ("Flora Price","E9"),
- ("Flora Price","E11")])
- return G
-
-def florentine_families_graph():
- """Return Florentine families graph.
-
- References
- ----------
- .. [1] Ronald L. Breiger and Philippa E. Pattison
- Cumulated social roles: The duality of persons and their algebras,1
- Social Networks, Volume 8, Issue 3, September 1986, Pages 215-256
- """
- G=nx.Graph()
- G.add_edge('Acciaiuoli','Medici')
- G.add_edge('Castellani','Peruzzi')
- G.add_edge('Castellani','Strozzi')
- G.add_edge('Castellani','Barbadori')
- G.add_edge('Medici','Barbadori')
- G.add_edge('Medici','Ridolfi')
- G.add_edge('Medici','Tornabuoni')
- G.add_edge('Medici','Albizzi')
- G.add_edge('Medici','Salviati')
- G.add_edge('Salviati','Pazzi')
- G.add_edge('Peruzzi','Strozzi')
- G.add_edge('Peruzzi','Bischeri')
- G.add_edge('Strozzi','Ridolfi')
- G.add_edge('Strozzi','Bischeri')
- G.add_edge('Ridolfi','Tornabuoni')
- G.add_edge('Tornabuoni','Guadagni')
- G.add_edge('Albizzi','Ginori')
- G.add_edge('Albizzi','Guadagni')
- G.add_edge('Bischeri','Guadagni')
- G.add_edge('Guadagni','Lamberteschi')
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/stochastic.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/stochastic.py
deleted file mode 100644
index 5553f5f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/stochastic.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Stocastic graph."""
-# Copyright (C) 2010-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = "Aric Hagberg <aric.hagberg@gmail.com>"
-__all__ = ['stochastic_graph']
-
-def stochastic_graph(G, copy=True, weight='weight'):
- """Return a right-stochastic representation of G.
-
- A right-stochastic graph is a weighted digraph in which all of
- the node (out) neighbors edge weights sum to 1.
-
- Parameters
- -----------
- G : graph
- A NetworkX graph
-
- copy : boolean, optional
- If True make a copy of the graph, otherwise modify the original graph
-
- weight : edge attribute key (optional, default='weight')
- Edge data key used for weight. If no attribute is found for an edge
- the edge weight is set to 1.
- """
- if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
- raise nx.NetworkXError('stochastic_graph not implemented '
- 'for multigraphs')
-
- if not G.is_directed():
- raise nx.NetworkXError('stochastic_graph not implemented '
- 'for undirected graphs')
-
- if copy:
- W = nx.DiGraph(G)
- else:
- W = G # reference original graph, no copy
-
- degree = W.out_degree(weight=weight)
- for (u,v,d) in W.edges(data=True):
- d[weight] = float(d.get(weight,1.0))/degree[u]
- return W
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_atlas.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_atlas.py
deleted file mode 100644
index 2428385..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_atlas.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from nose.tools import *
-import networkx as nx
-
-
-class TestAtlas(object):
- def setUp(self):
- self.GAG=nx.graph_atlas_g()
-
- def test_sizes(self):
- G=self.GAG[0]
- assert_equal(G.number_of_nodes(),0)
- assert_equal(G.number_of_edges(),0)
-
- G=self.GAG[7]
- assert_equal(G.number_of_nodes(),3)
- assert_equal(G.number_of_edges(),3)
-
- def test_names(self):
- i=0
- for g in self.GAG:
- name=g.name
- assert_equal(int(name[1:]),i)
- i+=1
-
- def test_monotone_nodes(self):
- # check for monotone increasing number of nodes
- previous=self.GAG[0]
- for g in self.GAG:
- assert_false(len(g)-len(previous) > 1)
- previous=g.copy()
-
- def test_monotone_nodes(self):
- # check for monotone increasing number of edges
- # (for fixed number of nodes)
- previous=self.GAG[0]
- for g in self.GAG:
- if len(g)==len(previous):
- assert_false(g.size()-previous.size() > 1)
- previous=g.copy()
-
- def test_monotone_degree_sequence(self):
- # check for monotone increasing degree sequence
- # (for fixed number f nodes and edges)
- # note that 111223 < 112222
- previous=self.GAG[0]
- for g in self.GAG:
- if len(g)==0:
- continue
- if len(g)==len(previous) & g.size()==previous.size():
- deg_seq=sorted(g.degree().values())
- previous_deg_seq=sorted(previous.degree().values())
- assert_true(previous_deg_seq < deg_seq)
- previous=g.copy()
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_bipartite.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_bipartite.py
deleted file mode 100644
index 63d336e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_bipartite.py
+++ /dev/null
@@ -1,176 +0,0 @@
-#!/usr/bin/env python
-
-from nose.tools import *
-from networkx import *
-from networkx.generators.bipartite import *
-
-"""Generators - Bipartite
-----------------------
-"""
-
-class TestGeneratorsBipartite():
- def test_configuration_model(self):
- aseq=[3,3,3,3]
- bseq=[2,2,2,2,2]
- assert_raises(networkx.exception.NetworkXError,
- bipartite_configuration_model, aseq, bseq)
-
- aseq=[3,3,3,3]
- bseq=[2,2,2,2,2,2]
- G=bipartite_configuration_model(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,2,2,2]
- bseq=[3,3,3,3]
- G=bipartite_configuration_model(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,1,1,1]
- bseq=[3,3,3]
- G=bipartite_configuration_model(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [1, 1, 1, 2, 2, 2, 3, 3, 3])
-
- GU=project(Graph(G),range(len(aseq)))
- assert_equal(GU.number_of_nodes(), 6)
-
- GD=project(Graph(G),range(len(aseq),len(aseq)+len(bseq)))
- assert_equal(GD.number_of_nodes(), 3)
-
- assert_raises(networkx.exception.NetworkXError,
- bipartite_configuration_model, aseq, bseq,
- create_using=DiGraph())
-
- def test_havel_hakimi_graph(self):
- aseq=[3,3,3,3]
- bseq=[2,2,2,2,2]
- assert_raises(networkx.exception.NetworkXError,
- bipartite_havel_hakimi_graph, aseq, bseq)
-
- bseq=[2,2,2,2,2,2]
- G=bipartite_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,2,2,2]
- bseq=[3,3,3,3]
- G=bipartite_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- GU=project(Graph(G),range(len(aseq)))
- assert_equal(GU.number_of_nodes(), 6)
-
- GD=project(Graph(G),range(len(aseq),len(aseq)+len(bseq)))
- assert_equal(GD.number_of_nodes(), 4)
- assert_raises(networkx.exception.NetworkXError,
- bipartite_havel_hakimi_graph, aseq, bseq,
- create_using=DiGraph())
-
- def test_reverse_havel_hakimi_graph(self):
- aseq=[3,3,3,3]
- bseq=[2,2,2,2,2]
- assert_raises(networkx.exception.NetworkXError,
- bipartite_reverse_havel_hakimi_graph, aseq, bseq)
-
- bseq=[2,2,2,2,2,2]
- G=bipartite_reverse_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,2,2,2]
- bseq=[3,3,3,3]
- G=bipartite_reverse_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,1,1,1]
- bseq=[3,3,3]
- G=bipartite_reverse_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [1, 1, 1, 2, 2, 2, 3, 3, 3])
-
- GU=project(Graph(G),range(len(aseq)))
- assert_equal(GU.number_of_nodes(), 6)
-
- GD=project(Graph(G),range(len(aseq),len(aseq)+len(bseq)))
- assert_equal(GD.number_of_nodes(), 3)
- assert_raises(networkx.exception.NetworkXError,
- bipartite_reverse_havel_hakimi_graph, aseq, bseq,
- create_using=DiGraph())
-
- def test_alternating_havel_hakimi_graph(self):
- aseq=[3,3,3,3]
- bseq=[2,2,2,2,2]
- assert_raises(networkx.exception.NetworkXError,
- bipartite_alternating_havel_hakimi_graph, aseq, bseq)
-
- bseq=[2,2,2,2,2,2]
- G=bipartite_alternating_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,2,2,2]
- bseq=[3,3,3,3]
- G=bipartite_alternating_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [2, 2, 2, 2, 2, 2, 3, 3, 3, 3])
-
- aseq=[2,2,2,1,1,1]
- bseq=[3,3,3]
- G=bipartite_alternating_havel_hakimi_graph(aseq,bseq)
- assert_equal(sorted(G.degree().values()),
- [1, 1, 1, 2, 2, 2, 3, 3, 3])
-
- GU=project(Graph(G),range(len(aseq)))
- assert_equal(GU.number_of_nodes(), 6)
-
- GD=project(Graph(G),range(len(aseq),len(aseq)+len(bseq)))
- assert_equal(GD.number_of_nodes(), 3)
-
- assert_raises(networkx.exception.NetworkXError,
- bipartite_alternating_havel_hakimi_graph, aseq, bseq,
- create_using=DiGraph())
-
- def test_preferential_attachment(self):
- aseq=[3,2,1,1]
- G=bipartite_preferential_attachment_graph(aseq,0.5)
- assert_raises(networkx.exception.NetworkXError,
- bipartite_preferential_attachment_graph, aseq, 0.5,
- create_using=DiGraph())
-
- def test_bipartite_random_graph(self):
- n=10
- m=20
- G=bipartite_random_graph(n,m,0.9)
- assert_equal(len(G),30)
- assert_true(is_bipartite(G))
- X,Y=nx.algorithms.bipartite.sets(G)
- assert_equal(set(range(n)),X)
- assert_equal(set(range(n,n+m)),Y)
-
- def test_directed_bipartite_random_graph(self):
- n=10
- m=20
- G=bipartite_random_graph(n,m,0.9,directed=True)
- assert_equal(len(G),30)
- assert_true(is_bipartite(G))
- X,Y=nx.algorithms.bipartite.sets(G)
- assert_equal(set(range(n)),X)
- assert_equal(set(range(n,n+m)),Y)
-
- def test_bipartite_gnmk_random_graph(self):
- n = 10
- m = 20
- edges = 100
- G = bipartite_gnmk_random_graph(n, m, edges)
- assert_equal(len(G),30)
- assert_true(is_bipartite(G))
- X,Y=nx.algorithms.bipartite.sets(G)
- print(X)
- assert_equal(set(range(n)),X)
- assert_equal(set(range(n,n+m)),Y)
- assert_equal(edges, len(G.edges()))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_classic.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_classic.py
deleted file mode 100644
index 96ca367..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_classic.py
+++ /dev/null
@@ -1,408 +0,0 @@
-#!/usr/bin/env python
-"""
-====================
-Generators - Classic
-====================
-
-Unit tests for various classic graph generators in generators/classic.py
-"""
-from nose.tools import *
-from networkx import *
-from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
-is_isomorphic=graph_could_be_isomorphic
-
-class TestGeneratorClassic():
- def test_balanced_tree(self):
- # balanced_tree(r,h) is a tree with (r**(h+1)-1)/(r-1) edges
- for r,h in [(2,2),(3,3),(6,2)]:
- t=balanced_tree(r,h)
- order=t.order()
- assert_true(order==(r**(h+1)-1)/(r-1))
- assert_true(is_connected(t))
- assert_true(t.size()==order-1)
- dh = degree_histogram(t)
- assert_equal(dh[0],0) # no nodes of 0
- assert_equal(dh[1],r**h) # nodes of degree 1 are leaves
- assert_equal(dh[r],1) # root is degree r
- assert_equal(dh[r+1],order-r**h-1)# everyone else is degree r+1
- assert_equal(len(dh),r+2)
-
- def test_balanced_tree_star(self):
- # balanced_tree(r,1) is the r-star
- t=balanced_tree(r=2,h=1)
- assert_true(is_isomorphic(t,star_graph(2)))
- t=balanced_tree(r=5,h=1)
- assert_true(is_isomorphic(t,star_graph(5)))
- t=balanced_tree(r=10,h=1)
- assert_true(is_isomorphic(t,star_graph(10)))
-
- def test_full_rary_tree(self):
- r=2
- n=9
- t=full_rary_tree(r,n)
- assert_equal(t.order(),n)
- assert_true(is_connected(t))
- dh = degree_histogram(t)
- assert_equal(dh[0],0) # no nodes of 0
- assert_equal(dh[1],5) # nodes of degree 1 are leaves
- assert_equal(dh[r],1) # root is degree r
- assert_equal(dh[r+1],9-5-1) # everyone else is degree r+1
- assert_equal(len(dh),r+2)
-
- def test_full_rary_tree_balanced(self):
- t=full_rary_tree(2,15)
- th=balanced_tree(2,3)
- assert_true(is_isomorphic(t,th))
-
- def test_full_rary_tree_path(self):
- t=full_rary_tree(1,10)
- assert_true(is_isomorphic(t,path_graph(10)))
-
- def test_full_rary_tree_empty(self):
- t=full_rary_tree(0,10)
- assert_true(is_isomorphic(t,empty_graph(10)))
- t=full_rary_tree(3,0)
- assert_true(is_isomorphic(t,empty_graph(0)))
-
- def test_full_rary_tree_3_20(self):
- t=full_rary_tree(3,20)
- assert_equal(t.order(),20)
-
- def test_barbell_graph(self):
- # number of nodes = 2*m1 + m2 (2 m1-complete graphs + m2-path + 2 edges)
- # number of edges = 2*(number_of_edges(m1-complete graph) + m2 + 1
- m1=3; m2=5
- b=barbell_graph(m1,m2)
- assert_true(number_of_nodes(b)==2*m1+m2)
- assert_true(number_of_edges(b)==m1*(m1-1) + m2 + 1)
- assert_equal(b.name, 'barbell_graph(3,5)')
-
- m1=4; m2=10
- b=barbell_graph(m1,m2)
- assert_true(number_of_nodes(b)==2*m1+m2)
- assert_true(number_of_edges(b)==m1*(m1-1) + m2 + 1)
- assert_equal(b.name, 'barbell_graph(4,10)')
-
- m1=3; m2=20
- b=barbell_graph(m1,m2)
- assert_true(number_of_nodes(b)==2*m1+m2)
- assert_true(number_of_edges(b)==m1*(m1-1) + m2 + 1)
- assert_equal(b.name, 'barbell_graph(3,20)')
-
- # Raise NetworkXError if m1<2
- m1=1; m2=20
- assert_raises(networkx.exception.NetworkXError, barbell_graph, m1, m2)
-
- # Raise NetworkXError if m2<0
- m1=5; m2=-2
- assert_raises(networkx.exception.NetworkXError, barbell_graph, m1, m2)
-
- # barbell_graph(2,m) = path_graph(m+4)
- m1=2; m2=5
- b=barbell_graph(m1,m2)
- assert_true(is_isomorphic(b, path_graph(m2+4)))
-
- m1=2; m2=10
- b=barbell_graph(m1,m2)
- assert_true(is_isomorphic(b, path_graph(m2+4)))
-
- m1=2; m2=20
- b=barbell_graph(m1,m2)
- assert_true(is_isomorphic(b, path_graph(m2+4)))
-
- assert_raises(networkx.exception.NetworkXError, barbell_graph, m1, m2,
- create_using=DiGraph())
-
- mb=barbell_graph(m1, m2, create_using=MultiGraph())
- assert_true(mb.edges()==b.edges())
-
- def test_complete_graph(self):
- # complete_graph(m) is a connected graph with
- # m nodes and m*(m+1)/2 edges
- for m in [0, 1, 3, 5]:
- g = complete_graph(m)
- assert_true(number_of_nodes(g) == m)
- assert_true(number_of_edges(g) == m * (m - 1) // 2)
-
-
- mg=complete_graph(m, create_using=MultiGraph())
- assert_true(mg.edges()==g.edges())
-
- def test_complete_digraph(self):
- # complete_graph(m) is a connected graph with
- # m nodes and m*(m+1)/2 edges
- for m in [0, 1, 3, 5]:
- g = complete_graph(m,create_using=nx.DiGraph())
- assert_true(number_of_nodes(g) == m)
- assert_true(number_of_edges(g) == m * (m - 1))
-
- def test_complete_bipartite_graph(self):
- G=complete_bipartite_graph(0,0)
- assert_true(is_isomorphic( G, null_graph() ))
-
- for i in [1, 5]:
- G=complete_bipartite_graph(i,0)
- assert_true(is_isomorphic( G, empty_graph(i) ))
- G=complete_bipartite_graph(0,i)
- assert_true(is_isomorphic( G, empty_graph(i) ))
-
- G=complete_bipartite_graph(2,2)
- assert_true(is_isomorphic( G, cycle_graph(4) ))
-
- G=complete_bipartite_graph(1,5)
- assert_true(is_isomorphic( G, star_graph(5) ))
-
- G=complete_bipartite_graph(5,1)
- assert_true(is_isomorphic( G, star_graph(5) ))
-
- # complete_bipartite_graph(m1,m2) is a connected graph with
- # m1+m2 nodes and m1*m2 edges
- for m1, m2 in [(5, 11), (7, 3)]:
- G=complete_bipartite_graph(m1,m2)
- assert_equal(number_of_nodes(G), m1 + m2)
- assert_equal(number_of_edges(G), m1 * m2)
-
- assert_raises(networkx.exception.NetworkXError,
- complete_bipartite_graph, 7, 3, create_using=DiGraph())
-
- mG=complete_bipartite_graph(7, 3, create_using=MultiGraph())
- assert_equal(mG.edges(), G.edges())
-
- def test_circular_ladder_graph(self):
- G=circular_ladder_graph(5)
- assert_raises(networkx.exception.NetworkXError, circular_ladder_graph,
- 5, create_using=DiGraph())
- mG=circular_ladder_graph(5, create_using=MultiGraph())
- assert_equal(mG.edges(), G.edges())
-
- def test_cycle_graph(self):
- G=cycle_graph(4)
- assert_equal(sorted(G.edges()), [(0, 1), (0, 3), (1, 2), (2, 3)])
- mG=cycle_graph(4, create_using=MultiGraph())
- assert_equal(sorted(mG.edges()), [(0, 1), (0, 3), (1, 2), (2, 3)])
- G=cycle_graph(4, create_using=DiGraph())
- assert_false(G.has_edge(2,1))
- assert_true(G.has_edge(1,2))
-
- def test_dorogovtsev_goltsev_mendes_graph(self):
- G=dorogovtsev_goltsev_mendes_graph(0)
- assert_equal(G.edges(), [(0, 1)])
- assert_equal(G.nodes(), [0, 1])
- G=dorogovtsev_goltsev_mendes_graph(1)
- assert_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
- assert_equal(average_clustering(G), 1.0)
- assert_equal(list(triangles(G).values()), [1, 1, 1])
- G=dorogovtsev_goltsev_mendes_graph(10)
- assert_equal(number_of_nodes(G), 29526)
- assert_equal(number_of_edges(G), 59049)
- assert_equal(G.degree(0), 1024)
- assert_equal(G.degree(1), 1024)
- assert_equal(G.degree(2), 1024)
-
- assert_raises(networkx.exception.NetworkXError,
- dorogovtsev_goltsev_mendes_graph, 7,
- create_using=DiGraph())
- assert_raises(networkx.exception.NetworkXError,
- dorogovtsev_goltsev_mendes_graph, 7,
- create_using=MultiGraph())
-
- def test_empty_graph(self):
- G=empty_graph()
- assert_equal(number_of_nodes(G), 0)
- G=empty_graph(42)
- assert_equal(number_of_nodes(G), 42)
- assert_equal(number_of_edges(G), 0)
- assert_equal(G.name, 'empty_graph(42)')
-
- # create empty digraph
- G=empty_graph(42,create_using=DiGraph(name="duh"))
- assert_equal(number_of_nodes(G), 42)
- assert_equal(number_of_edges(G), 0)
- assert_equal(G.name, 'empty_graph(42)')
- assert_true(isinstance(G,DiGraph))
-
- # create empty multigraph
- G=empty_graph(42,create_using=MultiGraph(name="duh"))
- assert_equal(number_of_nodes(G), 42)
- assert_equal(number_of_edges(G), 0)
- assert_equal(G.name, 'empty_graph(42)')
- assert_true(isinstance(G,MultiGraph))
-
- # create empty graph from another
- pete=petersen_graph()
- G=empty_graph(42,create_using=pete)
- assert_equal(number_of_nodes(G), 42)
- assert_equal(number_of_edges(G), 0)
- assert_equal(G.name, 'empty_graph(42)')
- assert_true(isinstance(G,Graph))
-
- def test_grid_2d_graph(self):
- n=5;m=6
- G=grid_2d_graph(n,m)
- assert_equal(number_of_nodes(G), n*m)
- assert_equal(degree_histogram(G), [0,0,4,2*(n+m)-8,(n-2)*(m-2)])
- DG=grid_2d_graph(n,m, create_using=DiGraph())
- assert_equal(DG.succ, G.adj)
- assert_equal(DG.pred, G.adj)
- MG=grid_2d_graph(n,m, create_using=MultiGraph())
- assert_equal(MG.edges(), G.edges())
-
- def test_grid_graph(self):
- """grid_graph([n,m]) is a connected simple graph with the
- following properties:
- number_of_nodes=n*m
- degree_histogram=[0,0,4,2*(n+m)-8,(n-2)*(m-2)]
- """
- for n, m in [(3, 5), (5, 3), (4, 5), (5, 4)]:
- dim=[n,m]
- g=grid_graph(dim)
- assert_equal(number_of_nodes(g), n*m)
- assert_equal(degree_histogram(g), [0,0,4,2*(n+m)-8,(n-2)*(m-2)])
- assert_equal(dim,[n,m])
-
- for n, m in [(1, 5), (5, 1)]:
- dim=[n,m]
- g=grid_graph(dim)
- assert_equal(number_of_nodes(g), n*m)
- assert_true(is_isomorphic(g,path_graph(5)))
- assert_equal(dim,[n,m])
-
-# mg=grid_graph([n,m], create_using=MultiGraph())
-# assert_equal(mg.edges(), g.edges())
-
- def test_hypercube_graph(self):
- for n, G in [(0, null_graph()), (1, path_graph(2)),
- (2, cycle_graph(4)), (3, cubical_graph())]:
- g=hypercube_graph(n)
- assert_true(is_isomorphic(g, G))
-
- g=hypercube_graph(4)
- assert_equal(degree_histogram(g), [0, 0, 0, 0, 16])
- g=hypercube_graph(5)
- assert_equal(degree_histogram(g), [0, 0, 0, 0, 0, 32])
- g=hypercube_graph(6)
- assert_equal(degree_histogram(g), [0, 0, 0, 0, 0, 0, 64])
-
-# mg=hypercube_graph(6, create_using=MultiGraph())
-# assert_equal(mg.edges(), g.edges())
-
- def test_ladder_graph(self):
- for i, G in [(0, empty_graph(0)), (1, path_graph(2)),
- (2, hypercube_graph(2)), (10, grid_graph([2,10]))]:
- assert_true(is_isomorphic(ladder_graph(i), G))
-
- assert_raises(networkx.exception.NetworkXError,
- ladder_graph, 2, create_using=DiGraph())
-
- g = ladder_graph(2)
- mg=ladder_graph(2, create_using=MultiGraph())
- assert_equal(mg.edges(), g.edges())
-
- def test_lollipop_graph(self):
- # number of nodes = m1 + m2
- # number of edges = number_of_edges(complete_graph(m1)) + m2
- for m1, m2 in [(3, 5), (4, 10), (3, 20)]:
- b=lollipop_graph(m1,m2)
- assert_equal(number_of_nodes(b), m1+m2)
- assert_equal(number_of_edges(b), m1*(m1-1)/2 + m2)
- assert_equal(b.name,
- 'lollipop_graph(' + str(m1) + ',' + str(m2) + ')')
-
- # Raise NetworkXError if m<2
- assert_raises(networkx.exception.NetworkXError,
- lollipop_graph, 1, 20)
-
- # Raise NetworkXError if n<0
- assert_raises(networkx.exception.NetworkXError,
- lollipop_graph, 5, -2)
-
- # lollipop_graph(2,m) = path_graph(m+2)
- for m1, m2 in [(2, 5), (2, 10), (2, 20)]:
- b=lollipop_graph(m1,m2)
- assert_true(is_isomorphic(b, path_graph(m2+2)))
-
- assert_raises(networkx.exception.NetworkXError,
- lollipop_graph, m1, m2, create_using=DiGraph())
-
- mb=lollipop_graph(m1, m2, create_using=MultiGraph())
- assert_true(mb.edges(), b.edges())
-
- def test_null_graph(self):
- assert_equal(number_of_nodes(null_graph()), 0)
-
- def test_path_graph(self):
- p=path_graph(0)
- assert_true(is_isomorphic(p, null_graph()))
- assert_equal(p.name, 'path_graph(0)')
-
- p=path_graph(1)
- assert_true(is_isomorphic( p, empty_graph(1)))
- assert_equal(p.name, 'path_graph(1)')
-
- p=path_graph(10)
- assert_true(is_connected(p))
- assert_equal(sorted(list(p.degree().values())),
- [1, 1, 2, 2, 2, 2, 2, 2, 2, 2])
- assert_equal(p.order()-1, p.size())
-
- dp=path_graph(3, create_using=DiGraph())
- assert_true(dp.has_edge(0,1))
- assert_false(dp.has_edge(1,0))
-
- mp=path_graph(10, create_using=MultiGraph())
- assert_true(mp.edges()==p.edges())
-
- def test_periodic_grid_2d_graph(self):
- g=grid_2d_graph(0,0, periodic=True)
- assert_equal(g.degree(), {})
-
- for m, n, G in [(2, 2, cycle_graph(4)), (1, 7, cycle_graph(7)),
- (7, 1, cycle_graph(7)), (2, 5, circular_ladder_graph(5)),
- (5, 2, circular_ladder_graph(5)), (2, 4, cubical_graph()),
- (4, 2, cubical_graph())]:
- g=grid_2d_graph(m,n, periodic=True)
- assert_true(is_isomorphic(g, G))
-
- DG=grid_2d_graph(4, 2, periodic=True, create_using=DiGraph())
- assert_equal(DG.succ,g.adj)
- assert_equal(DG.pred,g.adj)
- MG=grid_2d_graph(4, 2, periodic=True, create_using=MultiGraph())
- assert_equal(MG.edges(),g.edges())
-
- def test_star_graph(self):
- assert_true(is_isomorphic(star_graph(0), empty_graph(1)))
- assert_true(is_isomorphic(star_graph(1), path_graph(2)))
- assert_true(is_isomorphic(star_graph(2), path_graph(3)))
-
- s=star_graph(10)
- assert_equal(sorted(list(s.degree().values())),
- [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10])
-
- assert_raises(networkx.exception.NetworkXError,
- star_graph, 10, create_using=DiGraph())
-
- ms=star_graph(10, create_using=MultiGraph())
- assert_true(ms.edges()==s.edges())
-
- def test_trivial_graph(self):
- assert_equal(number_of_nodes(trivial_graph()), 1)
-
- def test_wheel_graph(self):
- for n, G in [(0, null_graph()), (1, empty_graph(1)),
- (2, path_graph(2)), (3, complete_graph(3)),
- (4, complete_graph(4))]:
- g=wheel_graph(n)
- assert_true(is_isomorphic( g, G))
-
- assert_equal(g.name, 'wheel_graph(4)')
-
- g=wheel_graph(10)
- assert_equal(sorted(list(g.degree().values())),
- [3, 3, 3, 3, 3, 3, 3, 3, 3, 9])
-
- assert_raises(networkx.exception.NetworkXError,
- wheel_graph, 10, create_using=DiGraph())
-
- mg=wheel_graph(10, create_using=MultiGraph())
- assert_equal(mg.edges(), g.edges())
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_degree_seq.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_degree_seq.py
deleted file mode 100644
index 0d09855..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_degree_seq.py
+++ /dev/null
@@ -1,169 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-from networkx import *
-from networkx.generators.degree_seq import *
-from networkx.utils import uniform_sequence,powerlaw_sequence
-
-def test_configuration_model_empty():
- # empty graph has empty degree sequence
- deg_seq=[]
- G=configuration_model(deg_seq)
- assert_equal(G.degree(), {})
-
-def test_configuration_model():
- deg_seq=[5,3,3,3,3,2,2,2,1,1,1]
- G=configuration_model(deg_seq,seed=12345678)
- assert_equal(sorted(G.degree().values(),reverse=True),
- [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1])
- assert_equal(sorted(G.degree(range(len(deg_seq))).values(),
- reverse=True),
- [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1])
-
- # test that fixed seed delivers the same graph
- deg_seq=[3,3,3,3,3,3,3,3,3,3,3,3]
- G1=configuration_model(deg_seq,seed=1000)
- G2=configuration_model(deg_seq,seed=1000)
- assert_true(is_isomorphic(G1,G2))
- G1=configuration_model(deg_seq,seed=10)
- G2=configuration_model(deg_seq,seed=10)
- assert_true(is_isomorphic(G1,G2))
-
-@raises(NetworkXError)
-def test_configuation_raise():
- z=[5,3,3,3,3,2,2,2,1,1,1]
- G = configuration_model(z, create_using=DiGraph())
-
-@raises(NetworkXError)
-def test_configuation_raise_odd():
- z=[5,3,3,3,3,2,2,2,1,1]
- G = configuration_model(z, create_using=DiGraph())
-
-@raises(NetworkXError)
-def test_directed_configuation_raise_unequal():
- zin = [5,3,3,3,3,2,2,2,1,1]
- zout = [5,3,3,3,3,2,2,2,1,2]
- G = directed_configuration_model(zin, zout)
-
-def test_directed_configuation_mode():
- G = directed_configuration_model([],[],seed=0)
- assert_equal(len(G),0)
-
-
-def test_expected_degree_graph_empty():
- # empty graph has empty degree sequence
- deg_seq=[]
- G=expected_degree_graph(deg_seq)
- assert_equal(G.degree(), {})
-
-
-def test_expected_degree_graph():
- # test that fixed seed delivers the same graph
- deg_seq=[3,3,3,3,3,3,3,3,3,3,3,3]
- G1=expected_degree_graph(deg_seq,seed=1000)
- G2=expected_degree_graph(deg_seq,seed=1000)
- assert_true(is_isomorphic(G1,G2))
-
- G1=expected_degree_graph(deg_seq,seed=10)
- G2=expected_degree_graph(deg_seq,seed=10)
- assert_true(is_isomorphic(G1,G2))
-
-
-def test_expected_degree_graph_selfloops():
- deg_seq=[3,3,3,3,3,3,3,3,3,3,3,3]
- G1=expected_degree_graph(deg_seq,seed=1000, selfloops=False)
- G2=expected_degree_graph(deg_seq,seed=1000, selfloops=False)
- assert_true(is_isomorphic(G1,G2))
-
-def test_expected_degree_graph_skew():
- deg_seq=[10,2,2,2,2]
- G1=expected_degree_graph(deg_seq,seed=1000)
- G2=expected_degree_graph(deg_seq,seed=1000)
- assert_true(is_isomorphic(G1,G2))
-
-
-def test_havel_hakimi_construction():
- G = havel_hakimi_graph([])
- assert_equal(len(G),0)
-
- z=[1000,3,3,3,3,2,2,2,1,1,1]
- assert_raises(networkx.exception.NetworkXError,
- havel_hakimi_graph, z)
- z=["A",3,3,3,3,2,2,2,1,1,1]
- assert_raises(networkx.exception.NetworkXError,
- havel_hakimi_graph, z)
-
- z=[5,4,3,3,3,2,2,2]
- G=havel_hakimi_graph(z)
- G=configuration_model(z)
- z=[6,5,4,4,2,1,1,1]
- assert_raises(networkx.exception.NetworkXError,
- havel_hakimi_graph, z)
-
- z=[10,3,3,3,3,2,2,2,2,2,2]
-
- G=havel_hakimi_graph(z)
-
- assert_raises(networkx.exception.NetworkXError,
- havel_hakimi_graph, z, create_using=DiGraph())
-
-def test_directed_havel_hakimi():
- # Test range of valid directed degree sequences
- n, r = 100, 10
- p = 1.0 / r
- for i in range(r):
- G1 = nx.erdos_renyi_graph(n,p*(i+1),None,True)
- din = list(G1.in_degree().values())
- dout = list(G1.out_degree().values())
- G2 = nx.directed_havel_hakimi_graph(din, dout)
- assert_true(din == list(G2.in_degree().values()))
- assert_true(dout == list(G2.out_degree().values()))
-
- # Test non-graphical sequence
- dout = [1000,3,3,3,3,2,2,2,1,1,1]
- din=[103,102,102,102,102,102,102,102,102,102]
- assert_raises(nx.exception.NetworkXError,
- nx.directed_havel_hakimi_graph, din, dout)
- # Test valid sequences
- dout=[1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- din=[2, 2, 2, 2, 2, 2, 2, 2, 0, 2]
- G2 = nx.directed_havel_hakimi_graph(din, dout)
- assert_true(din == list(G2.in_degree().values()))
- assert_true(dout == list(G2.out_degree().values()))
- # Test unequal sums
- din=[2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
- assert_raises(nx.exception.NetworkXError,
- nx.directed_havel_hakimi_graph, din, dout)
- # Test for negative values
- din=[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -2]
- assert_raises(nx.exception.NetworkXError,
- nx.directed_havel_hakimi_graph, din, dout)
-
-def test_degree_sequence_tree():
- z=[1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- G=degree_sequence_tree(z)
- assert_true(len(G.nodes())==len(z))
- assert_true(len(G.edges())==sum(z)/2)
-
- assert_raises(networkx.exception.NetworkXError,
- degree_sequence_tree, z, create_using=DiGraph())
-
- z=[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- assert_raises(networkx.exception.NetworkXError,
- degree_sequence_tree, z)
-
-def test_random_degree_sequence_graph():
- d=[1,2,2,3]
- G = nx.random_degree_sequence_graph(d)
- assert_equal(d, list(G.degree().values()))
-
-def test_random_degree_sequence_graph_raise():
- z=[1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
- assert_raises(networkx.exception.NetworkXUnfeasible,
- random_degree_sequence_graph, z)
-
-def test_random_degree_sequence_large():
- G = nx.fast_gnp_random_graph(100,0.1)
- d = G.degree().values()
- G = nx.random_degree_sequence_graph(d, seed=0)
- assert_equal(sorted(d), sorted(list(G.degree().values())))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_directed.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_directed.py
deleted file mode 100644
index 45b9ab1..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_directed.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-
-"""Generators - Directed Graphs
-----------------------------
-"""
-
-from nose.tools import *
-from networkx import *
-from networkx.generators.directed import *
-
-class TestGeneratorsDirected():
- def test_smoke_test_random_graphs(self):
- G=gn_graph(100)
- G=gnr_graph(100,0.5)
- G=gnc_graph(100)
- G=scale_free_graph(100)
-
- def test_create_using_keyword_arguments(self):
- assert_raises(networkx.exception.NetworkXError,
- gn_graph, 100, create_using=Graph())
- assert_raises(networkx.exception.NetworkXError,
- gnr_graph, 100, 0.5, create_using=Graph())
- assert_raises(networkx.exception.NetworkXError,
- gnc_graph, 100, create_using=Graph())
- assert_raises(networkx.exception.NetworkXError,
- scale_free_graph, 100, create_using=Graph())
- G=gn_graph(100,seed=1)
- MG=gn_graph(100,create_using=MultiDiGraph(),seed=1)
- assert_equal(G.edges(), MG.edges())
- G=gnr_graph(100,0.5,seed=1)
- MG=gnr_graph(100,0.5,create_using=MultiDiGraph(),seed=1)
- assert_equal(G.edges(), MG.edges())
- G=gnc_graph(100,seed=1)
- MG=gnc_graph(100,create_using=MultiDiGraph(),seed=1)
- assert_equal(G.edges(), MG.edges())
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_ego.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_ego.py
deleted file mode 100644
index da15b60..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_ego.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/usr/bin/env python
-"""
-ego graph
----------
-"""
-
-from nose.tools import assert_true, assert_equal
-import networkx as nx
-
-class TestGeneratorEgo():
- def test_ego(self):
- G=nx.star_graph(3)
- H=nx.ego_graph(G,0)
- assert_true(nx.is_isomorphic(G,H))
- G.add_edge(1,11)
- G.add_edge(2,22)
- G.add_edge(3,33)
- H=nx.ego_graph(G,0)
- assert_true(nx.is_isomorphic(nx.star_graph(3),H))
- G=nx.path_graph(3)
- H=nx.ego_graph(G,0)
- assert_equal(H.edges(), [(0, 1)])
- H=nx.ego_graph(G,0,undirected=True)
- assert_equal(H.edges(), [(0, 1)])
- H=nx.ego_graph(G,0,center=False)
- assert_equal(H.edges(), [])
-
-
- def test_ego_distance(self):
- G=nx.Graph()
- G.add_edge(0,1,weight=2,distance=1)
- G.add_edge(1,2,weight=2,distance=2)
- G.add_edge(2,3,weight=2,distance=1)
- assert_equal(sorted(nx.ego_graph(G,0,radius=3).nodes()),[0,1,2,3])
- eg=nx.ego_graph(G,0,radius=3,distance='weight')
- assert_equal(sorted(eg.nodes()),[0,1])
- eg=nx.ego_graph(G,0,radius=3,distance='weight',undirected=True)
- assert_equal(sorted(eg.nodes()),[0,1])
- eg=nx.ego_graph(G,0,radius=3,distance='distance')
- assert_equal(sorted(eg.nodes()),[0,1,2])
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_geometric.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_geometric.py
deleted file mode 100644
index 5e29e25..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_geometric.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestGeneratorsGeometric():
- def test_random_geometric_graph(self):
- G=nx.random_geometric_graph(50,0.25)
- assert_equal(len(G),50)
-
- def test_geographical_threshold_graph(self):
- G=nx.geographical_threshold_graph(50,100)
- assert_equal(len(G),50)
-
- def test_waxman_graph(self):
- G=nx.waxman_graph(50,0.5,0.1)
- assert_equal(len(G),50)
- G=nx.waxman_graph(50,0.5,0.1,L=1)
- assert_equal(len(G),50)
-
- def test_naviable_small_world(self):
- G = nx.navigable_small_world_graph(5,p=1,q=0)
- gg = nx.grid_2d_graph(5,5).to_directed()
- assert_true(nx.is_isomorphic(G,gg))
-
- G = nx.navigable_small_world_graph(5,p=1,q=0,dim=3)
- gg = nx.grid_graph([5,5,5]).to_directed()
- assert_true(nx.is_isomorphic(G,gg))
-
- G = nx.navigable_small_world_graph(5,p=1,q=0,dim=1)
- gg = nx.grid_graph([5]).to_directed()
- assert_true(nx.is_isomorphic(G,gg))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_hybrid.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_hybrid.py
deleted file mode 100644
index 88c7505..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_hybrid.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from nose.tools import *
-import networkx as nx
-
-def test_2d_grid_graph():
- # FC article claims 2d grid graph of size n is (3,3)-connected
- # and (5,9)-connected, but I don't think it is (5,9)-connected
- G=nx.grid_2d_graph(8,8,periodic=True)
- assert_true(nx.is_kl_connected(G,3,3))
- assert_false(nx.is_kl_connected(G,5,9))
- (H,graphOK)=nx.kl_connected_subgraph(G,5,9,same_as_graph=True)
- assert_false(graphOK)
-
-def test_small_graph():
- G=nx.Graph()
- G.add_edge(1,2)
- G.add_edge(1,3)
- G.add_edge(2,3)
- assert_true(nx.is_kl_connected(G,2,2))
- H=nx.kl_connected_subgraph(G,2,2)
- (H,graphOK)=nx.kl_connected_subgraph(G,2,2,
- low_memory=True,
- same_as_graph=True)
- assert_true(graphOK)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_intersection.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_intersection.py
deleted file mode 100644
index 26bbcf6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_intersection.py
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-
-class TestIntersectionGraph():
- def test_random_intersection_graph(self):
- G=nx.uniform_random_intersection_graph(10,5,0.5)
- assert_equal(len(G),10)
-
- def test_k_random_intersection_graph(self):
- G=nx.k_random_intersection_graph(10,5,2)
- assert_equal(len(G),10)
-
- def test_general_random_intersection_graph(self):
- G=nx.general_random_intersection_graph(10,5,[0.1,0.2,0.2,0.1,0.1])
- assert_equal(len(G),10)
- assert_raises(ValueError, nx.general_random_intersection_graph,10,5,
- [0.1,0.2,0.2,0.1])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_line.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_line.py
deleted file mode 100644
index 35b71be..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_line.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-
-"""line graph
-----------
-"""
-
-import networkx as nx
-from nose.tools import *
-
-
-class TestGeneratorLine():
- def test_line(self):
- G=nx.star_graph(5)
- L=nx.line_graph(G)
- assert_true(nx.is_isomorphic(L,nx.complete_graph(5)))
- G=nx.path_graph(5)
- L=nx.line_graph(G)
- assert_true(nx.is_isomorphic(L,nx.path_graph(4)))
- G=nx.cycle_graph(5)
- L=nx.line_graph(G)
- assert_true(nx.is_isomorphic(L,G))
- G=nx.DiGraph()
- G.add_edges_from([(0,1),(0,2),(0,3)])
- L=nx.line_graph(G)
- assert_equal(L.adj, {})
- G=nx.DiGraph()
- G.add_edges_from([(0,1),(1,2),(2,3)])
- L=nx.line_graph(G)
- assert_equal(sorted(L.edges()), [((0, 1), (1, 2)), ((1, 2), (2, 3))])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_random_clustered.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_random_clustered.py
deleted file mode 100644
index f052f2c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_random_clustered.py
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx
-
-class TestRandomClusteredGraph:
-
- def test_valid(self):
- node=[1,1,1,2,1,2,0,0]
- tri=[0,0,0,0,0,1,1,1]
- joint_degree_sequence=zip(node,tri)
- G = networkx.random_clustered_graph(joint_degree_sequence)
- assert_equal(G.number_of_nodes(),8)
- assert_equal(G.number_of_edges(),7)
-
- def test_valid2(self):
- G = networkx.random_clustered_graph(\
- [(1,2),(2,1),(1,1),(1,1),(1,1),(2,0)])
- assert_equal(G.number_of_nodes(),6)
- assert_equal(G.number_of_edges(),10)
-
- def test_invalid1(self):
- assert_raises((TypeError,networkx.NetworkXError),
- networkx.random_clustered_graph,[[1,1],[2,1],[0,1]])
-
- def test_invalid2(self):
- assert_raises((TypeError,networkx.NetworkXError),
- networkx.random_clustered_graph,[[1,1],[1,2],[0,1]])
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_random_graphs.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_random_graphs.py
deleted file mode 100644
index c49d74d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_random_graphs.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from networkx import *
-from networkx.generators.random_graphs import *
-
-class TestGeneratorsRandom():
- def smoke_test_random_graph(self):
- seed = 42
- G=gnp_random_graph(100,0.25,seed)
- G=binomial_graph(100,0.25,seed)
- G=erdos_renyi_graph(100,0.25,seed)
- G=fast_gnp_random_graph(100,0.25,seed)
- G=gnm_random_graph(100,20,seed)
- G=dense_gnm_random_graph(100,20,seed)
-
- G=watts_strogatz_graph(10,2,0.25,seed)
- assert_equal(len(G), 10)
- assert_equal(G.number_of_edges(), 10)
-
- G=connected_watts_strogatz_graph(10,2,0.1,seed)
- assert_equal(len(G), 10)
- assert_equal(G.number_of_edges(), 10)
-
- G=watts_strogatz_graph(10,4,0.25,seed)
- assert_equal(len(G), 10)
- assert_equal(G.number_of_edges(), 20)
-
- G=newman_watts_strogatz_graph(10,2,0.0,seed)
- assert_equal(len(G), 10)
- assert_equal(G.number_of_edges(), 10)
-
- G=newman_watts_strogatz_graph(10,4,0.25,seed)
- assert_equal(len(G), 10)
- assert_true(G.number_of_edges() >= 20)
-
- G=barabasi_albert_graph(100,1,seed)
- G=barabasi_albert_graph(100,3,seed)
- assert_equal(G.number_of_edges(),(97*3))
-
- G=powerlaw_cluster_graph(100,1,1.0,seed)
- G=powerlaw_cluster_graph(100,3,0.0,seed)
- assert_equal(G.number_of_edges(),(97*3))
-
- G=random_regular_graph(10,20,seed)
-
- assert_raises(networkx.exception.NetworkXError,
- random_regular_graph, 3, 21)
-
- constructor=[(10,20,0.8),(20,40,0.8)]
- G=random_shell_graph(constructor,seed)
-
- G=nx.random_lobster(10,0.1,0.5,seed)
-
- def test_gnp(self):
- G=gnp_random_graph(10,0.1)
- assert_equal(len(G),10)
-
- G=gnp_random_graph(10,0.1,seed=42)
- assert_equal(len(G),10)
-
- G=gnp_random_graph(10,1.1)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),45)
-
- G=gnp_random_graph(10,1.1,directed=True)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),90)
-
- G=gnp_random_graph(10,-1.1)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),0)
-
- G=binomial_graph(10,0.1)
- assert_equal(len(G),10)
-
- G=erdos_renyi_graph(10,0.1)
- assert_equal(len(G),10)
-
-
- def test_fast_gnp(self):
- G=fast_gnp_random_graph(10,0.1)
- assert_equal(len(G),10)
-
- G=fast_gnp_random_graph(10,0.1,seed=42)
- assert_equal(len(G),10)
-
- G=fast_gnp_random_graph(10,1.1)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),45)
-
- G=fast_gnp_random_graph(10,-1.1)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),0)
-
- G=fast_gnp_random_graph(10,0.1,directed=True)
- assert_true(G.is_directed())
- assert_equal(len(G),10)
-
-
- def test_gnm(self):
- G=gnm_random_graph(10,3)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),3)
-
- G=gnm_random_graph(10,3,seed=42)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),3)
-
- G=gnm_random_graph(10,100)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),45)
-
- G=gnm_random_graph(10,100,directed=True)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),90)
-
- G=gnm_random_graph(10,-1.1)
- assert_equal(len(G),10)
- assert_equal(len(G.edges()),0)
-
- def test_watts_strogatz_big_k(self):
- assert_raises(networkx.exception.NetworkXError,
- watts_strogatz_graph, 10, 10, 0.25)
- assert_raises(networkx.exception.NetworkXError,
- newman_watts_strogatz_graph, 10, 10, 0.25)
- # could create an infinite loop, now doesn't
- # infinite loop used to occur when a node has degree n-1 and needs to rewire
- watts_strogatz_graph(10, 9, 0.25, seed=0)
- newman_watts_strogatz_graph(10, 9, 0.5, seed=0)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_small.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_small.py
deleted file mode 100644
index 5228d3a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_small.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#!/usr/bin/env python
-
-from nose.tools import *
-from networkx import *
-from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
-is_isomorphic=graph_could_be_isomorphic
-
-"""Generators - Small
-=====================
-
-Some small graphs
-"""
-
-null=null_graph()
-
-class TestGeneratorsSmall():
- def test_make_small_graph(self):
- d=["adjacencylist","Bull Graph",5,[[2,3],[1,3,4],[1,2,5],[2],[3]]]
- G=make_small_graph(d)
- assert_true(is_isomorphic(G, bull_graph()))
-
- def test__LCF_graph(self):
- # If n<=0, then return the null_graph
- G=LCF_graph(-10,[1,2],100)
- assert_true(is_isomorphic(G,null))
- G=LCF_graph(0,[1,2],3)
- assert_true(is_isomorphic(G,null))
- G=LCF_graph(0,[1,2],10)
- assert_true(is_isomorphic(G,null))
-
- # Test that LCF(n,[],0) == cycle_graph(n)
- for a, b, c in [(5, [], 0), (10, [], 0), (5, [], 1), (10, [], 10)]:
- G=LCF_graph(a, b, c)
- assert_true(is_isomorphic(G,cycle_graph(a)))
-
- # Generate the utility graph K_{3,3}
- G=LCF_graph(6,[3,-3],3)
- utility_graph=complete_bipartite_graph(3,3)
- assert_true(is_isomorphic(G, utility_graph))
-
- def test_properties_named_small_graphs(self):
- G=bull_graph()
- assert_equal(G.number_of_nodes(), 5)
- assert_equal(G.number_of_edges(), 5)
- assert_equal(sorted(G.degree().values()), [1, 1, 2, 3, 3])
- assert_equal(diameter(G), 3)
- assert_equal(radius(G), 2)
-
- G=chvatal_graph()
- assert_equal(G.number_of_nodes(), 12)
- assert_equal(G.number_of_edges(), 24)
- assert_equal(list(G.degree().values()), 12 * [4])
- assert_equal(diameter(G), 2)
- assert_equal(radius(G), 2)
-
- G=cubical_graph()
- assert_equal(G.number_of_nodes(), 8)
- assert_equal(G.number_of_edges(), 12)
- assert_equal(list(G.degree().values()), 8*[3])
- assert_equal(diameter(G), 3)
- assert_equal(radius(G), 3)
-
- G=desargues_graph()
- assert_equal(G.number_of_nodes(), 20)
- assert_equal(G.number_of_edges(), 30)
- assert_equal(list(G.degree().values()), 20*[3])
-
- G=diamond_graph()
- assert_equal(G.number_of_nodes(), 4)
- assert_equal(sorted(G.degree().values()), [2, 2, 3, 3])
- assert_equal(diameter(G), 2)
- assert_equal(radius(G), 1)
-
- G=dodecahedral_graph()
- assert_equal(G.number_of_nodes(), 20)
- assert_equal(G.number_of_edges(), 30)
- assert_equal(list(G.degree().values()), 20*[3])
- assert_equal(diameter(G), 5)
- assert_equal(radius(G), 5)
-
- G=frucht_graph()
- assert_equal(G.number_of_nodes(), 12)
- assert_equal(G.number_of_edges(), 18)
- assert_equal(list(G.degree().values()), 12*[3])
- assert_equal(diameter(G), 4)
- assert_equal(radius(G), 3)
-
- G=heawood_graph()
- assert_equal(G.number_of_nodes(), 14)
- assert_equal(G.number_of_edges(), 21)
- assert_equal(list(G.degree().values()), 14*[3])
- assert_equal(diameter(G), 3)
- assert_equal(radius(G), 3)
-
- G=house_graph()
- assert_equal(G.number_of_nodes(), 5)
- assert_equal(G.number_of_edges(), 6)
- assert_equal(sorted(G.degree().values()), [2, 2, 2, 3, 3])
- assert_equal(diameter(G), 2)
- assert_equal(radius(G), 2)
-
- G=house_x_graph()
- assert_equal(G.number_of_nodes(), 5)
- assert_equal(G.number_of_edges(), 8)
- assert_equal(sorted(G.degree().values()), [2, 3, 3, 4, 4])
- assert_equal(diameter(G), 2)
- assert_equal(radius(G), 1)
-
- G=icosahedral_graph()
- assert_equal(G.number_of_nodes(), 12)
- assert_equal(G.number_of_edges(), 30)
- assert_equal(list(G.degree().values()),
- [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
- assert_equal(diameter(G), 3)
- assert_equal(radius(G), 3)
-
- G=krackhardt_kite_graph()
- assert_equal(G.number_of_nodes(), 10)
- assert_equal(G.number_of_edges(), 18)
- assert_equal(sorted(G.degree().values()),
- [1, 2, 3, 3, 3, 4, 4, 5, 5, 6])
-
- G=moebius_kantor_graph()
- assert_equal(G.number_of_nodes(), 16)
- assert_equal(G.number_of_edges(), 24)
- assert_equal(list(G.degree().values()), 16*[3])
- assert_equal(diameter(G), 4)
-
- G=octahedral_graph()
- assert_equal(G.number_of_nodes(), 6)
- assert_equal(G.number_of_edges(), 12)
- assert_equal(list(G.degree().values()), 6*[4])
- assert_equal(diameter(G), 2)
- assert_equal(radius(G), 2)
-
- G=pappus_graph()
- assert_equal(G.number_of_nodes(), 18)
- assert_equal(G.number_of_edges(), 27)
- assert_equal(list(G.degree().values()), 18*[3])
- assert_equal(diameter(G), 4)
-
- G=petersen_graph()
- assert_equal(G.number_of_nodes(), 10)
- assert_equal(G.number_of_edges(), 15)
- assert_equal(list(G.degree().values()), 10*[3])
- assert_equal(diameter(G), 2)
- assert_equal(radius(G), 2)
-
- G=sedgewick_maze_graph()
- assert_equal(G.number_of_nodes(), 8)
- assert_equal(G.number_of_edges(), 10)
- assert_equal(sorted(G.degree().values()), [1, 2, 2, 2, 3, 3, 3, 4])
-
- G=tetrahedral_graph()
- assert_equal(G.number_of_nodes(), 4)
- assert_equal(G.number_of_edges(), 6)
- assert_equal(list(G.degree().values()), [3, 3, 3, 3])
- assert_equal(diameter(G), 1)
- assert_equal(radius(G), 1)
-
- G=truncated_cube_graph()
- assert_equal(G.number_of_nodes(), 24)
- assert_equal(G.number_of_edges(), 36)
- assert_equal(list(G.degree().values()), 24*[3])
-
- G=truncated_tetrahedron_graph()
- assert_equal(G.number_of_nodes(), 12)
- assert_equal(G.number_of_edges(), 18)
- assert_equal(list(G.degree().values()), 12*[3])
-
- G=tutte_graph()
- assert_equal(G.number_of_nodes(), 46)
- assert_equal(G.number_of_edges(), 69)
- assert_equal(list(G.degree().values()), 46*[3])
-
- # Test create_using with directed or multigraphs on small graphs
- assert_raises(networkx.exception.NetworkXError, tutte_graph,
- create_using=DiGraph())
- MG=tutte_graph(create_using=MultiGraph())
- assert_equal(MG.edges(), G.edges())
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_stochastic.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_stochastic.py
deleted file mode 100644
index 15c6fef..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_stochastic.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from nose.tools import assert_true, assert_equal, raises
-import networkx as nx
-
-def test_stochastic():
- G=nx.DiGraph()
- G.add_edge(0,1)
- G.add_edge(0,2)
- S=nx.stochastic_graph(G)
- assert_true(nx.is_isomorphic(G,S))
- assert_equal(sorted(S.edges(data=True)),
- [(0, 1, {'weight': 0.5}),
- (0, 2, {'weight': 0.5})])
- S=nx.stochastic_graph(G,copy=True)
- assert_equal(sorted(S.edges(data=True)),
- [(0, 1, {'weight': 0.5}),
- (0, 2, {'weight': 0.5})])
-
-def test_stochastic_ints():
- G=nx.DiGraph()
- G.add_edge(0,1,weight=1)
- G.add_edge(0,2,weight=1)
- S=nx.stochastic_graph(G)
- assert_equal(sorted(S.edges(data=True)),
- [(0, 1, {'weight': 0.5}),
- (0, 2, {'weight': 0.5})])
-
-@raises(nx.NetworkXError)
-def test_stochastic_graph_input():
- S = nx.stochastic_graph(nx.Graph())
-
-@raises(nx.NetworkXError)
-def test_stochastic_multigraph_input():
- S = nx.stochastic_graph(nx.MultiGraph())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_threshold.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_threshold.py
deleted file mode 100644
index 198d7cc..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/test_threshold.py
+++ /dev/null
@@ -1,183 +0,0 @@
-#!/usr/bin/env python
-
-"""Threshold Graphs
-================
-"""
-
-from nose.tools import *
-from nose import SkipTest
-from nose.plugins.attrib import attr
-import networkx as nx
-import networkx.generators.threshold as nxt
-from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
-
-cnlti = nx.convert_node_labels_to_integers
-
-
-class TestGeneratorThreshold():
- def test_threshold_sequence_graph_test(self):
- G=nx.star_graph(10)
- assert_true(nxt.is_threshold_graph(G))
- assert_true(nxt.is_threshold_sequence(list(G.degree().values())))
-
- G=nx.complete_graph(10)
- assert_true(nxt.is_threshold_graph(G))
- assert_true(nxt.is_threshold_sequence(list(G.degree().values())))
-
- deg=[3,2,2,1,1,1]
- assert_false(nxt.is_threshold_sequence(deg))
-
- deg=[3,2,2,1]
- assert_true(nxt.is_threshold_sequence(deg))
-
- G=nx.generators.havel_hakimi_graph(deg)
- assert_true(nxt.is_threshold_graph(G))
-
- def test_creation_sequences(self):
- deg=[3,2,2,1]
- G=nx.generators.havel_hakimi_graph(deg)
- cs0=nxt.creation_sequence(deg)
- H0=nxt.threshold_graph(cs0)
- assert_equal(''.join(cs0), 'ddid')
-
- cs1=nxt.creation_sequence(deg, with_labels=True)
- H1=nxt.threshold_graph(cs1)
- assert_equal(cs1, [(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])
-
- cs2=nxt.creation_sequence(deg, compact=True)
- H2=nxt.threshold_graph(cs2)
- assert_equal(cs2, [2, 1, 1])
- assert_equal(''.join(nxt.uncompact(cs2)), 'ddid')
- assert_true(graph_could_be_isomorphic(H0,G))
- assert_true(graph_could_be_isomorphic(H0,H1))
- assert_true(graph_could_be_isomorphic(H0,H2))
-
- def test_shortest_path(self):
- deg=[3,2,2,1]
- G=nx.generators.havel_hakimi_graph(deg)
- cs1=nxt.creation_sequence(deg, with_labels=True)
- for n, m in [(3, 0), (0, 3), (0, 2), (0, 1), (1, 3),
- (3, 1), (1, 2), (2, 3)]:
- assert_equal(nxt.shortest_path(cs1,n,m),
- nx.shortest_path(G, n, m))
-
- spl=nxt.shortest_path_length(cs1,3)
- spl2=nxt.shortest_path_length([ t for v,t in cs1],2)
- assert_equal(spl, spl2)
-
- spld={}
- for j,pl in enumerate(spl):
- n=cs1[j][0]
- spld[n]=pl
- assert_equal(spld, nx.single_source_shortest_path_length(G, 3))
-
- def test_weights_thresholds(self):
- wseq=[3,4,3,3,5,6,5,4,5,6]
- cs=nxt.weights_to_creation_sequence(wseq,threshold=10)
- wseq=nxt.creation_sequence_to_weights(cs)
- cs2=nxt.weights_to_creation_sequence(wseq)
- assert_equal(cs, cs2)
-
- wseq=nxt.creation_sequence_to_weights(nxt.uncompact([3,1,2,3,3,2,3]))
- assert_equal(wseq,
- [s*0.125 for s in [4,4,4,3,5,5,2,2,2,6,6,6,1,1,7,7,7]])
-
- wseq=nxt.creation_sequence_to_weights([3,1,2,3,3,2,3])
- assert_equal(wseq,
- [s*0.125 for s in [4,4,4,3,5,5,2,2,2,6,6,6,1,1,7,7,7]])
-
- wseq=nxt.creation_sequence_to_weights(list(enumerate('ddidiiidididi')))
- assert_equal(wseq,
- [s*0.1 for s in [5,5,4,6,3,3,3,7,2,8,1,9,0]])
-
- wseq=nxt.creation_sequence_to_weights('ddidiiidididi')
- assert_equal(wseq,
- [s*0.1 for s in [5,5,4,6,3,3,3,7,2,8,1,9,0]])
-
- wseq=nxt.creation_sequence_to_weights('ddidiiidididid')
- ws=[s/float(12) for s in [6,6,5,7,4,4,4,8,3,9,2,10,1,11]]
- assert_true(sum([abs(c-d) for c,d in zip(wseq,ws)]) < 1e-14)
-
- def test_finding_routines(self):
- G=nx.Graph({1:[2],2:[3],3:[4],4:[5],5:[6]})
- G.add_edge(2,4)
- G.add_edge(2,5)
- G.add_edge(2,7)
- G.add_edge(3,6)
- G.add_edge(4,6)
-
- # Alternating 4 cycle
- assert_equal(nxt.find_alternating_4_cycle(G), [1, 2, 3, 6])
-
- # Threshold graph
- TG=nxt.find_threshold_graph(G)
- assert_true(nxt.is_threshold_graph(TG))
- assert_equal(sorted(TG.nodes()), [1, 2, 3, 4, 5, 7])
-
- cs=nxt.creation_sequence(TG.degree(),with_labels=True)
- assert_equal(nxt.find_creation_sequence(G), cs)
-
- def test_fast_versions_properties_threshold_graphs(self):
- cs='ddiiddid'
- G=nxt.threshold_graph(cs)
- assert_equal(nxt.density('ddiiddid'), nx.density(G))
- assert_equal(sorted(nxt.degree_sequence(cs)),
- sorted(G.degree().values()))
-
- ts=nxt.triangle_sequence(cs)
- assert_equal(ts, list(nx.triangles(G).values()))
- assert_equal(sum(ts) // 3, nxt.triangles(cs))
-
- c1=nxt.cluster_sequence(cs)
- c2=list(nx.clustering(G).values())
- assert_almost_equal(sum([abs(c-d) for c,d in zip(c1,c2)]), 0)
-
- b1=nx.betweenness_centrality(G).values()
- b2=nxt.betweenness_sequence(cs)
- assert_true(sum([abs(c-d) for c,d in zip(b1,b2)]) < 1e-14)
-
- assert_equal(nxt.eigenvalues(cs), [0, 1, 3, 3, 5, 7, 7, 8])
-
- # Degree Correlation
- assert_true(abs(nxt.degree_correlation(cs)+0.593038821954) < 1e-12)
- assert_equal(nxt.degree_correlation('diiiddi'), -0.8)
- assert_equal(nxt.degree_correlation('did'), -1.0)
- assert_equal(nxt.degree_correlation('ddd'), 1.0)
- assert_equal(nxt.eigenvalues('dddiii'), [0, 0, 0, 0, 3, 3])
- assert_equal(nxt.eigenvalues('dddiiid'), [0, 1, 1, 1, 4, 4, 7])
-
- def test_tg_creation_routines(self):
- s=nxt.left_d_threshold_sequence(5,7)
- s=nxt.right_d_threshold_sequence(5,7)
- s1=nxt.swap_d(s,1.0,1.0)
-
-
- @attr('numpy')
- def test_eigenvectors(self):
- try:
- import numpy as N
- eigenval=N.linalg.eigvals
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- cs='ddiiddid'
- G=nxt.threshold_graph(cs)
- (tgeval,tgevec)=nxt.eigenvectors(cs)
- dot=N.dot
- assert_equal([ abs(dot(lv,lv)-1.0)<1e-9 for lv in tgevec ], [True]*8)
- lapl=nx.laplacian_matrix(G)
-# tgev=[ dot(lv,dot(lapl,lv)) for lv in tgevec ]
-# assert_true(sum([abs(c-d) for c,d in zip(tgev,tgeval)]) < 1e-9)
-# tgev.sort()
-# lev=list(eigenval(lapl))
-# lev.sort()
-# assert_true(sum([abs(c-d) for c,d in zip(tgev,lev)]) < 1e-9)
-
- def test_create_using(self):
- cs='ddiiddid'
- G=nxt.threshold_graph(cs)
- assert_raises(nx.exception.NetworkXError,
- nxt.threshold_graph, cs, create_using=nx.DiGraph())
- MG=nxt.threshold_graph(cs,create_using=nx.MultiGraph())
- assert_equal(MG.edges(), G.edges())
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/generators/threshold.py b/lib/python2.7/site-packages/setoolsgui/networkx/generators/threshold.py
deleted file mode 100644
index 68a565e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/generators/threshold.py
+++ /dev/null
@@ -1,906 +0,0 @@
-"""
-Threshold Graphs - Creation, manipulation and identification.
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)\nDan Schult (dschult@colgate.edu)"""
-# Copyright (C) 2004-2008 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-#
-
-__all__=[]
-
-import random # for swap_d
-from math import sqrt
-import networkx
-
-def is_threshold_graph(G):
- """
- Returns True if G is a threshold graph.
- """
- return is_threshold_sequence(list(G.degree().values()))
-
-def is_threshold_sequence(degree_sequence):
- """
- Returns True if the sequence is a threshold degree seqeunce.
-
- Uses the property that a threshold graph must be constructed by
- adding either dominating or isolated nodes. Thus, it can be
- deconstructed iteratively by removing a node of degree zero or a
- node that connects to the remaining nodes. If this deconstruction
- failes then the sequence is not a threshold sequence.
- """
- ds=degree_sequence[:] # get a copy so we don't destroy original
- ds.sort()
- while ds:
- if ds[0]==0: # if isolated node
- ds.pop(0) # remove it
- continue
- if ds[-1]!=len(ds)-1: # is the largest degree node dominating?
- return False # no, not a threshold degree sequence
- ds.pop() # yes, largest is the dominating node
- ds=[ d-1 for d in ds ] # remove it and decrement all degrees
- return True
-
-
-def creation_sequence(degree_sequence,with_labels=False,compact=False):
- """
- Determines the creation sequence for the given threshold degree sequence.
-
- The creation sequence is a list of single characters 'd'
- or 'i': 'd' for dominating or 'i' for isolated vertices.
- Dominating vertices are connected to all vertices present when it
- is added. The first node added is by convention 'd'.
- This list can be converted to a string if desired using "".join(cs)
-
- If with_labels==True:
- Returns a list of 2-tuples containing the vertex number
- and a character 'd' or 'i' which describes the type of vertex.
-
- If compact==True:
- Returns the creation sequence in a compact form that is the number
- of 'i's and 'd's alternating.
- Examples:
- [1,2,2,3] represents d,i,i,d,d,i,i,i
- [3,1,2] represents d,d,d,i,d,d
-
- Notice that the first number is the first vertex to be used for
- construction and so is always 'd'.
-
- with_labels and compact cannot both be True.
-
- Returns None if the sequence is not a threshold sequence
- """
- if with_labels and compact:
- raise ValueError("compact sequences cannot be labeled")
-
- # make an indexed copy
- if isinstance(degree_sequence,dict): # labeled degree seqeunce
- ds = [ [degree,label] for (label,degree) in degree_sequence.items() ]
- else:
- ds=[ [d,i] for i,d in enumerate(degree_sequence) ]
- ds.sort()
- cs=[] # creation sequence
- while ds:
- if ds[0][0]==0: # isolated node
- (d,v)=ds.pop(0)
- if len(ds)>0: # make sure we start with a d
- cs.insert(0,(v,'i'))
- else:
- cs.insert(0,(v,'d'))
- continue
- if ds[-1][0]!=len(ds)-1: # Not dominating node
- return None # not a threshold degree sequence
- (d,v)=ds.pop()
- cs.insert(0,(v,'d'))
- ds=[ [d[0]-1,d[1]] for d in ds ] # decrement due to removing node
-
- if with_labels: return cs
- if compact: return make_compact(cs)
- return [ v[1] for v in cs ] # not labeled
-
-def make_compact(creation_sequence):
- """
- Returns the creation sequence in a compact form
- that is the number of 'i's and 'd's alternating.
- Examples:
- [1,2,2,3] represents d,i,i,d,d,i,i,i.
- [3,1,2] represents d,d,d,i,d,d.
- Notice that the first number is the first vertex
- to be used for construction and so is always 'd'.
-
- Labeled creation sequences lose their labels in the
- compact representation.
- """
- first=creation_sequence[0]
- if isinstance(first,str): # creation sequence
- cs = creation_sequence[:]
- elif isinstance(first,tuple): # labeled creation sequence
- cs = [ s[1] for s in creation_sequence ]
- elif isinstance(first,int): # compact creation sequence
- return creation_sequence
- else:
- raise TypeError("Not a valid creation sequence type")
-
- ccs=[]
- count=1 # count the run lengths of d's or i's.
- for i in range(1,len(cs)):
- if cs[i]==cs[i-1]:
- count+=1
- else:
- ccs.append(count)
- count=1
- ccs.append(count) # don't forget the last one
- return ccs
-
-def uncompact(creation_sequence):
- """
- Converts a compact creation sequence for a threshold
- graph to a standard creation sequence (unlabeled).
- If the creation_sequence is already standard, return it.
- See creation_sequence.
- """
- first=creation_sequence[0]
- if isinstance(first,str): # creation sequence
- return creation_sequence
- elif isinstance(first,tuple): # labeled creation sequence
- return creation_sequence
- elif isinstance(first,int): # compact creation sequence
- ccscopy=creation_sequence[:]
- else:
- raise TypeError("Not a valid creation sequence type")
- cs = []
- while ccscopy:
- cs.extend(ccscopy.pop(0)*['d'])
- if ccscopy:
- cs.extend(ccscopy.pop(0)*['i'])
- return cs
-
-def creation_sequence_to_weights(creation_sequence):
- """
- Returns a list of node weights which create the threshold
- graph designated by the creation sequence. The weights
- are scaled so that the threshold is 1.0. The order of the
- nodes is the same as that in the creation sequence.
- """
- # Turn input sequence into a labeled creation sequence
- first=creation_sequence[0]
- if isinstance(first,str): # creation sequence
- if isinstance(creation_sequence,list):
- wseq = creation_sequence[:]
- else:
- wseq = list(creation_sequence) # string like 'ddidid'
- elif isinstance(first,tuple): # labeled creation sequence
- wseq = [ v[1] for v in creation_sequence]
- elif isinstance(first,int): # compact creation sequence
- wseq = uncompact(creation_sequence)
- else:
- raise TypeError("Not a valid creation sequence type")
- # pass through twice--first backwards
- wseq.reverse()
- w=0
- prev='i'
- for j,s in enumerate(wseq):
- if s=='i':
- wseq[j]=w
- prev=s
- elif prev=='i':
- prev=s
- w+=1
- wseq.reverse() # now pass through forwards
- for j,s in enumerate(wseq):
- if s=='d':
- wseq[j]=w
- prev=s
- elif prev=='d':
- prev=s
- w+=1
- # Now scale weights
- if prev=='d': w+=1
- wscale=1./float(w)
- return [ ww*wscale for ww in wseq]
- #return wseq
-
-def weights_to_creation_sequence(weights,threshold=1,with_labels=False,compact=False):
- """
- Returns a creation sequence for a threshold graph
- determined by the weights and threshold given as input.
- If the sum of two node weights is greater than the
- threshold value, an edge is created between these nodes.
-
- The creation sequence is a list of single characters 'd'
- or 'i': 'd' for dominating or 'i' for isolated vertices.
- Dominating vertices are connected to all vertices present
- when it is added. The first node added is by convention 'd'.
-
- If with_labels==True:
- Returns a list of 2-tuples containing the vertex number
- and a character 'd' or 'i' which describes the type of vertex.
-
- If compact==True:
- Returns the creation sequence in a compact form that is the number
- of 'i's and 'd's alternating.
- Examples:
- [1,2,2,3] represents d,i,i,d,d,i,i,i
- [3,1,2] represents d,d,d,i,d,d
-
- Notice that the first number is the first vertex to be used for
- construction and so is always 'd'.
-
- with_labels and compact cannot both be True.
- """
- if with_labels and compact:
- raise ValueError("compact sequences cannot be labeled")
-
- # make an indexed copy
- if isinstance(weights,dict): # labeled weights
- wseq = [ [w,label] for (label,w) in weights.items() ]
- else:
- wseq = [ [w,i] for i,w in enumerate(weights) ]
- wseq.sort()
- cs=[] # creation sequence
- cutoff=threshold-wseq[-1][0]
- while wseq:
- if wseq[0][0]<cutoff: # isolated node
- (w,label)=wseq.pop(0)
- cs.append((label,'i'))
- else:
- (w,label)=wseq.pop()
- cs.append((label,'d'))
- cutoff=threshold-wseq[-1][0]
- if len(wseq)==1: # make sure we start with a d
- (w,label)=wseq.pop()
- cs.append((label,'d'))
- # put in correct order
- cs.reverse()
-
- if with_labels: return cs
- if compact: return make_compact(cs)
- return [ v[1] for v in cs ] # not labeled
-
-
-# Manipulating NetworkX.Graphs in context of threshold graphs
-def threshold_graph(creation_sequence, create_using=None):
- """
- Create a threshold graph from the creation sequence or compact
- creation_sequence.
-
- The input sequence can be a
-
- creation sequence (e.g. ['d','i','d','d','d','i'])
- labeled creation sequence (e.g. [(0,'d'),(2,'d'),(1,'i')])
- compact creation sequence (e.g. [2,1,1,2,0])
-
- Use cs=creation_sequence(degree_sequence,labeled=True)
- to convert a degree sequence to a creation sequence.
-
- Returns None if the sequence is not valid
- """
- # Turn input sequence into a labeled creation sequence
- first=creation_sequence[0]
- if isinstance(first,str): # creation sequence
- ci = list(enumerate(creation_sequence))
- elif isinstance(first,tuple): # labeled creation sequence
- ci = creation_sequence[:]
- elif isinstance(first,int): # compact creation sequence
- cs = uncompact(creation_sequence)
- ci = list(enumerate(cs))
- else:
- print("not a valid creation sequence type")
- return None
-
- if create_using is None:
- G = networkx.Graph()
- elif create_using.is_directed():
- raise networkx.NetworkXError("Directed Graph not supported")
- else:
- G = create_using
- G.clear()
-
- G.name="Threshold Graph"
-
- # add nodes and edges
- # if type is 'i' just add nodea
- # if type is a d connect to everything previous
- while ci:
- (v,node_type)=ci.pop(0)
- if node_type=='d': # dominating type, connect to all existing nodes
- for u in G.nodes():
- G.add_edge(v,u)
- G.add_node(v)
- return G
-
-
-
-def find_alternating_4_cycle(G):
- """
- Returns False if there aren't any alternating 4 cycles.
- Otherwise returns the cycle as [a,b,c,d] where (a,b)
- and (c,d) are edges and (a,c) and (b,d) are not.
- """
- for (u,v) in G.edges():
- for w in G.nodes():
- if not G.has_edge(u,w) and u!=w:
- for x in G.neighbors(w):
- if not G.has_edge(v,x) and v!=x:
- return [u,v,w,x]
- return False
-
-
-
-def find_threshold_graph(G, create_using=None):
- """
- Return a threshold subgraph that is close to largest in G.
- The threshold graph will contain the largest degree node in G.
-
- """
- return threshold_graph(find_creation_sequence(G),create_using)
-
-
-def find_creation_sequence(G):
- """
- Find a threshold subgraph that is close to largest in G.
- Returns the labeled creation sequence of that threshold graph.
- """
- cs=[]
- # get a local pointer to the working part of the graph
- H=G
- while H.order()>0:
- # get new degree sequence on subgraph
- dsdict=H.degree()
- ds=[ [d,v] for v,d in dsdict.items() ]
- ds.sort()
- # Update threshold graph nodes
- if ds[-1][0]==0: # all are isolated
- cs.extend( zip( dsdict, ['i']*(len(ds)-1)+['d']) )
- break # Done!
- # pull off isolated nodes
- while ds[0][0]==0:
- (d,iso)=ds.pop(0)
- cs.append((iso,'i'))
- # find new biggest node
- (d,bigv)=ds.pop()
- # add edges of star to t_g
- cs.append((bigv,'d'))
- # form subgraph of neighbors of big node
- H=H.subgraph(H.neighbors(bigv))
- cs.reverse()
- return cs
-
-
-
-### Properties of Threshold Graphs
-def triangles(creation_sequence):
- """
- Compute number of triangles in the threshold graph with the
- given creation sequence.
- """
- # shortcut algoritm that doesn't require computing number
- # of triangles at each node.
- cs=creation_sequence # alias
- dr=cs.count("d") # number of d's in sequence
- ntri=dr*(dr-1)*(dr-2)/6 # number of triangles in clique of nd d's
- # now add dr choose 2 triangles for every 'i' in sequence where
- # dr is the number of d's to the right of the current i
- for i,typ in enumerate(cs):
- if typ=="i":
- ntri+=dr*(dr-1)/2
- else:
- dr-=1
- return ntri
-
-
-def triangle_sequence(creation_sequence):
- """
- Return triangle sequence for the given threshold graph creation sequence.
-
- """
- cs=creation_sequence
- seq=[]
- dr=cs.count("d") # number of d's to the right of the current pos
- dcur=(dr-1)*(dr-2) // 2 # number of triangles through a node of clique dr
- irun=0 # number of i's in the last run
- drun=0 # number of d's in the last run
- for i,sym in enumerate(cs):
- if sym=="d":
- drun+=1
- tri=dcur+(dr-1)*irun # new triangles at this d
- else: # cs[i]="i":
- if prevsym=="d": # new string of i's
- dcur+=(dr-1)*irun # accumulate shared shortest paths
- irun=0 # reset i run counter
- dr-=drun # reduce number of d's to right
- drun=0 # reset d run counter
- irun+=1
- tri=dr*(dr-1) // 2 # new triangles at this i
- seq.append(tri)
- prevsym=sym
- return seq
-
-def cluster_sequence(creation_sequence):
- """
- Return cluster sequence for the given threshold graph creation sequence.
- """
- triseq=triangle_sequence(creation_sequence)
- degseq=degree_sequence(creation_sequence)
- cseq=[]
- for i,deg in enumerate(degseq):
- tri=triseq[i]
- if deg <= 1: # isolated vertex or single pair gets cc 0
- cseq.append(0)
- continue
- max_size=(deg*(deg-1)) // 2
- cseq.append(float(tri)/float(max_size))
- return cseq
-
-
-def degree_sequence(creation_sequence):
- """
- Return degree sequence for the threshold graph with the given
- creation sequence
- """
- cs=creation_sequence # alias
- seq=[]
- rd=cs.count("d") # number of d to the right
- for i,sym in enumerate(cs):
- if sym=="d":
- rd-=1
- seq.append(rd+i)
- else:
- seq.append(rd)
- return seq
-
-def density(creation_sequence):
- """
- Return the density of the graph with this creation_sequence.
- The density is the fraction of possible edges present.
- """
- N=len(creation_sequence)
- two_size=sum(degree_sequence(creation_sequence))
- two_possible=N*(N-1)
- den=two_size/float(two_possible)
- return den
-
-def degree_correlation(creation_sequence):
- """
- Return the degree-degree correlation over all edges.
- """
- cs=creation_sequence
- s1=0 # deg_i*deg_j
- s2=0 # deg_i^2+deg_j^2
- s3=0 # deg_i+deg_j
- m=0 # number of edges
- rd=cs.count("d") # number of d nodes to the right
- rdi=[ i for i,sym in enumerate(cs) if sym=="d"] # index of "d"s
- ds=degree_sequence(cs)
- for i,sym in enumerate(cs):
- if sym=="d":
- if i!=rdi[0]:
- print("Logic error in degree_correlation",i,rdi)
- raise ValueError
- rdi.pop(0)
- degi=ds[i]
- for dj in rdi:
- degj=ds[dj]
- s1+=degj*degi
- s2+=degi**2+degj**2
- s3+=degi+degj
- m+=1
- denom=(2*m*s2-s3*s3)
- numer=(4*m*s1-s3*s3)
- if denom==0:
- if numer==0:
- return 1
- raise ValueError("Zero Denominator but Numerator is %s"%numer)
- return numer/float(denom)
-
-
-def shortest_path(creation_sequence,u,v):
- """
- Find the shortest path between u and v in a
- threshold graph G with the given creation_sequence.
-
- For an unlabeled creation_sequence, the vertices
- u and v must be integers in (0,len(sequence)) refering
- to the position of the desired vertices in the sequence.
-
- For a labeled creation_sequence, u and v are labels of veritices.
-
- Use cs=creation_sequence(degree_sequence,with_labels=True)
- to convert a degree sequence to a creation sequence.
-
- Returns a list of vertices from u to v.
- Example: if they are neighbors, it returns [u,v]
- """
- # Turn input sequence into a labeled creation sequence
- first=creation_sequence[0]
- if isinstance(first,str): # creation sequence
- cs = [(i,creation_sequence[i]) for i in range(len(creation_sequence))]
- elif isinstance(first,tuple): # labeled creation sequence
- cs = creation_sequence[:]
- elif isinstance(first,int): # compact creation sequence
- ci = uncompact(creation_sequence)
- cs = [(i,ci[i]) for i in range(len(ci))]
- else:
- raise TypeError("Not a valid creation sequence type")
-
- verts=[ s[0] for s in cs ]
- if v not in verts:
- raise ValueError("Vertex %s not in graph from creation_sequence"%v)
- if u not in verts:
- raise ValueError("Vertex %s not in graph from creation_sequence"%u)
- # Done checking
- if u==v: return [u]
-
- uindex=verts.index(u)
- vindex=verts.index(v)
- bigind=max(uindex,vindex)
- if cs[bigind][1]=='d':
- return [u,v]
- # must be that cs[bigind][1]=='i'
- cs=cs[bigind:]
- while cs:
- vert=cs.pop()
- if vert[1]=='d':
- return [u,vert[0],v]
- # All after u are type 'i' so no connection
- return -1
-
-def shortest_path_length(creation_sequence,i):
- """
- Return the shortest path length from indicated node to
- every other node for the threshold graph with the given
- creation sequence.
- Node is indicated by index i in creation_sequence unless
- creation_sequence is labeled in which case, i is taken to
- be the label of the node.
-
- Paths lengths in threshold graphs are at most 2.
- Length to unreachable nodes is set to -1.
- """
- # Turn input sequence into a labeled creation sequence
- first=creation_sequence[0]
- if isinstance(first,str): # creation sequence
- if isinstance(creation_sequence,list):
- cs = creation_sequence[:]
- else:
- cs = list(creation_sequence)
- elif isinstance(first,tuple): # labeled creation sequence
- cs = [ v[1] for v in creation_sequence]
- i = [v[0] for v in creation_sequence].index(i)
- elif isinstance(first,int): # compact creation sequence
- cs = uncompact(creation_sequence)
- else:
- raise TypeError("Not a valid creation sequence type")
-
- # Compute
- N=len(cs)
- spl=[2]*N # length 2 to every node
- spl[i]=0 # except self which is 0
- # 1 for all d's to the right
- for j in range(i+1,N):
- if cs[j]=="d":
- spl[j]=1
- if cs[i]=='d': # 1 for all nodes to the left
- for j in range(i):
- spl[j]=1
- # and -1 for any trailing i to indicate unreachable
- for j in range(N-1,0,-1):
- if cs[j]=="d":
- break
- spl[j]=-1
- return spl
-
-
-def betweenness_sequence(creation_sequence,normalized=True):
- """
- Return betweenness for the threshold graph with the given creation
- sequence. The result is unscaled. To scale the values
- to the iterval [0,1] divide by (n-1)*(n-2).
- """
- cs=creation_sequence
- seq=[] # betweenness
- lastchar='d' # first node is always a 'd'
- dr=float(cs.count("d")) # number of d's to the right of curren pos
- irun=0 # number of i's in the last run
- drun=0 # number of d's in the last run
- dlast=0.0 # betweenness of last d
- for i,c in enumerate(cs):
- if c=='d': #cs[i]=="d":
- # betweennees = amt shared with eariler d's and i's
- # + new isolated nodes covered
- # + new paths to all previous nodes
- b=dlast + (irun-1)*irun/dr + 2*irun*(i-drun-irun)/dr
- drun+=1 # update counter
- else: # cs[i]="i":
- if lastchar=='d': # if this is a new run of i's
- dlast=b # accumulate betweenness
- dr-=drun # update number of d's to the right
- drun=0 # reset d counter
- irun=0 # reset i counter
- b=0 # isolated nodes have zero betweenness
- irun+=1 # add another i to the run
- seq.append(float(b))
- lastchar=c
-
- # normalize by the number of possible shortest paths
- if normalized:
- order=len(cs)
- scale=1.0/((order-1)*(order-2))
- seq=[ s*scale for s in seq ]
-
- return seq
-
-
-def eigenvectors(creation_sequence):
- """
- Return a 2-tuple of Laplacian eigenvalues and eigenvectors
- for the threshold network with creation_sequence.
- The first value is a list of eigenvalues.
- The second value is a list of eigenvectors.
- The lists are in the same order so corresponding eigenvectors
- and eigenvalues are in the same position in the two lists.
-
- Notice that the order of the eigenvalues returned by eigenvalues(cs)
- may not correspond to the order of these eigenvectors.
- """
- ccs=make_compact(creation_sequence)
- N=sum(ccs)
- vec=[0]*N
- val=vec[:]
- # get number of type d nodes to the right (all for first node)
- dr=sum(ccs[::2])
-
- nn=ccs[0]
- vec[0]=[1./sqrt(N)]*N
- val[0]=0
- e=dr
- dr-=nn
- type_d=True
- i=1
- dd=1
- while dd<nn:
- scale=1./sqrt(dd*dd+i)
- vec[i]=i*[-scale]+[dd*scale]+[0]*(N-i-1)
- val[i]=e
- i+=1
- dd+=1
- if len(ccs)==1: return (val,vec)
- for nn in ccs[1:]:
- scale=1./sqrt(nn*i*(i+nn))
- vec[i]=i*[-nn*scale]+nn*[i*scale]+[0]*(N-i-nn)
- # find eigenvalue
- type_d=not type_d
- if type_d:
- e=i+dr
- dr-=nn
- else:
- e=dr
- val[i]=e
- st=i
- i+=1
- dd=1
- while dd<nn:
- scale=1./sqrt(i-st+dd*dd)
- vec[i]=[0]*st+(i-st)*[-scale]+[dd*scale]+[0]*(N-i-1)
- val[i]=e
- i+=1
- dd+=1
- return (val,vec)
-
-def spectral_projection(u,eigenpairs):
- """
- Returns the coefficients of each eigenvector
- in a projection of the vector u onto the normalized
- eigenvectors which are contained in eigenpairs.
-
- eigenpairs should be a list of two objects. The
- first is a list of eigenvalues and the second a list
- of eigenvectors. The eigenvectors should be lists.
-
- There's not a lot of error checking on lengths of
- arrays, etc. so be careful.
- """
- coeff=[]
- evect=eigenpairs[1]
- for ev in evect:
- c=sum([ evv*uv for (evv,uv) in zip(ev,u)])
- coeff.append(c)
- return coeff
-
-
-
-def eigenvalues(creation_sequence):
- """
- Return sequence of eigenvalues of the Laplacian of the threshold
- graph for the given creation_sequence.
-
- Based on the Ferrer's diagram method. The spectrum is integral
- and is the conjugate of the degree sequence.
-
- See::
-
- @Article{degree-merris-1994,
- author = {Russel Merris},
- title = {Degree maximal graphs are Laplacian integral},
- journal = {Linear Algebra Appl.},
- year = {1994},
- volume = {199},
- pages = {381--389},
- }
-
- """
- degseq=degree_sequence(creation_sequence)
- degseq.sort()
- eiglist=[] # zero is always one eigenvalue
- eig=0
- row=len(degseq)
- bigdeg=degseq.pop()
- while row:
- if bigdeg<row:
- eiglist.append(eig)
- row-=1
- else:
- eig+=1
- if degseq:
- bigdeg=degseq.pop()
- else:
- bigdeg=0
- return eiglist
-
-
-### Threshold graph creation routines
-
-def random_threshold_sequence(n,p,seed=None):
- """
- Create a random threshold sequence of size n.
- A creation sequence is built by randomly choosing d's with
- probabiliy p and i's with probability 1-p.
-
- s=nx.random_threshold_sequence(10,0.5)
-
- returns a threshold sequence of length 10 with equal
- probably of an i or a d at each position.
-
- A "random" threshold graph can be built with
-
- G=nx.threshold_graph(s)
-
- """
- if not seed is None:
- random.seed(seed)
-
- if not (p<=1 and p>=0):
- raise ValueError("p must be in [0,1]")
-
- cs=['d'] # threshold sequences always start with a d
- for i in range(1,n):
- if random.random() < p:
- cs.append('d')
- else:
- cs.append('i')
- return cs
-
-
-
-
-
-# maybe *_d_threshold_sequence routines should
-# be (or be called from) a single routine with a more descriptive name
-# and a keyword parameter?
-def right_d_threshold_sequence(n,m):
- """
- Create a skewed threshold graph with a given number
- of vertices (n) and a given number of edges (m).
-
- The routine returns an unlabeled creation sequence
- for the threshold graph.
-
- FIXME: describe algorithm
-
- """
- cs=['d']+['i']*(n-1) # create sequence with n insolated nodes
-
- # m <n : not enough edges, make disconnected
- if m < n:
- cs[m]='d'
- return cs
-
- # too many edges
- if m > n*(n-1)/2:
- raise ValueError("Too many edges for this many nodes.")
-
- # connected case m >n-1
- ind=n-1
- sum=n-1
- while sum<m:
- cs[ind]='d'
- ind -= 1
- sum += ind
- ind=m-(sum-ind)
- cs[ind]='d'
- return cs
-
-def left_d_threshold_sequence(n,m):
- """
- Create a skewed threshold graph with a given number
- of vertices (n) and a given number of edges (m).
-
- The routine returns an unlabeled creation sequence
- for the threshold graph.
-
- FIXME: describe algorithm
-
- """
- cs=['d']+['i']*(n-1) # create sequence with n insolated nodes
-
- # m <n : not enough edges, make disconnected
- if m < n:
- cs[m]='d'
- return cs
-
- # too many edges
- if m > n*(n-1)/2:
- raise ValueError("Too many edges for this many nodes.")
-
- # Connected case when M>N-1
- cs[n-1]='d'
- sum=n-1
- ind=1
- while sum<m:
- cs[ind]='d'
- sum += ind
- ind += 1
- if sum>m: # be sure not to change the first vertex
- cs[sum-m]='i'
- return cs
-
-def swap_d(cs,p_split=1.0,p_combine=1.0,seed=None):
- """
- Perform a "swap" operation on a threshold sequence.
-
- The swap preserves the number of nodes and edges
- in the graph for the given sequence.
- The resulting sequence is still a threshold sequence.
-
- Perform one split and one combine operation on the
- 'd's of a creation sequence for a threshold graph.
- This operation maintains the number of nodes and edges
- in the graph, but shifts the edges from node to node
- maintaining the threshold quality of the graph.
- """
- if not seed is None:
- random.seed(seed)
-
- # preprocess the creation sequence
- dlist= [ i for (i,node_type) in enumerate(cs[1:-1]) if node_type=='d' ]
- # split
- if random.random()<p_split:
- choice=random.choice(dlist)
- split_to=random.choice(range(choice))
- flip_side=choice-split_to
- if split_to!=flip_side and cs[split_to]=='i' and cs[flip_side]=='i':
- cs[choice]='i'
- cs[split_to]='d'
- cs[flip_side]='d'
- dlist.remove(choice)
- # don't add or combine may reverse this action
- # dlist.extend([split_to,flip_side])
-# print >>sys.stderr,"split at %s to %s and %s"%(choice,split_to,flip_side)
- # combine
- if random.random()<p_combine and dlist:
- first_choice= random.choice(dlist)
- second_choice=random.choice(dlist)
- target=first_choice+second_choice
- if target >= len(cs) or cs[target]=='d' or first_choice==second_choice:
- return cs
- # OK to combine
- cs[first_choice]='i'
- cs[second_choice]='i'
- cs[target]='d'
-# print >>sys.stderr,"combine %s and %s to make %s."%(first_choice,second_choice,target)
-
- return cs
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/__init__.py
deleted file mode 100644
index 9f63c89..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from networkx.linalg.attrmatrix import *
-import networkx.linalg.attrmatrix
-from networkx.linalg.spectrum import *
-import networkx.linalg.spectrum
-from networkx.linalg.graphmatrix import *
-import networkx.linalg.graphmatrix
-from networkx.linalg.laplacianmatrix import *
-import networkx.linalg.laplacianmatrix
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/attrmatrix.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/attrmatrix.py
deleted file mode 100644
index df193bb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/attrmatrix.py
+++ /dev/null
@@ -1,458 +0,0 @@
-"""
- Functions for constructing matrix-like objects from graph attributes.
-"""
-
-__all__ = ['attr_matrix', 'attr_sparse_matrix']
-
-import networkx as nx
-
-def _node_value(G, node_attr):
- """Returns a function that returns a value from G.node[u].
-
- We return a function expecting a node as its sole argument. Then, in the
- simplest scenario, the returned function will return G.node[u][node_attr].
- However, we also handle the case when `node_attr` is None or when it is a
- function itself.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- node_attr : {None, str, callable}
- Specification of how the value of the node attribute should be obtained
- from the node attribute dictionary.
-
- Returns
- -------
- value : function
- A function expecting a node as its sole argument. The function will
- returns a value from G.node[u] that depends on `edge_attr`.
-
- """
- if node_attr is None:
- value = lambda u: u
- elif not hasattr(node_attr, '__call__'):
- # assume it is a key for the node attribute dictionary
- value = lambda u: G.node[u][node_attr]
- else:
- # Advanced: Allow users to specify something else.
- #
- # For example,
- # node_attr = lambda u: G.node[u].get('size', .5) * 3
- #
- value = node_attr
-
- return value
-
-def _edge_value(G, edge_attr):
- """Returns a function that returns a value from G[u][v].
-
- Suppose there exists an edge between u and v. Then we return a function
- expecting u and v as arguments. For Graph and DiGraph, G[u][v] is
- the edge attribute dictionary, and the function (essentially) returns
- G[u][v][edge_attr]. However, we also handle cases when `edge_attr` is None
- and when it is a function itself. For MultiGraph and MultiDiGraph, G[u][v]
- is a dictionary of all edges between u and v. In this case, the returned
- function sums the value of `edge_attr` for every edge between u and v.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- edge_attr : {None, str, callable}
- Specification of how the value of the edge attribute should be obtained
- from the edge attribute dictionary, G[u][v]. For multigraphs, G[u][v]
- is a dictionary of all the edges between u and v. This allows for
- special treatment of multiedges.
-
- Returns
- -------
- value : function
- A function expecting two nodes as parameters. The nodes should
- represent the from- and to- node of an edge. The function will
- return a value from G[u][v] that depends on `edge_attr`.
-
- """
-
- if edge_attr is None:
- # topological count of edges
-
- if G.is_multigraph():
- value = lambda u,v: len(G[u][v])
- else:
- value = lambda u,v: 1
-
- elif not hasattr(edge_attr, '__call__'):
- # assume it is a key for the edge attribute dictionary
-
- if edge_attr == 'weight':
- # provide a default value
- if G.is_multigraph():
- value = lambda u,v: sum([d.get(edge_attr, 1) for d in G[u][v].values()])
- else:
- value = lambda u,v: G[u][v].get(edge_attr, 1)
- else:
- # otherwise, the edge attribute MUST exist for each edge
- if G.is_multigraph():
- value = lambda u,v: sum([d[edge_attr] for d in G[u][v].values()])
- else:
- value = lambda u,v: G[u][v][edge_attr]
-
- else:
- # Advanced: Allow users to specify something else.
- #
- # Alternative default value:
- # edge_attr = lambda u,v: G[u][v].get('thickness', .5)
- #
- # Function on an attribute:
- # edge_attr = lambda u,v: abs(G[u][v]['weight'])
- #
- # Handle Multi(Di)Graphs differently:
- # edge_attr = lambda u,v: numpy.prod([d['size'] for d in G[u][v].values()])
- #
- # Ignore multiple edges
- # edge_attr = lambda u,v: 1 if len(G[u][v]) else 0
- #
- value = edge_attr
-
- return value
-
-def attr_matrix(G, edge_attr=None, node_attr=None, normalized=False,
- rc_order=None, dtype=None, order=None):
- """Returns a NumPy matrix using attributes from G.
-
- If only `G` is passed in, then the adjacency matrix is constructed.
-
- Let A be a discrete set of values for the node attribute `node_attr`. Then
- the elements of A represent the rows and columns of the constructed matrix.
- Now, iterate through every edge e=(u,v) in `G` and consider the value
- of the edge attribute `edge_attr`. If ua and va are the values of the
- node attribute `node_attr` for u and v, respectively, then the value of
- the edge attribute is added to the matrix element at (ua, va).
-
- Parameters
- ----------
- G : graph
- The NetworkX graph used to construct the NumPy matrix.
-
- edge_attr : str, optional
- Each element of the matrix represents a running total of the
- specified edge attribute for edges whose node attributes correspond
- to the rows/cols of the matirx. The attribute must be present for
- all edges in the graph. If no attribute is specified, then we
- just count the number of edges whose node attributes correspond
- to the matrix element.
-
- node_attr : str, optional
- Each row and column in the matrix represents a particular value
- of the node attribute. The attribute must be present for all nodes
- in the graph. Note, the values of this attribute should be reliably
- hashable. So, float values are not recommended. If no attribute is
- specified, then the rows and columns will be the nodes of the graph.
-
- normalized : bool, optional
- If True, then each row is normalized by the summation of its values.
-
- rc_order : list, optional
- A list of the node attribute values. This list specifies the ordering
- of rows and columns of the array. If no ordering is provided, then
- the ordering will be random (and also, a return value).
-
- Other Parameters
- ----------------
- dtype : NumPy data-type, optional
- A valid NumPy dtype used to initialize the array. Keep in mind certain
- dtypes can yield unexpected results if the array is to be normalized.
- The parameter is passed to numpy.zeros(). If unspecified, the NumPy
- default is used.
-
- order : {'C', 'F'}, optional
- Whether to store multidimensional data in C- or Fortran-contiguous
- (row- or column-wise) order in memory. This parameter is passed to
- numpy.zeros(). If unspecified, the NumPy default is used.
-
- Returns
- -------
- M : NumPy matrix
- The attribute matrix.
-
- ordering : list
- If `rc_order` was specified, then only the matrix is returned.
- However, if `rc_order` was None, then the ordering used to construct
- the matrix is returned as well.
-
- Examples
- --------
- Construct an adjacency matrix:
-
- >>> G = nx.Graph()
- >>> G.add_edge(0,1,thickness=1,weight=3)
- >>> G.add_edge(0,2,thickness=2)
- >>> G.add_edge(1,2,thickness=3)
- >>> nx.attr_matrix(G, rc_order=[0,1,2])
- matrix([[ 0., 1., 1.],
- [ 1., 0., 1.],
- [ 1., 1., 0.]])
-
- Alternatively, we can obtain the matrix describing edge thickness.
-
- >>> nx.attr_matrix(G, edge_attr='thickness', rc_order=[0,1,2])
- matrix([[ 0., 1., 2.],
- [ 1., 0., 3.],
- [ 2., 3., 0.]])
-
- We can also color the nodes and ask for the probability distribution over
- all edges (u,v) describing:
-
- Pr(v has color Y | u has color X)
-
- >>> G.node[0]['color'] = 'red'
- >>> G.node[1]['color'] = 'red'
- >>> G.node[2]['color'] = 'blue'
- >>> rc = ['red', 'blue']
- >>> nx.attr_matrix(G, node_attr='color', normalized=True, rc_order=rc)
- matrix([[ 0.33333333, 0.66666667],
- [ 1. , 0. ]])
-
- For example, the above tells us that for all edges (u,v):
-
- Pr( v is red | u is red) = 1/3
- Pr( v is blue | u is red) = 2/3
-
- Pr( v is red | u is blue) = 1
- Pr( v is blue | u is blue) = 0
-
- Finally, we can obtain the total weights listed by the node colors.
-
- >>> nx.attr_matrix(G, edge_attr='weight', node_attr='color', rc_order=rc)
- matrix([[ 3., 2.],
- [ 2., 0.]])
-
- Thus, the total weight over all edges (u,v) with u and v having colors:
-
- (red, red) is 3 # the sole contribution is from edge (0,1)
- (red, blue) is 2 # contributions from edges (0,2) and (1,2)
- (blue, red) is 2 # same as (red, blue) since graph is undirected
- (blue, blue) is 0 # there are no edges with blue endpoints
-
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(
- "attr_matrix() requires numpy: http://scipy.org/ ")
-
- edge_value = _edge_value(G, edge_attr)
- node_value = _node_value(G, node_attr)
-
- if rc_order is None:
- ordering = list(set([node_value(n) for n in G]))
- else:
- ordering = rc_order
-
- N = len(ordering)
- undirected = not G.is_directed()
- index = dict(zip(ordering, range(N)))
- M = np.zeros((N,N), dtype=dtype, order=order)
-
- seen = set([])
- for u,nbrdict in G.adjacency_iter():
- for v in nbrdict:
- # Obtain the node attribute values.
- i, j = index[node_value(u)], index[node_value(v)]
- if v not in seen:
- M[i,j] += edge_value(u,v)
- if undirected:
- M[j,i] = M[i,j]
-
- if undirected:
- seen.add(u)
-
- if normalized:
- M /= M.sum(axis=1).reshape((N,1))
-
- M = np.asmatrix(M)
-
- if rc_order is None:
- return M, ordering
- else:
- return M
-
-def attr_sparse_matrix(G, edge_attr=None, node_attr=None,
- normalized=False, rc_order=None, dtype=None):
- """Returns a SciPy sparse matrix using attributes from G.
-
- If only `G` is passed in, then the adjacency matrix is constructed.
-
- Let A be a discrete set of values for the node attribute `node_attr`. Then
- the elements of A represent the rows and columns of the constructed matrix.
- Now, iterate through every edge e=(u,v) in `G` and consider the value
- of the edge attribute `edge_attr`. If ua and va are the values of the
- node attribute `node_attr` for u and v, respectively, then the value of
- the edge attribute is added to the matrix element at (ua, va).
-
- Parameters
- ----------
- G : graph
- The NetworkX graph used to construct the NumPy matrix.
-
- edge_attr : str, optional
- Each element of the matrix represents a running total of the
- specified edge attribute for edges whose node attributes correspond
- to the rows/cols of the matirx. The attribute must be present for
- all edges in the graph. If no attribute is specified, then we
- just count the number of edges whose node attributes correspond
- to the matrix element.
-
- node_attr : str, optional
- Each row and column in the matrix represents a particular value
- of the node attribute. The attribute must be present for all nodes
- in the graph. Note, the values of this attribute should be reliably
- hashable. So, float values are not recommended. If no attribute is
- specified, then the rows and columns will be the nodes of the graph.
-
- normalized : bool, optional
- If True, then each row is normalized by the summation of its values.
-
- rc_order : list, optional
- A list of the node attribute values. This list specifies the ordering
- of rows and columns of the array. If no ordering is provided, then
- the ordering will be random (and also, a return value).
-
- Other Parameters
- ----------------
- dtype : NumPy data-type, optional
- A valid NumPy dtype used to initialize the array. Keep in mind certain
- dtypes can yield unexpected results if the array is to be normalized.
- The parameter is passed to numpy.zeros(). If unspecified, the NumPy
- default is used.
-
- Returns
- -------
- M : SciPy sparse matrix
- The attribute matrix.
-
- ordering : list
- If `rc_order` was specified, then only the matrix is returned.
- However, if `rc_order` was None, then the ordering used to construct
- the matrix is returned as well.
-
- Examples
- --------
- Construct an adjacency matrix:
-
- >>> G = nx.Graph()
- >>> G.add_edge(0,1,thickness=1,weight=3)
- >>> G.add_edge(0,2,thickness=2)
- >>> G.add_edge(1,2,thickness=3)
- >>> M = nx.attr_sparse_matrix(G, rc_order=[0,1,2])
- >>> M.todense()
- matrix([[ 0., 1., 1.],
- [ 1., 0., 1.],
- [ 1., 1., 0.]])
-
- Alternatively, we can obtain the matrix describing edge thickness.
-
- >>> M = nx.attr_sparse_matrix(G, edge_attr='thickness', rc_order=[0,1,2])
- >>> M.todense()
- matrix([[ 0., 1., 2.],
- [ 1., 0., 3.],
- [ 2., 3., 0.]])
-
- We can also color the nodes and ask for the probability distribution over
- all edges (u,v) describing:
-
- Pr(v has color Y | u has color X)
-
- >>> G.node[0]['color'] = 'red'
- >>> G.node[1]['color'] = 'red'
- >>> G.node[2]['color'] = 'blue'
- >>> rc = ['red', 'blue']
- >>> M = nx.attr_sparse_matrix(G, node_attr='color', \
- normalized=True, rc_order=rc)
- >>> M.todense()
- matrix([[ 0.33333333, 0.66666667],
- [ 1. , 0. ]])
-
- For example, the above tells us that for all edges (u,v):
-
- Pr( v is red | u is red) = 1/3
- Pr( v is blue | u is red) = 2/3
-
- Pr( v is red | u is blue) = 1
- Pr( v is blue | u is blue) = 0
-
- Finally, we can obtain the total weights listed by the node colors.
-
- >>> M = nx.attr_sparse_matrix(G, edge_attr='weight',\
- node_attr='color', rc_order=rc)
- >>> M.todense()
- matrix([[ 3., 2.],
- [ 2., 0.]])
-
- Thus, the total weight over all edges (u,v) with u and v having colors:
-
- (red, red) is 3 # the sole contribution is from edge (0,1)
- (red, blue) is 2 # contributions from edges (0,2) and (1,2)
- (blue, red) is 2 # same as (red, blue) since graph is undirected
- (blue, blue) is 0 # there are no edges with blue endpoints
-
- """
- try:
- import numpy as np
- from scipy import sparse
- except ImportError:
- raise ImportError(
- "attr_sparse_matrix() requires scipy: http://scipy.org/ ")
-
- edge_value = _edge_value(G, edge_attr)
- node_value = _node_value(G, node_attr)
-
- if rc_order is None:
- ordering = list(set([node_value(n) for n in G]))
- else:
- ordering = rc_order
-
- N = len(ordering)
- undirected = not G.is_directed()
- index = dict(zip(ordering, range(N)))
- M = sparse.lil_matrix((N,N), dtype=dtype)
-
- seen = set([])
- for u,nbrdict in G.adjacency_iter():
- for v in nbrdict:
- # Obtain the node attribute values.
- i, j = index[node_value(u)], index[node_value(v)]
- if v not in seen:
- M[i,j] += edge_value(u,v)
- if undirected:
- M[j,i] = M[i,j]
-
- if undirected:
- seen.add(u)
-
- if normalized:
- norms = np.asarray(M.sum(axis=1)).ravel()
- for i,norm in enumerate(norms):
- M[i,:] /= norm
-
- if rc_order is None:
- return M, ordering
- else:
- return M
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
- try:
- import scipy
- except:
- raise SkipTest("SciPy not available")
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/graphmatrix.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/graphmatrix.py
deleted file mode 100644
index c677619..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/graphmatrix.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""
-Adjacency matrix and incidence matrix of graphs.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-
-__all__ = ['incidence_matrix',
- 'adj_matrix', 'adjacency_matrix',
- ]
-
-
-def incidence_matrix(G, nodelist=None, edgelist=None,
- oriented=False, weight=None):
- """Return incidence matrix of G.
-
- The incidence matrix assigns each row to a node and each column to an edge.
- For a standard incidence matrix a 1 appears wherever a row's node is
- incident on the column's edge. For an oriented incidence matrix each
- edge is assigned an orientation (arbitrarily for undirected and aligning to
- direction for directed). A -1 appears for the tail of an edge and 1
- for the head of the edge. The elements are zero otherwise.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list, optional (default= all nodes in G)
- The rows are ordered according to the nodes in nodelist.
- If nodelist is None, then the ordering is produced by G.nodes().
-
- edgelist : list, optional (default= all edges in G)
- The columns are ordered according to the edges in edgelist.
- If edgelist is None, then the ordering is produced by G.edges().
-
- oriented: bool, optional (default=False)
- If True, matrix elements are +1 or -1 for the head or tail node
- respectively of each edge. If False, +1 occurs at both nodes.
-
- weight : string or None, optional (default=None)
- The edge data key used to provide each value in the matrix.
- If None, then each edge has weight 1. Edge weights, if used,
- should be positive so that the orientation can provide the sign.
-
- Returns
- -------
- A : NumPy matrix
- The incidence matrix of G.
-
- Notes
- -----
- For MultiGraph/MultiDiGraph, the edges in edgelist should be
- (u,v,key) 3-tuples.
-
- "Networks are the best discrete model for so many problems in
- applied mathematics" [1]_.
-
- References
- ----------
- .. [1] Gil Strang, Network applications: A = incidence matrix,
- http://academicearth.org/lectures/network-applications-incidence-matrix
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(
- "incidence_matrix() requires numpy: http://scipy.org/ ")
- if nodelist is None:
- nodelist = G.nodes()
- if edgelist is None:
- if G.is_multigraph():
- edgelist = G.edges(keys=True)
- else:
- edgelist = G.edges()
- A = np.zeros((len(nodelist),len(edgelist)))
- node_index = dict( (node,i) for i,node in enumerate(nodelist) )
- for ei,e in enumerate(edgelist):
- (u,v) = e[:2]
- if u == v: continue # self loops give zero column
- try:
- ui = node_index[u]
- vi = node_index[v]
- except KeyError:
- raise NetworkXError('node %s or %s in edgelist '
- 'but not in nodelist"%(u,v)')
- if weight is None:
- wt = 1
- else:
- if G.is_multigraph():
- ekey = e[2]
- wt = G[u][v][ekey].get(weight,1)
- else:
- wt = G[u][v].get(weight,1)
- if oriented:
- A[ui,ei] = -wt
- A[vi,ei] = wt
- else:
- A[ui,ei] = wt
- A[vi,ei] = wt
- return np.asmatrix(A)
-
-def adjacency_matrix(G, nodelist=None, weight='weight'):
- """Return adjacency matrix of G.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in nodelist.
- If nodelist is None, then the ordering is produced by G.nodes().
-
- weight : string or None, optional (default='weight')
- The edge data key used to provide each value in the matrix.
- If None, then each edge has weight 1.
-
- Returns
- -------
- A : numpy matrix
- Adjacency matrix representation of G.
-
- Notes
- -----
- If you want a pure Python adjacency matrix representation try
- networkx.convert.to_dict_of_dicts which will return a
- dictionary-of-dictionaries format that can be addressed as a
- sparse matrix.
-
- For MultiGraph/MultiDiGraph, the edges weights are summed.
- See to_numpy_matrix for other options.
-
- See Also
- --------
- to_numpy_matrix
- to_dict_of_dicts
- """
- return nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight)
-
-adj_matrix=adjacency_matrix
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/laplacianmatrix.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/laplacianmatrix.py
deleted file mode 100644
index ffb256c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/laplacianmatrix.py
+++ /dev/null
@@ -1,277 +0,0 @@
-"""
-Laplacian matrix of graphs.
-"""
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import require, not_implemented_for
-
-__author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult (dschult@colgate.edu)',
- 'Alejandro Weinstein <alejandro.weinstein@gmail.com>'])
-
-__all__ = ['laplacian_matrix',
- 'normalized_laplacian_matrix',
- 'directed_laplacian_matrix']
-
-@require('numpy')
-@not_implemented_for('directed')
-def laplacian_matrix(G, nodelist=None, weight='weight'):
- """Return the Laplacian matrix of G.
-
- The graph Laplacian is the matrix L = D - A, where
- A is the adjacency matrix and D is the diagonal matrix of node degrees.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in nodelist.
- If nodelist is None, then the ordering is produced by G.nodes().
-
- weight : string or None, optional (default='weight')
- The edge data key used to compute each value in the matrix.
- If None, then each edge has weight 1.
-
- Returns
- -------
- L : NumPy matrix
- The Laplacian matrix of G.
-
- Notes
- -----
- For MultiGraph/MultiDiGraph, the edges weights are summed.
- See to_numpy_matrix for other options.
-
- See Also
- --------
- to_numpy_matrix
- normalized_laplacian_matrix
- """
- import numpy as np
- if nodelist is None:
- nodelist = G.nodes()
- if G.is_multigraph():
- # this isn't the fastest way to do this...
- A = np.asarray(nx.to_numpy_matrix(G,nodelist=nodelist,weight=weight))
- I = np.identity(A.shape[0])
- D = I*np.sum(A,axis=1)
- L = D - A
- else:
- # Graph or DiGraph, this is faster than above
- n = len(nodelist)
- index = dict( (n,i) for i,n in enumerate(nodelist) )
- L = np.zeros((n,n))
- for ui,u in enumerate(nodelist):
- totalwt = 0.0
- for v,d in G[u].items():
- try:
- vi = index[v]
- except KeyError:
- continue
- wt = d.get(weight,1)
- L[ui,vi] = -wt
- totalwt += wt
- L[ui,ui] = totalwt
- return np.asmatrix(L)
-
-@require('numpy')
-@not_implemented_for('directed')
-def normalized_laplacian_matrix(G, nodelist=None, weight='weight'):
- r"""Return the normalized Laplacian matrix of G.
-
- The normalized graph Laplacian is the matrix
-
- .. math::
-
- NL = D^{-1/2} L D^{-1/2}
-
- where `L` is the graph Laplacian and `D` is the diagonal matrix of
- node degrees.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in nodelist.
- If nodelist is None, then the ordering is produced by G.nodes().
-
- weight : string or None, optional (default='weight')
- The edge data key used to compute each value in the matrix.
- If None, then each edge has weight 1.
-
- Returns
- -------
- L : NumPy matrix
- The normalized Laplacian matrix of G.
-
- Notes
- -----
- For MultiGraph/MultiDiGraph, the edges weights are summed.
- See to_numpy_matrix for other options.
-
- If the Graph contains selfloops, D is defined as diag(sum(A,1)), where A is
- the adjencency matrix [2]_.
-
- See Also
- --------
- laplacian_matrix
-
- References
- ----------
- .. [1] Fan Chung-Graham, Spectral Graph Theory,
- CBMS Regional Conference Series in Mathematics, Number 92, 1997.
- .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
- Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
- March 2007.
- """
- import numpy as np
- if G.is_multigraph():
- L = laplacian_matrix(G, nodelist=nodelist, weight=weight)
- D = np.diag(L)
- elif G.number_of_selfloops() == 0:
- L = laplacian_matrix(G, nodelist=nodelist, weight=weight)
- D = np.diag(L)
- else:
- A = np.array(nx.adj_matrix(G))
- D = np.sum(A, 1)
- L = np.diag(D) - A
-
- # Handle div by 0. It happens if there are unconnected nodes
- with np.errstate(divide='ignore'):
- Disqrt = np.diag(1 / np.sqrt(D))
- Disqrt[np.isinf(Disqrt)] = 0
- Ln = np.dot(Disqrt, np.dot(L,Disqrt))
- return Ln
-
-###############################################################################
-# Code based on
-# https://bitbucket.org/bedwards/networkx-community/src/370bd69fc02f/networkx/algorithms/community/
-
-@require('numpy')
-@not_implemented_for('undirected')
-@not_implemented_for('multigraph')
-def directed_laplacian_matrix(G, nodelist=None, weight='weight',
- walk_type=None, alpha=0.95):
- r"""Return the directed Laplacian matrix of G.
-
- The graph directed Laplacian is the matrix
-
- .. math::
-
- L = I - (\Phi^{1/2} P \Phi^{-1/2} + \Phi^{-1/2} P^T \Phi^{1/2} ) / 2
-
- where `I` is the identity matrix, `P` is the transition matrix of the
- graph, and `\Phi` a matrix with the Perron vector of `P` in the diagonal and
- zeros elsewhere.
-
- Depending on the value of walk_type, `P` can be the transition matrix
- induced by a random walk, a lazy random walk, or a random walk with
- teleportation (PageRank).
-
- Parameters
- ----------
- G : DiGraph
- A NetworkX graph
-
- nodelist : list, optional
- The rows and columns are ordered according to the nodes in nodelist.
- If nodelist is None, then the ordering is produced by G.nodes().
-
- weight : string or None, optional (default='weight')
- The edge data key used to compute each value in the matrix.
- If None, then each edge has weight 1.
-
- walk_type : string or None, optional (default=None)
- If None, `P` is selected depending on the properties of the
- graph. Otherwise is one of 'random', 'lazy', or 'pagerank'
-
- alpha : real
- (1 - alpha) is the teleportation probability used with pagerank
-
- Returns
- -------
- L : NumPy array
- Normalized Laplacian of G.
-
- Raises
- ------
- NetworkXError
- If NumPy cannot be imported
-
- NetworkXNotImplemnted
- If G is not a DiGraph
-
- Notes
- -----
- Only implemented for DiGraphs
-
- See Also
- --------
- laplacian_matrix
-
- References
- ----------
- .. [1] Fan Chung (2005).
- Laplacians and the Cheeger inequality for directed graphs.
- Annals of Combinatorics, 9(1), 2005
- """
- import numpy as np
- if walk_type is None:
- if nx.is_strongly_connected(G):
- if nx.is_aperiodic(G):
- walk_type = "random"
- else:
- walk_type = "lazy"
- else:
- walk_type = "pagerank"
-
- M = nx.to_numpy_matrix(G, nodelist=nodelist, weight=weight)
- n, m = M.shape
- if walk_type in ["random", "lazy"]:
- DI = np.diagflat(1.0 / np.sum(M, axis=1))
- if walk_type == "random":
- P = DI * M
- else:
- I = np.identity(n)
- P = (I + DI * M) / 2.0
- elif walk_type == "pagerank":
- if not (0 < alpha < 1):
- raise nx.NetworkXError('alpha must be between 0 and 1')
- # add constant to dangling nodes' row
- dangling = np.where(M.sum(axis=1) == 0)
- for d in dangling[0]:
- M[d] = 1.0 / n
- # normalize
- M = M / M.sum(axis=1)
- P = alpha * M + (1 - alpha) / n
- else:
- raise nx.NetworkXError("walk_type must be random, lazy, or pagerank")
-
- evals, evecs = np.linalg.eig(P.T)
- index = evals.argsort()[-1] # index of largest eval,evec
- # eigenvector of largest eigenvalue at ind[-1]
- v = np.array(evecs[:,index]).flatten().real
- p = v / v.sum()
- sp = np.sqrt(p)
- Q = np.diag(sp) * P * np.diag(1.0/sp)
- I = np.identity(len(G))
-
- return I - (Q + Q.T) /2.0
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/spectrum.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/spectrum.py
deleted file mode 100644
index bca7288..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/spectrum.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""
-Eigenvalue spectrum of graphs.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = "\n".join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)'])
-
-__all__ = ['laplacian_spectrum', 'adjacency_spectrum']
-
-
-def laplacian_spectrum(G, weight='weight'):
- """Return eigenvalues of the Laplacian of G
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- weight : string or None, optional (default='weight')
- The edge data key used to compute each value in the matrix.
- If None, then each edge has weight 1.
-
- Returns
- -------
- evals : NumPy array
- Eigenvalues
-
- Notes
- -----
- For MultiGraph/MultiDiGraph, the edges weights are summed.
- See to_numpy_matrix for other options.
-
- See Also
- --------
- laplacian_matrix
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(
- "laplacian_spectrum() requires NumPy: http://scipy.org/ ")
- return np.linalg.eigvals(nx.laplacian_matrix(G,weight=weight))
-
-def adjacency_spectrum(G, weight='weight'):
- """Return eigenvalues of the adjacency matrix of G.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- weight : string or None, optional (default='weight')
- The edge data key used to compute each value in the matrix.
- If None, then each edge has weight 1.
-
- Returns
- -------
- evals : NumPy array
- Eigenvalues
-
- Notes
- -----
- For MultiGraph/MultiDiGraph, the edges weights are summed.
- See to_numpy_matrix for other options.
-
- See Also
- --------
- adjacency_matrix
- """
- try:
- import numpy as np
- except ImportError:
- raise ImportError(
- "adjacency_spectrum() requires NumPy: http://scipy.org/ ")
- return np.linalg.eigvals(nx.adjacency_matrix(G,weight=weight))
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import numpy
- except:
- raise SkipTest("NumPy not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_graphmatrix.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_graphmatrix.py
deleted file mode 100644
index bba234e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_graphmatrix.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from nose import SkipTest
-
-import networkx as nx
-from networkx.generators.degree_seq import havel_hakimi_graph
-
-class TestGraphMatrix(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global numpy
- global assert_equal
- global assert_almost_equal
- try:
- import numpy
- from numpy.testing import assert_equal,assert_almost_equal
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def setUp(self):
- deg=[3,2,2,1,0]
- self.G=havel_hakimi_graph(deg)
- self.OI=numpy.array([[-1, -1, -1, 0],
- [1, 0, 0, -1],
- [0, 1, 0, 1],
- [0, 0, 1, 0],
- [0, 0, 0, 0]])
- self.A=numpy.array([[0, 1, 1, 1, 0],
- [1, 0, 1, 0, 0],
- [1, 1, 0, 0, 0],
- [1, 0, 0, 0, 0],
- [0, 0, 0, 0, 0]])
- self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
- for (u,v) in self.G.edges_iter() )
- self.WG.add_node(4)
- self.WA=numpy.array([[0 , 0.5, 0.5, 0.5, 0],
- [0.5, 0 , 0.5, 0 , 0],
- [0.5, 0.5, 0 , 0 , 0],
- [0.5, 0 , 0 , 0 , 0],
- [0 , 0 , 0 , 0 , 0]])
- self.MG=nx.MultiGraph(self.G)
- self.MG2=self.MG.copy()
- self.MG2.add_edge(0,1)
- self.MG2A=numpy.array([[0, 2, 1, 1, 0],
- [2, 0, 1, 0, 0],
- [1, 1, 0, 0, 0],
- [1, 0, 0, 0, 0],
- [0, 0, 0, 0, 0]])
- self.MGOI=numpy.array([[-1, -1, -1, -1, 0],
- [1, 1, 0, 0, -1],
- [0, 0, 1, 0, 1],
- [0, 0, 0, 1, 0],
- [0, 0, 0, 0, 0]])
-
- def test_incidence_matrix(self):
- "Conversion to incidence matrix"
- assert_equal(nx.incidence_matrix(self.G,oriented=True),self.OI)
- assert_equal(nx.incidence_matrix(self.G),numpy.abs(self.OI))
- assert_equal(nx.incidence_matrix(self.MG,oriented=True),self.OI)
- assert_equal(nx.incidence_matrix(self.MG),numpy.abs(self.OI))
- assert_equal(nx.incidence_matrix(self.MG2,oriented=True),self.MGOI)
- assert_equal(nx.incidence_matrix(self.MG2),numpy.abs(self.MGOI))
- assert_equal(nx.incidence_matrix(self.WG,oriented=True),self.OI)
- assert_equal(nx.incidence_matrix(self.WG),numpy.abs(self.OI))
- assert_equal(nx.incidence_matrix(self.WG,oriented=True,
- weight='weight'),0.5*self.OI)
- assert_equal(nx.incidence_matrix(self.WG,weight='weight'),
- numpy.abs(0.5*self.OI))
- assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other'),
- 0.3*self.OI)
- WMG=nx.MultiGraph(self.WG)
- WMG.add_edge(0,1,attr_dict={'weight':0.5,'other':0.3})
- assert_equal(nx.incidence_matrix(WMG,weight='weight'),
- numpy.abs(0.5*self.MGOI))
- assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True),
- 0.5*self.MGOI)
- assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True),
- 0.3*self.MGOI)
-
- def test_adjacency_matrix(self):
- "Conversion to adjacency matrix"
- assert_equal(nx.adj_matrix(self.G),self.A)
- assert_equal(nx.adj_matrix(self.MG),self.A)
- assert_equal(nx.adj_matrix(self.MG2),self.MG2A)
- assert_equal(nx.adj_matrix(self.G,nodelist=[0,1]),self.A[:2,:2])
- assert_equal(nx.adj_matrix(self.WG),self.WA)
- assert_equal(nx.adj_matrix(self.WG,weight=None),self.A)
- assert_equal(nx.adj_matrix(self.MG2,weight=None),self.MG2A)
- assert_equal(nx.adj_matrix(self.WG,weight='other'),0.6*self.WA)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_laplacian.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_laplacian.py
deleted file mode 100644
index 87725fe..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_laplacian.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from nose import SkipTest
-
-import networkx as nx
-from networkx.generators.degree_seq import havel_hakimi_graph
-
-class TestLaplacian(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global numpy
- global assert_equal
- global assert_almost_equal
- try:
- import numpy
- from numpy.testing import assert_equal,assert_almost_equal
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def setUp(self):
- deg=[3,2,2,1,0]
- self.G=havel_hakimi_graph(deg)
- self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
- for (u,v) in self.G.edges_iter() )
- self.WG.add_node(4)
- self.MG=nx.MultiGraph(self.G)
-
- # Graph with selfloops
- self.Gsl = self.G.copy()
- for node in self.Gsl.nodes():
- self.Gsl.add_edge(node, node)
-
-
- def test_laplacian(self):
- "Graph Laplacian"
- NL=numpy.array([[ 3, -1, -1, -1, 0],
- [-1, 2, -1, 0, 0],
- [-1, -1, 2, 0, 0],
- [-1, 0, 0, 1, 0],
- [ 0, 0, 0, 0, 0]])
- WL=0.5*NL
- OL=0.3*NL
- assert_equal(nx.laplacian_matrix(self.G),NL)
- assert_equal(nx.laplacian_matrix(self.MG),NL)
- assert_equal(nx.laplacian_matrix(self.G,nodelist=[0,1]),
- numpy.array([[ 1, -1],[-1, 1]]))
- assert_equal(nx.laplacian_matrix(self.WG),WL)
- assert_equal(nx.laplacian_matrix(self.WG,weight=None),NL)
- assert_equal(nx.laplacian_matrix(self.WG,weight='other'),OL)
-
- def test_normalized_laplacian(self):
- "Generalized Graph Laplacian"
- GL=numpy.array([[ 1.00, -0.408, -0.408, -0.577, 0.00],
- [-0.408, 1.00, -0.50, 0.00 , 0.00],
- [-0.408, -0.50, 1.00, 0.00, 0.00],
- [-0.577, 0.00, 0.00, 1.00, 0.00],
- [ 0.00, 0.00, 0.00, 0.00, 0.00]])
- Lsl = numpy.array([[ 0.75 , -0.2887, -0.2887, -0.3536, 0.],
- [-0.2887, 0.6667, -0.3333, 0. , 0.],
- [-0.2887, -0.3333, 0.6667, 0. , 0.],
- [-0.3536, 0. , 0. , 0.5 , 0.],
- [ 0. , 0. , 0. , 0. , 0.]])
-
- assert_almost_equal(nx.normalized_laplacian_matrix(self.G),GL,decimal=3)
- assert_almost_equal(nx.normalized_laplacian_matrix(self.MG),GL,decimal=3)
- assert_almost_equal(nx.normalized_laplacian_matrix(self.WG),GL,decimal=3)
- assert_almost_equal(nx.normalized_laplacian_matrix(self.WG,weight='other'),GL,decimal=3)
- assert_almost_equal(nx.normalized_laplacian_matrix(self.Gsl), Lsl, decimal=3)
-
- def test_directed_laplacian(self):
- "Directed Laplacian"
- # Graph used as an example in Sec. 4.1 of Langville and Meyer,
- # "Google's PageRank and Beyond". The graph contains dangling nodes, so
- # the pagerank random walk is selected by directed_laplacian
- G = nx.DiGraph()
- G.add_edges_from(((1,2), (1,3), (3,1), (3,2), (3,5), (4,5), (4,6),
- (5,4), (5,6), (6,4)))
- GL = numpy.array([[ 0.9833, -0.2941, -0.3882, -0.0291, -0.0231, -0.0261],
- [-0.2941, 0.8333, -0.2339, -0.0536, -0.0589, -0.0554],
- [-0.3882, -0.2339, 0.9833, -0.0278, -0.0896, -0.0251],
- [-0.0291, -0.0536, -0.0278, 0.9833, -0.4878, -0.6675],
- [-0.0231, -0.0589, -0.0896, -0.4878, 0.9833, -0.2078],
- [-0.0261, -0.0554, -0.0251, -0.6675, -0.2078, 0.9833]])
- assert_almost_equal(nx.directed_laplacian_matrix(G, alpha=0.9), GL, decimal=3)
-
- # Make the graph strongly connected, so we can use a random and lazy walk
- G.add_edges_from((((2,5), (6,1))))
- GL = numpy.array([[ 1. , -0.3062, -0.4714, 0. , 0. , -0.3227],
- [-0.3062, 1. , -0.1443, 0. , -0.3162, 0. ],
- [-0.4714, -0.1443, 1. , 0. , -0.0913, 0. ],
- [ 0. , 0. , 0. , 1. , -0.5 , -0.5 ],
- [ 0. , -0.3162, -0.0913, -0.5 , 1. , -0.25 ],
- [-0.3227, 0. , 0. , -0.5 , -0.25 , 1. ]])
- assert_almost_equal(nx.directed_laplacian_matrix(G, walk_type='random'), GL, decimal=3)
-
- GL = numpy.array([[ 0.5 , -0.1531, -0.2357, 0. , 0. , -0.1614],
- [-0.1531, 0.5 , -0.0722, 0. , -0.1581, 0. ],
- [-0.2357, -0.0722, 0.5 , 0. , -0.0456, 0. ],
- [ 0. , 0. , 0. , 0.5 , -0.25 , -0.25 ],
- [ 0. , -0.1581, -0.0456, -0.25 , 0.5 , -0.125 ],
- [-0.1614, 0. , 0. , -0.25 , -0.125 , 0.5 ]])
- assert_almost_equal(nx.directed_laplacian_matrix(G, walk_type='lazy'), GL, decimal=3)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_spectrum.py b/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_spectrum.py
deleted file mode 100644
index a2961cb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/linalg/tests/test_spectrum.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from nose import SkipTest
-
-import networkx as nx
-from networkx.generators.degree_seq import havel_hakimi_graph
-
-class TestSpectrum(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global numpy
- global assert_equal
- global assert_almost_equal
- try:
- import numpy
- from numpy.testing import assert_equal,assert_almost_equal
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def setUp(self):
- deg=[3,2,2,1,0]
- self.G=havel_hakimi_graph(deg)
- self.P=nx.path_graph(3)
- self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
- for (u,v) in self.G.edges_iter() )
- self.WG.add_node(4)
-
- def test_laplacian_spectrum(self):
- "Laplacian eigenvalues"
- evals=numpy.array([0, 0, 1, 3, 4])
- e=sorted(nx.laplacian_spectrum(self.G))
- assert_almost_equal(e,evals)
- e=sorted(nx.laplacian_spectrum(self.WG,weight=None))
- assert_almost_equal(e,evals)
- e=sorted(nx.laplacian_spectrum(self.WG))
- assert_almost_equal(e,0.5*evals)
- e=sorted(nx.laplacian_spectrum(self.WG,weight='other'))
- assert_almost_equal(e,0.3*evals)
-
- def test_adjacency_spectrum(self):
- "Adjacency eigenvalues"
- evals=numpy.array([-numpy.sqrt(2), 0, numpy.sqrt(2)])
- e=sorted(nx.adjacency_spectrum(self.P))
- assert_almost_equal(e,evals)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/__init__.py
deleted file mode 100644
index c806cd0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-A package for reading and writing graphs in various formats.
-
-"""
-from networkx.readwrite.adjlist import *
-from networkx.readwrite.multiline_adjlist import *
-from networkx.readwrite.edgelist import *
-from networkx.readwrite.gpickle import *
-from networkx.readwrite.pajek import *
-from networkx.readwrite.leda import *
-from networkx.readwrite.sparsegraph6 import *
-from networkx.readwrite.nx_yaml import *
-from networkx.readwrite.gml import *
-from networkx.readwrite.graphml import *
-from networkx.readwrite.gexf import *
-from networkx.readwrite.nx_shp import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/adjlist.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/adjlist.py
deleted file mode 100644
index 57f1e24..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/adjlist.py
+++ /dev/null
@@ -1,314 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-**************
-Adjacency List
-**************
-Read and write NetworkX graphs as adjacency lists.
-
-Adjacency list format is useful for graphs without data associated
-with nodes or edges and for nodes that can be meaningfully represented
-as strings.
-
-Format
-------
-The adjacency list format consists of lines with node labels. The
-first label in a line is the source node. Further labels in the line
-are considered target nodes and are added to the graph along with an edge
-between the source node and target node.
-
-The graph with edges a-b, a-c, d-e can be represented as the following
-adjacency list (anything following the # in a line is a comment)::
-
- a b c # source target target
- d e
-"""
-__author__ = '\n'.join(['Aric Hagberg <hagberg@lanl.gov>',
- 'Dan Schult <dschult@colgate.edu>',
- 'Loïc Séguin-C. <loicseguin@gmail.com>'])
-# Copyright (C) 2004-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['generate_adjlist',
- 'write_adjlist',
- 'parse_adjlist',
- 'read_adjlist']
-
-from networkx.utils import make_str, open_file
-import networkx as nx
-
-
-def generate_adjlist(G, delimiter = ' '):
- """Generate a single line of the graph G in adjacency list format.
-
- Parameters
- ----------
- G : NetworkX graph
-
- delimiter : string, optional
- Separator for node labels
-
- Returns
- -------
- lines : string
- Lines of data in adjlist format.
-
- Examples
- --------
- >>> G = nx.lollipop_graph(4, 3)
- >>> for line in nx.generate_adjlist(G):
- ... print(line)
- 0 1 2 3
- 1 2 3
- 2 3
- 3 4
- 4 5
- 5 6
- 6
-
- See Also
- --------
- write_adjlist, read_adjlist
-
- """
- directed=G.is_directed()
- seen=set()
- for s,nbrs in G.adjacency_iter():
- line = make_str(s)+delimiter
- for t,data in nbrs.items():
- if not directed and t in seen:
- continue
- if G.is_multigraph():
- for d in data.values():
- line += make_str(t) + delimiter
- else:
- line += make_str(t) + delimiter
- if not directed:
- seen.add(s)
- yield line[:-len(delimiter)]
-
-@open_file(1,mode='wb')
-def write_adjlist(G, path, comments="#", delimiter=' ', encoding = 'utf-8'):
- """Write graph G in single-line adjacency-list format to path.
-
-
- Parameters
- ----------
- G : NetworkX graph
-
- path : string or file
- Filename or file handle for data output.
- Filenames ending in .gz or .bz2 will be compressed.
-
- comments : string, optional
- Marker for comment lines
-
- delimiter : string, optional
- Separator for node labels
-
- encoding : string, optional
- Text encoding.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_adjlist(G,"test.adjlist")
-
- The path can be a filehandle or a string with the name of the file. If a
- filehandle is provided, it has to be opened in 'wb' mode.
-
- >>> fh=open("test.adjlist",'wb')
- >>> nx.write_adjlist(G, fh)
-
- Notes
- -----
- This format does not store graph, node, or edge data.
-
- See Also
- --------
- read_adjlist, generate_adjlist
- """
- import sys
- import time
- pargs=comments + " ".join(sys.argv) + '\n'
- header = (pargs
- + comments + " GMT %s\n" % (time.asctime(time.gmtime()))
- + comments + " %s\n" % (G.name))
- path.write(header.encode(encoding))
-
- for line in generate_adjlist(G, delimiter):
- line+='\n'
- path.write(line.encode(encoding))
-
-
-def parse_adjlist(lines, comments = '#', delimiter = None,
- create_using = None, nodetype = None):
- """Parse lines of a graph adjacency list representation.
-
- Parameters
- ----------
- lines : list or iterator of strings
- Input data in adjlist format
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
- nodetype : Python type, optional
- Convert nodes to this type.
-
- comments : string, optional
- Marker for comment lines
-
- delimiter : string, optional
- Separator for node labels. The default is whitespace.
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
-
- Returns
- -------
- G: NetworkX graph
- The graph corresponding to the lines in adjacency list format.
-
- Examples
- --------
- >>> lines = ['1 2 5',
- ... '2 3 4',
- ... '3 5',
- ... '4',
- ... '5']
- >>> G = nx.parse_adjlist(lines, nodetype = int)
- >>> G.nodes()
- [1, 2, 3, 4, 5]
- >>> G.edges()
- [(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)]
-
- See Also
- --------
- read_adjlist
-
- """
- if create_using is None:
- G=nx.Graph()
- else:
- try:
- G=create_using
- G.clear()
- except:
- raise TypeError("Input graph is not a NetworkX graph type")
-
- for line in lines:
- p=line.find(comments)
- if p>=0:
- line = line[:p]
- if not len(line):
- continue
- vlist=line.strip().split(delimiter)
- u=vlist.pop(0)
- # convert types
- if nodetype is not None:
- try:
- u=nodetype(u)
- except:
- raise TypeError("Failed to convert node (%s) to type %s"\
- %(u,nodetype))
- G.add_node(u)
- if nodetype is not None:
- try:
- vlist=map(nodetype,vlist)
- except:
- raise TypeError("Failed to convert nodes (%s) to type %s"\
- %(','.join(vlist),nodetype))
- G.add_edges_from([(u, v) for v in vlist])
- return G
-
-@open_file(0,mode='rb')
-def read_adjlist(path, comments="#", delimiter=None, create_using=None,
- nodetype=None, encoding = 'utf-8'):
- """Read graph in adjacency list format from path.
-
- Parameters
- ----------
- path : string or file
- Filename or file handle to read.
- Filenames ending in .gz or .bz2 will be uncompressed.
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
- nodetype : Python type, optional
- Convert nodes to this type.
-
- comments : string, optional
- Marker for comment lines
-
- delimiter : string, optional
- Separator for node labels. The default is whitespace.
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
-
- Returns
- -------
- G: NetworkX graph
- The graph corresponding to the lines in adjacency list format.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_adjlist(G, "test.adjlist")
- >>> G=nx.read_adjlist("test.adjlist")
-
- The path can be a filehandle or a string with the name of the file. If a
- filehandle is provided, it has to be opened in 'rb' mode.
-
- >>> fh=open("test.adjlist", 'rb')
- >>> G=nx.read_adjlist(fh)
-
- Filenames ending in .gz or .bz2 will be compressed.
-
- >>> nx.write_adjlist(G,"test.adjlist.gz")
- >>> G=nx.read_adjlist("test.adjlist.gz")
-
- The optional nodetype is a function to convert node strings to nodetype.
-
- For example
-
- >>> G=nx.read_adjlist("test.adjlist", nodetype=int)
-
- will attempt to convert all nodes to integer type.
-
- Since nodes must be hashable, the function nodetype must return hashable
- types (e.g. int, float, str, frozenset - or tuples of those, etc.)
-
- The optional create_using parameter is a NetworkX graph container.
- The default is Graph(), an undirected graph. To read the data as
- a directed graph use
-
- >>> G=nx.read_adjlist("test.adjlist", create_using=nx.DiGraph())
-
- Notes
- -----
- This format does not store graph or node data.
-
- See Also
- --------
- write_adjlist
- """
- lines = (line.decode(encoding) for line in path)
- return parse_adjlist(lines,
- comments = comments,
- delimiter = delimiter,
- create_using = create_using,
- nodetype = nodetype)
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.adjlist')
- os.unlink('test.adjlist.gz')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/edgelist.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/edgelist.py
deleted file mode 100644
index 4a1aea9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/edgelist.py
+++ /dev/null
@@ -1,464 +0,0 @@
-"""
-**********
-Edge Lists
-**********
-Read and write NetworkX graphs as edge lists.
-
-The multi-line adjacency list format is useful for graphs with nodes
-that can be meaningfully represented as strings. With the edgelist
-format simple edge data can be stored but node or graph data is not.
-There is no way of representing isolated nodes unless the node has a
-self-loop edge.
-
-Format
-------
-You can read or write three formats of edge lists with these functions.
-
-Node pairs with no data::
-
- 1 2
-
-Python dictionary as data::
-
- 1 2 {'weight':7, 'color':'green'}
-
-Arbitrary data::
-
- 1 2 7 green
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nDan Schult (dschult@colgate.edu)"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['generate_edgelist',
- 'write_edgelist',
- 'parse_edgelist',
- 'read_edgelist',
- 'read_weighted_edgelist',
- 'write_weighted_edgelist']
-
-from networkx.utils import open_file, make_str
-import networkx as nx
-
-def generate_edgelist(G, delimiter=' ', data=True):
- """Generate a single line of the graph G in edge list format.
-
- Parameters
- ----------
- G : NetworkX graph
-
- delimiter : string, optional
- Separator for node labels
-
- data : bool or list of keys
- If False generate no edge data. If True use a dictionary
- representation of edge data. If a list of keys use a list of data
- values corresponding to the keys.
-
- Returns
- -------
- lines : string
- Lines of data in adjlist format.
-
- Examples
- --------
- >>> G = nx.lollipop_graph(4, 3)
- >>> G[1][2]['weight'] = 3
- >>> G[3][4]['capacity'] = 12
- >>> for line in nx.generate_edgelist(G, data=False):
- ... print(line)
- 0 1
- 0 2
- 0 3
- 1 2
- 1 3
- 2 3
- 3 4
- 4 5
- 5 6
-
- >>> for line in nx.generate_edgelist(G):
- ... print(line)
- 0 1 {}
- 0 2 {}
- 0 3 {}
- 1 2 {'weight': 3}
- 1 3 {}
- 2 3 {}
- 3 4 {'capacity': 12}
- 4 5 {}
- 5 6 {}
-
- >>> for line in nx.generate_edgelist(G,data=['weight']):
- ... print(line)
- 0 1
- 0 2
- 0 3
- 1 2 3
- 1 3
- 2 3
- 3 4
- 4 5
- 5 6
-
- See Also
- --------
- write_adjlist, read_adjlist
- """
- if data is True or data is False:
- for e in G.edges(data=data):
- yield delimiter.join(map(make_str,e))
- else:
- for u,v,d in G.edges(data=True):
- e=[u,v]
- try:
- e.extend(d[k] for k in data)
- except KeyError:
- pass # missing data for this edge, should warn?
- yield delimiter.join(map(make_str,e))
-
-@open_file(1,mode='wb')
-def write_edgelist(G, path, comments="#", delimiter=' ', data=True,
- encoding = 'utf-8'):
- """Write graph as a list of edges.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- path : file or string
- File or filename to write. If a file is provided, it must be
- opened in 'wb' mode. Filenames ending in .gz or .bz2 will be compressed.
- comments : string, optional
- The character used to indicate the start of a comment
- delimiter : string, optional
- The string used to separate values. The default is whitespace.
- data : bool or list, optional
- If False write no edge data.
- If True write a string representation of the edge data dictionary..
- If a list (or other iterable) is provided, write the keys specified
- in the list.
- encoding: string, optional
- Specify which encoding to use when writing file.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_edgelist(G, "test.edgelist")
- >>> G=nx.path_graph(4)
- >>> fh=open("test.edgelist",'wb')
- >>> nx.write_edgelist(G, fh)
- >>> nx.write_edgelist(G, "test.edgelist.gz")
- >>> nx.write_edgelist(G, "test.edgelist.gz", data=False)
-
- >>> G=nx.Graph()
- >>> G.add_edge(1,2,weight=7,color='red')
- >>> nx.write_edgelist(G,'test.edgelist',data=False)
- >>> nx.write_edgelist(G,'test.edgelist',data=['color'])
- >>> nx.write_edgelist(G,'test.edgelist',data=['color','weight'])
-
- See Also
- --------
- write_edgelist()
- write_weighted_edgelist()
- """
-
- for line in generate_edgelist(G, delimiter, data):
- line+='\n'
- path.write(line.encode(encoding))
-
-def parse_edgelist(lines, comments='#', delimiter=None,
- create_using=None, nodetype=None, data=True):
- """Parse lines of an edge list representation of a graph.
-
-
- Returns
- -------
- G: NetworkX Graph
- The graph corresponding to lines
- data : bool or list of (label,type) tuples
- If False generate no edge data or if True use a dictionary
- representation of edge data or a list tuples specifying dictionary
- key names and types for edge data.
- create_using: NetworkX graph container, optional
- Use given NetworkX graph for holding nodes or edges.
- nodetype : Python type, optional
- Convert nodes to this type.
- comments : string, optional
- Marker for comment lines
- delimiter : string, optional
- Separator for node labels
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
- Examples
- --------
- Edgelist with no data:
-
- >>> lines = ["1 2",
- ... "2 3",
- ... "3 4"]
- >>> G = nx.parse_edgelist(lines, nodetype = int)
- >>> G.nodes()
- [1, 2, 3, 4]
- >>> G.edges()
- [(1, 2), (2, 3), (3, 4)]
-
- Edgelist with data in Python dictionary representation:
-
- >>> lines = ["1 2 {'weight':3}",
- ... "2 3 {'weight':27}",
- ... "3 4 {'weight':3.0}"]
- >>> G = nx.parse_edgelist(lines, nodetype = int)
- >>> G.nodes()
- [1, 2, 3, 4]
- >>> G.edges(data = True)
- [(1, 2, {'weight': 3}), (2, 3, {'weight': 27}), (3, 4, {'weight': 3.0})]
-
- Edgelist with data in a list:
-
- >>> lines = ["1 2 3",
- ... "2 3 27",
- ... "3 4 3.0"]
- >>> G = nx.parse_edgelist(lines, nodetype = int, data=(('weight',float),))
- >>> G.nodes()
- [1, 2, 3, 4]
- >>> G.edges(data = True)
- [(1, 2, {'weight': 3.0}), (2, 3, {'weight': 27.0}), (3, 4, {'weight': 3.0})]
-
- See Also
- --------
- read_weighted_edgelist
-
- """
- from ast import literal_eval
- if create_using is None:
- G=nx.Graph()
- else:
- try:
- G=create_using
- G.clear()
- except:
- raise TypeError("create_using input is not a NetworkX graph type")
-
- for line in lines:
- p=line.find(comments)
- if p>=0:
- line = line[:p]
- if not len(line):
- continue
- # split line, should have 2 or more
- s=line.strip().split(delimiter)
- if len(s)<2:
- continue
- u=s.pop(0)
- v=s.pop(0)
- d=s
- if nodetype is not None:
- try:
- u=nodetype(u)
- v=nodetype(v)
- except:
- raise TypeError("Failed to convert nodes %s,%s to type %s."
- %(u,v,nodetype))
-
- if len(d)==0 or data is False:
- # no data or data type specified
- edgedata={}
- elif data is True:
- # no edge types specified
- try: # try to evaluate as dictionary
- edgedata=dict(literal_eval(' '.join(d)))
- except:
- raise TypeError(
- "Failed to convert edge data (%s) to dictionary."%(d))
- else:
- # convert edge data to dictionary with specified keys and type
- if len(d)!=len(data):
- raise IndexError(
- "Edge data %s and data_keys %s are not the same length"%
- (d, data))
- edgedata={}
- for (edge_key,edge_type),edge_value in zip(data,d):
- try:
- edge_value=edge_type(edge_value)
- except:
- raise TypeError(
- "Failed to convert %s data %s to type %s."
- %(edge_key, edge_value, edge_type))
- edgedata.update({edge_key:edge_value})
- G.add_edge(u, v, attr_dict=edgedata)
- return G
-
-@open_file(0,mode='rb')
-def read_edgelist(path, comments="#", delimiter=None, create_using=None,
- nodetype=None, data=True, edgetype=None, encoding='utf-8'):
- """Read a graph from a list of edges.
-
- Parameters
- ----------
- path : file or string
- File or filename to write. If a file is provided, it must be
- opened in 'rb' mode.
- Filenames ending in .gz or .bz2 will be uncompressed.
- comments : string, optional
- The character used to indicate the start of a comment.
- delimiter : string, optional
- The string used to separate values. The default is whitespace.
- create_using : Graph container, optional,
- Use specified container to build graph. The default is networkx.Graph,
- an undirected graph.
- nodetype : int, float, str, Python type, optional
- Convert node data from strings to specified type
- data : bool or list of (label,type) tuples
- Tuples specifying dictionary key names and types for edge data
- edgetype : int, float, str, Python type, optional OBSOLETE
- Convert edge data from strings to specified type and use as 'weight'
- encoding: string, optional
- Specify which encoding to use when reading file.
-
- Returns
- -------
- G : graph
- A networkx Graph or other type specified with create_using
-
- Examples
- --------
- >>> nx.write_edgelist(nx.path_graph(4), "test.edgelist")
- >>> G=nx.read_edgelist("test.edgelist")
-
- >>> fh=open("test.edgelist", 'rb')
- >>> G=nx.read_edgelist(fh)
- >>> fh.close()
-
- >>> G=nx.read_edgelist("test.edgelist", nodetype=int)
- >>> G=nx.read_edgelist("test.edgelist",create_using=nx.DiGraph())
-
- Edgelist with data in a list:
-
- >>> textline = '1 2 3'
- >>> fh = open('test.edgelist','w')
- >>> d = fh.write(textline)
- >>> fh.close()
- >>> G = nx.read_edgelist('test.edgelist', nodetype=int, data=(('weight',float),))
- >>> G.nodes()
- [1, 2]
- >>> G.edges(data = True)
- [(1, 2, {'weight': 3.0})]
-
- See parse_edgelist() for more examples of formatting.
-
- See Also
- --------
- parse_edgelist
-
- Notes
- -----
- Since nodes must be hashable, the function nodetype must return hashable
- types (e.g. int, float, str, frozenset - or tuples of those, etc.)
- """
- lines = (line.decode(encoding) for line in path)
- return parse_edgelist(lines,comments=comments, delimiter=delimiter,
- create_using=create_using, nodetype=nodetype,
- data=data)
-
-
-def write_weighted_edgelist(G, path, comments="#",
- delimiter=' ', encoding='utf-8'):
- """Write graph G as a list of edges with numeric weights.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- path : file or string
- File or filename to write. If a file is provided, it must be
- opened in 'wb' mode.
- Filenames ending in .gz or .bz2 will be compressed.
- comments : string, optional
- The character used to indicate the start of a comment
- delimiter : string, optional
- The string used to separate values. The default is whitespace.
- encoding: string, optional
- Specify which encoding to use when writing file.
-
- Examples
- --------
- >>> G=nx.Graph()
- >>> G.add_edge(1,2,weight=7)
- >>> nx.write_weighted_edgelist(G, 'test.weighted.edgelist')
-
- See Also
- --------
- read_edgelist()
- write_edgelist()
- write_weighted_edgelist()
-
- """
- write_edgelist(G,path, comments=comments, delimiter=delimiter,
- data=('weight',), encoding = encoding)
-
-def read_weighted_edgelist(path, comments="#", delimiter=None,
- create_using=None, nodetype=None, encoding='utf-8'):
-
- """Read a graph as list of edges with numeric weights.
-
- Parameters
- ----------
- path : file or string
- File or filename to write. If a file is provided, it must be
- opened in 'rb' mode.
- Filenames ending in .gz or .bz2 will be uncompressed.
- comments : string, optional
- The character used to indicate the start of a comment.
- delimiter : string, optional
- The string used to separate values. The default is whitespace.
- create_using : Graph container, optional,
- Use specified container to build graph. The default is networkx.Graph,
- an undirected graph.
- nodetype : int, float, str, Python type, optional
- Convert node data from strings to specified type
- encoding: string, optional
- Specify which encoding to use when reading file.
-
- Returns
- -------
- G : graph
- A networkx Graph or other type specified with create_using
-
- Notes
- -----
- Since nodes must be hashable, the function nodetype must return hashable
- types (e.g. int, float, str, frozenset - or tuples of those, etc.)
-
- Example edgelist file format.
-
- With numeric edge data::
-
- # read with
- # >>> G=nx.read_weighted_edgelist(fh)
- # source target data
- a b 1
- a c 3.14159
- d e 42
- """
- return read_edgelist(path,
- comments=comments,
- delimiter=delimiter,
- create_using=create_using,
- nodetype=nodetype,
- data=(('weight',float),),
- encoding = encoding
- )
-
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.edgelist')
- os.unlink('test.edgelist.gz')
- os.unlink('test.weighted.edgelist')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gexf.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gexf.py
deleted file mode 100644
index 88503ac..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gexf.py
+++ /dev/null
@@ -1,926 +0,0 @@
-"""
-****
-GEXF
-****
-Read and write graphs in GEXF format.
-
-GEXF (Graph Exchange XML Format) is a language for describing complex
-network structures, their associated data and dynamics.
-
-This implementation does not support mixed graphs (directed and
-undirected edges together).
-
-Format
-------
-GEXF is an XML format. See http://gexf.net/format/schema.html for the
-specification and http://gexf.net/format/basic.html for examples.
-"""
-# Copyright (C) 2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-# Based on GraphML NetworkX GraphML reader
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['write_gexf', 'read_gexf', 'relabel_gexf_graph', 'generate_gexf']
-import itertools
-import networkx as nx
-from networkx.utils import open_file, make_str
-try:
- from xml.etree.cElementTree import Element, ElementTree, tostring
-except ImportError:
- try:
- from xml.etree.ElementTree import Element, ElementTree, tostring
- except ImportError:
- pass
-
-@open_file(1,mode='wb')
-def write_gexf(G, path, encoding='utf-8',prettyprint=True,version='1.1draft'):
- """Write G in GEXF format to path.
-
- "GEXF (Graph Exchange XML Format) is a language for describing
- complex networks structures, their associated data and dynamics" [1]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- path : file or string
- File or file name to write.
- File names ending in .gz or .bz2 will be compressed.
- encoding : string (optional)
- Encoding for text data.
- prettyprint : bool (optional)
- If True use line breaks and indenting in output XML.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_gexf(G, "test.gexf")
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and undirected
- edges together).
-
- The node id attribute is set to be the string of the node label.
- If you want to specify an id use set it as node data, e.g.
- node['a']['id']=1 to set the id of node 'a' to 1.
-
- References
- ----------
- .. [1] GEXF graph format, http://gexf.net/format/
- """
- writer = GEXFWriter(encoding=encoding,prettyprint=prettyprint,
- version=version)
- writer.add_graph(G)
- writer.write(path)
-
-def generate_gexf(G, encoding='utf-8',prettyprint=True,version='1.1draft'):
- """Generate lines of GEXF format representation of G"
-
- "GEXF (Graph Exchange XML Format) is a language for describing
- complex networks structures, their associated data and dynamics" [1]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- encoding : string (optional)
- Encoding for text data.
- prettyprint : bool (optional)
- If True use line breaks and indenting in output XML.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> linefeed=chr(10) # linefeed=\n
- >>> s=linefeed.join(nx.generate_gexf(G)) # doctest: +SKIP
- >>> for line in nx.generate_gexf(G): # doctest: +SKIP
- ... print line
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and undirected
- edges together).
-
- The node id attribute is set to be the string of the node label.
- If you want to specify an id use set it as node data, e.g.
- node['a']['id']=1 to set the id of node 'a' to 1.
-
- References
- ----------
- .. [1] GEXF graph format, http://gexf.net/format/
- """
- writer = GEXFWriter(encoding=encoding,prettyprint=prettyprint,
- version=version)
- writer.add_graph(G)
- for line in str(writer).splitlines():
- yield line
-
-@open_file(0,mode='rb')
-def read_gexf(path,node_type=None,relabel=False,version='1.1draft'):
- """Read graph in GEXF format from path.
-
- "GEXF (Graph Exchange XML Format) is a language for describing
- complex networks structures, their associated data and dynamics" [1]_.
-
- Parameters
- ----------
- path : file or string
- File or file name to write.
- File names ending in .gz or .bz2 will be compressed.
-
- node_type: Python type (default: None)
- Convert node ids to this type if not None.
-
- relabel : bool (default: False)
- If True relabel the nodes to use the GEXF node "label" attribute
- instead of the node "id" attribute as the NetworkX node label.
-
- Returns
- -------
- graph: NetworkX graph
- If no parallel edges are found a Graph or DiGraph is returned.
- Otherwise a MultiGraph or MultiDiGraph is returned.
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and undirected
- edges together).
-
- References
- ----------
- .. [1] GEXF graph format, http://gexf.net/format/
- """
- reader = GEXFReader(node_type=node_type,version=version)
- if relabel:
- G=relabel_gexf_graph(reader(path))
- else:
- G=reader(path)
- return G
-
-class GEXF(object):
-# global register_namespace
-
- versions={}
- d={'NS_GEXF':"http://www.gexf.net/1.1draft",
- 'NS_VIZ':"http://www.gexf.net/1.1draft/viz",
- 'NS_XSI':"http://www.w3.org/2001/XMLSchema-instance",
- 'SCHEMALOCATION':' '.join(['http://www.gexf.net/1.1draft',
- 'http://www.gexf.net/1.1draft/gexf.xsd'
- ]),
- 'VERSION':'1.1'
- }
- versions['1.1draft']=d
- d={'NS_GEXF':"http://www.gexf.net/1.2draft",
- 'NS_VIZ':"http://www.gexf.net/1.2draft/viz",
- 'NS_XSI':"http://www.w3.org/2001/XMLSchema-instance",
- 'SCHEMALOCATION':' '.join(['http://www.gexf.net/1.2draft',
- 'http://www.gexf.net/1.2draft/gexf.xsd'
- ]),
- 'VERSION':'1.2'
- }
- versions['1.2draft']=d
-
-
- types=[(int,"integer"),
- (float,"float"),
- (float,"double"),
- (bool,"boolean"),
- (list,"string"),
- (dict,"string"),
- ]
-
- try: # Python 3.x
- blurb = chr(1245) # just to trigger the exception
- types.extend([
- (str,"liststring"),
- (str,"anyURI"),
- (str,"string")])
- except ValueError: # Python 2.6+
- types.extend([
- (str,"liststring"),
- (str,"anyURI"),
- (str,"string"),
- (unicode,"liststring"),
- (unicode,"anyURI"),
- (unicode,"string")])
-
- xml_type = dict(types)
- python_type = dict(reversed(a) for a in types)
- convert_bool={'true':True,'false':False}
-
-# try:
-# register_namespace = ET.register_namespace
-# except AttributeError:
-# def register_namespace(prefix, uri):
-# ET._namespace_map[uri] = prefix
-
-
- def set_version(self,version):
- d=self.versions.get(version)
- if d is None:
- raise nx.NetworkXError('Unknown GEXF version %s'%version)
- self.NS_GEXF = d['NS_GEXF']
- self.NS_VIZ = d['NS_VIZ']
- self.NS_XSI = d['NS_XSI']
- self.SCHEMALOCATION = d['NS_XSI']
- self.VERSION=d['VERSION']
- self.version=version
-
-# register_namespace('viz', d['NS_VIZ'])
-
-
-class GEXFWriter(GEXF):
- # class for writing GEXF format files
- # use write_gexf() function
- def __init__(self, graph=None, encoding="utf-8",
- mode='static',prettyprint=True,
- version='1.1draft'):
- try:
- import xml.etree.ElementTree
- except ImportError:
- raise ImportError('GEXF writer requires '
- 'xml.elementtree.ElementTree')
- self.prettyprint=prettyprint
- self.mode=mode
- self.encoding = encoding
- self.set_version(version)
- self.xml = Element("gexf",
- {'xmlns':self.NS_GEXF,
- 'xmlns:xsi':self.NS_XSI,
- 'xmlns:viz':self.NS_VIZ,
- 'xsi:schemaLocation':self.SCHEMALOCATION,
- 'version':self.VERSION})
-
- # counters for edge and attribute identifiers
- self.edge_id=itertools.count()
- self.attr_id=itertools.count()
- # default attributes are stored in dictionaries
- self.attr={}
- self.attr['node']={}
- self.attr['edge']={}
- self.attr['node']['dynamic']={}
- self.attr['node']['static']={}
- self.attr['edge']['dynamic']={}
- self.attr['edge']['static']={}
-
- if graph is not None:
- self.add_graph(graph)
-
- def __str__(self):
- if self.prettyprint:
- self.indent(self.xml)
- s=tostring(self.xml).decode(self.encoding)
- return s
-
- def add_graph(self, G):
- # Add a graph element to the XML
- if G.is_directed():
- default='directed'
- else:
- default='undirected'
- graph_element = Element("graph",defaultedgetype=default,mode=self.mode)
- self.graph_element=graph_element
- self.add_nodes(G,graph_element)
- self.add_edges(G,graph_element)
- self.xml.append(graph_element)
-
-
- def add_nodes(self, G, graph_element):
- nodes_element = Element('nodes')
- for node,data in G.nodes_iter(data=True):
- node_data=data.copy()
- node_id = make_str(node_data.pop('id', node))
- kw={'id':node_id}
- label = make_str(node_data.pop('label', node))
- kw['label']=label
- try:
- pid=node_data.pop('pid')
- kw['pid'] = make_str(pid)
- except KeyError:
- pass
-
- # add node element with attributes
- node_element = Element("node", **kw)
-
- # add node element and attr subelements
- default=G.graph.get('node_default',{})
- node_data=self.add_parents(node_element, node_data)
- if self.version=='1.1':
- node_data=self.add_slices(node_element, node_data)
- else:
- node_data=self.add_spells(node_element, node_data)
- node_data=self.add_viz(node_element,node_data)
- node_data=self.add_attributes("node", node_element,
- node_data, default)
- nodes_element.append(node_element)
- graph_element.append(nodes_element)
-
-
- def add_edges(self, G, graph_element):
- def edge_key_data(G):
- # helper function to unify multigraph and graph edge iterator
- if G.is_multigraph():
- for u,v,key,data in G.edges_iter(data=True,keys=True):
- edge_data=data.copy()
- edge_data.update(key=key)
- edge_id=edge_data.pop('id',None)
- if edge_id is None:
- edge_id=next(self.edge_id)
- yield u,v,edge_id,edge_data
- else:
- for u,v,data in G.edges_iter(data=True):
- edge_data=data.copy()
- edge_id=edge_data.pop('id',None)
- if edge_id is None:
- edge_id=next(self.edge_id)
- yield u,v,edge_id,edge_data
-
- edges_element = Element('edges')
- for u,v,key,edge_data in edge_key_data(G):
- kw={'id':make_str(key)}
- try:
- edge_weight=edge_data.pop('weight')
- kw['weight']=make_str(edge_weight)
- except KeyError:
- pass
- try:
- edge_type=edge_data.pop('type')
- kw['type']=make_str(edge_type)
- except KeyError:
- pass
- source_id = make_str(G.node[u].get('id', u))
- target_id = make_str(G.node[v].get('id', v))
- edge_element = Element("edge",
- source=source_id,target=target_id,
- **kw)
- default=G.graph.get('edge_default',{})
- edge_data=self.add_viz(edge_element,edge_data)
- edge_data=self.add_attributes("edge", edge_element,
- edge_data, default)
- edges_element.append(edge_element)
- graph_element.append(edges_element)
-
-
- def add_attributes(self, node_or_edge, xml_obj, data, default):
- # Add attrvalues to node or edge
- attvalues=Element('attvalues')
- if len(data)==0:
- return data
- if 'start' in data or 'end' in data:
- mode='dynamic'
- else:
- mode='static'
- for k,v in data.items():
- # rename generic multigraph key to avoid any name conflict
- if k == 'key':
- k='networkx_key'
- attr_id = self.get_attr_id(make_str(k), self.xml_type[type(v)],
- node_or_edge, default, mode)
- if type(v)==list:
- # dynamic data
- for val,start,end in v:
- e=Element("attvalue")
- e.attrib['for']=attr_id
- e.attrib['value']=make_str(val)
- if start is not None:
- e.attrib['start']=make_str(start)
- if end is not None:
- e.attrib['end']=make_str(end)
- attvalues.append(e)
- else:
- # static data
- e=Element("attvalue")
- e.attrib['for']=attr_id
- e.attrib['value']=make_str(v)
- attvalues.append(e)
- xml_obj.append(attvalues)
- return data
-
- def get_attr_id(self, title, attr_type, edge_or_node, default, mode):
- # find the id of the attribute or generate a new id
- try:
- return self.attr[edge_or_node][mode][title]
- except KeyError:
- # generate new id
- new_id=str(next(self.attr_id))
- self.attr[edge_or_node][mode][title] = new_id
- attr_kwargs = {"id":new_id, "title":title, "type":attr_type}
- attribute=Element("attribute",**attr_kwargs)
- # add subelement for data default value if present
- default_title=default.get(title)
- if default_title is not None:
- default_element=Element("default")
- default_element.text=make_str(default_title)
- attribute.append(default_element)
- # new insert it into the XML
- attributes_element=None
- for a in self.graph_element.findall("attributes"):
- # find existing attributes element by class and mode
- a_class=a.get('class')
- a_mode=a.get('mode','static') # default mode is static
- if a_class==edge_or_node and a_mode==mode:
- attributes_element=a
- if attributes_element is None:
- # create new attributes element
- attr_kwargs = {"mode":mode,"class":edge_or_node}
- attributes_element=Element('attributes', **attr_kwargs)
- self.graph_element.insert(0,attributes_element)
- attributes_element.append(attribute)
- return new_id
-
-
- def add_viz(self,element,node_data):
- viz=node_data.pop('viz',False)
- if viz:
- color=viz.get('color')
- if color is not None:
- if self.VERSION=='1.1':
- e=Element("{%s}color"%self.NS_VIZ,
- r=str(color.get('r')),
- g=str(color.get('g')),
- b=str(color.get('b')),
- )
- else:
- e=Element("{%s}color"%self.NS_VIZ,
- r=str(color.get('r')),
- g=str(color.get('g')),
- b=str(color.get('b')),
- a=str(color.get('a')),
- )
- element.append(e)
-
- size=viz.get('size')
- if size is not None:
- e=Element("{%s}size"%self.NS_VIZ,value=str(size))
- element.append(e)
-
- thickness=viz.get('thickness')
- if thickness is not None:
- e=Element("{%s}thickness"%self.NS_VIZ,value=str(thickness))
- element.append(e)
-
- shape=viz.get('shape')
- if shape is not None:
- if shape.startswith('http'):
- e=Element("{%s}shape"%self.NS_VIZ,
- value='image',uri=str(shape))
- else:
- e=Element("{%s}shape"%self.NS_VIZ,value=str(shape))
- element.append(e)
-
- position=viz.get('position')
- if position is not None:
- e=Element("{%s}position"%self.NS_VIZ,
- x=str(position.get('x')),
- y=str(position.get('y')),
- z=str(position.get('z')),
- )
- element.append(e)
- return node_data
-
- def add_parents(self,node_element,node_data):
- parents=node_data.pop('parents',False)
- if parents:
- parents_element=Element('parents')
- for p in parents:
- e=Element('parent')
- e.attrib['for']=str(p)
- parents_element.append(e)
- node_element.append(parents_element)
- return node_data
-
- def add_slices(self,node_element,node_data):
- slices=node_data.pop('slices',False)
- if slices:
- slices_element=Element('slices')
- for start,end in slices:
- e=Element('slice',start=str(start),end=str(end))
- slices_element.append(e)
- node_element.append(slices_element)
- return node_data
-
-
- def add_spells(self,node_element,node_data):
- spells=node_data.pop('spells',False)
- if spells:
- spells_element=Element('spells')
- for start,end in spells:
- e=Element('spell')
- if start is not None:
- e.attrib['start']=make_str(start)
- if end is not None:
- e.attrib['end']=make_str(end)
- spells_element.append(e)
- node_element.append(spells_element)
- return node_data
-
-
- def write(self, fh):
- # Serialize graph G in GEXF to the open fh
- if self.prettyprint:
- self.indent(self.xml)
- document = ElementTree(self.xml)
- header='<?xml version="1.0" encoding="%s"?>'%self.encoding
- fh.write(header.encode(self.encoding))
- document.write(fh, encoding=self.encoding)
-
-
- def indent(self, elem, level=0):
- # in-place prettyprint formatter
- i = "\n" + level*" "
- if len(elem):
- if not elem.text or not elem.text.strip():
- elem.text = i + " "
- if not elem.tail or not elem.tail.strip():
- elem.tail = i
- for elem in elem:
- self.indent(elem, level+1)
- if not elem.tail or not elem.tail.strip():
- elem.tail = i
- else:
- if level and (not elem.tail or not elem.tail.strip()):
- elem.tail = i
-
-
-class GEXFReader(GEXF):
- # Class to read GEXF format files
- # use read_gexf() function
- def __init__(self, node_type=None,version='1.1draft'):
- try:
- import xml.etree.ElementTree
- except ImportError:
- raise ImportError('GEXF reader requires '
- 'xml.elementtree.ElementTree')
- self.node_type=node_type
- # assume simple graph and test for multigraph on read
- self.simple_graph=True
- self.set_version(version)
-
- def __call__(self, stream):
- self.xml = ElementTree(file=stream)
- g=self.xml.find("{%s}graph" % self.NS_GEXF)
- if g is not None:
- return self.make_graph(g)
- # try all the versions
- for version in self.versions:
- self.set_version(version)
- g=self.xml.find("{%s}graph" % self.NS_GEXF)
- if g is not None:
- return self.make_graph(g)
- raise nx.NetworkXError("No <graph> element in GEXF file")
-
-
- def make_graph(self, graph_xml):
- # mode is "static" or "dynamic"
- graph_mode = graph_xml.get("mode", "")
- self.dynamic=(graph_mode=='dynamic')
-
- # start with empty DiGraph or MultiDiGraph
- edgedefault = graph_xml.get("defaultedgetype", None)
- if edgedefault=='directed':
- G=nx.MultiDiGraph()
- else:
- G=nx.MultiGraph()
-
- # graph attributes
- graph_start=graph_xml.get('start')
- if graph_start is not None:
- G.graph['start']=graph_start
- graph_end=graph_xml.get('end')
- if graph_end is not None:
- G.graph['end']=graph_end
-
- # node and edge attributes
- attributes_elements=graph_xml.findall("{%s}attributes"%self.NS_GEXF)
- # dictionaries to hold attributes and attribute defaults
- node_attr={}
- node_default={}
- edge_attr={}
- edge_default={}
- for a in attributes_elements:
- attr_class = a.get("class")
- if attr_class=='node':
- na,nd = self.find_gexf_attributes(a)
- node_attr.update(na)
- node_default.update(nd)
- G.graph['node_default']=node_default
- elif attr_class=='edge':
- ea,ed = self.find_gexf_attributes(a)
- edge_attr.update(ea)
- edge_default.update(ed)
- G.graph['edge_default']=edge_default
- else:
- raise # unknown attribute class
-
- # Hack to handle Gephi0.7beta bug
- # add weight attribute
- ea={'weight':{'type': 'double', 'mode': 'static', 'title': 'weight'}}
- ed={}
- edge_attr.update(ea)
- edge_default.update(ed)
- G.graph['edge_default']=edge_default
-
- # add nodes
- nodes_element=graph_xml.find("{%s}nodes" % self.NS_GEXF)
- if nodes_element is not None:
- for node_xml in nodes_element.findall("{%s}node" % self.NS_GEXF):
- self.add_node(G, node_xml, node_attr)
-
- # add edges
- edges_element=graph_xml.find("{%s}edges" % self.NS_GEXF)
- if edges_element is not None:
- for edge_xml in edges_element.findall("{%s}edge" % self.NS_GEXF):
- self.add_edge(G, edge_xml, edge_attr)
-
- # switch to Graph or DiGraph if no parallel edges were found.
- if self.simple_graph:
- if G.is_directed():
- G=nx.DiGraph(G)
- else:
- G=nx.Graph(G)
- return G
-
- def add_node(self, G, node_xml, node_attr, node_pid=None):
- # add a single node with attributes to the graph
-
- # get attributes and subattributues for node
- data = self.decode_attr_elements(node_attr, node_xml)
- data = self.add_parents(data, node_xml) # add any parents
- if self.version=='1.1':
- data = self.add_slices(data, node_xml) # add slices
- else:
- data = self.add_spells(data, node_xml) # add spells
- data = self.add_viz(data, node_xml) # add viz
- data = self.add_start_end(data, node_xml) # add start/end
-
- # find the node id and cast it to the appropriate type
- node_id = node_xml.get("id")
- if self.node_type is not None:
- node_id=self.node_type(node_id)
-
- # every node should have a label
- node_label = node_xml.get("label")
- data['label']=node_label
-
- # parent node id
- node_pid = node_xml.get("pid", node_pid)
- if node_pid is not None:
- data['pid']=node_pid
-
- # check for subnodes, recursive
- subnodes=node_xml.find("{%s}nodes" % self.NS_GEXF)
- if subnodes is not None:
- for node_xml in subnodes.findall("{%s}node" % self.NS_GEXF):
- self.add_node(G, node_xml, node_attr, node_pid=node_id)
-
- G.add_node(node_id, data)
-
- def add_start_end(self, data, xml):
- # start and end times
- node_start = xml.get("start")
- if node_start is not None:
- data['start']=node_start
- node_end = xml.get("end")
- if node_end is not None:
- data['end']=node_end
- return data
-
-
- def add_viz(self, data, node_xml):
- # add viz element for node
- viz={}
- color=node_xml.find("{%s}color"%self.NS_VIZ)
- if color is not None:
- if self.VERSION=='1.1':
- viz['color']={'r':int(color.get('r')),
- 'g':int(color.get('g')),
- 'b':int(color.get('b'))}
- else:
- viz['color']={'r':int(color.get('r')),
- 'g':int(color.get('g')),
- 'b':int(color.get('b')),
- 'a':float(color.get('a', 1)),
- }
-
- size=node_xml.find("{%s}size"%self.NS_VIZ)
- if size is not None:
- viz['size']=float(size.get('value'))
-
- thickness=node_xml.find("{%s}thickness"%self.NS_VIZ)
- if thickness is not None:
- viz['thickness']=float(thickness.get('value'))
-
- shape=node_xml.find("{%s}shape"%self.NS_VIZ)
- if shape is not None:
- viz['shape']=shape.get('shape')
- if viz['shape']=='image':
- viz['shape']=shape.get('uri')
-
- position=node_xml.find("{%s}position"%self.NS_VIZ)
- if position is not None:
- viz['position']={'x':float(position.get('x',0)),
- 'y':float(position.get('y',0)),
- 'z':float(position.get('z',0))}
-
- if len(viz)>0:
- data['viz']=viz
- return data
-
- def add_parents(self, data, node_xml):
- parents_element=node_xml.find("{%s}parents"%self.NS_GEXF)
- if parents_element is not None:
- data['parents']=[]
- for p in parents_element.findall("{%s}parent"%self.NS_GEXF):
- parent=p.get('for')
- data['parents'].append(parent)
- return data
-
- def add_slices(self, data, node_xml):
- slices_element=node_xml.find("{%s}slices"%self.NS_GEXF)
- if slices_element is not None:
- data['slices']=[]
- for s in slices_element.findall("{%s}slice"%self.NS_GEXF):
- start=s.get('start')
- end=s.get('end')
- data['slices'].append((start,end))
- return data
-
- def add_spells(self, data, node_xml):
- spells_element=node_xml.find("{%s}spells"%self.NS_GEXF)
- if spells_element is not None:
- data['spells']=[]
- for s in spells_element.findall("{%s}spell"%self.NS_GEXF):
- start=s.get('start')
- end=s.get('end')
- data['spells'].append((start,end))
- return data
-
-
- def add_edge(self, G, edge_element, edge_attr):
- # add an edge to the graph
-
- # raise error if we find mixed directed and undirected edges
- edge_direction = edge_element.get("type")
- if G.is_directed() and edge_direction=='undirected':
- raise nx.NetworkXError(\
- "Undirected edge found in directed graph.")
- if (not G.is_directed()) and edge_direction=='directed':
- raise nx.NetworkXError(\
- "Directed edge found in undirected graph.")
-
- # Get source and target and recast type if required
- source = edge_element.get("source")
- target = edge_element.get("target")
- if self.node_type is not None:
- source=self.node_type(source)
- target=self.node_type(target)
-
- data = self.decode_attr_elements(edge_attr, edge_element)
- data = self.add_start_end(data,edge_element)
-
- # GEXF stores edge ids as an attribute
- # NetworkX uses them as keys in multigraphs
- # if networkx_key is not specified as an attribute
- edge_id = edge_element.get("id")
- if edge_id is not None:
- data["id"] = edge_id
-
- # check if there is a 'multigraph_key' and use that as edge_id
- multigraph_key = data.pop('networkx_key',None)
- if multigraph_key is not None:
- edge_id=multigraph_key
-
- weight = edge_element.get('weight')
- if weight is not None:
- data['weight']=float(weight)
-
- edge_label = edge_element.get("label")
- if edge_label is not None:
- data['label']=edge_label
-
-
-
- if G.has_edge(source,target):
- # seen this edge before - this is a multigraph
- self.simple_graph=False
- G.add_edge(source, target, key=edge_id, **data)
- if edge_direction=='mutual':
- G.add_edge(target, source, key=edge_id, **data)
-
- def decode_attr_elements(self, gexf_keys, obj_xml):
- # Use the key information to decode the attr XML
- attr = {}
- # look for outer "<attvalues>" element
- attr_element=obj_xml.find("{%s}attvalues" % self.NS_GEXF)
- if attr_element is not None:
- # loop over <attvalue> elements
- for a in attr_element.findall("{%s}attvalue" % self.NS_GEXF):
- key = a.get('for') # for is required
- try: # should be in our gexf_keys dictionary
- title=gexf_keys[key]['title']
- except KeyError:
- raise nx.NetworkXError("No attribute defined for=%s"%key)
- atype=gexf_keys[key]['type']
- value=a.get('value')
- if atype=='boolean':
- value=self.convert_bool[value]
- else:
- value=self.python_type[atype](value)
- if gexf_keys[key]['mode']=='dynamic':
- # for dynamic graphs use list of three-tuples
- # [(value1,start1,end1), (value2,start2,end2), etc]
- start=a.get('start')
- end=a.get('end')
- if title in attr:
- attr[title].append((value,start,end))
- else:
- attr[title]=[(value,start,end)]
- else:
- # for static graphs just assign the value
- attr[title] = value
- return attr
-
- def find_gexf_attributes(self, attributes_element):
- # Extract all the attributes and defaults
- attrs = {}
- defaults = {}
- mode=attributes_element.get('mode')
- for k in attributes_element.findall("{%s}attribute" % self.NS_GEXF):
- attr_id = k.get("id")
- title=k.get('title')
- atype=k.get('type')
- attrs[attr_id]={'title':title,'type':atype,'mode':mode}
- # check for the "default" subelement of key element and add
- default=k.find("{%s}default" % self.NS_GEXF)
- if default is not None:
- if atype=='boolean':
- value=self.convert_bool[default.text]
- else:
- value=self.python_type[atype](default.text)
- defaults[title]=value
- return attrs,defaults
-
-
-def relabel_gexf_graph(G):
- """Relabel graph using "label" node keyword for node label.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph read from GEXF data
-
- Returns
- -------
- H : graph
- A NetworkX graph with relabed nodes
-
- Notes
- -----
- This function relabels the nodes in a NetworkX graph with the
- "label" attribute. It also handles relabeling the specific GEXF
- node attributes "parents", and "pid".
- """
- # build mapping of node labels, do some error checking
- try:
- mapping=[(u,G.node[u]['label']) for u in G]
- except KeyError:
- raise nx.NetworkXError('Failed to relabel nodes: '
- 'missing node labels found. '
- 'Use relabel=False.')
- x,y=zip(*mapping)
- if len(set(y))!=len(G):
- raise nx.NetworkXError('Failed to relabel nodes: '
- 'duplicate node labels found. '
- 'Use relabel=False.')
- mapping=dict(mapping)
- H=nx.relabel_nodes(G,mapping)
- # relabel attributes
- for n in G:
- m=mapping[n]
- H.node[m]['id']=n
- H.node[m].pop('label')
- if 'pid' in H.node[m]:
- H.node[m]['pid']=mapping[G.node[n]['pid']]
- if 'parents' in H.node[m]:
- H.node[m]['parents']=[mapping[p] for p in G.node[n]['parents']]
- return H
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import xml.etree.cElementTree
- except:
- raise SkipTest("xml.etree.cElementTree not available")
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- try:
- os.unlink('test.gexf')
- except:
- pass
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gml.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gml.py
deleted file mode 100644
index d248eb4..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gml.py
+++ /dev/null
@@ -1,410 +0,0 @@
-"""
-Read graphs in GML format.
-
-"GML, the G>raph Modelling Language, is our proposal for a portable
-file format for graphs. GML's key features are portability, simple
-syntax, extensibility and flexibility. A GML file consists of a
-hierarchical key-value lists. Graphs can be annotated with arbitrary
-data structures. The idea for a common file format was born at the
-GD'95; this proposal is the outcome of many discussions. GML is the
-standard file format in the Graphlet graph editor system. It has been
-overtaken and adapted by several other systems for drawing graphs."
-
-See http://www.infosun.fim.uni-passau.de/Graphlet/GML/gml-tr.html
-
-Requires pyparsing: http://pyparsing.wikispaces.com/
-
-Format
-------
-See http://www.infosun.fim.uni-passau.de/Graphlet/GML/gml-tr.html
-for format specification.
-
-Example graphs in GML format:
-http://www-personal.umich.edu/~mejn/netdata/
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-# Copyright (C) 2008-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['read_gml', 'parse_gml', 'generate_gml', 'write_gml']
-
-import networkx as nx
-from networkx.exception import NetworkXError
-from networkx.utils import is_string_like, open_file
-
-
-@open_file(0,mode='rb')
-def read_gml(path,encoding='UTF-8',relabel=False):
- """Read graph in GML format from path.
-
- Parameters
- ----------
- path : filename or filehandle
- The filename or filehandle to read from.
-
- encoding : string, optional
- Text encoding.
-
- relabel : bool, optional
- If True use the GML node label attribute for node names otherwise use
- the node id.
-
- Returns
- -------
- G : MultiGraph or MultiDiGraph
-
- Raises
- ------
- ImportError
- If the pyparsing module is not available.
-
- See Also
- --------
- write_gml, parse_gml
-
- Notes
- -----
- Requires pyparsing: http://pyparsing.wikispaces.com/
-
- References
- ----------
- GML specification:
- http://www.infosun.fim.uni-passau.de/Graphlet/GML/gml-tr.html
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_gml(G,'test.gml')
- >>> H=nx.read_gml('test.gml')
- """
- lines=(line.decode(encoding) for line in path)
- G=parse_gml(lines,relabel=relabel)
- return G
-
-def parse_gml(lines, relabel=True):
- """Parse GML graph from a string or iterable.
-
- Parameters
- ----------
- lines : string or iterable
- Data in GML format.
-
- relabel : bool, optional
- If True use the GML node label attribute for node names otherwise use
- the node id.
-
- Returns
- -------
- G : MultiGraph or MultiDiGraph
-
- Raises
- ------
- ImportError
- If the pyparsing module is not available.
-
- See Also
- --------
- write_gml, read_gml
-
- Notes
- -----
- This stores nested GML attributes as dictionaries in the
- NetworkX graph, node, and edge attribute structures.
-
- Requires pyparsing: http://pyparsing.wikispaces.com/
-
- References
- ----------
- GML specification:
- http://www.infosun.fim.uni-passau.de/Graphlet/GML/gml-tr.html
- """
- try:
- from pyparsing import ParseException
- except ImportError:
- try:
- from matplotlib.pyparsing import ParseException
- except:
- raise ImportError('Import Error: not able to import pyparsing:',
- 'http://pyparsing.wikispaces.com/')
- try:
- data = "".join(lines)
- gml = pyparse_gml()
- tokens =gml.parseString(data)
- except ParseException as err:
- print((err.line))
- print((" "*(err.column-1) + "^"))
- print(err)
- raise
-
- # function to recursively make dicts of key/value pairs
- def wrap(tok):
- listtype=type(tok)
- result={}
- for k,v in tok:
- if type(v)==listtype:
- result[str(k)]=wrap(v)
- else:
- result[str(k)]=v
- return result
-
- # Set flag
- multigraph=False
- # but assume multigraphs to start
- if tokens.directed==1:
- G=nx.MultiDiGraph()
- else:
- G=nx.MultiGraph()
-
- for k,v in tokens.asList():
- if k=="node":
- vdict=wrap(v)
- node=vdict['id']
- G.add_node(node,attr_dict=vdict)
- elif k=="edge":
- vdict=wrap(v)
- source=vdict.pop('source')
- target=vdict.pop('target')
- if G.has_edge(source,target):
- multigraph=True
- G.add_edge(source,target,attr_dict=vdict)
- else:
- G.graph[k]=v
-
- # switch to Graph or DiGraph if no parallel edges were found.
- if not multigraph:
- if G.is_directed():
- G=nx.DiGraph(G)
- else:
- G=nx.Graph(G)
-
- if relabel:
- # relabel, but check for duplicate labels first
- mapping=[(n,d['label']) for n,d in G.node.items()]
- x,y=zip(*mapping)
- if len(set(y))!=len(G):
- raise NetworkXError('Failed to relabel nodes: '
- 'duplicate node labels found. '
- 'Use relabel=False.')
- G=nx.relabel_nodes(G,dict(mapping))
- return G
-
-
-def pyparse_gml():
- """A pyparsing tokenizer for GML graph format.
-
- This is not intended to be called directly.
-
- See Also
- --------
- write_gml, read_gml, parse_gml
- """
- try:
- from pyparsing import \
- Literal, CaselessLiteral, Word, Forward,\
- ZeroOrMore, Group, Dict, Optional, Combine,\
- ParseException, restOfLine, White, alphas, alphanums, nums,\
- OneOrMore,quotedString,removeQuotes,dblQuotedString, Regex
- except ImportError:
- try:
- from matplotlib.pyparsing import \
- Literal, CaselessLiteral, Word, Forward,\
- ZeroOrMore, Group, Dict, Optional, Combine,\
- ParseException, restOfLine, White, alphas, alphanums, nums,\
- OneOrMore,quotedString,removeQuotes,dblQuotedString, Regex
- except:
- raise ImportError('pyparsing not found',
- 'http://pyparsing.wikispaces.com/')
-
- lbrack = Literal("[").suppress()
- rbrack = Literal("]").suppress()
- pound = ("#")
- comment = pound + Optional( restOfLine )
- integer = Word(nums+'-').setParseAction(lambda s,l,t:[ int(t[0])])
- real = Regex(r"[+-]?\d+\.\d*([eE][+-]?\d+)?").setParseAction(
- lambda s,l,t:[ float(t[0]) ])
- dblQuotedString.setParseAction( removeQuotes )
- key = Word(alphas,alphanums+'_')
- value_atom = (real | integer | Word(alphanums) | dblQuotedString)
- value = Forward() # to be defined later with << operator
- keyvalue = Group(key+value)
- value << (value_atom | Group( lbrack + ZeroOrMore(keyvalue) + rbrack ))
- node = Group(Literal("node") + lbrack + Group(OneOrMore(keyvalue)) + rbrack)
- edge = Group(Literal("edge") + lbrack + Group(OneOrMore(keyvalue)) + rbrack)
-
- creator = Group(Literal("Creator")+ Optional( restOfLine ))
- version = Group(Literal("Version")+ Optional( restOfLine ))
- graphkey = Literal("graph").suppress()
-
- graph = Dict (Optional(creator)+Optional(version)+\
- graphkey + lbrack + ZeroOrMore( (node|edge|keyvalue) ) + rbrack )
- graph.ignore(comment)
-
- return graph
-
-def generate_gml(G):
- """Generate a single entry of the graph G in GML format.
-
- Parameters
- ----------
- G : NetworkX graph
-
- Returns
- -------
- lines: string
- Lines in GML format.
-
- Notes
- -----
- This implementation does not support all Python data types as GML
- data. Nodes, node attributes, edge attributes, and graph
- attributes must be either dictionaries or single stings or
- numbers. If they are not an attempt is made to represent them as
- strings. For example, a list as edge data
- G[1][2]['somedata']=[1,2,3], will be represented in the GML file
- as::
-
- edge [
- source 1
- target 2
- somedata "[1, 2, 3]"
- ]
- """
- # recursively make dicts into gml brackets
- def listify(d,indent,indentlevel):
- result='[ \n'
- for k,v in d.items():
- if type(v)==dict:
- v=listify(v,indent,indentlevel+1)
- result += (indentlevel+1)*indent + \
- string_item(k,v,indentlevel*indent)+'\n'
- return result+indentlevel*indent+"]"
-
- def string_item(k,v,indent):
- # try to make a string of the data
- if type(v)==dict:
- v=listify(v,indent,2)
- elif is_string_like(v):
- v='"%s"'%v
- elif type(v)==bool:
- v=int(v)
- return "%s %s"%(k,v)
-
- # check for attributes or assign empty dict
- if hasattr(G,'graph_attr'):
- graph_attr=G.graph_attr
- else:
- graph_attr={}
- if hasattr(G,'node_attr'):
- node_attr=G.node_attr
- else:
- node_attr={}
-
- indent=2*' '
- count=iter(range(len(G)))
- node_id={}
-
- yield "graph ["
- if G.is_directed():
- yield indent+"directed 1"
- # write graph attributes
- for k,v in G.graph.items():
- if k == 'directed':
- continue
- yield indent+string_item(k,v,indent)
- # write nodes
- for n in G:
- yield indent+"node ["
- # get id or assign number
- nid=G.node[n].get('id',next(count))
- node_id[n]=nid
- yield 2*indent+"id %s"%nid
- label=G.node[n].get('label',n)
- if is_string_like(label):
- label='"%s"'%label
- yield 2*indent+'label %s'%label
- if n in G:
- for k,v in G.node[n].items():
- if k=='id' or k == 'label': continue
- yield 2*indent+string_item(k,v,indent)
- yield indent+"]"
- # write edges
- for u,v,edgedata in G.edges_iter(data=True):
- yield indent+"edge ["
- yield 2*indent+"source %s"%node_id[u]
- yield 2*indent+"target %s"%node_id[v]
- for k,v in edgedata.items():
- if k=='source': continue
- if k=='target': continue
- yield 2*indent+string_item(k,v,indent)
- yield indent+"]"
- yield "]"
-
-@open_file(1,mode='wb')
-def write_gml(G, path):
- """
- Write the graph G in GML format to the file or file handle path.
-
- Parameters
- ----------
- path : filename or filehandle
- The filename or filehandle to write. Filenames ending in
- .gz or .gz2 will be compressed.
-
- See Also
- --------
- read_gml, parse_gml
-
- Notes
- -----
- GML specifications indicate that the file should only use
- 7bit ASCII text encoding.iso8859-1 (latin-1).
-
- This implementation does not support all Python data types as GML
- data. Nodes, node attributes, edge attributes, and graph
- attributes must be either dictionaries or single stings or
- numbers. If they are not an attempt is made to represent them as
- strings. For example, a list as edge data
- G[1][2]['somedata']=[1,2,3], will be represented in the GML file
- as::
-
- edge [
- source 1
- target 2
- somedata "[1, 2, 3]"
- ]
-
-
- Examples
- ---------
- >>> G=nx.path_graph(4)
- >>> nx.write_gml(G,"test.gml")
-
- Filenames ending in .gz or .bz2 will be compressed.
-
- >>> nx.write_gml(G,"test.gml.gz")
- """
- for line in generate_gml(G):
- line+='\n'
- path.write(line.encode('latin-1'))
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import pyparsing
- except:
- try:
- import matplotlib.pyparsing
- except:
- raise SkipTest("pyparsing not available")
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.gml')
- os.unlink('test.gml.gz')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gpickle.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gpickle.py
deleted file mode 100644
index 688a35c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/gpickle.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""
-**************
-Pickled Graphs
-**************
-Read and write NetworkX graphs as Python pickles.
-
-"The pickle module implements a fundamental, but powerful algorithm
-for serializing and de-serializing a Python object
-structure. "Pickling" is the process whereby a Python object hierarchy
-is converted into a byte stream, and "unpickling" is the inverse
-operation, whereby a byte stream is converted back into an object
-hierarchy."
-
-Note that NetworkX graphs can contain any hashable Python object as
-node (not just integers and strings). For arbitrary data types it may
-be difficult to represent the data as text. In that case using Python
-pickles to store the graph data can be used.
-
-Format
-------
-See http://docs.python.org/library/pickle.html
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)\nDan Schult (dschult@colgate.edu)"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['read_gpickle', 'write_gpickle']
-
-import networkx as nx
-from networkx.utils import open_file
-
-try:
- import cPickle as pickle
-except ImportError:
- import pickle
-
-@open_file(1,mode='wb')
-def write_gpickle(G, path):
- """Write graph in Python pickle format.
-
- Pickles are a serialized byte stream of a Python object [1]_.
- This format will preserve Python objects used as nodes or edges.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be compressed.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_gpickle(G,"test.gpickle")
-
- References
- ----------
- .. [1] http://docs.python.org/library/pickle.html
- """
- pickle.dump(G, path, pickle.HIGHEST_PROTOCOL)
-
-@open_file(0,mode='rb')
-def read_gpickle(path):
- """Read graph object in Python pickle format.
-
- Pickles are a serialized byte stream of a Python object [1]_.
- This format will preserve Python objects used as nodes or edges.
-
- Parameters
- ----------
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be uncompressed.
-
- Returns
- -------
- G : graph
- A NetworkX graph
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_gpickle(G,"test.gpickle")
- >>> G=nx.read_gpickle("test.gpickle")
-
- References
- ----------
- .. [1] http://docs.python.org/library/pickle.html
- """
- return pickle.load(path)
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.gpickle')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/graphml.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/graphml.py
deleted file mode 100644
index 7f896e0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/graphml.py
+++ /dev/null
@@ -1,579 +0,0 @@
-"""
-*******
-GraphML
-*******
-Read and write graphs in GraphML format.
-
-This implementation does not support mixed graphs (directed and unidirected
-edges together), hyperedges, nested graphs, or ports.
-
-"GraphML is a comprehensive and easy-to-use file format for graphs. It
-consists of a language core to describe the structural properties of a
-graph and a flexible extension mechanism to add application-specific
-data. Its main features include support of
-
- * directed, undirected, and mixed graphs,
- * hypergraphs,
- * hierarchical graphs,
- * graphical representations,
- * references to external data,
- * application-specific attribute data, and
- * light-weight parsers.
-
-Unlike many other file formats for graphs, GraphML does not use a
-custom syntax. Instead, it is based on XML and hence ideally suited as
-a common denominator for all kinds of services generating, archiving,
-or processing graphs."
-
-http://graphml.graphdrawing.org/
-
-Format
-------
-GraphML is an XML format. See
-http://graphml.graphdrawing.org/specification.html for the specification and
-http://graphml.graphdrawing.org/primer/graphml-primer.html
-for examples.
-"""
-__author__ = """\n""".join(['Salim Fadhley',
- 'Aric Hagberg (hagberg@lanl.gov)'
- ])
-
-__all__ = ['write_graphml', 'read_graphml', 'generate_graphml',
- 'parse_graphml', 'GraphMLWriter', 'GraphMLReader']
-
-import networkx as nx
-from networkx.utils import open_file, make_str
-import warnings
-try:
- from xml.etree.cElementTree import Element, ElementTree, tostring, fromstring
-except ImportError:
- try:
- from xml.etree.ElementTree import Element, ElementTree, tostring, fromstring
- except ImportError:
- pass
-
-@open_file(1,mode='wb')
-def write_graphml(G, path, encoding='utf-8',prettyprint=True):
- """Write G in GraphML XML format to path
-
- Parameters
- ----------
- G : graph
- A networkx graph
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be compressed.
- encoding : string (optional)
- Encoding for text data.
- prettyprint : bool (optional)
- If True use line breaks and indenting in output XML.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_graphml(G, "test.graphml")
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and unidirected
- edges together) hyperedges, nested graphs, or ports.
- """
- writer = GraphMLWriter(encoding=encoding,prettyprint=prettyprint)
- writer.add_graph_element(G)
- writer.dump(path)
-
-def generate_graphml(G, encoding='utf-8',prettyprint=True):
- """Generate GraphML lines for G
-
- Parameters
- ----------
- G : graph
- A networkx graph
- encoding : string (optional)
- Encoding for text data.
- prettyprint : bool (optional)
- If True use line breaks and indenting in output XML.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> linefeed=chr(10) # linefeed=\n
- >>> s=linefeed.join(nx.generate_graphml(G)) # doctest: +SKIP
- >>> for line in nx.generate_graphml(G): # doctest: +SKIP
- ... print(line)
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and unidirected
- edges together) hyperedges, nested graphs, or ports.
- """
- writer = GraphMLWriter(encoding=encoding,prettyprint=prettyprint)
- writer.add_graph_element(G)
- for line in str(writer).splitlines():
- yield line
-
-@open_file(0,mode='rb')
-def read_graphml(path,node_type=str):
- """Read graph in GraphML format from path.
-
- Parameters
- ----------
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be compressed.
-
- node_type: Python type (default: str)
- Convert node ids to this type
-
- Returns
- -------
- graph: NetworkX graph
- If no parallel edges are found a Graph or DiGraph is returned.
- Otherwise a MultiGraph or MultiDiGraph is returned.
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and unidirected
- edges together), hypergraphs, nested graphs, or ports.
-
- For multigraphs the GraphML edge "id" will be used as the edge
- key. If not specified then they "key" attribute will be used. If
- there is no "key" attribute a default NetworkX multigraph edge key
- will be provided.
-
- Files with the yEd "yfiles" extension will can be read but the graphics
- information is discarded.
-
- yEd compressed files ("file.graphmlz" extension) can be read by renaming
- the file to "file.graphml.gz".
-
- """
- reader = GraphMLReader(node_type=node_type)
- # need to check for multiple graphs
- glist=list(reader(path=path))
- return glist[0]
-
-
-def parse_graphml(graphml_string,node_type=str):
- """Read graph in GraphML format from string.
-
- Parameters
- ----------
- graphml_string : string
- String containing graphml information
- (e.g., contents of a graphml file).
-
- node_type: Python type (default: str)
- Convert node ids to this type
-
- Returns
- -------
- graph: NetworkX graph
- If no parallel edges are found a Graph or DiGraph is returned.
- Otherwise a MultiGraph or MultiDiGraph is returned.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> linefeed=chr(10) # linefeed=\n
- >>> s=linefeed.join(nx.generate_graphml(G))
- >>> H=nx.parse_graphml(s)
-
- Notes
- -----
- This implementation does not support mixed graphs (directed and unidirected
- edges together), hypergraphs, nested graphs, or ports.
-
- For multigraphs the GraphML edge "id" will be used as the edge
- key. If not specified then they "key" attribute will be used. If
- there is no "key" attribute a default NetworkX multigraph edge key
- will be provided.
-
- """
- reader = GraphMLReader(node_type=node_type)
- # need to check for multiple graphs
- glist=list(reader(string=graphml_string))
- return glist[0]
-
-
-class GraphML(object):
- NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
- NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
- #xmlns:y="http://www.yworks.com/xml/graphml"
- NS_Y = "http://www.yworks.com/xml/graphml"
- SCHEMALOCATION = \
- ' '.join(['http://graphml.graphdrawing.org/xmlns',
- 'http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd'])
-
- try:
- chr(12345) # Fails on Py!=3.
- unicode = str # Py3k's str is our unicode type
- long = int # Py3K's int is our long type
- except ValueError:
- # Python 2.x
- pass
-
- types=[(int,"integer"), # for Gephi GraphML bug
- (str,"yfiles"),(str,"string"), (unicode,"string"),
- (int,"int"), (long,"long"),
- (float,"float"), (float,"double"),
- (bool, "boolean")]
-
- xml_type = dict(types)
- python_type = dict(reversed(a) for a in types)
- convert_bool={'true':True,'false':False,
- 'True': True, 'False': False}
-
-
-
-class GraphMLWriter(GraphML):
- def __init__(self, graph=None, encoding="utf-8",prettyprint=True):
- try:
- import xml.etree.ElementTree
- except ImportError:
- raise ImportError('GraphML writer requires '
- 'xml.elementtree.ElementTree')
- self.prettyprint=prettyprint
- self.encoding = encoding
- self.xml = Element("graphml",
- {'xmlns':self.NS_GRAPHML,
- 'xmlns:xsi':self.NS_XSI,
- 'xsi:schemaLocation':self.SCHEMALOCATION}
- )
- self.keys={}
-
- if graph is not None:
- self.add_graph_element(graph)
-
-
- def __str__(self):
- if self.prettyprint:
- self.indent(self.xml)
- s=tostring(self.xml).decode(self.encoding)
- return s
-
- def get_key(self, name, attr_type, scope, default):
- keys_key = (name, attr_type, scope)
- try:
- return self.keys[keys_key]
- except KeyError:
- new_id = "d%i" % len(list(self.keys))
- self.keys[keys_key] = new_id
- key_kwargs = {"id":new_id,
- "for":scope,
- "attr.name":name,
- "attr.type":attr_type}
- key_element=Element("key",**key_kwargs)
- # add subelement for data default value if present
- if default is not None:
- default_element=Element("default")
- default_element.text=make_str(default)
- key_element.append(default_element)
- self.xml.insert(0,key_element)
- return new_id
-
-
- def add_data(self, name, element_type, value,
- scope="all",
- default=None):
- """
- Make a data element for an edge or a node. Keep a log of the
- type in the keys table.
- """
- if element_type not in self.xml_type:
- raise nx.NetworkXError('GraphML writer does not support '
- '%s as data values.'%element_type)
- key_id = self.get_key(name, self.xml_type[element_type], scope, default)
- data_element = Element("data", key=key_id)
- data_element.text = make_str(value)
- return data_element
-
- def add_attributes(self, scope, xml_obj, data, default):
- """Appends attributes to edges or nodes.
- """
- for k,v in data.items():
- default_value=default.get(k)
- obj=self.add_data(make_str(k), type(v), make_str(v),
- scope=scope, default=default_value)
- xml_obj.append(obj)
-
- def add_nodes(self, G, graph_element):
- for node,data in G.nodes_iter(data=True):
- node_element = Element("node", id = make_str(node))
- default=G.graph.get('node_default',{})
- self.add_attributes("node", node_element, data, default)
- graph_element.append(node_element)
-
- def add_edges(self, G, graph_element):
- if G.is_multigraph():
- for u,v,key,data in G.edges_iter(data=True,keys=True):
- edge_element = Element("edge",source=make_str(u),
- target=make_str(v))
- default=G.graph.get('edge_default',{})
- self.add_attributes("edge", edge_element, data, default)
- self.add_attributes("edge", edge_element,
- {'key':key}, default)
- graph_element.append(edge_element)
- else:
- for u,v,data in G.edges_iter(data=True):
- edge_element = Element("edge",source=make_str(u),
- target=make_str(v))
- default=G.graph.get('edge_default',{})
- self.add_attributes("edge", edge_element, data, default)
- graph_element.append(edge_element)
-
- def add_graph_element(self, G):
- """
- Serialize graph G in GraphML to the stream.
- """
- if G.is_directed():
- default_edge_type='directed'
- else:
- default_edge_type='undirected'
-
- graphid=G.graph.pop('id',None)
- if graphid is None:
- graph_element = Element("graph",
- edgedefault = default_edge_type)
- else:
- graph_element = Element("graph",
- edgedefault = default_edge_type,
- id=graphid)
-
- default={}
- data=dict((k,v) for (k,v) in G.graph.items()
- if k not in ['node_default','edge_default'])
- self.add_attributes("graph", graph_element, data, default)
- self.add_nodes(G,graph_element)
- self.add_edges(G,graph_element)
- self.xml.append(graph_element)
-
- def add_graphs(self, graph_list):
- """
- Add many graphs to this GraphML document.
- """
- for G in graph_list:
- self.add_graph_element(G)
-
- def dump(self, stream):
- if self.prettyprint:
- self.indent(self.xml)
- document = ElementTree(self.xml)
- header='<?xml version="1.0" encoding="%s"?>'%self.encoding
- stream.write(header.encode(self.encoding))
- document.write(stream, encoding=self.encoding)
-
- def indent(self, elem, level=0):
- # in-place prettyprint formatter
- i = "\n" + level*" "
- if len(elem):
- if not elem.text or not elem.text.strip():
- elem.text = i + " "
- if not elem.tail or not elem.tail.strip():
- elem.tail = i
- for elem in elem:
- self.indent(elem, level+1)
- if not elem.tail or not elem.tail.strip():
- elem.tail = i
- else:
- if level and (not elem.tail or not elem.tail.strip()):
- elem.tail = i
-
-
-class GraphMLReader(GraphML):
- """Read a GraphML document. Produces NetworkX graph objects.
- """
- def __init__(self, node_type=str):
- try:
- import xml.etree.ElementTree
- except ImportError:
- raise ImportError('GraphML reader requires '
- 'xml.elementtree.ElementTree')
- self.node_type=node_type
- self.multigraph=False # assume multigraph and test for parallel edges
-
- def __call__(self, path=None, string=None):
- if path is not None:
- self.xml = ElementTree(file=path)
- elif string is not None:
- self.xml = fromstring(string)
- else:
- raise ValueError("Must specify either 'path' or 'string' as kwarg.")
- (keys,defaults) = self.find_graphml_keys(self.xml)
- for g in self.xml.findall("{%s}graph" % self.NS_GRAPHML):
- yield self.make_graph(g, keys, defaults)
-
- def make_graph(self, graph_xml, graphml_keys, defaults):
- # set default graph type
- edgedefault = graph_xml.get("edgedefault", None)
- if edgedefault=='directed':
- G=nx.MultiDiGraph()
- else:
- G=nx.MultiGraph()
- # set defaults for graph attributes
- G.graph['node_default']={}
- G.graph['edge_default']={}
- for key_id,value in defaults.items():
- key_for=graphml_keys[key_id]['for']
- name=graphml_keys[key_id]['name']
- python_type=graphml_keys[key_id]['type']
- if key_for=='node':
- G.graph['node_default'].update({name:python_type(value)})
- if key_for=='edge':
- G.graph['edge_default'].update({name:python_type(value)})
- # hyperedges are not supported
- hyperedge=graph_xml.find("{%s}hyperedge" % self.NS_GRAPHML)
- if hyperedge is not None:
- raise nx.NetworkXError("GraphML reader does not support hyperedges")
- # add nodes
- for node_xml in graph_xml.findall("{%s}node" % self.NS_GRAPHML):
- self.add_node(G, node_xml, graphml_keys)
- # add edges
- for edge_xml in graph_xml.findall("{%s}edge" % self.NS_GRAPHML):
- self.add_edge(G, edge_xml, graphml_keys)
- # add graph data
- data = self.decode_data_elements(graphml_keys, graph_xml)
- G.graph.update(data)
-
- # switch to Graph or DiGraph if no parallel edges were found.
- if not self.multigraph:
- if G.is_directed():
- return nx.DiGraph(G)
- else:
- return nx.Graph(G)
- else:
- return G
-
- def add_node(self, G, node_xml, graphml_keys):
- """Add a node to the graph.
- """
- # warn on finding unsupported ports tag
- ports=node_xml.find("{%s}port" % self.NS_GRAPHML)
- if ports is not None:
- warnings.warn("GraphML port tag not supported.")
- # find the node by id and cast it to the appropriate type
- node_id = self.node_type(node_xml.get("id"))
- # get data/attributes for node
- data = self.decode_data_elements(graphml_keys, node_xml)
- G.add_node(node_id, data)
-
- def add_edge(self, G, edge_element, graphml_keys):
- """Add an edge to the graph.
- """
- # warn on finding unsupported ports tag
- ports=edge_element.find("{%s}port" % self.NS_GRAPHML)
- if ports is not None:
- warnings.warn("GraphML port tag not supported.")
-
- # raise error if we find mixed directed and undirected edges
- directed = edge_element.get("directed")
- if G.is_directed() and directed=='false':
- raise nx.NetworkXError(\
- "directed=false edge found in directed graph.")
- if (not G.is_directed()) and directed=='true':
- raise nx.NetworkXError(\
- "directed=true edge found in undirected graph.")
-
- source = self.node_type(edge_element.get("source"))
- target = self.node_type(edge_element.get("target"))
- data = self.decode_data_elements(graphml_keys, edge_element)
- # GraphML stores edge ids as an attribute
- # NetworkX uses them as keys in multigraphs too if no key
- # attribute is specified
- edge_id = edge_element.get("id")
- if edge_id:
- data["id"] = edge_id
- if G.has_edge(source,target):
- # mark this as a multigraph
- self.multigraph=True
- if edge_id is None:
- # no id specified, try using 'key' attribute as id
- edge_id=data.pop('key',None)
- G.add_edge(source, target, key=edge_id, **data)
-
- def decode_data_elements(self, graphml_keys, obj_xml):
- """Use the key information to decode the data XML if present."""
- data = {}
- for data_element in obj_xml.findall("{%s}data" % self.NS_GRAPHML):
- key = data_element.get("key")
- try:
- data_name=graphml_keys[key]['name']
- data_type=graphml_keys[key]['type']
- except KeyError:
- raise nx.NetworkXError("Bad GraphML data: no key %s"%key)
- text=data_element.text
- # assume anything with subelements is a yfiles extension
- if text is not None and len(list(data_element))==0:
- if data_type==bool:
- data[data_name] = self.convert_bool[text]
- else:
- data[data_name] = data_type(text)
- elif len(list(data_element)) > 0:
- # Assume yfiles as subelements, try to extract node_label
- node_label = None
- for node_type in ['ShapeNode', 'SVGNode', 'ImageNode']:
- geometry = data_element.find("{%s}%s/{%s}Geometry" %
- (self.NS_Y, node_type, self.NS_Y))
- if geometry is not None:
- data['x'] = geometry.get('x')
- data['y'] = geometry.get('y')
- if node_label is None:
- node_label = data_element.find("{%s}%s/{%s}NodeLabel" %
- (self.NS_Y, node_type, self.NS_Y))
- if node_label is not None:
- data['label'] = node_label.text
-
- # check all the diffrent types of edges avaivable in yEd.
- for e in ['PolyLineEdge', 'SplineEdge', 'QuadCurveEdge', 'BezierEdge', 'ArcEdge']:
- edge_label = data_element.find("{%s}%s/{%s}EdgeLabel"%
- (self.NS_Y, e, (self.NS_Y)))
- if edge_label is not None:
- break
-
- if edge_label is not None:
- data['label'] = edge_label.text
- return data
-
- def find_graphml_keys(self, graph_element):
- """Extracts all the keys and key defaults from the xml.
- """
- graphml_keys = {}
- graphml_key_defaults = {}
- for k in graph_element.findall("{%s}key" % self.NS_GRAPHML):
- attr_id = k.get("id")
- attr_type=k.get('attr.type')
- attr_name=k.get("attr.name")
- yfiles_type=k.get("yfiles.type")
- if yfiles_type is not None:
- attr_name = yfiles_type
- attr_type = 'yfiles'
- if attr_type is None:
- attr_type = "string"
- warnings.warn("No key type for id %s. Using string"%attr_id)
- if attr_name is None:
- raise nx.NetworkXError("Unknown key for id %s in file."%attr_id)
- graphml_keys[attr_id] = {
- "name":attr_name,
- "type":self.python_type[attr_type],
- "for":k.get("for")}
- # check for "default" subelement of key element
- default=k.find("{%s}default" % self.NS_GRAPHML)
- if default is not None:
- graphml_key_defaults[attr_id]=default.text
- return graphml_keys,graphml_key_defaults
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import xml.etree.ElementTree
- except:
- raise SkipTest("xml.etree.ElementTree not available")
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- try:
- os.unlink('test.graphml')
- except:
- pass
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/__init__.py
deleted file mode 100644
index ea3db6e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
-*********
-JSON data
-*********
-Generate and parse JSON serializable data for NetworkX graphs.
-"""
-from networkx.readwrite.json_graph.node_link import *
-from networkx.readwrite.json_graph.adjacency import *
-from networkx.readwrite.json_graph.tree import *
-from networkx.readwrite.json_graph.serialize import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/adjacency.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/adjacency.py
deleted file mode 100644
index d5b0df2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/adjacency.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright (C) 2011-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from copy import deepcopy
-from itertools import count,repeat
-import json
-import networkx as nx
-__author__ = """Aric Hagberg <aric.hagberg@gmail.com>"""
-__all__ = ['adjacency_data', 'adjacency_graph']
-
-def adjacency_data(G):
- """Return data in adjacency format that is suitable for JSON serialization
- and use in Javascript documents.
-
- Parameters
- ----------
- G : NetworkX graph
-
- Returns
- -------
- data : dict
- A dictionary with node-link formatted data.
-
- Examples
- --------
- >>> from networkx.readwrite import json_graph
- >>> G = nx.Graph([(1,2)])
- >>> data = json_graph.adjacency_data(G)
-
- To serialize with json
-
- >>> import json
- >>> s = json.dumps(data)
-
- Notes
- -----
- Graph, node, and link attributes will be written when using this format
- but attribute keys must be strings if you want to serialize the resulting
- data with JSON.
-
- See Also
- --------
- adjacency_graph, node_link_data, tree_data
- """
- multigraph = G.is_multigraph()
- data = {}
- data['directed'] = G.is_directed()
- data['multigraph'] = multigraph
- data['graph'] = list(G.graph.items())
- data['nodes'] = []
- data['adjacency'] = []
- for n,nbrdict in G.adjacency_iter():
- data['nodes'].append(dict(id=n, **G.node[n]))
- adj = []
- if multigraph:
- for nbr,key in nbrdict.items():
- for k,d in key.items():
- adj.append(dict(id=nbr, key=k, **d))
- else:
- for nbr,d in nbrdict.items():
- adj.append(dict(id=nbr, **d))
- data['adjacency'].append(adj)
- return data
-
-def adjacency_graph(data, directed=False, multigraph=True):
- """Return graph from adjacency data format.
-
- Parameters
- ----------
- data : dict
- Adjacency list formatted graph data
-
- Returns
- -------
- G : NetworkX graph
- A NetworkX graph object
-
- directed : bool
- If True, and direction not specified in data, return a directed graph.
-
- multigraph : bool
- If True, and multigraph not specified in data, return a multigraph.
-
- Examples
- --------
- >>> from networkx.readwrite import json_graph
- >>> G = nx.Graph([(1,2)])
- >>> data = json_graph.adjacency_data(G)
- >>> H = json_graph.adjacency_graph(data)
-
- See Also
- --------
- adjacency_graph, node_link_data, tree_data
- """
- multigraph = data.get('multigraph',multigraph)
- directed = data.get('directed',directed)
- if multigraph:
- graph = nx.MultiGraph()
- else:
- graph = nx.Graph()
- if directed:
- graph = graph.to_directed()
- graph.graph = dict(data.get('graph',[]))
- mapping=[]
- for d in data['nodes']:
- node_data = d.copy()
- node = node_data.pop('id')
- mapping.append(node)
- graph.add_node(node, attr_dict=node_data)
- for i,d in enumerate(data['adjacency']):
- source = mapping[i]
- for tdata in d:
- target_data = tdata.copy()
- target = target_data.pop('id')
- key = target_data.pop('key', None)
- if not multigraph or key is None:
- graph.add_edge(source,target,attr_dict=tdata)
- else:
- graph.add_edge(source,target,key=key, attr_dict=tdata)
- return graph
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/node_link.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/node_link.py
deleted file mode 100644
index 03f150f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/node_link.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (C) 2011-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from itertools import count,repeat
-import json
-import networkx as nx
-__author__ = """Aric Hagberg <hagberg@lanl.gov>"""
-__all__ = ['node_link_data', 'node_link_graph']
-
-def node_link_data(G):
- """Return data in node-link format that is suitable for JSON serialization
- and use in Javascript documents.
-
- Parameters
- ----------
- G : NetworkX graph
-
- Returns
- -------
- data : dict
- A dictionary with node-link formatted data.
-
- Examples
- --------
- >>> from networkx.readwrite import json_graph
- >>> G = nx.Graph([(1,2)])
- >>> data = json_graph.node_link_data(G)
-
- To serialize with json
-
- >>> import json
- >>> s = json.dumps(data)
-
- Notes
- -----
- Graph, node, and link attributes are stored in this format but keys
- for attributes must be strings if you want to serialize with JSON.
-
- See Also
- --------
- node_link_graph, adjacency_data, tree_data
- """
- multigraph = G.is_multigraph()
- mapping = dict(zip(G,count()))
- data = {}
- data['directed'] = G.is_directed()
- data['multigraph'] = multigraph
- data['graph'] = list(G.graph.items())
- data['nodes'] = [ dict(id=n, **G.node[n]) for n in G ]
- if multigraph:
- data['links'] = [ dict(source=mapping[u], target=mapping[v], key=k, **d)
- for u,v,k,d in G.edges(keys=True, data=True) ]
- else:
- data['links'] = [ dict(source=mapping[u], target=mapping[v], **d)
- for u,v,d in G.edges(data=True) ]
-
- return data
-
-
-def node_link_graph(data, directed=False, multigraph=True):
- """Return graph from node-link data format.
-
- Parameters
- ----------
- data : dict
- node-link formatted graph data
-
- directed : bool
- If True, and direction not specified in data, return a directed graph.
-
- multigraph : bool
- If True, and multigraph not specified in data, return a multigraph.
-
- Returns
- -------
- G : NetworkX graph
- A NetworkX graph object
-
- Examples
- --------
- >>> from networkx.readwrite import json_graph
- >>> G = nx.Graph([(1,2)])
- >>> data = json_graph.node_link_data(G)
- >>> H = json_graph.node_link_graph(data)
-
- See Also
- --------
- node_link_data, adjacency_data, tree_data
- """
- multigraph = data.get('multigraph',multigraph)
- directed = data.get('directed',directed)
- if multigraph:
- graph = nx.MultiGraph()
- else:
- graph = nx.Graph()
- if directed:
- graph = graph.to_directed()
- mapping=[]
- graph.graph = dict(data.get('graph',[]))
- c = count()
- for d in data['nodes']:
- node = d.get('id',next(c))
- mapping.append(node)
- nodedata = dict((str(k),v) for k,v in d.items() if k!='id')
- graph.add_node(node, **nodedata)
- for d in data['links']:
- link_data = d.copy()
- source = link_data.pop('source')
- target = link_data.pop('target')
- edgedata = dict((str(k),v) for k,v in d.items()
- if k!='source' and k!='target')
- graph.add_edge(mapping[source],mapping[target],**edgedata)
- return graph
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/serialize.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/serialize.py
deleted file mode 100644
index 5861836..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/serialize.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from functools import partial,update_wrapper
-import json
-from networkx.readwrite.json_graph import node_link_data,node_link_graph
-__author__ = """Aric Hagberg (hagberg@lanl.gov))"""
-__all__ = ['dumps','loads','dump','load']
-
-class NXJSONEncoder(json.JSONEncoder):
- def default(self, o):
- return node_link_data(o)
-
-
-class NXJSONDecoder(json.JSONDecoder):
- def decode(self, s):
- d = json.loads(s)
- return node_link_graph(d)
-
-# modification of json functions to serialize networkx graphs
-dumps = partial(json.dumps, cls=NXJSONEncoder)
-update_wrapper(dumps,json.dumps)
-loads = partial(json.loads, cls=NXJSONDecoder)
-update_wrapper(loads,json.loads)
-dump = partial(json.dump, cls=NXJSONEncoder)
-update_wrapper(dump,json.dump)
-load = partial(json.load, cls=NXJSONDecoder)
-update_wrapper(load,json.load)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_adjacency.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_adjacency.py
deleted file mode 100644
index f8bf214..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_adjacency.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import json
-from nose.tools import assert_equal, assert_raises, assert_not_equal,assert_true
-import networkx as nx
-from networkx.readwrite.json_graph import *
-
-class TestAdjacency:
-
- def test_graph(self):
- G = nx.path_graph(4)
- H = adjacency_graph(adjacency_data(G))
- nx.is_isomorphic(G,H)
-
- def test_graph_attributes(self):
- G = nx.path_graph(4)
- G.add_node(1,color='red')
- G.add_edge(1,2,width=7)
- G.graph['foo']='bar'
- G.graph[1]='one'
-
- H = adjacency_graph(adjacency_data(G))
- assert_equal(H.graph['foo'],'bar')
- assert_equal(H.node[1]['color'],'red')
- assert_equal(H[1][2]['width'],7)
-
- d = json.dumps(adjacency_data(G))
- H = adjacency_graph(json.loads(d))
- assert_equal(H.graph['foo'],'bar')
- assert_equal(H.graph[1],'one')
- assert_equal(H.node[1]['color'],'red')
- assert_equal(H[1][2]['width'],7)
-
- def test_digraph(self):
- G = nx.DiGraph()
- G.add_path([1,2,3])
- H = adjacency_graph(adjacency_data(G))
- assert_true(H.is_directed())
- nx.is_isomorphic(G,H)
-
- def test_multidigraph(self):
- G = nx.MultiDiGraph()
- G.add_path([1,2,3])
- H = adjacency_graph(adjacency_data(G))
- assert_true(H.is_directed())
- assert_true(H.is_multigraph())
-
- def test_multigraph(self):
- G = nx.MultiGraph()
- G.add_edge(1,2,key='first')
- G.add_edge(1,2,key='second',color='blue')
- H = adjacency_graph(adjacency_data(G))
- nx.is_isomorphic(G,H)
- assert_equal(H[1][2]['second']['color'],'blue')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_node_link.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_node_link.py
deleted file mode 100644
index 5430e0d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_node_link.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import json
-from nose.tools import assert_equal, assert_raises, assert_not_equal,assert_true
-import networkx as nx
-from networkx.readwrite.json_graph import *
-
-class TestNodeLink:
-
- def test_graph(self):
- G = nx.path_graph(4)
- H = node_link_graph(node_link_data(G))
- nx.is_isomorphic(G,H)
-
- def test_graph_attributes(self):
- G = nx.path_graph(4)
- G.add_node(1,color='red')
- G.add_edge(1,2,width=7)
- G.graph[1]='one'
- G.graph['foo']='bar'
-
- H = node_link_graph(node_link_data(G))
- assert_equal(H.graph['foo'],'bar')
- assert_equal(H.node[1]['color'],'red')
- assert_equal(H[1][2]['width'],7)
-
- d=json.dumps(node_link_data(G))
- H = node_link_graph(json.loads(d))
- assert_equal(H.graph['foo'],'bar')
- assert_equal(H.graph[1],'one')
- assert_equal(H.node[1]['color'],'red')
- assert_equal(H[1][2]['width'],7)
-
- def test_digraph(self):
- G = nx.DiGraph()
- H = node_link_graph(node_link_data(G))
- assert_true(H.is_directed())
-
-
- def test_multigraph(self):
- G = nx.MultiGraph()
- G.add_edge(1,2,key='first')
- G.add_edge(1,2,key='second',color='blue')
- H = node_link_graph(node_link_data(G))
- nx.is_isomorphic(G,H)
- assert_equal(H[1][2]['second']['color'],'blue')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_serialize.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_serialize.py
deleted file mode 100644
index cc8d7ff..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_serialize.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import json
-from nose.tools import assert_equal, assert_raises, assert_not_equal,assert_true
-import networkx as nx
-from networkx.readwrite.json_graph import *
-
-class TestAdjacency:
-
- def test_graph(self):
- G = nx.path_graph(4)
- H = loads(dumps(G))
- nx.is_isomorphic(G,H)
-
- def test_graph_attributes(self):
- G = nx.path_graph(4)
- G.add_node(1,color='red')
- G.add_edge(1,2,width=7)
- G.graph['foo']='bar'
- G.graph[1]='one'
-
- H = loads(dumps(G))
- assert_equal(H.graph['foo'],'bar')
- assert_equal(H.graph[1],'one')
- assert_equal(H.node[1]['color'],'red')
- assert_equal(H[1][2]['width'],7)
-
- try:
- from StringIO import StringIO
- except:
- from io import StringIO
- io = StringIO()
- dump(G,io)
- io.seek(0)
- H=load(io)
- assert_equal(H.graph['foo'],'bar')
- assert_equal(H.graph[1],'one')
- assert_equal(H.node[1]['color'],'red')
- assert_equal(H[1][2]['width'],7)
-
-
- def test_digraph(self):
- G = nx.DiGraph()
- H = loads(dumps(G))
- assert_true(H.is_directed())
-
- def test_multidigraph(self):
- G = nx.MultiDiGraph()
- H = loads(dumps(G))
- assert_true(H.is_directed())
- assert_true(H.is_multigraph())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_tree.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_tree.py
deleted file mode 100644
index 19ba8f2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tests/test_tree.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import json
-from nose.tools import assert_equal, assert_raises, assert_not_equal,assert_true
-import networkx as nx
-from networkx.readwrite.json_graph import *
-
-class TestTree:
-
- def test_graph(self):
- G=nx.DiGraph()
- G.add_nodes_from([1,2,3],color='red')
- G.add_edge(1,2,foo=7)
- G.add_edge(1,3,foo=10)
- G.add_edge(3,4,foo=10)
- H = tree_graph(tree_data(G,1))
- nx.is_isomorphic(G,H)
-
- def test_graph_attributes(self):
- G=nx.DiGraph()
- G.add_nodes_from([1,2,3],color='red')
- G.add_edge(1,2,foo=7)
- G.add_edge(1,3,foo=10)
- G.add_edge(3,4,foo=10)
- H = tree_graph(tree_data(G,1))
- assert_equal(H.node[1]['color'],'red')
-
- d = json.dumps(tree_data(G,1))
- H = tree_graph(json.loads(d))
- assert_equal(H.node[1]['color'],'red')
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tree.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tree.py
deleted file mode 100644
index d2229e9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/tree.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-from itertools import count,repeat
-import json
-import networkx as nx
-__author__ = """Aric Hagberg (hagberg@lanl.gov))"""
-__all__ = ['tree_data',
- 'tree_graph']
-
-def tree_data(G, root):
- """Return data in tree format that is suitable for JSON serialization
- and use in Javascript documents.
-
- Parameters
- ----------
- G : NetworkX graph
- G must be an oriented tree
-
- root : node
- The root of the tree
-
- Returns
- -------
- data : dict
- A dictionary with node-link formatted data.
-
- Examples
- --------
- >>> from networkx.readwrite import json_graph
- >>> G = nx.DiGraph([(1,2)])
- >>> data = json_graph.tree_data(G,root=1)
-
- To serialize with json
-
- >>> import json
- >>> s = json.dumps(data)
-
- Notes
- -----
- Node attributes are stored in this format but keys
- for attributes must be strings if you want to serialize with JSON.
-
- Graph and edge attributes are not stored.
-
- See Also
- --------
- tree_graph, node_link_data, node_link_data
- """
- if not G.number_of_nodes()==G.number_of_edges()+1:
- raise TypeError("G is not a tree.")
- if not G.is_directed():
- raise TypeError("G is not directed")
- def add_children(n, G):
- nbrs = G[n]
- if len(nbrs) == 0:
- return []
- children = []
- for child in nbrs:
- d = dict(id=child, **G.node[child])
- c = add_children(child,G)
- if c:
- d['children'] = c
- children.append(d)
- return children
- data = dict(id=root, **G.node[root])
- data['children'] = add_children(root,G)
- return data
-
-def tree_graph(data):
- """Return graph from tree data format.
-
- Parameters
- ----------
- data : dict
- Tree formatted graph data
-
- Returns
- -------
- G : NetworkX DiGraph
-
- Examples
- --------
- >>> from networkx.readwrite import json_graph
- >>> G = nx.DiGraph([(1,2)])
- >>> data = json_graph.tree_data(G,root=1)
- >>> H = json_graph.tree_graph(data)
-
- See Also
- --------
- tree_graph, node_link_data, adjacency_data
- """
- graph = nx.DiGraph()
- def add_children(parent, children):
- for data in children:
- child = data['id']
- graph.add_edge(parent, child)
- grandchildren = data.get('children',[])
- if grandchildren:
- add_children(child,grandchildren)
- nodedata = dict((str(k),v) for k,v in data.items()
- if k!='id' and k!='children')
- graph.add_node(child,attr_dict=nodedata)
- root = data['id']
- children = data.get('children',[])
- nodedata = dict((k,v) for k,v in data.items()
- if k!='id' and k!='children')
- graph.add_node(root, attr_dict=nodedata)
- add_children(root, children)
- return graph
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/leda.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/leda.py
deleted file mode 100644
index a4d17db..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/leda.py
+++ /dev/null
@@ -1,106 +0,0 @@
-"""
-Read graphs in LEDA format.
-
-LEDA is a C++ class library for efficient data types and algorithms.
-
-Format
-------
-See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
-
-"""
-# Original author: D. Eppstein, UC Irvine, August 12, 2003.
-# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['read_leda', 'parse_leda']
-
-import networkx as nx
-from networkx.exception import NetworkXError
-from networkx.utils import open_file, is_string_like
-
-@open_file(0,mode='rb')
-def read_leda(path, encoding='UTF-8'):
- """Read graph in LEDA format from path.
-
- Parameters
- ----------
- path : file or string
- File or filename to read. Filenames ending in .gz or .bz2 will be
- uncompressed.
-
- Returns
- -------
- G : NetworkX graph
-
- Examples
- --------
- G=nx.read_leda('file.leda')
-
- References
- ----------
- .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
- """
- lines=(line.decode(encoding) for line in path)
- G=parse_leda(lines)
- return G
-
-
-def parse_leda(lines):
- """Read graph in LEDA format from string or iterable.
-
- Parameters
- ----------
- lines : string or iterable
- Data in LEDA format.
-
- Returns
- -------
- G : NetworkX graph
-
- Examples
- --------
- G=nx.parse_leda(string)
-
- References
- ----------
- .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
- """
- if is_string_like(lines): lines=iter(lines.split('\n'))
- lines = iter([line.rstrip('\n') for line in lines \
- if not (line.startswith('#') or line.startswith('\n') or line=='')])
- for i in range(3):
- next(lines)
- # Graph
- du = int(next(lines)) # -1=directed, -2=undirected
- if du==-1:
- G = nx.DiGraph()
- else:
- G = nx.Graph()
-
- # Nodes
- n =int(next(lines)) # number of nodes
- node={}
- for i in range(1,n+1): # LEDA counts from 1 to n
- symbol=next(lines).rstrip().strip('|{}| ')
- if symbol=="": symbol=str(i) # use int if no label - could be trouble
- node[i]=symbol
-
- G.add_nodes_from([s for i,s in node.items()])
-
- # Edges
- m = int(next(lines)) # number of edges
- for i in range(m):
- try:
- s,t,reversal,label=next(lines).split()
- except:
- raise NetworkXError('Too few fields in LEDA.GRAPH edge %d'%(i+1))
- # BEWARE: no handling of reversal edges
- G.add_edge(node[int(s)],node[int(t)],label=label[2:-2])
- return G
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/multiline_adjlist.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/multiline_adjlist.py
deleted file mode 100644
index 30c3234..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/multiline_adjlist.py
+++ /dev/null
@@ -1,390 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-*************************
-Multi-line Adjacency List
-*************************
-Read and write NetworkX graphs as multi-line adjacency lists.
-
-The multi-line adjacency list format is useful for graphs with
-nodes that can be meaningfully represented as strings. With this format
-simple edge data can be stored but node or graph data is not.
-
-Format
-------
-The first label in a line is the source node label followed by the node degree
-d. The next d lines are target node labels and optional edge data.
-That pattern repeats for all nodes in the graph.
-
-The graph with edges a-b, a-c, d-e can be represented as the following
-adjacency list (anything following the # in a line is a comment)::
-
- # example.multiline-adjlist
- a 2
- b
- c
- d 1
- e
-"""
-__author__ = '\n'.join(['Aric Hagberg <hagberg@lanl.gov>',
- 'Dan Schult <dschult@colgate.edu>',
- 'Loïc Séguin-C. <loicseguin@gmail.com>'])
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['generate_multiline_adjlist',
- 'write_multiline_adjlist',
- 'parse_multiline_adjlist',
- 'read_multiline_adjlist']
-
-from networkx.utils import make_str, open_file
-import networkx as nx
-
-def generate_multiline_adjlist(G, delimiter = ' '):
- """Generate a single line of the graph G in multiline adjacency list format.
-
- Parameters
- ----------
- G : NetworkX graph
-
- delimiter : string, optional
- Separator for node labels
-
- Returns
- -------
- lines : string
- Lines of data in multiline adjlist format.
-
- Examples
- --------
- >>> G = nx.lollipop_graph(4, 3)
- >>> for line in nx.generate_multiline_adjlist(G):
- ... print(line)
- 0 3
- 1 {}
- 2 {}
- 3 {}
- 1 2
- 2 {}
- 3 {}
- 2 1
- 3 {}
- 3 1
- 4 {}
- 4 1
- 5 {}
- 5 1
- 6 {}
- 6 0
-
- See Also
- --------
- write_multiline_adjlist, read_multiline_adjlist
- """
- if G.is_directed():
- if G.is_multigraph():
- for s,nbrs in G.adjacency_iter():
- nbr_edges=[ (u,data)
- for u,datadict in nbrs.items()
- for key,data in datadict.items()]
- deg=len(nbr_edges)
- yield make_str(s)+delimiter+"%i"%(deg)
- for u,d in nbr_edges:
- if d is None:
- yield make_str(u)
- else:
- yield make_str(u)+delimiter+make_str(d)
- else: # directed single edges
- for s,nbrs in G.adjacency_iter():
- deg=len(nbrs)
- yield make_str(s)+delimiter+"%i"%(deg)
- for u,d in nbrs.items():
- if d is None:
- yield make_str(u)
- else:
- yield make_str(u)+delimiter+make_str(d)
- else: # undirected
- if G.is_multigraph():
- seen=set() # helper dict used to avoid duplicate edges
- for s,nbrs in G.adjacency_iter():
- nbr_edges=[ (u,data)
- for u,datadict in nbrs.items()
- if u not in seen
- for key,data in datadict.items()]
- deg=len(nbr_edges)
- yield make_str(s)+delimiter+"%i"%(deg)
- for u,d in nbr_edges:
- if d is None:
- yield make_str(u)
- else:
- yield make_str(u)+delimiter+make_str(d)
- seen.add(s)
- else: # undirected single edges
- seen=set() # helper dict used to avoid duplicate edges
- for s,nbrs in G.adjacency_iter():
- nbr_edges=[ (u,d) for u,d in nbrs.items() if u not in seen]
- deg=len(nbr_edges)
- yield make_str(s)+delimiter+"%i"%(deg)
- for u,d in nbr_edges:
- if d is None:
- yield make_str(u)
- else:
- yield make_str(u)+delimiter+make_str(d)
- seen.add(s)
-
-@open_file(1,mode='wb')
-def write_multiline_adjlist(G, path, delimiter=' ',
- comments='#', encoding = 'utf-8'):
- """ Write the graph G in multiline adjacency list format to path
-
- Parameters
- ----------
- G : NetworkX graph
-
- comments : string, optional
- Marker for comment lines
-
- delimiter : string, optional
- Separator for node labels
-
- encoding : string, optional
- Text encoding.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_multiline_adjlist(G,"test.adjlist")
-
- The path can be a file handle or a string with the name of the file. If a
- file handle is provided, it has to be opened in 'wb' mode.
-
- >>> fh=open("test.adjlist",'wb')
- >>> nx.write_multiline_adjlist(G,fh)
-
- Filenames ending in .gz or .bz2 will be compressed.
-
- >>> nx.write_multiline_adjlist(G,"test.adjlist.gz")
-
- See Also
- --------
- read_multiline_adjlist
- """
- import sys
- import time
-
- pargs=comments+" ".join(sys.argv)
- header = ("%s\n" % (pargs)
- + comments + " GMT %s\n" % (time.asctime(time.gmtime()))
- + comments + " %s\n" % (G.name))
- path.write(header.encode(encoding))
-
- for multiline in generate_multiline_adjlist(G, delimiter):
- multiline+='\n'
- path.write(multiline.encode(encoding))
-
-def parse_multiline_adjlist(lines, comments = '#', delimiter = None,
- create_using = None, nodetype = None,
- edgetype = None):
- """Parse lines of a multiline adjacency list representation of a graph.
-
- Parameters
- ----------
- lines : list or iterator of strings
- Input data in multiline adjlist format
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
- nodetype : Python type, optional
- Convert nodes to this type.
-
- comments : string, optional
- Marker for comment lines
-
- delimiter : string, optional
- Separator for node labels. The default is whitespace.
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
-
- Returns
- -------
- G: NetworkX graph
- The graph corresponding to the lines in multiline adjacency list format.
-
- Examples
- --------
- >>> lines = ['1 2',
- ... "2 {'weight':3, 'name': 'Frodo'}",
- ... "3 {}",
- ... "2 1",
- ... "5 {'weight':6, 'name': 'Saruman'}"]
- >>> G = nx.parse_multiline_adjlist(iter(lines), nodetype = int)
- >>> G.nodes()
- [1, 2, 3, 5]
- """
- from ast import literal_eval
- if create_using is None:
- G=nx.Graph()
- else:
- try:
- G=create_using
- G.clear()
- except:
- raise TypeError("Input graph is not a networkx graph type")
-
- for line in lines:
- p=line.find(comments)
- if p>=0:
- line = line[:p]
- if not line: continue
- try:
- (u,deg)=line.strip().split(delimiter)
- deg=int(deg)
- except:
- raise TypeError("Failed to read node and degree on line (%s)"%line)
- if nodetype is not None:
- try:
- u=nodetype(u)
- except:
- raise TypeError("Failed to convert node (%s) to type %s"\
- %(u,nodetype))
- G.add_node(u)
- for i in range(deg):
- while True:
- try:
- line = next(lines)
- except StopIteration:
- msg = "Failed to find neighbor for node (%s)" % (u,)
- raise TypeError(msg)
- p=line.find(comments)
- if p>=0:
- line = line[:p]
- if line: break
- vlist=line.strip().split(delimiter)
- numb=len(vlist)
- if numb<1:
- continue # isolated node
- v=vlist.pop(0)
- data=''.join(vlist)
- if nodetype is not None:
- try:
- v=nodetype(v)
- except:
- raise TypeError(
- "Failed to convert node (%s) to type %s"\
- %(v,nodetype))
- if edgetype is not None:
- try:
- edgedata={'weight':edgetype(data)}
- except:
- raise TypeError(
- "Failed to convert edge data (%s) to type %s"\
- %(data, edgetype))
- else:
- try: # try to evaluate
- edgedata=literal_eval(data)
- except:
- edgedata={}
- G.add_edge(u,v,attr_dict=edgedata)
-
- return G
-
-@open_file(0,mode='rb')
-def read_multiline_adjlist(path, comments="#", delimiter=None,
- create_using=None,
- nodetype=None, edgetype=None,
- encoding = 'utf-8'):
- """Read graph in multi-line adjacency list format from path.
-
- Parameters
- ----------
- path : string or file
- Filename or file handle to read.
- Filenames ending in .gz or .bz2 will be uncompressed.
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
- nodetype : Python type, optional
- Convert nodes to this type.
-
- edgetype : Python type, optional
- Convert edge data to this type.
-
- comments : string, optional
- Marker for comment lines
-
- delimiter : string, optional
- Separator for node labels. The default is whitespace.
-
- create_using: NetworkX graph container
- Use given NetworkX graph for holding nodes or edges.
-
-
- Returns
- -------
- G: NetworkX graph
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_multiline_adjlist(G,"test.adjlist")
- >>> G=nx.read_multiline_adjlist("test.adjlist")
-
- The path can be a file or a string with the name of the file. If a
- file s provided, it has to be opened in 'rb' mode.
-
- >>> fh=open("test.adjlist", 'rb')
- >>> G=nx.read_multiline_adjlist(fh)
-
- Filenames ending in .gz or .bz2 will be compressed.
-
- >>> nx.write_multiline_adjlist(G,"test.adjlist.gz")
- >>> G=nx.read_multiline_adjlist("test.adjlist.gz")
-
- The optional nodetype is a function to convert node strings to nodetype.
-
- For example
-
- >>> G=nx.read_multiline_adjlist("test.adjlist", nodetype=int)
-
- will attempt to convert all nodes to integer type.
-
- The optional edgetype is a function to convert edge data strings to
- edgetype.
-
- >>> G=nx.read_multiline_adjlist("test.adjlist")
-
- The optional create_using parameter is a NetworkX graph container.
- The default is Graph(), an undirected graph. To read the data as
- a directed graph use
-
- >>> G=nx.read_multiline_adjlist("test.adjlist", create_using=nx.DiGraph())
-
- Notes
- -----
- This format does not store graph, node, or edge data.
-
- See Also
- --------
- write_multiline_adjlist
- """
- lines = (line.decode(encoding) for line in path)
- return parse_multiline_adjlist(lines,
- comments = comments,
- delimiter = delimiter,
- create_using = create_using,
- nodetype = nodetype,
- edgetype = edgetype)
-
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.adjlist')
- os.unlink('test.adjlist.gz')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/nx_shp.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/nx_shp.py
deleted file mode 100644
index d873ea6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/nx_shp.py
+++ /dev/null
@@ -1,224 +0,0 @@
-"""
-*********
-Shapefile
-*********
-
-Generates a networkx.DiGraph from point and line shapefiles.
-
-"The Esri Shapefile or simply a shapefile is a popular geospatial vector
-data format for geographic information systems software. It is developed
-and regulated by Esri as a (mostly) open specification for data
-interoperability among Esri and other software products."
-See http://en.wikipedia.org/wiki/Shapefile for additional information.
-"""
-# Copyright (C) 2004-2010 by
-# Ben Reilly <benwreilly@gmail.com>
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """Ben Reilly (benwreilly@gmail.com)"""
-__all__ = ['read_shp', 'write_shp']
-
-
-def read_shp(path):
- """Generates a networkx.DiGraph from shapefiles. Point geometries are
- translated into nodes, lines into edges. Coordinate tuples are used as
- keys. Attributes are preserved, line geometries are simplified into start
- and end coordinates. Accepts a single shapefile or directory of many
- shapefiles.
-
- "The Esri Shapefile or simply a shapefile is a popular geospatial vector
- data format for geographic information systems software [1]_."
-
- Parameters
- ----------
- path : file or string
- File, directory, or filename to read.
-
- Returns
- -------
- G : NetworkX graph
-
- Examples
- --------
- >>> G=nx.read_shp('test.shp') # doctest: +SKIP
-
- References
- ----------
- .. [1] http://en.wikipedia.org/wiki/Shapefile
- """
- try:
- from osgeo import ogr
- except ImportError:
- raise ImportError("read_shp requires OGR: http://www.gdal.org/")
-
- net = nx.DiGraph()
-
- def getfieldinfo(lyr, feature, flds):
- f = feature
- return [f.GetField(f.GetFieldIndex(x)) for x in flds]
-
- def addlyr(lyr, fields):
- for findex in xrange(lyr.GetFeatureCount()):
- f = lyr.GetFeature(findex)
- flddata = getfieldinfo(lyr, f, fields)
- g = f.geometry()
- attributes = dict(zip(fields, flddata))
- attributes["ShpName"] = lyr.GetName()
- if g.GetGeometryType() == 1: # point
- net.add_node((g.GetPoint_2D(0)), attributes)
- if g.GetGeometryType() == 2: # linestring
- attributes["Wkb"] = g.ExportToWkb()
- attributes["Wkt"] = g.ExportToWkt()
- attributes["Json"] = g.ExportToJson()
- last = g.GetPointCount() - 1
- net.add_edge(g.GetPoint_2D(0), g.GetPoint_2D(last), attributes)
-
- if isinstance(path, str):
- shp = ogr.Open(path)
- lyrcount = shp.GetLayerCount() # multiple layers indicate a directory
- for lyrindex in xrange(lyrcount):
- lyr = shp.GetLayerByIndex(lyrindex)
- flds = [x.GetName() for x in lyr.schema]
- addlyr(lyr, flds)
- return net
-
-
-def write_shp(G, outdir):
- """Writes a networkx.DiGraph to two shapefiles, edges and nodes.
- Nodes and edges are expected to have a Well Known Binary (Wkb) or
- Well Known Text (Wkt) key in order to generate geometries. Also
- acceptable are nodes with a numeric tuple key (x,y).
-
- "The Esri Shapefile or simply a shapefile is a popular geospatial vector
- data format for geographic information systems software [1]_."
-
- Parameters
- ----------
- outdir : directory path
- Output directory for the two shapefiles.
-
- Returns
- -------
- None
-
- Examples
- --------
- nx.write_shp(digraph, '/shapefiles') # doctest +SKIP
-
- References
- ----------
- .. [1] http://en.wikipedia.org/wiki/Shapefile
- """
- try:
- from osgeo import ogr
- except ImportError:
- raise ImportError("write_shp requires OGR: http://www.gdal.org/")
- # easier to debug in python if ogr throws exceptions
- ogr.UseExceptions()
-
- def netgeometry(key, data):
- if 'Wkb' in data:
- geom = ogr.CreateGeometryFromWkb(data['Wkb'])
- elif 'Wkt' in data:
- geom = ogr.CreateGeometryFromWkt(data['Wkt'])
- elif type(key[0]).__name__ == 'tuple': # edge keys are packed tuples
- geom = ogr.Geometry(ogr.wkbLineString)
- _from, _to = key[0], key[1]
- try:
- geom.SetPoint(0, *_from)
- geom.SetPoint(1, *_to)
- except TypeError:
- # assume user used tuple of int and choked ogr
- _ffrom = [float(x) for x in _from]
- _fto = [float(x) for x in _to]
- geom.SetPoint(0, *_ffrom)
- geom.SetPoint(1, *_fto)
- else:
- geom = ogr.Geometry(ogr.wkbPoint)
- try:
- geom.SetPoint(0, *key)
- except TypeError:
- # assume user used tuple of int and choked ogr
- fkey = [float(x) for x in key]
- geom.SetPoint(0, *fkey)
-
- return geom
-
- # Create_feature with new optional attributes arg (should be dict type)
- def create_feature(geometry, lyr, attributes=None):
- feature = ogr.Feature(lyr.GetLayerDefn())
- feature.SetGeometry(g)
- if attributes != None:
- # Loop through attributes, assigning data to each field
- for field, data in attributes.iter():
- feature.SetField(field, data)
- lyr.CreateFeature(feature)
- feature.Destroy()
-
- drv = ogr.GetDriverByName("ESRI Shapefile")
- shpdir = drv.CreateDataSource(outdir)
- # delete pre-existing output first otherwise ogr chokes
- try:
- shpdir.DeleteLayer("nodes")
- except:
- pass
- nodes = shpdir.CreateLayer("nodes", None, ogr.wkbPoint)
- for n in G:
- data = G.node[n] or {}
- g = netgeometry(n, data)
- create_feature(g, nodes)
- try:
- shpdir.DeleteLayer("edges")
- except:
- pass
- edges = shpdir.CreateLayer("edges", None, ogr.wkbLineString)
-
- # New edge attribute write support merged into edge loop
- fields = {} # storage for field names and their data types
- attributes = {} # storage for attribute data (indexed by field names)
-
- # Conversion dict between python and ogr types
- OGRTypes = {int: ogr.OFTInteger, str: ogr.OFTString, float: ogr.OFTReal}
-
- # Edge loop
- for e in G.edges(data=True):
- data = G.get_edge_data(*e)
- g = netgeometry(e, data)
- # Loop through attribute data in edges
- for key, data in e[2].iter():
- # Reject spatial data not required for attribute table
- if (key != 'Json' and key != 'Wkt' and key != 'Wkb'
- and key != 'ShpName'):
- # For all edges check/add field and data type to fields dict
- if key not in fields:
- # Field not in previous edges so add to dict
- if type(data) in OGRTypes:
- fields[key] = OGRTypes[type(data)]
- else:
- # Data type not supported, default to string (char 80)
- fields[key] = ogr.OFTString
- # Create the new field
- newfield = ogr.FieldDefn(key, fields[key])
- edges.CreateField(newfield)
- # Store the data from new field to dict for CreateLayer()
- attributes[key] = data
- else:
- # Field already exists, add data to dict for CreateLayer()
- attributes[key] = data
- # Create the feature with, passing new attribute data
- create_feature(g, edges, attributes)
-
- nodes, edges = None, None
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import ogr
- except:
- raise SkipTest("OGR not available")
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/nx_yaml.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/nx_yaml.py
deleted file mode 100644
index 00c916f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/nx_yaml.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""
-****
-YAML
-****
-Read and write NetworkX graphs in YAML format.
-
-"YAML is a data serialization format designed for human readability
-and interaction with scripting languages."
-See http://www.yaml.org for documentation.
-
-Format
-------
-http://pyyaml.org/wiki/PyYAML
-
-"""
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['read_yaml', 'write_yaml']
-
-import networkx as nx
-from networkx.utils import open_file
-
-@open_file(1,mode='w')
-def write_yaml(G, path, encoding='UTF-8', **kwds):
- """Write graph G in YAML format to path.
-
- YAML is a data serialization format designed for human readability
- and interaction with scripting languages [1]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be compressed.
- encoding: string, optional
- Specify which encoding to use when writing file.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_yaml(G,'test.yaml')
-
- References
- ----------
- .. [1] http://www.yaml.org
- """
- try:
- import yaml
- except ImportError:
- raise ImportError("write_yaml() requires PyYAML: http://pyyaml.org/")
- yaml.dump(G, path, **kwds)
-
-@open_file(0,mode='r')
-def read_yaml(path):
- """Read graph in YAML format from path.
-
- YAML is a data serialization format designed for human readability
- and interaction with scripting languages [1]_.
-
- Parameters
- ----------
- path : file or string
- File or filename to read. Filenames ending in .gz or .bz2
- will be uncompressed.
-
- Returns
- -------
- G : NetworkX graph
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_yaml(G,'test.yaml')
- >>> G=nx.read_yaml('test.yaml')
-
- References
- ----------
- .. [1] http://www.yaml.org
-
- """
- try:
- import yaml
- except ImportError:
- raise ImportError("read_yaml() requires PyYAML: http://pyyaml.org/")
-
- G=yaml.load(path)
- return G
-
-
-# fixture for nose tests
-def setup_module(module):
- from nose import SkipTest
- try:
- import yaml
- except:
- raise SkipTest("PyYAML not available")
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.yaml')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/p2g.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/p2g.py
deleted file mode 100644
index 92dca9d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/p2g.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-This module provides the following: read and write of p2g format
-used in metabolic pathway studies.
-
-See http://www.cs.purdue.edu/homes/koyuturk/pathway/ for a description.
-
-The summary is included here:
-
-A file that describes a uniquely labeled graph (with extension ".gr")
-format looks like the following:
-
-
-name
-3 4
-a
-1 2
-b
-
-c
-0 2
-
-"name" is simply a description of what the graph corresponds to. The
-second line displays the number of nodes and number of edges,
-respectively. This sample graph contains three nodes labeled "a", "b",
-and "c". The rest of the graph contains two lines for each node. The
-first line for a node contains the node label. After the declaration
-of the node label, the out-edges of that node in the graph are
-provided. For instance, "a" is linked to nodes 1 and 2, which are
-labeled "b" and "c", while the node labeled "b" has no outgoing
-edges. Observe that node labeled "c" has an outgoing edge to
-itself. Indeed, self-loops are allowed. Node index starts from 0.
-
-"""
-# Copyright (C) 2008-2012 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx
-from networkx.utils import is_string_like,open_file
-__author__ = '\n'.join(['Willem Ligtenberg (w.p.a.ligtenberg@tue.nl)',
- 'Aric Hagberg (aric.hagberg@gmail.com)'])
-
-@open_file(1,mode='w')
-def write_p2g(G, path, encoding = 'utf-8'):
- """Write NetworkX graph in p2g format.
-
- Notes
- -----
- This format is meant to be used with directed graphs with
- possible self loops.
- """
- path.write(("%s\n"%G.name).encode(encoding))
- path.write(("%s %s\n"%(G.order(),G.size())).encode(encoding))
- nodes = G.nodes()
- # make dictionary mapping nodes to integers
- nodenumber=dict(zip(nodes,range(len(nodes))))
- for n in nodes:
- path.write(("%s\n"%n).encode(encoding))
- for nbr in G.neighbors(n):
- path.write(("%s "%nodenumber[nbr]).encode(encoding))
- path.write("\n".encode(encoding))
-
-@open_file(0,mode='r')
-def read_p2g(path, encoding='utf-8'):
- """Read graph in p2g format from path.
-
- Returns
- -------
- MultiDiGraph
-
- Notes
- -----
- If you want a DiGraph (with no self loops allowed and no edge data)
- use D=networkx.DiGraph(read_p2g(path))
- """
- lines = (line.decode(encoding) for line in path)
- G=parse_p2g(lines)
- return G
-
-def parse_p2g(lines):
- """Parse p2g format graph from string or iterable.
-
- Returns
- -------
- MultiDiGraph
- """
- description = next(lines).strip()
- # are multiedges (parallel edges) allowed?
- G=networkx.MultiDiGraph(name=description,selfloops=True)
- nnodes,nedges=map(int,next(lines).split())
- nodelabel={}
- nbrs={}
- # loop over the nodes keeping track of node labels and out neighbors
- # defer adding edges until all node labels are known
- for i in range(nnodes):
- n=next(lines).strip()
- nodelabel[i]=n
- G.add_node(n)
- nbrs[n]=map(int,next(lines).split())
- # now we know all of the node labels so we can add the edges
- # with the correct labels
- for n in G:
- for nbr in nbrs[n]:
- G.add_edge(n,nodelabel[nbr])
- return G
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/pajek.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/pajek.py
deleted file mode 100644
index ffc3641..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/pajek.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""
-*****
-Pajek
-*****
-Read graphs in Pajek format.
-
-This implementation handles directed and undirected graphs including
-those with self loops and parallel edges.
-
-Format
-------
-See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
-for format information.
-"""
-# Copyright (C) 2008-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-from networkx.utils import is_string_like, open_file, make_str
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-__all__ = ['read_pajek', 'parse_pajek', 'generate_pajek', 'write_pajek']
-
-def generate_pajek(G):
- """Generate lines in Pajek graph format.
-
- Parameters
- ----------
- G : graph
- A Networkx graph
-
- References
- ----------
- See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
- for format information.
- """
- if G.name=='':
- name='NetworkX'
- else:
- name=G.name
- # Apparently many Pajek format readers can't process this line
- # So we'll leave it out for now.
- # yield '*network %s'%name
-
- # write nodes with attributes
- yield '*vertices %s'%(G.order())
- nodes = G.nodes()
- # make dictionary mapping nodes to integers
- nodenumber=dict(zip(nodes,range(1,len(nodes)+1)))
- for n in nodes:
- na=G.node.get(n,{})
- x=na.get('x',0.0)
- y=na.get('y',0.0)
- id=int(na.get('id',nodenumber[n]))
- nodenumber[n]=id
- shape=na.get('shape','ellipse')
- s=' '.join(map(make_qstr,(id,n,x,y,shape)))
- for k,v in na.items():
- s+=' %s %s'%(make_qstr(k),make_qstr(v))
- yield s
-
- # write edges with attributes
- if G.is_directed():
- yield '*arcs'
- else:
- yield '*edges'
- for u,v,edgedata in G.edges(data=True):
- d=edgedata.copy()
- value=d.pop('weight',1.0) # use 1 as default edge value
- s=' '.join(map(make_qstr,(nodenumber[u],nodenumber[v],value)))
- for k,v in d.items():
- s+=' %s %s'%(make_qstr(k),make_qstr(v))
- s+=' %s %s'%(k,v)
- yield s
-
-@open_file(1,mode='wb')
-def write_pajek(G, path, encoding='UTF-8'):
- """Write graph in Pajek format to path.
-
- Parameters
- ----------
- G : graph
- A Networkx graph
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be compressed.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_pajek(G, "test.net")
-
- References
- ----------
- See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
- for format information.
- """
- for line in generate_pajek(G):
- line+='\n'
- path.write(line.encode(encoding))
-
-@open_file(0,mode='rb')
-def read_pajek(path,encoding='UTF-8'):
- """Read graph in Pajek format from path.
-
- Parameters
- ----------
- path : file or string
- File or filename to write.
- Filenames ending in .gz or .bz2 will be uncompressed.
-
- Returns
- -------
- G : NetworkX MultiGraph or MultiDiGraph.
-
- Examples
- --------
- >>> G=nx.path_graph(4)
- >>> nx.write_pajek(G, "test.net")
- >>> G=nx.read_pajek("test.net")
-
- To create a Graph instead of a MultiGraph use
-
- >>> G1=nx.Graph(G)
-
- References
- ----------
- See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
- for format information.
- """
- lines = (line.decode(encoding) for line in path)
- return parse_pajek(lines)
-
-def parse_pajek(lines):
- """Parse Pajek format graph from string or iterable.
-
- Parameters
- ----------
- lines : string or iterable
- Data in Pajek format.
-
- Returns
- -------
- G : NetworkX graph
-
- See Also
- --------
- read_pajek()
-
- """
- import shlex
- # multigraph=False
- if is_string_like(lines): lines=iter(lines.split('\n'))
- lines = iter([line.rstrip('\n') for line in lines])
- G=nx.MultiDiGraph() # are multiedges allowed in Pajek? assume yes
- while lines:
- try:
- l=next(lines)
- except: #EOF
- break
- if l.lower().startswith("*network"):
- label,name=l.split()
- G.name=name
- if l.lower().startswith("*vertices"):
- nodelabels={}
- l,nnodes=l.split()
- for i in range(int(nnodes)):
- splitline=shlex.split(str(next(lines)))
- id,label=splitline[0:2]
- G.add_node(label)
- nodelabels[id]=label
- G.node[label]={'id':id}
- try:
- x,y,shape=splitline[2:5]
- G.node[label].update({'x':float(x),
- 'y':float(y),
- 'shape':shape})
- except:
- pass
- extra_attr=zip(splitline[5::2],splitline[6::2])
- G.node[label].update(extra_attr)
- if l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
- if l.lower().startswith("*edge"):
- # switch from multidigraph to multigraph
- G=nx.MultiGraph(G)
- if l.lower().startswith("*arcs"):
- # switch to directed with multiple arcs for each existing edge
- G=G.to_directed()
- for l in lines:
- splitline=shlex.split(str(l))
- if len(splitline)<2:
- continue
- ui,vi=splitline[0:2]
- u=nodelabels.get(ui,ui)
- v=nodelabels.get(vi,vi)
- # parse the data attached to this edge and put in a dictionary
- edge_data={}
- try:
- # there should always be a single value on the edge?
- w=splitline[2:3]
- edge_data.update({'weight':float(w[0])})
- except:
- pass
- # if there isn't, just assign a 1
-# edge_data.update({'value':1})
- extra_attr=zip(splitline[3::2],splitline[4::2])
- edge_data.update(extra_attr)
- # if G.has_edge(u,v):
- # multigraph=True
- G.add_edge(u,v,**edge_data)
- return G
-
-
-
-def make_qstr(t):
- """Return the string representation of t.
- Add outer double-quotes if the string has a space.
- """
- if not is_string_like(t):
- t = str(t)
- if " " in t:
- t=r'"%s"'%t
- return t
-
-
-# fixture for nose tests
-def teardown_module(module):
- import os
- os.unlink('test.net')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/sparsegraph6.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/sparsegraph6.py
deleted file mode 100644
index 440c92a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/sparsegraph6.py
+++ /dev/null
@@ -1,169 +0,0 @@
-"""
-**************
-SparseGraph 6
-**************
-Read graphs in graph6 and sparse6 format.
-
-Format
-------
-
-"graph6 and sparse6 are formats for storing undirected graphs in a
-compact manner, using only printable ASCII characters. Files in these
-formats have text type and contain one line per graph."
-http://cs.anu.edu.au/~bdm/data/formats.html
-
-See http://cs.anu.edu.au/~bdm/data/formats.txt for details.
-"""
-# Original author: D. Eppstein, UC Irvine, August 12, 2003.
-# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
-__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
-# Copyright (C) 2004-2010 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-__all__ = ['read_graph6', 'parse_graph6', 'read_graph6_list',
- 'read_sparse6', 'parse_sparse6', 'read_sparse6_list']
-
-import networkx as nx
-from networkx.exception import NetworkXError
-from networkx.utils import open_file
-
-# graph6
-
-def read_graph6(path):
- """Read simple undirected graphs in graph6 format from path.
-
- Returns a single Graph.
- """
- return read_graph6_list(path)[0]
-
-def parse_graph6(str):
- """Read a simple undirected graph in graph6 format from string.
-
- Returns a single Graph.
- """
- def bits():
- """Return sequence of individual bits from 6-bit-per-value
- list of data values."""
- for d in data:
- for i in [5,4,3,2,1,0]:
- yield (d>>i)&1
-
- if str.startswith('>>graph6<<'):
- str = str[10:]
- data = graph6data(str)
- n, data = graph6n(data)
- nd = (n*(n-1)//2 + 5) // 6
- if len(data) != nd:
- raise NetworkXError(\
- 'Expected %d bits but got %d in graph6' % (n*(n-1)//2, len(data)*6))
-
- G=nx.Graph()
- G.add_nodes_from(range(n))
- for (i,j),b in zip([(i,j) for j in range(1,n) for i in range(j)], bits()):
- if b: G.add_edge(i,j)
- return G
-
-@open_file(0,mode='rt')
-def read_graph6_list(path):
- """Read simple undirected graphs in graph6 format from path.
-
- Returns a list of Graphs, one for each line in file.
- """
- glist=[]
- for line in path:
- line = line.strip()
- if not len(line): continue
- glist.append(parse_graph6(line))
- return glist
-
-# sparse6
-
-def read_sparse6(path):
- """Read simple undirected graphs in sparse6 format from path.
-
- Returns a single MultiGraph."""
- return read_sparse6_list(path)[0]
-
-@open_file(0,mode='rt')
-def read_sparse6_list(path):
- """Read undirected graphs in sparse6 format from path.
-
- Returns a list of MultiGraphs, one for each line in file."""
- glist=[]
- for line in path:
- line = line.strip()
- if not len(line): continue
- glist.append(parse_sparse6(line))
- return glist
-
-def parse_sparse6(string):
- """Read undirected graph in sparse6 format from string.
-
- Returns a MultiGraph.
- """
- if string.startswith('>>sparse6<<'):
- string = str[10:]
- if not string.startswith(':'):
- raise NetworkXError('Expected colon in sparse6')
- n, data = graph6n(graph6data(string[1:]))
- k = 1
- while 1<<k < n:
- k += 1
-
- def parseData():
- """Return stream of pairs b[i], x[i] for sparse6 format."""
- chunks = iter(data)
- d = None # partial data word
- dLen = 0 # how many unparsed bits are left in d
-
- while 1:
- if dLen < 1:
- d = next(chunks)
- dLen = 6
- dLen -= 1
- b = (d>>dLen) & 1 # grab top remaining bit
-
- x = d & ((1<<dLen)-1) # partially built up value of x
- xLen = dLen # how many bits included so far in x
- while xLen < k: # now grab full chunks until we have enough
- d = next(chunks)
- dLen = 6
- x = (x<<6) + d
- xLen += 6
- x = (x >> (xLen - k)) # shift back the extra bits
- dLen = xLen - k
- yield b,x
-
- v = 0
-
- G=nx.MultiGraph()
- G.add_nodes_from(range(n))
-
- for b,x in parseData():
- if b: v += 1
- if x >= n: break # padding with ones can cause overlarge number here
- elif x > v: v = x
- else:
- G.add_edge(x,v)
-
- return G
-
-# helper functions
-
-def graph6data(str):
- """Convert graph6 character sequence to 6-bit integers."""
- v = [ord(c)-63 for c in str]
- if min(v) < 0 or max(v) > 63:
- return None
- return v
-
-def graph6n(data):
- """Read initial one or four-unit value from graph6 sequence.
- Return value, rest of seq."""
- if data[0] <= 62:
- return data[0], data[1:]
- return (data[1]<<12) + (data[2]<<6) + data[3], data[4:]
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_adjlist.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_adjlist.py
deleted file mode 100644
index 9b3c0f2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_adjlist.py
+++ /dev/null
@@ -1,283 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
- Unit tests for adjlist.
-"""
-import io
-from nose.tools import assert_equal, assert_raises, assert_not_equal
-import os
-import tempfile
-import networkx as nx
-from networkx.testing import *
-
-
-class TestAdjlist():
-
- def setUp(self):
- self.G=nx.Graph(name="test")
- e=[('a','b'),('b','c'),('c','d'),('d','e'),('e','f'),('a','f')]
- self.G.add_edges_from(e)
- self.G.add_node('g')
- self.DG=nx.DiGraph(self.G)
- self.XG=nx.MultiGraph()
- self.XG.add_weighted_edges_from([(1,2,5),(1,2,5),(1,2,1),(3,3,42)])
- self. XDG=nx.MultiDiGraph(self.XG)
-
- def test_read_multiline_adjlist_1(self):
- # Unit test for https://networkx.lanl.gov/trac/ticket/252
- s = b"""# comment line
-1 2
-# comment line
-2
-3
-"""
- bytesIO = io.BytesIO(s)
- G = nx.read_multiline_adjlist(bytesIO)
- adj = {'1': {'3': {}, '2': {}}, '3': {'1': {}}, '2': {'1': {}}}
- assert_equal(G.adj, adj)
-
- def test_unicode(self):
- G = nx.Graph()
- try: # Python 3.x
- name1 = chr(2344) + chr(123) + chr(6543)
- name2 = chr(5543) + chr(1543) + chr(324)
- except ValueError: # Python 2.6+
- name1 = unichr(2344) + unichr(123) + unichr(6543)
- name2 = unichr(5543) + unichr(1543) + unichr(324)
- G.add_edge(name1, 'Radiohead', {name2: 3})
- fd, fname = tempfile.mkstemp()
- nx.write_multiline_adjlist(G, fname)
- H = nx.read_multiline_adjlist(fname)
- assert_equal(G.adj, H.adj)
- os.close(fd)
- os.unlink(fname)
-
- def test_latin1_error(self):
- G = nx.Graph()
- try: # Python 3.x
- name1 = chr(2344) + chr(123) + chr(6543)
- name2 = chr(5543) + chr(1543) + chr(324)
- except ValueError: # Python 2.6+
- name1 = unichr(2344) + unichr(123) + unichr(6543)
- name2 = unichr(5543) + unichr(1543) + unichr(324)
- G.add_edge(name1, 'Radiohead', {name2: 3})
- fd, fname = tempfile.mkstemp()
- assert_raises(UnicodeEncodeError,
- nx.write_multiline_adjlist,
- G, fname, encoding = 'latin-1')
- os.close(fd)
- os.unlink(fname)
-
- def test_latin1(self):
- G = nx.Graph()
- try: # Python 3.x
- blurb = chr(1245) # just to trigger the exception
- name1 = 'Bj' + chr(246) + 'rk'
- name2 = chr(220) + 'ber'
- except ValueError: # Python 2.6+
- name1 = 'Bj' + unichr(246) + 'rk'
- name2 = unichr(220) + 'ber'
- G.add_edge(name1, 'Radiohead', {name2: 3})
- fd, fname = tempfile.mkstemp()
- nx.write_multiline_adjlist(G, fname, encoding = 'latin-1')
- H = nx.read_multiline_adjlist(fname, encoding = 'latin-1')
- assert_equal(G.adj, H.adj)
- os.close(fd)
- os.unlink(fname)
-
-
-
- def test_adjlist_graph(self):
- G=self.G
- (fd,fname)=tempfile.mkstemp()
- nx.write_adjlist(G,fname)
- H=nx.read_adjlist(fname)
- H2=nx.read_adjlist(fname)
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_adjlist_digraph(self):
- G=self.DG
- (fd,fname)=tempfile.mkstemp()
- nx.write_adjlist(G,fname)
- H=nx.read_adjlist(fname,create_using=nx.DiGraph())
- H2=nx.read_adjlist(fname,create_using=nx.DiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_adjlist_integers(self):
- (fd,fname)=tempfile.mkstemp()
- G=nx.convert_node_labels_to_integers(self.G)
- nx.write_adjlist(G,fname)
- H=nx.read_adjlist(fname,nodetype=int)
- H2=nx.read_adjlist(fname,nodetype=int)
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_adjlist_digraph(self):
- G=self.DG
- (fd,fname)=tempfile.mkstemp()
- nx.write_adjlist(G,fname)
- H=nx.read_adjlist(fname,create_using=nx.DiGraph())
- H2=nx.read_adjlist(fname,create_using=nx.DiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_adjlist_multigraph(self):
- G=self.XG
- (fd,fname)=tempfile.mkstemp()
- nx.write_adjlist(G,fname)
- H=nx.read_adjlist(fname,nodetype=int,
- create_using=nx.MultiGraph())
- H2=nx.read_adjlist(fname,nodetype=int,
- create_using=nx.MultiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_adjlist_multidigraph(self):
- G=self.XDG
- (fd,fname)=tempfile.mkstemp()
- nx.write_adjlist(G,fname)
- H=nx.read_adjlist(fname,nodetype=int,
- create_using=nx.MultiDiGraph())
- H2=nx.read_adjlist(fname,nodetype=int,
- create_using=nx.MultiDiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_adjlist_delimiter(self):
- fh=io.BytesIO()
- G = nx.path_graph(3)
- nx.write_adjlist(G, fh, delimiter=':')
- fh.seek(0)
- H = nx.read_adjlist(fh, nodetype=int, delimiter=':')
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
-
-
-
-
-
-
-class TestMultilineAdjlist():
-
- def setUp(self):
- self.G=nx.Graph(name="test")
- e=[('a','b'),('b','c'),('c','d'),('d','e'),('e','f'),('a','f')]
- self.G.add_edges_from(e)
- self.G.add_node('g')
- self.DG=nx.DiGraph(self.G)
- self.DG.remove_edge('b','a')
- self.DG.remove_edge('b','c')
- self.XG=nx.MultiGraph()
- self.XG.add_weighted_edges_from([(1,2,5),(1,2,5),(1,2,1),(3,3,42)])
- self. XDG=nx.MultiDiGraph(self.XG)
-
-
- def test_multiline_adjlist_graph(self):
- G=self.G
- (fd,fname)=tempfile.mkstemp()
- nx.write_multiline_adjlist(G,fname)
- H=nx.read_multiline_adjlist(fname)
- H2=nx.read_multiline_adjlist(fname)
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_multiline_adjlist_digraph(self):
- G=self.DG
- (fd,fname)=tempfile.mkstemp()
- nx.write_multiline_adjlist(G,fname)
- H=nx.read_multiline_adjlist(fname,create_using=nx.DiGraph())
- H2=nx.read_multiline_adjlist(fname,create_using=nx.DiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_multiline_adjlist_integers(self):
- (fd,fname)=tempfile.mkstemp()
- G=nx.convert_node_labels_to_integers(self.G)
- nx.write_multiline_adjlist(G,fname)
- H=nx.read_multiline_adjlist(fname,nodetype=int)
- H2=nx.read_multiline_adjlist(fname,nodetype=int)
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_multiline_adjlist_digraph(self):
- G=self.DG
- (fd,fname)=tempfile.mkstemp()
- nx.write_multiline_adjlist(G,fname)
- H=nx.read_multiline_adjlist(fname,create_using=nx.DiGraph())
- H2=nx.read_multiline_adjlist(fname,create_using=nx.DiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_edges_equal(H.edges(),G.edges())
- os.close(fd)
- os.unlink(fname)
-
-
- def test_multiline_adjlist_multigraph(self):
- G=self.XG
- (fd,fname)=tempfile.mkstemp()
- nx.write_multiline_adjlist(G,fname)
- H=nx.read_multiline_adjlist(fname,nodetype=int,
- create_using=nx.MultiGraph())
- H2=nx.read_multiline_adjlist(fname,nodetype=int,
- create_using=nx.MultiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_multiline_adjlist_multidigraph(self):
- G=self.XDG
- (fd,fname)=tempfile.mkstemp()
- nx.write_multiline_adjlist(G,fname)
- H=nx.read_multiline_adjlist(fname,nodetype=int,
- create_using=nx.MultiDiGraph())
- H2=nx.read_multiline_adjlist(fname,nodetype=int,
- create_using=nx.MultiDiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_multiline_adjlist_delimiter(self):
- fh=io.BytesIO()
- G = nx.path_graph(3)
- nx.write_multiline_adjlist(G, fh, delimiter=':')
- fh.seek(0)
- H = nx.read_multiline_adjlist(fh, nodetype=int, delimiter=':')
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_edgelist.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_edgelist.py
deleted file mode 100644
index be70bba..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_edgelist.py
+++ /dev/null
@@ -1,234 +0,0 @@
-"""
- Unit tests for edgelists.
-"""
-from nose.tools import assert_equal, assert_raises, assert_not_equal
-import networkx as nx
-import io
-import tempfile
-import os
-
-def assert_equal_edges(elist1,elist2):
- if len(elist1[0]) == 2:
- return assert_equal(sorted(sorted(e) for e in elist1),
- sorted(sorted(e) for e in elist2))
- else:
- return assert_equal(sorted((sorted((u, v)), d) for u, v, d in elist1),
- sorted((sorted((u, v)), d) for u, v, d in elist2))
-
-class TestEdgelist:
-
- def setUp(self):
- self.G=nx.Graph(name="test")
- e=[('a','b'),('b','c'),('c','d'),('d','e'),('e','f'),('a','f')]
- self.G.add_edges_from(e)
- self.G.add_node('g')
- self.DG=nx.DiGraph(self.G)
- self.XG=nx.MultiGraph()
- self.XG.add_weighted_edges_from([(1,2,5),(1,2,5),(1,2,1),(3,3,42)])
- self. XDG=nx.MultiDiGraph(self.XG)
-
-
- def test_read_edgelist_1(self):
- s = b"""\
-# comment line
-1 2
-# comment line
-2 3
-"""
- bytesIO = io.BytesIO(s)
- G = nx.read_edgelist(bytesIO,nodetype=int)
- assert_equal_edges(G.edges(),[(1,2),(2,3)])
-
- def test_read_edgelist_2(self):
- s = b"""\
-# comment line
-1 2 2.0
-# comment line
-2 3 3.0
-"""
- bytesIO = io.BytesIO(s)
- G = nx.read_edgelist(bytesIO,nodetype=int,data=False)
- assert_equal_edges(G.edges(),[(1,2),(2,3)])
-
- bytesIO = io.BytesIO(s)
- G = nx.read_weighted_edgelist(bytesIO,nodetype=int)
- assert_equal_edges(G.edges(data=True),[(1,2,{'weight':2.0}),(2,3,{'weight':3.0})])
-
- def test_read_edgelist_3(self):
- s = b"""\
-# comment line
-1 2 {'weight':2.0}
-# comment line
-2 3 {'weight':3.0}
-"""
- bytesIO = io.BytesIO(s)
- G = nx.read_edgelist(bytesIO,nodetype=int,data=False)
- assert_equal_edges(G.edges(),[(1,2),(2,3)])
-
- bytesIO = io.BytesIO(s)
- G = nx.read_edgelist(bytesIO,nodetype=int,data=True)
- assert_equal_edges(G.edges(data=True),[(1,2,{'weight':2.0}),(2,3,{'weight':3.0})])
-
- def test_write_edgelist_1(self):
- fh=io.BytesIO()
- G=nx.Graph()
- G.add_edges_from([(1,2),(2,3)])
- nx.write_edgelist(G,fh,data=False)
- fh.seek(0)
- assert_equal(fh.read(),b"1 2\n2 3\n")
-
- def test_write_edgelist_2(self):
- fh=io.BytesIO()
- G=nx.Graph()
- G.add_edges_from([(1,2),(2,3)])
- nx.write_edgelist(G,fh,data=True)
- fh.seek(0)
- assert_equal(fh.read(),b"1 2 {}\n2 3 {}\n")
-
- def test_write_edgelist_3(self):
- fh=io.BytesIO()
- G=nx.Graph()
- G.add_edge(1,2,weight=2.0)
- G.add_edge(2,3,weight=3.0)
- nx.write_edgelist(G,fh,data=True)
- fh.seek(0)
- assert_equal(fh.read(),b"1 2 {'weight': 2.0}\n2 3 {'weight': 3.0}\n")
-
- def test_write_edgelist_4(self):
- fh=io.BytesIO()
- G=nx.Graph()
- G.add_edge(1,2,weight=2.0)
- G.add_edge(2,3,weight=3.0)
- nx.write_edgelist(G,fh,data=[('weight')])
- fh.seek(0)
- assert_equal(fh.read(),b"1 2 2.0\n2 3 3.0\n")
-
- def test_unicode(self):
- G = nx.Graph()
- try: # Python 3.x
- name1 = chr(2344) + chr(123) + chr(6543)
- name2 = chr(5543) + chr(1543) + chr(324)
- except ValueError: # Python 2.6+
- name1 = unichr(2344) + unichr(123) + unichr(6543)
- name2 = unichr(5543) + unichr(1543) + unichr(324)
- G.add_edge(name1, 'Radiohead', attr_dict={name2: 3})
- fd, fname = tempfile.mkstemp()
- nx.write_edgelist(G, fname)
- H = nx.read_edgelist(fname)
- assert_equal(G.adj, H.adj)
- os.close(fd)
- os.unlink(fname)
-
- def test_latin1_error(self):
- G = nx.Graph()
- try: # Python 3.x
- name1 = chr(2344) + chr(123) + chr(6543)
- name2 = chr(5543) + chr(1543) + chr(324)
- except ValueError: # Python 2.6+
- name1 = unichr(2344) + unichr(123) + unichr(6543)
- name2 = unichr(5543) + unichr(1543) + unichr(324)
- G.add_edge(name1, 'Radiohead', attr_dict={name2: 3})
- fd, fname = tempfile.mkstemp()
- assert_raises(UnicodeEncodeError,
- nx.write_edgelist,
- G, fname, encoding = 'latin-1')
- os.close(fd)
- os.unlink(fname)
-
- def test_latin1(self):
- G = nx.Graph()
- try: # Python 3.x
- blurb = chr(1245) # just to trigger the exception
- name1 = 'Bj' + chr(246) + 'rk'
- name2 = chr(220) + 'ber'
- except ValueError: # Python 2.6+
- name1 = 'Bj' + unichr(246) + 'rk'
- name2 = unichr(220) + 'ber'
- G.add_edge(name1, 'Radiohead', attr_dict={name2: 3})
- fd, fname = tempfile.mkstemp()
- nx.write_edgelist(G, fname, encoding = 'latin-1')
- H = nx.read_edgelist(fname, encoding = 'latin-1')
- assert_equal(G.adj, H.adj)
- os.close(fd)
- os.unlink(fname)
-
-
- def test_edgelist_graph(self):
- G=self.G
- (fd,fname)=tempfile.mkstemp()
- nx.write_edgelist(G,fname)
- H=nx.read_edgelist(fname)
- H2=nx.read_edgelist(fname)
- assert_not_equal(H,H2) # they should be different graphs
- G.remove_node('g') # isolated nodes are not written in edgelist
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_edgelist_digraph(self):
- G=self.DG
- (fd,fname)=tempfile.mkstemp()
- nx.write_edgelist(G,fname)
- H=nx.read_edgelist(fname,create_using=nx.DiGraph())
- H2=nx.read_edgelist(fname,create_using=nx.DiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- G.remove_node('g') # isolated nodes are not written in edgelist
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_edgelist_integers(self):
- G=nx.convert_node_labels_to_integers(self.G)
- (fd,fname)=tempfile.mkstemp()
- nx.write_edgelist(G,fname)
- H=nx.read_edgelist(fname,nodetype=int)
- # isolated nodes are not written in edgelist
- G.remove_nodes_from(nx.isolates(G))
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_edgelist_digraph(self):
- G=self.DG
- (fd,fname)=tempfile.mkstemp()
- nx.write_edgelist(G,fname)
- H=nx.read_edgelist(fname,create_using=nx.DiGraph())
- G.remove_node('g') # isolated nodes are not written in edgelist
- H2=nx.read_edgelist(fname,create_using=nx.DiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
- def test_edgelist_multigraph(self):
- G=self.XG
- (fd,fname)=tempfile.mkstemp()
- nx.write_edgelist(G,fname)
- H=nx.read_edgelist(fname,nodetype=int,create_using=nx.MultiGraph())
- H2=nx.read_edgelist(fname,nodetype=int,create_using=nx.MultiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_edgelist_multidigraph(self):
- G=self.XDG
- (fd,fname)=tempfile.mkstemp()
- nx.write_edgelist(G,fname)
- H=nx.read_edgelist(fname,nodetype=int,create_using=nx.MultiDiGraph())
- H2=nx.read_edgelist(fname,nodetype=int,create_using=nx.MultiDiGraph())
- assert_not_equal(H,H2) # they should be different graphs
- assert_equal(sorted(H.nodes()),sorted(G.nodes()))
- assert_equal(sorted(H.edges()),sorted(G.edges()))
- os.close(fd)
- os.unlink(fname)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gexf.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gexf.py
deleted file mode 100644
index bbd86ba..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gexf.py
+++ /dev/null
@@ -1,306 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-import io
-
-class TestGEXF(object):
- @classmethod
- def setupClass(cls):
- try:
- import xml.etree.ElementTree
- except ImportError:
- raise SkipTest('xml.etree.ElementTree not available.')
-
- def setUp(self):
- self.simple_directed_data="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" version="1.1">
- <graph mode="static" defaultedgetype="directed">
- <nodes>
- <node id="0" label="Hello" />
- <node id="1" label="Word" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1" />
- </edges>
- </graph>
-</gexf>
-"""
- self.simple_directed_graph=nx.DiGraph()
- self.simple_directed_graph.add_node('0',label='Hello')
- self.simple_directed_graph.add_node('1',label='World')
- self.simple_directed_graph.add_edge('0','1',id='0')
-
- self.simple_directed_fh = \
- io.BytesIO(self.simple_directed_data.encode('UTF-8'))
-
-
- self.attribute_data="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.gexf.net/1.1draft http://www.gexf.net/1.1draft/gexf.xsd" version="1.1">
- <meta lastmodifieddate="2009-03-20">
- <creator>Gephi.org</creator>
- <description>A Web network</description>
- </meta>
- <graph defaultedgetype="directed">
- <attributes class="node">
- <attribute id="0" title="url" type="string"/>
- <attribute id="1" title="indegree" type="integer"/>
- <attribute id="2" title="frog" type="boolean">
- <default>true</default>
- </attribute>
- </attributes>
- <nodes>
- <node id="0" label="Gephi">
- <attvalues>
- <attvalue for="0" value="http://gephi.org"/>
- <attvalue for="1" value="1"/>
- </attvalues>
- </node>
- <node id="1" label="Webatlas">
- <attvalues>
- <attvalue for="0" value="http://webatlas.fr"/>
- <attvalue for="1" value="2"/>
- </attvalues>
- </node>
- <node id="2" label="RTGI">
- <attvalues>
- <attvalue for="0" value="http://rtgi.fr"/>
- <attvalue for="1" value="1"/>
- </attvalues>
- </node>
- <node id="3" label="BarabasiLab">
- <attvalues>
- <attvalue for="0" value="http://barabasilab.com"/>
- <attvalue for="1" value="1"/>
- <attvalue for="2" value="false"/>
- </attvalues>
- </node>
- </nodes>
- <edges>
- <edge id="0" source="0" target="1"/>
- <edge id="1" source="0" target="2"/>
- <edge id="2" source="1" target="0"/>
- <edge id="3" source="2" target="1"/>
- <edge id="4" source="0" target="3"/>
- </edges>
- </graph>
-</gexf>
-"""
- self.attribute_graph=nx.DiGraph()
- self.attribute_graph.graph['node_default']={'frog':True}
- self.attribute_graph.add_node('0',
- label='Gephi',
- url='http://gephi.org',
- indegree=1)
- self.attribute_graph.add_node('1',
- label='Webatlas',
- url='http://webatlas.fr',
- indegree=2)
-
- self.attribute_graph.add_node('2',
- label='RTGI',
- url='http://rtgi.fr',
- indegree=1)
-
- self.attribute_graph.add_node('3',
- label='BarabasiLab',
- url='http://barabasilab.com',
- indegree=1,
- frog=False)
- self.attribute_graph.add_edge('0','1',id='0')
- self.attribute_graph.add_edge('0','2',id='1')
- self.attribute_graph.add_edge('1','0',id='2')
- self.attribute_graph.add_edge('2','1',id='3')
- self.attribute_graph.add_edge('0','3',id='4')
- self.attribute_fh = io.BytesIO(self.attribute_data.encode('UTF-8'))
-
- self.simple_undirected_data="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" version="1.1">
- <graph mode="static" defaultedgetype="undirected">
- <nodes>
- <node id="0" label="Hello" />
- <node id="1" label="Word" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1" />
- </edges>
- </graph>
-</gexf>
-"""
- self.simple_undirected_graph=nx.Graph()
- self.simple_undirected_graph.add_node('0',label='Hello')
- self.simple_undirected_graph.add_node('1',label='World')
- self.simple_undirected_graph.add_edge('0','1',id='0')
-
- self.simple_undirected_fh = io.BytesIO(self.simple_undirected_data.encode('UTF-8'))
-
-
- def test_read_simple_directed_graphml(self):
- G=self.simple_directed_graph
- H=nx.read_gexf(self.simple_directed_fh)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(sorted(G.edges()),sorted(H.edges()))
- assert_equal(sorted(G.edges(data=True)),
- sorted(H.edges(data=True)))
- self.simple_directed_fh.seek(0)
-
- def test_write_read_simple_directed_graphml(self):
- G=self.simple_directed_graph
- fh=io.BytesIO()
- nx.write_gexf(G,fh)
- fh.seek(0)
- H=nx.read_gexf(fh)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(sorted(G.edges()),sorted(H.edges()))
- assert_equal(sorted(G.edges(data=True)),
- sorted(H.edges(data=True)))
- self.simple_directed_fh.seek(0)
-
- def test_read_simple_undirected_graphml(self):
- G=self.simple_undirected_graph
- H=nx.read_gexf(self.simple_undirected_fh)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(
- sorted(sorted(e) for e in G.edges()),
- sorted(sorted(e) for e in H.edges()))
- self.simple_undirected_fh.seek(0)
-
- def test_read_attribute_graphml(self):
- G=self.attribute_graph
- H=nx.read_gexf(self.attribute_fh)
- assert_equal(sorted(G.nodes(True)),sorted(H.nodes(data=True)))
- ge=sorted(G.edges(data=True))
- he=sorted(H.edges(data=True))
- for a,b in zip(ge,he):
- assert_equal(a,b)
- self.attribute_fh.seek(0)
-
- def test_directed_edge_in_undirected(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" version="1.1">
- <graph mode="static" defaultedgetype="undirected">
- <nodes>
- <node id="0" label="Hello" />
- <node id="1" label="Word" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1" type="directed"/>
- </edges>
- </graph>
-</gexf>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_gexf,fh)
-
- def test_undirected_edge_in_directed(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" version="1.1">
- <graph mode="static" defaultedgetype="directed">
- <nodes>
- <node id="0" label="Hello" />
- <node id="1" label="Word" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1" type="undirected"/>
- </edges>
- </graph>
-</gexf>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_gexf,fh)
-
-
- def test_key_error(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" version="1.1">
- <graph mode="static" defaultedgetype="directed">
- <nodes>
- <node id="0" label="Hello">
- <attvalues>
- <attvalue for='0' value='1'/>
- </attvalues>
- </node>
- <node id="1" label="Word" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1" type="undirected"/>
- </edges>
- </graph>
-</gexf>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_gexf,fh)
-
- def test_relabel(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<gexf xmlns="http://www.gexf.net/1.1draft" version="1.1">
- <graph mode="static" defaultedgetype="directed">
- <nodes>
- <node id="0" label="Hello" />
- <node id="1" label="Word" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1"/>
- </edges>
- </graph>
-</gexf>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- G=nx.read_gexf(fh,relabel=True)
- assert_equal(sorted(G.nodes()),["Hello","Word"])
-
-
- def test_default_attribute(self):
- G=nx.Graph()
- G.add_node(1,label='1',color='green')
- G.add_path([0,1,2,3])
- G.add_edge(1,2,foo=3)
- G.graph['node_default']={'color':'yellow'}
- G.graph['edge_default']={'foo':7}
- fh = io.BytesIO()
- nx.write_gexf(G,fh)
- fh.seek(0)
- H=nx.read_gexf(fh,node_type=int)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(
- sorted(sorted(e) for e in G.edges()),
- sorted(sorted(e) for e in H.edges()))
- assert_equal(G.graph,H.graph)
-
- def test_serialize_ints_to_strings(self):
- G=nx.Graph()
- G.add_node(1,id=7,label=77)
- fh = io.BytesIO()
- nx.write_gexf(G,fh)
- fh.seek(0)
- H=nx.read_gexf(fh,node_type=int)
- assert_equal(H.nodes(),[7])
- assert_equal(H.node[7]['label'],'77')
-
- def test_write_with_node_attributes(self):
- # Addresses #673.
- G = nx.path_graph(4)
- for i in range(4):
- G.node[i]['id'] = i
- G.node[i]['label'] = i
- G.node[i]['pid'] = i
-
- expected = """<gexf version="1.1" xmlns="http://www.gexf.net/1.1draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
- <graph defaultedgetype="undirected" mode="static">
- <nodes>
- <node id="0" label="0" pid="0" />
- <node id="1" label="1" pid="1" />
- <node id="2" label="2" pid="2" />
- <node id="3" label="3" pid="3" />
- </nodes>
- <edges>
- <edge id="0" source="0" target="1" />
- <edge id="1" source="1" target="2" />
- <edge id="2" source="2" target="3" />
- </edges>
- </graph>
-</gexf>"""
- obtained = '\n'.join(nx.generate_gexf(G))
- assert_equal( expected, obtained )
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gml.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gml.py
deleted file mode 100644
index ed63bd2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gml.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/usr/bin/env python
-import io
-from nose.tools import *
-from nose import SkipTest
-import networkx
-
-class TestGraph(object):
- @classmethod
- def setupClass(cls):
- global pyparsing
- try:
- import pyparsing
- except ImportError:
- try:
- import matplotlib.pyparsing as pyparsing
- except:
- raise SkipTest('gml test: pyparsing not available.')
-
- def setUp(self):
- self.simple_data="""Creator me
-graph [
- comment "This is a sample graph"
- directed 1
- IsPlanar 1
- pos [ x 0 y 1 ]
- node [
- id 1
- label "Node 1"
- pos [ x 1 y 1 ]
- ]
- node [
- id 2
- pos [ x 1 y 2 ]
- label "Node 2"
- ]
- node [
- id 3
- label "Node 3"
- pos [ x 1 y 3 ]
- ]
- edge [
- source 1
- target 2
- label "Edge from node 1 to node 2"
- color [line "blue" thickness 3]
-
- ]
- edge [
- source 2
- target 3
- label "Edge from node 2 to node 3"
- ]
- edge [
- source 3
- target 1 label
- "Edge from node 3 to node 1"
- ]
-]
-"""
- def test_parse_gml(self):
- G=networkx.parse_gml(self.simple_data,relabel=True)
- assert_equals(sorted(G.nodes()),\
- ['Node 1', 'Node 2', 'Node 3'])
- assert_equals( [e for e in sorted(G.edges())],\
- [('Node 1', 'Node 2'),
- ('Node 2', 'Node 3'),
- ('Node 3', 'Node 1')])
-
- assert_equals( [e for e in sorted(G.edges(data=True))],\
- [('Node 1', 'Node 2',
- {'color': {'line': 'blue', 'thickness': 3},
- 'label': 'Edge from node 1 to node 2'}),
- ('Node 2', 'Node 3',
- {'label': 'Edge from node 2 to node 3'}),
- ('Node 3', 'Node 1',
- {'label': 'Edge from node 3 to node 1'})])
-
-
- def test_read_gml(self):
- import os,tempfile
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- fh.write(self.simple_data)
- fh.close()
- Gin=networkx.read_gml(fname,relabel=True)
- G=networkx.parse_gml(self.simple_data,relabel=True)
- assert_equals( sorted(G.nodes(data=True)), sorted(Gin.nodes(data=True)))
- assert_equals( sorted(G.edges(data=True)), sorted(Gin.edges(data=True)))
- os.close(fd)
- os.unlink(fname)
-
- def test_relabel_duplicate(self):
- data="""
-graph
-[
- label ""
- directed 1
- node
- [
- id 0
- label "same"
- ]
- node
- [
- id 1
- label "same"
- ]
-]
-"""
- fh = io.BytesIO(data.encode('UTF-8'))
- fh.seek(0)
- assert_raises(networkx.NetworkXError,networkx.read_gml,fh,relabel=True)
-
- def test_bool(self):
- G=networkx.Graph()
- G.add_node(1,on=True)
- G.add_edge(1,2,on=False)
- data = '\n'.join(list(networkx.generate_gml(G)))
- answer ="""graph [
- node [
- id 0
- label 1
- on 1
- ]
- node [
- id 1
- label 2
- ]
- edge [
- source 0
- target 1
- on 0
- ]
-]"""
- assert_equal(data,answer)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gpickle.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gpickle.py
deleted file mode 100644
index 429f753..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_gpickle.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import assert_equal
-import networkx as nx
-import os,tempfile
-
-class TestGpickle(object):
- def setUp(self):
- G=nx.Graph(name="test")
- e=[('a','b'),('b','c'),('c','d'),('d','e'),('e','f'),('a','f')]
- G.add_edges_from(e,width=10)
- G.add_node('g',color='green')
- G.graph['number']=1
- self.G=G
-
- def test_gpickle(self):
- G=self.G
- (fd,fname)=tempfile.mkstemp()
- nx.write_gpickle(G,fname);
- Gin=nx.read_gpickle(fname);
- assert_equal(sorted(G.nodes(data=True)),
- sorted(Gin.nodes(data=True)))
- assert_equal(sorted(G.edges(data=True)),
- sorted(Gin.edges(data=True)))
- assert_equal(G.graph,Gin.graph)
- os.close(fd)
- os.unlink(fname)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_graphml.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_graphml.py
deleted file mode 100644
index c21c89b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_graphml.py
+++ /dev/null
@@ -1,445 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-import io
-import tempfile
-import os
-
-class TestGraph(object):
- @classmethod
- def setupClass(cls):
- try:
- import xml.etree.ElementTree
- except ImportError:
- raise SkipTest('xml.etree.ElementTree not available.')
-
- def setUp(self):
- self.simple_directed_data="""<?xml version="1.0" encoding="UTF-8"?>
-<!-- This file was written by the JAVA GraphML Library.-->
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <graph id="G" edgedefault="directed">
- <node id="n0"/>
- <node id="n1"/>
- <node id="n2"/>
- <node id="n3"/>
- <node id="n4"/>
- <node id="n5"/>
- <node id="n6"/>
- <node id="n7"/>
- <node id="n8"/>
- <node id="n9"/>
- <node id="n10"/>
- <edge id="foo" source="n0" target="n2"/>
- <edge source="n1" target="n2"/>
- <edge source="n2" target="n3"/>
- <edge source="n3" target="n5"/>
- <edge source="n3" target="n4"/>
- <edge source="n4" target="n6"/>
- <edge source="n6" target="n5"/>
- <edge source="n5" target="n7"/>
- <edge source="n6" target="n8"/>
- <edge source="n8" target="n7"/>
- <edge source="n8" target="n9"/>
- </graph>
-</graphml>"""
- self.simple_directed_graph=nx.DiGraph()
- self.simple_directed_graph.add_node('n10')
- self.simple_directed_graph.add_edge('n0','n2',id='foo')
- self.simple_directed_graph.add_edges_from([('n1','n2'),
- ('n2','n3'),
- ('n3','n5'),
- ('n3','n4'),
- ('n4','n6'),
- ('n6','n5'),
- ('n5','n7'),
- ('n6','n8'),
- ('n8','n7'),
- ('n8','n9'),
- ])
-
- self.simple_directed_fh = \
- io.BytesIO(self.simple_directed_data.encode('UTF-8'))
-
-
- self.attribute_data="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
- xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
- xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
- http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <key id="d0" for="node" attr.name="color" attr.type="string">
- <default>yellow</default>
- </key>
- <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
- <graph id="G" edgedefault="directed">
- <node id="n0">
- <data key="d0">green</data>
- </node>
- <node id="n1"/>
- <node id="n2">
- <data key="d0">blue</data>
- </node>
- <node id="n3">
- <data key="d0">red</data>
- </node>
- <node id="n4"/>
- <node id="n5">
- <data key="d0">turquoise</data>
- </node>
- <edge id="e0" source="n0" target="n2">
- <data key="d1">1.0</data>
- </edge>
- <edge id="e1" source="n0" target="n1">
- <data key="d1">1.0</data>
- </edge>
- <edge id="e2" source="n1" target="n3">
- <data key="d1">2.0</data>
- </edge>
- <edge id="e3" source="n3" target="n2"/>
- <edge id="e4" source="n2" target="n4"/>
- <edge id="e5" source="n3" target="n5"/>
- <edge id="e6" source="n5" target="n4">
- <data key="d1">1.1</data>
- </edge>
- </graph>
-</graphml>
-"""
- self.attribute_graph=nx.DiGraph(id='G')
- self.attribute_graph.graph['node_default']={'color':'yellow'}
- self.attribute_graph.add_node('n0',color='green')
- self.attribute_graph.add_node('n2',color='blue')
- self.attribute_graph.add_node('n3',color='red')
- self.attribute_graph.add_node('n4')
- self.attribute_graph.add_node('n5',color='turquoise')
- self.attribute_graph.add_edge('n0','n2',id='e0',weight=1.0)
- self.attribute_graph.add_edge('n0','n1',id='e1',weight=1.0)
- self.attribute_graph.add_edge('n1','n3',id='e2',weight=2.0)
- self.attribute_graph.add_edge('n3','n2',id='e3')
- self.attribute_graph.add_edge('n2','n4',id='e4')
- self.attribute_graph.add_edge('n3','n5',id='e5')
- self.attribute_graph.add_edge('n5','n4',id='e6',weight=1.1)
- self.attribute_fh = io.BytesIO(self.attribute_data.encode('UTF-8'))
-
- self.simple_undirected_data="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <graph id="G">
- <node id="n0"/>
- <node id="n1"/>
- <node id="n2"/>
- <node id="n10"/>
- <edge id="foo" source="n0" target="n2"/>
- <edge source="n1" target="n2"/>
- <edge source="n2" target="n3"/>
- </graph>
-</graphml>"""
-# <edge source="n8" target="n10" directed="false"/>
- self.simple_undirected_graph=nx.Graph()
- self.simple_undirected_graph.add_node('n10')
- self.simple_undirected_graph.add_edge('n0','n2',id='foo')
- self.simple_undirected_graph.add_edges_from([('n1','n2'),
- ('n2','n3'),
- ])
-
- self.simple_undirected_fh = io.BytesIO(self.simple_undirected_data.encode('UTF-8'))
-
-
- def test_read_simple_directed_graphml(self):
- G=self.simple_directed_graph
- H=nx.read_graphml(self.simple_directed_fh)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(sorted(G.edges()),sorted(H.edges()))
- assert_equal(sorted(G.edges(data=True)),
- sorted(H.edges(data=True)))
- self.simple_directed_fh.seek(0)
-
- I=nx.parse_graphml(self.simple_directed_data)
- assert_equal(sorted(G.nodes()),sorted(I.nodes()))
- assert_equal(sorted(G.edges()),sorted(I.edges()))
- assert_equal(sorted(G.edges(data=True)),
- sorted(I.edges(data=True)))
-
- def test_write_read_simple_directed_graphml(self):
- G=self.simple_directed_graph
- fh=io.BytesIO()
- nx.write_graphml(G,fh)
- fh.seek(0)
- H=nx.read_graphml(fh)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(sorted(G.edges()),sorted(H.edges()))
- assert_equal(sorted(G.edges(data=True)),
- sorted(H.edges(data=True)))
- self.simple_directed_fh.seek(0)
-
- def test_read_simple_undirected_graphml(self):
- G=self.simple_undirected_graph
- H=nx.read_graphml(self.simple_undirected_fh)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(
- sorted(sorted(e) for e in G.edges()),
- sorted(sorted(e) for e in H.edges()))
- self.simple_undirected_fh.seek(0)
-
- I=nx.parse_graphml(self.simple_undirected_data)
- assert_equal(sorted(G.nodes()),sorted(I.nodes()))
- assert_equal(
- sorted(sorted(e) for e in G.edges()),
- sorted(sorted(e) for e in I.edges()))
-
- def test_read_attribute_graphml(self):
- G=self.attribute_graph
- H=nx.read_graphml(self.attribute_fh)
- assert_equal(sorted(G.nodes(True)),sorted(H.nodes(data=True)))
- ge=sorted(G.edges(data=True))
- he=sorted(H.edges(data=True))
- for a,b in zip(ge,he):
- assert_equal(a,b)
- self.attribute_fh.seek(0)
-
- I=nx.parse_graphml(self.attribute_data)
- assert_equal(sorted(G.nodes(True)),sorted(I.nodes(data=True)))
- ge=sorted(G.edges(data=True))
- he=sorted(I.edges(data=True))
- for a,b in zip(ge,he):
- assert_equal(a,b)
-
- def test_directed_edge_in_undirected(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <graph id="G">
- <node id="n0"/>
- <node id="n1"/>
- <node id="n2"/>
- <edge source="n0" target="n1"/>
- <edge source="n1" target="n2" directed='true'/>
- </graph>
-</graphml>"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_graphml,fh)
- assert_raises(nx.NetworkXError,nx.parse_graphml,s)
-
- def test_undirected_edge_in_directed(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <graph id="G" edgedefault='directed'>
- <node id="n0"/>
- <node id="n1"/>
- <node id="n2"/>
- <edge source="n0" target="n1"/>
- <edge source="n1" target="n2" directed='false'/>
- </graph>
-</graphml>"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_graphml,fh)
- assert_raises(nx.NetworkXError,nx.parse_graphml,s)
-
- def test_key_error(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
- xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
- xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
- http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <key id="d0" for="node" attr.name="color" attr.type="string">
- <default>yellow</default>
- </key>
- <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
- <graph id="G" edgedefault="directed">
- <node id="n0">
- <data key="d0">green</data>
- </node>
- <node id="n1"/>
- <node id="n2">
- <data key="d0">blue</data>
- </node>
- <edge id="e0" source="n0" target="n2">
- <data key="d2">1.0</data>
- </edge>
- </graph>
-</graphml>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_graphml,fh)
- assert_raises(nx.NetworkXError,nx.parse_graphml,s)
-
- def test_hyperedge_error(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
- xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
- xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
- http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <key id="d0" for="node" attr.name="color" attr.type="string">
- <default>yellow</default>
- </key>
- <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
- <graph id="G" edgedefault="directed">
- <node id="n0">
- <data key="d0">green</data>
- </node>
- <node id="n1"/>
- <node id="n2">
- <data key="d0">blue</data>
- </node>
- <hyperedge id="e0" source="n0" target="n2">
- <endpoint node="n0"/>
- <endpoint node="n1"/>
- <endpoint node="n2"/>
- </hyperedge>
- </graph>
-</graphml>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- assert_raises(nx.NetworkXError,nx.read_graphml,fh)
- assert_raises(nx.NetworkXError,nx.parse_graphml,s)
-
- # remove test until we get the "name" issue sorted
- # https://networkx.lanl.gov/trac/ticket/544
- def test_default_attribute(self):
- G=nx.Graph()
- G.add_node(1,label=1,color='green')
- G.add_path([0,1,2,3])
- G.add_edge(1,2,weight=3)
- G.graph['node_default']={'color':'yellow'}
- G.graph['edge_default']={'weight':7}
- fh = io.BytesIO()
- nx.write_graphml(G,fh)
- fh.seek(0)
- H=nx.read_graphml(fh,node_type=int)
- assert_equal(sorted(G.nodes()),sorted(H.nodes()))
- assert_equal(
- sorted(sorted(e) for e in G.edges()),
- sorted(sorted(e) for e in H.edges()))
- assert_equal(G.graph,H.graph)
-
- def test_multigraph_keys(self):
- # test that multigraphs use edge id attributes as key
- pass
-
- def test_multigraph_to_graph(self):
- # test converting multigraph to graph if no parallel edges are found
- pass
-
- def test_yfiles_extension(self):
- data="""<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:y="http://www.yworks.com/xml/graphml" xmlns:yed="http://www.yworks.com/xml/yed/3" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://www.yworks.com/xml/schema/graphml/1.1/ygraphml.xsd">
- <!--Created by yFiles for Java 2.7-->
- <key for="graphml" id="d0" yfiles.type="resources"/>
- <key attr.name="url" attr.type="string" for="node" id="d1"/>
- <key attr.name="description" attr.type="string" for="node" id="d2"/>
- <key for="node" id="d3" yfiles.type="nodegraphics"/>
- <key attr.name="Description" attr.type="string" for="graph" id="d4">
- <default/>
- </key>
- <key attr.name="url" attr.type="string" for="edge" id="d5"/>
- <key attr.name="description" attr.type="string" for="edge" id="d6"/>
- <key for="edge" id="d7" yfiles.type="edgegraphics"/>
- <graph edgedefault="directed" id="G">
- <node id="n0">
- <data key="d3">
- <y:ShapeNode>
- <y:Geometry height="30.0" width="30.0" x="125.0" y="100.0"/>
- <y:Fill color="#FFCC00" transparent="false"/>
- <y:BorderStyle color="#000000" type="line" width="1.0"/>
- <y:NodeLabel alignment="center" autoSizePolicy="content" borderDistance="0.0" fontFamily="Dialog" fontSize="13" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="19.1328125" modelName="internal" modelPosition="c" textColor="#000000" visible="true" width="12.27099609375" x="8.864501953125" y="5.43359375">1</y:NodeLabel>
- <y:Shape type="rectangle"/>
- </y:ShapeNode>
- </data>
- </node>
- <node id="n1">
- <data key="d3">
- <y:ShapeNode>
- <y:Geometry height="30.0" width="30.0" x="183.0" y="205.0"/>
- <y:Fill color="#FFCC00" transparent="false"/>
- <y:BorderStyle color="#000000" type="line" width="1.0"/>
- <y:NodeLabel alignment="center" autoSizePolicy="content" borderDistance="0.0" fontFamily="Dialog" fontSize="13" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="19.1328125" modelName="internal" modelPosition="c" textColor="#000000" visible="true" width="12.27099609375" x="8.864501953125" y="5.43359375">2</y:NodeLabel>
- <y:Shape type="rectangle"/>
- </y:ShapeNode>
- </data>
- </node>
- <edge id="e0" source="n0" target="n1">
- <data key="d7">
- <y:PolyLineEdge>
- <y:Path sx="0.0" sy="0.0" tx="0.0" ty="0.0"/>
- <y:LineStyle color="#000000" type="line" width="1.0"/>
- <y:Arrows source="none" target="standard"/>
- <y:BendStyle smoothed="false"/>
- </y:PolyLineEdge>
- </data>
- </edge>
- </graph>
- <data key="d0">
- <y:Resources/>
- </data>
-</graphml>
-"""
- fh = io.BytesIO(data.encode('UTF-8'))
- G=nx.read_graphml(fh)
- assert_equal(G.edges(),[('n0','n1')])
- assert_equal(G['n0']['n1']['id'],'e0')
- assert_equal(G.node['n0']['label'],'1')
- assert_equal(G.node['n1']['label'],'2')
-
- H=nx.parse_graphml(data)
- assert_equal(H.edges(),[('n0','n1')])
- assert_equal(H['n0']['n1']['id'],'e0')
- assert_equal(H.node['n0']['label'],'1')
- assert_equal(H.node['n1']['label'],'2')
-
- def test_unicode(self):
- G = nx.Graph()
- try: # Python 3.x
- name1 = chr(2344) + chr(123) + chr(6543)
- name2 = chr(5543) + chr(1543) + chr(324)
- node_type=str
- except ValueError: # Python 2.6+
- name1 = unichr(2344) + unichr(123) + unichr(6543)
- name2 = unichr(5543) + unichr(1543) + unichr(324)
- node_type=unicode
- G.add_edge(name1, 'Radiohead', attr_dict={'foo': name2})
- fd, fname = tempfile.mkstemp()
- nx.write_graphml(G, fname)
- H = nx.read_graphml(fname,node_type=node_type)
- assert_equal(G.adj, H.adj)
- os.close(fd)
- os.unlink(fname)
-
-
- def test_bool(self):
- s="""<?xml version="1.0" encoding="UTF-8"?>
-<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
- xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
- xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
- http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
- <key id="d0" for="node" attr.name="test" attr.type="boolean">
- <default>false</default>
- </key>
- <graph id="G" edgedefault="directed">
- <node id="n0">
- <data key="d0">True</data>
- </node>
- <node id="n1"/>
- <node id="n2">
- <data key="d0">False</data>
- </node>
- <node id="n3">
- <data key="d0">true</data>
- </node>
- <node id="n4">
- <data key="d0">false</data>
- </node>
-
-
- </graph>
-</graphml>
-"""
- fh = io.BytesIO(s.encode('UTF-8'))
- G=nx.read_graphml(fh)
- assert_equal(G.node['n0']['test'],True)
- assert_equal(G.node['n2']['test'],False)
-
- H=nx.parse_graphml(s)
- assert_equal(H.node['n0']['test'],True)
- assert_equal(H.node['n2']['test'],False)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_leda.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_leda.py
deleted file mode 100644
index 1c3614e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_leda.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-import os,tempfile
-
-class TestLEDA(object):
-
- def test_parse_leda(self):
- data="""#header section \nLEDA.GRAPH \nstring\nint\n-1\n#nodes section\n5 \n|{v1}| \n|{v2}| \n|{v3}| \n|{v4}| \n|{v5}| \n\n#edges section\n7 \n1 2 0 |{4}| \n1 3 0 |{3}| \n2 3 0 |{2}| \n3 4 0 |{3}| \n3 5 0 |{7}| \n4 5 0 |{6}| \n5 1 0 |{foo}|"""
- G=nx.parse_leda(data)
- G=nx.parse_leda(data.split('\n'))
- assert_equal(sorted(G.nodes()),
- ['v1', 'v2', 'v3', 'v4', 'v5'])
- assert_equal([e for e in sorted(G.edges(data=True))],
- [('v1', 'v2', {'label': '4'}),
- ('v1', 'v3', {'label': '3'}),
- ('v2', 'v3', {'label': '2'}),
- ('v3', 'v4', {'label': '3'}),
- ('v3', 'v5', {'label': '7'}),
- ('v4', 'v5', {'label': '6'}),
- ('v5', 'v1', {'label': 'foo'})])
-
-
- def test_read_LEDA(self):
- data="""#header section \nLEDA.GRAPH \nstring\nint\n-1\n#nodes section\n5 \n|{v1}| \n|{v2}| \n|{v3}| \n|{v4}| \n|{v5}| \n\n#edges section\n7 \n1 2 0 |{4}| \n1 3 0 |{3}| \n2 3 0 |{2}| \n3 4 0 |{3}| \n3 5 0 |{7}| \n4 5 0 |{6}| \n5 1 0 |{foo}|"""
- G=nx.parse_leda(data)
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- b=fh.write(data)
- fh.close()
- Gin=nx.read_leda(fname)
- assert_equal(sorted(G.nodes()),sorted(Gin.nodes()))
- assert_equal(sorted(G.edges()),sorted(Gin.edges()))
- os.close(fd)
- os.unlink(fname)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_p2g.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_p2g.py
deleted file mode 100644
index 6bd7d4e..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_p2g.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from nose.tools import assert_equal, assert_raises, assert_not_equal
-import networkx as nx
-import io
-import tempfile
-import os
-from networkx.readwrite.p2g import *
-from networkx.testing import *
-
-
-class TestP2G:
-
- def setUp(self):
- self.G=nx.Graph(name="test")
- e=[('a','b'),('b','c'),('c','d'),('d','e'),('e','f'),('a','f')]
- self.G.add_edges_from(e)
- self.G.add_node('g')
- self.DG=nx.DiGraph(self.G)
-
- def test_read_p2g(self):
- s = b"""\
-name
-3 4
-a
-1 2
-b
-
-c
-0 2
-"""
- bytesIO = io.BytesIO(s)
- G = read_p2g(bytesIO)
- assert_equal(G.name,'name')
- assert_equal(sorted(G),['a','b','c'])
- edges = [(str(u),str(v)) for u,v in G.edges()]
- assert_edges_equal(G.edges(),[('a','c'),('a','b'),('c','a'),('c','c')])
-
- def test_write_p2g(self):
- s=b"""foo
-3 2
-1
-1
-2
-2
-3
-
-"""
- fh=io.BytesIO()
- G=nx.DiGraph()
- G.name='foo'
- G.add_edges_from([(1,2),(2,3)])
- write_p2g(G,fh)
- fh.seek(0)
- r=fh.read()
- assert_equal(r,s)
-
- def test_write_read_p2g(self):
- fh=io.BytesIO()
- G=nx.DiGraph()
- G.name='foo'
- G.add_edges_from([('a','b'),('b','c')])
- write_p2g(G,fh)
- fh.seek(0)
- H=read_p2g(fh)
- assert_edges_equal(G.edges(),H.edges())
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_pajek.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_pajek.py
deleted file mode 100644
index a2e43d0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_pajek.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python
-"""
-Pajek tests
-"""
-from nose.tools import assert_equal
-from networkx import *
-import os,tempfile
-from io import open
-from networkx.testing import *
-
-class TestPajek(object):
- def setUp(self):
- self.data="""*network Tralala\n*vertices 4\n 1 "A1" 0.0938 0.0896 ellipse x_fact 1 y_fact 1\n 2 "Bb" 0.8188 0.2458 ellipse x_fact 1 y_fact 1\n 3 "C" 0.3688 0.7792 ellipse x_fact 1\n 4 "D2" 0.9583 0.8563 ellipse x_fact 1\n*arcs\n1 1 1 h2 0 w 3 c Blue s 3 a1 -130 k1 0.6 a2 -130 k2 0.6 ap 0.5 l "Bezier loop" lc BlueViolet fos 20 lr 58 lp 0.3 la 360\n2 1 1 h2 0 a1 120 k1 1.3 a2 -120 k2 0.3 ap 25 l "Bezier arc" lphi 270 la 180 lr 19 lp 0.5\n1 2 1 h2 0 a1 40 k1 2.8 a2 30 k2 0.8 ap 25 l "Bezier arc" lphi 90 la 0 lp 0.65\n4 2 -1 h2 0 w 1 k1 -2 k2 250 ap 25 l "Circular arc" c Red lc OrangeRed\n3 4 1 p Dashed h2 0 w 2 c OliveGreen ap 25 l "Straight arc" lc PineGreen\n1 3 1 p Dashed h2 0 w 5 k1 -1 k2 -20 ap 25 l "Oval arc" c Brown lc Black\n3 3 -1 h1 6 w 1 h2 12 k1 -2 k2 -15 ap 0.5 l "Circular loop" c Red lc OrangeRed lphi 270 la 180"""
- self.G=nx.MultiDiGraph()
- self.G.add_nodes_from(['A1', 'Bb', 'C', 'D2'])
- self.G.add_edges_from([('A1', 'A1'), ('A1', 'Bb'), ('A1', 'C'),
- ('Bb', 'A1'),('C', 'C'), ('C', 'D2'),
- ('D2', 'Bb')])
-
- self.G.graph['name']='Tralala'
- (self.fd,self.fname)=tempfile.mkstemp()
- fh=open(self.fname,'wb')
- fh.write(self.data.encode('UTF-8'))
- fh.close()
-
- def tearDown(self):
- os.close(self.fd)
- os.unlink(self.fname)
-
- def test_parse_pajek_simple(self):
- # Example without node positions or shape
- data="""*Vertices 2\n1 "1"\n2 "2"\n*Edges\n1 2\n2 1"""
- G=parse_pajek(data)
- assert_equal(sorted(G.nodes()), ['1', '2'])
- assert_edges_equal(G.edges(), [('1', '2'), ('1', '2')])
-
- def test_parse_pajek(self):
- G=parse_pajek(self.data)
- assert_equal(sorted(G.nodes()), ['A1', 'Bb', 'C', 'D2'])
- assert_edges_equal(G.edges(), [('A1', 'A1'), ('A1', 'Bb'),
- ('A1', 'C'), ('Bb', 'A1'),
- ('C', 'C'), ('C', 'D2'), ('D2', 'Bb')])
-
- def test_read_pajek(self):
- G=parse_pajek(self.data)
- Gin=read_pajek(self.fname)
- assert_equal(sorted(G.nodes()), sorted(Gin.nodes()))
- assert_edges_equal(G.edges(), Gin.edges())
- assert_equal(self.G.graph,Gin.graph)
- for n in G.node:
- assert_equal(G.node[n],Gin.node[n])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_shp.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_shp.py
deleted file mode 100644
index 91c392c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_shp.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""Unit tests for shp.
-"""
-
-import os
-import tempfile
-from nose import SkipTest
-from nose.tools import assert_equal
-
-import networkx as nx
-
-
-class TestShp(object):
- @classmethod
- def setupClass(cls):
- global ogr
- try:
- from osgeo import ogr
- except ImportError:
- raise SkipTest('ogr not available.')
-
- def deletetmp(self, drv, *paths):
- for p in paths:
- if os.path.exists(p):
- drv.DeleteDataSource(p)
-
- def setUp(self):
-
- def createlayer(driver):
- lyr = shp.CreateLayer("edges", None, ogr.wkbLineString)
- namedef = ogr.FieldDefn("Name", ogr.OFTString)
- namedef.SetWidth(32)
- lyr.CreateField(namedef)
- return lyr
-
- drv = ogr.GetDriverByName("ESRI Shapefile")
-
- testdir = os.path.join(tempfile.gettempdir(), 'shpdir')
- shppath = os.path.join(tempfile.gettempdir(), 'tmpshp.shp')
-
- self.deletetmp(drv, testdir, shppath)
- os.mkdir(testdir)
-
- shp = drv.CreateDataSource(shppath)
- lyr = createlayer(shp)
- self.names = ['a', 'b', 'c'] # edgenames
- self.paths = ( [(1.0, 1.0), (2.0, 2.0)],
- [(2.0, 2.0), (3.0, 3.0)],
- [(0.9, 0.9), (4.0, 2.0)]
- )
- for path, name in zip(self.paths, self.names):
- feat = ogr.Feature(lyr.GetLayerDefn())
- g = ogr.Geometry(ogr.wkbLineString)
- map(lambda xy: g.AddPoint_2D(*xy), path)
- feat.SetGeometry(g)
- feat.SetField("Name", name)
- lyr.CreateFeature(feat)
- self.shppath = shppath
- self.testdir = testdir
- self.drv = drv
-
- def testload(self):
- expected = nx.DiGraph()
- map(expected.add_path, self.paths)
- G = nx.read_shp(self.shppath)
- assert_equal(sorted(expected.node), sorted(G.node))
- assert_equal(sorted(expected.edges()), sorted(G.edges()))
- names = [G.get_edge_data(s, e)['Name'] for s, e in G.edges()]
- assert_equal(self.names, sorted(names))
-
- def checkgeom(self, lyr, expected):
- feature = lyr.GetNextFeature()
- actualwkt = []
- while feature:
- actualwkt.append(feature.GetGeometryRef().ExportToWkt())
- feature = lyr.GetNextFeature()
- assert_equal(sorted(expected), sorted(actualwkt))
-
- def test_geometryexport(self):
- expectedpoints = (
- "POINT (1 1)",
- "POINT (2 2)",
- "POINT (3 3)",
- "POINT (0.9 0.9)",
- "POINT (4 2)"
- )
- expectedlines = (
- "LINESTRING (1 1,2 2)",
- "LINESTRING (2 2,3 3)",
- "LINESTRING (0.9 0.9,4 2)"
- )
- tpath = os.path.join(tempfile.gettempdir(), 'shpdir')
- G = nx.read_shp(self.shppath)
- nx.write_shp(G, tpath)
- shpdir = ogr.Open(tpath)
- self.checkgeom(shpdir.GetLayerByName("nodes"), expectedpoints)
- self.checkgeom(shpdir.GetLayerByName("edges"), expectedlines)
-
- def test_attributeexport(self):
- def testattributes(lyr, graph):
- feature = lyr.GetNextFeature()
- while feature:
- coords = []
- ref = feature.GetGeometryRef()
- for i in xrange(ref.GetPointCount()):
- coords.append(ref.GetPoint_2D(i))
- name = feature.GetFieldAsString('Name')
- assert_equal(graph.get_edge_data(*coords)['Name'], name)
- feature = lyr.GetNextFeature()
-
- tpath = os.path.join(tempfile.gettempdir(), 'shpdir')
-
- G = nx.read_shp(self.shppath)
- nx.write_shp(G, tpath)
- shpdir = ogr.Open(tpath)
- edges = shpdir.GetLayerByName("edges")
- testattributes(edges, G)
-
- def test_wkt_export(self):
- G = nx.DiGraph()
- tpath = os.path.join(tempfile.gettempdir(), 'shpdir')
- points = (
- "POINT (0.9 0.9)",
- "POINT (4 2)"
- )
- line = (
- "LINESTRING (0.9 0.9,4 2)",
- )
- G.add_node(1, Wkt=points[0])
- G.add_node(2, Wkt=points[1])
- G.add_edge(1, 2, Wkt=line[0])
- try:
- nx.write_shp(G, tpath)
- except Exception as e:
- assert False, e
- shpdir = ogr.Open(tpath)
- self.checkgeom(shpdir.GetLayerByName("nodes"), points)
- self.checkgeom(shpdir.GetLayerByName("edges"), line)
-
- def tearDown(self):
- self.deletetmp(self.drv, self.testdir, self.shppath)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_sparsegraph6.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_sparsegraph6.py
deleted file mode 100644
index fea9161..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_sparsegraph6.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-import networkx as nx
-import os,tempfile
-
-class TestGraph6(object):
-
- def test_parse_graph6(self):
- data="""DF{"""
- G=nx.parse_graph6(data)
- assert_equal(sorted(G.nodes()),[0, 1, 2, 3, 4])
- assert_equal([e for e in sorted(G.edges())],
- [(0, 3), (0, 4), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)])
-
- def test_read_graph6(self):
- data="""DF{"""
- G=nx.parse_graph6(data)
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- b=fh.write(data)
- fh.close()
- Gin=nx.read_graph6(fname)
- assert_equal(sorted(G.nodes()),sorted(Gin.nodes()))
- assert_equal(sorted(G.edges()),sorted(Gin.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_read_many_graph6(self):
- # Read many graphs into list
- data="""DF{\nD`{\nDqK\nD~{\n"""
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- b=fh.write(data)
- fh.close()
- glist=nx.read_graph6_list(fname)
- assert_equal(len(glist),4)
- for G in glist:
- assert_equal(sorted(G.nodes()),[0, 1, 2, 3, 4])
- os.close(fd)
- os.unlink(fname)
-
-
-class TestSparseGraph6(object):
-
- def test_parse_sparse6(self):
- data=""":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM"""
- G=nx.parse_sparse6(data)
- assert_equal(sorted(G.nodes()),
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
- 10, 11, 12, 13, 14, 15, 16, 17])
- assert_equal([e for e in sorted(G.edges())],
- [(0, 1), (0, 2), (0, 3), (1, 12), (1, 14), (2, 13),
- (2, 15), (3, 16), (3, 17), (4, 7), (4, 9), (4, 11),
- (5, 6), (5, 8), (5, 9), (6, 10), (6, 11), (7, 8),
- (7, 10), (8, 12), (9, 15), (10, 14), (11, 13),
- (12, 16), (13, 17), (14, 17), (15, 16)])
-
-
- def test_read_sparse6(self):
- data=""":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM"""
- G=nx.parse_sparse6(data)
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- b=fh.write(data)
- fh.close()
- Gin=nx.read_sparse6(fname)
- assert_equal(sorted(G.nodes()),sorted(Gin.nodes()))
- assert_equal(sorted(G.edges()),sorted(Gin.edges()))
- os.close(fd)
- os.unlink(fname)
-
- def test_read_many_graph6(self):
- # Read many graphs into list
- data=""":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM\n:Q___dCfDEdcEgcbEGbFIaJ`JaHN`IM"""
- (fd,fname)=tempfile.mkstemp()
- fh=open(fname,'w')
- b=fh.write(data)
- fh.close()
- glist=nx.read_sparse6_list(fname)
- assert_equal(len(glist),2)
- for G in glist:
- assert_equal(sorted(G.nodes()),
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
- 10, 11, 12, 13, 14, 15, 16, 17])
- os.close(fd)
- os.unlink(fname)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_yaml.py b/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_yaml.py
deleted file mode 100644
index ca0ff56..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/test_yaml.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
- Unit tests for yaml.
-"""
-
-import os,tempfile
-from nose import SkipTest
-from nose.tools import assert_equal
-
-import networkx as nx
-
-class TestYaml(object):
- @classmethod
- def setupClass(cls):
- global yaml
- try:
- import yaml
- except ImportError:
- raise SkipTest('yaml not available.')
-
- def setUp(self):
- self.build_graphs()
-
- def build_graphs(self):
- self.G = nx.Graph(name="test")
- e = [('a','b'),('b','c'),('c','d'),('d','e'),('e','f'),('a','f')]
- self.G.add_edges_from(e)
- self.G.add_node('g')
-
- self.DG = nx.DiGraph(self.G)
-
- self.MG = nx.MultiGraph()
- self.MG.add_weighted_edges_from([(1,2,5),(1,2,5),(1,2,1),(3,3,42)])
-
- def assert_equal(self, G, data=False):
- (fd, fname) = tempfile.mkstemp()
- nx.write_yaml(G, fname)
- Gin = nx.read_yaml(fname);
-
- assert_equal(sorted(G.nodes()),sorted(Gin.nodes()))
- assert_equal(G.edges(data=data),Gin.edges(data=data))
-
- os.close(fd)
- os.unlink(fname)
-
- def testUndirected(self):
- self.assert_equal(self.G, False)
-
- def testDirected(self):
- self.assert_equal(self.DG, False)
-
- def testMultiGraph(self):
- self.assert_equal(self.MG, True)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/relabel.py b/lib/python2.7/site-packages/setoolsgui/networkx/relabel.py
deleted file mode 100644
index 4ff1196..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/relabel.py
+++ /dev/null
@@ -1,205 +0,0 @@
-# Copyright (C) 2006-2013 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
- 'Pieter Swart (swart@lanl.gov)',
- 'Dan Schult (dschult@colgate.edu)'])
-__all__ = ['convert_node_labels_to_integers', 'relabel_nodes']
-
-def relabel_nodes(G, mapping, copy=True):
- """Relabel the nodes of the graph G.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- mapping : dictionary
- A dictionary with the old labels as keys and new labels as values.
- A partial mapping is allowed.
-
- copy : bool (optional, default=True)
- If True return a copy, or if False relabel the nodes in place.
-
- Examples
- --------
- >>> G=nx.path_graph(3) # nodes 0-1-2
- >>> mapping={0:'a',1:'b',2:'c'}
- >>> H=nx.relabel_nodes(G,mapping)
- >>> print(sorted(H.nodes()))
- ['a', 'b', 'c']
-
- >>> G=nx.path_graph(26) # nodes 0..25
- >>> mapping=dict(zip(G.nodes(),"abcdefghijklmnopqrstuvwxyz"))
- >>> H=nx.relabel_nodes(G,mapping) # nodes a..z
- >>> mapping=dict(zip(G.nodes(),range(1,27)))
- >>> G1=nx.relabel_nodes(G,mapping) # nodes 1..26
-
- Partial in-place mapping:
-
- >>> G=nx.path_graph(3) # nodes 0-1-2
- >>> mapping={0:'a',1:'b'} # 0->'a' and 1->'b'
- >>> G=nx.relabel_nodes(G,mapping, copy=False)
-
- print(G.nodes())
- [2, 'b', 'a']
-
- Mapping as function:
-
- >>> G=nx.path_graph(3)
- >>> def mapping(x):
- ... return x**2
- >>> H=nx.relabel_nodes(G,mapping)
- >>> print(H.nodes())
- [0, 1, 4]
-
- Notes
- -----
- Only the nodes specified in the mapping will be relabeled.
-
- The keyword setting copy=False modifies the graph in place.
- This is not always possible if the mapping is circular.
- In that case use copy=True.
-
- See Also
- --------
- convert_node_labels_to_integers
- """
- # you can pass a function f(old_label)->new_label
- # but we'll just make a dictionary here regardless
- if not hasattr(mapping,"__getitem__"):
- m = dict((n,mapping(n)) for n in G)
- else:
- m=mapping
- if copy:
- return _relabel_copy(G,m)
- else:
- return _relabel_inplace(G,m)
-
-
-def _relabel_inplace(G, mapping):
- old_labels=set(mapping.keys())
- new_labels=set(mapping.values())
- if len(old_labels & new_labels) > 0:
- # labels sets overlap
- # can we topological sort and still do the relabeling?
- D=nx.DiGraph(list(mapping.items()))
- D.remove_edges_from(D.selfloop_edges())
- try:
- nodes=nx.topological_sort(D)
- except nx.NetworkXUnfeasible:
- raise nx.NetworkXUnfeasible('The node label sets are overlapping '
- 'and no ordering can resolve the '
- 'mapping. Use copy=True.')
- nodes.reverse() # reverse topological order
- else:
- # non-overlapping label sets
- nodes=old_labels
-
- multigraph = G.is_multigraph()
- directed = G.is_directed()
-
- for old in nodes:
- try:
- new=mapping[old]
- except KeyError:
- continue
- try:
- G.add_node(new,attr_dict=G.node[old])
- except KeyError:
- raise KeyError("Node %s is not in the graph"%old)
- if multigraph:
- new_edges=[(new,old == target and new or target,key,data)
- for (_,target,key,data)
- in G.edges(old,data=True,keys=True)]
- if directed:
- new_edges+=[(old == source and new or source,new,key,data)
- for (source,_,key,data)
- in G.in_edges(old,data=True,keys=True)]
- else:
- new_edges=[(new,old == target and new or target,data)
- for (_,target,data) in G.edges(old,data=True)]
- if directed:
- new_edges+=[(old == source and new or source,new,data)
- for (source,_,data) in G.in_edges(old,data=True)]
- G.remove_node(old)
- G.add_edges_from(new_edges)
- return G
-
-def _relabel_copy(G, mapping):
- H=G.__class__()
- H.name="(%s)" % G.name
- if G.is_multigraph():
- H.add_edges_from( (mapping.get(n1,n1),mapping.get(n2,n2),k,d.copy())
- for (n1,n2,k,d) in G.edges_iter(keys=True,data=True))
- else:
- H.add_edges_from( (mapping.get(n1,n1),mapping.get(n2,n2),d.copy())
- for (n1,n2,d) in G.edges_iter(data=True))
-
- H.add_nodes_from(mapping.get(n,n) for n in G)
- H.node.update(dict((mapping.get(n,n),d.copy()) for n,d in G.node.items()))
- H.graph.update(G.graph.copy())
-
- return H
-
-
-def convert_node_labels_to_integers(G, first_label=0, ordering="default",
- label_attribute=None):
- """Return a copy of the graph G with the nodes relabeled with integers.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- first_label : int, optional (default=0)
- An integer specifying the offset in numbering nodes.
- The n new integer labels are numbered first_label, ..., n-1+first_label.
-
- ordering : string
- "default" : inherit node ordering from G.nodes()
- "sorted" : inherit node ordering from sorted(G.nodes())
- "increasing degree" : nodes are sorted by increasing degree
- "decreasing degree" : nodes are sorted by decreasing degree
-
- label_attribute : string, optional (default=None)
- Name of node attribute to store old label. If None no attribute
- is created.
-
- Notes
- -----
- Node and edge attribute data are copied to the new (relabeled) graph.
-
- See Also
- --------
- relabel_nodes
- """
- N = G.number_of_nodes()+first_label
- if ordering == "default":
- mapping = dict(zip(G.nodes(),range(first_label,N)))
- elif ordering == "sorted":
- nlist = G.nodes()
- nlist.sort()
- mapping=dict(zip(nlist,range(first_label,N)))
- elif ordering == "increasing degree":
- dv_pairs=[(d,n) for (n,d) in G.degree_iter()]
- dv_pairs.sort() # in-place sort from lowest to highest degree
- mapping = dict(zip([n for d,n in dv_pairs],range(first_label,N)))
- elif ordering == "decreasing degree":
- dv_pairs = [(d,n) for (n,d) in G.degree_iter()]
- dv_pairs.sort() # in-place sort from lowest to highest degree
- dv_pairs.reverse()
- mapping = dict(zip([n for d,n in dv_pairs],range(first_label,N)))
- else:
- raise nx.NetworkXError('Unknown node ordering: %s'%ordering)
- H = relabel_nodes(G,mapping)
- H.name="("+G.name+")_with_int_labels"
- # create node attribute with the old label
- if label_attribute is not None:
- nx.set_node_attributes(H, label_attribute,
- dict((v,k) for k,v in mapping.items()))
- return H
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/release.py b/lib/python2.7/site-packages/setoolsgui/networkx/release.py
deleted file mode 100644
index 285db5f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/release.py
+++ /dev/null
@@ -1,254 +0,0 @@
-"""Release data for NetworkX.
-
-When NetworkX is imported a number of steps are followed to determine
-the version information.
-
- 1) If the release is not a development release (dev=False), then version
- information is read from version.py, a file containing statically
- defined version information. This file should exist on every
- downloadable release of NetworkX since setup.py creates it during
- packaging/installation. However, version.py might not exist if one
- is running NetworkX from the mercurial repository. In the event that
- version.py does not exist, then no vcs information will be available.
-
- 2) If the release is a development release, then version information
- is read dynamically, when possible. If no dynamic information can be
- read, then an attempt is made to read the information from version.py.
- If version.py does not exist, then no vcs information will be available.
-
-Clarification:
- version.py is created only by setup.py
-
-When setup.py creates version.py, it does so before packaging/installation.
-So the created file is included in the source distribution. When a user
-downloads a tar.gz file and extracts the files, the files will not be in a
-live version control repository. So when the user runs setup.py to install
-NetworkX, we must make sure write_versionfile() does not overwrite the
-revision information contained in the version.py that was included in the
-tar.gz file. This is why write_versionfile() includes an early escape.
-
-"""
-
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-
-from __future__ import absolute_import
-
-import os
-import sys
-import time
-import datetime
-import subprocess
-
-basedir = os.path.abspath(os.path.split(__file__)[0])
-
-def write_versionfile():
- """Creates a static file containing version information."""
- versionfile = os.path.join(basedir, 'version.py')
-
- text = '''"""
-Version information for NetworkX, created during installation.
-
-Do not add this file to the repository.
-
-"""
-
-import datetime
-
-version = %(version)r
-date = %(date)r
-
-# Was NetworkX built from a development version? If so, remember that the major
-# and minor versions reference the "target" (rather than "current") release.
-dev = %(dev)r
-
-# Format: (name, major, min, revision)
-version_info = %(version_info)r
-
-# Format: a 'datetime.datetime' instance
-date_info = %(date_info)r
-
-# Format: (vcs, vcs_tuple)
-vcs_info = %(vcs_info)r
-
-'''
-
- # Try to update all information
- date, date_info, version, version_info, vcs_info = get_info(dynamic=True)
-
- def writefile():
- fh = open(versionfile, 'w')
- subs = {
- 'dev' : dev,
- 'version': version,
- 'version_info': version_info,
- 'date': date,
- 'date_info': date_info,
- 'vcs_info': vcs_info
- }
- fh.write(text % subs)
- fh.close()
-
- if vcs_info[0] == 'mercurial':
- # Then, we want to update version.py.
- writefile()
- else:
- if os.path.isfile(versionfile):
- # This is *good*, and the most likely place users will be when
- # running setup.py. We do not want to overwrite version.py.
- # Grab the version so that setup can use it.
- sys.path.insert(0, basedir)
- from version import version
- del sys.path[0]
- else:
- # This is *bad*. It means the user might have a tarball that
- # does not include version.py. Let this error raise so we can
- # fix the tarball.
- ##raise Exception('version.py not found!')
-
- # We no longer require that prepared tarballs include a version.py
- # So we use the possibly trunctated value from get_info()
- # Then we write a new file.
- writefile()
-
- return version
-
-def get_revision():
- """Returns revision and vcs information, dynamically obtained."""
- vcs, revision, tag = None, None, None
-
- hgdir = os.path.join(basedir, '..', '.hg')
- gitdir = os.path.join(basedir, '..', '.git')
-
- if os.path.isdir(hgdir):
- vcs = 'mercurial'
- try:
- p = subprocess.Popen(['hg', 'id'],
- cwd=basedir,
- stdout=subprocess.PIPE)
- except OSError:
- # Could not run hg, even though this is a mercurial repository.
- pass
- else:
- stdout = p.communicate()[0]
- # Force strings instead of unicode.
- x = list(map(str, stdout.decode().strip().split()))
-
- if len(x) == 0:
- # Somehow stdout was empty. This can happen, for example,
- # if you're running in a terminal which has redirected stdout.
- # In this case, we do not use any revision/tag info.
- pass
- elif len(x) == 1:
- # We don't have 'tip' or anything similar...so no tag.
- revision = str(x[0])
- else:
- revision = str(x[0])
- tag = str(x[1])
-
- elif os.path.isdir(gitdir):
- vcs = 'git'
- # For now, we are not bothering with revision and tag.
-
- vcs_info = (vcs, (revision, tag))
-
- return revision, vcs_info
-
-def get_info(dynamic=True):
- ## Date information
- date_info = datetime.datetime.now()
- date = time.asctime(date_info.timetuple())
-
- revision, version, version_info, vcs_info = None, None, None, None
-
- import_failed = False
- dynamic_failed = False
-
- if dynamic:
- revision, vcs_info = get_revision()
- if revision is None:
- dynamic_failed = True
-
- if dynamic_failed or not dynamic:
- # This is where most final releases of NetworkX will be.
- # All info should come from version.py. If it does not exist, then
- # no vcs information will be provided.
- sys.path.insert(0, basedir)
- try:
- from version import date, date_info, version, version_info, vcs_info
- except ImportError:
- import_failed = True
- vcs_info = (None, (None, None))
- else:
- revision = vcs_info[1][0]
- del sys.path[0]
-
- if import_failed or (dynamic and not dynamic_failed):
- # We are here if:
- # we failed to determine static versioning info, or
- # we successfully obtained dynamic revision info
- version = ''.join([str(major), '.', str(minor)])
- if dev:
- version += '.dev_' + date_info.strftime("%Y%m%d%H%M%S")
- version_info = (name, major, minor, revision)
-
- return date, date_info, version, version_info, vcs_info
-
-## Version information
-name = 'networkx'
-major = "1"
-minor = "8.1"
-
-
-## Declare current release as a development release.
-## Change to False before tagging a release; then change back.
-dev = False
-
-
-description = "Python package for creating and manipulating graphs and networks"
-
-long_description = \
-"""
-NetworkX is a Python package for the creation, manipulation, and
-study of the structure, dynamics, and functions of complex networks.
-
-"""
-license = 'BSD'
-authors = {'Hagberg' : ('Aric Hagberg','hagberg@lanl.gov'),
- 'Schult' : ('Dan Schult','dschult@colgate.edu'),
- 'Swart' : ('Pieter Swart','swart@lanl.gov')
- }
-maintainer = "NetworkX Developers"
-maintainer_email = "networkx-discuss@googlegroups.com"
-url = 'http://networkx.lanl.gov/'
-download_url="http://networkx.lanl.gov/download/networkx"
-platforms = ['Linux','Mac OSX','Windows','Unix']
-keywords = ['Networks', 'Graph Theory', 'Mathematics', 'network', 'graph', 'discrete mathematics', 'math']
-classifiers = [
- 'Development Status :: 4 - Beta',
- 'Intended Audience :: Developers',
- 'Intended Audience :: Science/Research',
- 'License :: OSI Approved :: BSD License',
- 'Operating System :: OS Independent',
- 'Programming Language :: Python :: 2',
- 'Programming Language :: Python :: 2.6',
- 'Programming Language :: Python :: 2.7',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.1',
- 'Programming Language :: Python :: 3.2',
- 'Topic :: Software Development :: Libraries :: Python Modules',
- 'Topic :: Scientific/Engineering :: Bio-Informatics',
- 'Topic :: Scientific/Engineering :: Information Analysis',
- 'Topic :: Scientific/Engineering :: Mathematics',
- 'Topic :: Scientific/Engineering :: Physics']
-
-date, date_info, version, version_info, vcs_info = get_info()
-
-if __name__ == '__main__':
- # Write versionfile for nightly snapshots.
- write_versionfile()
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/testing/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/testing/__init__.py
deleted file mode 100644
index db57076..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/testing/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from networkx.testing.utils import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/testing/tests/test_utils.py b/lib/python2.7/site-packages/setoolsgui/networkx/testing/tests/test_utils.py
deleted file mode 100644
index 9c57649..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/testing/tests/test_utils.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from nose.tools import *
-import networkx as nx
-from networkx.testing import *
-
-# thanks to numpy for this GenericTest class (numpy/testing/test_utils.py)
-class _GenericTest(object):
- def _test_equal(self, a, b):
- self._assert_func(a, b)
-
- def _test_not_equal(self, a, b):
- try:
- self._assert_func(a, b)
- passed = True
- except AssertionError:
- pass
- else:
- raise AssertionError("a and b are found equal but are not")
-
-
-class TestNodesEqual(_GenericTest):
- def setUp(self):
- self._assert_func = assert_nodes_equal
-
- def test_nodes_equal(self):
- a = [1,2,5,4]
- b = [4,5,1,2]
- self._test_equal(a,b)
-
- def test_nodes_not_equal(self):
- a = [1,2,5,4]
- b = [4,5,1,3]
- self._test_not_equal(a,b)
-
- def test_nodes_with_data_equal(self):
- G = nx.Graph()
- G.add_nodes_from([1,2,3],color='red')
- H = nx.Graph()
- H.add_nodes_from([1,2,3],color='red')
- self._test_equal(G.nodes(data=True), H.nodes(data=True))
-
- def test_edges_with_data_not_equal(self):
- G = nx.Graph()
- G.add_nodes_from([1,2,3],color='red')
- H = nx.Graph()
- H.add_nodes_from([1,2,3],color='blue')
- self._test_not_equal(G.nodes(data=True), H.nodes(data=True))
-
-
-class TestEdgesEqual(_GenericTest):
- def setUp(self):
- self._assert_func = assert_edges_equal
-
- def test_edges_equal(self):
- a = [(1,2),(5,4)]
- b = [(4,5),(1,2)]
- self._test_equal(a,b)
-
- def test_edges_not_equal(self):
- a = [(1,2),(5,4)]
- b = [(4,5),(1,3)]
- self._test_not_equal(a,b)
-
- def test_edges_with_data_equal(self):
- G = nx.MultiGraph()
- G.add_path([0,1,2],weight=1)
- H = nx.MultiGraph()
- H.add_path([0,1,2],weight=1)
- self._test_equal(G.edges(data=True, keys=True),
- H.edges(data=True, keys=True))
-
- def test_edges_with_data_not_equal(self):
- G = nx.MultiGraph()
- G.add_path([0,1,2],weight=1)
- H = nx.MultiGraph()
- H.add_path([0,1,2],weight=2)
- self._test_not_equal(G.edges(data=True, keys=True),
- H.edges(data=True, keys=True))
-
-class TestGraphsEqual(_GenericTest):
- def setUp(self):
- self._assert_func = assert_graphs_equal
-
- def test_graphs_equal(self):
- G = nx.path_graph(4)
- H = nx.Graph()
- H.add_path(range(4))
- H.name='path_graph(4)'
- self._test_equal(G,H)
-
- def test_graphs_not_equal(self):
- G = nx.path_graph(4)
- H = nx.Graph()
- H.add_cycle(range(4))
- self._test_not_equal(G,H)
-
- def test_graphs_not_equal2(self):
- G = nx.path_graph(4)
- H = nx.Graph()
- H.add_path(range(3))
- H.name='path_graph(4)'
- self._test_not_equal(G,H)
-
- def test_graphs_not_equal3(self):
- G = nx.path_graph(4)
- H = nx.Graph()
- H.add_path(range(4))
- H.name='path_graph(foo)'
- self._test_not_equal(G,H)
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/testing/utils.py b/lib/python2.7/site-packages/setoolsgui/networkx/testing/utils.py
deleted file mode 100644
index 5cca7f8..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/testing/utils.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import operator
-from nose.tools import *
-__all__ = ['assert_nodes_equal', 'assert_edges_equal','assert_graphs_equal']
-
-def assert_nodes_equal(nlist1, nlist2):
- # Assumes lists are either nodes, or (node,datadict) tuples,
- # and also that nodes are orderable/sortable.
- try:
- l = len(nlist1[0])
- n1 = sorted(nlist1,key=operator.itemgetter(0))
- n2 = sorted(nlist2,key=operator.itemgetter(0))
- assert_equal(len(n1),len(n2))
- for a,b in zip(n1,n2):
- assert_equal(a,b)
- except TypeError:
- assert_equal(set(nlist1),set(nlist2))
- return
-
-def assert_edges_equal(elist1, elist2):
- # Assumes lists with u,v nodes either as
- # edge tuples (u,v)
- # edge tuples with data dicts (u,v,d)
- # edge tuples with keys and data dicts (u,v,k, d)
- # and also that nodes are orderable/sortable.
- e1 = sorted(elist1,key=lambda x: sorted(x[0:2]))
- e2 = sorted(elist2,key=lambda x: sorted(x[0:2]))
- assert_equal(len(e1),len(e2))
- if len(e1) == 0:
- return True
- if len(e1[0]) == 2:
- for a,b in zip(e1,e2):
- assert_equal(set(a[0:2]),set(b[0:2]))
- elif len(e1[0]) == 3:
- for a,b in zip(e1,e2):
- assert_equal(set(a[0:2]),set(b[0:2]))
- assert_equal(a[2],b[2])
- elif len(e1[0]) == 4:
- for a,b in zip(e1,e2):
- assert_equal(set(a[0:2]),set(b[0:2]))
- assert_equal(a[2],b[2])
- assert_equal(a[3],b[3])
-
-
-def assert_graphs_equal(graph1, graph2):
- if graph1.is_multigraph():
- edges1 = graph1.edges(data=True,keys=True)
- else:
- edges1 = graph1.edges(data=True)
- if graph2.is_multigraph():
- edges2 = graph2.edges(data=True,keys=True)
- else:
- edges2 = graph2.edges(data=True)
- assert_nodes_equal(graph1.nodes(data=True),
- graph2.nodes(data=True))
- assert_edges_equal(edges1, edges2)
- assert_equal(graph1.graph,graph2.graph)
- return
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/__init__.py
+++ /dev/null
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/benchmark.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/benchmark.py
deleted file mode 100644
index 5eb68d0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/benchmark.py
+++ /dev/null
@@ -1,248 +0,0 @@
-from timeit import Timer
-
-# This is gratefully modeled after the benchmarks found in
-# the numpy svn repository. http://svn.scipy.org/svn/numpy/trunk
-
-class Benchmark(object):
- """
- Benchmark a method or simple bit of code using different Graph classes.
- If the test code is the same for each graph class, then you can set it
- during instantiation through the argument test_string.
- The argument test_string can also be a tuple of test code and setup code.
- The code is entered as a string valid for use with the timeit module.
-
- Example:
- >>> b=Benchmark(['Graph','XGraph'])
- >>> b['Graph']=('G.add_nodes_from(nlist)','nlist=range(100)')
- >>> b.run()
- """
- def __init__(self,graph_classes,title='',test_string=None,runs=3,reps=1000):
- self.runs = runs
- self.reps = reps
- self.title = title
- self.class_tests = dict((gc,'') for gc in graph_classes)
- # set up the test string if it is the same for all classes.
- if test_string is not None:
- if isinstance(test_string,tuple):
- self['all']=test_string
- else:
- self['all']=(test_string,'')
-
- def __setitem__(self,graph_class,some_strs):
- """
- Set a simple bit of code and setup string for the test.
- Use this for cases where the code differs from one class to another.
- """
- test_str, setup_str = some_strs
- if graph_class == 'all':
- graph_class = self.class_tests.keys()
- elif not isinstance(graph_class,list):
- graph_class = [graph_class]
-
- for GC in graph_class:
- setup_string='import networkx as NX\nG=NX.%s.%s()\n'%\
- (GC.lower(),GC) + setup_str
- self.class_tests[GC] = Timer(test_str, setup_string)
-
-
- def run(self):
- """Run the benchmark for each class and print results."""
- column_len = max(len(G) for G in self.class_tests)
-
- print('='*72)
- if self.title:
- print("%s: %s runs, %s reps"% (self.title,self.runs,self.reps))
- print('='*72)
-
- times=[]
- for GC,timer in self.class_tests.items():
- name = GC.ljust(column_len)
- try:
- t=sum(timer.repeat(self.runs,self.reps))/self.runs
-# print "%s: %s" % (name, timer.repeat(self.runs,self.reps))
- times.append((t,name))
- except Exception as e:
- print("%s: Failed to benchmark (%s)." % (name,e))
-
-
- times.sort()
- tmin=times[0][0]
- for t,name in times:
- print("%s: %5.2f %s" % (name, t/tmin*100.,t))
- print('-'*72)
- print()
-
-if __name__ == "__main__":
- # set up for all routines:
- classes=['Graph','MultiGraph','DiGraph','MultiDiGraph']
- all_tests=['add_nodes','add_edges','remove_nodes','remove_edges',\
- 'neighbors','edges','degree','dijkstra','shortest path',\
- 'subgraph','edgedata_subgraph','laplacian']
- # Choose which tests to run
- tests=all_tests
- tests=['subgraph','edgedata_subgraph']
- #tests=all_tests[-1:]
- N=100
-
- if 'add_nodes' in tests:
- title='Benchmark: Adding nodes'
- test_string=('G.add_nodes_from(nlist)','nlist=range(%i)'%N)
- b=Benchmark(classes,title,test_string,runs=3,reps=1000)
- b.run()
-
- if 'add_edges' in tests:
- title='Benchmark: Adding edges'
- setup='elist=[(i,i+3) for i in range(%s-3)]\nG.add_nodes_from(range(%i))'%(N,N)
- test_string=('G.add_edges_from(elist)',setup)
- b=Benchmark(classes,title,test_string,runs=3,reps=1000)
- b.run()
-
- if 'remove_nodes' in tests:
- title='Benchmark: Adding and Deleting nodes'
- setup='nlist=range(%i)'%N
- test_string=('G.add_nodes_from(nlist)\nG.remove_nodes_from(nlist)',setup)
- b=Benchmark(classes,title,test_string,runs=3,reps=1000)
- b.run()
-
- if 'remove_edges' in tests:
- title='Benchmark: Adding and Deleting edges'
- setup='elist=[(i,i+3) for i in range(%s-3)]'%N
- test_string=('G.add_edges_from(elist)\nG.remove_edges_from(elist)',setup)
- b=Benchmark(classes,title,test_string,runs=3,reps=1000)
- b.run()
-
- if 'neighbors' in tests:
- N=500
- p=0.3
- title='Benchmark: reporting neighbors'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='for n in G:\n for nbr in G.neighbors(n):\n pass'
- all_setup='H=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)\n'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'edges' in tests:
- N=500
- p=0.3
- title='Benchmark: reporting edges'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='for n in G:\n for e in G.edges(n):\n pass'
- all_setup='H=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)\n'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'degree' in tests:
- N=500
- p=0.3
- title='Benchmark: reporting degree'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='for d in G.degree():\n pass'
- all_setup='H=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)\n'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'dijkstra' in tests:
- N=500
- p=0.3
- title='dijkstra single source shortest path'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='p=NX.single_source_dijkstra(G,i)'
- all_setup='i=6\nH=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'shortest path' in tests:
- N=500
- p=0.3
- title='single source shortest path'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='p=NX.single_source_shortest_path(G,i)'
- all_setup='i=6\nH=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'subgraph' in tests:
- N=500
- p=0.3
- title='subgraph method'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='G.subgraph(nlist)'
- all_setup='nlist=range(100,150)\nH=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'edgedata_subgraph' in tests:
- N=500
- p=0.3
- title='subgraph method with edge data present'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='G.subgraph(nlist)'
- all_setup='nlist=range(100,150)\nH=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v,hi=3)'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)],hi=2)'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v,hi=1)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)],hi=2)'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
-
- if 'laplacian' in tests:
- N=500
- p=0.3
- title='creation of laplacian matrix'
- b=Benchmark(classes,title,runs=3,reps=1)
- test_string='NX.laplacian(G)'
- all_setup='H=NX.binomial_graph(%s,%s)\nfor (u,v) in H.edges_iter():\n '%(N,p)
- setup=all_setup+'G.add_edge(u,v)'
- if 'Graph' in classes: b['Graph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'DiGraph' in classes: b['DiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edge(u,v)'
- if 'MultiGraph' in classes: b['MultiGraph']=(test_string,setup)
- setup=all_setup+'G.add_edges_from([(u,v),(v,u)])'
- if 'MultiDiGraph' in classes: b['MultiDiGraph']=(test_string,setup)
- b.run()
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/test.py
deleted file mode 100755
index e776d32..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test.py
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env python
-import sys
-from os import path,getcwd
-
-def run(verbosity=1,doctest=False,numpy=True):
- """Run NetworkX tests.
-
- Parameters
- ----------
- verbosity: integer, optional
- Level of detail in test reports. Higher numbers provide more detail.
-
- doctest: bool, optional
- True to run doctests in code modules
-
- numpy: bool, optional
- True to test modules dependent on numpy
- """
- try:
- import nose
- except ImportError:
- raise ImportError(\
- "The nose package is needed to run the NetworkX tests.")
-
- sys.stderr.write("Running NetworkX tests:")
- nx_install_dir=path.join(path.dirname(__file__), path.pardir)
- # stop if running from source directory
- if getcwd() == path.abspath(path.join(nx_install_dir,path.pardir)):
- raise RuntimeError("Can't run tests from source directory.\n"
- "Run 'nosetests' from the command line.")
-
- argv=[' ','--verbosity=%d'%verbosity,
- '-w',nx_install_dir,
- '-exe']
- if doctest:
- argv.extend(['--with-doctest','--doctest-extension=txt'])
- if not numpy:
- argv.extend(['-A not numpy'])
-
-
- nose.run(argv=argv)
-
-if __name__=="__main__":
- run()
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert.py
deleted file mode 100644
index 38a66e2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert.py
+++ /dev/null
@@ -1,224 +0,0 @@
-#!/usr/bin/env python
-
-"""Convert
-=======
-"""
-
-from nose.tools import *
-from networkx import *
-from networkx.convert import *
-from networkx.algorithms.operators import *
-from networkx.generators.classic import barbell_graph,cycle_graph
-
-class TestConvert():
- def edgelists_equal(self,e1,e2):
- return sorted(sorted(e) for e in e1)==sorted(sorted(e) for e in e2)
-
-
-
- def test_simple_graphs(self):
- for dest, source in [(to_dict_of_dicts, from_dict_of_dicts),
- (to_dict_of_lists, from_dict_of_lists)]:
- G=barbell_graph(10,3)
- dod=dest(G)
-
- # Dict of [dicts, lists]
- GG=source(dod)
- assert_equal(sorted(G.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(G.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dod)
- assert_equal(sorted(G.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(G.edges()), sorted(GW.edges()))
- GI=Graph(dod)
- assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(G.edges()), sorted(GI.edges()))
-
- # With nodelist keyword
- P4=path_graph(4)
- P3=path_graph(3)
- dod=dest(P4,nodelist=[0,1,2])
- Gdod=Graph(dod)
- assert_equal(sorted(Gdod.nodes()), sorted(P3.nodes()))
- assert_equal(sorted(Gdod.edges()), sorted(P3.edges()))
-
- def test_digraphs(self):
- for dest, source in [(to_dict_of_dicts, from_dict_of_dicts),
- (to_dict_of_lists, from_dict_of_lists)]:
- G=cycle_graph(10)
-
- # Dict of [dicts, lists]
- dod=dest(G)
- GG=source(dod)
- assert_equal(sorted(G.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(G.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dod)
- assert_equal(sorted(G.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(G.edges()), sorted(GW.edges()))
- GI=Graph(dod)
- assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(G.edges()), sorted(GI.edges()))
-
- G=cycle_graph(10,create_using=DiGraph())
- dod=dest(G)
- GG=source(dod, create_using=DiGraph())
- assert_equal(sorted(G.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(G.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dod, create_using=DiGraph())
- assert_equal(sorted(G.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(G.edges()), sorted(GW.edges()))
- GI=DiGraph(dod)
- assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(G.edges()), sorted(GI.edges()))
-
- def test_graph(self):
- G=cycle_graph(10)
- e=G.edges()
- source=[u for u,v in e]
- dest=[v for u,v in e]
- ex=zip(source,dest,source)
- G=Graph()
- G.add_weighted_edges_from(ex)
-
- # Dict of dicts
- dod=to_dict_of_dicts(G)
- GG=from_dict_of_dicts(dod,create_using=Graph())
- assert_equal(sorted(G.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(G.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dod,create_using=Graph())
- assert_equal(sorted(G.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(G.edges()), sorted(GW.edges()))
- GI=Graph(dod)
- assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(G.edges()), sorted(GI.edges()))
-
- # Dict of lists
- dol=to_dict_of_lists(G)
- GG=from_dict_of_lists(dol,create_using=Graph())
- # dict of lists throws away edge data so set it to none
- enone=[(u,v,{}) for (u,v,d) in G.edges(data=True)]
- assert_equal(sorted(G.nodes()), sorted(GG.nodes()))
- assert_equal(enone, sorted(GG.edges(data=True)))
- GW=to_networkx_graph(dol,create_using=Graph())
- assert_equal(sorted(G.nodes()), sorted(GW.nodes()))
- assert_equal(enone, sorted(GW.edges(data=True)))
- GI=Graph(dol)
- assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
- assert_equal(enone, sorted(GI.edges(data=True)))
-
-
- def test_with_multiedges_self_loops(self):
- G=cycle_graph(10)
- e=G.edges()
- source,dest = list(zip(*e))
- ex=list(zip(source,dest,source))
- XG=Graph()
- XG.add_weighted_edges_from(ex)
- XGM=MultiGraph()
- XGM.add_weighted_edges_from(ex)
- XGM.add_edge(0,1,weight=2) # multiedge
- XGS=Graph()
- XGS.add_weighted_edges_from(ex)
- XGS.add_edge(0,0,weight=100) # self loop
-
- # Dict of dicts
- # with self loops, OK
- dod=to_dict_of_dicts(XGS)
- GG=from_dict_of_dicts(dod,create_using=Graph())
- assert_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(XGS.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dod,create_using=Graph())
- assert_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(XGS.edges()), sorted(GW.edges()))
- GI=Graph(dod)
- assert_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(XGS.edges()), sorted(GI.edges()))
-
- # Dict of lists
- # with self loops, OK
- dol=to_dict_of_lists(XGS)
- GG=from_dict_of_lists(dol,create_using=Graph())
- # dict of lists throws away edge data so set it to none
- enone=[(u,v,{}) for (u,v,d) in XGS.edges(data=True)]
- assert_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
- assert_equal(enone, sorted(GG.edges(data=True)))
- GW=to_networkx_graph(dol,create_using=Graph())
- assert_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
- assert_equal(enone, sorted(GW.edges(data=True)))
- GI=Graph(dol)
- assert_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
- assert_equal(enone, sorted(GI.edges(data=True)))
-
- # Dict of dicts
- # with multiedges, OK
- dod=to_dict_of_dicts(XGM)
- GG=from_dict_of_dicts(dod,create_using=MultiGraph(),
- multigraph_input=True)
- assert_equal(sorted(XGM.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(XGM.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dod,create_using=MultiGraph(),multigraph_input=True)
- assert_equal(sorted(XGM.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(XGM.edges()), sorted(GW.edges()))
- GI=MultiGraph(dod) # convert can't tell whether to duplicate edges!
- assert_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
- #assert_not_equal(sorted(XGM.edges()), sorted(GI.edges()))
- assert_false(sorted(XGM.edges()) == sorted(GI.edges()))
- GE=from_dict_of_dicts(dod,create_using=MultiGraph(),
- multigraph_input=False)
- assert_equal(sorted(XGM.nodes()), sorted(GE.nodes()))
- assert_not_equal(sorted(XGM.edges()), sorted(GE.edges()))
- GI=MultiGraph(XGM)
- assert_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(XGM.edges()), sorted(GI.edges()))
- GM=MultiGraph(G)
- assert_equal(sorted(GM.nodes()), sorted(G.nodes()))
- assert_equal(sorted(GM.edges()), sorted(G.edges()))
-
- # Dict of lists
- # with multiedges, OK, but better write as DiGraph else you'll
- # get double edges
- dol=to_dict_of_lists(G)
- GG=from_dict_of_lists(dol,create_using=MultiGraph())
- assert_equal(sorted(G.nodes()), sorted(GG.nodes()))
- assert_equal(sorted(G.edges()), sorted(GG.edges()))
- GW=to_networkx_graph(dol,create_using=MultiGraph())
- assert_equal(sorted(G.nodes()), sorted(GW.nodes()))
- assert_equal(sorted(G.edges()), sorted(GW.edges()))
- GI=MultiGraph(dol)
- assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
- assert_equal(sorted(G.edges()), sorted(GI.edges()))
-
- def test_edgelists(self):
- P=path_graph(4)
- e=[(0,1),(1,2),(2,3)]
- G=Graph(e)
- assert_equal(sorted(G.nodes()), sorted(P.nodes()))
- assert_equal(sorted(G.edges()), sorted(P.edges()))
- assert_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
-
- e=[(0,1,{}),(1,2,{}),(2,3,{})]
- G=Graph(e)
- assert_equal(sorted(G.nodes()), sorted(P.nodes()))
- assert_equal(sorted(G.edges()), sorted(P.edges()))
- assert_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
-
- e=((n,n+1) for n in range(3))
- G=Graph(e)
- assert_equal(sorted(G.nodes()), sorted(P.nodes()))
- assert_equal(sorted(G.edges()), sorted(P.edges()))
- assert_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
-
- def test_directed_to_undirected(self):
- edges1 = [(0, 1), (1, 2), (2, 0)]
- edges2 = [(0, 1), (1, 2), (0, 2)]
- assert_true(self.edgelists_equal(nx.Graph(nx.DiGraph(edges1)).edges(),edges1))
- assert_true(self.edgelists_equal(nx.Graph(nx.DiGraph(edges2)).edges(),edges1))
- assert_true(self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges1)).edges(),edges1))
- assert_true(self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges2)).edges(),edges1))
-
- assert_true(self.edgelists_equal(nx.MultiGraph(nx.MultiDiGraph(edges1)).edges(),
- edges1))
- assert_true(self.edgelists_equal(nx.MultiGraph(nx.MultiDiGraph(edges2)).edges(),
- edges1))
-
- assert_true(self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges1)).edges(),edges1))
- assert_true(self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges2)).edges(),edges1))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert_numpy.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert_numpy.py
deleted file mode 100644
index 2a4ef96..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert_numpy.py
+++ /dev/null
@@ -1,172 +0,0 @@
-from nose import SkipTest
-from nose.tools import assert_raises, assert_true, assert_equal
-
-import networkx as nx
-from networkx.generators.classic import barbell_graph,cycle_graph,path_graph
-
-class TestConvertNumpy(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global np
- global np_assert_equal
- try:
- import numpy as np
- np_assert_equal=np.testing.assert_equal
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def __init__(self):
- self.G1 = barbell_graph(10, 3)
- self.G2 = cycle_graph(10, create_using=nx.DiGraph())
-
- self.G3 = self.create_weighted(nx.Graph())
- self.G4 = self.create_weighted(nx.DiGraph())
-
- def create_weighted(self, G):
- g = cycle_graph(4)
- e = g.edges()
- source = [u for u,v in e]
- dest = [v for u,v in e]
- weight = [s+10 for s in source]
- ex = zip(source, dest, weight)
- G.add_weighted_edges_from(ex)
- return G
-
- def assert_equal(self, G1, G2):
- assert_true( sorted(G1.nodes())==sorted(G2.nodes()) )
- assert_true( sorted(G1.edges())==sorted(G2.edges()) )
-
- def identity_conversion(self, G, A, create_using):
- GG = nx.from_numpy_matrix(A, create_using=create_using)
- self.assert_equal(G, GG)
- GW = nx.to_networkx_graph(A, create_using=create_using)
- self.assert_equal(G, GW)
- GI = create_using.__class__(A)
- self.assert_equal(G, GI)
-
- def test_shape(self):
- "Conversion from non-square array."
- A=np.array([[1,2,3],[4,5,6]])
- assert_raises(nx.NetworkXError, nx.from_numpy_matrix, A)
-
- def test_identity_graph_matrix(self):
- "Conversion from graph to matrix to graph."
- A = nx.to_numpy_matrix(self.G1)
- self.identity_conversion(self.G1, A, nx.Graph())
-
- def test_identity_graph_array(self):
- "Conversion from graph to array to graph."
- A = nx.to_numpy_matrix(self.G1)
- A = np.asarray(A)
- self.identity_conversion(self.G1, A, nx.Graph())
-
- def test_identity_digraph_matrix(self):
- """Conversion from digraph to matrix to digraph."""
- A = nx.to_numpy_matrix(self.G2)
- self.identity_conversion(self.G2, A, nx.DiGraph())
-
- def test_identity_digraph_array(self):
- """Conversion from digraph to array to digraph."""
- A = nx.to_numpy_matrix(self.G2)
- A = np.asarray(A)
- self.identity_conversion(self.G2, A, nx.DiGraph())
-
- def test_identity_weighted_graph_matrix(self):
- """Conversion from weighted graph to matrix to weighted graph."""
- A = nx.to_numpy_matrix(self.G3)
- self.identity_conversion(self.G3, A, nx.Graph())
-
- def test_identity_weighted_graph_array(self):
- """Conversion from weighted graph to array to weighted graph."""
- A = nx.to_numpy_matrix(self.G3)
- A = np.asarray(A)
- self.identity_conversion(self.G3, A, nx.Graph())
-
- def test_identity_weighted_digraph_matrix(self):
- """Conversion from weighted digraph to matrix to weighted digraph."""
- A = nx.to_numpy_matrix(self.G4)
- self.identity_conversion(self.G4, A, nx.DiGraph())
-
- def test_identity_weighted_digraph_array(self):
- """Conversion from weighted digraph to array to weighted digraph."""
- A = nx.to_numpy_matrix(self.G4)
- A = np.asarray(A)
- self.identity_conversion(self.G4, A, nx.DiGraph())
-
- def test_nodelist(self):
- """Conversion from graph to matrix to graph with nodelist."""
- P4 = path_graph(4)
- P3 = path_graph(3)
- nodelist = P3.nodes()
- A = nx.to_numpy_matrix(P4, nodelist=nodelist)
- GA = nx.Graph(A)
- self.assert_equal(GA, P3)
-
- # Make nodelist ambiguous by containing duplicates.
- nodelist += [nodelist[0]]
- assert_raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=nodelist)
-
- def test_weight_keyword(self):
- WP4 = nx.Graph()
- WP4.add_edges_from( (n,n+1,dict(weight=0.5,other=0.3)) for n in range(3) )
- P4 = path_graph(4)
- A = nx.to_numpy_matrix(P4)
- np_assert_equal(A, nx.to_numpy_matrix(WP4,weight=None))
- np_assert_equal(0.5*A, nx.to_numpy_matrix(WP4))
- np_assert_equal(0.3*A, nx.to_numpy_matrix(WP4,weight='other'))
-
- def test_from_numpy_matrix_type(self):
- A=np.matrix([[1]])
- G=nx.from_numpy_matrix(A)
- assert_equal(type(G[0][0]['weight']),int)
-
- A=np.matrix([[1]]).astype(np.float)
- G=nx.from_numpy_matrix(A)
- assert_equal(type(G[0][0]['weight']),float)
-
- A=np.matrix([[1]]).astype(np.str)
- G=nx.from_numpy_matrix(A)
- assert_equal(type(G[0][0]['weight']),str)
-
- A=np.matrix([[1]]).astype(np.bool)
- G=nx.from_numpy_matrix(A)
- assert_equal(type(G[0][0]['weight']),bool)
-
- A=np.matrix([[1]]).astype(np.complex)
- G=nx.from_numpy_matrix(A)
- assert_equal(type(G[0][0]['weight']),complex)
-
- A=np.matrix([[1]]).astype(np.object)
- assert_raises(TypeError,nx.from_numpy_matrix,A)
-
- def test_from_numpy_matrix_dtype(self):
- dt=[('weight',float),('cost',int)]
- A=np.matrix([[(1.0,2)]],dtype=dt)
- G=nx.from_numpy_matrix(A)
- assert_equal(type(G[0][0]['weight']),float)
- assert_equal(type(G[0][0]['cost']),int)
- assert_equal(G[0][0]['cost'],2)
- assert_equal(G[0][0]['weight'],1.0)
-
- def test_to_numpy_recarray(self):
- G=nx.Graph()
- G.add_edge(1,2,weight=7.0,cost=5)
- A=nx.to_numpy_recarray(G,dtype=[('weight',float),('cost',int)])
- assert_equal(sorted(A.dtype.names),['cost','weight'])
- assert_equal(A.weight[0,1],7.0)
- assert_equal(A.weight[0,0],0.0)
- assert_equal(A.cost[0,1],5)
- assert_equal(A.cost[0,0],0)
-
- def test_numpy_multigraph(self):
- G=nx.MultiGraph()
- G.add_edge(1,2,weight=7)
- G.add_edge(1,2,weight=70)
- A=nx.to_numpy_matrix(G)
- assert_equal(A[1,0],77)
- A=nx.to_numpy_matrix(G,multigraph_weight=min)
- assert_equal(A[1,0],7)
- A=nx.to_numpy_matrix(G,multigraph_weight=max)
- assert_equal(A[1,0],70)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert_scipy.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert_scipy.py
deleted file mode 100644
index f90dee7..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_convert_scipy.py
+++ /dev/null
@@ -1,179 +0,0 @@
-from nose import SkipTest
-from nose.tools import assert_raises, assert_true, assert_equal, raises
-
-import networkx as nx
-from networkx.generators.classic import barbell_graph,cycle_graph,path_graph
-
-class TestConvertNumpy(object):
- @classmethod
- def setupClass(cls):
- global np, sp, sparse, np_assert_equal
- try:
- import numpy as np
- import scipy as sp
- import scipy.sparse as sparse
- np_assert_equal=np.testing.assert_equal
- except ImportError:
- raise SkipTest('SciPy sparse library not available.')
-
- def __init__(self):
- self.G1 = barbell_graph(10, 3)
- self.G2 = cycle_graph(10, create_using=nx.DiGraph())
-
- self.G3 = self.create_weighted(nx.Graph())
- self.G4 = self.create_weighted(nx.DiGraph())
-
- def create_weighted(self, G):
- g = cycle_graph(4)
- e = g.edges()
- source = [u for u,v in e]
- dest = [v for u,v in e]
- weight = [s+10 for s in source]
- ex = zip(source, dest, weight)
- G.add_weighted_edges_from(ex)
- return G
-
- def assert_equal(self, G1, G2):
- assert_true( sorted(G1.nodes())==sorted(G2.nodes()) )
- assert_true( sorted(G1.edges())==sorted(G2.edges()) )
-
- def identity_conversion(self, G, A, create_using):
- GG = nx.from_scipy_sparse_matrix(A, create_using=create_using)
- self.assert_equal(G, GG)
-
- GW = nx.to_networkx_graph(A, create_using=create_using)
- self.assert_equal(G, GW)
-
- GI = create_using.__class__(A)
- self.assert_equal(G, GI)
-
- ACSR = A.tocsr()
- GI = create_using.__class__(ACSR)
- self.assert_equal(G, GI)
-
- ACOO = A.tocoo()
- GI = create_using.__class__(ACOO)
- self.assert_equal(G, GI)
-
- ACSC = A.tocsc()
- GI = create_using.__class__(ACSC)
- self.assert_equal(G, GI)
-
- AD = A.todense()
- GI = create_using.__class__(AD)
- self.assert_equal(G, GI)
-
- AA = A.toarray()
- GI = create_using.__class__(AA)
- self.assert_equal(G, GI)
-
- def test_shape(self):
- "Conversion from non-square sparse array."
- A = sp.sparse.lil_matrix([[1,2,3],[4,5,6]])
- assert_raises(nx.NetworkXError, nx.from_scipy_sparse_matrix, A)
-
- def test_identity_graph_matrix(self):
- "Conversion from graph to sparse matrix to graph."
- A = nx.to_scipy_sparse_matrix(self.G1)
- self.identity_conversion(self.G1, A, nx.Graph())
-
- def test_identity_digraph_matrix(self):
- "Conversion from digraph to sparse matrix to digraph."
- A = nx.to_scipy_sparse_matrix(self.G2)
- self.identity_conversion(self.G2, A, nx.DiGraph())
-
- def test_identity_weighted_graph_matrix(self):
- """Conversion from weighted graph to sparse matrix to weighted graph."""
- A = nx.to_scipy_sparse_matrix(self.G3)
- self.identity_conversion(self.G3, A, nx.Graph())
-
- def test_identity_weighted_digraph_matrix(self):
- """Conversion from weighted digraph to sparse matrix to weighted digraph."""
- A = nx.to_scipy_sparse_matrix(self.G4)
- self.identity_conversion(self.G4, A, nx.DiGraph())
-
- def test_nodelist(self):
- """Conversion from graph to sparse matrix to graph with nodelist."""
- P4 = path_graph(4)
- P3 = path_graph(3)
- nodelist = P3.nodes()
- A = nx.to_scipy_sparse_matrix(P4, nodelist=nodelist)
- GA = nx.Graph(A)
- self.assert_equal(GA, P3)
-
- # Make nodelist ambiguous by containing duplicates.
- nodelist += [nodelist[0]]
- assert_raises(nx.NetworkXError, nx.to_numpy_matrix, P3,
- nodelist=nodelist)
-
- def test_weight_keyword(self):
- WP4 = nx.Graph()
- WP4.add_edges_from( (n,n+1,dict(weight=0.5,other=0.3))
- for n in range(3) )
- P4 = path_graph(4)
- A = nx.to_scipy_sparse_matrix(P4)
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
- np_assert_equal(0.5*A.todense(),
- nx.to_scipy_sparse_matrix(WP4).todense())
- np_assert_equal(0.3*A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight='other').todense())
-
- def test_format_keyword(self):
- WP4 = nx.Graph()
- WP4.add_edges_from( (n,n+1,dict(weight=0.5,other=0.3))
- for n in range(3) )
- P4 = path_graph(4)
- A = nx.to_scipy_sparse_matrix(P4, format='csr')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- A = nx.to_scipy_sparse_matrix(P4, format='csc')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- A = nx.to_scipy_sparse_matrix(P4, format='coo')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- A = nx.to_scipy_sparse_matrix(P4, format='bsr')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- A = nx.to_scipy_sparse_matrix(P4, format='lil')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- A = nx.to_scipy_sparse_matrix(P4, format='dia')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- A = nx.to_scipy_sparse_matrix(P4, format='dok')
- np_assert_equal(A.todense(),
- nx.to_scipy_sparse_matrix(WP4,weight=None).todense())
-
- @raises(nx.NetworkXError)
- def test_format_keyword_fail(self):
- WP4 = nx.Graph()
- WP4.add_edges_from( (n,n+1,dict(weight=0.5,other=0.3))
- for n in range(3) )
- P4 = path_graph(4)
- nx.to_scipy_sparse_matrix(P4, format='any_other')
-
- @raises(nx.NetworkXError)
- def test_null_fail(self):
- nx.to_scipy_sparse_matrix(nx.Graph())
-
- def test_empty(self):
- G = nx.Graph()
- G.add_node(1)
- M = nx.to_scipy_sparse_matrix(G)
- np_assert_equal(M.todense(), np.matrix([[0]]))
-
- def test_ordering(self):
- G = nx.DiGraph()
- G.add_edge(1,2)
- G.add_edge(2,3)
- G.add_edge(3,1)
- M = nx.to_scipy_sparse_matrix(G,nodelist=[3,2,1])
- np_assert_equal(M.todense(), np.matrix([[0,0,1],[1,0,0],[0,1,0]]))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_exceptions.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_exceptions.py
deleted file mode 100644
index 796fdd2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_exceptions.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from nose.tools import raises
-import networkx as nx
-
-# smoke tests for exceptions
-
-@raises(nx.NetworkXException)
-def test_raises_networkx_exception():
- raise nx.NetworkXException
-
-@raises(nx.NetworkXError)
-def test_raises_networkx_error():
- raise nx.NetworkXError
-
-@raises(nx.NetworkXPointlessConcept)
-def test_raises_networkx_pointless_concept():
- raise nx.NetworkXPointlessConcept
-
-@raises(nx.NetworkXAlgorithmError)
-def test_raises_networkx_algorithm_error():
- raise nx.NetworkXAlgorithmError
-
-@raises(nx.NetworkXUnfeasible)
-def test_raises_networkx_unfeasible():
- raise nx.NetworkXUnfeasible
-
-@raises(nx.NetworkXNoPath)
-def test_raises_networkx_no_path():
- raise nx.NetworkXNoPath
-
-@raises(nx.NetworkXUnbounded)
-def test_raises_networkx_unbounded():
- raise nx.NetworkXUnbounded
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_relabel.py b/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_relabel.py
deleted file mode 100644
index e9dd2af..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/tests/test_relabel.py
+++ /dev/null
@@ -1,163 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from networkx import *
-from networkx.convert import *
-from networkx.algorithms.operators import *
-from networkx.generators.classic import barbell_graph,cycle_graph
-from networkx.testing import *
-
-class TestRelabel():
- def test_convert_node_labels_to_integers(self):
- # test that empty graph converts fine for all options
- G=empty_graph()
- H=convert_node_labels_to_integers(G,100)
- assert_equal(H.name, '(empty_graph(0))_with_int_labels')
- assert_equal(H.nodes(), [])
- assert_equal(H.edges(), [])
-
- for opt in ["default", "sorted", "increasing degree",
- "decreasing degree"]:
- G=empty_graph()
- H=convert_node_labels_to_integers(G,100, ordering=opt)
- assert_equal(H.name, '(empty_graph(0))_with_int_labels')
- assert_equal(H.nodes(), [])
- assert_equal(H.edges(), [])
-
- G=empty_graph()
- G.add_edges_from([('A','B'),('A','C'),('B','C'),('C','D')])
- G.name="paw"
- H=convert_node_labels_to_integers(G)
- degH=H.degree().values()
- degG=G.degree().values()
- assert_equal(sorted(degH), sorted(degG))
-
- H=convert_node_labels_to_integers(G,1000)
- degH=H.degree().values()
- degG=G.degree().values()
- assert_equal(sorted(degH), sorted(degG))
- assert_equal(H.nodes(), [1000, 1001, 1002, 1003])
-
- H=convert_node_labels_to_integers(G,ordering="increasing degree")
- degH=H.degree().values()
- degG=G.degree().values()
- assert_equal(sorted(degH), sorted(degG))
- assert_equal(degree(H,0), 1)
- assert_equal(degree(H,1), 2)
- assert_equal(degree(H,2), 2)
- assert_equal(degree(H,3), 3)
-
- H=convert_node_labels_to_integers(G,ordering="decreasing degree")
- degH=H.degree().values()
- degG=G.degree().values()
- assert_equal(sorted(degH), sorted(degG))
- assert_equal(degree(H,0), 3)
- assert_equal(degree(H,1), 2)
- assert_equal(degree(H,2), 2)
- assert_equal(degree(H,3), 1)
-
- H=convert_node_labels_to_integers(G,ordering="increasing degree",
- label_attribute='label')
- degH=H.degree().values()
- degG=G.degree().values()
- assert_equal(sorted(degH), sorted(degG))
- assert_equal(degree(H,0), 1)
- assert_equal(degree(H,1), 2)
- assert_equal(degree(H,2), 2)
- assert_equal(degree(H,3), 3)
-
- # check mapping
- assert_equal(H.node[3]['label'],'C')
- assert_equal(H.node[0]['label'],'D')
- assert_true(H.node[1]['label']=='A' or H.node[2]['label']=='A')
- assert_true(H.node[1]['label']=='B' or H.node[2]['label']=='B')
-
- def test_convert_to_integers2(self):
- G=empty_graph()
- G.add_edges_from([('C','D'),('A','B'),('A','C'),('B','C')])
- G.name="paw"
- H=convert_node_labels_to_integers(G,ordering="sorted")
- degH=H.degree().values()
- degG=G.degree().values()
- assert_equal(sorted(degH), sorted(degG))
-
- H=convert_node_labels_to_integers(G,ordering="sorted",
- label_attribute='label')
- assert_equal(H.node[0]['label'],'A')
- assert_equal(H.node[1]['label'],'B')
- assert_equal(H.node[2]['label'],'C')
- assert_equal(H.node[3]['label'],'D')
-
- @raises(nx.NetworkXError)
- def test_convert_to_integers_raise(self):
- G = nx.Graph()
- H=convert_node_labels_to_integers(G,ordering="increasing age")
-
-
- def test_relabel_nodes_copy(self):
- G=empty_graph()
- G.add_edges_from([('A','B'),('A','C'),('B','C'),('C','D')])
- mapping={'A':'aardvark','B':'bear','C':'cat','D':'dog'}
- H=relabel_nodes(G,mapping)
- assert_equal(sorted(H.nodes()), ['aardvark', 'bear', 'cat', 'dog'])
-
- def test_relabel_nodes_function(self):
- G=empty_graph()
- G.add_edges_from([('A','B'),('A','C'),('B','C'),('C','D')])
- # function mapping no longer encouraged but works
- def mapping(n):
- return ord(n)
- H=relabel_nodes(G,mapping)
- assert_equal(sorted(H.nodes()), [65, 66, 67, 68])
-
- def test_relabel_nodes_graph(self):
- G=Graph([('A','B'),('A','C'),('B','C'),('C','D')])
- mapping={'A':'aardvark','B':'bear','C':'cat','D':'dog'}
- H=relabel_nodes(G,mapping)
- assert_equal(sorted(H.nodes()), ['aardvark', 'bear', 'cat', 'dog'])
-
- def test_relabel_nodes_digraph(self):
- G=DiGraph([('A','B'),('A','C'),('B','C'),('C','D')])
- mapping={'A':'aardvark','B':'bear','C':'cat','D':'dog'}
- H=relabel_nodes(G,mapping,copy=False)
- assert_equal(sorted(H.nodes()), ['aardvark', 'bear', 'cat', 'dog'])
-
- def test_relabel_nodes_multigraph(self):
- G=MultiGraph([('a','b'),('a','b')])
- mapping={'a':'aardvark','b':'bear'}
- G=relabel_nodes(G,mapping,copy=False)
- assert_equal(sorted(G.nodes()), ['aardvark', 'bear'])
- assert_edges_equal(sorted(G.edges()),
- [('aardvark', 'bear'), ('aardvark', 'bear')])
-
- def test_relabel_nodes_multidigraph(self):
- G=MultiDiGraph([('a','b'),('a','b')])
- mapping={'a':'aardvark','b':'bear'}
- G=relabel_nodes(G,mapping,copy=False)
- assert_equal(sorted(G.nodes()), ['aardvark', 'bear'])
- assert_equal(sorted(G.edges()),
- [('aardvark', 'bear'), ('aardvark', 'bear')])
-
- @raises(KeyError)
- def test_relabel_nodes_missing(self):
- G=Graph([('A','B'),('A','C'),('B','C'),('C','D')])
- mapping={0:'aardvark'}
- G=relabel_nodes(G,mapping,copy=False)
-
-
- def test_relabel_toposort(self):
- K5=nx.complete_graph(4)
- G=nx.complete_graph(4)
- G=nx.relabel_nodes(G,dict( [(i,i+1) for i in range(4)]),copy=False)
- nx.is_isomorphic(K5,G)
- G=nx.complete_graph(4)
- G=nx.relabel_nodes(G,dict( [(i,i-1) for i in range(4)]),copy=False)
- nx.is_isomorphic(K5,G)
-
-
- def test_relabel_selfloop(self):
- G = nx.DiGraph([(1, 1), (1, 2), (2, 3)])
- G = nx.relabel_nodes(G, {1: 'One', 2: 'Two', 3: 'Three'}, copy=False)
- assert_equal(sorted(G.nodes()),['One','Three','Two'])
- G = nx.MultiDiGraph([(1, 1), (1, 2), (2, 3)])
- G = nx.relabel_nodes(G, {1: 'One', 2: 'Two', 3: 'Three'}, copy=False)
- assert_equal(sorted(G.nodes()),['One','Three','Two'])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/__init__.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/__init__.py
deleted file mode 100644
index d443064..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from networkx.utils.misc import *
-from networkx.utils.decorators import *
-from networkx.utils.random_sequence import *
-from networkx.utils.union_find import *
-from networkx.utils.rcm import *
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/decorators.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/decorators.py
deleted file mode 100644
index def1548..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/decorators.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import sys
-
-from collections import defaultdict
-from os.path import splitext
-
-import networkx as nx
-from networkx.external.decorator import decorator
-from networkx.utils import is_string_like
-
-def not_implemented_for(*graph_types):
- """Decorator to mark algorithms as not implemented
-
- Parameters
- ----------
- graph_types : container of strings
- Entries must be one of 'directed','undirected', 'multigraph', 'graph'.
-
- Returns
- -------
- _require : function
- The decorated function.
-
- Raises
- ------
- NetworkXNotImplemnted
- If any of the packages cannot be imported
-
- Notes
- -----
- Multiple types are joined logically with "and".
- For "or" use multiple @not_implemented_for() lines.
-
- Examples
- --------
- Decorate functions like this::
-
- @not_implemnted_for('directed')
- def sp_function():
- pass
-
- @not_implemnted_for('directed','multigraph')
- def sp_np_function():
- pass
- """
- @decorator
- def _not_implemented_for(f,*args,**kwargs):
- graph = args[0]
- terms= {'directed':graph.is_directed(),
- 'undirected':not graph.is_directed(),
- 'multigraph':graph.is_multigraph(),
- 'graph':not graph.is_multigraph()}
- match = True
- try:
- for t in graph_types:
- match = match and terms[t]
- except KeyError:
- raise KeyError('use one or more of ',
- 'directed, undirected, multigraph, graph')
- if match:
- raise nx.NetworkXNotImplemented('not implemented for %s type'%
- ' '.join(graph_types))
- else:
- return f(*args,**kwargs)
- return _not_implemented_for
-
-
-def require(*packages):
- """Decorator to check whether specific packages can be imported.
-
- If a package cannot be imported, then NetworkXError is raised.
- If all packages can be imported, then the original function is called.
-
- Parameters
- ----------
- packages : container of strings
- Container of module names that will be imported.
-
- Returns
- -------
- _require : function
- The decorated function.
-
- Raises
- ------
- NetworkXError
- If any of the packages cannot be imported
-
- Examples
- --------
- Decorate functions like this::
-
- @require('scipy')
- def sp_function():
- import scipy
- pass
-
- @require('numpy','scipy')
- def sp_np_function():
- import numpy
- import scipy
- pass
- """
- @decorator
- def _require(f,*args,**kwargs):
- for package in reversed(packages):
- try:
- __import__(package)
- except:
- msg = "{0} requires {1}"
- raise nx.NetworkXError( msg.format(f.__name__, package) )
- return f(*args,**kwargs)
- return _require
-
-
-def _open_gz(path, mode):
- import gzip
- return gzip.open(path,mode=mode)
-
-def _open_bz2(path, mode):
- import bz2
- return bz2.BZ2File(path,mode=mode)
-
-# To handle new extensions, define a function accepting a `path` and `mode`.
-# Then add the extension to _dispatch_dict.
-_dispatch_dict = defaultdict(lambda : open)
-_dispatch_dict['.gz'] = _open_gz
-_dispatch_dict['.bz2'] = _open_bz2
-_dispatch_dict['.gzip'] = _open_gz
-
-
-def open_file(path_arg, mode='r'):
- """Decorator to ensure clean opening and closing of files.
-
- Parameters
- ----------
- path_arg : int
- Location of the path argument in args. Even if the argument is a
- named positional argument (with a default value), you must specify its
- index as a positional argument.
- mode : str
- String for opening mode.
-
- Returns
- -------
- _open_file : function
- Function which cleanly executes the io.
-
- Examples
- --------
- Decorate functions like this::
-
- @open_file(0,'r')
- def read_function(pathname):
- pass
-
- @open_file(1,'w')
- def write_function(G,pathname):
- pass
-
- @open_file(1,'w')
- def write_function(G, pathname='graph.dot')
- pass
-
- @open_file('path', 'w+')
- def another_function(arg, **kwargs):
- path = kwargs['path']
- pass
- """
- # Note that this decorator solves the problem when a path argument is
- # specified as a string, but it does not handle the situation when the
- # function wants to accept a default of None (and then handle it).
- # Here is an example:
- #
- # @open_file('path')
- # def some_function(arg1, arg2, path=None):
- # if path is None:
- # fobj = tempfile.NamedTemporaryFile(delete=False)
- # close_fobj = True
- # else:
- # # `path` could have been a string or file object or something
- # # similar. In any event, the decorator has given us a file object
- # # and it will close it for us, if it should.
- # fobj = path
- # close_fobj = False
- #
- # try:
- # fobj.write('blah')
- # finally:
- # if close_fobj:
- # fobj.close()
- #
- # Normally, we'd want to use "with" to ensure that fobj gets closed.
- # However, recall that the decorator will make `path` a file object for
- # us, and using "with" would undesirably close that file object. Instead,
- # you use a try block, as shown above. When we exit the function, fobj will
- # be closed, if it should be, by the decorator.
-
- @decorator
- def _open_file(func, *args, **kwargs):
-
- # Note that since we have used @decorator, *args, and **kwargs have
- # already been resolved to match the function signature of func. This
- # means default values have been propagated. For example, the function
- # func(x, y, a=1, b=2, **kwargs) if called as func(0,1,b=5,c=10) would
- # have args=(0,1,1,5) and kwargs={'c':10}.
-
- # First we parse the arguments of the decorator. The path_arg could
- # be an positional argument or a keyword argument. Even if it is
- try:
- # path_arg is a required positional argument
- # This works precisely because we are using @decorator
- path = args[path_arg]
- except TypeError:
- # path_arg is a keyword argument. It is "required" in the sense
- # that it must exist, according to the decorator specification,
- # It can exist in `kwargs` by a developer specified default value
- # or it could have been explicitly set by the user.
- try:
- path = kwargs[path_arg]
- except KeyError:
- # Could not find the keyword. Thus, no default was specified
- # in the function signature and the user did not provide it.
- msg = 'Missing required keyword argument: {0}'
- raise nx.NetworkXError(msg.format(path_arg))
- else:
- is_kwarg = True
- except IndexError:
- # A "required" argument was missing. This can only happen if
- # the decorator of the function was incorrectly specified.
- # So this probably is not a user error, but a developer error.
- msg = "path_arg of open_file decorator is incorrect"
- raise nx.NetworkXError(msg)
- else:
- is_kwarg = False
-
- # Now we have the path_arg. There are two types of input to consider:
- # 1) string representing a path that should be opened
- # 2) an already opened file object
- if is_string_like(path):
- ext = splitext(path)[1]
- fobj = _dispatch_dict[ext](path, mode=mode)
- close_fobj = True
- elif hasattr(path, 'read'):
- # path is already a file-like object
- fobj = path
- close_fobj = False
- else:
- # could be None, in which case the algorithm will deal with it
- fobj = path
- close_fobj = False
-
- # Insert file object into args or kwargs.
- if is_kwarg:
- new_args = args
- kwargs[path_arg] = fobj
- else:
- # args is a tuple, so we must convert to list before modifying it.
- new_args = list(args)
- new_args[path_arg] = fobj
-
- # Finally, we call the original function, making sure to close the fobj.
- try:
- result = func(*new_args, **kwargs)
- finally:
- if close_fobj:
- fobj.close()
-
- return result
-
- return _open_file
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/misc.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/misc.py
deleted file mode 100644
index a942753..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/misc.py
+++ /dev/null
@@ -1,151 +0,0 @@
-"""
-Miscellaneous Helpers for NetworkX.
-
-These are not imported into the base networkx namespace but
-can be accessed, for example, as
-
->>> import networkx
->>> networkx.utils.is_string_like('spam')
-True
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import sys
-import subprocess
-import uuid
-
-import networkx as nx
-from networkx.external.decorator import decorator
-
-__author__ = '\n'.join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)',
- 'Ben Edwards(bedwards@cs.unm.edu)'])
-### some cookbook stuff
-# used in deciding whether something is a bunch of nodes, edges, etc.
-# see G.add_nodes and others in Graph Class in networkx/base.py
-
-def is_string_like(obj): # from John Hunter, types-free version
- """Check if obj is string."""
- try:
- obj + ''
- except (TypeError, ValueError):
- return False
- return True
-
-def iterable(obj):
- """ Return True if obj is iterable with a well-defined len()."""
- if hasattr(obj,"__iter__"): return True
- try:
- len(obj)
- except:
- return False
- return True
-
-def flatten(obj, result=None):
- """ Return flattened version of (possibly nested) iterable object. """
- if not iterable(obj) or is_string_like(obj):
- return obj
- if result is None:
- result = []
- for item in obj:
- if not iterable(item) or is_string_like(item):
- result.append(item)
- else:
- flatten(item, result)
- return obj.__class__(result)
-
-def is_list_of_ints( intlist ):
- """ Return True if list is a list of ints. """
- if not isinstance(intlist,list): return False
- for i in intlist:
- if not isinstance(i,int): return False
- return True
-
-def make_str(t):
- """Return the string representation of t."""
- if is_string_like(t): return t
- return str(t)
-
-def cumulative_sum(numbers):
- """Yield cumulative sum of numbers.
-
- >>> import networkx.utils as utils
- >>> list(utils.cumulative_sum([1,2,3,4]))
- [1, 3, 6, 10]
- """
- csum = 0
- for n in numbers:
- csum += n
- yield csum
-
-def generate_unique_node():
- """ Generate a unique node label."""
- return str(uuid.uuid1())
-
-def default_opener(filename):
- """Opens `filename` using system's default program.
-
- Parameters
- ----------
- filename : str
- The path of the file to be opened.
-
- """
- cmds = {'darwin': ['open'],
- 'linux2': ['xdg-open'],
- 'win32': ['cmd.exe', '/C', 'start', '']}
- cmd = cmds[sys.platform] + [filename]
- subprocess.call(cmd)
-
-
-def dict_to_numpy_array(d,mapping=None):
- """Convert a dictionary of dictionaries to a numpy array
- with optional mapping."""
- try:
- return dict_to_numpy_array2(d, mapping)
- except AttributeError:
- return dict_to_numpy_array1(d,mapping)
-
-def dict_to_numpy_array2(d,mapping=None):
- """Convert a dictionary of dictionaries to a 2d numpy array
- with optional mapping."""
- try:
- import numpy
- except ImportError:
- raise ImportError(
- "dict_to_numpy_array requires numpy : http://scipy.org/ ")
- if mapping is None:
- s=set(d.keys())
- for k,v in d.items():
- s.update(v.keys())
- mapping=dict(zip(s,range(len(s))))
- n=len(mapping)
- a = numpy.zeros((n, n))
- for k1, row in d.items():
- for k2, value in row.items():
- i=mapping[k1]
- j=mapping[k2]
- a[i,j] = value
- return a
-
-def dict_to_numpy_array1(d,mapping=None):
- """Convert a dictionary of numbers to a 1d numpy array
- with optional mapping."""
- try:
- import numpy
- except ImportError:
- raise ImportError(
- "dict_to_numpy_array requires numpy : http://scipy.org/ ")
- if mapping is None:
- s = set(d.keys())
- mapping = dict(zip(s,range(len(s))))
- n = len(mapping)
- a = numpy.zeros(n)
- for k1, value in d.items():
- i = mapping[k1]
- a[i] = value
- return a
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/random_sequence.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/random_sequence.py
deleted file mode 100644
index a2f947d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/random_sequence.py
+++ /dev/null
@@ -1,222 +0,0 @@
-"""
-Utilities for generating random numbers, random sequences, and
-random selections.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import random
-import sys
-import networkx as nx
-__author__ = '\n'.join(['Aric Hagberg (hagberg@lanl.gov)',
- 'Dan Schult(dschult@colgate.edu)',
- 'Ben Edwards(bedwards@cs.unm.edu)'])
-
-def create_degree_sequence(n, sfunction=None, max_tries=50, **kwds):
- """ Attempt to create a valid degree sequence of length n using
- specified function sfunction(n,**kwds).
-
- Parameters
- ----------
- n : int
- Length of degree sequence = number of nodes
- sfunction: function
- Function which returns a list of n real or integer values.
- Called as "sfunction(n,**kwds)".
- max_tries: int
- Max number of attempts at creating valid degree sequence.
-
- Notes
- -----
- Repeatedly create a degree sequence by calling sfunction(n,**kwds)
- until achieving a valid degree sequence. If unsuccessful after
- max_tries attempts, raise an exception.
-
- For examples of sfunctions that return sequences of random numbers,
- see networkx.Utils.
-
- Examples
- --------
- >>> from networkx.utils import uniform_sequence, create_degree_sequence
- >>> seq=create_degree_sequence(10,uniform_sequence)
- """
- tries=0
- max_deg=n
- while tries < max_tries:
- trialseq=sfunction(n,**kwds)
- # round to integer values in the range [0,max_deg]
- seq=[min(max_deg, max( int(round(s)),0 )) for s in trialseq]
- # if graphical return, else throw away and try again
- if nx.is_valid_degree_sequence(seq):
- return seq
- tries+=1
- raise nx.NetworkXError(\
- "Exceeded max (%d) attempts at a valid sequence."%max_tries)
-
-
-# The same helpers for choosing random sequences from distributions
-# uses Python's random module
-# http://www.python.org/doc/current/lib/module-random.html
-
-def pareto_sequence(n,exponent=1.0):
- """
- Return sample sequence of length n from a Pareto distribution.
- """
- return [random.paretovariate(exponent) for i in range(n)]
-
-
-def powerlaw_sequence(n,exponent=2.0):
- """
- Return sample sequence of length n from a power law distribution.
- """
- return [random.paretovariate(exponent-1) for i in range(n)]
-
-def zipf_rv(alpha, xmin=1, seed=None):
- r"""Return a random value chosen from the Zipf distribution.
-
- The return value is an integer drawn from the probability distribution
- ::math::
-
- p(x)=\frac{x^{-\alpha}}{\zeta(\alpha,x_{min})},
-
- where `\zeta(\alpha,x_{min})` is the Hurwitz zeta function.
-
- Parameters
- ----------
- alpha : float
- Exponent value of the distribution
- xmin : int
- Minimum value
- seed : int
- Seed value for random number generator
-
- Returns
- -------
- x : int
- Random value from Zipf distribution
-
- Raises
- ------
- ValueError:
- If xmin < 1 or
- If alpha <= 1
-
- Notes
- -----
- The rejection algorithm generates random values for a the power-law
- distribution in uniformly bounded expected time dependent on
- parameters. See [1] for details on its operation.
-
- Examples
- --------
- >>> nx.zipf_rv(alpha=2, xmin=3, seed=42) # doctest: +SKIP
-
- References
- ----------
- ..[1] Luc Devroye, Non-Uniform Random Variate Generation,
- Springer-Verlag, New York, 1986.
- """
- if xmin < 1:
- raise ValueError("xmin < 1")
- if alpha <= 1:
- raise ValueError("a <= 1.0")
- if not seed is None:
- random.seed(seed)
- a1 = alpha - 1.0
- b = 2**a1
- while True:
- u = 1.0 - random.random() # u in (0,1]
- v = random.random() # v in [0,1)
- x = int(xmin*u**-(1.0/a1))
- t = (1.0+(1.0/x))**a1
- if v*x*(t-1.0)/(b-1.0) <= t/b:
- break
- return x
-
-def zipf_sequence(n, alpha=2.0, xmin=1):
- """Return a sample sequence of length n from a Zipf distribution with
- exponent parameter alpha and minimum value xmin.
-
- See Also
- --------
- zipf_rv
- """
- return [ zipf_rv(alpha,xmin) for _ in range(n)]
-
-def uniform_sequence(n):
- """
- Return sample sequence of length n from a uniform distribution.
- """
- return [ random.uniform(0,n) for i in range(n)]
-
-
-def cumulative_distribution(distribution):
- """Return normalized cumulative distribution from discrete distribution."""
-
- cdf=[]
- cdf.append(0.0)
- psum=float(sum(distribution))
- for i in range(0,len(distribution)):
- cdf.append(cdf[i]+distribution[i]/psum)
- return cdf
-
-
-def discrete_sequence(n, distribution=None, cdistribution=None):
- """
- Return sample sequence of length n from a given discrete distribution
- or discrete cumulative distribution.
-
- One of the following must be specified.
-
- distribution = histogram of values, will be normalized
-
- cdistribution = normalized discrete cumulative distribution
-
- """
- import bisect
-
- if cdistribution is not None:
- cdf=cdistribution
- elif distribution is not None:
- cdf=cumulative_distribution(distribution)
- else:
- raise nx.NetworkXError(
- "discrete_sequence: distribution or cdistribution missing")
-
-
- # get a uniform random number
- inputseq=[random.random() for i in range(n)]
-
- # choose from CDF
- seq=[bisect.bisect_left(cdf,s)-1 for s in inputseq]
- return seq
-
-
-def random_weighted_sample(mapping, k):
- """Return k items without replacement from a weighted sample.
-
- The input is a dictionary of items with weights as values.
- """
- if k > len(mapping):
- raise ValueError("sample larger than population")
- sample = set()
- while len(sample) < k:
- sample.add(weighted_choice(mapping))
- return list(sample)
-
-def weighted_choice(mapping):
- """Return a single element from a weighted sample.
-
- The input is a dictionary of items with weights as values.
- """
- # use roulette method
- rnd = random.random() * sum(mapping.values())
- for k, w in mapping.items():
- rnd -= w
- if rnd < 0:
- return k
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/rcm.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/rcm.py
deleted file mode 100644
index b21bd9b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/rcm.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""
-Cuthill-McKee ordering of graph nodes to produce sparse matrices
-"""
-# Copyright (C) 2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# All rights reserved.
-# BSD license.
-from operator import itemgetter
-import networkx as nx
-__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>'])
-__all__ = ['cuthill_mckee_ordering',
- 'reverse_cuthill_mckee_ordering']
-
-def cuthill_mckee_ordering(G, start=None):
- """Generate an ordering (permutation) of the graph nodes to make
- a sparse matrix.
-
- Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- start : node, optional
- Start algorithm and specified node. The node should be on the
- periphery of the graph for best results.
-
- Returns
- -------
- nodes : generator
- Generator of nodes in Cuthill-McKee ordering.
-
- Examples
- --------
- >>> from networkx.utils import cuthill_mckee_ordering
- >>> G = nx.path_graph(4)
- >>> rcm = list(cuthill_mckee_ordering(G))
- >>> A = nx.adjacency_matrix(G, nodelist=rcm) # doctest: +SKIP
-
- See Also
- --------
- reverse_cuthill_mckee_ordering
-
- Notes
- -----
- The optimal solution the the bandwidth reduction is NP-complete [2]_.
-
- References
- ----------
- .. [1] E. Cuthill and J. McKee.
- Reducing the bandwidth of sparse symmetric matrices,
- In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
- http://doi.acm.org/10.1145/800195.805928
- .. [2] Steven S. Skiena. 1997. The Algorithm Design Manual.
- Springer-Verlag New York, Inc., New York, NY, USA.
- """
- for c in nx.connected_components(G):
- for n in connected_cuthill_mckee_ordering(G.subgraph(c), start):
- yield n
-
-def reverse_cuthill_mckee_ordering(G, start=None):
- """Generate an ordering (permutation) of the graph nodes to make
- a sparse matrix.
-
- Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)
- [1]_.
-
- Parameters
- ----------
- G : graph
- A NetworkX graph
-
- start : node, optional
- Start algorithm and specified node. The node should be on the
- periphery of the graph for best results.
-
- Returns
- -------
- nodes : generator
- Generator of nodes in reverse Cuthill-McKee ordering.
-
- Examples
- --------
- >>> from networkx.utils import reverse_cuthill_mckee_ordering
- >>> G = nx.path_graph(4)
- >>> rcm = list(reverse_cuthill_mckee_ordering(G))
- >>> A = nx.adjacency_matrix(G, nodelist=rcm) # doctest: +SKIP
-
- See Also
- --------
- cuthill_mckee_ordering
-
- Notes
- -----
- The optimal solution the the bandwidth reduction is NP-complete [2]_.
-
- References
- ----------
- .. [1] E. Cuthill and J. McKee.
- Reducing the bandwidth of sparse symmetric matrices,
- In Proc. 24th Nat. Conf. ACM, pages 157-72, 1969.
- http://doi.acm.org/10.1145/800195.805928
- .. [2] Steven S. Skiena. 1997. The Algorithm Design Manual.
- Springer-Verlag New York, Inc., New York, NY, USA.
- """
- return reversed(list(cuthill_mckee_ordering(G, start=start)))
-
-def connected_cuthill_mckee_ordering(G, start=None):
- # the cuthill mckee algorithm for connected graphs
- if start is None:
- (_, start) = find_pseudo_peripheral_node_pair(G)
- yield start
- visited = set([start])
- stack = [(start, iter(G[start]))]
- while stack:
- parent,children = stack[0]
- if parent not in visited:
- yield parent
- try:
- child = next(children)
- if child not in visited:
- yield child
- visited.add(child)
- # add children to stack, sorted by degree (lowest first)
- nd = sorted(G.degree(G[child]).items(), key=itemgetter(1))
- children = (n for n,d in nd)
- stack.append((child,children))
- except StopIteration:
- stack.pop(0)
-
-def find_pseudo_peripheral_node_pair(G, start=None):
- # helper for cuthill-mckee to find a "pseudo peripheral pair"
- # to use as good starting node
- if start is None:
- u = next(G.nodes_iter())
- else:
- u = start
- lp = 0
- v = u
- while True:
- spl = nx.shortest_path_length(G, v)
- l = max(spl.values())
- if l <= lp:
- break
- lp = l
- farthest = [n for n,dist in spl.items() if dist==l]
- v, deg = sorted(G.degree(farthest).items(), key=itemgetter(1))[0]
- return u, v
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_decorators.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_decorators.py
deleted file mode 100644
index 964b2c9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_decorators.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import tempfile
-import os
-
-from nose.tools import *
-
-import networkx as nx
-from networkx.utils.decorators import open_file,require,not_implemented_for
-
-def test_not_implemented_decorator():
- @not_implemented_for('directed')
- def test1(G):
- pass
- test1(nx.Graph())
-
-@raises(KeyError)
-def test_not_implemented_decorator_key():
- @not_implemented_for('foo')
- def test1(G):
- pass
- test1(nx.Graph())
-
-@raises(nx.NetworkXNotImplemented)
-def test_not_implemented_decorator_raise():
- @not_implemented_for('graph')
- def test1(G):
- pass
- test1(nx.Graph())
-
-
-def test_require_decorator1():
- @require('os','sys')
- def test1():
- import os
- import sys
- test1()
-
-def test_require_decorator2():
- @require('blahhh')
- def test2():
- import blahhh
- assert_raises(nx.NetworkXError, test2)
-
-class TestOpenFileDecorator(object):
- def setUp(self):
- self.text = ['Blah... ', 'BLAH ', 'BLAH!!!!']
- self.fobj = tempfile.NamedTemporaryFile('wb+', delete=False)
- self.name = self.fobj.name
-
- def write(self, path):
- for text in self.text:
- path.write(text.encode('ascii'))
-
- @open_file(1, 'r')
- def read(self, path):
- return path.readlines()[0]
-
- @staticmethod
- @open_file(0, 'wb')
- def writer_arg0(path):
- path.write('demo'.encode('ascii'))
-
- @open_file(1, 'wb+')
- def writer_arg1(self, path):
- self.write(path)
-
- @open_file(2, 'wb')
- def writer_arg2default(self, x, path=None):
- if path is None:
- fh = tempfile.NamedTemporaryFile('wb+', delete=False)
- close_fh = True
- else:
- fh = path
- close_fh = False
-
- try:
- self.write(fh)
- finally:
- if close_fh:
- fh.close()
-
- @open_file(4, 'wb')
- def writer_arg4default(self, x, y, other='hello', path=None, **kwargs):
- if path is None:
- fh = tempfile.NamedTemporaryFile('wb+', delete=False)
- close_fh = True
- else:
- fh = path
- close_fh = False
-
- try:
- self.write(fh)
- finally:
- if close_fh:
- fh.close()
-
- @open_file('path', 'wb')
- def writer_kwarg(self, **kwargs):
- path = kwargs.get('path', None)
- if path is None:
- fh = tempfile.NamedTemporaryFile('wb+', delete=False)
- close_fh = True
- else:
- fh = path
- close_fh = False
-
- try:
- self.write(fh)
- finally:
- if close_fh:
- fh.close()
-
- def test_writer_arg0_str(self):
- self.writer_arg0(self.name)
-
- def test_writer_arg0_fobj(self):
- self.writer_arg0(self.fobj)
-
- def test_writer_arg1_str(self):
- self.writer_arg1(self.name)
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_arg1_fobj(self):
- self.writer_arg1(self.fobj)
- assert_false(self.fobj.closed)
- self.fobj.close()
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_arg2default_str(self):
- self.writer_arg2default(0, path=None)
- self.writer_arg2default(0, path=self.name)
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_arg2default_fobj(self):
- self.writer_arg2default(0, path=self.fobj)
- assert_false(self.fobj.closed)
- self.fobj.close()
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_arg2default_fobj(self):
- self.writer_arg2default(0, path=None)
-
- def test_writer_arg4default_fobj(self):
- self.writer_arg4default(0, 1, dog='dog', other='other2')
- self.writer_arg4default(0, 1, dog='dog', other='other2', path=self.name)
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_kwarg_str(self):
- self.writer_kwarg(path=self.name)
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_kwarg_fobj(self):
- self.writer_kwarg(path=self.fobj)
- self.fobj.close()
- assert_equal( self.read(self.name), ''.join(self.text) )
-
- def test_writer_kwarg_fobj(self):
- self.writer_kwarg(path=None)
-
- def tearDown(self):
- self.fobj.close()
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_misc.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_misc.py
deleted file mode 100644
index 77b8196..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_misc.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from nose.tools import *
-from nose import SkipTest
-import networkx as nx
-from networkx.utils import *
-
-def test_is_string_like():
- assert_true(is_string_like("aaaa"))
- assert_false(is_string_like(None))
- assert_false(is_string_like(123))
-
-def test_iterable():
- assert_false(iterable(None))
- assert_false(iterable(10))
- assert_true(iterable([1,2,3]))
- assert_true(iterable((1,2,3)))
- assert_true(iterable({1:"A",2:"X"}))
- assert_true(iterable("ABC"))
-
-def test_graph_iterable():
- K=nx.complete_graph(10)
- assert_true(iterable(K))
- assert_true(iterable(K.nodes_iter()))
- assert_true(iterable(K.edges_iter()))
-
-def test_is_list_of_ints():
- assert_true(is_list_of_ints([1,2,3,42]))
- assert_false(is_list_of_ints([1,2,3,"kermit"]))
-
-def test_random_number_distribution():
- # smoke test only
- z=uniform_sequence(20)
- z=powerlaw_sequence(20,exponent=2.5)
- z=pareto_sequence(20,exponent=1.5)
- z=discrete_sequence(20,distribution=[0,0,0,0,1,1,1,1,2,2,3])
-
-class TestNumpyArray(object):
- numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
- @classmethod
- def setupClass(cls):
- global numpy
- global assert_equal
- global assert_almost_equal
- try:
- import numpy
- from numpy.testing import assert_equal,assert_almost_equal
- except ImportError:
- raise SkipTest('NumPy not available.')
-
- def test_dict_to_numpy_array1(self):
- d = {'a':1,'b':2}
- a = dict_to_numpy_array1(d)
- assert_equal(a, numpy.array([1,2]))
- a = dict_to_numpy_array1(d, mapping = {'b':0,'a':1})
- assert_equal(a, numpy.array([2,1]))
-
- def test_dict_to_numpy_array2(self):
- d = {'a': {'a':1,'b':2},
- 'b': {'a':10,'b':20}}
- a = dict_to_numpy_array(d)
- assert_equal(a, numpy.array([[1,2],[10,20]]))
- a = dict_to_numpy_array2(d, mapping = {'b':0,'a':1})
- assert_equal(a, numpy.array([[20,10],[2,1]]))
-
-
- def test_dict_to_numpy_array(self):
- d = {'a': {'a':1,'b':2},
- 'b': {'a':10,'b':20}}
- a = dict_to_numpy_array(d)
- assert_equal(a, numpy.array([[1,2],[10,20]]))
- d = {'a':1,'b':2}
- a = dict_to_numpy_array1(d)
- assert_equal(a, numpy.array([1,2]))
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_random_sequence.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_random_sequence.py
deleted file mode 100644
index 0c3634a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_random_sequence.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python
-from nose.tools import *
-from networkx.utils import uniform_sequence,powerlaw_sequence,\
- create_degree_sequence,zipf_rv,zipf_sequence,random_weighted_sample,\
- weighted_choice
-import networkx.utils
-
-def test_degree_sequences():
- seq=create_degree_sequence(10,uniform_sequence)
- assert_equal(len(seq), 10)
- seq=create_degree_sequence(10,powerlaw_sequence)
- assert_equal(len(seq), 10)
-
-def test_zipf_rv():
- r = zipf_rv(2.3)
- assert_true(type(r),int)
- assert_raises(ValueError,zipf_rv,0.5)
- assert_raises(ValueError,zipf_rv,2,xmin=0)
-
-def test_zipf_sequence():
- s = zipf_sequence(10)
- assert_equal(len(s),10)
-
-def test_random_weighted_sample():
- mapping={'a':10,'b':20}
- s = random_weighted_sample(mapping,2)
- assert_equal(sorted(s),sorted(mapping.keys()))
- assert_raises(ValueError,random_weighted_sample,mapping,3)
-
-def test_random_weighted_choice():
- mapping={'a':10,'b':0}
- c = weighted_choice(mapping)
- assert_equal(c,'a')
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_rcm.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_rcm.py
deleted file mode 100644
index f267586..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/test_rcm.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from nose.tools import *
-from networkx.utils import reverse_cuthill_mckee_ordering
-import networkx as nx
-
-def test_reverse_cuthill_mckee():
- # example graph from
- # http://www.boost.org/doc/libs/1_37_0/libs/graph/example/cuthill_mckee_ordering.cpp
- G = nx.Graph([(0,3),(0,5),(1,2),(1,4),(1,6),(1,9),(2,3),
- (2,4),(3,5),(3,8),(4,6),(5,6),(5,7),(6,7)])
- rcm = list(reverse_cuthill_mckee_ordering(G,start=0))
- assert_equal(rcm,[9, 1, 4, 6, 7, 2, 8, 5, 3, 0])
- rcm = list(reverse_cuthill_mckee_ordering(G))
- assert_equal(rcm,[0, 8, 5, 7, 3, 6, 4, 2, 1, 9])
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/utils/union_find.py b/lib/python2.7/site-packages/setoolsgui/networkx/utils/union_find.py
deleted file mode 100644
index d05dd92..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/utils/union_find.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""
-Union-find data structure.
-"""
-# Copyright (C) 2004-2011 by
-# Aric Hagberg <hagberg@lanl.gov>
-# Dan Schult <dschult@colgate.edu>
-# Pieter Swart <swart@lanl.gov>
-# All rights reserved.
-# BSD license.
-import networkx as nx
-
-class UnionFind:
- """Union-find data structure.
-
- Each unionFind instance X maintains a family of disjoint sets of
- hashable objects, supporting the following two methods:
-
- - X[item] returns a name for the set containing the given item.
- Each set is named by an arbitrarily-chosen one of its members; as
- long as the set remains unchanged it will keep the same name. If
- the item is not yet part of a set in X, a new singleton set is
- created for it.
-
- - X.union(item1, item2, ...) merges the sets containing each item
- into a single larger set. If any item is not yet part of a set
- in X, it is added to X as one of the members of the merged set.
-
- Union-find data structure. Based on Josiah Carlson's code,
- http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/215912
- with significant additional changes by D. Eppstein.
- http://www.ics.uci.edu/~eppstein/PADS/UnionFind.py
-
- """
-
- def __init__(self):
- """Create a new empty union-find structure."""
- self.weights = {}
- self.parents = {}
-
- def __getitem__(self, object):
- """Find and return the name of the set containing the object."""
-
- # check for previously unknown object
- if object not in self.parents:
- self.parents[object] = object
- self.weights[object] = 1
- return object
-
- # find path of objects leading to the root
- path = [object]
- root = self.parents[object]
- while root != path[-1]:
- path.append(root)
- root = self.parents[root]
-
- # compress the path and return
- for ancestor in path:
- self.parents[ancestor] = root
- return root
-
- def __iter__(self):
- """Iterate through all items ever found or unioned by this structure."""
- return iter(self.parents)
-
- def union(self, *objects):
- """Find the sets containing the objects and merge them all."""
- roots = [self[x] for x in objects]
- heaviest = max([(self.weights[r],r) for r in roots])[1]
- for r in roots:
- if r != heaviest:
- self.weights[heaviest] += self.weights[r]
- self.parents[r] = heaviest
-
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/version.py b/lib/python2.7/site-packages/setoolsgui/networkx/version.py
deleted file mode 100644
index 7369f8b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/networkx/version.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""
-Version information for NetworkX, created during installation.
-
-Do not add this file to the repository.
-
-"""
-
-import datetime
-
-version = '1.8.1'
-date = 'Sun Aug 4 07:56:54 2013'
-
-# Was NetworkX built from a development version? If so, remember that the major
-# and minor versions reference the "target" (rather than "current") release.
-dev = False
-
-# Format: (name, major, min, revision)
-version_info = ('networkx', '1', '8.1', None)
-
-# Format: a 'datetime.datetime' instance
-date_info = datetime.datetime(2013, 8, 4, 7, 56, 54, 416491)
-
-# Format: (vcs, vcs_tuple)
-vcs_info = (None, (None, None))
-
diff --git a/lib/python2.7/site-packages/setoolsgui/selinux/__init__.py b/lib/python2.7/site-packages/setoolsgui/selinux/__init__.py
deleted file mode 100644
index b81b031..0000000
--- a/lib/python2.7/site-packages/setoolsgui/selinux/__init__.py
+++ /dev/null
@@ -1,2445 +0,0 @@
-# This file was automatically generated by SWIG (http://www.swig.org).
-# Version 2.0.11
-#
-# Do not make changes to this file unless you know what you are doing--modify
-# the SWIG interface file instead.
-
-
-
-
-
-from sys import version_info
-if version_info >= (2,6,0):
- def swig_import_helper():
- from os.path import dirname
- import imp
- fp = None
- try:
- fp, pathname, description = imp.find_module('_selinux', [dirname(__file__)])
- except ImportError:
- import _selinux
- return _selinux
- if fp is not None:
- try:
- _mod = imp.load_module('_selinux', fp, pathname, description)
- finally:
- fp.close()
- return _mod
- _selinux = swig_import_helper()
- del swig_import_helper
-else:
- import _selinux
-del version_info
-try:
- _swig_property = property
-except NameError:
- pass # Python < 2.2 doesn't have 'property'.
-def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
- if (name == "thisown"): return self.this.own(value)
- if (name == "this"):
- if type(value).__name__ == 'SwigPyObject':
- self.__dict__[name] = value
- return
- method = class_type.__swig_setmethods__.get(name,None)
- if method: return method(self,value)
- if (not static):
- self.__dict__[name] = value
- else:
- raise AttributeError("You cannot add attributes to %s" % self)
-
-def _swig_setattr(self,class_type,name,value):
- return _swig_setattr_nondynamic(self,class_type,name,value,0)
-
-def _swig_getattr(self,class_type,name):
- if (name == "thisown"): return self.this.own()
- method = class_type.__swig_getmethods__.get(name,None)
- if method: return method(self)
- raise AttributeError(name)
-
-def _swig_repr(self):
- try: strthis = "proxy of " + self.this.__repr__()
- except: strthis = ""
- return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
-
-try:
- _object = object
- _newclass = 1
-except AttributeError:
- class _object : pass
- _newclass = 0
-
-
-import shutil, os, stat
-
-DISABLED = -1
-PERMISSIVE = 0
-ENFORCING = 1
-
-def restorecon(path, recursive=False):
- """ Restore SELinux context on a given path """
-
- try:
- mode = os.lstat(path)[stat.ST_MODE]
- status, context = matchpathcon(path, mode)
- except OSError:
- path = os.path.realpath(os.path.expanduser(path))
- mode = os.lstat(path)[stat.ST_MODE]
- status, context = matchpathcon(path, mode)
-
- if status == 0:
- status, oldcontext = lgetfilecon(path)
- if context != oldcontext:
- lsetfilecon(path, context)
-
- if recursive:
- for root, dirs, files in os.walk(path):
- for name in files + dirs:
- restorecon(os.path.join(root, name))
-
-def chcon(path, context, recursive=False):
- """ Set the SELinux context on a given path """
- lsetfilecon(path, context)
- if recursive:
- for root, dirs, files in os.walk(path):
- for name in files + dirs:
- lsetfilecon(os.path.join(root,name), context)
-
-def copytree(src, dest):
- """ An SELinux-friendly shutil.copytree method """
- shutil.copytree(src, dest)
- restorecon(dest, recursive=True)
-
-def install(src, dest):
- """ An SELinux-friendly shutil.move method """
- shutil.move(src, dest)
- restorecon(dest, recursive=True)
-
-class security_id(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, security_id, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, security_id, name)
- __repr__ = _swig_repr
- __swig_setmethods__["ctx"] = _selinux.security_id_ctx_set
- __swig_getmethods__["ctx"] = _selinux.security_id_ctx_get
- if _newclass:ctx = _swig_property(_selinux.security_id_ctx_get, _selinux.security_id_ctx_set)
- __swig_setmethods__["refcnt"] = _selinux.security_id_refcnt_set
- __swig_getmethods__["refcnt"] = _selinux.security_id_refcnt_get
- if _newclass:refcnt = _swig_property(_selinux.security_id_refcnt_get, _selinux.security_id_refcnt_set)
- def __init__(self):
- this = _selinux.new_security_id()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_security_id
- __del__ = lambda self : None;
-security_id_swigregister = _selinux.security_id_swigregister
-security_id_swigregister(security_id)
-
-
-def avc_sid_to_context(*args):
- return _selinux.avc_sid_to_context(*args)
-avc_sid_to_context = _selinux.avc_sid_to_context
-
-def avc_sid_to_context_raw(*args):
- return _selinux.avc_sid_to_context_raw(*args)
-avc_sid_to_context_raw = _selinux.avc_sid_to_context_raw
-
-def avc_context_to_sid(*args):
- return _selinux.avc_context_to_sid(*args)
-avc_context_to_sid = _selinux.avc_context_to_sid
-
-def avc_context_to_sid_raw(*args):
- return _selinux.avc_context_to_sid_raw(*args)
-avc_context_to_sid_raw = _selinux.avc_context_to_sid_raw
-
-def sidget(*args):
- return _selinux.sidget(*args)
-sidget = _selinux.sidget
-
-def sidput(*args):
- return _selinux.sidput(*args)
-sidput = _selinux.sidput
-
-def avc_get_initial_sid(*args):
- return _selinux.avc_get_initial_sid(*args)
-avc_get_initial_sid = _selinux.avc_get_initial_sid
-class avc_entry_ref(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, avc_entry_ref, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, avc_entry_ref, name)
- __repr__ = _swig_repr
- __swig_setmethods__["ae"] = _selinux.avc_entry_ref_ae_set
- __swig_getmethods__["ae"] = _selinux.avc_entry_ref_ae_get
- if _newclass:ae = _swig_property(_selinux.avc_entry_ref_ae_get, _selinux.avc_entry_ref_ae_set)
- def __init__(self):
- this = _selinux.new_avc_entry_ref()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_avc_entry_ref
- __del__ = lambda self : None;
-avc_entry_ref_swigregister = _selinux.avc_entry_ref_swigregister
-avc_entry_ref_swigregister(avc_entry_ref)
-
-class avc_memory_callback(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, avc_memory_callback, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, avc_memory_callback, name)
- __repr__ = _swig_repr
- __swig_setmethods__["func_malloc"] = _selinux.avc_memory_callback_func_malloc_set
- __swig_getmethods__["func_malloc"] = _selinux.avc_memory_callback_func_malloc_get
- if _newclass:func_malloc = _swig_property(_selinux.avc_memory_callback_func_malloc_get, _selinux.avc_memory_callback_func_malloc_set)
- __swig_setmethods__["func_free"] = _selinux.avc_memory_callback_func_free_set
- __swig_getmethods__["func_free"] = _selinux.avc_memory_callback_func_free_get
- if _newclass:func_free = _swig_property(_selinux.avc_memory_callback_func_free_get, _selinux.avc_memory_callback_func_free_set)
- def __init__(self):
- this = _selinux.new_avc_memory_callback()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_avc_memory_callback
- __del__ = lambda self : None;
-avc_memory_callback_swigregister = _selinux.avc_memory_callback_swigregister
-avc_memory_callback_swigregister(avc_memory_callback)
-
-class avc_log_callback(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, avc_log_callback, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, avc_log_callback, name)
- __repr__ = _swig_repr
- __swig_setmethods__["func_log"] = _selinux.avc_log_callback_func_log_set
- __swig_getmethods__["func_log"] = _selinux.avc_log_callback_func_log_get
- if _newclass:func_log = _swig_property(_selinux.avc_log_callback_func_log_get, _selinux.avc_log_callback_func_log_set)
- __swig_setmethods__["func_audit"] = _selinux.avc_log_callback_func_audit_set
- __swig_getmethods__["func_audit"] = _selinux.avc_log_callback_func_audit_get
- if _newclass:func_audit = _swig_property(_selinux.avc_log_callback_func_audit_get, _selinux.avc_log_callback_func_audit_set)
- def __init__(self):
- this = _selinux.new_avc_log_callback()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_avc_log_callback
- __del__ = lambda self : None;
-avc_log_callback_swigregister = _selinux.avc_log_callback_swigregister
-avc_log_callback_swigregister(avc_log_callback)
-
-class avc_thread_callback(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, avc_thread_callback, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, avc_thread_callback, name)
- __repr__ = _swig_repr
- __swig_setmethods__["func_create_thread"] = _selinux.avc_thread_callback_func_create_thread_set
- __swig_getmethods__["func_create_thread"] = _selinux.avc_thread_callback_func_create_thread_get
- if _newclass:func_create_thread = _swig_property(_selinux.avc_thread_callback_func_create_thread_get, _selinux.avc_thread_callback_func_create_thread_set)
- __swig_setmethods__["func_stop_thread"] = _selinux.avc_thread_callback_func_stop_thread_set
- __swig_getmethods__["func_stop_thread"] = _selinux.avc_thread_callback_func_stop_thread_get
- if _newclass:func_stop_thread = _swig_property(_selinux.avc_thread_callback_func_stop_thread_get, _selinux.avc_thread_callback_func_stop_thread_set)
- def __init__(self):
- this = _selinux.new_avc_thread_callback()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_avc_thread_callback
- __del__ = lambda self : None;
-avc_thread_callback_swigregister = _selinux.avc_thread_callback_swigregister
-avc_thread_callback_swigregister(avc_thread_callback)
-
-class avc_lock_callback(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, avc_lock_callback, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, avc_lock_callback, name)
- __repr__ = _swig_repr
- __swig_setmethods__["func_alloc_lock"] = _selinux.avc_lock_callback_func_alloc_lock_set
- __swig_getmethods__["func_alloc_lock"] = _selinux.avc_lock_callback_func_alloc_lock_get
- if _newclass:func_alloc_lock = _swig_property(_selinux.avc_lock_callback_func_alloc_lock_get, _selinux.avc_lock_callback_func_alloc_lock_set)
- __swig_setmethods__["func_get_lock"] = _selinux.avc_lock_callback_func_get_lock_set
- __swig_getmethods__["func_get_lock"] = _selinux.avc_lock_callback_func_get_lock_get
- if _newclass:func_get_lock = _swig_property(_selinux.avc_lock_callback_func_get_lock_get, _selinux.avc_lock_callback_func_get_lock_set)
- __swig_setmethods__["func_release_lock"] = _selinux.avc_lock_callback_func_release_lock_set
- __swig_getmethods__["func_release_lock"] = _selinux.avc_lock_callback_func_release_lock_get
- if _newclass:func_release_lock = _swig_property(_selinux.avc_lock_callback_func_release_lock_get, _selinux.avc_lock_callback_func_release_lock_set)
- __swig_setmethods__["func_free_lock"] = _selinux.avc_lock_callback_func_free_lock_set
- __swig_getmethods__["func_free_lock"] = _selinux.avc_lock_callback_func_free_lock_get
- if _newclass:func_free_lock = _swig_property(_selinux.avc_lock_callback_func_free_lock_get, _selinux.avc_lock_callback_func_free_lock_set)
- def __init__(self):
- this = _selinux.new_avc_lock_callback()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_avc_lock_callback
- __del__ = lambda self : None;
-avc_lock_callback_swigregister = _selinux.avc_lock_callback_swigregister
-avc_lock_callback_swigregister(avc_lock_callback)
-
-AVC_OPT_UNUSED = _selinux.AVC_OPT_UNUSED
-AVC_OPT_SETENFORCE = _selinux.AVC_OPT_SETENFORCE
-
-def avc_init(*args):
- return _selinux.avc_init(*args)
-avc_init = _selinux.avc_init
-
-def avc_open(*args):
- return _selinux.avc_open(*args)
-avc_open = _selinux.avc_open
-
-def avc_cleanup():
- return _selinux.avc_cleanup()
-avc_cleanup = _selinux.avc_cleanup
-
-def avc_reset():
- return _selinux.avc_reset()
-avc_reset = _selinux.avc_reset
-
-def avc_destroy():
- return _selinux.avc_destroy()
-avc_destroy = _selinux.avc_destroy
-
-def avc_has_perm_noaudit(*args):
- return _selinux.avc_has_perm_noaudit(*args)
-avc_has_perm_noaudit = _selinux.avc_has_perm_noaudit
-
-def avc_has_perm(*args):
- return _selinux.avc_has_perm(*args)
-avc_has_perm = _selinux.avc_has_perm
-
-def avc_audit(*args):
- return _selinux.avc_audit(*args)
-avc_audit = _selinux.avc_audit
-
-def avc_compute_create(*args):
- return _selinux.avc_compute_create(*args)
-avc_compute_create = _selinux.avc_compute_create
-
-def avc_compute_member(*args):
- return _selinux.avc_compute_member(*args)
-avc_compute_member = _selinux.avc_compute_member
-AVC_CALLBACK_GRANT = _selinux.AVC_CALLBACK_GRANT
-AVC_CALLBACK_TRY_REVOKE = _selinux.AVC_CALLBACK_TRY_REVOKE
-AVC_CALLBACK_REVOKE = _selinux.AVC_CALLBACK_REVOKE
-AVC_CALLBACK_RESET = _selinux.AVC_CALLBACK_RESET
-AVC_CALLBACK_AUDITALLOW_ENABLE = _selinux.AVC_CALLBACK_AUDITALLOW_ENABLE
-AVC_CALLBACK_AUDITALLOW_DISABLE = _selinux.AVC_CALLBACK_AUDITALLOW_DISABLE
-AVC_CALLBACK_AUDITDENY_ENABLE = _selinux.AVC_CALLBACK_AUDITDENY_ENABLE
-AVC_CALLBACK_AUDITDENY_DISABLE = _selinux.AVC_CALLBACK_AUDITDENY_DISABLE
-AVC_CACHE_STATS = _selinux.AVC_CACHE_STATS
-class avc_cache_stats(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, avc_cache_stats, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, avc_cache_stats, name)
- __repr__ = _swig_repr
- __swig_setmethods__["entry_lookups"] = _selinux.avc_cache_stats_entry_lookups_set
- __swig_getmethods__["entry_lookups"] = _selinux.avc_cache_stats_entry_lookups_get
- if _newclass:entry_lookups = _swig_property(_selinux.avc_cache_stats_entry_lookups_get, _selinux.avc_cache_stats_entry_lookups_set)
- __swig_setmethods__["entry_hits"] = _selinux.avc_cache_stats_entry_hits_set
- __swig_getmethods__["entry_hits"] = _selinux.avc_cache_stats_entry_hits_get
- if _newclass:entry_hits = _swig_property(_selinux.avc_cache_stats_entry_hits_get, _selinux.avc_cache_stats_entry_hits_set)
- __swig_setmethods__["entry_misses"] = _selinux.avc_cache_stats_entry_misses_set
- __swig_getmethods__["entry_misses"] = _selinux.avc_cache_stats_entry_misses_get
- if _newclass:entry_misses = _swig_property(_selinux.avc_cache_stats_entry_misses_get, _selinux.avc_cache_stats_entry_misses_set)
- __swig_setmethods__["entry_discards"] = _selinux.avc_cache_stats_entry_discards_set
- __swig_getmethods__["entry_discards"] = _selinux.avc_cache_stats_entry_discards_get
- if _newclass:entry_discards = _swig_property(_selinux.avc_cache_stats_entry_discards_get, _selinux.avc_cache_stats_entry_discards_set)
- __swig_setmethods__["cav_lookups"] = _selinux.avc_cache_stats_cav_lookups_set
- __swig_getmethods__["cav_lookups"] = _selinux.avc_cache_stats_cav_lookups_get
- if _newclass:cav_lookups = _swig_property(_selinux.avc_cache_stats_cav_lookups_get, _selinux.avc_cache_stats_cav_lookups_set)
- __swig_setmethods__["cav_hits"] = _selinux.avc_cache_stats_cav_hits_set
- __swig_getmethods__["cav_hits"] = _selinux.avc_cache_stats_cav_hits_get
- if _newclass:cav_hits = _swig_property(_selinux.avc_cache_stats_cav_hits_get, _selinux.avc_cache_stats_cav_hits_set)
- __swig_setmethods__["cav_probes"] = _selinux.avc_cache_stats_cav_probes_set
- __swig_getmethods__["cav_probes"] = _selinux.avc_cache_stats_cav_probes_get
- if _newclass:cav_probes = _swig_property(_selinux.avc_cache_stats_cav_probes_get, _selinux.avc_cache_stats_cav_probes_set)
- __swig_setmethods__["cav_misses"] = _selinux.avc_cache_stats_cav_misses_set
- __swig_getmethods__["cav_misses"] = _selinux.avc_cache_stats_cav_misses_get
- if _newclass:cav_misses = _swig_property(_selinux.avc_cache_stats_cav_misses_get, _selinux.avc_cache_stats_cav_misses_set)
- def __init__(self):
- this = _selinux.new_avc_cache_stats()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_avc_cache_stats
- __del__ = lambda self : None;
-avc_cache_stats_swigregister = _selinux.avc_cache_stats_swigregister
-avc_cache_stats_swigregister(avc_cache_stats)
-
-
-def avc_av_stats():
- return _selinux.avc_av_stats()
-avc_av_stats = _selinux.avc_av_stats
-
-def avc_sid_stats():
- return _selinux.avc_sid_stats()
-avc_sid_stats = _selinux.avc_sid_stats
-
-def avc_netlink_open(*args):
- return _selinux.avc_netlink_open(*args)
-avc_netlink_open = _selinux.avc_netlink_open
-
-def avc_netlink_loop():
- return _selinux.avc_netlink_loop()
-avc_netlink_loop = _selinux.avc_netlink_loop
-
-def avc_netlink_close():
- return _selinux.avc_netlink_close()
-avc_netlink_close = _selinux.avc_netlink_close
-
-def selinux_status_open(*args):
- return _selinux.selinux_status_open(*args)
-selinux_status_open = _selinux.selinux_status_open
-
-def selinux_status_close():
- return _selinux.selinux_status_close()
-selinux_status_close = _selinux.selinux_status_close
-
-def selinux_status_updated():
- return _selinux.selinux_status_updated()
-selinux_status_updated = _selinux.selinux_status_updated
-
-def selinux_status_getenforce():
- return _selinux.selinux_status_getenforce()
-selinux_status_getenforce = _selinux.selinux_status_getenforce
-
-def selinux_status_policyload():
- return _selinux.selinux_status_policyload()
-selinux_status_policyload = _selinux.selinux_status_policyload
-
-def selinux_status_deny_unknown():
- return _selinux.selinux_status_deny_unknown()
-selinux_status_deny_unknown = _selinux.selinux_status_deny_unknown
-COMMON_FILE__IOCTL = _selinux.COMMON_FILE__IOCTL
-COMMON_FILE__READ = _selinux.COMMON_FILE__READ
-COMMON_FILE__WRITE = _selinux.COMMON_FILE__WRITE
-COMMON_FILE__CREATE = _selinux.COMMON_FILE__CREATE
-COMMON_FILE__GETATTR = _selinux.COMMON_FILE__GETATTR
-COMMON_FILE__SETATTR = _selinux.COMMON_FILE__SETATTR
-COMMON_FILE__LOCK = _selinux.COMMON_FILE__LOCK
-COMMON_FILE__RELABELFROM = _selinux.COMMON_FILE__RELABELFROM
-COMMON_FILE__RELABELTO = _selinux.COMMON_FILE__RELABELTO
-COMMON_FILE__APPEND = _selinux.COMMON_FILE__APPEND
-COMMON_FILE__UNLINK = _selinux.COMMON_FILE__UNLINK
-COMMON_FILE__LINK = _selinux.COMMON_FILE__LINK
-COMMON_FILE__RENAME = _selinux.COMMON_FILE__RENAME
-COMMON_FILE__EXECUTE = _selinux.COMMON_FILE__EXECUTE
-COMMON_FILE__SWAPON = _selinux.COMMON_FILE__SWAPON
-COMMON_FILE__QUOTAON = _selinux.COMMON_FILE__QUOTAON
-COMMON_FILE__MOUNTON = _selinux.COMMON_FILE__MOUNTON
-COMMON_SOCKET__IOCTL = _selinux.COMMON_SOCKET__IOCTL
-COMMON_SOCKET__READ = _selinux.COMMON_SOCKET__READ
-COMMON_SOCKET__WRITE = _selinux.COMMON_SOCKET__WRITE
-COMMON_SOCKET__CREATE = _selinux.COMMON_SOCKET__CREATE
-COMMON_SOCKET__GETATTR = _selinux.COMMON_SOCKET__GETATTR
-COMMON_SOCKET__SETATTR = _selinux.COMMON_SOCKET__SETATTR
-COMMON_SOCKET__LOCK = _selinux.COMMON_SOCKET__LOCK
-COMMON_SOCKET__RELABELFROM = _selinux.COMMON_SOCKET__RELABELFROM
-COMMON_SOCKET__RELABELTO = _selinux.COMMON_SOCKET__RELABELTO
-COMMON_SOCKET__APPEND = _selinux.COMMON_SOCKET__APPEND
-COMMON_SOCKET__BIND = _selinux.COMMON_SOCKET__BIND
-COMMON_SOCKET__CONNECT = _selinux.COMMON_SOCKET__CONNECT
-COMMON_SOCKET__LISTEN = _selinux.COMMON_SOCKET__LISTEN
-COMMON_SOCKET__ACCEPT = _selinux.COMMON_SOCKET__ACCEPT
-COMMON_SOCKET__GETOPT = _selinux.COMMON_SOCKET__GETOPT
-COMMON_SOCKET__SETOPT = _selinux.COMMON_SOCKET__SETOPT
-COMMON_SOCKET__SHUTDOWN = _selinux.COMMON_SOCKET__SHUTDOWN
-COMMON_SOCKET__RECVFROM = _selinux.COMMON_SOCKET__RECVFROM
-COMMON_SOCKET__SENDTO = _selinux.COMMON_SOCKET__SENDTO
-COMMON_SOCKET__RECV_MSG = _selinux.COMMON_SOCKET__RECV_MSG
-COMMON_SOCKET__SEND_MSG = _selinux.COMMON_SOCKET__SEND_MSG
-COMMON_SOCKET__NAME_BIND = _selinux.COMMON_SOCKET__NAME_BIND
-COMMON_IPC__CREATE = _selinux.COMMON_IPC__CREATE
-COMMON_IPC__DESTROY = _selinux.COMMON_IPC__DESTROY
-COMMON_IPC__GETATTR = _selinux.COMMON_IPC__GETATTR
-COMMON_IPC__SETATTR = _selinux.COMMON_IPC__SETATTR
-COMMON_IPC__READ = _selinux.COMMON_IPC__READ
-COMMON_IPC__WRITE = _selinux.COMMON_IPC__WRITE
-COMMON_IPC__ASSOCIATE = _selinux.COMMON_IPC__ASSOCIATE
-COMMON_IPC__UNIX_READ = _selinux.COMMON_IPC__UNIX_READ
-COMMON_IPC__UNIX_WRITE = _selinux.COMMON_IPC__UNIX_WRITE
-COMMON_DATABASE__CREATE = _selinux.COMMON_DATABASE__CREATE
-COMMON_DATABASE__DROP = _selinux.COMMON_DATABASE__DROP
-COMMON_DATABASE__GETATTR = _selinux.COMMON_DATABASE__GETATTR
-COMMON_DATABASE__SETATTR = _selinux.COMMON_DATABASE__SETATTR
-COMMON_DATABASE__RELABELFROM = _selinux.COMMON_DATABASE__RELABELFROM
-COMMON_DATABASE__RELABELTO = _selinux.COMMON_DATABASE__RELABELTO
-FILESYSTEM__MOUNT = _selinux.FILESYSTEM__MOUNT
-FILESYSTEM__REMOUNT = _selinux.FILESYSTEM__REMOUNT
-FILESYSTEM__UNMOUNT = _selinux.FILESYSTEM__UNMOUNT
-FILESYSTEM__GETATTR = _selinux.FILESYSTEM__GETATTR
-FILESYSTEM__RELABELFROM = _selinux.FILESYSTEM__RELABELFROM
-FILESYSTEM__RELABELTO = _selinux.FILESYSTEM__RELABELTO
-FILESYSTEM__TRANSITION = _selinux.FILESYSTEM__TRANSITION
-FILESYSTEM__ASSOCIATE = _selinux.FILESYSTEM__ASSOCIATE
-FILESYSTEM__QUOTAMOD = _selinux.FILESYSTEM__QUOTAMOD
-FILESYSTEM__QUOTAGET = _selinux.FILESYSTEM__QUOTAGET
-DIR__IOCTL = _selinux.DIR__IOCTL
-DIR__READ = _selinux.DIR__READ
-DIR__WRITE = _selinux.DIR__WRITE
-DIR__CREATE = _selinux.DIR__CREATE
-DIR__GETATTR = _selinux.DIR__GETATTR
-DIR__SETATTR = _selinux.DIR__SETATTR
-DIR__LOCK = _selinux.DIR__LOCK
-DIR__RELABELFROM = _selinux.DIR__RELABELFROM
-DIR__RELABELTO = _selinux.DIR__RELABELTO
-DIR__APPEND = _selinux.DIR__APPEND
-DIR__UNLINK = _selinux.DIR__UNLINK
-DIR__LINK = _selinux.DIR__LINK
-DIR__RENAME = _selinux.DIR__RENAME
-DIR__EXECUTE = _selinux.DIR__EXECUTE
-DIR__SWAPON = _selinux.DIR__SWAPON
-DIR__QUOTAON = _selinux.DIR__QUOTAON
-DIR__MOUNTON = _selinux.DIR__MOUNTON
-DIR__ADD_NAME = _selinux.DIR__ADD_NAME
-DIR__REMOVE_NAME = _selinux.DIR__REMOVE_NAME
-DIR__REPARENT = _selinux.DIR__REPARENT
-DIR__SEARCH = _selinux.DIR__SEARCH
-DIR__RMDIR = _selinux.DIR__RMDIR
-DIR__OPEN = _selinux.DIR__OPEN
-FILE__IOCTL = _selinux.FILE__IOCTL
-FILE__READ = _selinux.FILE__READ
-FILE__WRITE = _selinux.FILE__WRITE
-FILE__CREATE = _selinux.FILE__CREATE
-FILE__GETATTR = _selinux.FILE__GETATTR
-FILE__SETATTR = _selinux.FILE__SETATTR
-FILE__LOCK = _selinux.FILE__LOCK
-FILE__RELABELFROM = _selinux.FILE__RELABELFROM
-FILE__RELABELTO = _selinux.FILE__RELABELTO
-FILE__APPEND = _selinux.FILE__APPEND
-FILE__UNLINK = _selinux.FILE__UNLINK
-FILE__LINK = _selinux.FILE__LINK
-FILE__RENAME = _selinux.FILE__RENAME
-FILE__EXECUTE = _selinux.FILE__EXECUTE
-FILE__SWAPON = _selinux.FILE__SWAPON
-FILE__QUOTAON = _selinux.FILE__QUOTAON
-FILE__MOUNTON = _selinux.FILE__MOUNTON
-FILE__EXECUTE_NO_TRANS = _selinux.FILE__EXECUTE_NO_TRANS
-FILE__ENTRYPOINT = _selinux.FILE__ENTRYPOINT
-FILE__EXECMOD = _selinux.FILE__EXECMOD
-FILE__OPEN = _selinux.FILE__OPEN
-LNK_FILE__IOCTL = _selinux.LNK_FILE__IOCTL
-LNK_FILE__READ = _selinux.LNK_FILE__READ
-LNK_FILE__WRITE = _selinux.LNK_FILE__WRITE
-LNK_FILE__CREATE = _selinux.LNK_FILE__CREATE
-LNK_FILE__GETATTR = _selinux.LNK_FILE__GETATTR
-LNK_FILE__SETATTR = _selinux.LNK_FILE__SETATTR
-LNK_FILE__LOCK = _selinux.LNK_FILE__LOCK
-LNK_FILE__RELABELFROM = _selinux.LNK_FILE__RELABELFROM
-LNK_FILE__RELABELTO = _selinux.LNK_FILE__RELABELTO
-LNK_FILE__APPEND = _selinux.LNK_FILE__APPEND
-LNK_FILE__UNLINK = _selinux.LNK_FILE__UNLINK
-LNK_FILE__LINK = _selinux.LNK_FILE__LINK
-LNK_FILE__RENAME = _selinux.LNK_FILE__RENAME
-LNK_FILE__EXECUTE = _selinux.LNK_FILE__EXECUTE
-LNK_FILE__SWAPON = _selinux.LNK_FILE__SWAPON
-LNK_FILE__QUOTAON = _selinux.LNK_FILE__QUOTAON
-LNK_FILE__MOUNTON = _selinux.LNK_FILE__MOUNTON
-CHR_FILE__IOCTL = _selinux.CHR_FILE__IOCTL
-CHR_FILE__READ = _selinux.CHR_FILE__READ
-CHR_FILE__WRITE = _selinux.CHR_FILE__WRITE
-CHR_FILE__CREATE = _selinux.CHR_FILE__CREATE
-CHR_FILE__GETATTR = _selinux.CHR_FILE__GETATTR
-CHR_FILE__SETATTR = _selinux.CHR_FILE__SETATTR
-CHR_FILE__LOCK = _selinux.CHR_FILE__LOCK
-CHR_FILE__RELABELFROM = _selinux.CHR_FILE__RELABELFROM
-CHR_FILE__RELABELTO = _selinux.CHR_FILE__RELABELTO
-CHR_FILE__APPEND = _selinux.CHR_FILE__APPEND
-CHR_FILE__UNLINK = _selinux.CHR_FILE__UNLINK
-CHR_FILE__LINK = _selinux.CHR_FILE__LINK
-CHR_FILE__RENAME = _selinux.CHR_FILE__RENAME
-CHR_FILE__EXECUTE = _selinux.CHR_FILE__EXECUTE
-CHR_FILE__SWAPON = _selinux.CHR_FILE__SWAPON
-CHR_FILE__QUOTAON = _selinux.CHR_FILE__QUOTAON
-CHR_FILE__MOUNTON = _selinux.CHR_FILE__MOUNTON
-CHR_FILE__EXECUTE_NO_TRANS = _selinux.CHR_FILE__EXECUTE_NO_TRANS
-CHR_FILE__ENTRYPOINT = _selinux.CHR_FILE__ENTRYPOINT
-CHR_FILE__EXECMOD = _selinux.CHR_FILE__EXECMOD
-CHR_FILE__OPEN = _selinux.CHR_FILE__OPEN
-BLK_FILE__IOCTL = _selinux.BLK_FILE__IOCTL
-BLK_FILE__READ = _selinux.BLK_FILE__READ
-BLK_FILE__WRITE = _selinux.BLK_FILE__WRITE
-BLK_FILE__CREATE = _selinux.BLK_FILE__CREATE
-BLK_FILE__GETATTR = _selinux.BLK_FILE__GETATTR
-BLK_FILE__SETATTR = _selinux.BLK_FILE__SETATTR
-BLK_FILE__LOCK = _selinux.BLK_FILE__LOCK
-BLK_FILE__RELABELFROM = _selinux.BLK_FILE__RELABELFROM
-BLK_FILE__RELABELTO = _selinux.BLK_FILE__RELABELTO
-BLK_FILE__APPEND = _selinux.BLK_FILE__APPEND
-BLK_FILE__UNLINK = _selinux.BLK_FILE__UNLINK
-BLK_FILE__LINK = _selinux.BLK_FILE__LINK
-BLK_FILE__RENAME = _selinux.BLK_FILE__RENAME
-BLK_FILE__EXECUTE = _selinux.BLK_FILE__EXECUTE
-BLK_FILE__SWAPON = _selinux.BLK_FILE__SWAPON
-BLK_FILE__QUOTAON = _selinux.BLK_FILE__QUOTAON
-BLK_FILE__MOUNTON = _selinux.BLK_FILE__MOUNTON
-BLK_FILE__OPEN = _selinux.BLK_FILE__OPEN
-SOCK_FILE__IOCTL = _selinux.SOCK_FILE__IOCTL
-SOCK_FILE__READ = _selinux.SOCK_FILE__READ
-SOCK_FILE__WRITE = _selinux.SOCK_FILE__WRITE
-SOCK_FILE__CREATE = _selinux.SOCK_FILE__CREATE
-SOCK_FILE__GETATTR = _selinux.SOCK_FILE__GETATTR
-SOCK_FILE__SETATTR = _selinux.SOCK_FILE__SETATTR
-SOCK_FILE__LOCK = _selinux.SOCK_FILE__LOCK
-SOCK_FILE__RELABELFROM = _selinux.SOCK_FILE__RELABELFROM
-SOCK_FILE__RELABELTO = _selinux.SOCK_FILE__RELABELTO
-SOCK_FILE__APPEND = _selinux.SOCK_FILE__APPEND
-SOCK_FILE__UNLINK = _selinux.SOCK_FILE__UNLINK
-SOCK_FILE__LINK = _selinux.SOCK_FILE__LINK
-SOCK_FILE__RENAME = _selinux.SOCK_FILE__RENAME
-SOCK_FILE__EXECUTE = _selinux.SOCK_FILE__EXECUTE
-SOCK_FILE__SWAPON = _selinux.SOCK_FILE__SWAPON
-SOCK_FILE__QUOTAON = _selinux.SOCK_FILE__QUOTAON
-SOCK_FILE__MOUNTON = _selinux.SOCK_FILE__MOUNTON
-FIFO_FILE__IOCTL = _selinux.FIFO_FILE__IOCTL
-FIFO_FILE__READ = _selinux.FIFO_FILE__READ
-FIFO_FILE__WRITE = _selinux.FIFO_FILE__WRITE
-FIFO_FILE__CREATE = _selinux.FIFO_FILE__CREATE
-FIFO_FILE__GETATTR = _selinux.FIFO_FILE__GETATTR
-FIFO_FILE__SETATTR = _selinux.FIFO_FILE__SETATTR
-FIFO_FILE__LOCK = _selinux.FIFO_FILE__LOCK
-FIFO_FILE__RELABELFROM = _selinux.FIFO_FILE__RELABELFROM
-FIFO_FILE__RELABELTO = _selinux.FIFO_FILE__RELABELTO
-FIFO_FILE__APPEND = _selinux.FIFO_FILE__APPEND
-FIFO_FILE__UNLINK = _selinux.FIFO_FILE__UNLINK
-FIFO_FILE__LINK = _selinux.FIFO_FILE__LINK
-FIFO_FILE__RENAME = _selinux.FIFO_FILE__RENAME
-FIFO_FILE__EXECUTE = _selinux.FIFO_FILE__EXECUTE
-FIFO_FILE__SWAPON = _selinux.FIFO_FILE__SWAPON
-FIFO_FILE__QUOTAON = _selinux.FIFO_FILE__QUOTAON
-FIFO_FILE__MOUNTON = _selinux.FIFO_FILE__MOUNTON
-FIFO_FILE__OPEN = _selinux.FIFO_FILE__OPEN
-FD__USE = _selinux.FD__USE
-SOCKET__IOCTL = _selinux.SOCKET__IOCTL
-SOCKET__READ = _selinux.SOCKET__READ
-SOCKET__WRITE = _selinux.SOCKET__WRITE
-SOCKET__CREATE = _selinux.SOCKET__CREATE
-SOCKET__GETATTR = _selinux.SOCKET__GETATTR
-SOCKET__SETATTR = _selinux.SOCKET__SETATTR
-SOCKET__LOCK = _selinux.SOCKET__LOCK
-SOCKET__RELABELFROM = _selinux.SOCKET__RELABELFROM
-SOCKET__RELABELTO = _selinux.SOCKET__RELABELTO
-SOCKET__APPEND = _selinux.SOCKET__APPEND
-SOCKET__BIND = _selinux.SOCKET__BIND
-SOCKET__CONNECT = _selinux.SOCKET__CONNECT
-SOCKET__LISTEN = _selinux.SOCKET__LISTEN
-SOCKET__ACCEPT = _selinux.SOCKET__ACCEPT
-SOCKET__GETOPT = _selinux.SOCKET__GETOPT
-SOCKET__SETOPT = _selinux.SOCKET__SETOPT
-SOCKET__SHUTDOWN = _selinux.SOCKET__SHUTDOWN
-SOCKET__RECVFROM = _selinux.SOCKET__RECVFROM
-SOCKET__SENDTO = _selinux.SOCKET__SENDTO
-SOCKET__RECV_MSG = _selinux.SOCKET__RECV_MSG
-SOCKET__SEND_MSG = _selinux.SOCKET__SEND_MSG
-SOCKET__NAME_BIND = _selinux.SOCKET__NAME_BIND
-TCP_SOCKET__IOCTL = _selinux.TCP_SOCKET__IOCTL
-TCP_SOCKET__READ = _selinux.TCP_SOCKET__READ
-TCP_SOCKET__WRITE = _selinux.TCP_SOCKET__WRITE
-TCP_SOCKET__CREATE = _selinux.TCP_SOCKET__CREATE
-TCP_SOCKET__GETATTR = _selinux.TCP_SOCKET__GETATTR
-TCP_SOCKET__SETATTR = _selinux.TCP_SOCKET__SETATTR
-TCP_SOCKET__LOCK = _selinux.TCP_SOCKET__LOCK
-TCP_SOCKET__RELABELFROM = _selinux.TCP_SOCKET__RELABELFROM
-TCP_SOCKET__RELABELTO = _selinux.TCP_SOCKET__RELABELTO
-TCP_SOCKET__APPEND = _selinux.TCP_SOCKET__APPEND
-TCP_SOCKET__BIND = _selinux.TCP_SOCKET__BIND
-TCP_SOCKET__CONNECT = _selinux.TCP_SOCKET__CONNECT
-TCP_SOCKET__LISTEN = _selinux.TCP_SOCKET__LISTEN
-TCP_SOCKET__ACCEPT = _selinux.TCP_SOCKET__ACCEPT
-TCP_SOCKET__GETOPT = _selinux.TCP_SOCKET__GETOPT
-TCP_SOCKET__SETOPT = _selinux.TCP_SOCKET__SETOPT
-TCP_SOCKET__SHUTDOWN = _selinux.TCP_SOCKET__SHUTDOWN
-TCP_SOCKET__RECVFROM = _selinux.TCP_SOCKET__RECVFROM
-TCP_SOCKET__SENDTO = _selinux.TCP_SOCKET__SENDTO
-TCP_SOCKET__RECV_MSG = _selinux.TCP_SOCKET__RECV_MSG
-TCP_SOCKET__SEND_MSG = _selinux.TCP_SOCKET__SEND_MSG
-TCP_SOCKET__NAME_BIND = _selinux.TCP_SOCKET__NAME_BIND
-TCP_SOCKET__CONNECTTO = _selinux.TCP_SOCKET__CONNECTTO
-TCP_SOCKET__NEWCONN = _selinux.TCP_SOCKET__NEWCONN
-TCP_SOCKET__ACCEPTFROM = _selinux.TCP_SOCKET__ACCEPTFROM
-TCP_SOCKET__NODE_BIND = _selinux.TCP_SOCKET__NODE_BIND
-TCP_SOCKET__NAME_CONNECT = _selinux.TCP_SOCKET__NAME_CONNECT
-UDP_SOCKET__IOCTL = _selinux.UDP_SOCKET__IOCTL
-UDP_SOCKET__READ = _selinux.UDP_SOCKET__READ
-UDP_SOCKET__WRITE = _selinux.UDP_SOCKET__WRITE
-UDP_SOCKET__CREATE = _selinux.UDP_SOCKET__CREATE
-UDP_SOCKET__GETATTR = _selinux.UDP_SOCKET__GETATTR
-UDP_SOCKET__SETATTR = _selinux.UDP_SOCKET__SETATTR
-UDP_SOCKET__LOCK = _selinux.UDP_SOCKET__LOCK
-UDP_SOCKET__RELABELFROM = _selinux.UDP_SOCKET__RELABELFROM
-UDP_SOCKET__RELABELTO = _selinux.UDP_SOCKET__RELABELTO
-UDP_SOCKET__APPEND = _selinux.UDP_SOCKET__APPEND
-UDP_SOCKET__BIND = _selinux.UDP_SOCKET__BIND
-UDP_SOCKET__CONNECT = _selinux.UDP_SOCKET__CONNECT
-UDP_SOCKET__LISTEN = _selinux.UDP_SOCKET__LISTEN
-UDP_SOCKET__ACCEPT = _selinux.UDP_SOCKET__ACCEPT
-UDP_SOCKET__GETOPT = _selinux.UDP_SOCKET__GETOPT
-UDP_SOCKET__SETOPT = _selinux.UDP_SOCKET__SETOPT
-UDP_SOCKET__SHUTDOWN = _selinux.UDP_SOCKET__SHUTDOWN
-UDP_SOCKET__RECVFROM = _selinux.UDP_SOCKET__RECVFROM
-UDP_SOCKET__SENDTO = _selinux.UDP_SOCKET__SENDTO
-UDP_SOCKET__RECV_MSG = _selinux.UDP_SOCKET__RECV_MSG
-UDP_SOCKET__SEND_MSG = _selinux.UDP_SOCKET__SEND_MSG
-UDP_SOCKET__NAME_BIND = _selinux.UDP_SOCKET__NAME_BIND
-UDP_SOCKET__NODE_BIND = _selinux.UDP_SOCKET__NODE_BIND
-RAWIP_SOCKET__IOCTL = _selinux.RAWIP_SOCKET__IOCTL
-RAWIP_SOCKET__READ = _selinux.RAWIP_SOCKET__READ
-RAWIP_SOCKET__WRITE = _selinux.RAWIP_SOCKET__WRITE
-RAWIP_SOCKET__CREATE = _selinux.RAWIP_SOCKET__CREATE
-RAWIP_SOCKET__GETATTR = _selinux.RAWIP_SOCKET__GETATTR
-RAWIP_SOCKET__SETATTR = _selinux.RAWIP_SOCKET__SETATTR
-RAWIP_SOCKET__LOCK = _selinux.RAWIP_SOCKET__LOCK
-RAWIP_SOCKET__RELABELFROM = _selinux.RAWIP_SOCKET__RELABELFROM
-RAWIP_SOCKET__RELABELTO = _selinux.RAWIP_SOCKET__RELABELTO
-RAWIP_SOCKET__APPEND = _selinux.RAWIP_SOCKET__APPEND
-RAWIP_SOCKET__BIND = _selinux.RAWIP_SOCKET__BIND
-RAWIP_SOCKET__CONNECT = _selinux.RAWIP_SOCKET__CONNECT
-RAWIP_SOCKET__LISTEN = _selinux.RAWIP_SOCKET__LISTEN
-RAWIP_SOCKET__ACCEPT = _selinux.RAWIP_SOCKET__ACCEPT
-RAWIP_SOCKET__GETOPT = _selinux.RAWIP_SOCKET__GETOPT
-RAWIP_SOCKET__SETOPT = _selinux.RAWIP_SOCKET__SETOPT
-RAWIP_SOCKET__SHUTDOWN = _selinux.RAWIP_SOCKET__SHUTDOWN
-RAWIP_SOCKET__RECVFROM = _selinux.RAWIP_SOCKET__RECVFROM
-RAWIP_SOCKET__SENDTO = _selinux.RAWIP_SOCKET__SENDTO
-RAWIP_SOCKET__RECV_MSG = _selinux.RAWIP_SOCKET__RECV_MSG
-RAWIP_SOCKET__SEND_MSG = _selinux.RAWIP_SOCKET__SEND_MSG
-RAWIP_SOCKET__NAME_BIND = _selinux.RAWIP_SOCKET__NAME_BIND
-RAWIP_SOCKET__NODE_BIND = _selinux.RAWIP_SOCKET__NODE_BIND
-NODE__TCP_RECV = _selinux.NODE__TCP_RECV
-NODE__TCP_SEND = _selinux.NODE__TCP_SEND
-NODE__UDP_RECV = _selinux.NODE__UDP_RECV
-NODE__UDP_SEND = _selinux.NODE__UDP_SEND
-NODE__RAWIP_RECV = _selinux.NODE__RAWIP_RECV
-NODE__RAWIP_SEND = _selinux.NODE__RAWIP_SEND
-NODE__ENFORCE_DEST = _selinux.NODE__ENFORCE_DEST
-NODE__DCCP_RECV = _selinux.NODE__DCCP_RECV
-NODE__DCCP_SEND = _selinux.NODE__DCCP_SEND
-NODE__RECVFROM = _selinux.NODE__RECVFROM
-NODE__SENDTO = _selinux.NODE__SENDTO
-NETIF__TCP_RECV = _selinux.NETIF__TCP_RECV
-NETIF__TCP_SEND = _selinux.NETIF__TCP_SEND
-NETIF__UDP_RECV = _selinux.NETIF__UDP_RECV
-NETIF__UDP_SEND = _selinux.NETIF__UDP_SEND
-NETIF__RAWIP_RECV = _selinux.NETIF__RAWIP_RECV
-NETIF__RAWIP_SEND = _selinux.NETIF__RAWIP_SEND
-NETIF__DCCP_RECV = _selinux.NETIF__DCCP_RECV
-NETIF__DCCP_SEND = _selinux.NETIF__DCCP_SEND
-NETIF__INGRESS = _selinux.NETIF__INGRESS
-NETIF__EGRESS = _selinux.NETIF__EGRESS
-NETLINK_SOCKET__IOCTL = _selinux.NETLINK_SOCKET__IOCTL
-NETLINK_SOCKET__READ = _selinux.NETLINK_SOCKET__READ
-NETLINK_SOCKET__WRITE = _selinux.NETLINK_SOCKET__WRITE
-NETLINK_SOCKET__CREATE = _selinux.NETLINK_SOCKET__CREATE
-NETLINK_SOCKET__GETATTR = _selinux.NETLINK_SOCKET__GETATTR
-NETLINK_SOCKET__SETATTR = _selinux.NETLINK_SOCKET__SETATTR
-NETLINK_SOCKET__LOCK = _selinux.NETLINK_SOCKET__LOCK
-NETLINK_SOCKET__RELABELFROM = _selinux.NETLINK_SOCKET__RELABELFROM
-NETLINK_SOCKET__RELABELTO = _selinux.NETLINK_SOCKET__RELABELTO
-NETLINK_SOCKET__APPEND = _selinux.NETLINK_SOCKET__APPEND
-NETLINK_SOCKET__BIND = _selinux.NETLINK_SOCKET__BIND
-NETLINK_SOCKET__CONNECT = _selinux.NETLINK_SOCKET__CONNECT
-NETLINK_SOCKET__LISTEN = _selinux.NETLINK_SOCKET__LISTEN
-NETLINK_SOCKET__ACCEPT = _selinux.NETLINK_SOCKET__ACCEPT
-NETLINK_SOCKET__GETOPT = _selinux.NETLINK_SOCKET__GETOPT
-NETLINK_SOCKET__SETOPT = _selinux.NETLINK_SOCKET__SETOPT
-NETLINK_SOCKET__SHUTDOWN = _selinux.NETLINK_SOCKET__SHUTDOWN
-NETLINK_SOCKET__RECVFROM = _selinux.NETLINK_SOCKET__RECVFROM
-NETLINK_SOCKET__SENDTO = _selinux.NETLINK_SOCKET__SENDTO
-NETLINK_SOCKET__RECV_MSG = _selinux.NETLINK_SOCKET__RECV_MSG
-NETLINK_SOCKET__SEND_MSG = _selinux.NETLINK_SOCKET__SEND_MSG
-NETLINK_SOCKET__NAME_BIND = _selinux.NETLINK_SOCKET__NAME_BIND
-PACKET_SOCKET__IOCTL = _selinux.PACKET_SOCKET__IOCTL
-PACKET_SOCKET__READ = _selinux.PACKET_SOCKET__READ
-PACKET_SOCKET__WRITE = _selinux.PACKET_SOCKET__WRITE
-PACKET_SOCKET__CREATE = _selinux.PACKET_SOCKET__CREATE
-PACKET_SOCKET__GETATTR = _selinux.PACKET_SOCKET__GETATTR
-PACKET_SOCKET__SETATTR = _selinux.PACKET_SOCKET__SETATTR
-PACKET_SOCKET__LOCK = _selinux.PACKET_SOCKET__LOCK
-PACKET_SOCKET__RELABELFROM = _selinux.PACKET_SOCKET__RELABELFROM
-PACKET_SOCKET__RELABELTO = _selinux.PACKET_SOCKET__RELABELTO
-PACKET_SOCKET__APPEND = _selinux.PACKET_SOCKET__APPEND
-PACKET_SOCKET__BIND = _selinux.PACKET_SOCKET__BIND
-PACKET_SOCKET__CONNECT = _selinux.PACKET_SOCKET__CONNECT
-PACKET_SOCKET__LISTEN = _selinux.PACKET_SOCKET__LISTEN
-PACKET_SOCKET__ACCEPT = _selinux.PACKET_SOCKET__ACCEPT
-PACKET_SOCKET__GETOPT = _selinux.PACKET_SOCKET__GETOPT
-PACKET_SOCKET__SETOPT = _selinux.PACKET_SOCKET__SETOPT
-PACKET_SOCKET__SHUTDOWN = _selinux.PACKET_SOCKET__SHUTDOWN
-PACKET_SOCKET__RECVFROM = _selinux.PACKET_SOCKET__RECVFROM
-PACKET_SOCKET__SENDTO = _selinux.PACKET_SOCKET__SENDTO
-PACKET_SOCKET__RECV_MSG = _selinux.PACKET_SOCKET__RECV_MSG
-PACKET_SOCKET__SEND_MSG = _selinux.PACKET_SOCKET__SEND_MSG
-PACKET_SOCKET__NAME_BIND = _selinux.PACKET_SOCKET__NAME_BIND
-KEY_SOCKET__IOCTL = _selinux.KEY_SOCKET__IOCTL
-KEY_SOCKET__READ = _selinux.KEY_SOCKET__READ
-KEY_SOCKET__WRITE = _selinux.KEY_SOCKET__WRITE
-KEY_SOCKET__CREATE = _selinux.KEY_SOCKET__CREATE
-KEY_SOCKET__GETATTR = _selinux.KEY_SOCKET__GETATTR
-KEY_SOCKET__SETATTR = _selinux.KEY_SOCKET__SETATTR
-KEY_SOCKET__LOCK = _selinux.KEY_SOCKET__LOCK
-KEY_SOCKET__RELABELFROM = _selinux.KEY_SOCKET__RELABELFROM
-KEY_SOCKET__RELABELTO = _selinux.KEY_SOCKET__RELABELTO
-KEY_SOCKET__APPEND = _selinux.KEY_SOCKET__APPEND
-KEY_SOCKET__BIND = _selinux.KEY_SOCKET__BIND
-KEY_SOCKET__CONNECT = _selinux.KEY_SOCKET__CONNECT
-KEY_SOCKET__LISTEN = _selinux.KEY_SOCKET__LISTEN
-KEY_SOCKET__ACCEPT = _selinux.KEY_SOCKET__ACCEPT
-KEY_SOCKET__GETOPT = _selinux.KEY_SOCKET__GETOPT
-KEY_SOCKET__SETOPT = _selinux.KEY_SOCKET__SETOPT
-KEY_SOCKET__SHUTDOWN = _selinux.KEY_SOCKET__SHUTDOWN
-KEY_SOCKET__RECVFROM = _selinux.KEY_SOCKET__RECVFROM
-KEY_SOCKET__SENDTO = _selinux.KEY_SOCKET__SENDTO
-KEY_SOCKET__RECV_MSG = _selinux.KEY_SOCKET__RECV_MSG
-KEY_SOCKET__SEND_MSG = _selinux.KEY_SOCKET__SEND_MSG
-KEY_SOCKET__NAME_BIND = _selinux.KEY_SOCKET__NAME_BIND
-UNIX_STREAM_SOCKET__IOCTL = _selinux.UNIX_STREAM_SOCKET__IOCTL
-UNIX_STREAM_SOCKET__READ = _selinux.UNIX_STREAM_SOCKET__READ
-UNIX_STREAM_SOCKET__WRITE = _selinux.UNIX_STREAM_SOCKET__WRITE
-UNIX_STREAM_SOCKET__CREATE = _selinux.UNIX_STREAM_SOCKET__CREATE
-UNIX_STREAM_SOCKET__GETATTR = _selinux.UNIX_STREAM_SOCKET__GETATTR
-UNIX_STREAM_SOCKET__SETATTR = _selinux.UNIX_STREAM_SOCKET__SETATTR
-UNIX_STREAM_SOCKET__LOCK = _selinux.UNIX_STREAM_SOCKET__LOCK
-UNIX_STREAM_SOCKET__RELABELFROM = _selinux.UNIX_STREAM_SOCKET__RELABELFROM
-UNIX_STREAM_SOCKET__RELABELTO = _selinux.UNIX_STREAM_SOCKET__RELABELTO
-UNIX_STREAM_SOCKET__APPEND = _selinux.UNIX_STREAM_SOCKET__APPEND
-UNIX_STREAM_SOCKET__BIND = _selinux.UNIX_STREAM_SOCKET__BIND
-UNIX_STREAM_SOCKET__CONNECT = _selinux.UNIX_STREAM_SOCKET__CONNECT
-UNIX_STREAM_SOCKET__LISTEN = _selinux.UNIX_STREAM_SOCKET__LISTEN
-UNIX_STREAM_SOCKET__ACCEPT = _selinux.UNIX_STREAM_SOCKET__ACCEPT
-UNIX_STREAM_SOCKET__GETOPT = _selinux.UNIX_STREAM_SOCKET__GETOPT
-UNIX_STREAM_SOCKET__SETOPT = _selinux.UNIX_STREAM_SOCKET__SETOPT
-UNIX_STREAM_SOCKET__SHUTDOWN = _selinux.UNIX_STREAM_SOCKET__SHUTDOWN
-UNIX_STREAM_SOCKET__RECVFROM = _selinux.UNIX_STREAM_SOCKET__RECVFROM
-UNIX_STREAM_SOCKET__SENDTO = _selinux.UNIX_STREAM_SOCKET__SENDTO
-UNIX_STREAM_SOCKET__RECV_MSG = _selinux.UNIX_STREAM_SOCKET__RECV_MSG
-UNIX_STREAM_SOCKET__SEND_MSG = _selinux.UNIX_STREAM_SOCKET__SEND_MSG
-UNIX_STREAM_SOCKET__NAME_BIND = _selinux.UNIX_STREAM_SOCKET__NAME_BIND
-UNIX_STREAM_SOCKET__CONNECTTO = _selinux.UNIX_STREAM_SOCKET__CONNECTTO
-UNIX_STREAM_SOCKET__NEWCONN = _selinux.UNIX_STREAM_SOCKET__NEWCONN
-UNIX_STREAM_SOCKET__ACCEPTFROM = _selinux.UNIX_STREAM_SOCKET__ACCEPTFROM
-UNIX_DGRAM_SOCKET__IOCTL = _selinux.UNIX_DGRAM_SOCKET__IOCTL
-UNIX_DGRAM_SOCKET__READ = _selinux.UNIX_DGRAM_SOCKET__READ
-UNIX_DGRAM_SOCKET__WRITE = _selinux.UNIX_DGRAM_SOCKET__WRITE
-UNIX_DGRAM_SOCKET__CREATE = _selinux.UNIX_DGRAM_SOCKET__CREATE
-UNIX_DGRAM_SOCKET__GETATTR = _selinux.UNIX_DGRAM_SOCKET__GETATTR
-UNIX_DGRAM_SOCKET__SETATTR = _selinux.UNIX_DGRAM_SOCKET__SETATTR
-UNIX_DGRAM_SOCKET__LOCK = _selinux.UNIX_DGRAM_SOCKET__LOCK
-UNIX_DGRAM_SOCKET__RELABELFROM = _selinux.UNIX_DGRAM_SOCKET__RELABELFROM
-UNIX_DGRAM_SOCKET__RELABELTO = _selinux.UNIX_DGRAM_SOCKET__RELABELTO
-UNIX_DGRAM_SOCKET__APPEND = _selinux.UNIX_DGRAM_SOCKET__APPEND
-UNIX_DGRAM_SOCKET__BIND = _selinux.UNIX_DGRAM_SOCKET__BIND
-UNIX_DGRAM_SOCKET__CONNECT = _selinux.UNIX_DGRAM_SOCKET__CONNECT
-UNIX_DGRAM_SOCKET__LISTEN = _selinux.UNIX_DGRAM_SOCKET__LISTEN
-UNIX_DGRAM_SOCKET__ACCEPT = _selinux.UNIX_DGRAM_SOCKET__ACCEPT
-UNIX_DGRAM_SOCKET__GETOPT = _selinux.UNIX_DGRAM_SOCKET__GETOPT
-UNIX_DGRAM_SOCKET__SETOPT = _selinux.UNIX_DGRAM_SOCKET__SETOPT
-UNIX_DGRAM_SOCKET__SHUTDOWN = _selinux.UNIX_DGRAM_SOCKET__SHUTDOWN
-UNIX_DGRAM_SOCKET__RECVFROM = _selinux.UNIX_DGRAM_SOCKET__RECVFROM
-UNIX_DGRAM_SOCKET__SENDTO = _selinux.UNIX_DGRAM_SOCKET__SENDTO
-UNIX_DGRAM_SOCKET__RECV_MSG = _selinux.UNIX_DGRAM_SOCKET__RECV_MSG
-UNIX_DGRAM_SOCKET__SEND_MSG = _selinux.UNIX_DGRAM_SOCKET__SEND_MSG
-UNIX_DGRAM_SOCKET__NAME_BIND = _selinux.UNIX_DGRAM_SOCKET__NAME_BIND
-PROCESS__FORK = _selinux.PROCESS__FORK
-PROCESS__TRANSITION = _selinux.PROCESS__TRANSITION
-PROCESS__SIGCHLD = _selinux.PROCESS__SIGCHLD
-PROCESS__SIGKILL = _selinux.PROCESS__SIGKILL
-PROCESS__SIGSTOP = _selinux.PROCESS__SIGSTOP
-PROCESS__SIGNULL = _selinux.PROCESS__SIGNULL
-PROCESS__SIGNAL = _selinux.PROCESS__SIGNAL
-PROCESS__PTRACE = _selinux.PROCESS__PTRACE
-PROCESS__GETSCHED = _selinux.PROCESS__GETSCHED
-PROCESS__SETSCHED = _selinux.PROCESS__SETSCHED
-PROCESS__GETSESSION = _selinux.PROCESS__GETSESSION
-PROCESS__GETPGID = _selinux.PROCESS__GETPGID
-PROCESS__SETPGID = _selinux.PROCESS__SETPGID
-PROCESS__GETCAP = _selinux.PROCESS__GETCAP
-PROCESS__SETCAP = _selinux.PROCESS__SETCAP
-PROCESS__SHARE = _selinux.PROCESS__SHARE
-PROCESS__GETATTR = _selinux.PROCESS__GETATTR
-PROCESS__SETEXEC = _selinux.PROCESS__SETEXEC
-PROCESS__SETFSCREATE = _selinux.PROCESS__SETFSCREATE
-PROCESS__NOATSECURE = _selinux.PROCESS__NOATSECURE
-PROCESS__SIGINH = _selinux.PROCESS__SIGINH
-PROCESS__SETRLIMIT = _selinux.PROCESS__SETRLIMIT
-PROCESS__RLIMITINH = _selinux.PROCESS__RLIMITINH
-PROCESS__DYNTRANSITION = _selinux.PROCESS__DYNTRANSITION
-PROCESS__SETCURRENT = _selinux.PROCESS__SETCURRENT
-PROCESS__EXECMEM = _selinux.PROCESS__EXECMEM
-PROCESS__EXECSTACK = _selinux.PROCESS__EXECSTACK
-PROCESS__EXECHEAP = _selinux.PROCESS__EXECHEAP
-PROCESS__SETKEYCREATE = _selinux.PROCESS__SETKEYCREATE
-PROCESS__SETSOCKCREATE = _selinux.PROCESS__SETSOCKCREATE
-IPC__CREATE = _selinux.IPC__CREATE
-IPC__DESTROY = _selinux.IPC__DESTROY
-IPC__GETATTR = _selinux.IPC__GETATTR
-IPC__SETATTR = _selinux.IPC__SETATTR
-IPC__READ = _selinux.IPC__READ
-IPC__WRITE = _selinux.IPC__WRITE
-IPC__ASSOCIATE = _selinux.IPC__ASSOCIATE
-IPC__UNIX_READ = _selinux.IPC__UNIX_READ
-IPC__UNIX_WRITE = _selinux.IPC__UNIX_WRITE
-SEM__CREATE = _selinux.SEM__CREATE
-SEM__DESTROY = _selinux.SEM__DESTROY
-SEM__GETATTR = _selinux.SEM__GETATTR
-SEM__SETATTR = _selinux.SEM__SETATTR
-SEM__READ = _selinux.SEM__READ
-SEM__WRITE = _selinux.SEM__WRITE
-SEM__ASSOCIATE = _selinux.SEM__ASSOCIATE
-SEM__UNIX_READ = _selinux.SEM__UNIX_READ
-SEM__UNIX_WRITE = _selinux.SEM__UNIX_WRITE
-MSGQ__CREATE = _selinux.MSGQ__CREATE
-MSGQ__DESTROY = _selinux.MSGQ__DESTROY
-MSGQ__GETATTR = _selinux.MSGQ__GETATTR
-MSGQ__SETATTR = _selinux.MSGQ__SETATTR
-MSGQ__READ = _selinux.MSGQ__READ
-MSGQ__WRITE = _selinux.MSGQ__WRITE
-MSGQ__ASSOCIATE = _selinux.MSGQ__ASSOCIATE
-MSGQ__UNIX_READ = _selinux.MSGQ__UNIX_READ
-MSGQ__UNIX_WRITE = _selinux.MSGQ__UNIX_WRITE
-MSGQ__ENQUEUE = _selinux.MSGQ__ENQUEUE
-MSG__SEND = _selinux.MSG__SEND
-MSG__RECEIVE = _selinux.MSG__RECEIVE
-SHM__CREATE = _selinux.SHM__CREATE
-SHM__DESTROY = _selinux.SHM__DESTROY
-SHM__GETATTR = _selinux.SHM__GETATTR
-SHM__SETATTR = _selinux.SHM__SETATTR
-SHM__READ = _selinux.SHM__READ
-SHM__WRITE = _selinux.SHM__WRITE
-SHM__ASSOCIATE = _selinux.SHM__ASSOCIATE
-SHM__UNIX_READ = _selinux.SHM__UNIX_READ
-SHM__UNIX_WRITE = _selinux.SHM__UNIX_WRITE
-SHM__LOCK = _selinux.SHM__LOCK
-SECURITY__COMPUTE_AV = _selinux.SECURITY__COMPUTE_AV
-SECURITY__COMPUTE_CREATE = _selinux.SECURITY__COMPUTE_CREATE
-SECURITY__COMPUTE_MEMBER = _selinux.SECURITY__COMPUTE_MEMBER
-SECURITY__CHECK_CONTEXT = _selinux.SECURITY__CHECK_CONTEXT
-SECURITY__LOAD_POLICY = _selinux.SECURITY__LOAD_POLICY
-SECURITY__COMPUTE_RELABEL = _selinux.SECURITY__COMPUTE_RELABEL
-SECURITY__COMPUTE_USER = _selinux.SECURITY__COMPUTE_USER
-SECURITY__SETENFORCE = _selinux.SECURITY__SETENFORCE
-SECURITY__SETBOOL = _selinux.SECURITY__SETBOOL
-SECURITY__SETSECPARAM = _selinux.SECURITY__SETSECPARAM
-SECURITY__SETCHECKREQPROT = _selinux.SECURITY__SETCHECKREQPROT
-SYSTEM__IPC_INFO = _selinux.SYSTEM__IPC_INFO
-SYSTEM__SYSLOG_READ = _selinux.SYSTEM__SYSLOG_READ
-SYSTEM__SYSLOG_MOD = _selinux.SYSTEM__SYSLOG_MOD
-SYSTEM__SYSLOG_CONSOLE = _selinux.SYSTEM__SYSLOG_CONSOLE
-CAPABILITY__CHOWN = _selinux.CAPABILITY__CHOWN
-CAPABILITY__DAC_OVERRIDE = _selinux.CAPABILITY__DAC_OVERRIDE
-CAPABILITY__DAC_READ_SEARCH = _selinux.CAPABILITY__DAC_READ_SEARCH
-CAPABILITY__FOWNER = _selinux.CAPABILITY__FOWNER
-CAPABILITY__FSETID = _selinux.CAPABILITY__FSETID
-CAPABILITY__KILL = _selinux.CAPABILITY__KILL
-CAPABILITY__SETGID = _selinux.CAPABILITY__SETGID
-CAPABILITY__SETUID = _selinux.CAPABILITY__SETUID
-CAPABILITY__SETPCAP = _selinux.CAPABILITY__SETPCAP
-CAPABILITY__LINUX_IMMUTABLE = _selinux.CAPABILITY__LINUX_IMMUTABLE
-CAPABILITY__NET_BIND_SERVICE = _selinux.CAPABILITY__NET_BIND_SERVICE
-CAPABILITY__NET_BROADCAST = _selinux.CAPABILITY__NET_BROADCAST
-CAPABILITY__NET_ADMIN = _selinux.CAPABILITY__NET_ADMIN
-CAPABILITY__NET_RAW = _selinux.CAPABILITY__NET_RAW
-CAPABILITY__IPC_LOCK = _selinux.CAPABILITY__IPC_LOCK
-CAPABILITY__IPC_OWNER = _selinux.CAPABILITY__IPC_OWNER
-CAPABILITY__SYS_MODULE = _selinux.CAPABILITY__SYS_MODULE
-CAPABILITY__SYS_RAWIO = _selinux.CAPABILITY__SYS_RAWIO
-CAPABILITY__SYS_CHROOT = _selinux.CAPABILITY__SYS_CHROOT
-CAPABILITY__SYS_PTRACE = _selinux.CAPABILITY__SYS_PTRACE
-CAPABILITY__SYS_PACCT = _selinux.CAPABILITY__SYS_PACCT
-CAPABILITY__SYS_ADMIN = _selinux.CAPABILITY__SYS_ADMIN
-CAPABILITY__SYS_BOOT = _selinux.CAPABILITY__SYS_BOOT
-CAPABILITY__SYS_NICE = _selinux.CAPABILITY__SYS_NICE
-CAPABILITY__SYS_RESOURCE = _selinux.CAPABILITY__SYS_RESOURCE
-CAPABILITY__SYS_TIME = _selinux.CAPABILITY__SYS_TIME
-CAPABILITY__SYS_TTY_CONFIG = _selinux.CAPABILITY__SYS_TTY_CONFIG
-CAPABILITY__MKNOD = _selinux.CAPABILITY__MKNOD
-CAPABILITY__LEASE = _selinux.CAPABILITY__LEASE
-CAPABILITY__AUDIT_WRITE = _selinux.CAPABILITY__AUDIT_WRITE
-CAPABILITY__AUDIT_CONTROL = _selinux.CAPABILITY__AUDIT_CONTROL
-CAPABILITY__SETFCAP = _selinux.CAPABILITY__SETFCAP
-CAPABILITY2__MAC_OVERRIDE = _selinux.CAPABILITY2__MAC_OVERRIDE
-CAPABILITY2__MAC_ADMIN = _selinux.CAPABILITY2__MAC_ADMIN
-PASSWD__PASSWD = _selinux.PASSWD__PASSWD
-PASSWD__CHFN = _selinux.PASSWD__CHFN
-PASSWD__CHSH = _selinux.PASSWD__CHSH
-PASSWD__ROOTOK = _selinux.PASSWD__ROOTOK
-PASSWD__CRONTAB = _selinux.PASSWD__CRONTAB
-X_DRAWABLE__CREATE = _selinux.X_DRAWABLE__CREATE
-X_DRAWABLE__DESTROY = _selinux.X_DRAWABLE__DESTROY
-X_DRAWABLE__READ = _selinux.X_DRAWABLE__READ
-X_DRAWABLE__WRITE = _selinux.X_DRAWABLE__WRITE
-X_DRAWABLE__BLEND = _selinux.X_DRAWABLE__BLEND
-X_DRAWABLE__GETATTR = _selinux.X_DRAWABLE__GETATTR
-X_DRAWABLE__SETATTR = _selinux.X_DRAWABLE__SETATTR
-X_DRAWABLE__LIST_CHILD = _selinux.X_DRAWABLE__LIST_CHILD
-X_DRAWABLE__ADD_CHILD = _selinux.X_DRAWABLE__ADD_CHILD
-X_DRAWABLE__REMOVE_CHILD = _selinux.X_DRAWABLE__REMOVE_CHILD
-X_DRAWABLE__LIST_PROPERTY = _selinux.X_DRAWABLE__LIST_PROPERTY
-X_DRAWABLE__GET_PROPERTY = _selinux.X_DRAWABLE__GET_PROPERTY
-X_DRAWABLE__SET_PROPERTY = _selinux.X_DRAWABLE__SET_PROPERTY
-X_DRAWABLE__MANAGE = _selinux.X_DRAWABLE__MANAGE
-X_DRAWABLE__OVERRIDE = _selinux.X_DRAWABLE__OVERRIDE
-X_DRAWABLE__SHOW = _selinux.X_DRAWABLE__SHOW
-X_DRAWABLE__HIDE = _selinux.X_DRAWABLE__HIDE
-X_DRAWABLE__SEND = _selinux.X_DRAWABLE__SEND
-X_DRAWABLE__RECEIVE = _selinux.X_DRAWABLE__RECEIVE
-X_SCREEN__GETATTR = _selinux.X_SCREEN__GETATTR
-X_SCREEN__SETATTR = _selinux.X_SCREEN__SETATTR
-X_SCREEN__HIDE_CURSOR = _selinux.X_SCREEN__HIDE_CURSOR
-X_SCREEN__SHOW_CURSOR = _selinux.X_SCREEN__SHOW_CURSOR
-X_SCREEN__SAVER_GETATTR = _selinux.X_SCREEN__SAVER_GETATTR
-X_SCREEN__SAVER_SETATTR = _selinux.X_SCREEN__SAVER_SETATTR
-X_SCREEN__SAVER_HIDE = _selinux.X_SCREEN__SAVER_HIDE
-X_SCREEN__SAVER_SHOW = _selinux.X_SCREEN__SAVER_SHOW
-X_GC__CREATE = _selinux.X_GC__CREATE
-X_GC__DESTROY = _selinux.X_GC__DESTROY
-X_GC__GETATTR = _selinux.X_GC__GETATTR
-X_GC__SETATTR = _selinux.X_GC__SETATTR
-X_GC__USE = _selinux.X_GC__USE
-X_FONT__CREATE = _selinux.X_FONT__CREATE
-X_FONT__DESTROY = _selinux.X_FONT__DESTROY
-X_FONT__GETATTR = _selinux.X_FONT__GETATTR
-X_FONT__ADD_GLYPH = _selinux.X_FONT__ADD_GLYPH
-X_FONT__REMOVE_GLYPH = _selinux.X_FONT__REMOVE_GLYPH
-X_FONT__USE = _selinux.X_FONT__USE
-X_COLORMAP__CREATE = _selinux.X_COLORMAP__CREATE
-X_COLORMAP__DESTROY = _selinux.X_COLORMAP__DESTROY
-X_COLORMAP__READ = _selinux.X_COLORMAP__READ
-X_COLORMAP__WRITE = _selinux.X_COLORMAP__WRITE
-X_COLORMAP__GETATTR = _selinux.X_COLORMAP__GETATTR
-X_COLORMAP__ADD_COLOR = _selinux.X_COLORMAP__ADD_COLOR
-X_COLORMAP__REMOVE_COLOR = _selinux.X_COLORMAP__REMOVE_COLOR
-X_COLORMAP__INSTALL = _selinux.X_COLORMAP__INSTALL
-X_COLORMAP__UNINSTALL = _selinux.X_COLORMAP__UNINSTALL
-X_COLORMAP__USE = _selinux.X_COLORMAP__USE
-X_PROPERTY__CREATE = _selinux.X_PROPERTY__CREATE
-X_PROPERTY__DESTROY = _selinux.X_PROPERTY__DESTROY
-X_PROPERTY__READ = _selinux.X_PROPERTY__READ
-X_PROPERTY__WRITE = _selinux.X_PROPERTY__WRITE
-X_PROPERTY__APPEND = _selinux.X_PROPERTY__APPEND
-X_PROPERTY__GETATTR = _selinux.X_PROPERTY__GETATTR
-X_PROPERTY__SETATTR = _selinux.X_PROPERTY__SETATTR
-X_SELECTION__READ = _selinux.X_SELECTION__READ
-X_SELECTION__WRITE = _selinux.X_SELECTION__WRITE
-X_SELECTION__GETATTR = _selinux.X_SELECTION__GETATTR
-X_SELECTION__SETATTR = _selinux.X_SELECTION__SETATTR
-X_CURSOR__CREATE = _selinux.X_CURSOR__CREATE
-X_CURSOR__DESTROY = _selinux.X_CURSOR__DESTROY
-X_CURSOR__READ = _selinux.X_CURSOR__READ
-X_CURSOR__WRITE = _selinux.X_CURSOR__WRITE
-X_CURSOR__GETATTR = _selinux.X_CURSOR__GETATTR
-X_CURSOR__SETATTR = _selinux.X_CURSOR__SETATTR
-X_CURSOR__USE = _selinux.X_CURSOR__USE
-X_CLIENT__DESTROY = _selinux.X_CLIENT__DESTROY
-X_CLIENT__GETATTR = _selinux.X_CLIENT__GETATTR
-X_CLIENT__SETATTR = _selinux.X_CLIENT__SETATTR
-X_CLIENT__MANAGE = _selinux.X_CLIENT__MANAGE
-X_DEVICE__GETATTR = _selinux.X_DEVICE__GETATTR
-X_DEVICE__SETATTR = _selinux.X_DEVICE__SETATTR
-X_DEVICE__USE = _selinux.X_DEVICE__USE
-X_DEVICE__READ = _selinux.X_DEVICE__READ
-X_DEVICE__WRITE = _selinux.X_DEVICE__WRITE
-X_DEVICE__GETFOCUS = _selinux.X_DEVICE__GETFOCUS
-X_DEVICE__SETFOCUS = _selinux.X_DEVICE__SETFOCUS
-X_DEVICE__BELL = _selinux.X_DEVICE__BELL
-X_DEVICE__FORCE_CURSOR = _selinux.X_DEVICE__FORCE_CURSOR
-X_DEVICE__FREEZE = _selinux.X_DEVICE__FREEZE
-X_DEVICE__GRAB = _selinux.X_DEVICE__GRAB
-X_DEVICE__MANAGE = _selinux.X_DEVICE__MANAGE
-X_SERVER__GETATTR = _selinux.X_SERVER__GETATTR
-X_SERVER__SETATTR = _selinux.X_SERVER__SETATTR
-X_SERVER__RECORD = _selinux.X_SERVER__RECORD
-X_SERVER__DEBUG = _selinux.X_SERVER__DEBUG
-X_SERVER__GRAB = _selinux.X_SERVER__GRAB
-X_SERVER__MANAGE = _selinux.X_SERVER__MANAGE
-X_EXTENSION__QUERY = _selinux.X_EXTENSION__QUERY
-X_EXTENSION__USE = _selinux.X_EXTENSION__USE
-X_RESOURCE__READ = _selinux.X_RESOURCE__READ
-X_RESOURCE__WRITE = _selinux.X_RESOURCE__WRITE
-X_EVENT__SEND = _selinux.X_EVENT__SEND
-X_EVENT__RECEIVE = _selinux.X_EVENT__RECEIVE
-X_SYNTHETIC_EVENT__SEND = _selinux.X_SYNTHETIC_EVENT__SEND
-X_SYNTHETIC_EVENT__RECEIVE = _selinux.X_SYNTHETIC_EVENT__RECEIVE
-NETLINK_ROUTE_SOCKET__IOCTL = _selinux.NETLINK_ROUTE_SOCKET__IOCTL
-NETLINK_ROUTE_SOCKET__READ = _selinux.NETLINK_ROUTE_SOCKET__READ
-NETLINK_ROUTE_SOCKET__WRITE = _selinux.NETLINK_ROUTE_SOCKET__WRITE
-NETLINK_ROUTE_SOCKET__CREATE = _selinux.NETLINK_ROUTE_SOCKET__CREATE
-NETLINK_ROUTE_SOCKET__GETATTR = _selinux.NETLINK_ROUTE_SOCKET__GETATTR
-NETLINK_ROUTE_SOCKET__SETATTR = _selinux.NETLINK_ROUTE_SOCKET__SETATTR
-NETLINK_ROUTE_SOCKET__LOCK = _selinux.NETLINK_ROUTE_SOCKET__LOCK
-NETLINK_ROUTE_SOCKET__RELABELFROM = _selinux.NETLINK_ROUTE_SOCKET__RELABELFROM
-NETLINK_ROUTE_SOCKET__RELABELTO = _selinux.NETLINK_ROUTE_SOCKET__RELABELTO
-NETLINK_ROUTE_SOCKET__APPEND = _selinux.NETLINK_ROUTE_SOCKET__APPEND
-NETLINK_ROUTE_SOCKET__BIND = _selinux.NETLINK_ROUTE_SOCKET__BIND
-NETLINK_ROUTE_SOCKET__CONNECT = _selinux.NETLINK_ROUTE_SOCKET__CONNECT
-NETLINK_ROUTE_SOCKET__LISTEN = _selinux.NETLINK_ROUTE_SOCKET__LISTEN
-NETLINK_ROUTE_SOCKET__ACCEPT = _selinux.NETLINK_ROUTE_SOCKET__ACCEPT
-NETLINK_ROUTE_SOCKET__GETOPT = _selinux.NETLINK_ROUTE_SOCKET__GETOPT
-NETLINK_ROUTE_SOCKET__SETOPT = _selinux.NETLINK_ROUTE_SOCKET__SETOPT
-NETLINK_ROUTE_SOCKET__SHUTDOWN = _selinux.NETLINK_ROUTE_SOCKET__SHUTDOWN
-NETLINK_ROUTE_SOCKET__RECVFROM = _selinux.NETLINK_ROUTE_SOCKET__RECVFROM
-NETLINK_ROUTE_SOCKET__SENDTO = _selinux.NETLINK_ROUTE_SOCKET__SENDTO
-NETLINK_ROUTE_SOCKET__RECV_MSG = _selinux.NETLINK_ROUTE_SOCKET__RECV_MSG
-NETLINK_ROUTE_SOCKET__SEND_MSG = _selinux.NETLINK_ROUTE_SOCKET__SEND_MSG
-NETLINK_ROUTE_SOCKET__NAME_BIND = _selinux.NETLINK_ROUTE_SOCKET__NAME_BIND
-NETLINK_ROUTE_SOCKET__NLMSG_READ = _selinux.NETLINK_ROUTE_SOCKET__NLMSG_READ
-NETLINK_ROUTE_SOCKET__NLMSG_WRITE = _selinux.NETLINK_ROUTE_SOCKET__NLMSG_WRITE
-NETLINK_FIREWALL_SOCKET__IOCTL = _selinux.NETLINK_FIREWALL_SOCKET__IOCTL
-NETLINK_FIREWALL_SOCKET__READ = _selinux.NETLINK_FIREWALL_SOCKET__READ
-NETLINK_FIREWALL_SOCKET__WRITE = _selinux.NETLINK_FIREWALL_SOCKET__WRITE
-NETLINK_FIREWALL_SOCKET__CREATE = _selinux.NETLINK_FIREWALL_SOCKET__CREATE
-NETLINK_FIREWALL_SOCKET__GETATTR = _selinux.NETLINK_FIREWALL_SOCKET__GETATTR
-NETLINK_FIREWALL_SOCKET__SETATTR = _selinux.NETLINK_FIREWALL_SOCKET__SETATTR
-NETLINK_FIREWALL_SOCKET__LOCK = _selinux.NETLINK_FIREWALL_SOCKET__LOCK
-NETLINK_FIREWALL_SOCKET__RELABELFROM = _selinux.NETLINK_FIREWALL_SOCKET__RELABELFROM
-NETLINK_FIREWALL_SOCKET__RELABELTO = _selinux.NETLINK_FIREWALL_SOCKET__RELABELTO
-NETLINK_FIREWALL_SOCKET__APPEND = _selinux.NETLINK_FIREWALL_SOCKET__APPEND
-NETLINK_FIREWALL_SOCKET__BIND = _selinux.NETLINK_FIREWALL_SOCKET__BIND
-NETLINK_FIREWALL_SOCKET__CONNECT = _selinux.NETLINK_FIREWALL_SOCKET__CONNECT
-NETLINK_FIREWALL_SOCKET__LISTEN = _selinux.NETLINK_FIREWALL_SOCKET__LISTEN
-NETLINK_FIREWALL_SOCKET__ACCEPT = _selinux.NETLINK_FIREWALL_SOCKET__ACCEPT
-NETLINK_FIREWALL_SOCKET__GETOPT = _selinux.NETLINK_FIREWALL_SOCKET__GETOPT
-NETLINK_FIREWALL_SOCKET__SETOPT = _selinux.NETLINK_FIREWALL_SOCKET__SETOPT
-NETLINK_FIREWALL_SOCKET__SHUTDOWN = _selinux.NETLINK_FIREWALL_SOCKET__SHUTDOWN
-NETLINK_FIREWALL_SOCKET__RECVFROM = _selinux.NETLINK_FIREWALL_SOCKET__RECVFROM
-NETLINK_FIREWALL_SOCKET__SENDTO = _selinux.NETLINK_FIREWALL_SOCKET__SENDTO
-NETLINK_FIREWALL_SOCKET__RECV_MSG = _selinux.NETLINK_FIREWALL_SOCKET__RECV_MSG
-NETLINK_FIREWALL_SOCKET__SEND_MSG = _selinux.NETLINK_FIREWALL_SOCKET__SEND_MSG
-NETLINK_FIREWALL_SOCKET__NAME_BIND = _selinux.NETLINK_FIREWALL_SOCKET__NAME_BIND
-NETLINK_FIREWALL_SOCKET__NLMSG_READ = _selinux.NETLINK_FIREWALL_SOCKET__NLMSG_READ
-NETLINK_FIREWALL_SOCKET__NLMSG_WRITE = _selinux.NETLINK_FIREWALL_SOCKET__NLMSG_WRITE
-NETLINK_TCPDIAG_SOCKET__IOCTL = _selinux.NETLINK_TCPDIAG_SOCKET__IOCTL
-NETLINK_TCPDIAG_SOCKET__READ = _selinux.NETLINK_TCPDIAG_SOCKET__READ
-NETLINK_TCPDIAG_SOCKET__WRITE = _selinux.NETLINK_TCPDIAG_SOCKET__WRITE
-NETLINK_TCPDIAG_SOCKET__CREATE = _selinux.NETLINK_TCPDIAG_SOCKET__CREATE
-NETLINK_TCPDIAG_SOCKET__GETATTR = _selinux.NETLINK_TCPDIAG_SOCKET__GETATTR
-NETLINK_TCPDIAG_SOCKET__SETATTR = _selinux.NETLINK_TCPDIAG_SOCKET__SETATTR
-NETLINK_TCPDIAG_SOCKET__LOCK = _selinux.NETLINK_TCPDIAG_SOCKET__LOCK
-NETLINK_TCPDIAG_SOCKET__RELABELFROM = _selinux.NETLINK_TCPDIAG_SOCKET__RELABELFROM
-NETLINK_TCPDIAG_SOCKET__RELABELTO = _selinux.NETLINK_TCPDIAG_SOCKET__RELABELTO
-NETLINK_TCPDIAG_SOCKET__APPEND = _selinux.NETLINK_TCPDIAG_SOCKET__APPEND
-NETLINK_TCPDIAG_SOCKET__BIND = _selinux.NETLINK_TCPDIAG_SOCKET__BIND
-NETLINK_TCPDIAG_SOCKET__CONNECT = _selinux.NETLINK_TCPDIAG_SOCKET__CONNECT
-NETLINK_TCPDIAG_SOCKET__LISTEN = _selinux.NETLINK_TCPDIAG_SOCKET__LISTEN
-NETLINK_TCPDIAG_SOCKET__ACCEPT = _selinux.NETLINK_TCPDIAG_SOCKET__ACCEPT
-NETLINK_TCPDIAG_SOCKET__GETOPT = _selinux.NETLINK_TCPDIAG_SOCKET__GETOPT
-NETLINK_TCPDIAG_SOCKET__SETOPT = _selinux.NETLINK_TCPDIAG_SOCKET__SETOPT
-NETLINK_TCPDIAG_SOCKET__SHUTDOWN = _selinux.NETLINK_TCPDIAG_SOCKET__SHUTDOWN
-NETLINK_TCPDIAG_SOCKET__RECVFROM = _selinux.NETLINK_TCPDIAG_SOCKET__RECVFROM
-NETLINK_TCPDIAG_SOCKET__SENDTO = _selinux.NETLINK_TCPDIAG_SOCKET__SENDTO
-NETLINK_TCPDIAG_SOCKET__RECV_MSG = _selinux.NETLINK_TCPDIAG_SOCKET__RECV_MSG
-NETLINK_TCPDIAG_SOCKET__SEND_MSG = _selinux.NETLINK_TCPDIAG_SOCKET__SEND_MSG
-NETLINK_TCPDIAG_SOCKET__NAME_BIND = _selinux.NETLINK_TCPDIAG_SOCKET__NAME_BIND
-NETLINK_TCPDIAG_SOCKET__NLMSG_READ = _selinux.NETLINK_TCPDIAG_SOCKET__NLMSG_READ
-NETLINK_TCPDIAG_SOCKET__NLMSG_WRITE = _selinux.NETLINK_TCPDIAG_SOCKET__NLMSG_WRITE
-NETLINK_NFLOG_SOCKET__IOCTL = _selinux.NETLINK_NFLOG_SOCKET__IOCTL
-NETLINK_NFLOG_SOCKET__READ = _selinux.NETLINK_NFLOG_SOCKET__READ
-NETLINK_NFLOG_SOCKET__WRITE = _selinux.NETLINK_NFLOG_SOCKET__WRITE
-NETLINK_NFLOG_SOCKET__CREATE = _selinux.NETLINK_NFLOG_SOCKET__CREATE
-NETLINK_NFLOG_SOCKET__GETATTR = _selinux.NETLINK_NFLOG_SOCKET__GETATTR
-NETLINK_NFLOG_SOCKET__SETATTR = _selinux.NETLINK_NFLOG_SOCKET__SETATTR
-NETLINK_NFLOG_SOCKET__LOCK = _selinux.NETLINK_NFLOG_SOCKET__LOCK
-NETLINK_NFLOG_SOCKET__RELABELFROM = _selinux.NETLINK_NFLOG_SOCKET__RELABELFROM
-NETLINK_NFLOG_SOCKET__RELABELTO = _selinux.NETLINK_NFLOG_SOCKET__RELABELTO
-NETLINK_NFLOG_SOCKET__APPEND = _selinux.NETLINK_NFLOG_SOCKET__APPEND
-NETLINK_NFLOG_SOCKET__BIND = _selinux.NETLINK_NFLOG_SOCKET__BIND
-NETLINK_NFLOG_SOCKET__CONNECT = _selinux.NETLINK_NFLOG_SOCKET__CONNECT
-NETLINK_NFLOG_SOCKET__LISTEN = _selinux.NETLINK_NFLOG_SOCKET__LISTEN
-NETLINK_NFLOG_SOCKET__ACCEPT = _selinux.NETLINK_NFLOG_SOCKET__ACCEPT
-NETLINK_NFLOG_SOCKET__GETOPT = _selinux.NETLINK_NFLOG_SOCKET__GETOPT
-NETLINK_NFLOG_SOCKET__SETOPT = _selinux.NETLINK_NFLOG_SOCKET__SETOPT
-NETLINK_NFLOG_SOCKET__SHUTDOWN = _selinux.NETLINK_NFLOG_SOCKET__SHUTDOWN
-NETLINK_NFLOG_SOCKET__RECVFROM = _selinux.NETLINK_NFLOG_SOCKET__RECVFROM
-NETLINK_NFLOG_SOCKET__SENDTO = _selinux.NETLINK_NFLOG_SOCKET__SENDTO
-NETLINK_NFLOG_SOCKET__RECV_MSG = _selinux.NETLINK_NFLOG_SOCKET__RECV_MSG
-NETLINK_NFLOG_SOCKET__SEND_MSG = _selinux.NETLINK_NFLOG_SOCKET__SEND_MSG
-NETLINK_NFLOG_SOCKET__NAME_BIND = _selinux.NETLINK_NFLOG_SOCKET__NAME_BIND
-NETLINK_XFRM_SOCKET__IOCTL = _selinux.NETLINK_XFRM_SOCKET__IOCTL
-NETLINK_XFRM_SOCKET__READ = _selinux.NETLINK_XFRM_SOCKET__READ
-NETLINK_XFRM_SOCKET__WRITE = _selinux.NETLINK_XFRM_SOCKET__WRITE
-NETLINK_XFRM_SOCKET__CREATE = _selinux.NETLINK_XFRM_SOCKET__CREATE
-NETLINK_XFRM_SOCKET__GETATTR = _selinux.NETLINK_XFRM_SOCKET__GETATTR
-NETLINK_XFRM_SOCKET__SETATTR = _selinux.NETLINK_XFRM_SOCKET__SETATTR
-NETLINK_XFRM_SOCKET__LOCK = _selinux.NETLINK_XFRM_SOCKET__LOCK
-NETLINK_XFRM_SOCKET__RELABELFROM = _selinux.NETLINK_XFRM_SOCKET__RELABELFROM
-NETLINK_XFRM_SOCKET__RELABELTO = _selinux.NETLINK_XFRM_SOCKET__RELABELTO
-NETLINK_XFRM_SOCKET__APPEND = _selinux.NETLINK_XFRM_SOCKET__APPEND
-NETLINK_XFRM_SOCKET__BIND = _selinux.NETLINK_XFRM_SOCKET__BIND
-NETLINK_XFRM_SOCKET__CONNECT = _selinux.NETLINK_XFRM_SOCKET__CONNECT
-NETLINK_XFRM_SOCKET__LISTEN = _selinux.NETLINK_XFRM_SOCKET__LISTEN
-NETLINK_XFRM_SOCKET__ACCEPT = _selinux.NETLINK_XFRM_SOCKET__ACCEPT
-NETLINK_XFRM_SOCKET__GETOPT = _selinux.NETLINK_XFRM_SOCKET__GETOPT
-NETLINK_XFRM_SOCKET__SETOPT = _selinux.NETLINK_XFRM_SOCKET__SETOPT
-NETLINK_XFRM_SOCKET__SHUTDOWN = _selinux.NETLINK_XFRM_SOCKET__SHUTDOWN
-NETLINK_XFRM_SOCKET__RECVFROM = _selinux.NETLINK_XFRM_SOCKET__RECVFROM
-NETLINK_XFRM_SOCKET__SENDTO = _selinux.NETLINK_XFRM_SOCKET__SENDTO
-NETLINK_XFRM_SOCKET__RECV_MSG = _selinux.NETLINK_XFRM_SOCKET__RECV_MSG
-NETLINK_XFRM_SOCKET__SEND_MSG = _selinux.NETLINK_XFRM_SOCKET__SEND_MSG
-NETLINK_XFRM_SOCKET__NAME_BIND = _selinux.NETLINK_XFRM_SOCKET__NAME_BIND
-NETLINK_XFRM_SOCKET__NLMSG_READ = _selinux.NETLINK_XFRM_SOCKET__NLMSG_READ
-NETLINK_XFRM_SOCKET__NLMSG_WRITE = _selinux.NETLINK_XFRM_SOCKET__NLMSG_WRITE
-NETLINK_SELINUX_SOCKET__IOCTL = _selinux.NETLINK_SELINUX_SOCKET__IOCTL
-NETLINK_SELINUX_SOCKET__READ = _selinux.NETLINK_SELINUX_SOCKET__READ
-NETLINK_SELINUX_SOCKET__WRITE = _selinux.NETLINK_SELINUX_SOCKET__WRITE
-NETLINK_SELINUX_SOCKET__CREATE = _selinux.NETLINK_SELINUX_SOCKET__CREATE
-NETLINK_SELINUX_SOCKET__GETATTR = _selinux.NETLINK_SELINUX_SOCKET__GETATTR
-NETLINK_SELINUX_SOCKET__SETATTR = _selinux.NETLINK_SELINUX_SOCKET__SETATTR
-NETLINK_SELINUX_SOCKET__LOCK = _selinux.NETLINK_SELINUX_SOCKET__LOCK
-NETLINK_SELINUX_SOCKET__RELABELFROM = _selinux.NETLINK_SELINUX_SOCKET__RELABELFROM
-NETLINK_SELINUX_SOCKET__RELABELTO = _selinux.NETLINK_SELINUX_SOCKET__RELABELTO
-NETLINK_SELINUX_SOCKET__APPEND = _selinux.NETLINK_SELINUX_SOCKET__APPEND
-NETLINK_SELINUX_SOCKET__BIND = _selinux.NETLINK_SELINUX_SOCKET__BIND
-NETLINK_SELINUX_SOCKET__CONNECT = _selinux.NETLINK_SELINUX_SOCKET__CONNECT
-NETLINK_SELINUX_SOCKET__LISTEN = _selinux.NETLINK_SELINUX_SOCKET__LISTEN
-NETLINK_SELINUX_SOCKET__ACCEPT = _selinux.NETLINK_SELINUX_SOCKET__ACCEPT
-NETLINK_SELINUX_SOCKET__GETOPT = _selinux.NETLINK_SELINUX_SOCKET__GETOPT
-NETLINK_SELINUX_SOCKET__SETOPT = _selinux.NETLINK_SELINUX_SOCKET__SETOPT
-NETLINK_SELINUX_SOCKET__SHUTDOWN = _selinux.NETLINK_SELINUX_SOCKET__SHUTDOWN
-NETLINK_SELINUX_SOCKET__RECVFROM = _selinux.NETLINK_SELINUX_SOCKET__RECVFROM
-NETLINK_SELINUX_SOCKET__SENDTO = _selinux.NETLINK_SELINUX_SOCKET__SENDTO
-NETLINK_SELINUX_SOCKET__RECV_MSG = _selinux.NETLINK_SELINUX_SOCKET__RECV_MSG
-NETLINK_SELINUX_SOCKET__SEND_MSG = _selinux.NETLINK_SELINUX_SOCKET__SEND_MSG
-NETLINK_SELINUX_SOCKET__NAME_BIND = _selinux.NETLINK_SELINUX_SOCKET__NAME_BIND
-NETLINK_AUDIT_SOCKET__IOCTL = _selinux.NETLINK_AUDIT_SOCKET__IOCTL
-NETLINK_AUDIT_SOCKET__READ = _selinux.NETLINK_AUDIT_SOCKET__READ
-NETLINK_AUDIT_SOCKET__WRITE = _selinux.NETLINK_AUDIT_SOCKET__WRITE
-NETLINK_AUDIT_SOCKET__CREATE = _selinux.NETLINK_AUDIT_SOCKET__CREATE
-NETLINK_AUDIT_SOCKET__GETATTR = _selinux.NETLINK_AUDIT_SOCKET__GETATTR
-NETLINK_AUDIT_SOCKET__SETATTR = _selinux.NETLINK_AUDIT_SOCKET__SETATTR
-NETLINK_AUDIT_SOCKET__LOCK = _selinux.NETLINK_AUDIT_SOCKET__LOCK
-NETLINK_AUDIT_SOCKET__RELABELFROM = _selinux.NETLINK_AUDIT_SOCKET__RELABELFROM
-NETLINK_AUDIT_SOCKET__RELABELTO = _selinux.NETLINK_AUDIT_SOCKET__RELABELTO
-NETLINK_AUDIT_SOCKET__APPEND = _selinux.NETLINK_AUDIT_SOCKET__APPEND
-NETLINK_AUDIT_SOCKET__BIND = _selinux.NETLINK_AUDIT_SOCKET__BIND
-NETLINK_AUDIT_SOCKET__CONNECT = _selinux.NETLINK_AUDIT_SOCKET__CONNECT
-NETLINK_AUDIT_SOCKET__LISTEN = _selinux.NETLINK_AUDIT_SOCKET__LISTEN
-NETLINK_AUDIT_SOCKET__ACCEPT = _selinux.NETLINK_AUDIT_SOCKET__ACCEPT
-NETLINK_AUDIT_SOCKET__GETOPT = _selinux.NETLINK_AUDIT_SOCKET__GETOPT
-NETLINK_AUDIT_SOCKET__SETOPT = _selinux.NETLINK_AUDIT_SOCKET__SETOPT
-NETLINK_AUDIT_SOCKET__SHUTDOWN = _selinux.NETLINK_AUDIT_SOCKET__SHUTDOWN
-NETLINK_AUDIT_SOCKET__RECVFROM = _selinux.NETLINK_AUDIT_SOCKET__RECVFROM
-NETLINK_AUDIT_SOCKET__SENDTO = _selinux.NETLINK_AUDIT_SOCKET__SENDTO
-NETLINK_AUDIT_SOCKET__RECV_MSG = _selinux.NETLINK_AUDIT_SOCKET__RECV_MSG
-NETLINK_AUDIT_SOCKET__SEND_MSG = _selinux.NETLINK_AUDIT_SOCKET__SEND_MSG
-NETLINK_AUDIT_SOCKET__NAME_BIND = _selinux.NETLINK_AUDIT_SOCKET__NAME_BIND
-NETLINK_AUDIT_SOCKET__NLMSG_READ = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_READ
-NETLINK_AUDIT_SOCKET__NLMSG_WRITE = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_WRITE
-NETLINK_AUDIT_SOCKET__NLMSG_RELAY = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_RELAY
-NETLINK_AUDIT_SOCKET__NLMSG_READPRIV = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_READPRIV
-NETLINK_AUDIT_SOCKET__NLMSG_TTY_AUDIT = _selinux.NETLINK_AUDIT_SOCKET__NLMSG_TTY_AUDIT
-NETLINK_IP6FW_SOCKET__IOCTL = _selinux.NETLINK_IP6FW_SOCKET__IOCTL
-NETLINK_IP6FW_SOCKET__READ = _selinux.NETLINK_IP6FW_SOCKET__READ
-NETLINK_IP6FW_SOCKET__WRITE = _selinux.NETLINK_IP6FW_SOCKET__WRITE
-NETLINK_IP6FW_SOCKET__CREATE = _selinux.NETLINK_IP6FW_SOCKET__CREATE
-NETLINK_IP6FW_SOCKET__GETATTR = _selinux.NETLINK_IP6FW_SOCKET__GETATTR
-NETLINK_IP6FW_SOCKET__SETATTR = _selinux.NETLINK_IP6FW_SOCKET__SETATTR
-NETLINK_IP6FW_SOCKET__LOCK = _selinux.NETLINK_IP6FW_SOCKET__LOCK
-NETLINK_IP6FW_SOCKET__RELABELFROM = _selinux.NETLINK_IP6FW_SOCKET__RELABELFROM
-NETLINK_IP6FW_SOCKET__RELABELTO = _selinux.NETLINK_IP6FW_SOCKET__RELABELTO
-NETLINK_IP6FW_SOCKET__APPEND = _selinux.NETLINK_IP6FW_SOCKET__APPEND
-NETLINK_IP6FW_SOCKET__BIND = _selinux.NETLINK_IP6FW_SOCKET__BIND
-NETLINK_IP6FW_SOCKET__CONNECT = _selinux.NETLINK_IP6FW_SOCKET__CONNECT
-NETLINK_IP6FW_SOCKET__LISTEN = _selinux.NETLINK_IP6FW_SOCKET__LISTEN
-NETLINK_IP6FW_SOCKET__ACCEPT = _selinux.NETLINK_IP6FW_SOCKET__ACCEPT
-NETLINK_IP6FW_SOCKET__GETOPT = _selinux.NETLINK_IP6FW_SOCKET__GETOPT
-NETLINK_IP6FW_SOCKET__SETOPT = _selinux.NETLINK_IP6FW_SOCKET__SETOPT
-NETLINK_IP6FW_SOCKET__SHUTDOWN = _selinux.NETLINK_IP6FW_SOCKET__SHUTDOWN
-NETLINK_IP6FW_SOCKET__RECVFROM = _selinux.NETLINK_IP6FW_SOCKET__RECVFROM
-NETLINK_IP6FW_SOCKET__SENDTO = _selinux.NETLINK_IP6FW_SOCKET__SENDTO
-NETLINK_IP6FW_SOCKET__RECV_MSG = _selinux.NETLINK_IP6FW_SOCKET__RECV_MSG
-NETLINK_IP6FW_SOCKET__SEND_MSG = _selinux.NETLINK_IP6FW_SOCKET__SEND_MSG
-NETLINK_IP6FW_SOCKET__NAME_BIND = _selinux.NETLINK_IP6FW_SOCKET__NAME_BIND
-NETLINK_IP6FW_SOCKET__NLMSG_READ = _selinux.NETLINK_IP6FW_SOCKET__NLMSG_READ
-NETLINK_IP6FW_SOCKET__NLMSG_WRITE = _selinux.NETLINK_IP6FW_SOCKET__NLMSG_WRITE
-NETLINK_DNRT_SOCKET__IOCTL = _selinux.NETLINK_DNRT_SOCKET__IOCTL
-NETLINK_DNRT_SOCKET__READ = _selinux.NETLINK_DNRT_SOCKET__READ
-NETLINK_DNRT_SOCKET__WRITE = _selinux.NETLINK_DNRT_SOCKET__WRITE
-NETLINK_DNRT_SOCKET__CREATE = _selinux.NETLINK_DNRT_SOCKET__CREATE
-NETLINK_DNRT_SOCKET__GETATTR = _selinux.NETLINK_DNRT_SOCKET__GETATTR
-NETLINK_DNRT_SOCKET__SETATTR = _selinux.NETLINK_DNRT_SOCKET__SETATTR
-NETLINK_DNRT_SOCKET__LOCK = _selinux.NETLINK_DNRT_SOCKET__LOCK
-NETLINK_DNRT_SOCKET__RELABELFROM = _selinux.NETLINK_DNRT_SOCKET__RELABELFROM
-NETLINK_DNRT_SOCKET__RELABELTO = _selinux.NETLINK_DNRT_SOCKET__RELABELTO
-NETLINK_DNRT_SOCKET__APPEND = _selinux.NETLINK_DNRT_SOCKET__APPEND
-NETLINK_DNRT_SOCKET__BIND = _selinux.NETLINK_DNRT_SOCKET__BIND
-NETLINK_DNRT_SOCKET__CONNECT = _selinux.NETLINK_DNRT_SOCKET__CONNECT
-NETLINK_DNRT_SOCKET__LISTEN = _selinux.NETLINK_DNRT_SOCKET__LISTEN
-NETLINK_DNRT_SOCKET__ACCEPT = _selinux.NETLINK_DNRT_SOCKET__ACCEPT
-NETLINK_DNRT_SOCKET__GETOPT = _selinux.NETLINK_DNRT_SOCKET__GETOPT
-NETLINK_DNRT_SOCKET__SETOPT = _selinux.NETLINK_DNRT_SOCKET__SETOPT
-NETLINK_DNRT_SOCKET__SHUTDOWN = _selinux.NETLINK_DNRT_SOCKET__SHUTDOWN
-NETLINK_DNRT_SOCKET__RECVFROM = _selinux.NETLINK_DNRT_SOCKET__RECVFROM
-NETLINK_DNRT_SOCKET__SENDTO = _selinux.NETLINK_DNRT_SOCKET__SENDTO
-NETLINK_DNRT_SOCKET__RECV_MSG = _selinux.NETLINK_DNRT_SOCKET__RECV_MSG
-NETLINK_DNRT_SOCKET__SEND_MSG = _selinux.NETLINK_DNRT_SOCKET__SEND_MSG
-NETLINK_DNRT_SOCKET__NAME_BIND = _selinux.NETLINK_DNRT_SOCKET__NAME_BIND
-DBUS__ACQUIRE_SVC = _selinux.DBUS__ACQUIRE_SVC
-DBUS__SEND_MSG = _selinux.DBUS__SEND_MSG
-NSCD__GETPWD = _selinux.NSCD__GETPWD
-NSCD__GETGRP = _selinux.NSCD__GETGRP
-NSCD__GETHOST = _selinux.NSCD__GETHOST
-NSCD__GETSTAT = _selinux.NSCD__GETSTAT
-NSCD__ADMIN = _selinux.NSCD__ADMIN
-NSCD__SHMEMPWD = _selinux.NSCD__SHMEMPWD
-NSCD__SHMEMGRP = _selinux.NSCD__SHMEMGRP
-NSCD__SHMEMHOST = _selinux.NSCD__SHMEMHOST
-NSCD__GETSERV = _selinux.NSCD__GETSERV
-NSCD__SHMEMSERV = _selinux.NSCD__SHMEMSERV
-ASSOCIATION__SENDTO = _selinux.ASSOCIATION__SENDTO
-ASSOCIATION__RECVFROM = _selinux.ASSOCIATION__RECVFROM
-ASSOCIATION__SETCONTEXT = _selinux.ASSOCIATION__SETCONTEXT
-ASSOCIATION__POLMATCH = _selinux.ASSOCIATION__POLMATCH
-NETLINK_KOBJECT_UEVENT_SOCKET__IOCTL = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__IOCTL
-NETLINK_KOBJECT_UEVENT_SOCKET__READ = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__READ
-NETLINK_KOBJECT_UEVENT_SOCKET__WRITE = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__WRITE
-NETLINK_KOBJECT_UEVENT_SOCKET__CREATE = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__CREATE
-NETLINK_KOBJECT_UEVENT_SOCKET__GETATTR = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__GETATTR
-NETLINK_KOBJECT_UEVENT_SOCKET__SETATTR = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SETATTR
-NETLINK_KOBJECT_UEVENT_SOCKET__LOCK = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__LOCK
-NETLINK_KOBJECT_UEVENT_SOCKET__RELABELFROM = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RELABELFROM
-NETLINK_KOBJECT_UEVENT_SOCKET__RELABELTO = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RELABELTO
-NETLINK_KOBJECT_UEVENT_SOCKET__APPEND = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__APPEND
-NETLINK_KOBJECT_UEVENT_SOCKET__BIND = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__BIND
-NETLINK_KOBJECT_UEVENT_SOCKET__CONNECT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__CONNECT
-NETLINK_KOBJECT_UEVENT_SOCKET__LISTEN = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__LISTEN
-NETLINK_KOBJECT_UEVENT_SOCKET__ACCEPT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__ACCEPT
-NETLINK_KOBJECT_UEVENT_SOCKET__GETOPT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__GETOPT
-NETLINK_KOBJECT_UEVENT_SOCKET__SETOPT = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SETOPT
-NETLINK_KOBJECT_UEVENT_SOCKET__SHUTDOWN = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SHUTDOWN
-NETLINK_KOBJECT_UEVENT_SOCKET__RECVFROM = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RECVFROM
-NETLINK_KOBJECT_UEVENT_SOCKET__SENDTO = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SENDTO
-NETLINK_KOBJECT_UEVENT_SOCKET__RECV_MSG = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__RECV_MSG
-NETLINK_KOBJECT_UEVENT_SOCKET__SEND_MSG = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__SEND_MSG
-NETLINK_KOBJECT_UEVENT_SOCKET__NAME_BIND = _selinux.NETLINK_KOBJECT_UEVENT_SOCKET__NAME_BIND
-APPLETALK_SOCKET__IOCTL = _selinux.APPLETALK_SOCKET__IOCTL
-APPLETALK_SOCKET__READ = _selinux.APPLETALK_SOCKET__READ
-APPLETALK_SOCKET__WRITE = _selinux.APPLETALK_SOCKET__WRITE
-APPLETALK_SOCKET__CREATE = _selinux.APPLETALK_SOCKET__CREATE
-APPLETALK_SOCKET__GETATTR = _selinux.APPLETALK_SOCKET__GETATTR
-APPLETALK_SOCKET__SETATTR = _selinux.APPLETALK_SOCKET__SETATTR
-APPLETALK_SOCKET__LOCK = _selinux.APPLETALK_SOCKET__LOCK
-APPLETALK_SOCKET__RELABELFROM = _selinux.APPLETALK_SOCKET__RELABELFROM
-APPLETALK_SOCKET__RELABELTO = _selinux.APPLETALK_SOCKET__RELABELTO
-APPLETALK_SOCKET__APPEND = _selinux.APPLETALK_SOCKET__APPEND
-APPLETALK_SOCKET__BIND = _selinux.APPLETALK_SOCKET__BIND
-APPLETALK_SOCKET__CONNECT = _selinux.APPLETALK_SOCKET__CONNECT
-APPLETALK_SOCKET__LISTEN = _selinux.APPLETALK_SOCKET__LISTEN
-APPLETALK_SOCKET__ACCEPT = _selinux.APPLETALK_SOCKET__ACCEPT
-APPLETALK_SOCKET__GETOPT = _selinux.APPLETALK_SOCKET__GETOPT
-APPLETALK_SOCKET__SETOPT = _selinux.APPLETALK_SOCKET__SETOPT
-APPLETALK_SOCKET__SHUTDOWN = _selinux.APPLETALK_SOCKET__SHUTDOWN
-APPLETALK_SOCKET__RECVFROM = _selinux.APPLETALK_SOCKET__RECVFROM
-APPLETALK_SOCKET__SENDTO = _selinux.APPLETALK_SOCKET__SENDTO
-APPLETALK_SOCKET__RECV_MSG = _selinux.APPLETALK_SOCKET__RECV_MSG
-APPLETALK_SOCKET__SEND_MSG = _selinux.APPLETALK_SOCKET__SEND_MSG
-APPLETALK_SOCKET__NAME_BIND = _selinux.APPLETALK_SOCKET__NAME_BIND
-PACKET__SEND = _selinux.PACKET__SEND
-PACKET__RECV = _selinux.PACKET__RECV
-PACKET__RELABELTO = _selinux.PACKET__RELABELTO
-PACKET__FLOW_IN = _selinux.PACKET__FLOW_IN
-PACKET__FLOW_OUT = _selinux.PACKET__FLOW_OUT
-PACKET__FORWARD_IN = _selinux.PACKET__FORWARD_IN
-PACKET__FORWARD_OUT = _selinux.PACKET__FORWARD_OUT
-KEY__VIEW = _selinux.KEY__VIEW
-KEY__READ = _selinux.KEY__READ
-KEY__WRITE = _selinux.KEY__WRITE
-KEY__SEARCH = _selinux.KEY__SEARCH
-KEY__LINK = _selinux.KEY__LINK
-KEY__SETATTR = _selinux.KEY__SETATTR
-KEY__CREATE = _selinux.KEY__CREATE
-CONTEXT__TRANSLATE = _selinux.CONTEXT__TRANSLATE
-CONTEXT__CONTAINS = _selinux.CONTEXT__CONTAINS
-DCCP_SOCKET__IOCTL = _selinux.DCCP_SOCKET__IOCTL
-DCCP_SOCKET__READ = _selinux.DCCP_SOCKET__READ
-DCCP_SOCKET__WRITE = _selinux.DCCP_SOCKET__WRITE
-DCCP_SOCKET__CREATE = _selinux.DCCP_SOCKET__CREATE
-DCCP_SOCKET__GETATTR = _selinux.DCCP_SOCKET__GETATTR
-DCCP_SOCKET__SETATTR = _selinux.DCCP_SOCKET__SETATTR
-DCCP_SOCKET__LOCK = _selinux.DCCP_SOCKET__LOCK
-DCCP_SOCKET__RELABELFROM = _selinux.DCCP_SOCKET__RELABELFROM
-DCCP_SOCKET__RELABELTO = _selinux.DCCP_SOCKET__RELABELTO
-DCCP_SOCKET__APPEND = _selinux.DCCP_SOCKET__APPEND
-DCCP_SOCKET__BIND = _selinux.DCCP_SOCKET__BIND
-DCCP_SOCKET__CONNECT = _selinux.DCCP_SOCKET__CONNECT
-DCCP_SOCKET__LISTEN = _selinux.DCCP_SOCKET__LISTEN
-DCCP_SOCKET__ACCEPT = _selinux.DCCP_SOCKET__ACCEPT
-DCCP_SOCKET__GETOPT = _selinux.DCCP_SOCKET__GETOPT
-DCCP_SOCKET__SETOPT = _selinux.DCCP_SOCKET__SETOPT
-DCCP_SOCKET__SHUTDOWN = _selinux.DCCP_SOCKET__SHUTDOWN
-DCCP_SOCKET__RECVFROM = _selinux.DCCP_SOCKET__RECVFROM
-DCCP_SOCKET__SENDTO = _selinux.DCCP_SOCKET__SENDTO
-DCCP_SOCKET__RECV_MSG = _selinux.DCCP_SOCKET__RECV_MSG
-DCCP_SOCKET__SEND_MSG = _selinux.DCCP_SOCKET__SEND_MSG
-DCCP_SOCKET__NAME_BIND = _selinux.DCCP_SOCKET__NAME_BIND
-DCCP_SOCKET__NODE_BIND = _selinux.DCCP_SOCKET__NODE_BIND
-DCCP_SOCKET__NAME_CONNECT = _selinux.DCCP_SOCKET__NAME_CONNECT
-MEMPROTECT__MMAP_ZERO = _selinux.MEMPROTECT__MMAP_ZERO
-DB_DATABASE__CREATE = _selinux.DB_DATABASE__CREATE
-DB_DATABASE__DROP = _selinux.DB_DATABASE__DROP
-DB_DATABASE__GETATTR = _selinux.DB_DATABASE__GETATTR
-DB_DATABASE__SETATTR = _selinux.DB_DATABASE__SETATTR
-DB_DATABASE__RELABELFROM = _selinux.DB_DATABASE__RELABELFROM
-DB_DATABASE__RELABELTO = _selinux.DB_DATABASE__RELABELTO
-DB_DATABASE__ACCESS = _selinux.DB_DATABASE__ACCESS
-DB_DATABASE__INSTALL_MODULE = _selinux.DB_DATABASE__INSTALL_MODULE
-DB_DATABASE__LOAD_MODULE = _selinux.DB_DATABASE__LOAD_MODULE
-DB_DATABASE__GET_PARAM = _selinux.DB_DATABASE__GET_PARAM
-DB_DATABASE__SET_PARAM = _selinux.DB_DATABASE__SET_PARAM
-DB_TABLE__CREATE = _selinux.DB_TABLE__CREATE
-DB_TABLE__DROP = _selinux.DB_TABLE__DROP
-DB_TABLE__GETATTR = _selinux.DB_TABLE__GETATTR
-DB_TABLE__SETATTR = _selinux.DB_TABLE__SETATTR
-DB_TABLE__RELABELFROM = _selinux.DB_TABLE__RELABELFROM
-DB_TABLE__RELABELTO = _selinux.DB_TABLE__RELABELTO
-DB_TABLE__USE = _selinux.DB_TABLE__USE
-DB_TABLE__SELECT = _selinux.DB_TABLE__SELECT
-DB_TABLE__UPDATE = _selinux.DB_TABLE__UPDATE
-DB_TABLE__INSERT = _selinux.DB_TABLE__INSERT
-DB_TABLE__DELETE = _selinux.DB_TABLE__DELETE
-DB_TABLE__LOCK = _selinux.DB_TABLE__LOCK
-DB_PROCEDURE__CREATE = _selinux.DB_PROCEDURE__CREATE
-DB_PROCEDURE__DROP = _selinux.DB_PROCEDURE__DROP
-DB_PROCEDURE__GETATTR = _selinux.DB_PROCEDURE__GETATTR
-DB_PROCEDURE__SETATTR = _selinux.DB_PROCEDURE__SETATTR
-DB_PROCEDURE__RELABELFROM = _selinux.DB_PROCEDURE__RELABELFROM
-DB_PROCEDURE__RELABELTO = _selinux.DB_PROCEDURE__RELABELTO
-DB_PROCEDURE__EXECUTE = _selinux.DB_PROCEDURE__EXECUTE
-DB_PROCEDURE__ENTRYPOINT = _selinux.DB_PROCEDURE__ENTRYPOINT
-DB_COLUMN__CREATE = _selinux.DB_COLUMN__CREATE
-DB_COLUMN__DROP = _selinux.DB_COLUMN__DROP
-DB_COLUMN__GETATTR = _selinux.DB_COLUMN__GETATTR
-DB_COLUMN__SETATTR = _selinux.DB_COLUMN__SETATTR
-DB_COLUMN__RELABELFROM = _selinux.DB_COLUMN__RELABELFROM
-DB_COLUMN__RELABELTO = _selinux.DB_COLUMN__RELABELTO
-DB_COLUMN__USE = _selinux.DB_COLUMN__USE
-DB_COLUMN__SELECT = _selinux.DB_COLUMN__SELECT
-DB_COLUMN__UPDATE = _selinux.DB_COLUMN__UPDATE
-DB_COLUMN__INSERT = _selinux.DB_COLUMN__INSERT
-DB_TUPLE__RELABELFROM = _selinux.DB_TUPLE__RELABELFROM
-DB_TUPLE__RELABELTO = _selinux.DB_TUPLE__RELABELTO
-DB_TUPLE__USE = _selinux.DB_TUPLE__USE
-DB_TUPLE__SELECT = _selinux.DB_TUPLE__SELECT
-DB_TUPLE__UPDATE = _selinux.DB_TUPLE__UPDATE
-DB_TUPLE__INSERT = _selinux.DB_TUPLE__INSERT
-DB_TUPLE__DELETE = _selinux.DB_TUPLE__DELETE
-DB_BLOB__CREATE = _selinux.DB_BLOB__CREATE
-DB_BLOB__DROP = _selinux.DB_BLOB__DROP
-DB_BLOB__GETATTR = _selinux.DB_BLOB__GETATTR
-DB_BLOB__SETATTR = _selinux.DB_BLOB__SETATTR
-DB_BLOB__RELABELFROM = _selinux.DB_BLOB__RELABELFROM
-DB_BLOB__RELABELTO = _selinux.DB_BLOB__RELABELTO
-DB_BLOB__READ = _selinux.DB_BLOB__READ
-DB_BLOB__WRITE = _selinux.DB_BLOB__WRITE
-DB_BLOB__IMPORT = _selinux.DB_BLOB__IMPORT
-DB_BLOB__EXPORT = _selinux.DB_BLOB__EXPORT
-PEER__RECV = _selinux.PEER__RECV
-X_APPLICATION_DATA__PASTE = _selinux.X_APPLICATION_DATA__PASTE
-X_APPLICATION_DATA__PASTE_AFTER_CONFIRM = _selinux.X_APPLICATION_DATA__PASTE_AFTER_CONFIRM
-X_APPLICATION_DATA__COPY = _selinux.X_APPLICATION_DATA__COPY
-class context_s_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, context_s_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, context_s_t, name)
- __repr__ = _swig_repr
- __swig_setmethods__["ptr"] = _selinux.context_s_t_ptr_set
- __swig_getmethods__["ptr"] = _selinux.context_s_t_ptr_get
- if _newclass:ptr = _swig_property(_selinux.context_s_t_ptr_get, _selinux.context_s_t_ptr_set)
- def __init__(self):
- this = _selinux.new_context_s_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_context_s_t
- __del__ = lambda self : None;
-context_s_t_swigregister = _selinux.context_s_t_swigregister
-context_s_t_swigregister(context_s_t)
-
-
-def context_new(*args):
- return _selinux.context_new(*args)
-context_new = _selinux.context_new
-
-def context_str(*args):
- return _selinux.context_str(*args)
-context_str = _selinux.context_str
-
-def context_free(*args):
- return _selinux.context_free(*args)
-context_free = _selinux.context_free
-
-def context_type_get(*args):
- return _selinux.context_type_get(*args)
-context_type_get = _selinux.context_type_get
-
-def context_range_get(*args):
- return _selinux.context_range_get(*args)
-context_range_get = _selinux.context_range_get
-
-def context_role_get(*args):
- return _selinux.context_role_get(*args)
-context_role_get = _selinux.context_role_get
-
-def context_user_get(*args):
- return _selinux.context_user_get(*args)
-context_user_get = _selinux.context_user_get
-
-def context_type_set(*args):
- return _selinux.context_type_set(*args)
-context_type_set = _selinux.context_type_set
-
-def context_range_set(*args):
- return _selinux.context_range_set(*args)
-context_range_set = _selinux.context_range_set
-
-def context_role_set(*args):
- return _selinux.context_role_set(*args)
-context_role_set = _selinux.context_role_set
-
-def context_user_set(*args):
- return _selinux.context_user_set(*args)
-context_user_set = _selinux.context_user_set
-SECCLASS_SECURITY = _selinux.SECCLASS_SECURITY
-SECCLASS_PROCESS = _selinux.SECCLASS_PROCESS
-SECCLASS_SYSTEM = _selinux.SECCLASS_SYSTEM
-SECCLASS_CAPABILITY = _selinux.SECCLASS_CAPABILITY
-SECCLASS_FILESYSTEM = _selinux.SECCLASS_FILESYSTEM
-SECCLASS_FILE = _selinux.SECCLASS_FILE
-SECCLASS_DIR = _selinux.SECCLASS_DIR
-SECCLASS_FD = _selinux.SECCLASS_FD
-SECCLASS_LNK_FILE = _selinux.SECCLASS_LNK_FILE
-SECCLASS_CHR_FILE = _selinux.SECCLASS_CHR_FILE
-SECCLASS_BLK_FILE = _selinux.SECCLASS_BLK_FILE
-SECCLASS_SOCK_FILE = _selinux.SECCLASS_SOCK_FILE
-SECCLASS_FIFO_FILE = _selinux.SECCLASS_FIFO_FILE
-SECCLASS_SOCKET = _selinux.SECCLASS_SOCKET
-SECCLASS_TCP_SOCKET = _selinux.SECCLASS_TCP_SOCKET
-SECCLASS_UDP_SOCKET = _selinux.SECCLASS_UDP_SOCKET
-SECCLASS_RAWIP_SOCKET = _selinux.SECCLASS_RAWIP_SOCKET
-SECCLASS_NODE = _selinux.SECCLASS_NODE
-SECCLASS_NETIF = _selinux.SECCLASS_NETIF
-SECCLASS_NETLINK_SOCKET = _selinux.SECCLASS_NETLINK_SOCKET
-SECCLASS_PACKET_SOCKET = _selinux.SECCLASS_PACKET_SOCKET
-SECCLASS_KEY_SOCKET = _selinux.SECCLASS_KEY_SOCKET
-SECCLASS_UNIX_STREAM_SOCKET = _selinux.SECCLASS_UNIX_STREAM_SOCKET
-SECCLASS_UNIX_DGRAM_SOCKET = _selinux.SECCLASS_UNIX_DGRAM_SOCKET
-SECCLASS_SEM = _selinux.SECCLASS_SEM
-SECCLASS_MSG = _selinux.SECCLASS_MSG
-SECCLASS_MSGQ = _selinux.SECCLASS_MSGQ
-SECCLASS_SHM = _selinux.SECCLASS_SHM
-SECCLASS_IPC = _selinux.SECCLASS_IPC
-SECCLASS_PASSWD = _selinux.SECCLASS_PASSWD
-SECCLASS_X_DRAWABLE = _selinux.SECCLASS_X_DRAWABLE
-SECCLASS_X_SCREEN = _selinux.SECCLASS_X_SCREEN
-SECCLASS_X_GC = _selinux.SECCLASS_X_GC
-SECCLASS_X_FONT = _selinux.SECCLASS_X_FONT
-SECCLASS_X_COLORMAP = _selinux.SECCLASS_X_COLORMAP
-SECCLASS_X_PROPERTY = _selinux.SECCLASS_X_PROPERTY
-SECCLASS_X_SELECTION = _selinux.SECCLASS_X_SELECTION
-SECCLASS_X_CURSOR = _selinux.SECCLASS_X_CURSOR
-SECCLASS_X_CLIENT = _selinux.SECCLASS_X_CLIENT
-SECCLASS_X_DEVICE = _selinux.SECCLASS_X_DEVICE
-SECCLASS_X_SERVER = _selinux.SECCLASS_X_SERVER
-SECCLASS_X_EXTENSION = _selinux.SECCLASS_X_EXTENSION
-SECCLASS_NETLINK_ROUTE_SOCKET = _selinux.SECCLASS_NETLINK_ROUTE_SOCKET
-SECCLASS_NETLINK_FIREWALL_SOCKET = _selinux.SECCLASS_NETLINK_FIREWALL_SOCKET
-SECCLASS_NETLINK_TCPDIAG_SOCKET = _selinux.SECCLASS_NETLINK_TCPDIAG_SOCKET
-SECCLASS_NETLINK_NFLOG_SOCKET = _selinux.SECCLASS_NETLINK_NFLOG_SOCKET
-SECCLASS_NETLINK_XFRM_SOCKET = _selinux.SECCLASS_NETLINK_XFRM_SOCKET
-SECCLASS_NETLINK_SELINUX_SOCKET = _selinux.SECCLASS_NETLINK_SELINUX_SOCKET
-SECCLASS_NETLINK_AUDIT_SOCKET = _selinux.SECCLASS_NETLINK_AUDIT_SOCKET
-SECCLASS_NETLINK_IP6FW_SOCKET = _selinux.SECCLASS_NETLINK_IP6FW_SOCKET
-SECCLASS_NETLINK_DNRT_SOCKET = _selinux.SECCLASS_NETLINK_DNRT_SOCKET
-SECCLASS_DBUS = _selinux.SECCLASS_DBUS
-SECCLASS_NSCD = _selinux.SECCLASS_NSCD
-SECCLASS_ASSOCIATION = _selinux.SECCLASS_ASSOCIATION
-SECCLASS_NETLINK_KOBJECT_UEVENT_SOCKET = _selinux.SECCLASS_NETLINK_KOBJECT_UEVENT_SOCKET
-SECCLASS_APPLETALK_SOCKET = _selinux.SECCLASS_APPLETALK_SOCKET
-SECCLASS_PACKET = _selinux.SECCLASS_PACKET
-SECCLASS_KEY = _selinux.SECCLASS_KEY
-SECCLASS_CONTEXT = _selinux.SECCLASS_CONTEXT
-SECCLASS_DCCP_SOCKET = _selinux.SECCLASS_DCCP_SOCKET
-SECCLASS_MEMPROTECT = _selinux.SECCLASS_MEMPROTECT
-SECCLASS_DB_DATABASE = _selinux.SECCLASS_DB_DATABASE
-SECCLASS_DB_TABLE = _selinux.SECCLASS_DB_TABLE
-SECCLASS_DB_PROCEDURE = _selinux.SECCLASS_DB_PROCEDURE
-SECCLASS_DB_COLUMN = _selinux.SECCLASS_DB_COLUMN
-SECCLASS_DB_TUPLE = _selinux.SECCLASS_DB_TUPLE
-SECCLASS_DB_BLOB = _selinux.SECCLASS_DB_BLOB
-SECCLASS_PEER = _selinux.SECCLASS_PEER
-SECCLASS_CAPABILITY2 = _selinux.SECCLASS_CAPABILITY2
-SECCLASS_X_RESOURCE = _selinux.SECCLASS_X_RESOURCE
-SECCLASS_X_EVENT = _selinux.SECCLASS_X_EVENT
-SECCLASS_X_SYNTHETIC_EVENT = _selinux.SECCLASS_X_SYNTHETIC_EVENT
-SECCLASS_X_APPLICATION_DATA = _selinux.SECCLASS_X_APPLICATION_DATA
-SECINITSID_KERNEL = _selinux.SECINITSID_KERNEL
-SECINITSID_SECURITY = _selinux.SECINITSID_SECURITY
-SECINITSID_UNLABELED = _selinux.SECINITSID_UNLABELED
-SECINITSID_FS = _selinux.SECINITSID_FS
-SECINITSID_FILE = _selinux.SECINITSID_FILE
-SECINITSID_FILE_LABELS = _selinux.SECINITSID_FILE_LABELS
-SECINITSID_INIT = _selinux.SECINITSID_INIT
-SECINITSID_ANY_SOCKET = _selinux.SECINITSID_ANY_SOCKET
-SECINITSID_PORT = _selinux.SECINITSID_PORT
-SECINITSID_NETIF = _selinux.SECINITSID_NETIF
-SECINITSID_NETMSG = _selinux.SECINITSID_NETMSG
-SECINITSID_NODE = _selinux.SECINITSID_NODE
-SECINITSID_IGMP_PACKET = _selinux.SECINITSID_IGMP_PACKET
-SECINITSID_ICMP_SOCKET = _selinux.SECINITSID_ICMP_SOCKET
-SECINITSID_TCP_SOCKET = _selinux.SECINITSID_TCP_SOCKET
-SECINITSID_SYSCTL_MODPROBE = _selinux.SECINITSID_SYSCTL_MODPROBE
-SECINITSID_SYSCTL = _selinux.SECINITSID_SYSCTL
-SECINITSID_SYSCTL_FS = _selinux.SECINITSID_SYSCTL_FS
-SECINITSID_SYSCTL_KERNEL = _selinux.SECINITSID_SYSCTL_KERNEL
-SECINITSID_SYSCTL_NET = _selinux.SECINITSID_SYSCTL_NET
-SECINITSID_SYSCTL_NET_UNIX = _selinux.SECINITSID_SYSCTL_NET_UNIX
-SECINITSID_SYSCTL_VM = _selinux.SECINITSID_SYSCTL_VM
-SECINITSID_SYSCTL_DEV = _selinux.SECINITSID_SYSCTL_DEV
-SECINITSID_KMOD = _selinux.SECINITSID_KMOD
-SECINITSID_POLICY = _selinux.SECINITSID_POLICY
-SECINITSID_SCMP_PACKET = _selinux.SECINITSID_SCMP_PACKET
-SECINITSID_DEVNULL = _selinux.SECINITSID_DEVNULL
-SECINITSID_NUM = _selinux.SECINITSID_NUM
-SELINUX_DEFAULTUSER = _selinux.SELINUX_DEFAULTUSER
-
-def get_ordered_context_list(*args):
- return _selinux.get_ordered_context_list(*args)
-get_ordered_context_list = _selinux.get_ordered_context_list
-
-def get_ordered_context_list_with_level(*args):
- return _selinux.get_ordered_context_list_with_level(*args)
-get_ordered_context_list_with_level = _selinux.get_ordered_context_list_with_level
-
-def get_default_context(*args):
- return _selinux.get_default_context(*args)
-get_default_context = _selinux.get_default_context
-
-def get_default_context_with_level(*args):
- return _selinux.get_default_context_with_level(*args)
-get_default_context_with_level = _selinux.get_default_context_with_level
-
-def get_default_context_with_role(*args):
- return _selinux.get_default_context_with_role(*args)
-get_default_context_with_role = _selinux.get_default_context_with_role
-
-def get_default_context_with_rolelevel(*args):
- return _selinux.get_default_context_with_rolelevel(*args)
-get_default_context_with_rolelevel = _selinux.get_default_context_with_rolelevel
-
-def query_user_context():
- return _selinux.query_user_context()
-query_user_context = _selinux.query_user_context
-
-def manual_user_enter_context(*args):
- return _selinux.manual_user_enter_context(*args)
-manual_user_enter_context = _selinux.manual_user_enter_context
-
-def selinux_default_type_path():
- return _selinux.selinux_default_type_path()
-selinux_default_type_path = _selinux.selinux_default_type_path
-
-def get_default_type(*args):
- return _selinux.get_default_type(*args)
-get_default_type = _selinux.get_default_type
-SELABEL_CTX_FILE = _selinux.SELABEL_CTX_FILE
-SELABEL_CTX_MEDIA = _selinux.SELABEL_CTX_MEDIA
-SELABEL_CTX_X = _selinux.SELABEL_CTX_X
-SELABEL_CTX_DB = _selinux.SELABEL_CTX_DB
-SELABEL_CTX_ANDROID_PROP = _selinux.SELABEL_CTX_ANDROID_PROP
-SELABEL_OPT_UNUSED = _selinux.SELABEL_OPT_UNUSED
-SELABEL_OPT_VALIDATE = _selinux.SELABEL_OPT_VALIDATE
-SELABEL_OPT_BASEONLY = _selinux.SELABEL_OPT_BASEONLY
-SELABEL_OPT_PATH = _selinux.SELABEL_OPT_PATH
-SELABEL_OPT_SUBSET = _selinux.SELABEL_OPT_SUBSET
-SELABEL_NOPT = _selinux.SELABEL_NOPT
-
-def selabel_open(*args):
- return _selinux.selabel_open(*args)
-selabel_open = _selinux.selabel_open
-
-def selabel_close(*args):
- return _selinux.selabel_close(*args)
-selabel_close = _selinux.selabel_close
-
-def selabel_lookup(*args):
- return _selinux.selabel_lookup(*args)
-selabel_lookup = _selinux.selabel_lookup
-
-def selabel_lookup_raw(*args):
- return _selinux.selabel_lookup_raw(*args)
-selabel_lookup_raw = _selinux.selabel_lookup_raw
-
-def selabel_partial_match(*args):
- return _selinux.selabel_partial_match(*args)
-selabel_partial_match = _selinux.selabel_partial_match
-
-def selabel_lookup_best_match(*args):
- return _selinux.selabel_lookup_best_match(*args)
-selabel_lookup_best_match = _selinux.selabel_lookup_best_match
-
-def selabel_lookup_best_match_raw(*args):
- return _selinux.selabel_lookup_best_match_raw(*args)
-selabel_lookup_best_match_raw = _selinux.selabel_lookup_best_match_raw
-
-def selabel_stats(*args):
- return _selinux.selabel_stats(*args)
-selabel_stats = _selinux.selabel_stats
-SELABEL_X_PROP = _selinux.SELABEL_X_PROP
-SELABEL_X_EXT = _selinux.SELABEL_X_EXT
-SELABEL_X_CLIENT = _selinux.SELABEL_X_CLIENT
-SELABEL_X_EVENT = _selinux.SELABEL_X_EVENT
-SELABEL_X_SELN = _selinux.SELABEL_X_SELN
-SELABEL_X_POLYPROP = _selinux.SELABEL_X_POLYPROP
-SELABEL_X_POLYSELN = _selinux.SELABEL_X_POLYSELN
-SELABEL_DB_DATABASE = _selinux.SELABEL_DB_DATABASE
-SELABEL_DB_SCHEMA = _selinux.SELABEL_DB_SCHEMA
-SELABEL_DB_TABLE = _selinux.SELABEL_DB_TABLE
-SELABEL_DB_COLUMN = _selinux.SELABEL_DB_COLUMN
-SELABEL_DB_SEQUENCE = _selinux.SELABEL_DB_SEQUENCE
-SELABEL_DB_VIEW = _selinux.SELABEL_DB_VIEW
-SELABEL_DB_PROCEDURE = _selinux.SELABEL_DB_PROCEDURE
-SELABEL_DB_BLOB = _selinux.SELABEL_DB_BLOB
-SELABEL_DB_TUPLE = _selinux.SELABEL_DB_TUPLE
-SELABEL_DB_LANGUAGE = _selinux.SELABEL_DB_LANGUAGE
-SELABEL_DB_EXCEPTION = _selinux.SELABEL_DB_EXCEPTION
-SELABEL_DB_DATATYPE = _selinux.SELABEL_DB_DATATYPE
-
-def is_selinux_enabled():
- return _selinux.is_selinux_enabled()
-is_selinux_enabled = _selinux.is_selinux_enabled
-
-def is_selinux_mls_enabled():
- return _selinux.is_selinux_mls_enabled()
-is_selinux_mls_enabled = _selinux.is_selinux_mls_enabled
-
-def getcon():
- return _selinux.getcon()
-getcon = _selinux.getcon
-
-def getcon_raw():
- return _selinux.getcon_raw()
-getcon_raw = _selinux.getcon_raw
-
-def setcon(*args):
- return _selinux.setcon(*args)
-setcon = _selinux.setcon
-
-def setcon_raw(*args):
- return _selinux.setcon_raw(*args)
-setcon_raw = _selinux.setcon_raw
-
-def getpidcon(*args):
- return _selinux.getpidcon(*args)
-getpidcon = _selinux.getpidcon
-
-def getpidcon_raw(*args):
- return _selinux.getpidcon_raw(*args)
-getpidcon_raw = _selinux.getpidcon_raw
-
-def getprevcon():
- return _selinux.getprevcon()
-getprevcon = _selinux.getprevcon
-
-def getprevcon_raw():
- return _selinux.getprevcon_raw()
-getprevcon_raw = _selinux.getprevcon_raw
-
-def getexeccon():
- return _selinux.getexeccon()
-getexeccon = _selinux.getexeccon
-
-def getexeccon_raw():
- return _selinux.getexeccon_raw()
-getexeccon_raw = _selinux.getexeccon_raw
-
-def setexeccon(*args):
- return _selinux.setexeccon(*args)
-setexeccon = _selinux.setexeccon
-
-def setexeccon_raw(*args):
- return _selinux.setexeccon_raw(*args)
-setexeccon_raw = _selinux.setexeccon_raw
-
-def getfscreatecon():
- return _selinux.getfscreatecon()
-getfscreatecon = _selinux.getfscreatecon
-
-def getfscreatecon_raw():
- return _selinux.getfscreatecon_raw()
-getfscreatecon_raw = _selinux.getfscreatecon_raw
-
-def setfscreatecon(*args):
- return _selinux.setfscreatecon(*args)
-setfscreatecon = _selinux.setfscreatecon
-
-def setfscreatecon_raw(*args):
- return _selinux.setfscreatecon_raw(*args)
-setfscreatecon_raw = _selinux.setfscreatecon_raw
-
-def getkeycreatecon():
- return _selinux.getkeycreatecon()
-getkeycreatecon = _selinux.getkeycreatecon
-
-def getkeycreatecon_raw():
- return _selinux.getkeycreatecon_raw()
-getkeycreatecon_raw = _selinux.getkeycreatecon_raw
-
-def setkeycreatecon(*args):
- return _selinux.setkeycreatecon(*args)
-setkeycreatecon = _selinux.setkeycreatecon
-
-def setkeycreatecon_raw(*args):
- return _selinux.setkeycreatecon_raw(*args)
-setkeycreatecon_raw = _selinux.setkeycreatecon_raw
-
-def getsockcreatecon():
- return _selinux.getsockcreatecon()
-getsockcreatecon = _selinux.getsockcreatecon
-
-def getsockcreatecon_raw():
- return _selinux.getsockcreatecon_raw()
-getsockcreatecon_raw = _selinux.getsockcreatecon_raw
-
-def setsockcreatecon(*args):
- return _selinux.setsockcreatecon(*args)
-setsockcreatecon = _selinux.setsockcreatecon
-
-def setsockcreatecon_raw(*args):
- return _selinux.setsockcreatecon_raw(*args)
-setsockcreatecon_raw = _selinux.setsockcreatecon_raw
-
-def getfilecon(*args):
- return _selinux.getfilecon(*args)
-getfilecon = _selinux.getfilecon
-
-def getfilecon_raw(*args):
- return _selinux.getfilecon_raw(*args)
-getfilecon_raw = _selinux.getfilecon_raw
-
-def lgetfilecon(*args):
- return _selinux.lgetfilecon(*args)
-lgetfilecon = _selinux.lgetfilecon
-
-def lgetfilecon_raw(*args):
- return _selinux.lgetfilecon_raw(*args)
-lgetfilecon_raw = _selinux.lgetfilecon_raw
-
-def fgetfilecon(*args):
- return _selinux.fgetfilecon(*args)
-fgetfilecon = _selinux.fgetfilecon
-
-def fgetfilecon_raw(*args):
- return _selinux.fgetfilecon_raw(*args)
-fgetfilecon_raw = _selinux.fgetfilecon_raw
-
-def setfilecon(*args):
- return _selinux.setfilecon(*args)
-setfilecon = _selinux.setfilecon
-
-def setfilecon_raw(*args):
- return _selinux.setfilecon_raw(*args)
-setfilecon_raw = _selinux.setfilecon_raw
-
-def lsetfilecon(*args):
- return _selinux.lsetfilecon(*args)
-lsetfilecon = _selinux.lsetfilecon
-
-def lsetfilecon_raw(*args):
- return _selinux.lsetfilecon_raw(*args)
-lsetfilecon_raw = _selinux.lsetfilecon_raw
-
-def fsetfilecon(*args):
- return _selinux.fsetfilecon(*args)
-fsetfilecon = _selinux.fsetfilecon
-
-def fsetfilecon_raw(*args):
- return _selinux.fsetfilecon_raw(*args)
-fsetfilecon_raw = _selinux.fsetfilecon_raw
-
-def getpeercon(*args):
- return _selinux.getpeercon(*args)
-getpeercon = _selinux.getpeercon
-
-def getpeercon_raw(*args):
- return _selinux.getpeercon_raw(*args)
-getpeercon_raw = _selinux.getpeercon_raw
-class av_decision(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, av_decision, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, av_decision, name)
- __repr__ = _swig_repr
- __swig_setmethods__["allowed"] = _selinux.av_decision_allowed_set
- __swig_getmethods__["allowed"] = _selinux.av_decision_allowed_get
- if _newclass:allowed = _swig_property(_selinux.av_decision_allowed_get, _selinux.av_decision_allowed_set)
- __swig_setmethods__["decided"] = _selinux.av_decision_decided_set
- __swig_getmethods__["decided"] = _selinux.av_decision_decided_get
- if _newclass:decided = _swig_property(_selinux.av_decision_decided_get, _selinux.av_decision_decided_set)
- __swig_setmethods__["auditallow"] = _selinux.av_decision_auditallow_set
- __swig_getmethods__["auditallow"] = _selinux.av_decision_auditallow_get
- if _newclass:auditallow = _swig_property(_selinux.av_decision_auditallow_get, _selinux.av_decision_auditallow_set)
- __swig_setmethods__["auditdeny"] = _selinux.av_decision_auditdeny_set
- __swig_getmethods__["auditdeny"] = _selinux.av_decision_auditdeny_get
- if _newclass:auditdeny = _swig_property(_selinux.av_decision_auditdeny_get, _selinux.av_decision_auditdeny_set)
- __swig_setmethods__["seqno"] = _selinux.av_decision_seqno_set
- __swig_getmethods__["seqno"] = _selinux.av_decision_seqno_get
- if _newclass:seqno = _swig_property(_selinux.av_decision_seqno_get, _selinux.av_decision_seqno_set)
- __swig_setmethods__["flags"] = _selinux.av_decision_flags_set
- __swig_getmethods__["flags"] = _selinux.av_decision_flags_get
- if _newclass:flags = _swig_property(_selinux.av_decision_flags_get, _selinux.av_decision_flags_set)
- def __init__(self):
- this = _selinux.new_av_decision()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_av_decision
- __del__ = lambda self : None;
-av_decision_swigregister = _selinux.av_decision_swigregister
-av_decision_swigregister(av_decision)
-
-SELINUX_AVD_FLAGS_PERMISSIVE = _selinux.SELINUX_AVD_FLAGS_PERMISSIVE
-class selinux_opt(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, selinux_opt, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, selinux_opt, name)
- __repr__ = _swig_repr
- __swig_setmethods__["type"] = _selinux.selinux_opt_type_set
- __swig_getmethods__["type"] = _selinux.selinux_opt_type_get
- if _newclass:type = _swig_property(_selinux.selinux_opt_type_get, _selinux.selinux_opt_type_set)
- __swig_setmethods__["value"] = _selinux.selinux_opt_value_set
- __swig_getmethods__["value"] = _selinux.selinux_opt_value_get
- if _newclass:value = _swig_property(_selinux.selinux_opt_value_get, _selinux.selinux_opt_value_set)
- def __init__(self):
- this = _selinux.new_selinux_opt()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_selinux_opt
- __del__ = lambda self : None;
-selinux_opt_swigregister = _selinux.selinux_opt_swigregister
-selinux_opt_swigregister(selinux_opt)
-
-class selinux_callback(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, selinux_callback, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, selinux_callback, name)
- __repr__ = _swig_repr
- __swig_setmethods__["func_log"] = _selinux.selinux_callback_func_log_set
- __swig_getmethods__["func_log"] = _selinux.selinux_callback_func_log_get
- if _newclass:func_log = _swig_property(_selinux.selinux_callback_func_log_get, _selinux.selinux_callback_func_log_set)
- __swig_setmethods__["func_audit"] = _selinux.selinux_callback_func_audit_set
- __swig_getmethods__["func_audit"] = _selinux.selinux_callback_func_audit_get
- if _newclass:func_audit = _swig_property(_selinux.selinux_callback_func_audit_get, _selinux.selinux_callback_func_audit_set)
- __swig_setmethods__["func_validate"] = _selinux.selinux_callback_func_validate_set
- __swig_getmethods__["func_validate"] = _selinux.selinux_callback_func_validate_get
- if _newclass:func_validate = _swig_property(_selinux.selinux_callback_func_validate_get, _selinux.selinux_callback_func_validate_set)
- __swig_setmethods__["func_setenforce"] = _selinux.selinux_callback_func_setenforce_set
- __swig_getmethods__["func_setenforce"] = _selinux.selinux_callback_func_setenforce_get
- if _newclass:func_setenforce = _swig_property(_selinux.selinux_callback_func_setenforce_get, _selinux.selinux_callback_func_setenforce_set)
- __swig_setmethods__["func_policyload"] = _selinux.selinux_callback_func_policyload_set
- __swig_getmethods__["func_policyload"] = _selinux.selinux_callback_func_policyload_get
- if _newclass:func_policyload = _swig_property(_selinux.selinux_callback_func_policyload_get, _selinux.selinux_callback_func_policyload_set)
- def __init__(self):
- this = _selinux.new_selinux_callback()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_selinux_callback
- __del__ = lambda self : None;
-selinux_callback_swigregister = _selinux.selinux_callback_swigregister
-selinux_callback_swigregister(selinux_callback)
-
-SELINUX_CB_LOG = _selinux.SELINUX_CB_LOG
-SELINUX_CB_AUDIT = _selinux.SELINUX_CB_AUDIT
-SELINUX_CB_VALIDATE = _selinux.SELINUX_CB_VALIDATE
-SELINUX_CB_SETENFORCE = _selinux.SELINUX_CB_SETENFORCE
-SELINUX_CB_POLICYLOAD = _selinux.SELINUX_CB_POLICYLOAD
-
-def selinux_get_callback(*args):
- return _selinux.selinux_get_callback(*args)
-selinux_get_callback = _selinux.selinux_get_callback
-
-def selinux_set_callback(*args):
- return _selinux.selinux_set_callback(*args)
-selinux_set_callback = _selinux.selinux_set_callback
-SELINUX_ERROR = _selinux.SELINUX_ERROR
-SELINUX_WARNING = _selinux.SELINUX_WARNING
-SELINUX_INFO = _selinux.SELINUX_INFO
-SELINUX_AVC = _selinux.SELINUX_AVC
-SELINUX_TRANS_DIR = _selinux.SELINUX_TRANS_DIR
-
-def security_compute_av(*args):
- return _selinux.security_compute_av(*args)
-security_compute_av = _selinux.security_compute_av
-
-def security_compute_av_raw(*args):
- return _selinux.security_compute_av_raw(*args)
-security_compute_av_raw = _selinux.security_compute_av_raw
-
-def security_compute_av_flags(*args):
- return _selinux.security_compute_av_flags(*args)
-security_compute_av_flags = _selinux.security_compute_av_flags
-
-def security_compute_av_flags_raw(*args):
- return _selinux.security_compute_av_flags_raw(*args)
-security_compute_av_flags_raw = _selinux.security_compute_av_flags_raw
-
-def security_compute_create(*args):
- return _selinux.security_compute_create(*args)
-security_compute_create = _selinux.security_compute_create
-
-def security_compute_create_raw(*args):
- return _selinux.security_compute_create_raw(*args)
-security_compute_create_raw = _selinux.security_compute_create_raw
-
-def security_compute_create_name(*args):
- return _selinux.security_compute_create_name(*args)
-security_compute_create_name = _selinux.security_compute_create_name
-
-def security_compute_create_name_raw(*args):
- return _selinux.security_compute_create_name_raw(*args)
-security_compute_create_name_raw = _selinux.security_compute_create_name_raw
-
-def security_compute_relabel(*args):
- return _selinux.security_compute_relabel(*args)
-security_compute_relabel = _selinux.security_compute_relabel
-
-def security_compute_relabel_raw(*args):
- return _selinux.security_compute_relabel_raw(*args)
-security_compute_relabel_raw = _selinux.security_compute_relabel_raw
-
-def security_compute_member(*args):
- return _selinux.security_compute_member(*args)
-security_compute_member = _selinux.security_compute_member
-
-def security_compute_member_raw(*args):
- return _selinux.security_compute_member_raw(*args)
-security_compute_member_raw = _selinux.security_compute_member_raw
-
-def security_compute_user(*args):
- return _selinux.security_compute_user(*args)
-security_compute_user = _selinux.security_compute_user
-
-def security_compute_user_raw(*args):
- return _selinux.security_compute_user_raw(*args)
-security_compute_user_raw = _selinux.security_compute_user_raw
-
-def security_load_policy(*args):
- return _selinux.security_load_policy(*args)
-security_load_policy = _selinux.security_load_policy
-
-def security_get_initial_context(*args):
- return _selinux.security_get_initial_context(*args)
-security_get_initial_context = _selinux.security_get_initial_context
-
-def security_get_initial_context_raw(*args):
- return _selinux.security_get_initial_context_raw(*args)
-security_get_initial_context_raw = _selinux.security_get_initial_context_raw
-
-def selinux_mkload_policy(*args):
- return _selinux.selinux_mkload_policy(*args)
-selinux_mkload_policy = _selinux.selinux_mkload_policy
-
-def selinux_init_load_policy():
- return _selinux.selinux_init_load_policy()
-selinux_init_load_policy = _selinux.selinux_init_load_policy
-class SELboolean(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, SELboolean, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, SELboolean, name)
- __repr__ = _swig_repr
- __swig_setmethods__["name"] = _selinux.SELboolean_name_set
- __swig_getmethods__["name"] = _selinux.SELboolean_name_get
- if _newclass:name = _swig_property(_selinux.SELboolean_name_get, _selinux.SELboolean_name_set)
- __swig_setmethods__["value"] = _selinux.SELboolean_value_set
- __swig_getmethods__["value"] = _selinux.SELboolean_value_get
- if _newclass:value = _swig_property(_selinux.SELboolean_value_get, _selinux.SELboolean_value_set)
- def __init__(self):
- this = _selinux.new_SELboolean()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_SELboolean
- __del__ = lambda self : None;
-SELboolean_swigregister = _selinux.SELboolean_swigregister
-SELboolean_swigregister(SELboolean)
-
-
-def security_set_boolean_list(*args):
- return _selinux.security_set_boolean_list(*args)
-security_set_boolean_list = _selinux.security_set_boolean_list
-
-def security_load_booleans(*args):
- return _selinux.security_load_booleans(*args)
-security_load_booleans = _selinux.security_load_booleans
-
-def security_check_context(*args):
- return _selinux.security_check_context(*args)
-security_check_context = _selinux.security_check_context
-
-def security_check_context_raw(*args):
- return _selinux.security_check_context_raw(*args)
-security_check_context_raw = _selinux.security_check_context_raw
-
-def security_canonicalize_context(*args):
- return _selinux.security_canonicalize_context(*args)
-security_canonicalize_context = _selinux.security_canonicalize_context
-
-def security_canonicalize_context_raw(*args):
- return _selinux.security_canonicalize_context_raw(*args)
-security_canonicalize_context_raw = _selinux.security_canonicalize_context_raw
-
-def security_getenforce():
- return _selinux.security_getenforce()
-security_getenforce = _selinux.security_getenforce
-
-def security_setenforce(*args):
- return _selinux.security_setenforce(*args)
-security_setenforce = _selinux.security_setenforce
-
-def security_deny_unknown():
- return _selinux.security_deny_unknown()
-security_deny_unknown = _selinux.security_deny_unknown
-
-def security_disable():
- return _selinux.security_disable()
-security_disable = _selinux.security_disable
-
-def security_policyvers():
- return _selinux.security_policyvers()
-security_policyvers = _selinux.security_policyvers
-
-def security_get_boolean_names():
- return _selinux.security_get_boolean_names()
-security_get_boolean_names = _selinux.security_get_boolean_names
-
-def security_get_boolean_pending(*args):
- return _selinux.security_get_boolean_pending(*args)
-security_get_boolean_pending = _selinux.security_get_boolean_pending
-
-def security_get_boolean_active(*args):
- return _selinux.security_get_boolean_active(*args)
-security_get_boolean_active = _selinux.security_get_boolean_active
-
-def security_set_boolean(*args):
- return _selinux.security_set_boolean(*args)
-security_set_boolean = _selinux.security_set_boolean
-
-def security_commit_booleans():
- return _selinux.security_commit_booleans()
-security_commit_booleans = _selinux.security_commit_booleans
-class security_class_mapping(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, security_class_mapping, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, security_class_mapping, name)
- __repr__ = _swig_repr
- __swig_setmethods__["name"] = _selinux.security_class_mapping_name_set
- __swig_getmethods__["name"] = _selinux.security_class_mapping_name_get
- if _newclass:name = _swig_property(_selinux.security_class_mapping_name_get, _selinux.security_class_mapping_name_set)
- __swig_setmethods__["perms"] = _selinux.security_class_mapping_perms_set
- __swig_getmethods__["perms"] = _selinux.security_class_mapping_perms_get
- if _newclass:perms = _swig_property(_selinux.security_class_mapping_perms_get, _selinux.security_class_mapping_perms_set)
- def __init__(self):
- this = _selinux.new_security_class_mapping()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _selinux.delete_security_class_mapping
- __del__ = lambda self : None;
-security_class_mapping_swigregister = _selinux.security_class_mapping_swigregister
-security_class_mapping_swigregister(security_class_mapping)
-
-
-def selinux_set_mapping(*args):
- return _selinux.selinux_set_mapping(*args)
-selinux_set_mapping = _selinux.selinux_set_mapping
-
-def mode_to_security_class(*args):
- return _selinux.mode_to_security_class(*args)
-mode_to_security_class = _selinux.mode_to_security_class
-
-def string_to_security_class(*args):
- return _selinux.string_to_security_class(*args)
-string_to_security_class = _selinux.string_to_security_class
-
-def security_class_to_string(*args):
- return _selinux.security_class_to_string(*args)
-security_class_to_string = _selinux.security_class_to_string
-
-def security_av_perm_to_string(*args):
- return _selinux.security_av_perm_to_string(*args)
-security_av_perm_to_string = _selinux.security_av_perm_to_string
-
-def string_to_av_perm(*args):
- return _selinux.string_to_av_perm(*args)
-string_to_av_perm = _selinux.string_to_av_perm
-
-def security_av_string(*args):
- return _selinux.security_av_string(*args)
-security_av_string = _selinux.security_av_string
-
-def print_access_vector(*args):
- return _selinux.print_access_vector(*args)
-print_access_vector = _selinux.print_access_vector
-MATCHPATHCON_BASEONLY = _selinux.MATCHPATHCON_BASEONLY
-MATCHPATHCON_NOTRANS = _selinux.MATCHPATHCON_NOTRANS
-MATCHPATHCON_VALIDATE = _selinux.MATCHPATHCON_VALIDATE
-
-def set_matchpathcon_flags(*args):
- return _selinux.set_matchpathcon_flags(*args)
-set_matchpathcon_flags = _selinux.set_matchpathcon_flags
-
-def matchpathcon_init(*args):
- return _selinux.matchpathcon_init(*args)
-matchpathcon_init = _selinux.matchpathcon_init
-
-def matchpathcon_init_prefix(*args):
- return _selinux.matchpathcon_init_prefix(*args)
-matchpathcon_init_prefix = _selinux.matchpathcon_init_prefix
-
-def matchpathcon_fini():
- return _selinux.matchpathcon_fini()
-matchpathcon_fini = _selinux.matchpathcon_fini
-
-def realpath_not_final(*args):
- return _selinux.realpath_not_final(*args)
-realpath_not_final = _selinux.realpath_not_final
-
-def matchpathcon(*args):
- return _selinux.matchpathcon(*args)
-matchpathcon = _selinux.matchpathcon
-
-def matchpathcon_index(*args):
- return _selinux.matchpathcon_index(*args)
-matchpathcon_index = _selinux.matchpathcon_index
-
-def matchpathcon_filespec_add(*args):
- return _selinux.matchpathcon_filespec_add(*args)
-matchpathcon_filespec_add = _selinux.matchpathcon_filespec_add
-
-def matchpathcon_filespec_destroy():
- return _selinux.matchpathcon_filespec_destroy()
-matchpathcon_filespec_destroy = _selinux.matchpathcon_filespec_destroy
-
-def matchpathcon_filespec_eval():
- return _selinux.matchpathcon_filespec_eval()
-matchpathcon_filespec_eval = _selinux.matchpathcon_filespec_eval
-
-def matchpathcon_checkmatches(*args):
- return _selinux.matchpathcon_checkmatches(*args)
-matchpathcon_checkmatches = _selinux.matchpathcon_checkmatches
-
-def matchmediacon(*args):
- return _selinux.matchmediacon(*args)
-matchmediacon = _selinux.matchmediacon
-
-def selinux_getenforcemode():
- return _selinux.selinux_getenforcemode()
-selinux_getenforcemode = _selinux.selinux_getenforcemode
-
-def selinux_boolean_sub(*args):
- return _selinux.selinux_boolean_sub(*args)
-selinux_boolean_sub = _selinux.selinux_boolean_sub
-
-def selinux_getpolicytype():
- return _selinux.selinux_getpolicytype()
-selinux_getpolicytype = _selinux.selinux_getpolicytype
-
-def selinux_policy_root():
- return _selinux.selinux_policy_root()
-selinux_policy_root = _selinux.selinux_policy_root
-
-def selinux_set_policy_root(*args):
- return _selinux.selinux_set_policy_root(*args)
-selinux_set_policy_root = _selinux.selinux_set_policy_root
-
-def selinux_current_policy_path():
- return _selinux.selinux_current_policy_path()
-selinux_current_policy_path = _selinux.selinux_current_policy_path
-
-def selinux_binary_policy_path():
- return _selinux.selinux_binary_policy_path()
-selinux_binary_policy_path = _selinux.selinux_binary_policy_path
-
-def selinux_failsafe_context_path():
- return _selinux.selinux_failsafe_context_path()
-selinux_failsafe_context_path = _selinux.selinux_failsafe_context_path
-
-def selinux_removable_context_path():
- return _selinux.selinux_removable_context_path()
-selinux_removable_context_path = _selinux.selinux_removable_context_path
-
-def selinux_default_context_path():
- return _selinux.selinux_default_context_path()
-selinux_default_context_path = _selinux.selinux_default_context_path
-
-def selinux_user_contexts_path():
- return _selinux.selinux_user_contexts_path()
-selinux_user_contexts_path = _selinux.selinux_user_contexts_path
-
-def selinux_file_context_path():
- return _selinux.selinux_file_context_path()
-selinux_file_context_path = _selinux.selinux_file_context_path
-
-def selinux_file_context_homedir_path():
- return _selinux.selinux_file_context_homedir_path()
-selinux_file_context_homedir_path = _selinux.selinux_file_context_homedir_path
-
-def selinux_file_context_local_path():
- return _selinux.selinux_file_context_local_path()
-selinux_file_context_local_path = _selinux.selinux_file_context_local_path
-
-def selinux_file_context_subs_path():
- return _selinux.selinux_file_context_subs_path()
-selinux_file_context_subs_path = _selinux.selinux_file_context_subs_path
-
-def selinux_file_context_subs_dist_path():
- return _selinux.selinux_file_context_subs_dist_path()
-selinux_file_context_subs_dist_path = _selinux.selinux_file_context_subs_dist_path
-
-def selinux_homedir_context_path():
- return _selinux.selinux_homedir_context_path()
-selinux_homedir_context_path = _selinux.selinux_homedir_context_path
-
-def selinux_media_context_path():
- return _selinux.selinux_media_context_path()
-selinux_media_context_path = _selinux.selinux_media_context_path
-
-def selinux_virtual_domain_context_path():
- return _selinux.selinux_virtual_domain_context_path()
-selinux_virtual_domain_context_path = _selinux.selinux_virtual_domain_context_path
-
-def selinux_virtual_image_context_path():
- return _selinux.selinux_virtual_image_context_path()
-selinux_virtual_image_context_path = _selinux.selinux_virtual_image_context_path
-
-def selinux_lxc_contexts_path():
- return _selinux.selinux_lxc_contexts_path()
-selinux_lxc_contexts_path = _selinux.selinux_lxc_contexts_path
-
-def selinux_x_context_path():
- return _selinux.selinux_x_context_path()
-selinux_x_context_path = _selinux.selinux_x_context_path
-
-def selinux_sepgsql_context_path():
- return _selinux.selinux_sepgsql_context_path()
-selinux_sepgsql_context_path = _selinux.selinux_sepgsql_context_path
-
-def selinux_systemd_contexts_path():
- return _selinux.selinux_systemd_contexts_path()
-selinux_systemd_contexts_path = _selinux.selinux_systemd_contexts_path
-
-def selinux_contexts_path():
- return _selinux.selinux_contexts_path()
-selinux_contexts_path = _selinux.selinux_contexts_path
-
-def selinux_securetty_types_path():
- return _selinux.selinux_securetty_types_path()
-selinux_securetty_types_path = _selinux.selinux_securetty_types_path
-
-def selinux_booleans_subs_path():
- return _selinux.selinux_booleans_subs_path()
-selinux_booleans_subs_path = _selinux.selinux_booleans_subs_path
-
-def selinux_booleans_path():
- return _selinux.selinux_booleans_path()
-selinux_booleans_path = _selinux.selinux_booleans_path
-
-def selinux_customizable_types_path():
- return _selinux.selinux_customizable_types_path()
-selinux_customizable_types_path = _selinux.selinux_customizable_types_path
-
-def selinux_users_path():
- return _selinux.selinux_users_path()
-selinux_users_path = _selinux.selinux_users_path
-
-def selinux_usersconf_path():
- return _selinux.selinux_usersconf_path()
-selinux_usersconf_path = _selinux.selinux_usersconf_path
-
-def selinux_translations_path():
- return _selinux.selinux_translations_path()
-selinux_translations_path = _selinux.selinux_translations_path
-
-def selinux_colors_path():
- return _selinux.selinux_colors_path()
-selinux_colors_path = _selinux.selinux_colors_path
-
-def selinux_netfilter_context_path():
- return _selinux.selinux_netfilter_context_path()
-selinux_netfilter_context_path = _selinux.selinux_netfilter_context_path
-
-def selinux_path():
- return _selinux.selinux_path()
-selinux_path = _selinux.selinux_path
-
-def selinux_check_access(*args):
- return _selinux.selinux_check_access(*args)
-selinux_check_access = _selinux.selinux_check_access
-
-def selinux_check_passwd_access(*args):
- return _selinux.selinux_check_passwd_access(*args)
-selinux_check_passwd_access = _selinux.selinux_check_passwd_access
-
-def checkPasswdAccess(*args):
- return _selinux.checkPasswdAccess(*args)
-checkPasswdAccess = _selinux.checkPasswdAccess
-
-def selinux_check_securetty_context(*args):
- return _selinux.selinux_check_securetty_context(*args)
-selinux_check_securetty_context = _selinux.selinux_check_securetty_context
-
-def set_selinuxmnt(*args):
- return _selinux.set_selinuxmnt(*args)
-set_selinuxmnt = _selinux.set_selinuxmnt
-
-def selinuxfs_exists():
- return _selinux.selinuxfs_exists()
-selinuxfs_exists = _selinux.selinuxfs_exists
-
-def fini_selinuxmnt():
- return _selinux.fini_selinuxmnt()
-fini_selinuxmnt = _selinux.fini_selinuxmnt
-
-def setexecfilecon(*args):
- return _selinux.setexecfilecon(*args)
-setexecfilecon = _selinux.setexecfilecon
-
-def rpm_execcon(*args):
- return _selinux.rpm_execcon(*args)
-rpm_execcon = _selinux.rpm_execcon
-
-def is_context_customizable(*args):
- return _selinux.is_context_customizable(*args)
-is_context_customizable = _selinux.is_context_customizable
-
-def selinux_trans_to_raw_context(*args):
- return _selinux.selinux_trans_to_raw_context(*args)
-selinux_trans_to_raw_context = _selinux.selinux_trans_to_raw_context
-
-def selinux_raw_to_trans_context(*args):
- return _selinux.selinux_raw_to_trans_context(*args)
-selinux_raw_to_trans_context = _selinux.selinux_raw_to_trans_context
-
-def selinux_raw_context_to_color(*args):
- return _selinux.selinux_raw_context_to_color(*args)
-selinux_raw_context_to_color = _selinux.selinux_raw_context_to_color
-
-def getseuserbyname(*args):
- return _selinux.getseuserbyname(*args)
-getseuserbyname = _selinux.getseuserbyname
-
-def getseuser(*args):
- return _selinux.getseuser(*args)
-getseuser = _selinux.getseuser
-
-def selinux_file_context_cmp(*args):
- return _selinux.selinux_file_context_cmp(*args)
-selinux_file_context_cmp = _selinux.selinux_file_context_cmp
-
-def selinux_file_context_verify(*args):
- return _selinux.selinux_file_context_verify(*args)
-selinux_file_context_verify = _selinux.selinux_file_context_verify
-
-def selinux_lsetfilecon_default(*args):
- return _selinux.selinux_lsetfilecon_default(*args)
-selinux_lsetfilecon_default = _selinux.selinux_lsetfilecon_default
-
-def selinux_reset_config():
- return _selinux.selinux_reset_config()
-selinux_reset_config = _selinux.selinux_reset_config
-# This file is compatible with both classic and new-style classes.
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/selinux/_selinux.so b/lib/python2.7/site-packages/setoolsgui/selinux/_selinux.so
deleted file mode 100755
index f4a045f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/selinux/_selinux.so
+++ /dev/null
Binary files differ
diff --git a/lib/python2.7/site-packages/setoolsgui/selinux/audit2why.so b/lib/python2.7/site-packages/setoolsgui/selinux/audit2why.so
deleted file mode 100755
index e7daaab..0000000
--- a/lib/python2.7/site-packages/setoolsgui/selinux/audit2why.so
+++ /dev/null
Binary files differ
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/__init__.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/__init__.py
+++ /dev/null
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/access.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/access.py
deleted file mode 100644
index cf13210..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/access.py
+++ /dev/null
@@ -1,331 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-Classes representing basic access.
-
-SELinux - at the most basic level - represents access as
-the 4-tuple subject (type or context), target (type or context),
-object class, permission. The policy language elaborates this basic
-access to faciliate more concise rules (e.g., allow rules can have multiple
-source or target types - see refpolicy for more information).
-
-This module has objects for representing the most basic access (AccessVector)
-and sets of that access (AccessVectorSet). These objects are used in Madison
-in a variety of ways, but they are the fundamental representation of access.
-"""
-
-import refpolicy
-from selinux import audit2why
-
-def is_idparam(id):
- """Determine if an id is a paramater in the form $N, where N is
- an integer.
-
- Returns:
- True if the id is a paramater
- False if the id is not a paramater
- """
- if len(id) > 1 and id[0] == '$':
- try:
- int(id[1:])
- except ValueError:
- return False
- return True
- else:
- return False
-
-class AccessVector:
- """
- An access vector is the basic unit of access in SELinux.
-
- Access vectors are the most basic representation of access within
- SELinux. It represents the access a source type has to a target
- type in terms of an object class and a set of permissions.
-
- Access vectors are distinct from AVRules in that they can only
- store a single source type, target type, and object class. The
- simplicity of AccessVectors makes them useful for storing access
- in a form that is easy to search and compare.
-
- The source, target, and object are stored as string. No checking
- done to verify that the strings are valid SELinux identifiers.
- Identifiers in the form $N (where N is an integer) are reserved as
- interface parameters and are treated as wild cards in many
- circumstances.
-
- Properties:
- .src_type - The source type allowed access. [String or None]
- .tgt_type - The target type to which access is allowed. [String or None]
- .obj_class - The object class to which access is allowed. [String or None]
- .perms - The permissions allowed to the object class. [IdSet]
- .audit_msgs - The audit messages that generated this access vector [List of strings]
- """
- def __init__(self, init_list=None):
- if init_list:
- self.from_list(init_list)
- else:
- self.src_type = None
- self.tgt_type = None
- self.obj_class = None
- self.perms = refpolicy.IdSet()
- self.audit_msgs = []
- self.type = audit2why.TERULE
- self.data = []
-
- # The direction of the information flow represented by this
- # access vector - used for matching
- self.info_flow_dir = None
-
- def from_list(self, list):
- """Initialize an access vector from a list.
-
- Initialize an access vector from a list treating the list as
- positional arguments - i.e., 0 = src_type, 1 = tgt_type, etc.
- All of the list elements 3 and greater are treated as perms.
- For example, the list ['foo_t', 'bar_t', 'file', 'read', 'write']
- would create an access vector list with the source type 'foo_t',
- target type 'bar_t', object class 'file', and permissions 'read'
- and 'write'.
-
- This format is useful for very simple storage to strings or disc
- (see to_list) and for initializing access vectors.
- """
- if len(list) < 4:
- raise ValueError("List must contain at least four elements %s" % str(list))
- self.src_type = list[0]
- self.tgt_type = list[1]
- self.obj_class = list[2]
- self.perms = refpolicy.IdSet(list[3:])
-
- def to_list(self):
- """
- Convert an access vector to a list.
-
- Convert an access vector to a list treating the list as positional
- values. See from_list for more information on how an access vector
- is represented in a list.
- """
- l = [self.src_type, self.tgt_type, self.obj_class]
- l.extend(self.perms)
- return l
-
- def __str__(self):
- return self.to_string()
-
- def to_string(self):
- return "allow %s %s:%s %s;" % (self.src_type, self.tgt_type,
- self.obj_class, self.perms.to_space_str())
-
- def __cmp__(self, other):
- if self.src_type != other.src_type:
- return cmp(self.src_type, other.src_type)
- if self.tgt_type != other.tgt_type:
- return cmp(self.tgt_type, other.tgt_type)
- if self.obj_class != self.obj_class:
- return cmp(self.obj_class, other.obj_class)
- if len(self.perms) != len(other.perms):
- return cmp(len(self.perms), len(other.perms))
- x = list(self.perms)
- x.sort()
- y = list(other.perms)
- y.sort()
- for pa, pb in zip(x, y):
- if pa != pb:
- return cmp(pa, pb)
- return 0
-
-def avrule_to_access_vectors(avrule):
- """Convert an avrule into a list of access vectors.
-
- AccessVectors and AVRules are similary, but differ in that
- an AVRule can more than one source type, target type, and
- object class. This function expands a single avrule into a
- list of one or more AccessVectors representing the access
- defined in the AVRule.
-
-
- """
- if isinstance(avrule, AccessVector):
- return [avrule]
- a = []
- for src_type in avrule.src_types:
- for tgt_type in avrule.tgt_types:
- for obj_class in avrule.obj_classes:
- access = AccessVector()
- access.src_type = src_type
- access.tgt_type = tgt_type
- access.obj_class = obj_class
- access.perms = avrule.perms.copy()
- a.append(access)
- return a
-
-class AccessVectorSet:
- """A non-overlapping set of access vectors.
-
- An AccessVectorSet is designed to store one or more access vectors
- that are non-overlapping. Access can be added to the set
- incrementally and access vectors will be added or merged as
- necessary. For example, adding the following access vectors using
- add_av:
- allow $1 etc_t : read;
- allow $1 etc_t : write;
- allow $1 var_log_t : read;
- Would result in an access vector set with the access vectors:
- allow $1 etc_t : { read write};
- allow $1 var_log_t : read;
- """
- def __init__(self):
- """Initialize an access vector set.
- """
- self.src = {}
- # The information flow direction of this access vector
- # set - see objectmodel.py for more information. This
- # stored here to speed up searching - see matching.py.
- self.info_dir = None
-
- def __iter__(self):
- """Iterate over all of the unique access vectors in the set."""
- for tgts in self.src.values():
- for objs in tgts.values():
- for av in objs.values():
- yield av
-
- def __len__(self):
- """Return the number of unique access vectors in the set.
-
- Because of the inernal representation of the access vector set,
- __len__ is not a constant time operation. Worst case is O(N)
- where N is the number of unique access vectors, but the common
- case is probably better.
- """
- l = 0
- for tgts in self.src.values():
- for objs in tgts.values():
- l += len(objs)
- return l
-
- def to_list(self):
- """Return the unique access vectors in the set as a list.
-
- The format of the returned list is a set of nested lists,
- each access vector represented by a list. This format is
- designed to be simply serializable to a file.
-
- For example, consider an access vector set with the following
- access vectors:
- allow $1 user_t : file read;
- allow $1 etc_t : file { read write};
- to_list would return the following:
- [[$1, user_t, file, read]
- [$1, etc_t, file, read, write]]
-
- See AccessVector.to_list for more information.
- """
- l = []
- for av in self:
- l.append(av.to_list())
-
- return l
-
- def from_list(self, l):
- """Add access vectors stored in a list.
-
- See to list for more information on the list format that this
- method accepts.
-
- This will add all of the access from the list. Any existing
- access vectors in the set will be retained.
- """
- for av in l:
- self.add_av(AccessVector(av))
-
- def add(self, src_type, tgt_type, obj_class, perms, audit_msg=None, avc_type=audit2why.TERULE, data=[]):
- """Add an access vector to the set.
- """
- tgt = self.src.setdefault(src_type, { })
- cls = tgt.setdefault(tgt_type, { })
-
- if cls.has_key((obj_class, avc_type)):
- access = cls[obj_class, avc_type]
- else:
- access = AccessVector()
- access.src_type = src_type
- access.tgt_type = tgt_type
- access.obj_class = obj_class
- access.data = data
- access.type = avc_type
- cls[obj_class, avc_type] = access
-
- access.perms.update(perms)
- if audit_msg:
- access.audit_msgs.append(audit_msg)
-
- def add_av(self, av, audit_msg=None):
- """Add an access vector to the set."""
- self.add(av.src_type, av.tgt_type, av.obj_class, av.perms)
-
-
-def avs_extract_types(avs):
- types = refpolicy.IdSet()
- for av in avs:
- types.add(av.src_type)
- types.add(av.tgt_type)
-
- return types
-
-def avs_extract_obj_perms(avs):
- perms = { }
- for av in avs:
- if perms.has_key(av.obj_class):
- s = perms[av.obj_class]
- else:
- s = refpolicy.IdSet()
- perms[av.obj_class] = s
- s.update(av.perms)
- return perms
-
-class RoleTypeSet:
- """A non-overlapping set of role type statements.
-
- This clas allows the incremental addition of role type statements and
- maintains a non-overlapping list of statements.
- """
- def __init__(self):
- """Initialize an access vector set."""
- self.role_types = {}
-
- def __iter__(self):
- """Iterate over all of the unique role allows statements in the set."""
- for role_type in self.role_types.values():
- yield role_type
-
- def __len__(self):
- """Return the unique number of role allow statements."""
- return len(self.role_types.keys())
-
- def add(self, role, type):
- if self.role_types.has_key(role):
- role_type = self.role_types[role]
- else:
- role_type = refpolicy.RoleType()
- role_type.role = role
- self.role_types[role] = role_type
-
- role_type.types.add(type)
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/audit.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/audit.py
deleted file mode 100644
index 56919be..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/audit.py
+++ /dev/null
@@ -1,549 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-import refpolicy
-import access
-import re
-import sys
-
-# Convenience functions
-
-def get_audit_boot_msgs():
- """Obtain all of the avc and policy load messages from the audit
- log. This function uses ausearch and requires that the current
- process have sufficient rights to run ausearch.
-
- Returns:
- string contain all of the audit messages returned by ausearch.
- """
- import subprocess
- import time
- fd=open("/proc/uptime", "r")
- off=float(fd.read().split()[0])
- fd.close
- s = time.localtime(time.time() - off)
- bootdate = time.strftime("%x", s)
- boottime = time.strftime("%X", s)
- output = subprocess.Popen(["/sbin/ausearch", "-m", "AVC,USER_AVC,MAC_POLICY_LOAD,DAEMON_START,SELINUX_ERR", "-ts", bootdate, boottime],
- stdout=subprocess.PIPE).communicate()[0]
- return output
-
-def get_audit_msgs():
- """Obtain all of the avc and policy load messages from the audit
- log. This function uses ausearch and requires that the current
- process have sufficient rights to run ausearch.
-
- Returns:
- string contain all of the audit messages returned by ausearch.
- """
- import subprocess
- output = subprocess.Popen(["/sbin/ausearch", "-m", "AVC,USER_AVC,MAC_POLICY_LOAD,DAEMON_START,SELINUX_ERR"],
- stdout=subprocess.PIPE).communicate()[0]
- return output
-
-def get_dmesg_msgs():
- """Obtain all of the avc and policy load messages from /bin/dmesg.
-
- Returns:
- string contain all of the audit messages returned by dmesg.
- """
- import subprocess
- output = subprocess.Popen(["/bin/dmesg"],
- stdout=subprocess.PIPE).communicate()[0]
- return output
-
-# Classes representing audit messages
-
-class AuditMessage:
- """Base class for all objects representing audit messages.
-
- AuditMessage is a base class for all audit messages and only
- provides storage for the raw message (as a string) and a
- parsing function that does nothing.
- """
- def __init__(self, message):
- self.message = message
- self.header = ""
-
- def from_split_string(self, recs):
- """Parse a string that has been split into records by space into
- an audit message.
-
- This method should be overridden by subclasses. Error reporting
- should be done by raise ValueError exceptions.
- """
- for msg in recs:
- fields = msg.split("=")
- if len(fields) != 2:
- if msg[:6] == "audit(":
- self.header = msg
- return
- else:
- continue
-
- if fields[0] == "msg":
- self.header = fields[1]
- return
-
-
-class InvalidMessage(AuditMessage):
- """Class representing invalid audit messages. This is used to differentiate
- between audit messages that aren't recognized (that should return None from
- the audit message parser) and a message that is recognized but is malformed
- in some way.
- """
- def __init__(self, message):
- AuditMessage.__init__(self, message)
-
-class PathMessage(AuditMessage):
- """Class representing a path message"""
- def __init__(self, message):
- AuditMessage.__init__(self, message)
- self.path = ""
-
- def from_split_string(self, recs):
- AuditMessage.from_split_string(self, recs)
-
- for msg in recs:
- fields = msg.split("=")
- if len(fields) != 2:
- continue
- if fields[0] == "path":
- self.path = fields[1][1:-1]
- return
-import selinux.audit2why as audit2why
-
-avcdict = {}
-
-class AVCMessage(AuditMessage):
- """AVC message representing an access denial or granted message.
-
- This is a very basic class and does not represent all possible fields
- in an avc message. Currently the fields are:
- scontext - context for the source (process) that generated the message
- tcontext - context for the target
- tclass - object class for the target (only one)
- comm - the process name
- exe - the on-disc binary
- path - the path of the target
- access - list of accesses that were allowed or denied
- denial - boolean indicating whether this was a denial (True) or granted
- (False) message.
-
- An example audit message generated from the audit daemon looks like (line breaks
- added):
- 'type=AVC msg=audit(1155568085.407:10877): avc: denied { search } for
- pid=677 comm="python" name="modules" dev=dm-0 ino=13716388
- scontext=user_u:system_r:setroubleshootd_t:s0
- tcontext=system_u:object_r:modules_object_t:s0 tclass=dir'
-
- An example audit message stored in syslog (not processed by the audit daemon - line
- breaks added):
- 'Sep 12 08:26:43 dhcp83-5 kernel: audit(1158064002.046:4): avc: denied { read }
- for pid=2 496 comm="bluez-pin" name=".gdm1K3IFT" dev=dm-0 ino=3601333
- scontext=user_u:system_r:bluetooth_helper_t:s0-s0:c0
- tcontext=system_u:object_r:xdm_tmp_t:s0 tclass=file
- """
- def __init__(self, message):
- AuditMessage.__init__(self, message)
- self.scontext = refpolicy.SecurityContext()
- self.tcontext = refpolicy.SecurityContext()
- self.tclass = ""
- self.comm = ""
- self.exe = ""
- self.path = ""
- self.name = ""
- self.accesses = []
- self.denial = True
- self.type = audit2why.TERULE
-
- def __parse_access(self, recs, start):
- # This is kind of sucky - the access that is in a space separated
- # list like '{ read write }'. This doesn't fit particularly well with splitting
- # the string on spaces. This function takes the list of recs and a starting
- # position one beyond the open brace. It then adds the accesses until it finds
- # the close brace or the end of the list (which is an error if reached without
- # seeing a close brace).
- found_close = False
- i = start
- if i == (len(recs) - 1):
- raise ValueError("AVC message in invalid format [%s]\n" % self.message)
- while i < len(recs):
- if recs[i] == "}":
- found_close = True
- break
- self.accesses.append(recs[i])
- i = i + 1
- if not found_close:
- raise ValueError("AVC message in invalid format [%s]\n" % self.message)
- return i + 1
-
-
- def from_split_string(self, recs):
- AuditMessage.from_split_string(self, recs)
- # FUTURE - fully parse avc messages and store all possible fields
- # Required fields
- found_src = False
- found_tgt = False
- found_class = False
- found_access = False
-
- for i in range(len(recs)):
- if recs[i] == "{":
- i = self.__parse_access(recs, i + 1)
- found_access = True
- continue
- elif recs[i] == "granted":
- self.denial = False
-
- fields = recs[i].split("=")
- if len(fields) != 2:
- continue
- if fields[0] == "scontext":
- self.scontext = refpolicy.SecurityContext(fields[1])
- found_src = True
- elif fields[0] == "tcontext":
- self.tcontext = refpolicy.SecurityContext(fields[1])
- found_tgt = True
- elif fields[0] == "tclass":
- self.tclass = fields[1]
- found_class = True
- elif fields[0] == "comm":
- self.comm = fields[1][1:-1]
- elif fields[0] == "exe":
- self.exe = fields[1][1:-1]
- elif fields[0] == "name":
- self.name = fields[1][1:-1]
-
- if not found_src or not found_tgt or not found_class or not found_access:
- raise ValueError("AVC message in invalid format [%s]\n" % self.message)
- self.analyze()
-
- def analyze(self):
- tcontext = self.tcontext.to_string()
- scontext = self.scontext.to_string()
- access_tuple = tuple( self.accesses)
- self.data = []
-
- if (scontext, tcontext, self.tclass, access_tuple) in avcdict.keys():
- self.type, self.data = avcdict[(scontext, tcontext, self.tclass, access_tuple)]
- else:
- self.type, self.data = audit2why.analyze(scontext, tcontext, self.tclass, self.accesses);
- if self.type == audit2why.NOPOLICY:
- self.type = audit2why.TERULE
- if self.type == audit2why.BADTCON:
- raise ValueError("Invalid Target Context %s\n" % tcontext)
- if self.type == audit2why.BADSCON:
- raise ValueError("Invalid Source Context %s\n" % scontext)
- if self.type == audit2why.BADSCON:
- raise ValueError("Invalid Type Class %s\n" % self.tclass)
- if self.type == audit2why.BADPERM:
- raise ValueError("Invalid permission %s\n" % " ".join(self.accesses))
- if self.type == audit2why.BADCOMPUTE:
- raise ValueError("Error during access vector computation")
-
- if self.type == audit2why.CONSTRAINT:
- self.data = [ self.data ]
- if self.scontext.user != self.tcontext.user:
- self.data.append(("user (%s)" % self.scontext.user, 'user (%s)' % self.tcontext.user))
- if self.scontext.role != self.tcontext.role and self.tcontext.role != "object_r":
- self.data.append(("role (%s)" % self.scontext.role, 'role (%s)' % self.tcontext.role))
- if self.scontext.level != self.tcontext.level:
- self.data.append(("level (%s)" % self.scontext.level, 'level (%s)' % self.tcontext.level))
-
- avcdict[(scontext, tcontext, self.tclass, access_tuple)] = (self.type, self.data)
-
-class PolicyLoadMessage(AuditMessage):
- """Audit message indicating that the policy was reloaded."""
- def __init__(self, message):
- AuditMessage.__init__(self, message)
-
-class DaemonStartMessage(AuditMessage):
- """Audit message indicating that a daemon was started."""
- def __init__(self, message):
- AuditMessage.__init__(self, message)
- self.auditd = False
-
- def from_split_string(self, recs):
- AuditMessage.from_split_string(self, recs)
- if "auditd" in recs:
- self.auditd = True
-
-
-class ComputeSidMessage(AuditMessage):
- """Audit message indicating that a sid was not valid.
-
- Compute sid messages are generated on attempting to create a security
- context that is not valid. Security contexts are invalid if the role is
- not authorized for the user or the type is not authorized for the role.
-
- This class does not store all of the fields from the compute sid message -
- just the type and role.
- """
- def __init__(self, message):
- AuditMessage.__init__(self, message)
- self.invalid_context = refpolicy.SecurityContext()
- self.scontext = refpolicy.SecurityContext()
- self.tcontext = refpolicy.SecurityContext()
- self.tclass = ""
-
- def from_split_string(self, recs):
- AuditMessage.from_split_string(self, recs)
- if len(recs) < 10:
- raise ValueError("Split string does not represent a valid compute sid message")
-
- try:
- self.invalid_context = refpolicy.SecurityContext(recs[5])
- self.scontext = refpolicy.SecurityContext(recs[7].split("=")[1])
- self.tcontext = refpolicy.SecurityContext(recs[8].split("=")[1])
- self.tclass = recs[9].split("=")[1]
- except:
- raise ValueError("Split string does not represent a valid compute sid message")
- def output(self):
- return "role %s types %s;\n" % (self.role, self.type)
-
-# Parser for audit messages
-
-class AuditParser:
- """Parser for audit messages.
-
- This class parses audit messages and stores them according to their message
- type. This is not a general purpose audit message parser - it only extracts
- selinux related messages.
-
- Each audit messages are stored in one of four lists:
- avc_msgs - avc denial or granted messages. Messages are stored in
- AVCMessage objects.
- comput_sid_messages - invalid sid messages. Messages are stored in
- ComputSidMessage objects.
- invalid_msgs - selinux related messages that are not valid. Messages
- are stored in InvalidMessageObjects.
- policy_load_messages - policy load messages. Messages are stored in
- PolicyLoadMessage objects.
-
- These lists will be reset when a policy load message is seen if
- AuditParser.last_load_only is set to true. It is assumed that messages
- are fed to the parser in chronological order - time stamps are not
- parsed.
- """
- def __init__(self, last_load_only=False):
- self.__initialize()
- self.last_load_only = last_load_only
-
- def __initialize(self):
- self.avc_msgs = []
- self.compute_sid_msgs = []
- self.invalid_msgs = []
- self.policy_load_msgs = []
- self.path_msgs = []
- self.by_header = { }
- self.check_input_file = False
-
- # Low-level parsing function - tries to determine if this audit
- # message is an SELinux related message and then parses it into
- # the appropriate AuditMessage subclass. This function deliberately
- # does not impose policy (e.g., on policy load message) or store
- # messages to make as simple and reusable as possible.
- #
- # Return values:
- # None - no recognized audit message found in this line
- #
- # InvalidMessage - a recognized but invalid message was found.
- #
- # AuditMessage (or subclass) - object representing a parsed
- # and valid audit message.
- def __parse_line(self, line):
- rec = line.split()
- for i in rec:
- found = False
- if i == "avc:" or i == "message=avc:" or i == "msg='avc:":
- msg = AVCMessage(line)
- found = True
- elif i == "security_compute_sid:":
- msg = ComputeSidMessage(line)
- found = True
- elif i == "type=MAC_POLICY_LOAD" or i == "type=1403":
- msg = PolicyLoadMessage(line)
- found = True
- elif i == "type=AVC_PATH":
- msg = PathMessage(line)
- found = True
- elif i == "type=DAEMON_START":
- msg = DaemonStartMessage(list)
- found = True
-
- if found:
- self.check_input_file = True
- try:
- msg.from_split_string(rec)
- except ValueError:
- msg = InvalidMessage(line)
- return msg
- return None
-
- # Higher-level parse function - take a line, parse it into an
- # AuditMessage object, and store it in the appropriate list.
- # This function will optionally reset all of the lists when
- # it sees a load policy message depending on the value of
- # self.last_load_only.
- def __parse(self, line):
- msg = self.__parse_line(line)
- if msg is None:
- return
-
- # Append to the correct list
- if isinstance(msg, PolicyLoadMessage):
- if self.last_load_only:
- self.__initialize()
- elif isinstance(msg, DaemonStartMessage):
- # We initialize every time the auditd is started. This
- # is less than ideal, but unfortunately it is the only
- # way to catch reboots since the initial policy load
- # by init is not stored in the audit log.
- if msg.auditd and self.last_load_only:
- self.__initialize()
- self.policy_load_msgs.append(msg)
- elif isinstance(msg, AVCMessage):
- self.avc_msgs.append(msg)
- elif isinstance(msg, ComputeSidMessage):
- self.compute_sid_msgs.append(msg)
- elif isinstance(msg, InvalidMessage):
- self.invalid_msgs.append(msg)
- elif isinstance(msg, PathMessage):
- self.path_msgs.append(msg)
-
- # Group by audit header
- if msg.header != "":
- if self.by_header.has_key(msg.header):
- self.by_header[msg.header].append(msg)
- else:
- self.by_header[msg.header] = [msg]
-
-
- # Post processing will add additional information from AVC messages
- # from related messages - only works on messages generated by
- # the audit system.
- def __post_process(self):
- for value in self.by_header.values():
- avc = []
- path = None
- for msg in value:
- if isinstance(msg, PathMessage):
- path = msg
- elif isinstance(msg, AVCMessage):
- avc.append(msg)
- if len(avc) > 0 and path:
- for a in avc:
- a.path = path.path
-
- def parse_file(self, input):
- """Parse the contents of a file object. This method can be called
- multiple times (along with parse_string)."""
- line = input.readline()
- while line:
- self.__parse(line)
- line = input.readline()
- if not self.check_input_file:
- sys.stderr.write("Nothing to do\n")
- sys.exit(0)
- self.__post_process()
-
- def parse_string(self, input):
- """Parse a string containing audit messages - messages should
- be separated by new lines. This method can be called multiple
- times (along with parse_file)."""
- lines = input.split('\n')
- for l in lines:
- self.__parse(l)
- self.__post_process()
-
- def to_role(self, role_filter=None):
- """Return RoleAllowSet statements matching the specified filter
-
- Filter out types that match the filer, or all roles
-
- Params:
- role_filter - [optional] Filter object used to filter the
- output.
- Returns:
- Access vector set representing the denied access in the
- audit logs parsed by this object.
- """
- role_types = access.RoleTypeSet()
- for cs in self.compute_sid_msgs:
- if not role_filter or role_filter.filter(cs):
- role_types.add(cs.invalid_context.role, cs.invalid_context.type)
-
- return role_types
-
- def to_access(self, avc_filter=None, only_denials=True):
- """Convert the audit logs access into a an access vector set.
-
- Convert the audit logs into an access vector set, optionally
- filtering the restults with the passed in filter object.
-
- Filter objects are object instances with a .filter method
- that takes and access vector and returns True if the message
- should be included in the final output and False otherwise.
-
- Params:
- avc_filter - [optional] Filter object used to filter the
- output.
- Returns:
- Access vector set representing the denied access in the
- audit logs parsed by this object.
- """
- av_set = access.AccessVectorSet()
- for avc in self.avc_msgs:
- if avc.denial != True and only_denials:
- continue
- if avc_filter:
- if avc_filter.filter(avc):
- av_set.add(avc.scontext.type, avc.tcontext.type, avc.tclass,
- avc.accesses, avc, avc_type=avc.type, data=avc.data)
- else:
- av_set.add(avc.scontext.type, avc.tcontext.type, avc.tclass,
- avc.accesses, avc, avc_type=avc.type, data=avc.data)
- return av_set
-
-class AVCTypeFilter:
- def __init__(self, regex):
- self.regex = re.compile(regex)
-
- def filter(self, avc):
- if self.regex.match(avc.scontext.type):
- return True
- if self.regex.match(avc.tcontext.type):
- return True
- return False
-
-class ComputeSidTypeFilter:
- def __init__(self, regex):
- self.regex = re.compile(regex)
-
- def filter(self, avc):
- if self.regex.match(avc.invalid_context.type):
- return True
- if self.regex.match(avc.scontext.type):
- return True
- if self.regex.match(avc.tcontext.type):
- return True
- return False
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/classperms.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/classperms.py
deleted file mode 100644
index c925dee..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/classperms.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-import sys
-
-tokens = ('DEFINE',
- 'NAME',
- 'TICK',
- 'SQUOTE',
- 'OBRACE',
- 'CBRACE',
- 'SEMI',
- 'OPAREN',
- 'CPAREN',
- 'COMMA')
-
-reserved = {
- 'define' : 'DEFINE' }
-
-t_TICK = r'\`'
-t_SQUOTE = r'\''
-t_OBRACE = r'\{'
-t_CBRACE = r'\}'
-t_SEMI = r'\;'
-t_OPAREN = r'\('
-t_CPAREN = r'\)'
-t_COMMA = r'\,'
-
-t_ignore = " \t\n"
-
-def t_NAME(t):
- r'[a-zA-Z_][a-zA-Z0-9_]*'
- t.type = reserved.get(t.value,'NAME')
- return t
-
-def t_error(t):
- print "Illegal character '%s'" % t.value[0]
- t.skip(1)
-
-import lex
-lex.lex()
-
-def p_statements(p):
- '''statements : define_stmt
- | define_stmt statements
- '''
- if len(p) == 2:
- p[0] = [p[1]]
- else:
- p[0] = [p[1]] + [p[2]]
-
-def p_define_stmt(p):
- # This sucks - corresponds to 'define(`foo',`{ read write }')
- '''define_stmt : DEFINE OPAREN TICK NAME SQUOTE COMMA TICK list SQUOTE CPAREN
- '''
-
- p[0] = [p[4], p[8]]
-
-def p_list(p):
- '''list : NAME
- | OBRACE names CBRACE
- '''
- if p[1] == "{":
- p[0] = p[2]
- else:
- p[0] = [p[1]]
-
-def p_names(p):
- '''names : NAME
- | NAME names
- '''
- if len(p) == 2:
- p[0] = [p[1]]
- else:
- p[0] = [p[1]] + p[2]
-
-def p_error(p):
- print "Syntax error on line %d %s [type=%s]" % (p.lineno, p.value, p.type)
-
-import yacc
-yacc.yacc()
-
-
-f = open("all_perms.spt")
-txt = f.read()
-f.close()
-
-#lex.input(txt)
-#while 1:
-# tok = lex.token()
-# if not tok:
-# break
-# print tok
-
-test = "define(`foo',`{ read write append }')"
-test2 = """define(`all_filesystem_perms',`{ mount remount unmount getattr relabelfrom relabelto transition associate quotamod quotaget }')
-define(`all_security_perms',`{ compute_av compute_create compute_member check_context load_policy compute_relabel compute_user setenforce setbool setsecparam setcheckreqprot }')
-"""
-result = yacc.parse(txt)
-print result
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/defaults.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/defaults.py
deleted file mode 100644
index 218bc7c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/defaults.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-import os
-import re
-
-# Select the correct location for the development files based on a
-# path variable (optionally read from a configuration file)
-class PathChoooser(object):
- def __init__(self, pathname):
- self.config = dict()
- if not os.path.exists(pathname):
- self.config_pathname = "(defaults)"
- self.config["SELINUX_DEVEL_PATH"] = "/usr/share/selinux/default:/usr/share/selinux/mls:/usr/share/selinux/devel"
- return
- self.config_pathname = pathname
- ignore = re.compile(r"^\s*(?:#.+)?$")
- consider = re.compile(r"^\s*(\w+)\s*=\s*(.+?)\s*$")
- for lineno, line in enumerate(open(pathname)):
- if ignore.match(line): continue
- mo = consider.match(line)
- if not mo:
- raise ValueError, "%s:%d: line is not in key = value format" % (pathname, lineno+1)
- self.config[mo.group(1)] = mo.group(2)
-
- # We're only exporting one useful function, so why not be a function
- def __call__(self, testfilename, pathset="SELINUX_DEVEL_PATH"):
- paths = self.config.get(pathset, None)
- if paths is None:
- raise ValueError, "%s was not in %s" % (pathset, self.config_pathname)
- paths = paths.split(":")
- for p in paths:
- target = os.path.join(p, testfilename)
- if os.path.exists(target): return target
- return os.path.join(paths[0], testfilename)
-
-
-"""
-Various default settings, including file and directory locations.
-"""
-
-def data_dir():
- return "/var/lib/sepolgen"
-
-def perm_map():
- return data_dir() + "/perm_map"
-
-def interface_info():
- return data_dir() + "/interface_info"
-
-def attribute_info():
- return data_dir() + "/attribute_info"
-
-def refpolicy_makefile():
- chooser = PathChoooser("/etc/selinux/sepolgen.conf")
- return chooser("Makefile")
-
-def headers():
- chooser = PathChoooser("/etc/selinux/sepolgen.conf")
- return chooser("include")
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/interfaces.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/interfaces.py
deleted file mode 100644
index 88a6dc3..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/interfaces.py
+++ /dev/null
@@ -1,509 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-Classes for representing and manipulating interfaces.
-"""
-
-import access
-import refpolicy
-import itertools
-import objectmodel
-import matching
-
-from sepolgeni18n import _
-
-import copy
-
-class Param:
- """
- Object representing a paramater for an interface.
- """
- def __init__(self):
- self.__name = ""
- self.type = refpolicy.SRC_TYPE
- self.obj_classes = refpolicy.IdSet()
- self.required = True
-
- def set_name(self, name):
- if not access.is_idparam(name):
- raise ValueError("Name [%s] is not a param" % name)
- self.__name = name
-
- def get_name(self):
- return self.__name
-
- name = property(get_name, set_name)
-
- num = property(fget=lambda self: int(self.name[1:]))
-
- def __repr__(self):
- return "<sepolgen.policygen.Param instance [%s, %s, %s]>" % \
- (self.name, refpolicy.field_to_str[self.type], " ".join(self.obj_classes))
-
-
-# Helper for extract perms
-def __param_insert(name, type, av, params):
- ret = 0
- if name in params:
- p = params[name]
- # The entries are identical - we're done
- if type == p.type:
- return
- # Hanldle implicitly typed objects (like process)
- if (type == refpolicy.SRC_TYPE or type == refpolicy.TGT_TYPE) and \
- (p.type == refpolicy.TGT_TYPE or p.type == refpolicy.SRC_TYPE):
- #print name, refpolicy.field_to_str[p.type]
- # If the object is not implicitly typed, tell the
- # caller there is a likely conflict.
- ret = 1
- if av:
- avobjs = [av.obj_class]
- else:
- avobjs = []
- for obj in itertools.chain(p.obj_classes, avobjs):
- if obj in objectmodel.implicitly_typed_objects:
- ret = 0
- break
- # "Promote" to a SRC_TYPE as this is the likely usage.
- # We do this even if the above test fails on purpose
- # as there is really no sane way to resolve the conflict
- # here. The caller can take other actions if needed.
- p.type = refpolicy.SRC_TYPE
- else:
- # There is some conflict - no way to resolve it really
- # so we just leave the first entry and tell the caller
- # there was a conflict.
- ret = 1
- else:
- p = Param()
- p.name = name
- p.type = type
- params[p.name] = p
-
- if av:
- p.obj_classes.add(av.obj_class)
- return ret
-
-
-
-def av_extract_params(av, params):
- """Extract the paramaters from an access vector.
-
- Extract the paramaters (in the form $N) from an access
- vector, storing them as Param objects in a dictionary.
- Some attempt is made at resolving conflicts with other
- entries in the dict, but if an unresolvable conflict is
- found it is reported to the caller.
-
- The goal here is to figure out how interface paramaters are
- actually used in the interface - e.g., that $1 is a domain used as
- a SRC_TYPE. In general an interface will look like this:
-
- interface(`foo', `
- allow $1 foo : file read;
- ')
-
- This is simple to figure out - $1 is a SRC_TYPE. A few interfaces
- are more complex, for example:
-
- interface(`foo_trans',`
- domain_auto_trans($1,fingerd_exec_t,fingerd_t)
-
- allow $1 fingerd_t:fd use;
- allow fingerd_t $1:fd use;
- allow fingerd_t $1:fifo_file rw_file_perms;
- allow fingerd_t $1:process sigchld;
- ')
-
- Here the usage seems ambigious, but it is not. $1 is still domain
- and therefore should be returned as a SRC_TYPE.
-
- Returns:
- 0 - success
- 1 - conflict found
- """
- ret = 0
- found_src = False
- if access.is_idparam(av.src_type):
- if __param_insert(av.src_type, refpolicy.SRC_TYPE, av, params) == 1:
- ret = 1
-
- if access.is_idparam(av.tgt_type):
- if __param_insert(av.tgt_type, refpolicy.TGT_TYPE, av, params) == 1:
- ret = 1
-
- if access.is_idparam(av.obj_class):
- if __param_insert(av.obj_class, refpolicy.OBJ_CLASS, av, params) == 1:
- ret = 1
-
- for perm in av.perms:
- if access.is_idparam(perm):
- if __param_insert(perm, PERM) == 1:
- ret = 1
-
- return ret
-
-def role_extract_params(role, params):
- if access.is_idparam(role.role):
- return __param_insert(role.role, refpolicy.ROLE, None, params)
-
-def type_rule_extract_params(rule, params):
- def extract_from_set(set, type):
- ret = 0
- for x in set:
- if access.is_idparam(x):
- if __param_insert(x, type, None, params):
- ret = 1
- return ret
-
- ret = 0
- if extract_from_set(rule.src_types, refpolicy.SRC_TYPE):
- ret = 1
-
- if extract_from_set(rule.tgt_types, refpolicy.TGT_TYPE):
- ret = 1
-
- if extract_from_set(rule.obj_classes, refpolicy.OBJ_CLASS):
- ret = 1
-
- if access.is_idparam(rule.dest_type):
- if __param_insert(rule.dest_type, refpolicy.DEST_TYPE, None, params):
- ret = 1
-
- return ret
-
-def ifcall_extract_params(ifcall, params):
- ret = 0
- for arg in ifcall.args:
- if access.is_idparam(arg):
- # Assume interface arguments are source types. Fairly safe
- # assumption for most interfaces
- if __param_insert(arg, refpolicy.SRC_TYPE, None, params):
- ret = 1
-
- return ret
-
-class AttributeVector:
- def __init__(self):
- self.name = ""
- self.access = access.AccessVectorSet()
-
- def add_av(self, av):
- self.access.add_av(av)
-
-class AttributeSet:
- def __init__(self):
- self.attributes = { }
-
- def add_attr(self, attr):
- self.attributes[attr.name] = attr
-
- def from_file(self, fd):
- def parse_attr(line):
- fields = line[1:-1].split()
- if len(fields) != 2 or fields[0] != "Attribute":
- raise SyntaxError("Syntax error Attribute statement %s" % line)
- a = AttributeVector()
- a.name = fields[1]
-
- return a
-
- a = None
- for line in fd:
- line = line[:-1]
- if line[0] == "[":
- if a:
- self.add_attr(a)
- a = parse_attr(line)
- elif a:
- l = line.split(",")
- av = access.AccessVector(l)
- a.add_av(av)
- if a:
- self.add_attr(a)
-
-class InterfaceVector:
- def __init__(self, interface=None, attributes={}):
- # Enabled is a loose concept currently - we are essentially
- # not enabling interfaces that we can't handle currently.
- # See InterfaceVector.add_ifv for more information.
- self.enabled = True
- self.name = ""
- # The access that is enabled by this interface - eventually
- # this will include indirect access from typeattribute
- # statements.
- self.access = access.AccessVectorSet()
- # Paramaters are stored in a dictionary (key: param name
- # value: Param object).
- self.params = { }
- if interface:
- self.from_interface(interface, attributes)
- self.expanded = False
-
- def from_interface(self, interface, attributes={}):
- self.name = interface.name
-
- # Add allow rules
- for avrule in interface.avrules():
- if avrule.rule_type != refpolicy.AVRule.ALLOW:
- continue
- # Handle some policy bugs
- if "dontaudit" in interface.name:
- #print "allow rule in interface: %s" % interface
- continue
- avs = access.avrule_to_access_vectors(avrule)
- for av in avs:
- self.add_av(av)
-
- # Add typeattribute access
- if attributes:
- for typeattribute in interface.typeattributes():
- for attr in typeattribute.attributes:
- if not attributes.attributes.has_key(attr):
- # print "missing attribute " + attr
- continue
- attr_vec = attributes.attributes[attr]
- for a in attr_vec.access:
- av = copy.copy(a)
- if av.src_type == attr_vec.name:
- av.src_type = typeattribute.type
- if av.tgt_type == attr_vec.name:
- av.tgt_type = typeattribute.type
- self.add_av(av)
-
-
- # Extract paramaters from roles
- for role in interface.roles():
- if role_extract_params(role, self.params):
- pass
- #print "found conflicting role param %s for interface %s" % \
- # (role.name, interface.name)
- # Extract paramaters from type rules
- for rule in interface.typerules():
- if type_rule_extract_params(rule, self.params):
- pass
- #print "found conflicting params in rule %s in interface %s" % \
- # (str(rule), interface.name)
-
- for ifcall in interface.interface_calls():
- if ifcall_extract_params(ifcall, self.params):
- pass
- #print "found conflicting params in ifcall %s in interface %s" % \
- # (str(ifcall), interface.name)
-
-
- def add_av(self, av):
- if av_extract_params(av, self.params) == 1:
- pass
- #print "found conflicting perms [%s]" % str(av)
- self.access.add_av(av)
-
- def to_string(self):
- s = []
- s.append("[InterfaceVector %s]" % self.name)
- for av in self.access:
- s.append(str(av))
- return "\n".join(s)
-
- def __str__(self):
- return self.__repr__()
-
- def __repr__(self):
- return "<InterfaceVector %s:%s>" % (self.name, self.enabled)
-
-
-class InterfaceSet:
- def __init__(self, output=None):
- self.interfaces = { }
- self.tgt_type_map = { }
- self.tgt_type_all = []
- self.output = output
-
- def o(self, str):
- if self.output:
- self.output.write(str + "\n")
-
- def to_file(self, fd):
- for iv in self.interfaces.values():
- fd.write("[InterfaceVector %s " % iv.name)
- for param in iv.params.values():
- fd.write("%s:%s " % (param.name, refpolicy.field_to_str[param.type]))
- fd.write("]\n")
- avl = iv.access.to_list()
- for av in avl:
- fd.write(",".join(av))
- fd.write("\n")
-
- def from_file(self, fd):
- def parse_ifv(line):
- fields = line[1:-1].split()
- if len(fields) < 2 or fields[0] != "InterfaceVector":
- raise SyntaxError("Syntax error InterfaceVector statement %s" % line)
- ifv = InterfaceVector()
- ifv.name = fields[1]
- if len(fields) == 2:
- return
- for field in fields[2:]:
- p = field.split(":")
- if len(p) != 2:
- raise SyntaxError("Invalid param in InterfaceVector statement %s" % line)
- param = Param()
- param.name = p[0]
- param.type = refpolicy.str_to_field[p[1]]
- ifv.params[param.name] = param
- return ifv
-
- ifv = None
- for line in fd:
- line = line[:-1]
- if line[0] == "[":
- if ifv:
- self.add_ifv(ifv)
- ifv = parse_ifv(line)
- elif ifv:
- l = line.split(",")
- av = access.AccessVector(l)
- ifv.add_av(av)
- if ifv:
- self.add_ifv(ifv)
-
- self.index()
-
- def add_ifv(self, ifv):
- self.interfaces[ifv.name] = ifv
-
- def index(self):
- for ifv in self.interfaces.values():
- tgt_types = set()
- for av in ifv.access:
- if access.is_idparam(av.tgt_type):
- self.tgt_type_all.append(ifv)
- tgt_types = set()
- break
- tgt_types.add(av.tgt_type)
-
- for type in tgt_types:
- l = self.tgt_type_map.setdefault(type, [])
- l.append(ifv)
-
- def add(self, interface, attributes={}):
- ifv = InterfaceVector(interface, attributes)
- self.add_ifv(ifv)
-
- def add_headers(self, headers, output=None, attributes={}):
- for i in itertools.chain(headers.interfaces(), headers.templates()):
- self.add(i, attributes)
-
- self.expand_ifcalls(headers)
- self.index()
-
- def map_param(self, id, ifcall):
- if access.is_idparam(id):
- num = int(id[1:])
- if num > len(ifcall.args):
- # Tell caller to drop this because it must have
- # been generated from an optional param.
- return None
- else:
- arg = ifcall.args[num - 1]
- if isinstance(arg, list):
- return arg
- else:
- return [arg]
- else:
- return [id]
-
- def map_add_av(self, ifv, av, ifcall):
- src_types = self.map_param(av.src_type, ifcall)
- if src_types is None:
- return
-
- tgt_types = self.map_param(av.tgt_type, ifcall)
- if tgt_types is None:
- return
-
- obj_classes = self.map_param(av.obj_class, ifcall)
- if obj_classes is None:
- return
-
- new_perms = refpolicy.IdSet()
- for perm in av.perms:
- p = self.map_param(perm, ifcall)
- if p is None:
- continue
- else:
- new_perms.update(p)
- if len(new_perms) == 0:
- return
-
- for src_type in src_types:
- for tgt_type in tgt_types:
- for obj_class in obj_classes:
- ifv.access.add(src_type, tgt_type, obj_class, new_perms)
-
- def do_expand_ifcalls(self, interface, if_by_name):
- # Descend an interface call tree adding the access
- # from each interface. This is a depth first walk
- # of the tree.
-
- stack = [(interface, None)]
- ifv = self.interfaces[interface.name]
- ifv.expanded = True
-
- while len(stack) > 0:
- cur, cur_ifcall = stack.pop(-1)
-
- cur_ifv = self.interfaces[cur.name]
- if cur != interface:
-
- for av in cur_ifv.access:
- self.map_add_av(ifv, av, cur_ifcall)
-
- # If we have already fully expanded this interface
- # there is no reason to descend further.
- if cur_ifv.expanded:
- continue
-
- for ifcall in cur.interface_calls():
- if ifcall.ifname == interface.name:
- self.o(_("Found circular interface class"))
- return
- try:
- newif = if_by_name[ifcall.ifname]
- except KeyError:
- self.o(_("Missing interface definition for %s" % ifcall.ifname))
- continue
-
- stack.append((newif, ifcall))
-
-
- def expand_ifcalls(self, headers):
- # Create a map of interface names to interfaces -
- # this mirrors the interface vector map we already
- # have.
- if_by_name = { }
-
- for i in itertools.chain(headers.interfaces(), headers.templates()):
- if_by_name[i.name] = i
-
-
- for interface in itertools.chain(headers.interfaces(), headers.templates()):
- self.do_expand_ifcalls(interface, if_by_name)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/lex.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/lex.py
deleted file mode 100644
index c149366..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/lex.py
+++ /dev/null
@@ -1,866 +0,0 @@
-#-----------------------------------------------------------------------------
-# ply: lex.py
-#
-# Author: David M. Beazley (dave@dabeaz.com)
-#
-# Copyright (C) 2001-2006, David M. Beazley
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See the file COPYING for a complete copy of the LGPL.
-#-----------------------------------------------------------------------------
-
-__version__ = "2.2"
-
-import re, sys, types
-
-# Regular expression used to match valid token names
-_is_identifier = re.compile(r'^[a-zA-Z0-9_]+$')
-
-# Available instance types. This is used when lexers are defined by a class.
-# It's a little funky because I want to preserve backwards compatibility
-# with Python 2.0 where types.ObjectType is undefined.
-
-try:
- _INSTANCETYPE = (types.InstanceType, types.ObjectType)
-except AttributeError:
- _INSTANCETYPE = types.InstanceType
- class object: pass # Note: needed if no new-style classes present
-
-# Exception thrown when invalid token encountered and no default error
-# handler is defined.
-class LexError(Exception):
- def __init__(self,message,s):
- self.args = (message,)
- self.text = s
-
-# Token class
-class LexToken(object):
- def __str__(self):
- return "LexToken(%s,%r,%d,%d)" % (self.type,self.value,self.lineno,self.lexpos)
- def __repr__(self):
- return str(self)
- def skip(self,n):
- self.lexer.skip(n)
-
-# -----------------------------------------------------------------------------
-# Lexer class
-#
-# This class encapsulates all of the methods and data associated with a lexer.
-#
-# input() - Store a new string in the lexer
-# token() - Get the next token
-# -----------------------------------------------------------------------------
-
-class Lexer:
- def __init__(self):
- self.lexre = None # Master regular expression. This is a list of
- # tuples (re,findex) where re is a compiled
- # regular expression and findex is a list
- # mapping regex group numbers to rules
- self.lexretext = None # Current regular expression strings
- self.lexstatere = {} # Dictionary mapping lexer states to master regexs
- self.lexstateretext = {} # Dictionary mapping lexer states to regex strings
- self.lexstate = "INITIAL" # Current lexer state
- self.lexstatestack = [] # Stack of lexer states
- self.lexstateinfo = None # State information
- self.lexstateignore = {} # Dictionary of ignored characters for each state
- self.lexstateerrorf = {} # Dictionary of error functions for each state
- self.lexreflags = 0 # Optional re compile flags
- self.lexdata = None # Actual input data (as a string)
- self.lexpos = 0 # Current position in input text
- self.lexlen = 0 # Length of the input text
- self.lexerrorf = None # Error rule (if any)
- self.lextokens = None # List of valid tokens
- self.lexignore = "" # Ignored characters
- self.lexliterals = "" # Literal characters that can be passed through
- self.lexmodule = None # Module
- self.lineno = 1 # Current line number
- self.lexdebug = 0 # Debugging mode
- self.lexoptimize = 0 # Optimized mode
-
- def clone(self,object=None):
- c = Lexer()
- c.lexstatere = self.lexstatere
- c.lexstateinfo = self.lexstateinfo
- c.lexstateretext = self.lexstateretext
- c.lexstate = self.lexstate
- c.lexstatestack = self.lexstatestack
- c.lexstateignore = self.lexstateignore
- c.lexstateerrorf = self.lexstateerrorf
- c.lexreflags = self.lexreflags
- c.lexdata = self.lexdata
- c.lexpos = self.lexpos
- c.lexlen = self.lexlen
- c.lextokens = self.lextokens
- c.lexdebug = self.lexdebug
- c.lineno = self.lineno
- c.lexoptimize = self.lexoptimize
- c.lexliterals = self.lexliterals
- c.lexmodule = self.lexmodule
-
- # If the object parameter has been supplied, it means we are attaching the
- # lexer to a new object. In this case, we have to rebind all methods in
- # the lexstatere and lexstateerrorf tables.
-
- if object:
- newtab = { }
- for key, ritem in self.lexstatere.items():
- newre = []
- for cre, findex in ritem:
- newfindex = []
- for f in findex:
- if not f or not f[0]:
- newfindex.append(f)
- continue
- newfindex.append((getattr(object,f[0].__name__),f[1]))
- newre.append((cre,newfindex))
- newtab[key] = newre
- c.lexstatere = newtab
- c.lexstateerrorf = { }
- for key, ef in self.lexstateerrorf.items():
- c.lexstateerrorf[key] = getattr(object,ef.__name__)
- c.lexmodule = object
-
- # Set up other attributes
- c.begin(c.lexstate)
- return c
-
- # ------------------------------------------------------------
- # writetab() - Write lexer information to a table file
- # ------------------------------------------------------------
- def writetab(self,tabfile):
- tf = open(tabfile+".py","w")
- tf.write("# %s.py. This file automatically created by PLY (version %s). Don't edit!\n" % (tabfile,__version__))
- tf.write("_lextokens = %s\n" % repr(self.lextokens))
- tf.write("_lexreflags = %s\n" % repr(self.lexreflags))
- tf.write("_lexliterals = %s\n" % repr(self.lexliterals))
- tf.write("_lexstateinfo = %s\n" % repr(self.lexstateinfo))
-
- tabre = { }
- for key, lre in self.lexstatere.items():
- titem = []
- for i in range(len(lre)):
- titem.append((self.lexstateretext[key][i],_funcs_to_names(lre[i][1])))
- tabre[key] = titem
-
- tf.write("_lexstatere = %s\n" % repr(tabre))
- tf.write("_lexstateignore = %s\n" % repr(self.lexstateignore))
-
- taberr = { }
- for key, ef in self.lexstateerrorf.items():
- if ef:
- taberr[key] = ef.__name__
- else:
- taberr[key] = None
- tf.write("_lexstateerrorf = %s\n" % repr(taberr))
- tf.close()
-
- # ------------------------------------------------------------
- # readtab() - Read lexer information from a tab file
- # ------------------------------------------------------------
- def readtab(self,tabfile,fdict):
- exec "import %s as lextab" % tabfile
- self.lextokens = lextab._lextokens
- self.lexreflags = lextab._lexreflags
- self.lexliterals = lextab._lexliterals
- self.lexstateinfo = lextab._lexstateinfo
- self.lexstateignore = lextab._lexstateignore
- self.lexstatere = { }
- self.lexstateretext = { }
- for key,lre in lextab._lexstatere.items():
- titem = []
- txtitem = []
- for i in range(len(lre)):
- titem.append((re.compile(lre[i][0],lextab._lexreflags),_names_to_funcs(lre[i][1],fdict)))
- txtitem.append(lre[i][0])
- self.lexstatere[key] = titem
- self.lexstateretext[key] = txtitem
- self.lexstateerrorf = { }
- for key,ef in lextab._lexstateerrorf.items():
- self.lexstateerrorf[key] = fdict[ef]
- self.begin('INITIAL')
-
- # ------------------------------------------------------------
- # input() - Push a new string into the lexer
- # ------------------------------------------------------------
- def input(self,s):
- if not (isinstance(s,types.StringType) or isinstance(s,types.UnicodeType)):
- raise ValueError, "Expected a string"
- self.lexdata = s
- self.lexpos = 0
- self.lexlen = len(s)
-
- # ------------------------------------------------------------
- # begin() - Changes the lexing state
- # ------------------------------------------------------------
- def begin(self,state):
- if not self.lexstatere.has_key(state):
- raise ValueError, "Undefined state"
- self.lexre = self.lexstatere[state]
- self.lexretext = self.lexstateretext[state]
- self.lexignore = self.lexstateignore.get(state,"")
- self.lexerrorf = self.lexstateerrorf.get(state,None)
- self.lexstate = state
-
- # ------------------------------------------------------------
- # push_state() - Changes the lexing state and saves old on stack
- # ------------------------------------------------------------
- def push_state(self,state):
- self.lexstatestack.append(self.lexstate)
- self.begin(state)
-
- # ------------------------------------------------------------
- # pop_state() - Restores the previous state
- # ------------------------------------------------------------
- def pop_state(self):
- self.begin(self.lexstatestack.pop())
-
- # ------------------------------------------------------------
- # current_state() - Returns the current lexing state
- # ------------------------------------------------------------
- def current_state(self):
- return self.lexstate
-
- # ------------------------------------------------------------
- # skip() - Skip ahead n characters
- # ------------------------------------------------------------
- def skip(self,n):
- self.lexpos += n
-
- # ------------------------------------------------------------
- # token() - Return the next token from the Lexer
- #
- # Note: This function has been carefully implemented to be as fast
- # as possible. Don't make changes unless you really know what
- # you are doing
- # ------------------------------------------------------------
- def token(self):
- # Make local copies of frequently referenced attributes
- lexpos = self.lexpos
- lexlen = self.lexlen
- lexignore = self.lexignore
- lexdata = self.lexdata
-
- while lexpos < lexlen:
- # This code provides some short-circuit code for whitespace, tabs, and other ignored characters
- if lexdata[lexpos] in lexignore:
- lexpos += 1
- continue
-
- # Look for a regular expression match
- for lexre,lexindexfunc in self.lexre:
- m = lexre.match(lexdata,lexpos)
- if not m: continue
-
- # Set last match in lexer so that rules can access it if they want
- self.lexmatch = m
-
- # Create a token for return
- tok = LexToken()
- tok.value = m.group()
- tok.lineno = self.lineno
- tok.lexpos = lexpos
- tok.lexer = self
-
- lexpos = m.end()
- i = m.lastindex
- func,tok.type = lexindexfunc[i]
- self.lexpos = lexpos
-
- if not func:
- # If no token type was set, it's an ignored token
- if tok.type: return tok
- break
-
- # if func not callable, it means it's an ignored token
- if not callable(func):
- break
-
- # If token is processed by a function, call it
- newtok = func(tok)
-
- # Every function must return a token, if nothing, we just move to next token
- if not newtok:
- lexpos = self.lexpos # This is here in case user has updated lexpos.
- break
-
- # Verify type of the token. If not in the token map, raise an error
- if not self.lexoptimize:
- if not self.lextokens.has_key(newtok.type):
- raise LexError, ("%s:%d: Rule '%s' returned an unknown token type '%s'" % (
- func.func_code.co_filename, func.func_code.co_firstlineno,
- func.__name__, newtok.type),lexdata[lexpos:])
-
- return newtok
- else:
- # No match, see if in literals
- if lexdata[lexpos] in self.lexliterals:
- tok = LexToken()
- tok.value = lexdata[lexpos]
- tok.lineno = self.lineno
- tok.lexer = self
- tok.type = tok.value
- tok.lexpos = lexpos
- self.lexpos = lexpos + 1
- return tok
-
- # No match. Call t_error() if defined.
- if self.lexerrorf:
- tok = LexToken()
- tok.value = self.lexdata[lexpos:]
- tok.lineno = self.lineno
- tok.type = "error"
- tok.lexer = self
- tok.lexpos = lexpos
- self.lexpos = lexpos
- newtok = self.lexerrorf(tok)
- if lexpos == self.lexpos:
- # Error method didn't change text position at all. This is an error.
- raise LexError, ("Scanning error. Illegal character '%s'" % (lexdata[lexpos]), lexdata[lexpos:])
- lexpos = self.lexpos
- if not newtok: continue
- return newtok
-
- self.lexpos = lexpos
- raise LexError, ("Illegal character '%s' at index %d" % (lexdata[lexpos],lexpos), lexdata[lexpos:])
-
- self.lexpos = lexpos + 1
- if self.lexdata is None:
- raise RuntimeError, "No input string given with input()"
- return None
-
-# -----------------------------------------------------------------------------
-# _validate_file()
-#
-# This checks to see if there are duplicated t_rulename() functions or strings
-# in the parser input file. This is done using a simple regular expression
-# match on each line in the filename.
-# -----------------------------------------------------------------------------
-
-def _validate_file(filename):
- import os.path
- base,ext = os.path.splitext(filename)
- if ext != '.py': return 1 # No idea what the file is. Return OK
-
- try:
- f = open(filename)
- lines = f.readlines()
- f.close()
- except IOError:
- return 1 # Oh well
-
- fre = re.compile(r'\s*def\s+(t_[a-zA-Z_0-9]*)\(')
- sre = re.compile(r'\s*(t_[a-zA-Z_0-9]*)\s*=')
- counthash = { }
- linen = 1
- noerror = 1
- for l in lines:
- m = fre.match(l)
- if not m:
- m = sre.match(l)
- if m:
- name = m.group(1)
- prev = counthash.get(name)
- if not prev:
- counthash[name] = linen
- else:
- print "%s:%d: Rule %s redefined. Previously defined on line %d" % (filename,linen,name,prev)
- noerror = 0
- linen += 1
- return noerror
-
-# -----------------------------------------------------------------------------
-# _funcs_to_names()
-#
-# Given a list of regular expression functions, this converts it to a list
-# suitable for output to a table file
-# -----------------------------------------------------------------------------
-
-def _funcs_to_names(funclist):
- result = []
- for f in funclist:
- if f and f[0]:
- result.append((f[0].__name__,f[1]))
- else:
- result.append(f)
- return result
-
-# -----------------------------------------------------------------------------
-# _names_to_funcs()
-#
-# Given a list of regular expression function names, this converts it back to
-# functions.
-# -----------------------------------------------------------------------------
-
-def _names_to_funcs(namelist,fdict):
- result = []
- for n in namelist:
- if n and n[0]:
- result.append((fdict[n[0]],n[1]))
- else:
- result.append(n)
- return result
-
-# -----------------------------------------------------------------------------
-# _form_master_re()
-#
-# This function takes a list of all of the regex components and attempts to
-# form the master regular expression. Given limitations in the Python re
-# module, it may be necessary to break the master regex into separate expressions.
-# -----------------------------------------------------------------------------
-
-def _form_master_re(relist,reflags,ldict):
- if not relist: return []
- regex = "|".join(relist)
- try:
- lexre = re.compile(regex,re.VERBOSE | reflags)
-
- # Build the index to function map for the matching engine
- lexindexfunc = [ None ] * (max(lexre.groupindex.values())+1)
- for f,i in lexre.groupindex.items():
- handle = ldict.get(f,None)
- if type(handle) in (types.FunctionType, types.MethodType):
- lexindexfunc[i] = (handle,handle.__name__[2:])
- elif handle is not None:
- # If rule was specified as a string, we build an anonymous
- # callback function to carry out the action
- if f.find("ignore_") > 0:
- lexindexfunc[i] = (None,None)
- print "IGNORE", f
- else:
- lexindexfunc[i] = (None, f[2:])
-
- return [(lexre,lexindexfunc)],[regex]
- except Exception,e:
- m = int(len(relist)/2)
- if m == 0: m = 1
- llist, lre = _form_master_re(relist[:m],reflags,ldict)
- rlist, rre = _form_master_re(relist[m:],reflags,ldict)
- return llist+rlist, lre+rre
-
-# -----------------------------------------------------------------------------
-# def _statetoken(s,names)
-#
-# Given a declaration name s of the form "t_" and a dictionary whose keys are
-# state names, this function returns a tuple (states,tokenname) where states
-# is a tuple of state names and tokenname is the name of the token. For example,
-# calling this with s = "t_foo_bar_SPAM" might return (('foo','bar'),'SPAM')
-# -----------------------------------------------------------------------------
-
-def _statetoken(s,names):
- nonstate = 1
- parts = s.split("_")
- for i in range(1,len(parts)):
- if not names.has_key(parts[i]) and parts[i] != 'ANY': break
- if i > 1:
- states = tuple(parts[1:i])
- else:
- states = ('INITIAL',)
-
- if 'ANY' in states:
- states = tuple(names.keys())
-
- tokenname = "_".join(parts[i:])
- return (states,tokenname)
-
-# -----------------------------------------------------------------------------
-# lex(module)
-#
-# Build all of the regular expression rules from definitions in the supplied module
-# -----------------------------------------------------------------------------
-def lex(module=None,object=None,debug=0,optimize=0,lextab="lextab",reflags=0,nowarn=0):
- global lexer
- ldict = None
- stateinfo = { 'INITIAL' : 'inclusive'}
- error = 0
- files = { }
- lexobj = Lexer()
- lexobj.lexdebug = debug
- lexobj.lexoptimize = optimize
- global token,input
-
- if nowarn: warn = 0
- else: warn = 1
-
- if object: module = object
-
- if module:
- # User supplied a module object.
- if isinstance(module, types.ModuleType):
- ldict = module.__dict__
- elif isinstance(module, _INSTANCETYPE):
- _items = [(k,getattr(module,k)) for k in dir(module)]
- ldict = { }
- for (i,v) in _items:
- ldict[i] = v
- else:
- raise ValueError,"Expected a module or instance"
- lexobj.lexmodule = module
-
- else:
- # No module given. We might be able to get information from the caller.
- try:
- raise RuntimeError
- except RuntimeError:
- e,b,t = sys.exc_info()
- f = t.tb_frame
- f = f.f_back # Walk out to our calling function
- ldict = f.f_globals # Grab its globals dictionary
-
- if optimize and lextab:
- try:
- lexobj.readtab(lextab,ldict)
- token = lexobj.token
- input = lexobj.input
- lexer = lexobj
- return lexobj
-
- except ImportError:
- pass
-
- # Get the tokens, states, and literals variables (if any)
- if (module and isinstance(module,_INSTANCETYPE)):
- tokens = getattr(module,"tokens",None)
- states = getattr(module,"states",None)
- literals = getattr(module,"literals","")
- else:
- tokens = ldict.get("tokens",None)
- states = ldict.get("states",None)
- literals = ldict.get("literals","")
-
- if not tokens:
- raise SyntaxError,"lex: module does not define 'tokens'"
- if not (isinstance(tokens,types.ListType) or isinstance(tokens,types.TupleType)):
- raise SyntaxError,"lex: tokens must be a list or tuple."
-
- # Build a dictionary of valid token names
- lexobj.lextokens = { }
- if not optimize:
- for n in tokens:
- if not _is_identifier.match(n):
- print "lex: Bad token name '%s'" % n
- error = 1
- if warn and lexobj.lextokens.has_key(n):
- print "lex: Warning. Token '%s' multiply defined." % n
- lexobj.lextokens[n] = None
- else:
- for n in tokens: lexobj.lextokens[n] = None
-
- if debug:
- print "lex: tokens = '%s'" % lexobj.lextokens.keys()
-
- try:
- for c in literals:
- if not (isinstance(c,types.StringType) or isinstance(c,types.UnicodeType)) or len(c) > 1:
- print "lex: Invalid literal %s. Must be a single character" % repr(c)
- error = 1
- continue
-
- except TypeError:
- print "lex: Invalid literals specification. literals must be a sequence of characters."
- error = 1
-
- lexobj.lexliterals = literals
-
- # Build statemap
- if states:
- if not (isinstance(states,types.TupleType) or isinstance(states,types.ListType)):
- print "lex: states must be defined as a tuple or list."
- error = 1
- else:
- for s in states:
- if not isinstance(s,types.TupleType) or len(s) != 2:
- print "lex: invalid state specifier %s. Must be a tuple (statename,'exclusive|inclusive')" % repr(s)
- error = 1
- continue
- name, statetype = s
- if not isinstance(name,types.StringType):
- print "lex: state name %s must be a string" % repr(name)
- error = 1
- continue
- if not (statetype == 'inclusive' or statetype == 'exclusive'):
- print "lex: state type for state %s must be 'inclusive' or 'exclusive'" % name
- error = 1
- continue
- if stateinfo.has_key(name):
- print "lex: state '%s' already defined." % name
- error = 1
- continue
- stateinfo[name] = statetype
-
- # Get a list of symbols with the t_ or s_ prefix
- tsymbols = [f for f in ldict.keys() if f[:2] == 't_' ]
-
- # Now build up a list of functions and a list of strings
-
- funcsym = { } # Symbols defined as functions
- strsym = { } # Symbols defined as strings
- toknames = { } # Mapping of symbols to token names
-
- for s in stateinfo.keys():
- funcsym[s] = []
- strsym[s] = []
-
- ignore = { } # Ignore strings by state
- errorf = { } # Error functions by state
-
- if len(tsymbols) == 0:
- raise SyntaxError,"lex: no rules of the form t_rulename are defined."
-
- for f in tsymbols:
- t = ldict[f]
- states, tokname = _statetoken(f,stateinfo)
- toknames[f] = tokname
-
- if callable(t):
- for s in states: funcsym[s].append((f,t))
- elif (isinstance(t, types.StringType) or isinstance(t,types.UnicodeType)):
- for s in states: strsym[s].append((f,t))
- else:
- print "lex: %s not defined as a function or string" % f
- error = 1
-
- # Sort the functions by line number
- for f in funcsym.values():
- f.sort(lambda x,y: cmp(x[1].func_code.co_firstlineno,y[1].func_code.co_firstlineno))
-
- # Sort the strings by regular expression length
- for s in strsym.values():
- s.sort(lambda x,y: (len(x[1]) < len(y[1])) - (len(x[1]) > len(y[1])))
-
- regexs = { }
-
- # Build the master regular expressions
- for state in stateinfo.keys():
- regex_list = []
-
- # Add rules defined by functions first
- for fname, f in funcsym[state]:
- line = f.func_code.co_firstlineno
- file = f.func_code.co_filename
- files[file] = None
- tokname = toknames[fname]
-
- ismethod = isinstance(f, types.MethodType)
-
- if not optimize:
- nargs = f.func_code.co_argcount
- if ismethod:
- reqargs = 2
- else:
- reqargs = 1
- if nargs > reqargs:
- print "%s:%d: Rule '%s' has too many arguments." % (file,line,f.__name__)
- error = 1
- continue
-
- if nargs < reqargs:
- print "%s:%d: Rule '%s' requires an argument." % (file,line,f.__name__)
- error = 1
- continue
-
- if tokname == 'ignore':
- print "%s:%d: Rule '%s' must be defined as a string." % (file,line,f.__name__)
- error = 1
- continue
-
- if tokname == 'error':
- errorf[state] = f
- continue
-
- if f.__doc__:
- if not optimize:
- try:
- c = re.compile("(?P<%s>%s)" % (f.__name__,f.__doc__), re.VERBOSE | reflags)
- if c.match(""):
- print "%s:%d: Regular expression for rule '%s' matches empty string." % (file,line,f.__name__)
- error = 1
- continue
- except re.error,e:
- print "%s:%d: Invalid regular expression for rule '%s'. %s" % (file,line,f.__name__,e)
- if '#' in f.__doc__:
- print "%s:%d. Make sure '#' in rule '%s' is escaped with '\\#'." % (file,line, f.__name__)
- error = 1
- continue
-
- if debug:
- print "lex: Adding rule %s -> '%s' (state '%s')" % (f.__name__,f.__doc__, state)
-
- # Okay. The regular expression seemed okay. Let's append it to the master regular
- # expression we're building
-
- regex_list.append("(?P<%s>%s)" % (f.__name__,f.__doc__))
- else:
- print "%s:%d: No regular expression defined for rule '%s'" % (file,line,f.__name__)
-
- # Now add all of the simple rules
- for name,r in strsym[state]:
- tokname = toknames[name]
-
- if tokname == 'ignore':
- ignore[state] = r
- continue
-
- if not optimize:
- if tokname == 'error':
- raise SyntaxError,"lex: Rule '%s' must be defined as a function" % name
- error = 1
- continue
-
- if not lexobj.lextokens.has_key(tokname) and tokname.find("ignore_") < 0:
- print "lex: Rule '%s' defined for an unspecified token %s." % (name,tokname)
- error = 1
- continue
- try:
- c = re.compile("(?P<%s>%s)" % (name,r),re.VERBOSE | reflags)
- if (c.match("")):
- print "lex: Regular expression for rule '%s' matches empty string." % name
- error = 1
- continue
- except re.error,e:
- print "lex: Invalid regular expression for rule '%s'. %s" % (name,e)
- if '#' in r:
- print "lex: Make sure '#' in rule '%s' is escaped with '\\#'." % name
-
- error = 1
- continue
- if debug:
- print "lex: Adding rule %s -> '%s' (state '%s')" % (name,r,state)
-
- regex_list.append("(?P<%s>%s)" % (name,r))
-
- if not regex_list:
- print "lex: No rules defined for state '%s'" % state
- error = 1
-
- regexs[state] = regex_list
-
-
- if not optimize:
- for f in files.keys():
- if not _validate_file(f):
- error = 1
-
- if error:
- raise SyntaxError,"lex: Unable to build lexer."
-
- # From this point forward, we're reasonably confident that we can build the lexer.
- # No more errors will be generated, but there might be some warning messages.
-
- # Build the master regular expressions
-
- for state in regexs.keys():
- lexre, re_text = _form_master_re(regexs[state],reflags,ldict)
- lexobj.lexstatere[state] = lexre
- lexobj.lexstateretext[state] = re_text
- if debug:
- for i in range(len(re_text)):
- print "lex: state '%s'. regex[%d] = '%s'" % (state, i, re_text[i])
-
- # For inclusive states, we need to add the INITIAL state
- for state,type in stateinfo.items():
- if state != "INITIAL" and type == 'inclusive':
- lexobj.lexstatere[state].extend(lexobj.lexstatere['INITIAL'])
- lexobj.lexstateretext[state].extend(lexobj.lexstateretext['INITIAL'])
-
- lexobj.lexstateinfo = stateinfo
- lexobj.lexre = lexobj.lexstatere["INITIAL"]
- lexobj.lexretext = lexobj.lexstateretext["INITIAL"]
-
- # Set up ignore variables
- lexobj.lexstateignore = ignore
- lexobj.lexignore = lexobj.lexstateignore.get("INITIAL","")
-
- # Set up error functions
- lexobj.lexstateerrorf = errorf
- lexobj.lexerrorf = errorf.get("INITIAL",None)
- if warn and not lexobj.lexerrorf:
- print "lex: Warning. no t_error rule is defined."
-
- # Check state information for ignore and error rules
- for s,stype in stateinfo.items():
- if stype == 'exclusive':
- if warn and not errorf.has_key(s):
- print "lex: Warning. no error rule is defined for exclusive state '%s'" % s
- if warn and not ignore.has_key(s) and lexobj.lexignore:
- print "lex: Warning. no ignore rule is defined for exclusive state '%s'" % s
- elif stype == 'inclusive':
- if not errorf.has_key(s):
- errorf[s] = errorf.get("INITIAL",None)
- if not ignore.has_key(s):
- ignore[s] = ignore.get("INITIAL","")
-
-
- # Create global versions of the token() and input() functions
- token = lexobj.token
- input = lexobj.input
- lexer = lexobj
-
- # If in optimize mode, we write the lextab
- if lextab and optimize:
- lexobj.writetab(lextab)
-
- return lexobj
-
-# -----------------------------------------------------------------------------
-# runmain()
-#
-# This runs the lexer as a main program
-# -----------------------------------------------------------------------------
-
-def runmain(lexer=None,data=None):
- if not data:
- try:
- filename = sys.argv[1]
- f = open(filename)
- data = f.read()
- f.close()
- except IndexError:
- print "Reading from standard input (type EOF to end):"
- data = sys.stdin.read()
-
- if lexer:
- _input = lexer.input
- else:
- _input = input
- _input(data)
- if lexer:
- _token = lexer.token
- else:
- _token = token
-
- while 1:
- tok = _token()
- if not tok: break
- print "(%s,%r,%d,%d)" % (tok.type, tok.value, tok.lineno,tok.lexpos)
-
-
-# -----------------------------------------------------------------------------
-# @TOKEN(regex)
-#
-# This decorator function can be used to set the regex expression on a function
-# when its docstring might need to be set in an alternative way
-# -----------------------------------------------------------------------------
-
-def TOKEN(r):
- def set_doc(f):
- f.__doc__ = r
- return f
- return set_doc
-
-# Alternative spelling of the TOKEN decorator
-Token = TOKEN
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/matching.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/matching.py
deleted file mode 100644
index d56dd92..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/matching.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-Classes and algorithms for matching requested access to access vectors.
-"""
-
-import access
-import objectmodel
-import itertools
-
-class Match:
- def __init__(self, interface=None, dist=0):
- self.interface = interface
- self.dist = dist
- self.info_dir_change = False
-
- def __cmp__(self, other):
- if self.dist == other.dist:
- if self.info_dir_change:
- if other.info_dir_change:
- return 0
- else:
- return 1
- else:
- if other.info_dir_change:
- return -1
- else:
- return 0
- else:
- if self.dist < other.dist:
- return -1
- else:
- return 1
-
-class MatchList:
- DEFAULT_THRESHOLD = 150
- def __init__(self):
- # Match objects that pass the threshold
- self.children = []
- # Match objects over the threshold
- self.bastards = []
- self.threshold = self.DEFAULT_THRESHOLD
- self.allow_info_dir_change = False
- self.av = None
-
- def best(self):
- if len(self.children):
- return self.children[0]
- if len(self.bastards):
- return self.bastards[0]
- return None
-
- def __len__(self):
- # Only return the length of the matches so
- # that this can be used to test if there is
- # a match.
- return len(self.children) + len(self.bastards)
-
- def __iter__(self):
- return iter(self.children)
-
- def all(self):
- return itertools.chain(self.children, self.bastards)
-
- def append(self, match):
- if match.dist <= self.threshold:
- if not match.info_dir_change or self.allow_info_dir_change:
- self.children.append(match)
- else:
- self.bastards.append(match)
- else:
- self.bastards.append(match)
-
- def sort(self):
- self.children.sort()
- self.bastards.sort()
-
-
-class AccessMatcher:
- def __init__(self, perm_maps=None):
- self.type_penalty = 10
- self.obj_penalty = 10
- if perm_maps:
- self.perm_maps = perm_maps
- else:
- self.perm_maps = objectmodel.PermMappings()
- # We want a change in the information flow direction
- # to be a strong penalty - stronger than access to
- # a few unrelated types.
- self.info_dir_penalty = 100
-
- def type_distance(self, a, b):
- if a == b or access.is_idparam(b):
- return 0
- else:
- return -self.type_penalty
-
-
- def perm_distance(self, av_req, av_prov):
- # First check that we have enough perms
- diff = av_req.perms.difference(av_prov.perms)
-
- if len(diff) != 0:
- total = self.perm_maps.getdefault_distance(av_req.obj_class, diff)
- return -total
- else:
- diff = av_prov.perms.difference(av_req.perms)
- return self.perm_maps.getdefault_distance(av_req.obj_class, diff)
-
- def av_distance(self, req, prov):
- """Determine the 'distance' between 2 access vectors.
-
- This function is used to find an access vector that matches
- a 'required' access. To do this we comput a signed numeric
- value that indicates how close the req access is to the
- 'provided' access vector. The closer the value is to 0
- the closer the match, with 0 being an exact match.
-
- A value over 0 indicates that the prov access vector provides more
- access than the req (in practice, this means that the source type,
- target type, and object class is the same and the perms in prov is
- a superset of those in req.
-
- A value under 0 indicates that the prov access less - or unrelated
- - access to the req access. A different type or object class will
- result in a very low value.
-
- The values other than 0 should only be interpreted relative to
- one another - they have no exact meaning and are likely to
- change.
-
- Params:
- req - [AccessVector] The access that is required. This is the
- access being matched.
- prov - [AccessVector] The access provided. This is the potential
- match that is being evaluated for req.
- Returns:
- 0 : Exact match between the acess vectors.
-
- < 0 : The prov av does not provide all of the access in req.
- A smaller value indicates that the access is further.
-
- > 0 : The prov av provides more access than req. The larger
- the value the more access over req.
- """
- # FUTURE - this is _very_ expensive and probably needs some
- # thorough performance work. This version is meant to give
- # meaningful results relatively simply.
- dist = 0
-
- # Get the difference between the types. The addition is safe
- # here because type_distance only returns 0 or negative.
- dist += self.type_distance(req.src_type, prov.src_type)
- dist += self.type_distance(req.tgt_type, prov.tgt_type)
-
- # Object class distance
- if req.obj_class != prov.obj_class and not access.is_idparam(prov.obj_class):
- dist -= self.obj_penalty
-
- # Permission distance
-
- # If this av doesn't have a matching source type, target type, and object class
- # count all of the permissions against it. Otherwise determine the perm
- # distance and dir.
- if dist < 0:
- pdist = self.perm_maps.getdefault_distance(prov.obj_class, prov.perms)
- else:
- pdist = self.perm_distance(req, prov)
-
- # Combine the perm and other distance
- if dist < 0:
- if pdist < 0:
- return dist + pdist
- else:
- return dist - pdist
- elif dist >= 0:
- if pdist < 0:
- return pdist - dist
- else:
- return dist + pdist
-
- def av_set_match(self, av_set, av):
- """
-
- """
- dist = None
-
- # Get the distance for each access vector
- for x in av_set:
- tmp = self.av_distance(av, x)
- if dist is None:
- dist = tmp
- elif tmp >= 0:
- if dist >= 0:
- dist += tmp
- else:
- dist = tmp + -dist
- else:
- if dist < 0:
- dist += tmp
- else:
- dist -= tmp
-
- # Penalize for information flow - we want to prevent the
- # addition of a write if the requested is read none. We are
- # much less concerned about the reverse.
- av_dir = self.perm_maps.getdefault_direction(av.obj_class, av.perms)
-
- if av_set.info_dir is None:
- av_set.info_dir = objectmodel.FLOW_NONE
- for x in av_set:
- av_set.info_dir = av_set.info_dir | \
- self.perm_maps.getdefault_direction(x.obj_class, x.perms)
- if (av_dir & objectmodel.FLOW_WRITE == 0) and (av_set.info_dir & objectmodel.FLOW_WRITE):
- if dist < 0:
- dist -= self.info_dir_penalty
- else:
- dist += self.info_dir_penalty
-
- return dist
-
- def search_ifs(self, ifset, av, match_list):
- match_list.av = av
- for iv in itertools.chain(ifset.tgt_type_all,
- ifset.tgt_type_map.get(av.tgt_type, [])):
- if not iv.enabled:
- #print "iv %s not enabled" % iv.name
- continue
-
- dist = self.av_set_match(iv.access, av)
- if dist >= 0:
- m = Match(iv, dist)
- match_list.append(m)
-
-
- match_list.sort()
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/module.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/module.py
deleted file mode 100644
index 7fc9443..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/module.py
+++ /dev/null
@@ -1,213 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-Utilities for dealing with the compilation of modules and creation
-of module tress.
-"""
-
-import defaults
-
-import selinux
-
-import re
-import tempfile
-import commands
-import os
-import os.path
-import subprocess
-import shutil
-
-def is_valid_name(modname):
- """Check that a module name is valid.
- """
- m = re.findall("[^a-zA-Z0-9_\-\.]", modname)
- if len(m) == 0 and modname[0].isalpha():
- return True
- else:
- return False
-
-class ModuleTree:
- def __init__(self, modname):
- self.modname = modname
- self.dirname = None
-
- def dir_name(self):
- return self.dirname
-
- def te_name(self):
- return self.dirname + "/" + self.modname + ".te"
-
- def fc_name(self):
- return self.dirname + "/" + self.modname + ".fc"
-
- def if_name(self):
- return self.dirname + "/" + self.modname + ".if"
-
- def package_name(self):
- return self.dirname + "/" + self.modname + ".pp"
-
- def makefile_name(self):
- return self.dirname + "/Makefile"
-
- def create(self, parent_dirname, makefile_include=None):
- self.dirname = parent_dirname + "/" + self.modname
- os.mkdir(self.dirname)
- fd = open(self.makefile_name(), "w")
- if makefile_include:
- fd.write("include " + makefile_include)
- else:
- fd.write("include " + defaults.refpolicy_makefile())
- fd.close()
-
- # Create empty files for the standard refpolicy
- # module files
- open(self.te_name(), "w").close()
- open(self.fc_name(), "w").close()
- open(self.if_name(), "w").close()
-
-def modname_from_sourcename(sourcename):
- return os.path.splitext(os.path.split(sourcename)[1])[0]
-
-class ModuleCompiler:
- """ModuleCompiler eases running of the module compiler.
-
- The ModuleCompiler class encapsulates running the commandline
- module compiler (checkmodule) and module packager (semodule_package).
- You are likely interested in the create_module_package method.
-
- Several options are controlled via paramaters (only effects the
- non-refpol builds):
-
- .mls [boolean] Generate an MLS module (by passed -M to
- checkmodule). True to generate an MLS module, false
- otherwise.
-
- .module [boolean] Generate a module instead of a base module.
- True to generate a module, false to generate a base.
-
- .checkmodule [string] Fully qualified path to the module compiler.
- Default is /usr/bin/checkmodule.
-
- .semodule_package [string] Fully qualified path to the module
- packager. Defaults to /usr/bin/semodule_package.
- .output [file object] File object used to write verbose
- output of the compililation and packaging process.
- """
- def __init__(self, output=None):
- """Create a ModuleCompiler instance, optionally with an
- output file object for verbose output of the compilation process.
- """
- self.mls = selinux.is_selinux_mls_enabled()
- self.module = True
- self.checkmodule = "/usr/bin/checkmodule"
- self.semodule_package = "/usr/bin/semodule_package"
- self.output = output
- self.last_output = ""
- self.refpol_makefile = defaults.refpolicy_makefile()
- self.make = "/usr/bin/make"
-
- def o(self, str):
- if self.output:
- self.output.write(str + "\n")
- self.last_output = str
-
- def run(self, command):
- self.o(command)
- rc, output = commands.getstatusoutput(command)
- self.o(output)
-
- return rc
-
- def gen_filenames(self, sourcename):
- """Generate the module and policy package filenames from
- a source file name. The source file must be in the form
- of "foo.te". This will generate "foo.mod" and "foo.pp".
-
- Returns a tuple with (modname, policypackage).
- """
- splitname = sourcename.split(".")
- if len(splitname) < 2:
- raise RuntimeError("invalid sourcefile name %s (must end in .te)", sourcename)
- # Handle other periods in the filename correctly
- basename = ".".join(splitname[0:-1])
- modname = basename + ".mod"
- packagename = basename + ".pp"
-
- return (modname, packagename)
-
- def create_module_package(self, sourcename, refpolicy=True):
- """Create a module package saved in a packagename from a
- sourcename.
-
- The create_module_package creates a module package saved in a
- file named sourcename (.pp is the standard extension) from a
- source file (.te is the standard extension). The source file
- should contain SELinux policy statements appropriate for a
- base or non-base module (depending on the setting of .module).
-
- Only file names are accepted, not open file objects or
- descriptors because the command line SELinux tools are used.
-
- On error a RuntimeError will be raised with a descriptive
- error message.
- """
- if refpolicy:
- self.refpol_build(sourcename)
- else:
- modname, packagename = self.gen_filenames(sourcename)
- self.compile(sourcename, modname)
- self.package(modname, packagename)
- os.unlink(modname)
-
- def refpol_build(self, sourcename):
- # Compile
- command = self.make + " -f " + self.refpol_makefile
- rc = self.run(command)
-
- # Raise an error if the process failed
- if rc != 0:
- raise RuntimeError("compilation failed:\n%s" % self.last_output)
-
- def compile(self, sourcename, modname):
- s = [self.checkmodule]
- if self.mls:
- s.append("-M")
- if self.module:
- s.append("-m")
- s.append("-o")
- s.append(modname)
- s.append(sourcename)
-
- rc = self.run(" ".join(s))
- if rc != 0:
- raise RuntimeError("compilation failed:\n%s" % self.last_output)
-
- def package(self, modname, packagename):
- s = [self.semodule_package]
- s.append("-o")
- s.append(packagename)
- s.append("-m")
- s.append(modname)
-
- rc = self.run(" ".join(s))
- if rc != 0:
- raise RuntimeError("packaging failed [%s]" % self.last_output)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/objectmodel.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/objectmodel.py
deleted file mode 100644
index 88c8a1f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/objectmodel.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-This module provides knowledge object classes and permissions. It should
-be used to keep this knowledge from leaking into the more generic parts of
-the policy generation.
-"""
-
-# Objects that can be implicitly typed - these objects do
-# not _have_ to be implicitly typed (e.g., sockets can be
-# explicitly labeled), but they often are.
-#
-# File is in this list for /proc/self
-#
-# This list is useful when dealing with rules that have a
-# type (or param) used as both a subject and object. For
-# example:
-#
-# allow httpd_t httpd_t : socket read;
-#
-# This rule makes sense because the socket was (presumably) created
-# by a process with the type httpd_t.
-implicitly_typed_objects = ["socket", "fd", "process", "file", "lnk_file", "fifo_file",
- "dbus", "capability", "unix_stream_socket"]
-
-#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-#
-#Information Flow
-#
-# All of the permissions in SELinux can be described in terms of
-# information flow. For example, a read of a file is a flow of
-# information from that file to the process reading. Viewing
-# permissions in these terms can be used to model a varity of
-# security properties.
-#
-# Here we have some infrastructure for understanding permissions
-# in terms of information flow
-#
-#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-
-# Information flow deals with information either flowing from a subject
-# to and object ("write") or to a subject from an object ("read"). Read
-# or write is described from the subject point-of-view. It is also possible
-# for a permission to represent both a read and write (though the flow is
-# typical asymettric in terms of bandwidth). It is also possible for
-# permission to not flow information (meaning that the result is pure
-# side-effect).
-#
-# The following constants are for representing the directionality
-# of information flow.
-FLOW_NONE = 0
-FLOW_READ = 1
-FLOW_WRITE = 2
-FLOW_BOTH = FLOW_READ | FLOW_WRITE
-
-# These are used by the parser and for nice disply of the directions
-str_to_dir = { "n" : FLOW_NONE, "r" : FLOW_READ, "w" : FLOW_WRITE, "b" : FLOW_BOTH }
-dir_to_str = { FLOW_NONE : "n", FLOW_READ : "r", FLOW_WRITE : "w", FLOW_BOTH : "b" }
-
-class PermMap:
- """A mapping between a permission and its information flow properties.
-
- PermMap represents the information flow properties of a single permission
- including the direction (read, write, etc.) and an abstract representation
- of the bandwidth of the flow (weight).
- """
- def __init__(self, perm, dir, weight):
- self.perm = perm
- self.dir = dir
- self.weight = weight
-
- def __repr__(self):
- return "<sepolgen.objectmodel.PermMap %s %s %d>" % (self.perm,
- dir_to_str[self.dir],
- self.weight)
-
-class PermMappings:
- """The information flow properties of a set of object classes and permissions.
-
- PermMappings maps one or more classes and permissions to their PermMap objects
- describing their information flow charecteristics.
- """
- def __init__(self):
- self.classes = { }
- self.default_weight = 5
- self.default_dir = FLOW_BOTH
-
- def from_file(self, fd):
- """Read the permission mappings from a file. This reads the format used
- by Apol in the setools suite.
- """
- # This parsing is deliberitely picky and bails at the least error. It
- # is assumed that the permission map file will be shipped as part
- # of sepolgen and not user modified, so this is a reasonable design
- # choice. If user supplied permission mappings are needed the parser
- # should be made a little more robust and give better error messages.
- cur = None
- for line in fd:
- fields = line.split()
- if len(fields) == 0 or len(fields) == 1 or fields[0] == "#":
- continue
- if fields[0] == "class":
- c = fields[1]
- if self.classes.has_key(c):
- raise ValueError("duplicate class in perm map")
- self.classes[c] = { }
- cur = self.classes[c]
- else:
- if len(fields) != 3:
- raise ValueError("error in object classs permissions")
- if cur is None:
- raise ValueError("permission outside of class")
- pm = PermMap(fields[0], str_to_dir[fields[1]], int(fields[2]))
- cur[pm.perm] = pm
-
- def get(self, obj, perm):
- """Get the permission map for the object permission.
-
- Returns:
- PermMap representing the permission
- Raises:
- KeyError if the object or permission is not defined
- """
- return self.classes[obj][perm]
-
- def getdefault(self, obj, perm):
- """Get the permission map for the object permission or a default.
-
- getdefault is the same as get except that a default PermMap is
- returned if the object class or permission is not defined. The
- default is FLOW_BOTH with a weight of 5.
- """
- try:
- pm = self.classes[obj][perm]
- except KeyError:
- return PermMap(perm, self.default_dir, self.default_weight)
- return pm
-
- def getdefault_direction(self, obj, perms):
- dir = FLOW_NONE
- for perm in perms:
- pm = self.getdefault(obj, perm)
- dir = dir | pm.dir
- return dir
-
- def getdefault_distance(self, obj, perms):
- total = 0
- for perm in perms:
- pm = self.getdefault(obj, perm)
- total += pm.weight
-
- return total
-
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/output.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/output.py
deleted file mode 100644
index 739452d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/output.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-Classes and functions for the output of reference policy modules.
-
-This module takes a refpolicy.Module object and formats it for
-output using the ModuleWriter object. By separating the output
-in this way the other parts of Madison can focus solely on
-generating policy. This keeps the semantic / syntactic issues
-cleanly separated from the formatting issues.
-"""
-
-import refpolicy
-import util
-
-class ModuleWriter:
- def __init__(self):
- self.fd = None
- self.module = None
- self.sort = True
- self.requires = True
-
- def write(self, module, fd):
- self.module = module
-
- if self.sort:
- sort_filter(self.module)
-
- # FIXME - make this handle nesting
- for node, depth in refpolicy.walktree(self.module, showdepth=True):
- fd.write("%s\n" % str(node))
-
-# Helper functions for sort_filter - this is all done old school
-# C style rather than with polymorphic methods because this sorting
-# is specific to output. It is not necessarily the comparison you
-# want generally.
-
-# Compare two IdSets - we could probably do something clever
-# with different here, but this works.
-def id_set_cmp(x, y):
- xl = util.set_to_list(x)
- xl.sort()
- yl = util.set_to_list(y)
- yl.sort()
-
- if len(xl) != len(yl):
- return cmp(xl[0], yl[0])
- for v in zip(xl, yl):
- if v[0] != v[1]:
- return cmp(v[0], v[1])
- return 0
-
-# Compare two avrules
-def avrule_cmp(a, b):
- ret = id_set_cmp(a.src_types, b.src_types)
- if ret is not 0:
- return ret
- ret = id_set_cmp(a.tgt_types, b.tgt_types)
- if ret is not 0:
- return ret
- ret = id_set_cmp(a.obj_classes, b.obj_classes)
- if ret is not 0:
- return ret
-
- # At this point, who cares - just return something
- return cmp(len(a.perms), len(b.perms))
-
-# Compare two interface calls
-def ifcall_cmp(a, b):
- if a.args[0] != b.args[0]:
- return cmp(a.args[0], b.args[0])
- return cmp(a.ifname, b.ifname)
-
-# Compare an two avrules or interface calls
-def rule_cmp(a, b):
- if isinstance(a, refpolicy.InterfaceCall):
- if isinstance(b, refpolicy.InterfaceCall):
- return ifcall_cmp(a, b)
- else:
- return id_set_cmp([a.args[0]], b.src_types)
- else:
- if isinstance(b, refpolicy.AVRule):
- return avrule_cmp(a,b)
- else:
- return id_set_cmp(a.src_types, [b.args[0]])
-
-def role_type_cmp(a, b):
- return cmp(a.role, b.role)
-
-def sort_filter(module):
- """Sort and group the output for readability.
- """
- def sort_node(node):
- c = []
-
- # Module statement
- for mod in node.module_declarations():
- c.append(mod)
- c.append(refpolicy.Comment())
-
- # Requires
- for require in node.requires():
- c.append(require)
- c.append(refpolicy.Comment())
-
- # Rules
- #
- # We are going to group output by source type (which
- # we assume is the first argument for interfaces).
- rules = []
- rules.extend(node.avrules())
- rules.extend(node.interface_calls())
- rules.sort(rule_cmp)
-
- cur = None
- sep_rules = []
- for rule in rules:
- if isinstance(rule, refpolicy.InterfaceCall):
- x = rule.args[0]
- else:
- x = util.first(rule.src_types)
-
- if cur != x:
- if cur:
- sep_rules.append(refpolicy.Comment())
- cur = x
- comment = refpolicy.Comment()
- comment.lines.append("============= %s ==============" % cur)
- sep_rules.append(comment)
- sep_rules.append(rule)
-
- c.extend(sep_rules)
-
-
- ras = []
- ras.extend(node.role_types())
- ras.sort(role_type_cmp)
- if len(ras):
- comment = refpolicy.Comment()
- comment.lines.append("============= ROLES ==============")
- c.append(comment)
-
-
- c.extend(ras)
-
- # Everything else
- for child in node.children:
- if child not in c:
- c.append(child)
-
- node.children = c
-
- for node in module.nodes():
- sort_node(node)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/policygen.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/policygen.py
deleted file mode 100644
index 5f38577..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/policygen.py
+++ /dev/null
@@ -1,402 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-"""
-classes and algorithms for the generation of SELinux policy.
-"""
-
-import itertools
-import textwrap
-
-import refpolicy
-import objectmodel
-import access
-import interfaces
-import matching
-import selinux.audit2why as audit2why
-try:
- from setools import *
-except:
- pass
-
-# Constants for the level of explanation from the generation
-# routines
-NO_EXPLANATION = 0
-SHORT_EXPLANATION = 1
-LONG_EXPLANATION = 2
-
-class PolicyGenerator:
- """Generate a reference policy module from access vectors.
-
- PolicyGenerator generates a new reference policy module
- or updates an existing module based on requested access
- in the form of access vectors.
-
- It generates allow rules and optionally module require
- statements and reference policy interfaces. By default
- only allow rules are generated. The methods .set_gen_refpol
- and .set_gen_requires turns on interface generation and
- requires generation respectively.
-
- PolicyGenerator can also optionally add comments explaining
- why a particular access was allowed based on the audit
- messages that generated the access. The access vectors
- passed in must have the .audit_msgs field set correctly
- and .explain set to SHORT|LONG_EXPLANATION to enable this
- feature.
-
- The module created by PolicyGenerator can be passed to
- output.ModuleWriter to output a text representation.
- """
- def __init__(self, module=None):
- """Initialize a PolicyGenerator with an optional
- existing module.
-
- If the module paramater is not None then access
- will be added to the passed in module. Otherwise
- a new reference policy module will be created.
- """
- self.ifgen = None
- self.explain = NO_EXPLANATION
- self.gen_requires = False
- if module:
- self.moduel = module
- else:
- self.module = refpolicy.Module()
-
- self.dontaudit = False
-
- self.domains = None
- def set_gen_refpol(self, if_set=None, perm_maps=None):
- """Set whether reference policy interfaces are generated.
-
- To turn on interface generation pass in an interface set
- to use for interface generation. To turn off interface
- generation pass in None.
-
- If interface generation is enabled requires generation
- will also be enabled.
- """
- if if_set:
- self.ifgen = InterfaceGenerator(if_set, perm_maps)
- self.gen_requires = True
- else:
- self.ifgen = None
- self.__set_module_style()
-
-
- def set_gen_requires(self, status=True):
- """Set whether module requires are generated.
-
- Passing in true will turn on requires generation and
- False will disable generation. If requires generation is
- disabled interface generation will also be disabled and
- can only be re-enabled via .set_gen_refpol.
- """
- self.gen_requires = status
-
- def set_gen_explain(self, explain=SHORT_EXPLANATION):
- """Set whether access is explained.
- """
- self.explain = explain
-
- def set_gen_dontaudit(self, dontaudit):
- self.dontaudit = dontaudit
-
- def __set_module_style(self):
- if self.ifgen:
- refpolicy = True
- else:
- refpolicy = False
- for mod in self.module.module_declarations():
- mod.refpolicy = refpolicy
-
- def set_module_name(self, name, version="1.0"):
- """Set the name of the module and optionally the version.
- """
- # find an existing module declaration
- m = None
- for mod in self.module.module_declarations():
- m = mod
- if not m:
- m = refpolicy.ModuleDeclaration()
- self.module.children.insert(0, m)
- m.name = name
- m.version = version
- if self.ifgen:
- m.refpolicy = True
- else:
- m.refpolicy = False
-
- def get_module(self):
- # Generate the requires
- if self.gen_requires:
- gen_requires(self.module)
-
- """Return the generated module"""
- return self.module
-
- def __add_allow_rules(self, avs):
- for av in avs:
- rule = refpolicy.AVRule(av)
- if self.dontaudit:
- rule.rule_type = rule.DONTAUDIT
- rule.comment = ""
- if self.explain:
- rule.comment = str(refpolicy.Comment(explain_access(av, verbosity=self.explain)))
- if av.type == audit2why.ALLOW:
- rule.comment += "\n#!!!! This avc is allowed in the current policy"
- if av.type == audit2why.DONTAUDIT:
- rule.comment += "\n#!!!! This avc has a dontaudit rule in the current policy"
-
- if av.type == audit2why.BOOLEAN:
- if len(av.data) > 1:
- rule.comment += "\n#!!!! This avc can be allowed using one of the these booleans:\n# %s" % ", ".join(map(lambda x: x[0], av.data))
- else:
- rule.comment += "\n#!!!! This avc can be allowed using the boolean '%s'" % av.data[0][0]
-
- if av.type == audit2why.CONSTRAINT:
- rule.comment += "\n#!!!! This avc is a constraint violation. You would need to modify the attributes of either the source or target types to allow this access."
- rule.comment += "\n#Constraint rule: "
- rule.comment += "\n\t" + av.data[0]
- for reason in av.data[1:]:
- rule.comment += "\n#\tPossible cause is the source %s and target %s are different." % reason
-
- try:
- if ( av.type == audit2why.TERULE and
- "write" in av.perms and
- ( "dir" in av.obj_class or "open" in av.perms )):
- if not self.domains:
- self.domains = seinfo(ATTRIBUTE, name="domain")[0]["types"]
- types=[]
-
- for i in map(lambda x: x[TCONTEXT], sesearch([ALLOW], {SCONTEXT: av.src_type, CLASS: av.obj_class, PERMS: av.perms})):
- if i not in self.domains:
- types.append(i)
- if len(types) == 1:
- rule.comment += "\n#!!!! The source type '%s' can write to a '%s' of the following type:\n# %s\n" % ( av.src_type, av.obj_class, ", ".join(types))
- elif len(types) >= 1:
- rule.comment += "\n#!!!! The source type '%s' can write to a '%s' of the following types:\n# %s\n" % ( av.src_type, av.obj_class, ", ".join(types))
- except:
- pass
- self.module.children.append(rule)
-
-
- def add_access(self, av_set):
- """Add the access from the access vector set to this
- module.
- """
- # Use the interface generator to split the access
- # into raw allow rules and interfaces. After this
- # a will contain a list of access that should be
- # used as raw allow rules and the interfaces will
- # be added to the module.
- if self.ifgen:
- raw_allow, ifcalls = self.ifgen.gen(av_set, self.explain)
- self.module.children.extend(ifcalls)
- else:
- raw_allow = av_set
-
- # Generate the raw allow rules from the filtered list
- self.__add_allow_rules(raw_allow)
-
- def add_role_types(self, role_type_set):
- for role_type in role_type_set:
- self.module.children.append(role_type)
-
-def explain_access(av, ml=None, verbosity=SHORT_EXPLANATION):
- """Explain why a policy statement was generated.
-
- Return a string containing a text explanation of
- why a policy statement was generated. The string is
- commented and wrapped and can be directly inserted
- into a policy.
-
- Params:
- av - access vector representing the access. Should
- have .audit_msgs set appropriately.
- verbosity - the amount of explanation provided. Should
- be set to NO_EXPLANATION, SHORT_EXPLANATION, or
- LONG_EXPLANATION.
- Returns:
- list of strings - strings explaining the access or an empty
- string if verbosity=NO_EXPLANATION or there is not sufficient
- information to provide an explanation.
- """
- s = []
-
- def explain_interfaces():
- if not ml:
- return
- s.append(" Interface options:")
- for match in ml.all():
- ifcall = call_interface(match.interface, ml.av)
- s.append(' %s # [%d]' % (ifcall.to_string(), match.dist))
-
-
- # Format the raw audit data to explain why the
- # access was requested - either long or short.
- if verbosity == LONG_EXPLANATION:
- for msg in av.audit_msgs:
- s.append(' %s' % msg.header)
- s.append(' scontext="%s" tcontext="%s"' %
- (str(msg.scontext), str(msg.tcontext)))
- s.append(' class="%s" perms="%s"' %
- (msg.tclass, refpolicy.list_to_space_str(msg.accesses)))
- s.append(' comm="%s" exe="%s" path="%s"' % (msg.comm, msg.exe, msg.path))
- s.extend(textwrap.wrap('message="' + msg.message + '"', 80, initial_indent=" ",
- subsequent_indent=" "))
- explain_interfaces()
- elif verbosity:
- s.append(' src="%s" tgt="%s" class="%s", perms="%s"' %
- (av.src_type, av.tgt_type, av.obj_class, av.perms.to_space_str()))
- # For the short display we are only going to use the additional information
- # from the first audit message. For the vast majority of cases this info
- # will always be the same anyway.
- if len(av.audit_msgs) > 0:
- msg = av.audit_msgs[0]
- s.append(' comm="%s" exe="%s" path="%s"' % (msg.comm, msg.exe, msg.path))
- explain_interfaces()
- return s
-
-def param_comp(a, b):
- return cmp(b.num, a.num)
-
-def call_interface(interface, av):
- params = []
- args = []
-
- params.extend(interface.params.values())
- params.sort(param_comp)
-
- ifcall = refpolicy.InterfaceCall()
- ifcall.ifname = interface.name
-
- for i in range(len(params)):
- if params[i].type == refpolicy.SRC_TYPE:
- ifcall.args.append(av.src_type)
- elif params[i].type == refpolicy.TGT_TYPE:
- ifcall.args.append(av.tgt_type)
- elif params[i].type == refpolicy.OBJ_CLASS:
- ifcall.args.append(av.obj_class)
- else:
- print params[i].type
- assert(0)
-
- assert(len(ifcall.args) > 0)
-
- return ifcall
-
-class InterfaceGenerator:
- def __init__(self, ifs, perm_maps=None):
- self.ifs = ifs
- self.hack_check_ifs(ifs)
- self.matcher = matching.AccessMatcher(perm_maps)
- self.calls = []
-
- def hack_check_ifs(self, ifs):
- # FIXME: Disable interfaces we can't call - this is a hack.
- # Because we don't handle roles, multiple paramaters, etc.,
- # etc., we must make certain we can actually use a returned
- # interface.
- for x in ifs.interfaces.values():
- params = []
- params.extend(x.params.values())
- params.sort(param_comp)
- for i in range(len(params)):
- # Check that the paramater position matches
- # the number (e.g., $1 is the first arg). This
- # will fail if the parser missed something.
- if (i + 1) != params[i].num:
- x.enabled = False
- break
- # Check that we can handle the param type (currently excludes
- # roles.
- if params[i].type not in [refpolicy.SRC_TYPE, refpolicy.TGT_TYPE,
- refpolicy.OBJ_CLASS]:
- x.enabled = False
- break
-
- def gen(self, avs, verbosity):
- raw_av = self.match(avs)
- ifcalls = []
- for ml in self.calls:
- ifcall = call_interface(ml.best().interface, ml.av)
- if verbosity:
- ifcall.comment = refpolicy.Comment(explain_access(ml.av, ml, verbosity))
- ifcalls.append((ifcall, ml))
-
- d = []
- for ifcall, ifs in ifcalls:
- found = False
- for o_ifcall in d:
- if o_ifcall.matches(ifcall):
- if o_ifcall.comment and ifcall.comment:
- o_ifcall.comment.merge(ifcall.comment)
- found = True
- if not found:
- d.append(ifcall)
-
- return (raw_av, d)
-
-
- def match(self, avs):
- raw_av = []
- for av in avs:
- ans = matching.MatchList()
- self.matcher.search_ifs(self.ifs, av, ans)
- if len(ans):
- self.calls.append(ans)
- else:
- raw_av.append(av)
-
- return raw_av
-
-
-def gen_requires(module):
- """Add require statements to the module.
- """
- def collect_requires(node):
- r = refpolicy.Require()
- for avrule in node.avrules():
- r.types.update(avrule.src_types)
- r.types.update(avrule.tgt_types)
- for obj in avrule.obj_classes:
- r.add_obj_class(obj, avrule.perms)
-
- for ifcall in node.interface_calls():
- for arg in ifcall.args:
- # FIXME - handle non-type arguments when we
- # can actually figure those out.
- r.types.add(arg)
-
- for role_type in node.role_types():
- r.roles.add(role_type.role)
- r.types.update(role_type.types)
-
- r.types.discard("self")
-
- node.children.insert(0, r)
-
- # FUTURE - this is untested on modules with any sort of
- # nesting
- for node in module.nodes():
- collect_requires(node)
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/refparser.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/refparser.py
deleted file mode 100644
index 83542d3..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/refparser.py
+++ /dev/null
@@ -1,1128 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006-2007 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-# OVERVIEW
-#
-#
-# This is a parser for the refpolicy policy "language" - i.e., the
-# normal SELinux policy language plus the refpolicy style M4 macro
-# constructs on top of that base language. This parser is primarily
-# aimed at parsing the policy headers in order to create an abstract
-# policy representation suitable for generating policy.
-#
-# Both the lexer and parser are included in this file. The are implemented
-# using the Ply library (included with sepolgen).
-
-import sys
-import os
-import re
-import traceback
-
-import refpolicy
-import access
-import defaults
-
-import lex
-import yacc
-
-# :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-#
-# lexer
-#
-# :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-
-tokens = (
- # basic tokens, punctuation
- 'TICK',
- 'SQUOTE',
- 'OBRACE',
- 'CBRACE',
- 'SEMI',
- 'COLON',
- 'OPAREN',
- 'CPAREN',
- 'COMMA',
- 'MINUS',
- 'TILDE',
- 'ASTERISK',
- 'AMP',
- 'BAR',
- 'EXPL',
- 'EQUAL',
- 'FILENAME',
- 'IDENTIFIER',
- 'NUMBER',
- 'PATH',
- 'IPV6_ADDR',
- # reserved words
- # module
- 'MODULE',
- 'POLICY_MODULE',
- 'REQUIRE',
- # flask
- 'SID',
- 'GENFSCON',
- 'FS_USE_XATTR',
- 'FS_USE_TRANS',
- 'FS_USE_TASK',
- 'PORTCON',
- 'NODECON',
- 'NETIFCON',
- 'PIRQCON',
- 'IOMEMCON',
- 'IOPORTCON',
- 'PCIDEVICECON',
- 'DEVICETREECON',
- # object classes
- 'CLASS',
- # types and attributes
- 'TYPEATTRIBUTE',
- 'ROLEATTRIBUTE',
- 'TYPE',
- 'ATTRIBUTE',
- 'ATTRIBUTE_ROLE',
- 'ALIAS',
- 'TYPEALIAS',
- # conditional policy
- 'BOOL',
- 'TRUE',
- 'FALSE',
- 'IF',
- 'ELSE',
- # users and roles
- 'ROLE',
- 'TYPES',
- # rules
- 'ALLOW',
- 'DONTAUDIT',
- 'AUDITALLOW',
- 'NEVERALLOW',
- 'PERMISSIVE',
- 'TYPE_TRANSITION',
- 'TYPE_CHANGE',
- 'TYPE_MEMBER',
- 'RANGE_TRANSITION',
- 'ROLE_TRANSITION',
- # refpolicy keywords
- 'OPT_POLICY',
- 'INTERFACE',
- 'TUNABLE_POLICY',
- 'GEN_REQ',
- 'TEMPLATE',
- 'GEN_CONTEXT',
- # m4
- 'IFELSE',
- 'IFDEF',
- 'IFNDEF',
- 'DEFINE'
- )
-
-# All reserved keywords - see t_IDENTIFIER for how these are matched in
-# the lexer.
-reserved = {
- # module
- 'module' : 'MODULE',
- 'policy_module' : 'POLICY_MODULE',
- 'require' : 'REQUIRE',
- # flask
- 'sid' : 'SID',
- 'genfscon' : 'GENFSCON',
- 'fs_use_xattr' : 'FS_USE_XATTR',
- 'fs_use_trans' : 'FS_USE_TRANS',
- 'fs_use_task' : 'FS_USE_TASK',
- 'portcon' : 'PORTCON',
- 'nodecon' : 'NODECON',
- 'netifcon' : 'NETIFCON',
- 'pirqcon' : 'PIRQCON',
- 'iomemcon' : 'IOMEMCON',
- 'ioportcon' : 'IOPORTCON',
- 'pcidevicecon' : 'PCIDEVICECON',
- 'devicetreecon' : 'DEVICETREECON',
- # object classes
- 'class' : 'CLASS',
- # types and attributes
- 'typeattribute' : 'TYPEATTRIBUTE',
- 'roleattribute' : 'ROLEATTRIBUTE',
- 'type' : 'TYPE',
- 'attribute' : 'ATTRIBUTE',
- 'attribute_role' : 'ATTRIBUTE_ROLE',
- 'alias' : 'ALIAS',
- 'typealias' : 'TYPEALIAS',
- # conditional policy
- 'bool' : 'BOOL',
- 'true' : 'TRUE',
- 'false' : 'FALSE',
- 'if' : 'IF',
- 'else' : 'ELSE',
- # users and roles
- 'role' : 'ROLE',
- 'types' : 'TYPES',
- # rules
- 'allow' : 'ALLOW',
- 'dontaudit' : 'DONTAUDIT',
- 'auditallow' : 'AUDITALLOW',
- 'neverallow' : 'NEVERALLOW',
- 'permissive' : 'PERMISSIVE',
- 'type_transition' : 'TYPE_TRANSITION',
- 'type_change' : 'TYPE_CHANGE',
- 'type_member' : 'TYPE_MEMBER',
- 'range_transition' : 'RANGE_TRANSITION',
- 'role_transition' : 'ROLE_TRANSITION',
- # refpolicy keywords
- 'optional_policy' : 'OPT_POLICY',
- 'interface' : 'INTERFACE',
- 'tunable_policy' : 'TUNABLE_POLICY',
- 'gen_require' : 'GEN_REQ',
- 'template' : 'TEMPLATE',
- 'gen_context' : 'GEN_CONTEXT',
- # M4
- 'ifelse' : 'IFELSE',
- 'ifndef' : 'IFNDEF',
- 'ifdef' : 'IFDEF',
- 'define' : 'DEFINE'
- }
-
-# The ply lexer allows definition of tokens in 2 ways: regular expressions
-# or functions.
-
-# Simple regex tokens
-t_TICK = r'\`'
-t_SQUOTE = r'\''
-t_OBRACE = r'\{'
-t_CBRACE = r'\}'
-# This will handle spurios extra ';' via the +
-t_SEMI = r'\;+'
-t_COLON = r'\:'
-t_OPAREN = r'\('
-t_CPAREN = r'\)'
-t_COMMA = r'\,'
-t_MINUS = r'\-'
-t_TILDE = r'\~'
-t_ASTERISK = r'\*'
-t_AMP = r'\&'
-t_BAR = r'\|'
-t_EXPL = r'\!'
-t_EQUAL = r'\='
-t_NUMBER = r'[0-9\.]+'
-t_PATH = r'/[a-zA-Z0-9)_\.\*/]*'
-#t_IPV6_ADDR = r'[a-fA-F0-9]{0,4}:[a-fA-F0-9]{0,4}:([a-fA-F0-9]{0,4}:)*'
-
-# Ignore whitespace - this is a special token for ply that more efficiently
-# ignores uninteresting tokens.
-t_ignore = " \t"
-
-# More complex tokens
-def t_IPV6_ADDR(t):
- r'[a-fA-F0-9]{0,4}:[a-fA-F0-9]{0,4}:([a-fA-F0-9]|:)*'
- # This is a function simply to force it sooner into
- # the regex list
- return t
-
-def t_m4comment(t):
- r'dnl.*\n'
- # Ignore all comments
- t.lexer.lineno += 1
-
-def t_refpolicywarn1(t):
- r'define.*refpolicywarn\(.*\n'
- # Ignore refpolicywarn statements - they sometimes
- # contain text that we can't parse.
- t.skip(1)
-
-def t_refpolicywarn(t):
- r'refpolicywarn\(.*\n'
- # Ignore refpolicywarn statements - they sometimes
- # contain text that we can't parse.
- t.lexer.lineno += 1
-
-def t_IDENTIFIER(t):
- r'[a-zA-Z_\$][a-zA-Z0-9_\-\+\.\$\*~]*'
- # Handle any keywords
- t.type = reserved.get(t.value,'IDENTIFIER')
- return t
-
-def t_FILENAME(t):
- r'\"[a-zA-Z0-9_\-\+\.\$\*~ :]+\"'
- # Handle any keywords
- t.type = reserved.get(t.value,'FILENAME')
- return t
-
-def t_comment(t):
- r'\#.*\n'
- # Ignore all comments
- t.lexer.lineno += 1
-
-def t_error(t):
- print "Illegal character '%s'" % t.value[0]
- t.skip(1)
-
-def t_newline(t):
- r'\n+'
- t.lexer.lineno += len(t.value)
-
-# :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-#
-# Parser
-#
-# :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-
-# Global data used during parsing - making it global is easier than
-# passing the state through the parsing functions.
-
-# m is the top-level data structure (stands for modules).
-m = None
-# error is either None (indicating no error) or a string error message.
-error = None
-parse_file = ""
-# spt is the support macros (e.g., obj/perm sets) - it is an instance of
-# refpolicy.SupportMacros and should always be present during parsing
-# though it may not contain any macros.
-spt = None
-success = True
-
-# utilities
-def collect(stmts, parent, val=None):
- if stmts is None:
- return
- for s in stmts:
- if s is None:
- continue
- s.parent = parent
- if val is not None:
- parent.children.insert(0, (val, s))
- else:
- parent.children.insert(0, s)
-
-def expand(ids, s):
- for id in ids:
- if spt.has_key(id):
- s.update(spt.by_name(id))
- else:
- s.add(id)
-
-# Top-level non-terminal
-def p_statements(p):
- '''statements : statement
- | statements statement
- | empty
- '''
- if len(p) == 2 and p[1]:
- m.children.append(p[1])
- elif len(p) > 2 and p[2]:
- m.children.append(p[2])
-
-def p_statement(p):
- '''statement : interface
- | template
- | obj_perm_set
- | policy
- | policy_module_stmt
- | module_stmt
- '''
- p[0] = p[1]
-
-def p_empty(p):
- 'empty :'
- pass
-
-#
-# Reference policy language constructs
-#
-
-# This is for the policy module statement (e.g., policy_module(foo,1.2.0)).
-# We have a separate terminal for either the basic language module statement
-# and interface calls to make it easier to identifier.
-def p_policy_module_stmt(p):
- 'policy_module_stmt : POLICY_MODULE OPAREN IDENTIFIER COMMA NUMBER CPAREN'
- m = refpolicy.ModuleDeclaration()
- m.name = p[3]
- m.version = p[5]
- m.refpolicy = True
- p[0] = m
-
-def p_interface(p):
- '''interface : INTERFACE OPAREN TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN
- '''
- x = refpolicy.Interface(p[4])
- collect(p[8], x)
- p[0] = x
-
-def p_template(p):
- '''template : TEMPLATE OPAREN TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN
- | DEFINE OPAREN TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN
- '''
- x = refpolicy.Template(p[4])
- collect(p[8], x)
- p[0] = x
-
-def p_define(p):
- '''define : DEFINE OPAREN TICK IDENTIFIER SQUOTE CPAREN'''
- # This is for defining single M4 values (to be used later in ifdef statements).
- # Example: define(`sulogin_no_pam'). We don't currently do anything with these
- # but we should in the future when we correctly resolve ifdef statements.
- p[0] = None
-
-def p_interface_stmts(p):
- '''interface_stmts : policy
- | interface_stmts policy
- | empty
- '''
- if len(p) == 2 and p[1]:
- p[0] = p[1]
- elif len(p) > 2:
- if not p[1]:
- if p[2]:
- p[0] = p[2]
- elif not p[2]:
- p[0] = p[1]
- else:
- p[0] = p[1] + p[2]
-
-def p_optional_policy(p):
- '''optional_policy : OPT_POLICY OPAREN TICK interface_stmts SQUOTE CPAREN
- | OPT_POLICY OPAREN TICK interface_stmts SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN
- '''
- o = refpolicy.OptionalPolicy()
- collect(p[4], o, val=True)
- if len(p) > 7:
- collect(p[8], o, val=False)
- p[0] = [o]
-
-def p_tunable_policy(p):
- '''tunable_policy : TUNABLE_POLICY OPAREN TICK cond_expr SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN
- | TUNABLE_POLICY OPAREN TICK cond_expr SQUOTE COMMA TICK interface_stmts SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN
- '''
- x = refpolicy.TunablePolicy()
- x.cond_expr = p[4]
- collect(p[8], x, val=True)
- if len(p) > 11:
- collect(p[12], x, val=False)
- p[0] = [x]
-
-def p_ifelse(p):
- '''ifelse : IFELSE OPAREN TICK IDENTIFIER SQUOTE COMMA COMMA TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN optional_semi
- | IFELSE OPAREN TICK IDENTIFIER SQUOTE COMMA TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN optional_semi
- '''
-# x = refpolicy.IfDef(p[4])
-# v = True
-# collect(p[8], x, val=v)
-# if len(p) > 12:
-# collect(p[12], x, val=False)
-# p[0] = [x]
- pass
-
-
-def p_ifdef(p):
- '''ifdef : IFDEF OPAREN TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN optional_semi
- | IFNDEF OPAREN TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN optional_semi
- | IFDEF OPAREN TICK IDENTIFIER SQUOTE COMMA TICK interface_stmts SQUOTE COMMA TICK interface_stmts SQUOTE CPAREN optional_semi
- '''
- x = refpolicy.IfDef(p[4])
- if p[1] == 'ifdef':
- v = True
- else:
- v = False
- collect(p[8], x, val=v)
- if len(p) > 12:
- collect(p[12], x, val=False)
- p[0] = [x]
-
-def p_interface_call(p):
- '''interface_call : IDENTIFIER OPAREN interface_call_param_list CPAREN
- | IDENTIFIER OPAREN CPAREN
- | IDENTIFIER OPAREN interface_call_param_list CPAREN SEMI'''
- # Allow spurious semi-colons at the end of interface calls
- i = refpolicy.InterfaceCall(ifname=p[1])
- if len(p) > 4:
- i.args.extend(p[3])
- p[0] = i
-
-def p_interface_call_param(p):
- '''interface_call_param : IDENTIFIER
- | IDENTIFIER MINUS IDENTIFIER
- | nested_id_set
- | TRUE
- | FALSE
- | FILENAME
- '''
- # Intentionally let single identifiers pass through
- # List means set, non-list identifier
- if len(p) == 2:
- p[0] = p[1]
- else:
- p[0] = [p[1], "-" + p[3]]
-
-def p_interface_call_param_list(p):
- '''interface_call_param_list : interface_call_param
- | interface_call_param_list COMMA interface_call_param
- '''
- if len(p) == 2:
- p[0] = [p[1]]
- else:
- p[0] = p[1] + [p[3]]
-
-
-def p_obj_perm_set(p):
- 'obj_perm_set : DEFINE OPAREN TICK IDENTIFIER SQUOTE COMMA TICK names SQUOTE CPAREN'
- s = refpolicy.ObjPermSet(p[4])
- s.perms = p[8]
- p[0] = s
-
-#
-# Basic SELinux policy language
-#
-
-def p_policy(p):
- '''policy : policy_stmt
- | optional_policy
- | tunable_policy
- | ifdef
- | ifelse
- | conditional
- '''
- p[0] = p[1]
-
-def p_policy_stmt(p):
- '''policy_stmt : gen_require
- | avrule_def
- | typerule_def
- | typeattribute_def
- | roleattribute_def
- | interface_call
- | role_def
- | role_allow
- | permissive
- | type_def
- | typealias_def
- | attribute_def
- | attribute_role_def
- | range_transition_def
- | role_transition_def
- | bool
- | define
- | initial_sid
- | genfscon
- | fs_use
- | portcon
- | nodecon
- | netifcon
- | pirqcon
- | iomemcon
- | ioportcon
- | pcidevicecon
- | devicetreecon
- '''
- if p[1]:
- p[0] = [p[1]]
-
-def p_module_stmt(p):
- 'module_stmt : MODULE IDENTIFIER NUMBER SEMI'
- m = refpolicy.ModuleDeclaration()
- m.name = p[2]
- m.version = p[3]
- m.refpolicy = False
- p[0] = m
-
-def p_gen_require(p):
- '''gen_require : GEN_REQ OPAREN TICK requires SQUOTE CPAREN
- | REQUIRE OBRACE requires CBRACE'''
- # We ignore the require statements - they are redundant data from our point-of-view.
- # Checkmodule will verify them later anyway so we just assume that they match what
- # is in the rest of the interface.
- pass
-
-def p_requires(p):
- '''requires : require
- | requires require
- | ifdef
- | requires ifdef
- '''
- pass
-
-def p_require(p):
- '''require : TYPE comma_list SEMI
- | ROLE comma_list SEMI
- | ATTRIBUTE comma_list SEMI
- | ATTRIBUTE_ROLE comma_list SEMI
- | CLASS comma_list SEMI
- | BOOL comma_list SEMI
- '''
- pass
-
-def p_security_context(p):
- '''security_context : IDENTIFIER COLON IDENTIFIER COLON IDENTIFIER
- | IDENTIFIER COLON IDENTIFIER COLON IDENTIFIER COLON mls_range_def'''
- # This will likely need some updates to handle complex levels
- s = refpolicy.SecurityContext()
- s.user = p[1]
- s.role = p[3]
- s.type = p[5]
- if len(p) > 6:
- s.level = p[7]
-
- p[0] = s
-
-def p_gen_context(p):
- '''gen_context : GEN_CONTEXT OPAREN security_context COMMA mls_range_def CPAREN
- '''
- # We actually store gen_context statements in a SecurityContext
- # object - it knows how to output either a bare context or a
- # gen_context statement.
- s = p[3]
- s.level = p[5]
-
- p[0] = s
-
-def p_context(p):
- '''context : security_context
- | gen_context
- '''
- p[0] = p[1]
-
-def p_initial_sid(p):
- '''initial_sid : SID IDENTIFIER context'''
- s = refpolicy.InitialSid()
- s.name = p[2]
- s.context = p[3]
- p[0] = s
-
-def p_genfscon(p):
- '''genfscon : GENFSCON IDENTIFIER PATH context'''
-
- g = refpolicy.GenfsCon()
- g.filesystem = p[2]
- g.path = p[3]
- g.context = p[4]
-
- p[0] = g
-
-def p_fs_use(p):
- '''fs_use : FS_USE_XATTR IDENTIFIER context SEMI
- | FS_USE_TASK IDENTIFIER context SEMI
- | FS_USE_TRANS IDENTIFIER context SEMI
- '''
- f = refpolicy.FilesystemUse()
- if p[1] == "fs_use_xattr":
- f.type = refpolicy.FilesystemUse.XATTR
- elif p[1] == "fs_use_task":
- f.type = refpolicy.FilesystemUse.TASK
- elif p[1] == "fs_use_trans":
- f.type = refpolicy.FilesystemUse.TRANS
-
- f.filesystem = p[2]
- f.context = p[3]
-
- p[0] = f
-
-def p_portcon(p):
- '''portcon : PORTCON IDENTIFIER NUMBER context
- | PORTCON IDENTIFIER NUMBER MINUS NUMBER context'''
- c = refpolicy.PortCon()
- c.port_type = p[2]
- if len(p) == 5:
- c.port_number = p[3]
- c.context = p[4]
- else:
- c.port_number = p[3] + "-" + p[4]
- c.context = p[5]
-
- p[0] = c
-
-def p_nodecon(p):
- '''nodecon : NODECON NUMBER NUMBER context
- | NODECON IPV6_ADDR IPV6_ADDR context
- '''
- n = refpolicy.NodeCon()
- n.start = p[2]
- n.end = p[3]
- n.context = p[4]
-
- p[0] = n
-
-def p_netifcon(p):
- 'netifcon : NETIFCON IDENTIFIER context context'
- n = refpolicy.NetifCon()
- n.interface = p[2]
- n.interface_context = p[3]
- n.packet_context = p[4]
-
- p[0] = n
-
-def p_pirqcon(p):
- 'pirqcon : PIRQCON NUMBER context'
- c = refpolicy.PirqCon()
- c.pirq_number = p[2]
- c.context = p[3]
-
- p[0] = c
-
-def p_iomemcon(p):
- '''iomemcon : IOMEMCON NUMBER context
- | IOMEMCON NUMBER MINUS NUMBER context'''
- c = refpolicy.IomemCon()
- if len(p) == 4:
- c.device_mem = p[2]
- c.context = p[3]
- else:
- c.device_mem = p[2] + "-" + p[3]
- c.context = p[4]
-
- p[0] = c
-
-def p_ioportcon(p):
- '''ioportcon : IOPORTCON NUMBER context
- | IOPORTCON NUMBER MINUS NUMBER context'''
- c = refpolicy.IoportCon()
- if len(p) == 4:
- c.ioport = p[2]
- c.context = p[3]
- else:
- c.ioport = p[2] + "-" + p[3]
- c.context = p[4]
-
- p[0] = c
-
-def p_pcidevicecon(p):
- 'pcidevicecon : PCIDEVICECON NUMBER context'
- c = refpolicy.PciDeviceCon()
- c.device = p[2]
- c.context = p[3]
-
- p[0] = c
-
-def p_devicetreecon(p):
- 'devicetreecon : DEVICETREECON NUMBER context'
- c = refpolicy.DevicetTeeCon()
- c.path = p[2]
- c.context = p[3]
-
- p[0] = c
-
-def p_mls_range_def(p):
- '''mls_range_def : mls_level_def MINUS mls_level_def
- | mls_level_def
- '''
- p[0] = p[1]
- if len(p) > 2:
- p[0] = p[0] + "-" + p[3]
-
-def p_mls_level_def(p):
- '''mls_level_def : IDENTIFIER COLON comma_list
- | IDENTIFIER
- '''
- p[0] = p[1]
- if len(p) > 2:
- p[0] = p[0] + ":" + ",".join(p[3])
-
-def p_type_def(p):
- '''type_def : TYPE IDENTIFIER COMMA comma_list SEMI
- | TYPE IDENTIFIER SEMI
- | TYPE IDENTIFIER ALIAS names SEMI
- | TYPE IDENTIFIER ALIAS names COMMA comma_list SEMI
- '''
- t = refpolicy.Type(p[2])
- if len(p) == 6:
- if p[3] == ',':
- t.attributes.update(p[4])
- else:
- t.aliases = p[4]
- elif len(p) > 4:
- t.aliases = p[4]
- if len(p) == 8:
- t.attributes.update(p[6])
- p[0] = t
-
-def p_attribute_def(p):
- 'attribute_def : ATTRIBUTE IDENTIFIER SEMI'
- a = refpolicy.Attribute(p[2])
- p[0] = a
-
-def p_attribute_role_def(p):
- 'attribute_role_def : ATTRIBUTE_ROLE IDENTIFIER SEMI'
- a = refpolicy.Attribute_Role(p[2])
- p[0] = a
-
-def p_typealias_def(p):
- 'typealias_def : TYPEALIAS IDENTIFIER ALIAS names SEMI'
- t = refpolicy.TypeAlias()
- t.type = p[2]
- t.aliases = p[4]
- p[0] = t
-
-def p_role_def(p):
- '''role_def : ROLE IDENTIFIER TYPES comma_list SEMI
- | ROLE IDENTIFIER SEMI'''
- r = refpolicy.Role()
- r.role = p[2]
- if len(p) > 4:
- r.types.update(p[4])
- p[0] = r
-
-def p_role_allow(p):
- 'role_allow : ALLOW names names SEMI'
- r = refpolicy.RoleAllow()
- r.src_roles = p[2]
- r.tgt_roles = p[3]
- p[0] = r
-
-def p_permissive(p):
- 'permissive : PERMISSIVE names SEMI'
- t.skip(1)
-
-def p_avrule_def(p):
- '''avrule_def : ALLOW names names COLON names names SEMI
- | DONTAUDIT names names COLON names names SEMI
- | AUDITALLOW names names COLON names names SEMI
- | NEVERALLOW names names COLON names names SEMI
- '''
- a = refpolicy.AVRule()
- if p[1] == 'dontaudit':
- a.rule_type = refpolicy.AVRule.DONTAUDIT
- elif p[1] == 'auditallow':
- a.rule_type = refpolicy.AVRule.AUDITALLOW
- elif p[1] == 'neverallow':
- a.rule_type = refpolicy.AVRule.NEVERALLOW
- a.src_types = p[2]
- a.tgt_types = p[3]
- a.obj_classes = p[5]
- a.perms = p[6]
- p[0] = a
-
-def p_typerule_def(p):
- '''typerule_def : TYPE_TRANSITION names names COLON names IDENTIFIER SEMI
- | TYPE_TRANSITION names names COLON names IDENTIFIER FILENAME SEMI
- | TYPE_TRANSITION names names COLON names IDENTIFIER IDENTIFIER SEMI
- | TYPE_CHANGE names names COLON names IDENTIFIER SEMI
- | TYPE_MEMBER names names COLON names IDENTIFIER SEMI
- '''
- t = refpolicy.TypeRule()
- if p[1] == 'type_change':
- t.rule_type = refpolicy.TypeRule.TYPE_CHANGE
- elif p[1] == 'type_member':
- t.rule_type = refpolicy.TypeRule.TYPE_MEMBER
- t.src_types = p[2]
- t.tgt_types = p[3]
- t.obj_classes = p[5]
- t.dest_type = p[6]
- t.file_name = p[7]
- p[0] = t
-
-def p_bool(p):
- '''bool : BOOL IDENTIFIER TRUE SEMI
- | BOOL IDENTIFIER FALSE SEMI'''
- b = refpolicy.Bool()
- b.name = p[2]
- if p[3] == "true":
- b.state = True
- else:
- b.state = False
- p[0] = b
-
-def p_conditional(p):
- ''' conditional : IF OPAREN cond_expr CPAREN OBRACE interface_stmts CBRACE
- | IF OPAREN cond_expr CPAREN OBRACE interface_stmts CBRACE ELSE OBRACE interface_stmts CBRACE
- '''
- c = refpolicy.Conditional()
- c.cond_expr = p[3]
- collect(p[6], c, val=True)
- if len(p) > 8:
- collect(p[10], c, val=False)
- p[0] = [c]
-
-def p_typeattribute_def(p):
- '''typeattribute_def : TYPEATTRIBUTE IDENTIFIER comma_list SEMI'''
- t = refpolicy.TypeAttribute()
- t.type = p[2]
- t.attributes.update(p[3])
- p[0] = t
-
-def p_roleattribute_def(p):
- '''roleattribute_def : ROLEATTRIBUTE IDENTIFIER comma_list SEMI'''
- t = refpolicy.RoleAttribute()
- t.role = p[2]
- t.roleattributes.update(p[3])
- p[0] = t
-
-def p_range_transition_def(p):
- '''range_transition_def : RANGE_TRANSITION names names COLON names mls_range_def SEMI
- | RANGE_TRANSITION names names names SEMI'''
- pass
-
-def p_role_transition_def(p):
- '''role_transition_def : ROLE_TRANSITION names names names SEMI'''
- pass
-
-def p_cond_expr(p):
- '''cond_expr : IDENTIFIER
- | EXPL cond_expr
- | cond_expr AMP AMP cond_expr
- | cond_expr BAR BAR cond_expr
- | cond_expr EQUAL EQUAL cond_expr
- | cond_expr EXPL EQUAL cond_expr
- '''
- l = len(p)
- if l == 2:
- p[0] = [p[1]]
- elif l == 3:
- p[0] = [p[1]] + p[2]
- else:
- p[0] = p[1] + [p[2] + p[3]] + p[4]
-
-
-#
-# Basic terminals
-#
-
-# Identifiers and lists of identifiers. These must
-# be handled somewhat gracefully. Names returns an IdSet and care must
-# be taken that this is _assigned_ to an object to correctly update
-# all of the flags (as opposed to using update). The other terminals
-# return list - this is to preserve ordering if it is important for
-# parsing (for example, interface_call must retain the ordering). Other
-# times the list should be used to update an IdSet.
-
-def p_names(p):
- '''names : identifier
- | nested_id_set
- | asterisk
- | TILDE identifier
- | TILDE nested_id_set
- | IDENTIFIER MINUS IDENTIFIER
- '''
- s = refpolicy.IdSet()
- if len(p) < 3:
- expand(p[1], s)
- elif len(p) == 3:
- expand(p[2], s)
- s.compliment = True
- else:
- expand([p[1]])
- s.add("-" + p[3])
- p[0] = s
-
-def p_identifier(p):
- 'identifier : IDENTIFIER'
- p[0] = [p[1]]
-
-def p_asterisk(p):
- 'asterisk : ASTERISK'
- p[0] = [p[1]]
-
-def p_nested_id_set(p):
- '''nested_id_set : OBRACE nested_id_list CBRACE
- '''
- p[0] = p[2]
-
-def p_nested_id_list(p):
- '''nested_id_list : nested_id_element
- | nested_id_list nested_id_element
- '''
- if len(p) == 2:
- p[0] = p[1]
- else:
- p[0] = p[1] + p[2]
-
-def p_nested_id_element(p):
- '''nested_id_element : identifier
- | MINUS IDENTIFIER
- | nested_id_set
- '''
- if len(p) == 2:
- p[0] = p[1]
- else:
- # For now just leave the '-'
- str = "-" + p[2]
- p[0] = [str]
-
-def p_comma_list(p):
- '''comma_list : nested_id_list
- | comma_list COMMA nested_id_list
- '''
- if len(p) > 2:
- p[1] = p[1] + p[3]
- p[0] = p[1]
-
-def p_optional_semi(p):
- '''optional_semi : SEMI
- | empty'''
- pass
-
-
-#
-# Interface to the parser
-#
-
-def p_error(tok):
- global error, parse_file, success, parser
- error = "%s: Syntax error on line %d %s [type=%s]" % (parse_file, tok.lineno, tok.value, tok.type)
- print error
- success = False
-
-def prep_spt(spt):
- if not spt:
- return { }
- map = {}
- for x in spt:
- map[x.name] = x
-
-parser = None
-lexer = None
-def create_globals(module, support, debug):
- global parser, lexer, m, spt
-
- if not parser:
- lexer = lex.lex()
- parser = yacc.yacc(method="LALR", debug=debug, write_tables=0)
-
- if module is not None:
- m = module
- else:
- m = refpolicy.Module()
-
- if not support:
- spt = refpolicy.SupportMacros()
- else:
- spt = support
-
-def parse(text, module=None, support=None, debug=False):
- create_globals(module, support, debug)
- global error, parser, lexer, success
-
- success = True
-
- try:
- parser.parse(text, debug=debug, lexer=lexer)
- except Exception, e:
- parser = None
- lexer = None
- error = "internal parser error: %s" % str(e) + "\n" + traceback.format_exc()
-
- if not success:
- # force the parser and lexer to be rebuilt - we have some problems otherwise
- parser = None
- msg = 'could not parse text: "%s"' % error
- raise ValueError(msg)
- return m
-
-def list_headers(root):
- modules = []
- support_macros = None
-
- for dirpath, dirnames, filenames in os.walk(root):
- for name in filenames:
- modname = os.path.splitext(name)
- filename = os.path.join(dirpath, name)
-
- if modname[1] == '.spt':
- if name == "obj_perm_sets.spt":
- support_macros = filename
- elif len(re.findall("patterns", modname[0])):
- modules.append((modname[0], filename))
- elif modname[1] == '.if':
- modules.append((modname[0], filename))
-
- return (modules, support_macros)
-
-
-def parse_headers(root, output=None, expand=True, debug=False):
- import util
-
- headers = refpolicy.Headers()
-
- modules = []
- support_macros = None
-
- if os.path.isfile(root):
- name = os.path.split(root)[1]
- if name == '':
- raise ValueError("Invalid file name %s" % root)
- modname = os.path.splitext(name)
- modules.append((modname[0], root))
- all_modules, support_macros = list_headers(defaults.headers())
- else:
- modules, support_macros = list_headers(root)
-
- if expand and not support_macros:
- raise ValueError("could not find support macros (obj_perm_sets.spt)")
-
- def o(msg):
- if output:
- output.write(msg)
-
- def parse_file(f, module, spt=None):
- global parse_file
- if debug:
- o("parsing file %s\n" % f)
- try:
- fd = open(f)
- txt = fd.read()
- fd.close()
- parse_file = f
- parse(txt, module, spt, debug)
- except IOError, e:
- return
- except ValueError, e:
- raise ValueError("error parsing file %s: %s" % (f, str(e)))
-
- spt = None
- if support_macros:
- o("Parsing support macros (%s): " % support_macros)
- spt = refpolicy.SupportMacros()
- parse_file(support_macros, spt)
-
- headers.children.append(spt)
-
- # FIXME: Total hack - add in can_exec rather than parse the insanity
- # of misc_macros. We are just going to pretend that this is an interface
- # to make the expansion work correctly.
- can_exec = refpolicy.Interface("can_exec")
- av = access.AccessVector(["$1","$2","file","execute_no_trans","open", "read",
- "getattr","lock","execute","ioctl"])
-
- can_exec.children.append(refpolicy.AVRule(av))
- headers.children.append(can_exec)
-
- o("done.\n")
-
- if output and not debug:
- status = util.ConsoleProgressBar(sys.stdout, steps=len(modules))
- status.start("Parsing interface files")
-
- failures = []
- for x in modules:
- m = refpolicy.Module()
- m.name = x[0]
- try:
- if expand:
- parse_file(x[1], m, spt)
- else:
- parse_file(x[1], m)
- except ValueError, e:
- o(str(e) + "\n")
- failures.append(x[1])
- continue
-
- headers.children.append(m)
- if output and not debug:
- status.step()
-
- if len(failures):
- o("failed to parse some headers: %s" % ", ".join(failures))
-
- return headers
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/refpolicy.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/refpolicy.py
deleted file mode 100644
index b8ed5c1..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/refpolicy.py
+++ /dev/null
@@ -1,917 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-import string
-import itertools
-import selinux
-
-# OVERVIEW
-#
-# This file contains objects and functions used to represent the reference
-# policy (including the headers, M4 macros, and policy language statements).
-#
-# This representation is very different from the semantic representation
-# used in libsepol. Instead, it is a more typical abstract representation
-# used by the first stage of compilers. It is basically a parse tree.
-#
-# This choice is intentional as it allows us to handle the unprocessed
-# M4 statements - including the $1 style arguments - and to more easily generate
-# the data structures that we need for policy generation.
-#
-
-# Constans for referring to fields
-SRC_TYPE = 0
-TGT_TYPE = 1
-OBJ_CLASS = 2
-PERMS = 3
-ROLE = 4
-DEST_TYPE = 5
-
-# String represenations of the above constants
-field_to_str = ["source", "target", "object", "permission", "role", "destination" ]
-str_to_field = { "source" : SRC_TYPE, "target" : TGT_TYPE, "object" : OBJ_CLASS,
- "permission" : PERMS, "role" : ROLE, "destination" : DEST_TYPE }
-
-# Base Classes
-
-class PolicyBase:
- def __init__(self, parent=None):
- self.parent = None
- self.comment = None
-
-class Node(PolicyBase):
- """Base class objects produced from parsing the reference policy.
-
- The Node class is used as the base class for any non-leaf
- object produced by parsing the reference policy. This object
- should contain a reference to its parent (or None for a top-level
- object) and 0 or more children.
-
- The general idea here is to have a very simple tree structure. Children
- are not separated out by type. Instead the tree structure represents
- fairly closely the real structure of the policy statements.
-
- The object should be iterable - by default over all children but
- subclasses are free to provide additional iterators over a subset
- of their childre (see Interface for example).
- """
-
- def __init__(self, parent=None):
- PolicyBase.__init__(self, parent)
- self.children = []
-
- def __iter__(self):
- return iter(self.children)
-
- # Not all of the iterators will return something on all Nodes, but
- # they won't explode either. Putting them here is just easier.
-
- # Top level nodes
-
- def nodes(self):
- return itertools.ifilter(lambda x: isinstance(x, Node), walktree(self))
-
- def modules(self):
- return itertools.ifilter(lambda x: isinstance(x, Module), walktree(self))
-
- def interfaces(self):
- return itertools.ifilter(lambda x: isinstance(x, Interface), walktree(self))
-
- def templates(self):
- return itertools.ifilter(lambda x: isinstance(x, Template), walktree(self))
-
- def support_macros(self):
- return itertools.ifilter(lambda x: isinstance(x, SupportMacros), walktree(self))
-
- # Common policy statements
-
- def module_declarations(self):
- return itertools.ifilter(lambda x: isinstance(x, ModuleDeclaration), walktree(self))
-
- def interface_calls(self):
- return itertools.ifilter(lambda x: isinstance(x, InterfaceCall), walktree(self))
-
- def avrules(self):
- return itertools.ifilter(lambda x: isinstance(x, AVRule), walktree(self))
-
- def typerules(self):
- return itertools.ifilter(lambda x: isinstance(x, TypeRule), walktree(self))
-
- def typeattributes(self):
- """Iterate over all of the TypeAttribute children of this Interface."""
- return itertools.ifilter(lambda x: isinstance(x, TypeAttribute), walktree(self))
-
- def roleattributes(self):
- """Iterate over all of the RoleAttribute children of this Interface."""
- return itertools.ifilter(lambda x: isinstance(x, RoleAttribute), walktree(self))
-
- def requires(self):
- return itertools.ifilter(lambda x: isinstance(x, Require), walktree(self))
-
- def roles(self):
- return itertools.ifilter(lambda x: isinstance(x, Role), walktree(self))
-
- def role_allows(self):
- return itertools.ifilter(lambda x: isinstance(x, RoleAllow), walktree(self))
-
- def role_types(self):
- return itertools.ifilter(lambda x: isinstance(x, RoleType), walktree(self))
-
- def __str__(self):
- if self.comment:
- return str(self.comment) + "\n" + self.to_string()
- else:
- return self.to_string()
-
- def __repr__(self):
- return "<%s(%s)>" % (self.__class__.__name__, self.to_string())
-
- def to_string(self):
- return ""
-
-
-class Leaf(PolicyBase):
- def __init__(self, parent=None):
- PolicyBase.__init__(self, parent)
-
- def __str__(self):
- if self.comment:
- return str(self.comment) + "\n" + self.to_string()
- else:
- return self.to_string()
-
- def __repr__(self):
- return "<%s(%s)>" % (self.__class__.__name__, self.to_string())
-
- def to_string(self):
- return ""
-
-
-
-# Utility functions
-
-def walktree(node, depthfirst=True, showdepth=False, type=None):
- """Iterate over a Node and its Children.
-
- The walktree function iterates over a tree containing Nodes and
- leaf objects. The iteration can perform a depth first or a breadth
- first traversal of the tree (controlled by the depthfirst
- paramater. The passed in node will be returned.
-
- This function will only work correctly for trees - arbitrary graphs
- will likely cause infinite looping.
- """
- # We control depth first / versus breadth first by
- # how we pop items off of the node stack.
- if depthfirst:
- index = -1
- else:
- index = 0
-
- stack = [(node, 0)]
- while len(stack) > 0:
- cur, depth = stack.pop(index)
- if showdepth:
- yield cur, depth
- else:
- yield cur
-
- # If the node is not a Node instance it must
- # be a leaf - so no need to add it to the stack
- if isinstance(cur, Node):
- items = []
- i = len(cur.children) - 1
- while i >= 0:
- if type is None or isinstance(cur.children[i], type):
- items.append((cur.children[i], depth + 1))
- i -= 1
-
- stack.extend(items)
-
-def walknode(node, type=None):
- """Iterate over the direct children of a Node.
-
- The walktree function iterates over the children of a Node.
- Unlike walktree it does note return the passed in node or
- the children of any Node objects (that is, it does not go
- beyond the current level in the tree).
- """
- for x in node:
- if type is None or isinstance(x, type):
- yield x
-
-
-def list_to_space_str(s, cont=('{', '}')):
- """Convert a set (or any sequence type) into a string representation
- formatted to match SELinux space separated list conventions.
-
- For example the list ['read', 'write'] would be converted into:
- '{ read write }'
- """
- l = len(s)
- str = ""
- if l < 1:
- raise ValueError("cannot convert 0 len set to string")
- str = " ".join(s)
- if l == 1:
- return str
- else:
- return cont[0] + " " + str + " " + cont[1]
-
-def list_to_comma_str(s):
- l = len(s)
- if l < 1:
- raise ValueError("cannot conver 0 len set to comma string")
-
- return ", ".join(s)
-
-# Basic SELinux types
-
-class IdSet(set):
- def __init__(self, list=None):
- if list:
- set.__init__(self, list)
- else:
- set.__init__(self)
- self.compliment = False
-
- def to_space_str(self):
- return list_to_space_str(self)
-
- def to_comma_str(self):
- return list_to_comma_str(self)
-
-class SecurityContext(Leaf):
- """An SELinux security context with optional MCS / MLS fields."""
- def __init__(self, context=None, parent=None):
- """Create a SecurityContext object, optionally from a string.
-
- Parameters:
- [context] - string representing a security context. Same format
- as a string passed to the from_string method.
- """
- Leaf.__init__(self, parent)
- self.user = ""
- self.role = ""
- self.type = ""
- self.level = None
- if context is not None:
- self.from_string(context)
-
- def from_string(self, context):
- """Parse a string representing a context into a SecurityContext.
-
- The string should be in the standard format - e.g.,
- 'user:role:type:level'.
-
- Raises ValueError if the string is not parsable as a security context.
- """
- fields = context.split(":")
- if len(fields) < 3:
- raise ValueError("context string [%s] not in a valid format" % context)
-
- self.user = fields[0]
- self.role = fields[1]
- self.type = fields[2]
- if len(fields) > 3:
- # FUTURE - normalize level fields to allow more comparisons to succeed.
- self.level = string.join(fields[3:], ':')
- else:
- self.level = None
-
- def __eq__(self, other):
- """Compare two SecurityContext objects - all fields must be exactly the
- the same for the comparison to work. It is possible for the level fields
- to be semantically the same yet syntactically different - in this case
- this function will return false.
- """
- return self.user == other.user and \
- self.role == other.role and \
- self.type == other.type and \
- self.level == other.level
-
- def to_string(self, default_level=None):
- """Return a string representing this security context.
-
- By default, the string will contiain a MCS / MLS level
- potentially from the default which is passed in if none was
- set.
-
- Arguments:
- default_level - the default level to use if self.level is an
- empty string.
-
- Returns:
- A string represening the security context in the form
- 'user:role:type:level'.
- """
- fields = [self.user, self.role, self.type]
- if self.level is None:
- if default_level is None:
- if selinux.is_selinux_mls_enabled() == 1:
- fields.append("s0")
- else:
- fields.append(default_level)
- else:
- fields.append(self.level)
- return ":".join(fields)
-
-class ObjectClass(Leaf):
- """SELinux object class and permissions.
-
- This class is a basic representation of an SELinux object
- class - it does not represent separate common permissions -
- just the union of the common and class specific permissions.
- It is meant to be convenient for policy generation.
- """
- def __init__(self, name="", parent=None):
- Leaf.__init__(self, parent)
- self.name = name
- self.perms = IdSet()
-
-# Basic statements
-
-class TypeAttribute(Leaf):
- """SElinux typeattribute statement.
-
- This class represents a typeattribute statement.
- """
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.type = ""
- self.attributes = IdSet()
-
- def to_string(self):
- return "typeattribute %s %s;" % (self.type, self.attributes.to_comma_str())
-
-class RoleAttribute(Leaf):
- """SElinux roleattribute statement.
-
- This class represents a roleattribute statement.
- """
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.role = ""
- self.roleattributes = IdSet()
-
- def to_string(self):
- return "roleattribute %s %s;" % (self.role, self.roleattributes.to_comma_str())
-
-
-class Role(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.role = ""
- self.types = IdSet()
-
- def to_string(self):
- s = ""
- for t in self.types:
- s += "role %s types %s;\n" % (self.role, t)
- return s
-
-class Type(Leaf):
- def __init__(self, name="", parent=None):
- Leaf.__init__(self, parent)
- self.name = name
- self.attributes = IdSet()
- self.aliases = IdSet()
-
- def to_string(self):
- s = "type %s" % self.name
- if len(self.aliases) > 0:
- s = s + "alias %s" % self.aliases.to_space_str()
- if len(self.attributes) > 0:
- s = s + ", %s" % self.attributes.to_comma_str()
- return s + ";"
-
-class TypeAlias(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.type = ""
- self.aliases = IdSet()
-
- def to_string(self):
- return "typealias %s alias %s;" % (self.type, self.aliases.to_space_str())
-
-class Attribute(Leaf):
- def __init__(self, name="", parent=None):
- Leaf.__init__(self, parent)
- self.name = name
-
- def to_string(self):
- return "attribute %s;" % self.name
-
-class Attribute_Role(Leaf):
- def __init__(self, name="", parent=None):
- Leaf.__init__(self, parent)
- self.name = name
-
- def to_string(self):
- return "attribute_role %s;" % self.name
-
-
-# Classes representing rules
-
-class AVRule(Leaf):
- """SELinux access vector (AV) rule.
-
- The AVRule class represents all varieties of AV rules including
- allow, dontaudit, and auditallow (indicated by the flags self.ALLOW,
- self.DONTAUDIT, and self.AUDITALLOW respectively).
-
- The source and target types, object classes, and perms are all represented
- by sets containing strings. Sets are used to make it simple to add
- strings repeatedly while avoiding duplicates.
-
- No checking is done to make certain that the symbols are valid or
- consistent (e.g., perms that don't match the object classes). It is
- even possible to put invalid types like '$1' into the rules to allow
- storage of the reference policy interfaces.
- """
- ALLOW = 0
- DONTAUDIT = 1
- AUDITALLOW = 2
- NEVERALLOW = 3
-
- def __init__(self, av=None, parent=None):
- Leaf.__init__(self, parent)
- self.src_types = IdSet()
- self.tgt_types = IdSet()
- self.obj_classes = IdSet()
- self.perms = IdSet()
- self.rule_type = self.ALLOW
- if av:
- self.from_av(av)
-
- def __rule_type_str(self):
- if self.rule_type == self.ALLOW:
- return "allow"
- elif self.rule_type == self.DONTAUDIT:
- return "dontaudit"
- else:
- return "auditallow"
-
- def from_av(self, av):
- """Add the access from an access vector to this allow
- rule.
- """
- self.src_types.add(av.src_type)
- if av.src_type == av.tgt_type:
- self.tgt_types.add("self")
- else:
- self.tgt_types.add(av.tgt_type)
- self.obj_classes.add(av.obj_class)
- self.perms.update(av.perms)
-
- def to_string(self):
- """Return a string representation of the rule
- that is a valid policy language representation (assuming
- that the types, object class, etc. are valie).
- """
- return "%s %s %s:%s %s;" % (self.__rule_type_str(),
- self.src_types.to_space_str(),
- self.tgt_types.to_space_str(),
- self.obj_classes.to_space_str(),
- self.perms.to_space_str())
-class TypeRule(Leaf):
- """SELinux type rules.
-
- This class is very similar to the AVRule class, but is for representing
- the type rules (type_trans, type_change, and type_member). The major
- difference is the lack of perms and only and sing destination type.
- """
- TYPE_TRANSITION = 0
- TYPE_CHANGE = 1
- TYPE_MEMBER = 2
-
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.src_types = IdSet()
- self.tgt_types = IdSet()
- self.obj_classes = IdSet()
- self.dest_type = ""
- self.rule_type = self.TYPE_TRANSITION
-
- def __rule_type_str(self):
- if self.rule_type == self.TYPE_TRANSITION:
- return "type_transition"
- elif self.rule_type == self.TYPE_CHANGE:
- return "type_change"
- else:
- return "type_member"
-
- def to_string(self):
- return "%s %s %s:%s %s;" % (self.__rule_type_str(),
- self.src_types.to_space_str(),
- self.tgt_types.to_space_str(),
- self.obj_classes.to_space_str(),
- self.dest_type)
-
-class RoleAllow(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.src_roles = IdSet()
- self.tgt_roles = IdSet()
-
- def to_string(self):
- return "allow %s %s;" % (self.src_roles.to_comma_str(),
- self.tgt_roles.to_comma_str())
-
-class RoleType(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.role = ""
- self.types = IdSet()
-
- def to_string(self):
- s = ""
- for t in self.types:
- s += "role %s types %s;\n" % (self.role, t)
- return s
-
-class ModuleDeclaration(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.name = ""
- self.version = ""
- self.refpolicy = False
-
- def to_string(self):
- if self.refpolicy:
- return "policy_module(%s, %s)" % (self.name, self.version)
- else:
- return "module %s %s;" % (self.name, self.version)
-
-class Conditional(Node):
- def __init__(self, parent=None):
- Node.__init__(self, parent)
- self.cond_expr = []
-
- def to_string(self):
- return "[If %s]" % list_to_space_str(self.cond_expr, cont=("", ""))
-
-class Bool(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.name = ""
- self.state = False
-
- def to_string(self):
- s = "bool %s " % self.name
- if s.state:
- return s + "true"
- else:
- return s + "false"
-
-class InitialSid(Leaf):
- def __init(self, parent=None):
- Leaf.__init__(self, parent)
- self.name = ""
- self.context = None
-
- def to_string(self):
- return "sid %s %s" % (self.name, str(self.context))
-
-class GenfsCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.filesystem = ""
- self.path = ""
- self.context = None
-
- def to_string(self):
- return "genfscon %s %s %s" % (self.filesystem, self.path, str(self.context))
-
-class FilesystemUse(Leaf):
- XATTR = 1
- TRANS = 2
- TASK = 3
-
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.type = self.XATTR
- self.filesystem = ""
- self.context = None
-
- def to_string(self):
- s = ""
- if self.type == XATTR:
- s = "fs_use_xattr "
- elif self.type == TRANS:
- s = "fs_use_trans "
- elif self.type == TASK:
- s = "fs_use_task "
-
- return "%s %s %s;" % (s, self.filesystem, str(self.context))
-
-class PortCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.port_type = ""
- self.port_number = ""
- self.context = None
-
- def to_string(self):
- return "portcon %s %s %s" % (self.port_type, self.port_number, str(self.context))
-
-class NodeCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.start = ""
- self.end = ""
- self.context = None
-
- def to_string(self):
- return "nodecon %s %s %s" % (self.start, self.end, str(self.context))
-
-class NetifCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.interface = ""
- self.interface_context = None
- self.packet_context = None
-
- def to_string(self):
- return "netifcon %s %s %s" % (self.interface, str(self.interface_context),
- str(self.packet_context))
-class PirqCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.pirq_number = ""
- self.context = None
-
- def to_string(self):
- return "pirqcon %s %s" % (self.pirq_number, str(self.context))
-
-class IomemCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.device_mem = ""
- self.context = None
-
- def to_string(self):
- return "iomemcon %s %s" % (self.device_mem, str(self.context))
-
-class IoportCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.ioport = ""
- self.context = None
-
- def to_string(self):
- return "ioportcon %s %s" % (self.ioport, str(self.context))
-
-class PciDeviceCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.device = ""
- self.context = None
-
- def to_string(self):
- return "pcidevicecon %s %s" % (self.device, str(self.context))
-
-class DeviceTreeCon(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.path = ""
- self.context = None
-
- def to_string(self):
- return "devicetreecon %s %s" % (self.path, str(self.context))
-
-# Reference policy specific types
-
-def print_tree(head):
- for node, depth in walktree(head, showdepth=True):
- s = ""
- for i in range(depth):
- s = s + "\t"
- print s + str(node)
-
-
-class Headers(Node):
- def __init__(self, parent=None):
- Node.__init__(self, parent)
-
- def to_string(self):
- return "[Headers]"
-
-
-class Module(Node):
- def __init__(self, parent=None):
- Node.__init__(self, parent)
-
- def to_string(self):
- return ""
-
-class Interface(Node):
- """A reference policy interface definition.
-
- This class represents a reference policy interface definition.
- """
- def __init__(self, name="", parent=None):
- Node.__init__(self, parent)
- self.name = name
-
- def to_string(self):
- return "[Interface name: %s]" % self.name
-
-class TunablePolicy(Node):
- def __init__(self, parent=None):
- Node.__init__(self, parent)
- self.cond_expr = []
-
- def to_string(self):
- return "[Tunable Policy %s]" % list_to_space_str(self.cond_expr, cont=("", ""))
-
-class Template(Node):
- def __init__(self, name="", parent=None):
- Node.__init__(self, parent)
- self.name = name
-
- def to_string(self):
- return "[Template name: %s]" % self.name
-
-class IfDef(Node):
- def __init__(self, name="", parent=None):
- Node.__init__(self, parent)
- self.name = name
-
- def to_string(self):
- return "[Ifdef name: %s]" % self.name
-
-class InterfaceCall(Leaf):
- def __init__(self, ifname="", parent=None):
- Leaf.__init__(self, parent)
- self.ifname = ifname
- self.args = []
- self.comments = []
-
- def matches(self, other):
- if self.ifname != other.ifname:
- return False
- if len(self.args) != len(other.args):
- return False
- for a,b in zip(self.args, other.args):
- if a != b:
- return False
- return True
-
- def to_string(self):
- s = "%s(" % self.ifname
- i = 0
- for a in self.args:
- if isinstance(a, list):
- str = list_to_space_str(a)
- else:
- str = a
-
- if i != 0:
- s = s + ", %s" % str
- else:
- s = s + str
- i += 1
- return s + ")"
-
-class OptionalPolicy(Node):
- def __init__(self, parent=None):
- Node.__init__(self, parent)
-
- def to_string(self):
- return "[Optional Policy]"
-
-class SupportMacros(Node):
- def __init__(self, parent=None):
- Node.__init__(self, parent)
- self.map = None
-
- def to_string(self):
- return "[Support Macros]"
-
- def __expand_perm(self, perm):
- # Recursive expansion - the assumption is that these
- # are ordered correctly so that no macro is used before
- # it is defined
- s = set()
- if self.map.has_key(perm):
- for p in self.by_name(perm):
- s.update(self.__expand_perm(p))
- else:
- s.add(perm)
- return s
-
- def __gen_map(self):
- self.map = {}
- for x in self:
- exp_perms = set()
- for perm in x.perms:
- exp_perms.update(self.__expand_perm(perm))
- self.map[x.name] = exp_perms
-
- def by_name(self, name):
- if not self.map:
- self.__gen_map()
- return self.map[name]
-
- def has_key(self, name):
- if not self.map:
- self.__gen_map()
- return self.map.has_key(name)
-
-class Require(Leaf):
- def __init__(self, parent=None):
- Leaf.__init__(self, parent)
- self.types = IdSet()
- self.obj_classes = { }
- self.roles = IdSet()
- self.data = IdSet()
- self.users = IdSet()
-
- def add_obj_class(self, obj_class, perms):
- p = self.obj_classes.setdefault(obj_class, IdSet())
- p.update(perms)
-
-
- def to_string(self):
- s = []
- s.append("require {")
- for type in self.types:
- s.append("\ttype %s;" % type)
- for obj_class, perms in self.obj_classes.items():
- s.append("\tclass %s %s;" % (obj_class, perms.to_space_str()))
- for role in self.roles:
- s.append("\trole %s;" % role)
- for bool in self.data:
- s.append("\tbool %s;" % bool)
- for user in self.users:
- s.append("\tuser %s;" % user)
- s.append("}")
-
- # Handle empty requires
- if len(s) == 2:
- return ""
-
- return "\n".join(s)
-
-
-class ObjPermSet:
- def __init__(self, name):
- self.name = name
- self.perms = set()
-
- def to_string(self):
- return "define(`%s', `%s')" % (self.name, self.perms.to_space_str())
-
-class ClassMap:
- def __init__(self, obj_class, perms):
- self.obj_class = obj_class
- self.perms = perms
-
- def to_string(self):
- return self.obj_class + ": " + self.perms
-
-class Comment:
- def __init__(self, l=None):
- if l:
- self.lines = l
- else:
- self.lines = []
-
- def to_string(self):
- # If there are no lines, treat this as a spacer between
- # policy statements and return a new line.
- if len(self.lines) == 0:
- return ""
- else:
- out = []
- for line in self.lines:
- out.append("#" + line)
- return "\n".join(out)
-
- def merge(self, other):
- if len(other.lines):
- for line in other.lines:
- if line != "":
- self.lines.append(line)
-
- def __str__(self):
- return self.to_string()
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/sepolgeni18n.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/sepolgeni18n.py
deleted file mode 100644
index 998c435..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/sepolgeni18n.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-try:
- import gettext
- t = gettext.translation( 'yumex' )
- _ = t.gettext
-except:
- def _(str):
- return str
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/util.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/util.py
deleted file mode 100644
index 74a11f5..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/util.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Authors: Karl MacMillan <kmacmillan@mentalrootkit.com>
-#
-# Copyright (C) 2006 Red Hat
-# see file 'COPYING' for use and warranty information
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License as
-# published by the Free Software Foundation; version 2 only
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-class ConsoleProgressBar:
- def __init__(self, out, steps=100, indicator='#'):
- self.blocks = 0
- self.current = 0
- self.steps = steps
- self.indicator = indicator
- self.out = out
- self.done = False
-
- def start(self, message=None):
- self.done = False
- if message:
- self.out.write('\n%s:\n' % message)
- self.out.write('%--10---20---30---40---50---60---70---80---90--100\n')
-
- def step(self, n=1):
- self.current += n
-
- old = self.blocks
- self.blocks = int(round(self.current / float(self.steps) * 100) / 2)
-
- if self.blocks > 50:
- self.blocks = 50
-
- new = self.blocks - old
-
- self.out.write(self.indicator * new)
- self.out.flush()
-
- if self.blocks == 50 and not self.done:
- self.done = True
- self.out.write("\n")
-
-def set_to_list(s):
- l = []
- l.extend(s)
- return l
-
-def first(s, sorted=False):
- """
- Return the first element of a set.
-
- It sometimes useful to return the first element from a set but,
- because sets are not indexable, this is rather hard. This function
- will return the first element from a set. If sorted is True, then
- the set will first be sorted (making this an expensive operation).
- Otherwise a random element will be returned (as sets are not ordered).
- """
- if not len(s):
- raise IndexError("empty containter")
-
- if sorted:
- l = set_to_list(s)
- l.sort()
- return l[0]
- else:
- for x in s:
- return x
-
-if __name__ == "__main__":
- import sys
- import time
- p = ConsoleProgressBar(sys.stdout, steps=999)
- p.start("computing pi")
- for i in range(999):
- p.step()
- time.sleep(0.001)
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sepolgen/yacc.py b/lib/python2.7/site-packages/setoolsgui/sepolgen/yacc.py
deleted file mode 100644
index bc4536d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sepolgen/yacc.py
+++ /dev/null
@@ -1,2209 +0,0 @@
-#-----------------------------------------------------------------------------
-# ply: yacc.py
-#
-# Author(s): David M. Beazley (dave@dabeaz.com)
-#
-# Copyright (C) 2001-2006, David M. Beazley
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See the file COPYING for a complete copy of the LGPL.
-#
-#
-# This implements an LR parser that is constructed from grammar rules defined
-# as Python functions. The grammer is specified by supplying the BNF inside
-# Python documentation strings. The inspiration for this technique was borrowed
-# from John Aycock's Spark parsing system. PLY might be viewed as cross between
-# Spark and the GNU bison utility.
-#
-# The current implementation is only somewhat object-oriented. The
-# LR parser itself is defined in terms of an object (which allows multiple
-# parsers to co-exist). However, most of the variables used during table
-# construction are defined in terms of global variables. Users shouldn't
-# notice unless they are trying to define multiple parsers at the same
-# time using threads (in which case they should have their head examined).
-#
-# This implementation supports both SLR and LALR(1) parsing. LALR(1)
-# support was originally implemented by Elias Ioup (ezioup@alumni.uchicago.edu),
-# using the algorithm found in Aho, Sethi, and Ullman "Compilers: Principles,
-# Techniques, and Tools" (The Dragon Book). LALR(1) has since been replaced
-# by the more efficient DeRemer and Pennello algorithm.
-#
-# :::::::: WARNING :::::::
-#
-# Construction of LR parsing tables is fairly complicated and expensive.
-# To make this module run fast, a *LOT* of work has been put into
-# optimization---often at the expensive of readability and what might
-# consider to be good Python "coding style." Modify the code at your
-# own risk!
-# ----------------------------------------------------------------------------
-
-__version__ = "2.2"
-
-#-----------------------------------------------------------------------------
-# === User configurable parameters ===
-#
-# Change these to modify the default behavior of yacc (if you wish)
-#-----------------------------------------------------------------------------
-
-yaccdebug = 1 # Debugging mode. If set, yacc generates a
- # a 'parser.out' file in the current directory
-
-debug_file = 'parser.out' # Default name of the debugging file
-tab_module = 'parsetab' # Default name of the table module
-default_lr = 'LALR' # Default LR table generation method
-
-error_count = 3 # Number of symbols that must be shifted to leave recovery mode
-
-import re, types, sys, cStringIO, hashlib, os.path
-
-# Exception raised for yacc-related errors
-class YaccError(Exception): pass
-
-#-----------------------------------------------------------------------------
-# === LR Parsing Engine ===
-#
-# The following classes are used for the LR parser itself. These are not
-# used during table construction and are independent of the actual LR
-# table generation algorithm
-#-----------------------------------------------------------------------------
-
-# This class is used to hold non-terminal grammar symbols during parsing.
-# It normally has the following attributes set:
-# .type = Grammar symbol type
-# .value = Symbol value
-# .lineno = Starting line number
-# .endlineno = Ending line number (optional, set automatically)
-# .lexpos = Starting lex position
-# .endlexpos = Ending lex position (optional, set automatically)
-
-class YaccSymbol:
- def __str__(self): return self.type
- def __repr__(self): return str(self)
-
-# This class is a wrapper around the objects actually passed to each
-# grammar rule. Index lookup and assignment actually assign the
-# .value attribute of the underlying YaccSymbol object.
-# The lineno() method returns the line number of a given
-# item (or 0 if not defined). The linespan() method returns
-# a tuple of (startline,endline) representing the range of lines
-# for a symbol. The lexspan() method returns a tuple (lexpos,endlexpos)
-# representing the range of positional information for a symbol.
-
-class YaccProduction:
- def __init__(self,s,stack=None):
- self.slice = s
- self.pbstack = []
- self.stack = stack
-
- def __getitem__(self,n):
- if type(n) == types.IntType:
- if n >= 0: return self.slice[n].value
- else: return self.stack[n].value
- else:
- return [s.value for s in self.slice[n.start:n.stop:n.step]]
-
- def __setitem__(self,n,v):
- self.slice[n].value = v
-
- def __len__(self):
- return len(self.slice)
-
- def lineno(self,n):
- return getattr(self.slice[n],"lineno",0)
-
- def linespan(self,n):
- startline = getattr(self.slice[n],"lineno",0)
- endline = getattr(self.slice[n],"endlineno",startline)
- return startline,endline
-
- def lexpos(self,n):
- return getattr(self.slice[n],"lexpos",0)
-
- def lexspan(self,n):
- startpos = getattr(self.slice[n],"lexpos",0)
- endpos = getattr(self.slice[n],"endlexpos",startpos)
- return startpos,endpos
-
- def pushback(self,n):
- if n <= 0:
- raise ValueError, "Expected a positive value"
- if n > (len(self.slice)-1):
- raise ValueError, "Can't push %d tokens. Only %d are available." % (n,len(self.slice)-1)
- for i in range(0,n):
- self.pbstack.append(self.slice[-i-1])
-
-# The LR Parsing engine. This is defined as a class so that multiple parsers
-# can exist in the same process. A user never instantiates this directly.
-# Instead, the global yacc() function should be used to create a suitable Parser
-# object.
-
-class Parser:
- def __init__(self,magic=None):
-
- # This is a hack to keep users from trying to instantiate a Parser
- # object directly.
-
- if magic != "xyzzy":
- raise YaccError, "Can't instantiate Parser. Use yacc() instead."
-
- # Reset internal state
- self.productions = None # List of productions
- self.errorfunc = None # Error handling function
- self.action = { } # LR Action table
- self.goto = { } # LR goto table
- self.require = { } # Attribute require table
- self.method = "Unknown LR" # Table construction method used
-
- def errok(self):
- self.errorcount = 0
-
- def restart(self):
- del self.statestack[:]
- del self.symstack[:]
- sym = YaccSymbol()
- sym.type = '$end'
- self.symstack.append(sym)
- self.statestack.append(0)
-
- def parse(self,input=None,lexer=None,debug=0):
- lookahead = None # Current lookahead symbol
- lookaheadstack = [ ] # Stack of lookahead symbols
- actions = self.action # Local reference to action table
- goto = self.goto # Local reference to goto table
- prod = self.productions # Local reference to production list
- pslice = YaccProduction(None) # Production object passed to grammar rules
- pslice.parser = self # Parser object
- self.errorcount = 0 # Used during error recovery
-
- # If no lexer was given, we will try to use the lex module
- if not lexer:
- import lex
- lexer = lex.lexer
-
- pslice.lexer = lexer
-
- # If input was supplied, pass to lexer
- if input:
- lexer.input(input)
-
- # Tokenize function
- get_token = lexer.token
-
- statestack = [ ] # Stack of parsing states
- self.statestack = statestack
- symstack = [ ] # Stack of grammar symbols
- self.symstack = symstack
-
- pslice.stack = symstack # Put in the production
- errtoken = None # Err token
-
- # The start state is assumed to be (0,$end)
- statestack.append(0)
- sym = YaccSymbol()
- sym.type = '$end'
- symstack.append(sym)
-
- while 1:
- # Get the next symbol on the input. If a lookahead symbol
- # is already set, we just use that. Otherwise, we'll pull
- # the next token off of the lookaheadstack or from the lexer
- if debug > 1:
- print 'state', statestack[-1]
- if not lookahead:
- if not lookaheadstack:
- lookahead = get_token() # Get the next token
- else:
- lookahead = lookaheadstack.pop()
- if not lookahead:
- lookahead = YaccSymbol()
- lookahead.type = '$end'
- if debug:
- errorlead = ("%s . %s" % (" ".join([xx.type for xx in symstack][1:]), str(lookahead))).lstrip()
-
- # Check the action table
- s = statestack[-1]
- ltype = lookahead.type
- t = actions.get((s,ltype),None)
-
- if debug > 1:
- print 'action', t
- if t is not None:
- if t > 0:
- # shift a symbol on the stack
- if ltype == '$end':
- # Error, end of input
- sys.stderr.write("yacc: Parse error. EOF\n")
- return
- statestack.append(t)
- if debug > 1:
- sys.stderr.write("%-60s shift state %s\n" % (errorlead, t))
- symstack.append(lookahead)
- lookahead = None
-
- # Decrease error count on successful shift
- if self.errorcount > 0:
- self.errorcount -= 1
-
- continue
-
- if t < 0:
- # reduce a symbol on the stack, emit a production
- p = prod[-t]
- pname = p.name
- plen = p.len
-
- # Get production function
- sym = YaccSymbol()
- sym.type = pname # Production name
- sym.value = None
- if debug > 1:
- sys.stderr.write("%-60s reduce %d\n" % (errorlead, -t))
-
- if plen:
- targ = symstack[-plen-1:]
- targ[0] = sym
- try:
- sym.lineno = targ[1].lineno
- sym.endlineno = getattr(targ[-1],"endlineno",targ[-1].lineno)
- sym.lexpos = targ[1].lexpos
- sym.endlexpos = getattr(targ[-1],"endlexpos",targ[-1].lexpos)
- except AttributeError:
- sym.lineno = 0
- del symstack[-plen:]
- del statestack[-plen:]
- else:
- sym.lineno = 0
- targ = [ sym ]
- pslice.slice = targ
- pslice.pbstack = []
- # Call the grammar rule with our special slice object
- p.func(pslice)
-
- # If there was a pushback, put that on the stack
- if pslice.pbstack:
- lookaheadstack.append(lookahead)
- for _t in pslice.pbstack:
- lookaheadstack.append(_t)
- lookahead = None
-
- symstack.append(sym)
- statestack.append(goto[statestack[-1],pname])
- continue
-
- if t == 0:
- n = symstack[-1]
- return getattr(n,"value",None)
- sys.stderr.write(errorlead, "\n")
-
- if t == None:
- if debug:
- sys.stderr.write(errorlead + "\n")
- # We have some kind of parsing error here. To handle
- # this, we are going to push the current token onto
- # the tokenstack and replace it with an 'error' token.
- # If there are any synchronization rules, they may
- # catch it.
- #
- # In addition to pushing the error token, we call call
- # the user defined p_error() function if this is the
- # first syntax error. This function is only called if
- # errorcount == 0.
- if not self.errorcount:
- self.errorcount = error_count
- errtoken = lookahead
- if errtoken.type == '$end':
- errtoken = None # End of file!
- if self.errorfunc:
- global errok,token,restart
- errok = self.errok # Set some special functions available in error recovery
- token = get_token
- restart = self.restart
- tok = self.errorfunc(errtoken)
- del errok, token, restart # Delete special functions
-
- if not self.errorcount:
- # User must have done some kind of panic
- # mode recovery on their own. The
- # returned token is the next lookahead
- lookahead = tok
- errtoken = None
- continue
- else:
- if errtoken:
- if hasattr(errtoken,"lineno"): lineno = lookahead.lineno
- else: lineno = 0
- if lineno:
- sys.stderr.write("yacc: Syntax error at line %d, token=%s\n" % (lineno, errtoken.type))
- else:
- sys.stderr.write("yacc: Syntax error, token=%s" % errtoken.type)
- else:
- sys.stderr.write("yacc: Parse error in input. EOF\n")
- return
-
- else:
- self.errorcount = error_count
-
- # case 1: the statestack only has 1 entry on it. If we're in this state, the
- # entire parse has been rolled back and we're completely hosed. The token is
- # discarded and we just keep going.
-
- if len(statestack) <= 1 and lookahead.type != '$end':
- lookahead = None
- errtoken = None
- # Nuke the pushback stack
- del lookaheadstack[:]
- continue
-
- # case 2: the statestack has a couple of entries on it, but we're
- # at the end of the file. nuke the top entry and generate an error token
-
- # Start nuking entries on the stack
- if lookahead.type == '$end':
- # Whoa. We're really hosed here. Bail out
- return
-
- if lookahead.type != 'error':
- sym = symstack[-1]
- if sym.type == 'error':
- # Hmmm. Error is on top of stack, we'll just nuke input
- # symbol and continue
- lookahead = None
- continue
- t = YaccSymbol()
- t.type = 'error'
- if hasattr(lookahead,"lineno"):
- t.lineno = lookahead.lineno
- t.value = lookahead
- lookaheadstack.append(lookahead)
- lookahead = t
- else:
- symstack.pop()
- statestack.pop()
-
- continue
-
- # Call an error function here
- raise RuntimeError, "yacc: internal parser error!!!\n"
-
-# -----------------------------------------------------------------------------
-# === Parser Construction ===
-#
-# The following functions and variables are used to implement the yacc() function
-# itself. This is pretty hairy stuff involving lots of error checking,
-# construction of LR items, kernels, and so forth. Although a lot of
-# this work is done using global variables, the resulting Parser object
-# is completely self contained--meaning that it is safe to repeatedly
-# call yacc() with different grammars in the same application.
-# -----------------------------------------------------------------------------
-
-# -----------------------------------------------------------------------------
-# validate_file()
-#
-# This function checks to see if there are duplicated p_rulename() functions
-# in the parser module file. Without this function, it is really easy for
-# users to make mistakes by cutting and pasting code fragments (and it's a real
-# bugger to try and figure out why the resulting parser doesn't work). Therefore,
-# we just do a little regular expression pattern matching of def statements
-# to try and detect duplicates.
-# -----------------------------------------------------------------------------
-
-def validate_file(filename):
- base,ext = os.path.splitext(filename)
- if ext != '.py': return 1 # No idea. Assume it's okay.
-
- try:
- f = open(filename)
- lines = f.readlines()
- f.close()
- except IOError:
- return 1 # Oh well
-
- # Match def p_funcname(
- fre = re.compile(r'\s*def\s+(p_[a-zA-Z_0-9]*)\(')
- counthash = { }
- linen = 1
- noerror = 1
- for l in lines:
- m = fre.match(l)
- if m:
- name = m.group(1)
- prev = counthash.get(name)
- if not prev:
- counthash[name] = linen
- else:
- sys.stderr.write("%s:%d: Function %s redefined. Previously defined on line %d\n" % (filename,linen,name,prev))
- noerror = 0
- linen += 1
- return noerror
-
-# This function looks for functions that might be grammar rules, but which don't have the proper p_suffix.
-def validate_dict(d):
- for n,v in d.items():
- if n[0:2] == 'p_' and type(v) in (types.FunctionType, types.MethodType): continue
- if n[0:2] == 't_': continue
-
- if n[0:2] == 'p_':
- sys.stderr.write("yacc: Warning. '%s' not defined as a function\n" % n)
- if 1 and isinstance(v,types.FunctionType) and v.func_code.co_argcount == 1:
- try:
- doc = v.__doc__.split(" ")
- if doc[1] == ':':
- sys.stderr.write("%s:%d: Warning. Possible grammar rule '%s' defined without p_ prefix.\n" % (v.func_code.co_filename, v.func_code.co_firstlineno,n))
- except StandardError:
- pass
-
-# -----------------------------------------------------------------------------
-# === GRAMMAR FUNCTIONS ===
-#
-# The following global variables and functions are used to store, manipulate,
-# and verify the grammar rules specified by the user.
-# -----------------------------------------------------------------------------
-
-# Initialize all of the global variables used during grammar construction
-def initialize_vars():
- global Productions, Prodnames, Prodmap, Terminals
- global Nonterminals, First, Follow, Precedence, LRitems
- global Errorfunc, Signature, Requires
-
- Productions = [None] # A list of all of the productions. The first
- # entry is always reserved for the purpose of
- # building an augmented grammar
-
- Prodnames = { } # A dictionary mapping the names of nonterminals to a list of all
- # productions of that nonterminal.
-
- Prodmap = { } # A dictionary that is only used to detect duplicate
- # productions.
-
- Terminals = { } # A dictionary mapping the names of terminal symbols to a
- # list of the rules where they are used.
-
- Nonterminals = { } # A dictionary mapping names of nonterminals to a list
- # of rule numbers where they are used.
-
- First = { } # A dictionary of precomputed FIRST(x) symbols
-
- Follow = { } # A dictionary of precomputed FOLLOW(x) symbols
-
- Precedence = { } # Precedence rules for each terminal. Contains tuples of the
- # form ('right',level) or ('nonassoc', level) or ('left',level)
-
- LRitems = [ ] # A list of all LR items for the grammar. These are the
- # productions with the "dot" like E -> E . PLUS E
-
- Errorfunc = None # User defined error handler
-
- Signature = hashlib.sha256() # Digital signature of the grammar rules, precedence
- # and other information. Used to determined when a
- # parsing table needs to be regenerated.
-
- Requires = { } # Requires list
-
- # File objects used when creating the parser.out debugging file
- global _vf, _vfc
- _vf = cStringIO.StringIO()
- _vfc = cStringIO.StringIO()
-
-# -----------------------------------------------------------------------------
-# class Production:
-#
-# This class stores the raw information about a single production or grammar rule.
-# It has a few required attributes:
-#
-# name - Name of the production (nonterminal)
-# prod - A list of symbols making up its production
-# number - Production number.
-#
-# In addition, a few additional attributes are used to help with debugging or
-# optimization of table generation.
-#
-# file - File where production action is defined.
-# lineno - Line number where action is defined
-# func - Action function
-# prec - Precedence level
-# lr_next - Next LR item. Example, if we are ' E -> E . PLUS E'
-# then lr_next refers to 'E -> E PLUS . E'
-# lr_index - LR item index (location of the ".") in the prod list.
-# lookaheads - LALR lookahead symbols for this item
-# len - Length of the production (number of symbols on right hand side)
-# -----------------------------------------------------------------------------
-
-class Production:
- def __init__(self,**kw):
- for k,v in kw.items():
- setattr(self,k,v)
- self.lr_index = -1
- self.lr0_added = 0 # Flag indicating whether or not added to LR0 closure
- self.lr1_added = 0 # Flag indicating whether or not added to LR1
- self.usyms = [ ]
- self.lookaheads = { }
- self.lk_added = { }
- self.setnumbers = [ ]
-
- def __str__(self):
- if self.prod:
- s = "%s -> %s" % (self.name," ".join(self.prod))
- else:
- s = "%s -> <empty>" % self.name
- return s
-
- def __repr__(self):
- return str(self)
-
- # Compute lr_items from the production
- def lr_item(self,n):
- if n > len(self.prod): return None
- p = Production()
- p.name = self.name
- p.prod = list(self.prod)
- p.number = self.number
- p.lr_index = n
- p.lookaheads = { }
- p.setnumbers = self.setnumbers
- p.prod.insert(n,".")
- p.prod = tuple(p.prod)
- p.len = len(p.prod)
- p.usyms = self.usyms
-
- # Precompute list of productions immediately following
- try:
- p.lrafter = Prodnames[p.prod[n+1]]
- except (IndexError,KeyError),e:
- p.lrafter = []
- try:
- p.lrbefore = p.prod[n-1]
- except IndexError:
- p.lrbefore = None
-
- return p
-
-class MiniProduction:
- pass
-
-# regex matching identifiers
-_is_identifier = re.compile(r'^[a-zA-Z0-9_-~]+$')
-
-# -----------------------------------------------------------------------------
-# add_production()
-#
-# Given an action function, this function assembles a production rule.
-# The production rule is assumed to be found in the function's docstring.
-# This rule has the general syntax:
-#
-# name1 ::= production1
-# | production2
-# | production3
-# ...
-# | productionn
-# name2 ::= production1
-# | production2
-# ...
-# -----------------------------------------------------------------------------
-
-def add_production(f,file,line,prodname,syms):
-
- if Terminals.has_key(prodname):
- sys.stderr.write("%s:%d: Illegal rule name '%s'. Already defined as a token.\n" % (file,line,prodname))
- return -1
- if prodname == 'error':
- sys.stderr.write("%s:%d: Illegal rule name '%s'. error is a reserved word.\n" % (file,line,prodname))
- return -1
-
- if not _is_identifier.match(prodname):
- sys.stderr.write("%s:%d: Illegal rule name '%s'\n" % (file,line,prodname))
- return -1
-
- for x in range(len(syms)):
- s = syms[x]
- if s[0] in "'\"":
- try:
- c = eval(s)
- if (len(c) > 1):
- sys.stderr.write("%s:%d: Literal token %s in rule '%s' may only be a single character\n" % (file,line,s, prodname))
- return -1
- if not Terminals.has_key(c):
- Terminals[c] = []
- syms[x] = c
- continue
- except SyntaxError:
- pass
- if not _is_identifier.match(s) and s != '%prec':
- sys.stderr.write("%s:%d: Illegal name '%s' in rule '%s'\n" % (file,line,s, prodname))
- return -1
-
- # See if the rule is already in the rulemap
- map = "%s -> %s" % (prodname,syms)
- if Prodmap.has_key(map):
- m = Prodmap[map]
- sys.stderr.write("%s:%d: Duplicate rule %s.\n" % (file,line, m))
- sys.stderr.write("%s:%d: Previous definition at %s:%d\n" % (file,line, m.file, m.line))
- return -1
-
- p = Production()
- p.name = prodname
- p.prod = syms
- p.file = file
- p.line = line
- p.func = f
- p.number = len(Productions)
-
-
- Productions.append(p)
- Prodmap[map] = p
- if not Nonterminals.has_key(prodname):
- Nonterminals[prodname] = [ ]
-
- # Add all terminals to Terminals
- i = 0
- while i < len(p.prod):
- t = p.prod[i]
- if t == '%prec':
- try:
- precname = p.prod[i+1]
- except IndexError:
- sys.stderr.write("%s:%d: Syntax error. Nothing follows %%prec.\n" % (p.file,p.line))
- return -1
-
- prec = Precedence.get(precname,None)
- if not prec:
- sys.stderr.write("%s:%d: Nothing known about the precedence of '%s'\n" % (p.file,p.line,precname))
- return -1
- else:
- p.prec = prec
- del p.prod[i]
- del p.prod[i]
- continue
-
- if Terminals.has_key(t):
- Terminals[t].append(p.number)
- # Is a terminal. We'll assign a precedence to p based on this
- if not hasattr(p,"prec"):
- p.prec = Precedence.get(t,('right',0))
- else:
- if not Nonterminals.has_key(t):
- Nonterminals[t] = [ ]
- Nonterminals[t].append(p.number)
- i += 1
-
- if not hasattr(p,"prec"):
- p.prec = ('right',0)
-
- # Set final length of productions
- p.len = len(p.prod)
- p.prod = tuple(p.prod)
-
- # Calculate unique syms in the production
- p.usyms = [ ]
- for s in p.prod:
- if s not in p.usyms:
- p.usyms.append(s)
-
- # Add to the global productions list
- try:
- Prodnames[p.name].append(p)
- except KeyError:
- Prodnames[p.name] = [ p ]
- return 0
-
-# Given a raw rule function, this function rips out its doc string
-# and adds rules to the grammar
-
-def add_function(f):
- line = f.func_code.co_firstlineno
- file = f.func_code.co_filename
- error = 0
-
- if isinstance(f,types.MethodType):
- reqdargs = 2
- else:
- reqdargs = 1
-
- if f.func_code.co_argcount > reqdargs:
- sys.stderr.write("%s:%d: Rule '%s' has too many arguments.\n" % (file,line,f.__name__))
- return -1
-
- if f.func_code.co_argcount < reqdargs:
- sys.stderr.write("%s:%d: Rule '%s' requires an argument.\n" % (file,line,f.__name__))
- return -1
-
- if f.__doc__:
- # Split the doc string into lines
- pstrings = f.__doc__.splitlines()
- lastp = None
- dline = line
- for ps in pstrings:
- dline += 1
- p = ps.split()
- if not p: continue
- try:
- if p[0] == '|':
- # This is a continuation of a previous rule
- if not lastp:
- sys.stderr.write("%s:%d: Misplaced '|'.\n" % (file,dline))
- return -1
- prodname = lastp
- if len(p) > 1:
- syms = p[1:]
- else:
- syms = [ ]
- else:
- prodname = p[0]
- lastp = prodname
- assign = p[1]
- if len(p) > 2:
- syms = p[2:]
- else:
- syms = [ ]
- if assign != ':' and assign != '::=':
- sys.stderr.write("%s:%d: Syntax error. Expected ':'\n" % (file,dline))
- return -1
-
-
- e = add_production(f,file,dline,prodname,syms)
- error += e
-
-
- except StandardError:
- sys.stderr.write("%s:%d: Syntax error in rule '%s'\n" % (file,dline,ps))
- error -= 1
- else:
- sys.stderr.write("%s:%d: No documentation string specified in function '%s'\n" % (file,line,f.__name__))
- return error
-
-
-# Cycle checking code (Michael Dyck)
-
-def compute_reachable():
- '''
- Find each symbol that can be reached from the start symbol.
- Print a warning for any nonterminals that can't be reached.
- (Unused terminals have already had their warning.)
- '''
- Reachable = { }
- for s in Terminals.keys() + Nonterminals.keys():
- Reachable[s] = 0
-
- mark_reachable_from( Productions[0].prod[0], Reachable )
-
- for s in Nonterminals.keys():
- if not Reachable[s]:
- sys.stderr.write("yacc: Symbol '%s' is unreachable.\n" % s)
-
-def mark_reachable_from(s, Reachable):
- '''
- Mark all symbols that are reachable from symbol s.
- '''
- if Reachable[s]:
- # We've already reached symbol s.
- return
- Reachable[s] = 1
- for p in Prodnames.get(s,[]):
- for r in p.prod:
- mark_reachable_from(r, Reachable)
-
-# -----------------------------------------------------------------------------
-# compute_terminates()
-#
-# This function looks at the various parsing rules and tries to detect
-# infinite recursion cycles (grammar rules where there is no possible way
-# to derive a string of only terminals).
-# -----------------------------------------------------------------------------
-def compute_terminates():
- '''
- Raise an error for any symbols that don't terminate.
- '''
- Terminates = {}
-
- # Terminals:
- for t in Terminals.keys():
- Terminates[t] = 1
-
- Terminates['$end'] = 1
-
- # Nonterminals:
-
- # Initialize to false:
- for n in Nonterminals.keys():
- Terminates[n] = 0
-
- # Then propagate termination until no change:
- while 1:
- some_change = 0
- for (n,pl) in Prodnames.items():
- # Nonterminal n terminates iff any of its productions terminates.
- for p in pl:
- # Production p terminates iff all of its rhs symbols terminate.
- for s in p.prod:
- if not Terminates[s]:
- # The symbol s does not terminate,
- # so production p does not terminate.
- p_terminates = 0
- break
- else:
- # didn't break from the loop,
- # so every symbol s terminates
- # so production p terminates.
- p_terminates = 1
-
- if p_terminates:
- # symbol n terminates!
- if not Terminates[n]:
- Terminates[n] = 1
- some_change = 1
- # Don't need to consider any more productions for this n.
- break
-
- if not some_change:
- break
-
- some_error = 0
- for (s,terminates) in Terminates.items():
- if not terminates:
- if not Prodnames.has_key(s) and not Terminals.has_key(s) and s != 'error':
- # s is used-but-not-defined, and we've already warned of that,
- # so it would be overkill to say that it's also non-terminating.
- pass
- else:
- sys.stderr.write("yacc: Infinite recursion detected for symbol '%s'.\n" % s)
- some_error = 1
-
- return some_error
-
-# -----------------------------------------------------------------------------
-# verify_productions()
-#
-# This function examines all of the supplied rules to see if they seem valid.
-# -----------------------------------------------------------------------------
-def verify_productions(cycle_check=1):
- error = 0
- for p in Productions:
- if not p: continue
-
- for s in p.prod:
- if not Prodnames.has_key(s) and not Terminals.has_key(s) and s != 'error':
- sys.stderr.write("%s:%d: Symbol '%s' used, but not defined as a token or a rule.\n" % (p.file,p.line,s))
- error = 1
- continue
-
- unused_tok = 0
- # Now verify all of the tokens
- if yaccdebug:
- _vf.write("Unused terminals:\n\n")
- for s,v in Terminals.items():
- if s != 'error' and not v:
- sys.stderr.write("yacc: Warning. Token '%s' defined, but not used.\n" % s)
- if yaccdebug: _vf.write(" %s\n"% s)
- unused_tok += 1
-
- # Print out all of the productions
- if yaccdebug:
- _vf.write("\nGrammar\n\n")
- for i in range(1,len(Productions)):
- _vf.write("Rule %-5d %s\n" % (i, Productions[i]))
-
- unused_prod = 0
- # Verify the use of all productions
- for s,v in Nonterminals.items():
- if not v:
- p = Prodnames[s][0]
- sys.stderr.write("%s:%d: Warning. Rule '%s' defined, but not used.\n" % (p.file,p.line, s))
- unused_prod += 1
-
-
- if unused_tok == 1:
- sys.stderr.write("yacc: Warning. There is 1 unused token.\n")
- if unused_tok > 1:
- sys.stderr.write("yacc: Warning. There are %d unused tokens.\n" % unused_tok)
-
- if unused_prod == 1:
- sys.stderr.write("yacc: Warning. There is 1 unused rule.\n")
- if unused_prod > 1:
- sys.stderr.write("yacc: Warning. There are %d unused rules.\n" % unused_prod)
-
- if yaccdebug:
- _vf.write("\nTerminals, with rules where they appear\n\n")
- ks = Terminals.keys()
- ks.sort()
- for k in ks:
- _vf.write("%-20s : %s\n" % (k, " ".join([str(s) for s in Terminals[k]])))
- _vf.write("\nNonterminals, with rules where they appear\n\n")
- ks = Nonterminals.keys()
- ks.sort()
- for k in ks:
- _vf.write("%-20s : %s\n" % (k, " ".join([str(s) for s in Nonterminals[k]])))
-
- if (cycle_check):
- compute_reachable()
- error += compute_terminates()
-# error += check_cycles()
- return error
-
-# -----------------------------------------------------------------------------
-# build_lritems()
-#
-# This function walks the list of productions and builds a complete set of the
-# LR items. The LR items are stored in two ways: First, they are uniquely
-# numbered and placed in the list _lritems. Second, a linked list of LR items
-# is built for each production. For example:
-#
-# E -> E PLUS E
-#
-# Creates the list
-#
-# [E -> . E PLUS E, E -> E . PLUS E, E -> E PLUS . E, E -> E PLUS E . ]
-# -----------------------------------------------------------------------------
-
-def build_lritems():
- for p in Productions:
- lastlri = p
- lri = p.lr_item(0)
- i = 0
- while 1:
- lri = p.lr_item(i)
- lastlri.lr_next = lri
- if not lri: break
- lri.lr_num = len(LRitems)
- LRitems.append(lri)
- lastlri = lri
- i += 1
-
- # In order for the rest of the parser generator to work, we need to
- # guarantee that no more lritems are generated. Therefore, we nuke
- # the p.lr_item method. (Only used in debugging)
- # Production.lr_item = None
-
-# -----------------------------------------------------------------------------
-# add_precedence()
-#
-# Given a list of precedence rules, add to the precedence table.
-# -----------------------------------------------------------------------------
-
-def add_precedence(plist):
- plevel = 0
- error = 0
- for p in plist:
- plevel += 1
- try:
- prec = p[0]
- terms = p[1:]
- if prec != 'left' and prec != 'right' and prec != 'nonassoc':
- sys.stderr.write("yacc: Invalid precedence '%s'\n" % prec)
- return -1
- for t in terms:
- if Precedence.has_key(t):
- sys.stderr.write("yacc: Precedence already specified for terminal '%s'\n" % t)
- error += 1
- continue
- Precedence[t] = (prec,plevel)
- except:
- sys.stderr.write("yacc: Invalid precedence table.\n")
- error += 1
-
- return error
-
-# -----------------------------------------------------------------------------
-# augment_grammar()
-#
-# Compute the augmented grammar. This is just a rule S' -> start where start
-# is the starting symbol.
-# -----------------------------------------------------------------------------
-
-def augment_grammar(start=None):
- if not start:
- start = Productions[1].name
- Productions[0] = Production(name="S'",prod=[start],number=0,len=1,prec=('right',0),func=None)
- Productions[0].usyms = [ start ]
- Nonterminals[start].append(0)
-
-
-# -------------------------------------------------------------------------
-# first()
-#
-# Compute the value of FIRST1(beta) where beta is a tuple of symbols.
-#
-# During execution of compute_first1, the result may be incomplete.
-# Afterward (e.g., when called from compute_follow()), it will be complete.
-# -------------------------------------------------------------------------
-def first(beta):
-
- # We are computing First(x1,x2,x3,...,xn)
- result = [ ]
- for x in beta:
- x_produces_empty = 0
-
- # Add all the non-<empty> symbols of First[x] to the result.
- for f in First[x]:
- if f == '<empty>':
- x_produces_empty = 1
- else:
- if f not in result: result.append(f)
-
- if x_produces_empty:
- # We have to consider the next x in beta,
- # i.e. stay in the loop.
- pass
- else:
- # We don't have to consider any further symbols in beta.
- break
- else:
- # There was no 'break' from the loop,
- # so x_produces_empty was true for all x in beta,
- # so beta produces empty as well.
- result.append('<empty>')
-
- return result
-
-
-# FOLLOW(x)
-# Given a non-terminal. This function computes the set of all symbols
-# that might follow it. Dragon book, p. 189.
-
-def compute_follow(start=None):
- # Add '$end' to the follow list of the start symbol
- for k in Nonterminals.keys():
- Follow[k] = [ ]
-
- if not start:
- start = Productions[1].name
-
- Follow[start] = [ '$end' ]
-
- while 1:
- didadd = 0
- for p in Productions[1:]:
- # Here is the production set
- for i in range(len(p.prod)):
- B = p.prod[i]
- if Nonterminals.has_key(B):
- # Okay. We got a non-terminal in a production
- fst = first(p.prod[i+1:])
- hasempty = 0
- for f in fst:
- if f != '<empty>' and f not in Follow[B]:
- Follow[B].append(f)
- didadd = 1
- if f == '<empty>':
- hasempty = 1
- if hasempty or i == (len(p.prod)-1):
- # Add elements of follow(a) to follow(b)
- for f in Follow[p.name]:
- if f not in Follow[B]:
- Follow[B].append(f)
- didadd = 1
- if not didadd: break
-
- if 0 and yaccdebug:
- _vf.write('\nFollow:\n')
- for k in Nonterminals.keys():
- _vf.write("%-20s : %s\n" % (k, " ".join([str(s) for s in Follow[k]])))
-
-# -------------------------------------------------------------------------
-# compute_first1()
-#
-# Compute the value of FIRST1(X) for all symbols
-# -------------------------------------------------------------------------
-def compute_first1():
-
- # Terminals:
- for t in Terminals.keys():
- First[t] = [t]
-
- First['$end'] = ['$end']
- First['#'] = ['#'] # what's this for?
-
- # Nonterminals:
-
- # Initialize to the empty set:
- for n in Nonterminals.keys():
- First[n] = []
-
- # Then propagate symbols until no change:
- while 1:
- some_change = 0
- for n in Nonterminals.keys():
- for p in Prodnames[n]:
- for f in first(p.prod):
- if f not in First[n]:
- First[n].append( f )
- some_change = 1
- if not some_change:
- break
-
- if 0 and yaccdebug:
- _vf.write('\nFirst:\n')
- for k in Nonterminals.keys():
- _vf.write("%-20s : %s\n" %
- (k, " ".join([str(s) for s in First[k]])))
-
-# -----------------------------------------------------------------------------
-# === SLR Generation ===
-#
-# The following functions are used to construct SLR (Simple LR) parsing tables
-# as described on p.221-229 of the dragon book.
-# -----------------------------------------------------------------------------
-
-# Global variables for the LR parsing engine
-def lr_init_vars():
- global _lr_action, _lr_goto, _lr_method
- global _lr_goto_cache, _lr0_cidhash
-
- _lr_action = { } # Action table
- _lr_goto = { } # Goto table
- _lr_method = "Unknown" # LR method used
- _lr_goto_cache = { }
- _lr0_cidhash = { }
-
-
-# Compute the LR(0) closure operation on I, where I is a set of LR(0) items.
-# prodlist is a list of productions.
-
-_add_count = 0 # Counter used to detect cycles
-
-def lr0_closure(I):
- global _add_count
-
- _add_count += 1
- prodlist = Productions
-
- # Add everything in I to J
- J = I[:]
- didadd = 1
- while didadd:
- didadd = 0
- for j in J:
- for x in j.lrafter:
- if x.lr0_added == _add_count: continue
- # Add B --> .G to J
- J.append(x.lr_next)
- x.lr0_added = _add_count
- didadd = 1
-
- return J
-
-# Compute the LR(0) goto function goto(I,X) where I is a set
-# of LR(0) items and X is a grammar symbol. This function is written
-# in a way that guarantees uniqueness of the generated goto sets
-# (i.e. the same goto set will never be returned as two different Python
-# objects). With uniqueness, we can later do fast set comparisons using
-# id(obj) instead of element-wise comparison.
-
-def lr0_goto(I,x):
- # First we look for a previously cached entry
- g = _lr_goto_cache.get((id(I),x),None)
- if g: return g
-
- # Now we generate the goto set in a way that guarantees uniqueness
- # of the result
-
- s = _lr_goto_cache.get(x,None)
- if not s:
- s = { }
- _lr_goto_cache[x] = s
-
- gs = [ ]
- for p in I:
- n = p.lr_next
- if n and n.lrbefore == x:
- s1 = s.get(id(n),None)
- if not s1:
- s1 = { }
- s[id(n)] = s1
- gs.append(n)
- s = s1
- g = s.get('$end',None)
- if not g:
- if gs:
- g = lr0_closure(gs)
- s['$end'] = g
- else:
- s['$end'] = gs
- _lr_goto_cache[(id(I),x)] = g
- return g
-
-_lr0_cidhash = { }
-
-# Compute the LR(0) sets of item function
-def lr0_items():
-
- C = [ lr0_closure([Productions[0].lr_next]) ]
- i = 0
- for I in C:
- _lr0_cidhash[id(I)] = i
- i += 1
-
- # Loop over the items in C and each grammar symbols
- i = 0
- while i < len(C):
- I = C[i]
- i += 1
-
- # Collect all of the symbols that could possibly be in the goto(I,X) sets
- asyms = { }
- for ii in I:
- for s in ii.usyms:
- asyms[s] = None
-
- for x in asyms.keys():
- g = lr0_goto(I,x)
- if not g: continue
- if _lr0_cidhash.has_key(id(g)): continue
- _lr0_cidhash[id(g)] = len(C)
- C.append(g)
-
- return C
-
-# -----------------------------------------------------------------------------
-# ==== LALR(1) Parsing ====
-#
-# LALR(1) parsing is almost exactly the same as SLR except that instead of
-# relying upon Follow() sets when performing reductions, a more selective
-# lookahead set that incorporates the state of the LR(0) machine is utilized.
-# Thus, we mainly just have to focus on calculating the lookahead sets.
-#
-# The method used here is due to DeRemer and Pennelo (1982).
-#
-# DeRemer, F. L., and T. J. Pennelo: "Efficient Computation of LALR(1)
-# Lookahead Sets", ACM Transactions on Programming Languages and Systems,
-# Vol. 4, No. 4, Oct. 1982, pp. 615-649
-#
-# Further details can also be found in:
-#
-# J. Tremblay and P. Sorenson, "The Theory and Practice of Compiler Writing",
-# McGraw-Hill Book Company, (1985).
-#
-# Note: This implementation is a complete replacement of the LALR(1)
-# implementation in PLY-1.x releases. That version was based on
-# a less efficient algorithm and it had bugs in its implementation.
-# -----------------------------------------------------------------------------
-
-# -----------------------------------------------------------------------------
-# compute_nullable_nonterminals()
-#
-# Creates a dictionary containing all of the non-terminals that might produce
-# an empty production.
-# -----------------------------------------------------------------------------
-
-def compute_nullable_nonterminals():
- nullable = {}
- num_nullable = 0
- while 1:
- for p in Productions[1:]:
- if p.len == 0:
- nullable[p.name] = 1
- continue
- for t in p.prod:
- if not nullable.has_key(t): break
- else:
- nullable[p.name] = 1
- if len(nullable) == num_nullable: break
- num_nullable = len(nullable)
- return nullable
-
-# -----------------------------------------------------------------------------
-# find_nonterminal_trans(C)
-#
-# Given a set of LR(0) items, this functions finds all of the non-terminal
-# transitions. These are transitions in which a dot appears immediately before
-# a non-terminal. Returns a list of tuples of the form (state,N) where state
-# is the state number and N is the nonterminal symbol.
-#
-# The input C is the set of LR(0) items.
-# -----------------------------------------------------------------------------
-
-def find_nonterminal_transitions(C):
- trans = []
- for state in range(len(C)):
- for p in C[state]:
- if p.lr_index < p.len - 1:
- t = (state,p.prod[p.lr_index+1])
- if Nonterminals.has_key(t[1]):
- if t not in trans: trans.append(t)
- state = state + 1
- return trans
-
-# -----------------------------------------------------------------------------
-# dr_relation()
-#
-# Computes the DR(p,A) relationships for non-terminal transitions. The input
-# is a tuple (state,N) where state is a number and N is a nonterminal symbol.
-#
-# Returns a list of terminals.
-# -----------------------------------------------------------------------------
-
-def dr_relation(C,trans,nullable):
- dr_set = { }
- state,N = trans
- terms = []
-
- g = lr0_goto(C[state],N)
- for p in g:
- if p.lr_index < p.len - 1:
- a = p.prod[p.lr_index+1]
- if Terminals.has_key(a):
- if a not in terms: terms.append(a)
-
- # This extra bit is to handle the start state
- if state == 0 and N == Productions[0].prod[0]:
- terms.append('$end')
-
- return terms
-
-# -----------------------------------------------------------------------------
-# reads_relation()
-#
-# Computes the READS() relation (p,A) READS (t,C).
-# -----------------------------------------------------------------------------
-
-def reads_relation(C, trans, empty):
- # Look for empty transitions
- rel = []
- state, N = trans
-
- g = lr0_goto(C[state],N)
- j = _lr0_cidhash.get(id(g),-1)
- for p in g:
- if p.lr_index < p.len - 1:
- a = p.prod[p.lr_index + 1]
- if empty.has_key(a):
- rel.append((j,a))
-
- return rel
-
-# -----------------------------------------------------------------------------
-# compute_lookback_includes()
-#
-# Determines the lookback and includes relations
-#
-# LOOKBACK:
-#
-# This relation is determined by running the LR(0) state machine forward.
-# For example, starting with a production "N : . A B C", we run it forward
-# to obtain "N : A B C ." We then build a relationship between this final
-# state and the starting state. These relationships are stored in a dictionary
-# lookdict.
-#
-# INCLUDES:
-#
-# Computes the INCLUDE() relation (p,A) INCLUDES (p',B).
-#
-# This relation is used to determine non-terminal transitions that occur
-# inside of other non-terminal transition states. (p,A) INCLUDES (p', B)
-# if the following holds:
-#
-# B -> LAT, where T -> epsilon and p' -L-> p
-#
-# L is essentially a prefix (which may be empty), T is a suffix that must be
-# able to derive an empty string. State p' must lead to state p with the string L.
-#
-# -----------------------------------------------------------------------------
-
-def compute_lookback_includes(C,trans,nullable):
-
- lookdict = {} # Dictionary of lookback relations
- includedict = {} # Dictionary of include relations
-
- # Make a dictionary of non-terminal transitions
- dtrans = {}
- for t in trans:
- dtrans[t] = 1
-
- # Loop over all transitions and compute lookbacks and includes
- for state,N in trans:
- lookb = []
- includes = []
- for p in C[state]:
- if p.name != N: continue
-
- # Okay, we have a name match. We now follow the production all the way
- # through the state machine until we get the . on the right hand side
-
- lr_index = p.lr_index
- j = state
- while lr_index < p.len - 1:
- lr_index = lr_index + 1
- t = p.prod[lr_index]
-
- # Check to see if this symbol and state are a non-terminal transition
- if dtrans.has_key((j,t)):
- # Yes. Okay, there is some chance that this is an includes relation
- # the only way to know for certain is whether the rest of the
- # production derives empty
-
- li = lr_index + 1
- while li < p.len:
- if Terminals.has_key(p.prod[li]): break # No forget it
- if not nullable.has_key(p.prod[li]): break
- li = li + 1
- else:
- # Appears to be a relation between (j,t) and (state,N)
- includes.append((j,t))
-
- g = lr0_goto(C[j],t) # Go to next set
- j = _lr0_cidhash.get(id(g),-1) # Go to next state
-
- # When we get here, j is the final state, now we have to locate the production
- for r in C[j]:
- if r.name != p.name: continue
- if r.len != p.len: continue
- i = 0
- # This look is comparing a production ". A B C" with "A B C ."
- while i < r.lr_index:
- if r.prod[i] != p.prod[i+1]: break
- i = i + 1
- else:
- lookb.append((j,r))
- for i in includes:
- if not includedict.has_key(i): includedict[i] = []
- includedict[i].append((state,N))
- lookdict[(state,N)] = lookb
-
- return lookdict,includedict
-
-# -----------------------------------------------------------------------------
-# digraph()
-# traverse()
-#
-# The following two functions are used to compute set valued functions
-# of the form:
-#
-# F(x) = F'(x) U U{F(y) | x R y}
-#
-# This is used to compute the values of Read() sets as well as FOLLOW sets
-# in LALR(1) generation.
-#
-# Inputs: X - An input set
-# R - A relation
-# FP - Set-valued function
-# ------------------------------------------------------------------------------
-
-def digraph(X,R,FP):
- N = { }
- for x in X:
- N[x] = 0
- stack = []
- F = { }
- for x in X:
- if N[x] == 0: traverse(x,N,stack,F,X,R,FP)
- return F
-
-def traverse(x,N,stack,F,X,R,FP):
- stack.append(x)
- d = len(stack)
- N[x] = d
- F[x] = FP(x) # F(X) <- F'(x)
-
- rel = R(x) # Get y's related to x
- for y in rel:
- if N[y] == 0:
- traverse(y,N,stack,F,X,R,FP)
- N[x] = min(N[x],N[y])
- for a in F.get(y,[]):
- if a not in F[x]: F[x].append(a)
- if N[x] == d:
- N[stack[-1]] = sys.maxint
- F[stack[-1]] = F[x]
- element = stack.pop()
- while element != x:
- N[stack[-1]] = sys.maxint
- F[stack[-1]] = F[x]
- element = stack.pop()
-
-# -----------------------------------------------------------------------------
-# compute_read_sets()
-#
-# Given a set of LR(0) items, this function computes the read sets.
-#
-# Inputs: C = Set of LR(0) items
-# ntrans = Set of nonterminal transitions
-# nullable = Set of empty transitions
-#
-# Returns a set containing the read sets
-# -----------------------------------------------------------------------------
-
-def compute_read_sets(C, ntrans, nullable):
- FP = lambda x: dr_relation(C,x,nullable)
- R = lambda x: reads_relation(C,x,nullable)
- F = digraph(ntrans,R,FP)
- return F
-
-# -----------------------------------------------------------------------------
-# compute_follow_sets()
-#
-# Given a set of LR(0) items, a set of non-terminal transitions, a readset,
-# and an include set, this function computes the follow sets
-#
-# Follow(p,A) = Read(p,A) U U {Follow(p',B) | (p,A) INCLUDES (p',B)}
-#
-# Inputs:
-# ntrans = Set of nonterminal transitions
-# readsets = Readset (previously computed)
-# inclsets = Include sets (previously computed)
-#
-# Returns a set containing the follow sets
-# -----------------------------------------------------------------------------
-
-def compute_follow_sets(ntrans,readsets,inclsets):
- FP = lambda x: readsets[x]
- R = lambda x: inclsets.get(x,[])
- F = digraph(ntrans,R,FP)
- return F
-
-# -----------------------------------------------------------------------------
-# add_lookaheads()
-#
-# Attaches the lookahead symbols to grammar rules.
-#
-# Inputs: lookbacks - Set of lookback relations
-# followset - Computed follow set
-#
-# This function directly attaches the lookaheads to productions contained
-# in the lookbacks set
-# -----------------------------------------------------------------------------
-
-def add_lookaheads(lookbacks,followset):
- for trans,lb in lookbacks.items():
- # Loop over productions in lookback
- for state,p in lb:
- if not p.lookaheads.has_key(state):
- p.lookaheads[state] = []
- f = followset.get(trans,[])
- for a in f:
- if a not in p.lookaheads[state]: p.lookaheads[state].append(a)
-
-# -----------------------------------------------------------------------------
-# add_lalr_lookaheads()
-#
-# This function does all of the work of adding lookahead information for use
-# with LALR parsing
-# -----------------------------------------------------------------------------
-
-def add_lalr_lookaheads(C):
- # Determine all of the nullable nonterminals
- nullable = compute_nullable_nonterminals()
-
- # Find all non-terminal transitions
- trans = find_nonterminal_transitions(C)
-
- # Compute read sets
- readsets = compute_read_sets(C,trans,nullable)
-
- # Compute lookback/includes relations
- lookd, included = compute_lookback_includes(C,trans,nullable)
-
- # Compute LALR FOLLOW sets
- followsets = compute_follow_sets(trans,readsets,included)
-
- # Add all of the lookaheads
- add_lookaheads(lookd,followsets)
-
-# -----------------------------------------------------------------------------
-# lr_parse_table()
-#
-# This function constructs the parse tables for SLR or LALR
-# -----------------------------------------------------------------------------
-def lr_parse_table(method):
- global _lr_method
- goto = _lr_goto # Goto array
- action = _lr_action # Action array
- actionp = { } # Action production array (temporary)
-
- _lr_method = method
-
- n_srconflict = 0
- n_rrconflict = 0
-
- if yaccdebug:
- sys.stderr.write("yacc: Generating %s parsing table...\n" % method)
- _vf.write("\n\nParsing method: %s\n\n" % method)
-
- # Step 1: Construct C = { I0, I1, ... IN}, collection of LR(0) items
- # This determines the number of states
-
- C = lr0_items()
-
- if method == 'LALR':
- add_lalr_lookaheads(C)
-
- # Build the parser table, state by state
- st = 0
- for I in C:
- # Loop over each production in I
- actlist = [ ] # List of actions
-
- if yaccdebug:
- _vf.write("\nstate %d\n\n" % st)
- for p in I:
- _vf.write(" (%d) %s\n" % (p.number, str(p)))
- _vf.write("\n")
-
- for p in I:
- try:
- if p.prod[-1] == ".":
- if p.name == "S'":
- # Start symbol. Accept!
- action[st,"$end"] = 0
- actionp[st,"$end"] = p
- else:
- # We are at the end of a production. Reduce!
- if method == 'LALR':
- laheads = p.lookaheads[st]
- else:
- laheads = Follow[p.name]
- for a in laheads:
- actlist.append((a,p,"reduce using rule %d (%s)" % (p.number,p)))
- r = action.get((st,a),None)
- if r is not None:
- # Whoa. Have a shift/reduce or reduce/reduce conflict
- if r > 0:
- # Need to decide on shift or reduce here
- # By default we favor shifting. Need to add
- # some precedence rules here.
- sprec,slevel = Productions[actionp[st,a].number].prec
- rprec,rlevel = Precedence.get(a,('right',0))
- if (slevel < rlevel) or ((slevel == rlevel) and (rprec == 'left')):
- # We really need to reduce here.
- action[st,a] = -p.number
- actionp[st,a] = p
- if not slevel and not rlevel:
- _vfc.write("shift/reduce conflict in state %d resolved as reduce.\n" % st)
- _vf.write(" ! shift/reduce conflict for %s resolved as reduce.\n" % a)
- n_srconflict += 1
- elif (slevel == rlevel) and (rprec == 'nonassoc'):
- action[st,a] = None
- else:
- # Hmmm. Guess we'll keep the shift
- if not rlevel:
- _vfc.write("shift/reduce conflict in state %d resolved as shift.\n" % st)
- _vf.write(" ! shift/reduce conflict for %s resolved as shift.\n" % a)
- n_srconflict +=1
- elif r < 0:
- # Reduce/reduce conflict. In this case, we favor the rule
- # that was defined first in the grammar file
- oldp = Productions[-r]
- pp = Productions[p.number]
- if oldp.line > pp.line:
- action[st,a] = -p.number
- actionp[st,a] = p
- # sys.stderr.write("Reduce/reduce conflict in state %d\n" % st)
- n_rrconflict += 1
- _vfc.write("reduce/reduce conflict in state %d resolved using rule %d (%s).\n" % (st, actionp[st,a].number, actionp[st,a]))
- _vf.write(" ! reduce/reduce conflict for %s resolved using rule %d (%s).\n" % (a,actionp[st,a].number, actionp[st,a]))
- else:
- sys.stderr.write("Unknown conflict in state %d\n" % st)
- else:
- action[st,a] = -p.number
- actionp[st,a] = p
- else:
- i = p.lr_index
- a = p.prod[i+1] # Get symbol right after the "."
- if Terminals.has_key(a):
- g = lr0_goto(I,a)
- j = _lr0_cidhash.get(id(g),-1)
- if j >= 0:
- # We are in a shift state
- actlist.append((a,p,"shift and go to state %d" % j))
- r = action.get((st,a),None)
- if r is not None:
- # Whoa have a shift/reduce or shift/shift conflict
- if r > 0:
- if r != j:
- sys.stderr.write("Shift/shift conflict in state %d\n" % st)
- elif r < 0:
- # Do a precedence check.
- # - if precedence of reduce rule is higher, we reduce.
- # - if precedence of reduce is same and left assoc, we reduce.
- # - otherwise we shift
- rprec,rlevel = Productions[actionp[st,a].number].prec
- sprec,slevel = Precedence.get(a,('right',0))
- if (slevel > rlevel) or ((slevel == rlevel) and (rprec != 'left')):
- # We decide to shift here... highest precedence to shift
- action[st,a] = j
- actionp[st,a] = p
- if not rlevel:
- n_srconflict += 1
- _vfc.write("shift/reduce conflict in state %d resolved as shift.\n" % st)
- _vf.write(" ! shift/reduce conflict for %s resolved as shift.\n" % a)
- elif (slevel == rlevel) and (rprec == 'nonassoc'):
- action[st,a] = None
- else:
- # Hmmm. Guess we'll keep the reduce
- if not slevel and not rlevel:
- n_srconflict +=1
- _vfc.write("shift/reduce conflict in state %d resolved as reduce.\n" % st)
- _vf.write(" ! shift/reduce conflict for %s resolved as reduce.\n" % a)
-
- else:
- sys.stderr.write("Unknown conflict in state %d\n" % st)
- else:
- action[st,a] = j
- actionp[st,a] = p
-
- except StandardError,e:
- raise YaccError, "Hosed in lr_parse_table", e
-
- # Print the actions associated with each terminal
- if yaccdebug:
- _actprint = { }
- for a,p,m in actlist:
- if action.has_key((st,a)):
- if p is actionp[st,a]:
- _vf.write(" %-15s %s\n" % (a,m))
- _actprint[(a,m)] = 1
- _vf.write("\n")
- for a,p,m in actlist:
- if action.has_key((st,a)):
- if p is not actionp[st,a]:
- if not _actprint.has_key((a,m)):
- _vf.write(" ! %-15s [ %s ]\n" % (a,m))
- _actprint[(a,m)] = 1
-
- # Construct the goto table for this state
- if yaccdebug:
- _vf.write("\n")
- nkeys = { }
- for ii in I:
- for s in ii.usyms:
- if Nonterminals.has_key(s):
- nkeys[s] = None
- for n in nkeys.keys():
- g = lr0_goto(I,n)
- j = _lr0_cidhash.get(id(g),-1)
- if j >= 0:
- goto[st,n] = j
- if yaccdebug:
- _vf.write(" %-30s shift and go to state %d\n" % (n,j))
-
- st += 1
-
- if yaccdebug:
- if n_srconflict == 1:
- sys.stderr.write("yacc: %d shift/reduce conflict\n" % n_srconflict)
- if n_srconflict > 1:
- sys.stderr.write("yacc: %d shift/reduce conflicts\n" % n_srconflict)
- if n_rrconflict == 1:
- sys.stderr.write("yacc: %d reduce/reduce conflict\n" % n_rrconflict)
- if n_rrconflict > 1:
- sys.stderr.write("yacc: %d reduce/reduce conflicts\n" % n_rrconflict)
-
-# -----------------------------------------------------------------------------
-# ==== LR Utility functions ====
-# -----------------------------------------------------------------------------
-
-# -----------------------------------------------------------------------------
-# _lr_write_tables()
-#
-# This function writes the LR parsing tables to a file
-# -----------------------------------------------------------------------------
-
-def lr_write_tables(modulename=tab_module,outputdir=''):
- filename = os.path.join(outputdir,modulename) + ".py"
- try:
- f = open(filename,"w")
-
- f.write("""
-# %s
-# This file is automatically generated. Do not edit.
-
-_lr_method = %s
-
-_lr_signature = %s
-""" % (filename, repr(_lr_method), repr(Signature.digest())))
-
- # Change smaller to 0 to go back to original tables
- smaller = 1
-
- # Factor out names to try and make smaller
- if smaller:
- items = { }
-
- for k,v in _lr_action.items():
- i = items.get(k[1])
- if not i:
- i = ([],[])
- items[k[1]] = i
- i[0].append(k[0])
- i[1].append(v)
-
- f.write("\n_lr_action_items = {")
- for k,v in items.items():
- f.write("%r:([" % k)
- for i in v[0]:
- f.write("%r," % i)
- f.write("],[")
- for i in v[1]:
- f.write("%r," % i)
-
- f.write("]),")
- f.write("}\n")
-
- f.write("""
-_lr_action = { }
-for _k, _v in _lr_action_items.items():
- for _x,_y in zip(_v[0],_v[1]):
- _lr_action[(_x,_k)] = _y
-del _lr_action_items
-""")
-
- else:
- f.write("\n_lr_action = { ");
- for k,v in _lr_action.items():
- f.write("(%r,%r):%r," % (k[0],k[1],v))
- f.write("}\n");
-
- if smaller:
- # Factor out names to try and make smaller
- items = { }
-
- for k,v in _lr_goto.items():
- i = items.get(k[1])
- if not i:
- i = ([],[])
- items[k[1]] = i
- i[0].append(k[0])
- i[1].append(v)
-
- f.write("\n_lr_goto_items = {")
- for k,v in items.items():
- f.write("%r:([" % k)
- for i in v[0]:
- f.write("%r," % i)
- f.write("],[")
- for i in v[1]:
- f.write("%r," % i)
-
- f.write("]),")
- f.write("}\n")
-
- f.write("""
-_lr_goto = { }
-for _k, _v in _lr_goto_items.items():
- for _x,_y in zip(_v[0],_v[1]):
- _lr_goto[(_x,_k)] = _y
-del _lr_goto_items
-""")
- else:
- f.write("\n_lr_goto = { ");
- for k,v in _lr_goto.items():
- f.write("(%r,%r):%r," % (k[0],k[1],v))
- f.write("}\n");
-
- # Write production table
- f.write("_lr_productions = [\n")
- for p in Productions:
- if p:
- if (p.func):
- f.write(" (%r,%d,%r,%r,%d),\n" % (p.name, p.len, p.func.__name__,p.file,p.line))
- else:
- f.write(" (%r,%d,None,None,None),\n" % (p.name, p.len))
- else:
- f.write(" None,\n")
- f.write("]\n")
-
- f.close()
-
- except IOError,e:
- print "Unable to create '%s'" % filename
- print e
- return
-
-def lr_read_tables(module=tab_module,optimize=0):
- global _lr_action, _lr_goto, _lr_productions, _lr_method
- try:
- exec "import %s as parsetab" % module
-
- if (optimize) or (Signature.digest() == parsetab._lr_signature):
- _lr_action = parsetab._lr_action
- _lr_goto = parsetab._lr_goto
- _lr_productions = parsetab._lr_productions
- _lr_method = parsetab._lr_method
- return 1
- else:
- return 0
-
- except (ImportError,AttributeError):
- return 0
-
-
-# Available instance types. This is used when parsers are defined by a class.
-# it's a little funky because I want to preserve backwards compatibility
-# with Python 2.0 where types.ObjectType is undefined.
-
-try:
- _INSTANCETYPE = (types.InstanceType, types.ObjectType)
-except AttributeError:
- _INSTANCETYPE = types.InstanceType
-
-# -----------------------------------------------------------------------------
-# yacc(module)
-#
-# Build the parser module
-# -----------------------------------------------------------------------------
-
-def yacc(method=default_lr, debug=yaccdebug, module=None, tabmodule=tab_module, start=None, check_recursion=1, optimize=0,write_tables=1,debugfile=debug_file,outputdir=''):
- global yaccdebug
- yaccdebug = debug
-
- initialize_vars()
- files = { }
- error = 0
-
-
- # Add parsing method to signature
- Signature.update(method)
-
- # If a "module" parameter was supplied, extract its dictionary.
- # Note: a module may in fact be an instance as well.
-
- if module:
- # User supplied a module object.
- if isinstance(module, types.ModuleType):
- ldict = module.__dict__
- elif isinstance(module, _INSTANCETYPE):
- _items = [(k,getattr(module,k)) for k in dir(module)]
- ldict = { }
- for i in _items:
- ldict[i[0]] = i[1]
- else:
- raise ValueError,"Expected a module"
-
- else:
- # No module given. We might be able to get information from the caller.
- # Throw an exception and unwind the traceback to get the globals
-
- try:
- raise RuntimeError
- except RuntimeError:
- e,b,t = sys.exc_info()
- f = t.tb_frame
- f = f.f_back # Walk out to our calling function
- ldict = f.f_globals # Grab its globals dictionary
-
- # Add starting symbol to signature
- if not start:
- start = ldict.get("start",None)
- if start:
- Signature.update(start)
-
- # If running in optimized mode. We're going to
-
- if (optimize and lr_read_tables(tabmodule,1)):
- # Read parse table
- del Productions[:]
- for p in _lr_productions:
- if not p:
- Productions.append(None)
- else:
- m = MiniProduction()
- m.name = p[0]
- m.len = p[1]
- m.file = p[3]
- m.line = p[4]
- if p[2]:
- m.func = ldict[p[2]]
- Productions.append(m)
-
- else:
- # Get the tokens map
- if (module and isinstance(module,_INSTANCETYPE)):
- tokens = getattr(module,"tokens",None)
- else:
- tokens = ldict.get("tokens",None)
-
- if not tokens:
- raise YaccError,"module does not define a list 'tokens'"
- if not (isinstance(tokens,types.ListType) or isinstance(tokens,types.TupleType)):
- raise YaccError,"tokens must be a list or tuple."
-
- # Check to see if a requires dictionary is defined.
- requires = ldict.get("require",None)
- if requires:
- if not (isinstance(requires,types.DictType)):
- raise YaccError,"require must be a dictionary."
-
- for r,v in requires.items():
- try:
- if not (isinstance(v,types.ListType)):
- raise TypeError
- v1 = [x.split(".") for x in v]
- Requires[r] = v1
- except StandardError:
- print "Invalid specification for rule '%s' in require. Expected a list of strings" % r
-
-
- # Build the dictionary of terminals. We a record a 0 in the
- # dictionary to track whether or not a terminal is actually
- # used in the grammar
-
- if 'error' in tokens:
- print "yacc: Illegal token 'error'. Is a reserved word."
- raise YaccError,"Illegal token name"
-
- for n in tokens:
- if Terminals.has_key(n):
- print "yacc: Warning. Token '%s' multiply defined." % n
- Terminals[n] = [ ]
-
- Terminals['error'] = [ ]
-
- # Get the precedence map (if any)
- prec = ldict.get("precedence",None)
- if prec:
- if not (isinstance(prec,types.ListType) or isinstance(prec,types.TupleType)):
- raise YaccError,"precedence must be a list or tuple."
- add_precedence(prec)
- Signature.update(repr(prec))
-
- for n in tokens:
- if not Precedence.has_key(n):
- Precedence[n] = ('right',0) # Default, right associative, 0 precedence
-
- # Look for error handler
- ef = ldict.get('p_error',None)
- if ef:
- if isinstance(ef,types.FunctionType):
- ismethod = 0
- elif isinstance(ef, types.MethodType):
- ismethod = 1
- else:
- raise YaccError,"'p_error' defined, but is not a function or method."
- eline = ef.func_code.co_firstlineno
- efile = ef.func_code.co_filename
- files[efile] = None
-
- if (ef.func_code.co_argcount != 1+ismethod):
- raise YaccError,"%s:%d: p_error() requires 1 argument." % (efile,eline)
- global Errorfunc
- Errorfunc = ef
- else:
- print "yacc: Warning. no p_error() function is defined."
-
- # Get the list of built-in functions with p_ prefix
- symbols = [ldict[f] for f in ldict.keys()
- if (type(ldict[f]) in (types.FunctionType, types.MethodType) and ldict[f].__name__[:2] == 'p_'
- and ldict[f].__name__ != 'p_error')]
-
- # Check for non-empty symbols
- if len(symbols) == 0:
- raise YaccError,"no rules of the form p_rulename are defined."
-
- # Sort the symbols by line number
- symbols.sort(lambda x,y: cmp(x.func_code.co_firstlineno,y.func_code.co_firstlineno))
-
- # Add all of the symbols to the grammar
- for f in symbols:
- if (add_function(f)) < 0:
- error += 1
- else:
- files[f.func_code.co_filename] = None
-
- # Make a signature of the docstrings
- for f in symbols:
- if f.__doc__:
- Signature.update(f.__doc__)
-
- lr_init_vars()
-
- if error:
- raise YaccError,"Unable to construct parser."
-
- if not lr_read_tables(tabmodule):
-
- # Validate files
- for filename in files.keys():
- if not validate_file(filename):
- error = 1
-
- # Validate dictionary
- validate_dict(ldict)
-
- if start and not Prodnames.has_key(start):
- raise YaccError,"Bad starting symbol '%s'" % start
-
- augment_grammar(start)
- error = verify_productions(cycle_check=check_recursion)
- otherfunc = [ldict[f] for f in ldict.keys()
- if (type(f) in (types.FunctionType,types.MethodType) and ldict[f].__name__[:2] != 'p_')]
-
- if error:
- raise YaccError,"Unable to construct parser."
-
- build_lritems()
- compute_first1()
- compute_follow(start)
-
- if method in ['SLR','LALR']:
- lr_parse_table(method)
- else:
- raise YaccError, "Unknown parsing method '%s'" % method
-
- if write_tables:
- lr_write_tables(tabmodule,outputdir)
-
- if yaccdebug:
- try:
- f = open(os.path.join(outputdir,debugfile),"w")
- f.write(_vfc.getvalue())
- f.write("\n\n")
- f.write(_vf.getvalue())
- f.close()
- except IOError,e:
- print "yacc: can't create '%s'" % debugfile,e
-
- # Made it here. Create a parser object and set up its internal state.
- # Set global parse() method to bound method of parser object.
-
- p = Parser("xyzzy")
- p.productions = Productions
- p.errorfunc = Errorfunc
- p.action = _lr_action
- p.goto = _lr_goto
- p.method = _lr_method
- p.require = Requires
-
- global parse
- parse = p.parse
-
- global parser
- parser = p
-
- # Clean up all of the globals we created
- if (not optimize):
- yacc_cleanup()
- return p
-
-# yacc_cleanup function. Delete all of the global variables
-# used during table construction
-
-def yacc_cleanup():
- global _lr_action, _lr_goto, _lr_method, _lr_goto_cache
- del _lr_action, _lr_goto, _lr_method, _lr_goto_cache
-
- global Productions, Prodnames, Prodmap, Terminals
- global Nonterminals, First, Follow, Precedence, LRitems
- global Errorfunc, Signature, Requires
-
- del Productions, Prodnames, Prodmap, Terminals
- del Nonterminals, First, Follow, Precedence, LRitems
- del Errorfunc, Signature, Requires
-
- global _vf, _vfc
- del _vf, _vfc
-
-
-# Stub that raises an error if parsing is attempted without first calling yacc()
-def parse(*args,**kwargs):
- raise YaccError, "yacc: No parser built with yacc()"
-
diff --git a/lib/python2.7/site-packages/setoolsgui/sesearch b/lib/python2.7/site-packages/setoolsgui/sesearch
deleted file mode 100755
index e861db6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/sesearch
+++ /dev/null
@@ -1,206 +0,0 @@
-#!/usr/bin/python
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with SETools. If not, see <http://www.gnu.org/licenses/>.
-#
-
-from __future__ import print_function
-import setools
-import argparse
-import sys
-import logging
-
-parser = argparse.ArgumentParser(
- description="SELinux policy rule search tool.",
- epilog="TE/MLS rule searches cannot be mixed with RBAC rule searches.")
-parser.add_argument("--version", action="version", version=setools.__version__)
-parser.add_argument("policy", help="Path to the SELinux policy to search.", nargs="?")
-parser.add_argument("-v", "--verbose", action="store_true",
- help="Print extra informational messages")
-parser.add_argument("--debug", action="store_true", dest="debug", help="Enable debugging.")
-
-rtypes = parser.add_argument_group("TE Rule Types")
-rtypes.add_argument("-A", "--allow", action="append_const",
- const="allow", dest="tertypes",
- help="Search allow rules.")
-rtypes.add_argument("--auditallow", action="append_const",
- const="auditallow", dest="tertypes",
- help="Search auditallow rules.")
-rtypes.add_argument("--dontaudit", action="append_const",
- const="dontaudit", dest="tertypes",
- help="Search dontaudit rules.")
-rtypes.add_argument("-T", "--type_trans", action="append_const",
- const="type_transition", dest="tertypes",
- help="Search type_transition rules.")
-rtypes.add_argument("--type_change", action="append_const",
- const="type_change", dest="tertypes",
- help="Search type_change rules.")
-rtypes.add_argument("--type_member", action="append_const",
- const="type_member", dest="tertypes",
- help="Search type_member rules.")
-
-rbacrtypes = parser.add_argument_group("RBAC Rule Types")
-rbacrtypes.add_argument("--role_allow", action="append_const",
- const="allow", dest="rbacrtypes",
- help="Search role allow rules.")
-rbacrtypes.add_argument("--role_trans", action="append_const",
- const="role_transition", dest="rbacrtypes",
- help="Search role_transition rules.")
-
-mlsrtypes = parser.add_argument_group("MLS Rule Types")
-mlsrtypes.add_argument("--range_trans", action="append_const",
- const="range_transition", dest="mlsrtypes",
- help="Search range_transition rules.")
-
-expr = parser.add_argument_group("Expressions")
-expr.add_argument("-s", "--source",
- help="Source type/role of the TE/RBAC rule.")
-expr.add_argument("-t", "--target",
- help="Target type/role of the TE/RBAC rule.")
-expr.add_argument("-c", "--class", dest="tclass",
- help="Comma separated list of object classes")
-expr.add_argument("-p", "--perms", metavar="PERMS",
- help="Comma separated list of permissions.")
-expr.add_argument("-D", "--default",
- help="Default of the rule. (type/role/range transition rules)")
-expr.add_argument("-b", "--bool", dest="boolean", metavar="BOOL",
- help="Comma separated list of Booleans in the conditional expression.")
-
-opts = parser.add_argument_group("Search options")
-opts.add_argument("-eb", action="store_true", dest="boolean_equal",
- help="Match Boolean list exactly instead of matching any listed Boolean.")
-opts.add_argument("-ep", action="store_true", dest="perms_equal",
- help="Match permission set exactly instead of matching any listed permission.")
-opts.add_argument("-ds", action="store_false", dest="source_indirect",
- help="Match source attributes directly instead of matching member types/roles.")
-opts.add_argument("-dt", action="store_false", dest="target_indirect",
- help="Match target attributes directly instead of matching member types/roles.")
-opts.add_argument("-rs", action="store_true", dest="source_regex",
- help="Use regular expression matching for the source type/role.")
-opts.add_argument("-rt", action="store_true", dest="target_regex",
- help="Use regular expression matching for the target type/role.")
-opts.add_argument("-rc", action="store_true", dest="tclass_regex",
- help="Use regular expression matching for the object class.")
-opts.add_argument("-rd", action="store_true", dest="default_regex",
- help="Use regular expression matching for the default type/role.")
-opts.add_argument("-rb", action="store_true", dest="boolean_regex",
- help="Use regular expression matching for Booleans.")
-
-args = parser.parse_args()
-
-if not args.tertypes and not args.mlsrtypes and not args.rbacrtypes:
- parser.error("At least one rule type must be specified.")
-
-if args.debug:
- logging.basicConfig(level=logging.DEBUG,
- format='%(asctime)s|%(levelname)s|%(name)s|%(message)s')
-elif args.verbose:
- logging.basicConfig(level=logging.INFO, format='%(message)s')
-else:
- logging.basicConfig(level=logging.WARNING, format='%(message)s')
-
-try:
- p = setools.SELinuxPolicy(args.policy)
-
- if args.tertypes:
- q = setools.TERuleQuery(p,
- ruletype=args.tertypes,
- source=args.source,
- source_indirect=args.source_indirect,
- source_regex=args.source_regex,
- target=args.target,
- target_indirect=args.target_indirect,
- target_regex=args.target_regex,
- tclass_regex=args.tclass_regex,
- perms_equal=args.perms_equal,
- default=args.default,
- default_regex=args.default_regex,
- boolean_regex=args.boolean_regex,
- boolean_equal=args.boolean_equal)
-
- # these are broken out from the above statement to prevent making a list
- # with an empty string in it (split on empty string)
- if args.tclass:
- if args.tclass_regex:
- q.tclass = args.tclass
- else:
- q.tclass = args.tclass.split(",")
-
- if args.perms:
- q.perms = args.perms.split(",")
-
- if args.boolean:
- if args.boolean_regex:
- q.boolean = args.boolean
- else:
- q.boolean = args.boolean.split(",")
-
- for r in sorted(q.results()):
- print(r)
-
- if args.rbacrtypes:
- q = setools.RBACRuleQuery(p,
- ruletype=args.rbacrtypes,
- source=args.source,
- source_indirect=args.source_indirect,
- source_regex=args.source_regex,
- target=args.target,
- target_indirect=args.target_indirect,
- target_regex=args.target_regex,
- default=args.default,
- default_regex=args.default_regex,
- tclass_regex=args.tclass_regex)
-
- # these are broken out from the above statement to prevent making a list
- # with an empty string in it (split on empty string)
- if args.tclass:
- if args.tclass_regex:
- q.tclass = args.tclass
- else:
- q.tclass = args.tclass.split(",")
-
- for r in sorted(q.results()):
- print(r)
-
- if args.mlsrtypes:
- q = setools.MLSRuleQuery(p,
- ruletype=args.mlsrtypes,
- source=args.source,
- source_regex=args.source_regex,
- target=args.target,
- target_regex=args.target_regex,
- tclass_regex=args.tclass_regex,
- default=args.default)
-
- # these are broken out from the above statement to prevent making a list
- # with an empty string in it (split on empty string)
- if args.tclass:
- if args.tclass_regex:
- q.tclass = args.tclass
- else:
- q.tclass = args.tclass.split(",")
-
- for r in sorted(q.results()):
- print(r)
-
-except Exception as err:
- if args.debug:
- import traceback
- traceback.print_exc()
- else:
- print(err)
-
- sys.exit(-1)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/__init__.py b/lib/python2.7/site-packages/setoolsgui/setools/__init__.py
deleted file mode 100644
index 4d03553..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/__init__.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""The SETools SELinux policy analysis library."""
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-#try:
-# import pkg_resources
-# # pylint: disable=no-member
-# __version__ = pkg_resources.get_distribution("setools").version
-#except ImportError: # pragma: no cover
-# __version__ = "unknown"
-__version__ = "3.3.8"
-
-# Python classes for policy representation
-from . import policyrep
-from .policyrep import SELinuxPolicy
-
-# Exceptions
-from . import exception
-
-# Component Queries
-from .boolquery import BoolQuery
-from .categoryquery import CategoryQuery
-from .commonquery import CommonQuery
-from .objclassquery import ObjClassQuery
-from .polcapquery import PolCapQuery
-from .rolequery import RoleQuery
-from .sensitivityquery import SensitivityQuery
-from .typequery import TypeQuery
-from .typeattrquery import TypeAttributeQuery
-from .userquery import UserQuery
-
-# Rule Queries
-from .mlsrulequery import MLSRuleQuery
-from .rbacrulequery import RBACRuleQuery
-from .terulequery import TERuleQuery
-
-# Constraint queries
-from .constraintquery import ConstraintQuery
-
-# In-policy Context Queries
-from .fsusequery import FSUseQuery
-from .genfsconquery import GenfsconQuery
-from .initsidquery import InitialSIDQuery
-from .netifconquery import NetifconQuery
-from .nodeconquery import NodeconQuery
-from .portconquery import PortconQuery
-
-# Information Flow Analysis
-from .infoflow import InfoFlowAnalysis
-from .permmap import PermissionMap
-
-# Domain Transition Analysis
-from .dta import DomainTransitionAnalysis
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/boolquery.py b/lib/python2.7/site-packages/setoolsgui/setools/boolquery.py
deleted file mode 100644
index b70b7d5..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/boolquery.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import compquery
-from .descriptors import CriteriaDescriptor
-
-
-class BoolQuery(compquery.ComponentQuery):
-
- """Query SELinux policy Booleans.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The Boolean name to match.
- name_regex If true, regular expression matching
- will be used on the Boolean name.
- default The default state to match. If this
- is None, the default state not be matched.
- """
-
- _default = None
-
- @property
- def default(self):
- return self._default
-
- @default.setter
- def default(self, value):
- if value is None:
- self._default = None
- else:
- self._default = bool(value)
-
- def results(self):
- """Generator which yields all Booleans matching the criteria."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Default: {0.default}".format(self))
-
- for boolean in self.policy.bools():
- if not self._match_name(boolean):
- continue
-
- if self.default is not None and boolean.state != self.default:
- continue
-
- yield boolean
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/categoryquery.py b/lib/python2.7/site-packages/setoolsgui/setools/categoryquery.py
deleted file mode 100644
index d4d7c4c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/categoryquery.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import compquery
-from . import mixins
-
-
-class CategoryQuery(mixins.MatchAlias, compquery.ComponentQuery):
-
- """
- Query MLS Categories
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The name of the category to match.
- name_regex If true, regular expression matching will
- be used for matching the name.
- alias The alias name to match.
- alias_regex If true, regular expression matching
- will be used on the alias names.
- """
-
- def results(self):
- """Generator which yields all matching categories."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Alias: {0.alias}, regex: {0.alias_regex}".format(self))
-
- for cat in self.policy.categories():
- if not self._match_name(cat):
- continue
-
- if not self._match_alias(cat):
- continue
-
- yield cat
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/commonquery.py b/lib/python2.7/site-packages/setoolsgui/setools/commonquery.py
deleted file mode 100644
index e105ccb..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/commonquery.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import compquery, mixins
-
-
-class CommonQuery(mixins.MatchPermission, compquery.ComponentQuery):
-
- """
- Query common permission sets.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The name of the common to match.
- name_regex If true, regular expression matching will
- be used for matching the name.
- perms The permissions to match.
- perms_equal If true, only commons with permission sets
- that are equal to the criteria will
- match. Otherwise, any intersection
- will match.
- perms_regex If true, regular expression matching will be used
- on the permission names instead of set logic.
- """
-
- def results(self):
- """Generator which yields all matching commons."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Perms: {0.perms!r}, regex: {0.perms_regex}, eq: {0.perms_equal}".
- format(self))
-
- for com in self.policy.commons():
- if not self._match_name(com):
- continue
-
- if not self._match_perms(com):
- continue
-
- yield com
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/compquery.py b/lib/python2.7/site-packages/setoolsgui/setools/compquery.py
deleted file mode 100644
index 3d8851a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/compquery.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-# pylint: disable=no-member,attribute-defined-outside-init,abstract-method
-import re
-
-from . import query
-from .descriptors import CriteriaDescriptor
-
-
-class ComponentQuery(query.PolicyQuery):
-
- """Base class for SETools component queries."""
-
- name = CriteriaDescriptor("name_regex")
- name_regex = False
-
- def _match_name(self, obj):
- """Match the object to the name criteria."""
- if not self.name:
- # if there is no criteria, everything matches.
- return True
-
- return self._match_regex(obj, self.name, self.name_regex)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/constraintquery.py b/lib/python2.7/site-packages/setoolsgui/setools/constraintquery.py
deleted file mode 100644
index 82a6fc2..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/constraintquery.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import mixins, query
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor, RuletypeDescriptor
-from .policyrep.exception import ConstraintUseError
-
-
-class ConstraintQuery(mixins.MatchObjClass, mixins.MatchPermission, query.PolicyQuery):
-
- """
- Query constraint rules, (mls)constrain/(mls)validatetrans.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- ruletype The list of rule type(s) to match.
- tclass The object class(es) to match.
- tclass_regex If true, use a regular expression for
- matching the rule's object class.
- perms The permission(s) to match.
- perms_equal If true, the permission set of the rule
- must exactly match the permissions
- criteria. If false, any set intersection
- will match.
- perms_regex If true, regular expression matching will be used
- on the permission names instead of set logic.
- role The name of the role to match in the
- constraint expression.
- role_indirect If true, members of an attribute will be
- matched rather than the attribute itself.
- role_regex If true, regular expression matching will
- be used on the role.
- type_ The name of the type/attribute to match in the
- constraint expression.
- type_indirect If true, members of an attribute will be
- matched rather than the attribute itself.
- type_regex If true, regular expression matching will
- be used on the type/attribute.
- user The name of the user to match in the
- constraint expression.
- user_regex If true, regular expression matching will
- be used on the user.
- """
-
- ruletype = RuletypeDescriptor("validate_constraint_ruletype")
- user = CriteriaDescriptor("user_regex", "lookup_user")
- user_regex = False
- role = CriteriaDescriptor("role_regex", "lookup_role")
- role_regex = False
- role_indirect = True
- type_ = CriteriaDescriptor("type_regex", "lookup_type_or_attr")
- type_regex = False
- type_indirect = True
-
- def _match_expr(self, expr, criteria, indirect, regex):
- """
- Match roles/types/users in a constraint expression,
- optionally by expanding the contents of attributes.
-
- Parameters:
- expr The expression to match.
- criteria The criteria to match.
- indirect If attributes in the expression should be expanded.
- regex If regular expression matching should be used.
- """
-
- if indirect:
- obj = set()
- for item in expr:
- obj.update(item.expand())
- else:
- obj = expr
-
- return self._match_in_set(obj, criteria, regex)
-
- def results(self):
- """Generator which yields all matching constraints rules."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Ruletypes: {0.ruletype}".format(self))
- self.log.debug("Class: {0.tclass!r}, regex: {0.tclass_regex}".format(self))
- self.log.debug("Perms: {0.perms!r}, regex: {0.perms_regex}, eq: {0.perms_equal}".
- format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
-
- for c in self.policy.constraints():
- if self.ruletype:
- if c.ruletype not in self.ruletype:
- continue
-
- if not self._match_object_class(c):
- continue
-
- try:
- if not self._match_perms(c):
- continue
- except ConstraintUseError:
- continue
-
- if self.role and not self._match_expr(
- c.roles,
- self.role,
- self.role_indirect,
- self.role_regex):
- continue
-
- if self.type_ and not self._match_expr(
- c.types,
- self.type_,
- self.type_indirect,
- self.type_regex):
- continue
-
- if self.user and not self._match_expr(
- c.users,
- self.user,
- False,
- self.user_regex):
- continue
-
- yield c
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/contextquery.py b/lib/python2.7/site-packages/setoolsgui/setools/contextquery.py
deleted file mode 100644
index 5ce1632..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/contextquery.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-# pylint: disable=attribute-defined-outside-init,no-member
-import re
-
-from . import query
-from .descriptors import CriteriaDescriptor
-
-
-class ContextQuery(query.PolicyQuery):
-
- """
- Base class for SETools in-policy labeling/context queries.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- context The object to match.
- user The user to match in the context.
- user_regex If true, regular expression matching
- will be used on the user.
- role The role to match in the context.
- role_regex If true, regular expression matching
- will be used on the role.
- type_ The type to match in the context.
- type_regex If true, regular expression matching
- will be used on the type.
- range_ The range to match in the context.
- range_subset If true, the criteria will match if it
- is a subset of the context's range.
- range_overlap If true, the criteria will match if it
- overlaps any of the context's range.
- range_superset If true, the criteria will match if it
- is a superset of the context's range.
- range_proper If true, use proper superset/subset
- on range matching operations.
- No effect if not using set operations.
- """
-
- user = CriteriaDescriptor("user_regex", "lookup_user")
- user_regex = False
- role = CriteriaDescriptor("role_regex", "lookup_role")
- role_regex = False
- type_ = CriteriaDescriptor("type_regex", "lookup_type")
- type_regex = False
- range_ = CriteriaDescriptor(lookup_function="lookup_range")
- range_overlap = False
- range_subset = False
- range_superset = False
- range_proper = False
-
- def _match_context(self, context):
-
- if self.user and not query.PolicyQuery._match_regex(
- context.user,
- self.user,
- self.user_regex):
- return False
-
- if self.role and not query.PolicyQuery._match_regex(
- context.role,
- self.role,
- self.role_regex):
- return False
-
- if self.type_ and not query.PolicyQuery._match_regex(
- context.type_,
- self.type_,
- self.type_regex):
- return False
-
- if self.range_ and not query.PolicyQuery._match_range(
- context.range_,
- self.range_,
- self.range_subset,
- self.range_overlap,
- self.range_superset,
- self.range_proper):
- return False
-
- return True
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/descriptors.py b/lib/python2.7/site-packages/setoolsgui/setools/descriptors.py
deleted file mode 100644
index eab9210..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/descriptors.py
+++ /dev/null
@@ -1,230 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-"""
-SETools descriptors.
-
-These classes override how a class's attributes are get/set/deleted.
-This is how the @property decorator works.
-
-See https://docs.python.org/3/howto/descriptor.html
-for more details.
-"""
-
-import re
-from collections import defaultdict
-from weakref import WeakKeyDictionary
-
-#
-# Query criteria descriptors
-#
-# Implementation note: if the name_regex attribute value
-# is changed the criteria must be reset.
-#
-
-
-class CriteriaDescriptor(object):
-
- """
- Single item criteria descriptor.
-
- Parameters:
- name_regex The name of instance's regex setting attribute;
- used as name_regex below. If unset,
- regular expressions will never be used.
- lookup_function The name of the SELinuxPolicy lookup function,
- e.g. lookup_type or lookup_boolean.
- default_value The default value of the criteria. The default
- is None.
-
- Read-only instance attribute use (obj parameter):
- policy The instance of SELinuxPolicy
- name_regex This attribute is read to determine if
- the criteria should be looked up or
- compiled into a regex. If the attribute
- does not exist, False is assumed.
- """
-
- def __init__(self, name_regex=None, lookup_function=None, default_value=None):
- assert name_regex or lookup_function, "A simple attribute should be used if there is " \
- "no regex nor lookup function."
- self.regex = name_regex
- self.default_value = default_value
- self.lookup_function = lookup_function
-
- # use weak references so instances can be
- # garbage collected, rather than unnecessarily
- # kept around due to this descriptor.
- self.instances = WeakKeyDictionary()
-
- def __get__(self, obj, objtype=None):
- if obj is None:
- return self
-
- return self.instances.setdefault(obj, self.default_value)
-
- def __set__(self, obj, value):
- if not value:
- self.instances[obj] = None
- elif self.regex and getattr(obj, self.regex, False):
- self.instances[obj] = re.compile(value)
- elif self.lookup_function:
- lookup = getattr(obj.policy, self.lookup_function)
- self.instances[obj] = lookup(value)
- else:
- self.instances[obj] = value
-
-
-class CriteriaSetDescriptor(CriteriaDescriptor):
-
- """Descriptor for a set of criteria."""
-
- def __set__(self, obj, value):
- if not value:
- self.instances[obj] = None
- elif self.regex and getattr(obj, self.regex, False):
- self.instances[obj] = re.compile(value)
- elif self.lookup_function:
- lookup = getattr(obj.policy, self.lookup_function)
- self.instances[obj] = set(lookup(v) for v in value)
- else:
- self.instances[obj] = set(value)
-
-
-class RuletypeDescriptor(object):
-
- """
- Descriptor for a list of rule types.
-
- Parameters:
- validator The name of the SELinuxPolicy ruletype
- validator function, e.g. validate_te_ruletype
- default_value The default value of the criteria. The default
- is None.
-
- Read-only instance attribute use (obj parameter):
- policy The instance of SELinuxPolicy
- """
-
- def __init__(self, validator):
- self.validator = validator
-
- # use weak references so instances can be
- # garbage collected, rather than unnecessarily
- # kept around due to this descriptor.
- self.instances = WeakKeyDictionary()
-
- def __get__(self, obj, objtype=None):
- if obj is None:
- return self
-
- return self.instances.setdefault(obj, None)
-
- def __set__(self, obj, value):
- if value:
- validate = getattr(obj.policy, self.validator)
- validate(value)
- self.instances[obj] = value
- else:
- self.instances[obj] = None
-
-
-#
-# NetworkX Graph Descriptors
-#
-# These descriptors are used to simplify all
-# of the dictionary use in the NetworkX graph.
-#
-
-
-class NetworkXGraphEdgeDescriptor(object):
-
- """
- Descriptor base class for NetworkX graph edge attributes.
-
- Parameter:
- name The edge property name
-
- Instance class attribute use (obj parameter):
- G The NetworkX graph
- source The edge's source node
- target The edge's target node
- """
-
- def __init__(self, propname):
- self.name = propname
-
- def __get__(self, obj, objtype=None):
- if obj is None:
- return self
-
- return obj.G[obj.source][obj.target][self.name]
-
- def __set__(self, obj, value):
- raise NotImplementedError
-
- def __delete__(self, obj):
- raise NotImplementedError
-
-
-class EdgeAttrDict(NetworkXGraphEdgeDescriptor):
-
- """A descriptor for edge attributes that are dictionaries."""
-
- def __set__(self, obj, value):
- # None is a special value to initialize the attribute
- if value is None:
- obj.G[obj.source][obj.target][self.name] = defaultdict(list)
- else:
- raise ValueError("{0} dictionaries should not be assigned directly".format(self.name))
-
- def __delete__(self, obj):
- obj.G[obj.source][obj.target][self.name].clear()
-
-
-class EdgeAttrIntMax(NetworkXGraphEdgeDescriptor):
-
- """
- A descriptor for edge attributes that are non-negative integers that always
- keep the max assigned value until re-initialized.
- """
-
- def __set__(self, obj, value):
- # None is a special value to initialize
- if value is None:
- obj.G[obj.source][obj.target][self.name] = 0
- else:
- current_value = obj.G[obj.source][obj.target][self.name]
- obj.G[obj.source][obj.target][self.name] = max(current_value, value)
-
-
-class EdgeAttrList(NetworkXGraphEdgeDescriptor):
-
- """A descriptor for edge attributes that are lists."""
-
- def __set__(self, obj, value):
- # None is a special value to initialize
- if value is None:
- obj.G[obj.source][obj.target][self.name] = []
- else:
- raise ValueError("{0} lists should not be assigned directly".format(self.name))
-
- def __delete__(self, obj):
- # in Python3 a .clear() function was added for lists
- # keep this implementation for Python 2 compat
- del obj.G[obj.source][obj.target][self.name][:]
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/dta.py b/lib/python2.7/site-packages/setoolsgui/setools/dta.py
deleted file mode 100644
index 271efc4..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/dta.py
+++ /dev/null
@@ -1,603 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import itertools
-import logging
-from collections import defaultdict, namedtuple
-
-import networkx as nx
-from networkx.exception import NetworkXError, NetworkXNoPath
-
-from .descriptors import EdgeAttrDict, EdgeAttrList
-
-__all__ = ['DomainTransitionAnalysis']
-
-# Return values for the analysis
-# are in the following tuple formats:
-step_output = namedtuple("step", ["source",
- "target",
- "transition",
- "entrypoints",
- "setexec",
- "dyntransition",
- "setcurrent"])
-
-entrypoint_output = namedtuple("entrypoints", ["name",
- "entrypoint",
- "execute",
- "type_transition"])
-
-
-class DomainTransitionAnalysis(object):
-
- """Domain transition analysis."""
-
- def __init__(self, policy, reverse=False, exclude=None):
- """
- Parameter:
- policy The policy to analyze.
- """
- self.log = logging.getLogger(self.__class__.__name__)
-
- self.policy = policy
- self.exclude = exclude
- self.reverse = reverse
- self.rebuildgraph = True
- self.rebuildsubgraph = True
- self.G = nx.DiGraph()
- self.subG = None
-
- @property
- def reverse(self):
- return self._reverse
-
- @reverse.setter
- def reverse(self, direction):
- self._reverse = bool(direction)
- self.rebuildsubgraph = True
-
- @property
- def exclude(self):
- return self._exclude
-
- @exclude.setter
- def exclude(self, types):
- if types:
- self._exclude = [self.policy.lookup_type(t) for t in types]
- else:
- self._exclude = None
-
- self.rebuildsubgraph = True
-
- def shortest_path(self, source, target):
- """
- Generator which yields one shortest domain transition path
- between the source and target types (there may be more).
-
- Parameters:
- source The source type.
- target The target type.
-
- Yield: generator(steps)
-
- steps A generator that returns the tuple of
- source, target, and rules for each
- domain transition.
- """
- s = self.policy.lookup_type(source)
- t = self.policy.lookup_type(target)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating one shortest path from {0} to {1}...".format(s, t))
-
- try:
- yield self.__generate_steps(nx.shortest_path(self.subG, s, t))
- except (NetworkXNoPath, NetworkXError):
- # NetworkXError: the type is valid but not in graph, e.g. excluded
- # NetworkXNoPath: no paths or the target type is
- # not in the graph
- pass
-
- def all_paths(self, source, target, maxlen=2):
- """
- Generator which yields all domain transition paths between
- the source and target up to the specified maximum path
- length.
-
- Parameters:
- source The source type.
- target The target type.
- maxlen Maximum length of paths.
-
- Yield: generator(steps)
-
- steps A generator that returns the tuple of
- source, target, and rules for each
- domain transition.
- """
- if maxlen < 1:
- raise ValueError("Maximum path length must be positive.")
-
- s = self.policy.lookup_type(source)
- t = self.policy.lookup_type(target)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating all paths from {0} to {1}, max len {2}...".format(s, t, maxlen))
-
- try:
- for path in nx.all_simple_paths(self.subG, s, t, maxlen):
- yield self.__generate_steps(path)
- except (NetworkXNoPath, NetworkXError):
- # NetworkXError: the type is valid but not in graph, e.g. excluded
- # NetworkXNoPath: no paths or the target type is
- # not in the graph
- pass
-
- def all_shortest_paths(self, source, target):
- """
- Generator which yields all shortest domain transition paths
- between the source and target types.
-
- Parameters:
- source The source type.
- target The target type.
-
- Yield: generator(steps)
-
- steps A generator that returns the tuple of
- source, target, and rules for each
- domain transition.
- """
- s = self.policy.lookup_type(source)
- t = self.policy.lookup_type(target)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating all shortest paths from {0} to {1}...".format(s, t))
-
- try:
- for path in nx.all_shortest_paths(self.subG, s, t):
- yield self.__generate_steps(path)
- except (NetworkXNoPath, NetworkXError, KeyError):
- # NetworkXError: the type is valid but not in graph, e.g. excluded
- # NetworkXNoPath: no paths or the target type is
- # not in the graph
- # KeyError: work around NetworkX bug
- # when the source node is not in the graph
- pass
-
- def transitions(self, type_):
- """
- Generator which yields all domain transitions out of a
- specified source type.
-
- Parameters:
- type_ The starting type.
-
- Yield: generator(steps)
-
- steps A generator that returns the tuple of
- source, target, and rules for each
- domain transition.
- """
- s = self.policy.lookup_type(type_)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating all transitions {1} {0}".
- format(s, "in to" if self.reverse else "out from"))
-
- try:
- for source, target in self.subG.out_edges_iter(s):
- edge = Edge(self.subG, source, target)
-
- if self.reverse:
- real_source, real_target = target, source
- else:
- real_source, real_target = source, target
-
- yield step_output(real_source, real_target,
- edge.transition,
- self.__generate_entrypoints(edge),
- edge.setexec,
- edge.dyntransition,
- edge.setcurrent)
-
- except NetworkXError:
- # NetworkXError: the type is valid but not in graph, e.g. excluded
- pass
-
- def get_stats(self): # pragma: no cover
- """
- Get the domain transition graph statistics.
-
- Return: tuple(nodes, edges)
-
- nodes The number of nodes (types) in the graph.
- edges The number of edges (domain transitions) in the graph.
- """
- return (self.G.number_of_nodes(), self.G.number_of_edges())
-
- #
- # Internal functions follow
- #
- @staticmethod
- def __generate_entrypoints(edge):
- """
- Generator which yields the entrypoint, execute, and
- type_transition rules for each entrypoint.
-
- Parameter:
- data The dictionary of entrypoints.
-
- Yield: tuple(type, entry, exec, trans)
-
- type The entrypoint type.
- entry The list of entrypoint rules.
- exec The list of execute rules.
- trans The list of type_transition rules.
- """
- for e in edge.entrypoint:
- yield entrypoint_output(e, edge.entrypoint[e], edge.execute[e], edge.type_transition[e])
-
- def __generate_steps(self, path):
- """
- Generator which yields the source, target, and associated rules
- for each domain transition.
-
- Parameter:
- path A list of graph node names representing an information flow path.
-
- Yield: tuple(source, target, transition, entrypoints,
- setexec, dyntransition, setcurrent)
-
- source The source type for this step of the domain transition.
- target The target type for this step of the domain transition.
- transition The list of transition rules.
- entrypoints Generator which yields entrypoint-related rules.
- setexec The list of setexec rules.
- dyntranstion The list of dynamic transition rules.
- setcurrent The list of setcurrent rules.
- """
-
- for s in range(1, len(path)):
- source = path[s - 1]
- target = path[s]
- edge = Edge(self.subG, source, target)
-
- # Yield the actual source and target.
- # The above perspective is reversed
- # if the graph has been reversed.
- if self.reverse:
- real_source, real_target = target, source
- else:
- real_source, real_target = source, target
-
- yield step_output(real_source, real_target,
- edge.transition,
- self.__generate_entrypoints(edge),
- edge.setexec,
- edge.dyntransition,
- edge.setcurrent)
-
- #
- # Graph building functions
- #
-
- # Domain transition requirements:
- #
- # Standard transitions a->b:
- # allow a b:process transition;
- # allow a b_exec:file execute;
- # allow b b_exec:file entrypoint;
- #
- # and at least one of:
- # allow a self:process setexec;
- # type_transition a b_exec:process b;
- #
- # Dynamic transition x->y:
- # allow x y:process dyntransition;
- # allow x self:process setcurrent;
- #
- # Algorithm summary:
- # 1. iterate over all rules
- # 1. skip non allow/type_transition rules
- # 2. if process transition or dyntransition, create edge,
- # initialize rule lists, add the (dyn)transition rule
- # 3. if process setexec or setcurrent, add to appropriate dict
- # keyed on the subject
- # 4. if file exec, entrypoint, or type_transition:process,
- # add to appropriate dict keyed on subject,object.
- # 2. Iterate over all graph edges:
- # 1. if there is a transition rule (else add to invalid
- # transition list):
- # 1. use set intersection to find matching exec
- # and entrypoint rules. If none, add to invalid
- # transition list.
- # 2. for each valid entrypoint, add rules to the
- # edge's lists if there is either a
- # type_transition for it or the source process
- # has setexec permissions.
- # 3. If there are neither type_transitions nor
- # setexec permissions, add to the invalid
- # transition list
- # 2. if there is a dyntransition rule (else add to invalid
- # dyntrans list):
- # 1. If the source has a setcurrent rule, add it
- # to the edge's list, else add to invalid
- # dyntransition list.
- # 3. Iterate over all graph edges:
- # 1. if the edge has an invalid trans and dyntrans, delete
- # the edge.
- # 2. if the edge has an invalid trans, clear the related
- # lists on the edge.
- # 3. if the edge has an invalid dyntrans, clear the related
- # lists on the edge.
- #
- def _build_graph(self):
- self.G.clear()
-
- self.log.info("Building graph from {0}...".format(self.policy))
-
- # hash tables keyed on domain type
- setexec = defaultdict(list)
- setcurrent = defaultdict(list)
-
- # hash tables keyed on (domain, entrypoint file type)
- # the parameter for defaultdict has to be callable
- # hence the lambda for the nested defaultdict
- execute = defaultdict(lambda: defaultdict(list))
- entrypoint = defaultdict(lambda: defaultdict(list))
-
- # hash table keyed on (domain, entrypoint, target domain)
- type_trans = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
-
- for rule in self.policy.terules():
- if rule.ruletype == "allow":
- if rule.tclass not in ["process", "file"]:
- continue
-
- perms = rule.perms
-
- if rule.tclass == "process":
- if "transition" in perms:
- for s, t in itertools.product(rule.source.expand(), rule.target.expand()):
- # only add edges if they actually
- # transition to a new type
- if s != t:
- edge = Edge(self.G, s, t, create=True)
- edge.transition.append(rule)
-
- if "dyntransition" in perms:
- for s, t in itertools.product(rule.source.expand(), rule.target.expand()):
- # only add edges if they actually
- # transition to a new type
- if s != t:
- e = Edge(self.G, s, t, create=True)
- e.dyntransition.append(rule)
-
- if "setexec" in perms:
- for s in rule.source.expand():
- setexec[s].append(rule)
-
- if "setcurrent" in perms:
- for s in rule.source.expand():
- setcurrent[s].append(rule)
-
- else:
- if "execute" in perms:
- for s, t in itertools.product(
- rule.source.expand(),
- rule.target.expand()):
- execute[s][t].append(rule)
-
- if "entrypoint" in perms:
- for s, t in itertools.product(rule.source.expand(), rule.target.expand()):
- entrypoint[s][t].append(rule)
-
- elif rule.ruletype == "type_transition":
- if rule.tclass != "process":
- continue
-
- d = rule.default
- for s, t in itertools.product(rule.source.expand(), rule.target.expand()):
- type_trans[s][t][d].append(rule)
-
- invalid_edge = []
- clear_transition = []
- clear_dyntransition = []
-
- for s, t in self.G.edges_iter():
- edge = Edge(self.G, s, t)
- invalid_trans = False
- invalid_dyntrans = False
-
- if edge.transition:
- # get matching domain exec w/entrypoint type
- entry = set(entrypoint[t].keys())
- exe = set(execute[s].keys())
- match = entry.intersection(exe)
-
- if not match:
- # there are no valid entrypoints
- invalid_trans = True
- else:
- # TODO try to improve the
- # efficiency in this loop
- for m in match:
- if s in setexec or type_trans[s][m]:
- # add key for each entrypoint
- edge.entrypoint[m] += entrypoint[t][m]
- edge.execute[m] += execute[s][m]
-
- if type_trans[s][m][t]:
- edge.type_transition[m] += type_trans[s][m][t]
-
- if s in setexec:
- edge.setexec.extend(setexec[s])
-
- if not edge.setexec and not edge.type_transition:
- invalid_trans = True
- else:
- invalid_trans = True
-
- if edge.dyntransition:
- if s in setcurrent:
- edge.setcurrent.extend(setcurrent[s])
- else:
- invalid_dyntrans = True
- else:
- invalid_dyntrans = True
-
- # cannot change the edges while iterating over them,
- # so keep appropriate lists
- if invalid_trans and invalid_dyntrans:
- invalid_edge.append(edge)
- elif invalid_trans:
- clear_transition.append(edge)
- elif invalid_dyntrans:
- clear_dyntransition.append(edge)
-
- # Remove invalid transitions
- self.G.remove_edges_from(invalid_edge)
- for edge in clear_transition:
- # if only the regular transition is invalid,
- # clear the relevant lists
- del edge.transition
- del edge.execute
- del edge.entrypoint
- del edge.type_transition
- del edge.setexec
- for edge in clear_dyntransition:
- # if only the dynamic transition is invalid,
- # clear the relevant lists
- del edge.dyntransition
- del edge.setcurrent
-
- self.rebuildgraph = False
- self.rebuildsubgraph = True
- self.log.info("Completed building graph.")
-
- def __remove_excluded_entrypoints(self):
- invalid_edges = []
- for source, target in self.subG.edges_iter():
- edge = Edge(self.subG, source, target)
- entrypoints = set(edge.entrypoint)
- entrypoints.intersection_update(self.exclude)
-
- if not entrypoints:
- # short circuit if there are no
- # excluded entrypoint types on
- # this edge.
- continue
-
- for e in entrypoints:
- # clear the entrypoint data
- del edge.entrypoint[e]
- del edge.execute[e]
-
- try:
- del edge.type_transition[e]
- except KeyError: # setexec
- pass
-
- # cannot delete the edges while iterating over them
- if not edge.entrypoint and not edge.dyntransition:
- invalid_edges.append(edge)
-
- self.subG.remove_edges_from(invalid_edges)
-
- def _build_subgraph(self):
- if self.rebuildgraph:
- self._build_graph()
-
- self.log.info("Building subgraph.")
- self.log.debug("Excluding {0}".format(self.exclude))
- self.log.debug("Reverse {0}".format(self.reverse))
-
- # reverse graph for reverse DTA
- if self.reverse:
- self.subG = self.G.reverse(copy=True)
- else:
- self.subG = self.G.copy()
-
- if self.exclude:
- # delete excluded domains from subgraph
- self.subG.remove_nodes_from(self.exclude)
-
- # delete excluded entrypoints from subgraph
- self.__remove_excluded_entrypoints()
-
- self.rebuildsubgraph = False
- self.log.info("Completed building subgraph.")
-
-
-class Edge(object):
-
- """
- A graph edge. Also used for returning domain transition steps.
-
- Parameters:
- source The source type of the edge.
- target The target tyep of the edge.
-
- Keyword Parameters:
- create (T/F) create the edge if it does not exist.
- The default is False.
- """
-
- transition = EdgeAttrList('transition')
- setexec = EdgeAttrList('setexec')
- dyntransition = EdgeAttrList('dyntransition')
- setcurrent = EdgeAttrList('setcurrent')
- entrypoint = EdgeAttrDict('entrypoint')
- execute = EdgeAttrDict('execute')
- type_transition = EdgeAttrDict('type_transition')
-
- def __init__(self, graph, source, target, create=False):
- self.G = graph
- self.source = source
- self.target = target
-
- # a bit of a hack to make Edges work
- # in NetworkX functions that work on
- # 2-tuples of (source, target)
- # (see __getitem__ below)
- self.st_tuple = (source, target)
-
- if not self.G.has_edge(source, target):
- if not create:
- raise ValueError("Edge does not exist in graph")
- else:
- self.G.add_edge(source, target)
- self.transition = None
- self.entrypoint = None
- self.execute = None
- self.type_transition = None
- self.setexec = None
- self.dyntransition = None
- self.setcurrent = None
-
- def __getitem__(self, key):
- return self.st_tuple[key]
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/exception.py b/lib/python2.7/site-packages/setoolsgui/setools/exception.py
deleted file mode 100644
index c3505cd..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/exception.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-
-#
-# Base class for exceptions
-#
-
-
-class SEToolsException(Exception):
-
- """Base class for all SETools exceptions."""
- pass
-
-#
-# Permission map exceptions
-#
-
-
-class PermissionMapException(SEToolsException):
-
- """Base class for all permission map exceptions."""
- pass
-
-
-class PermissionMapParseError(PermissionMapException):
-
- """Exception for parse errors while reading permission map files."""
- pass
-
-
-class RuleTypeError(PermissionMapException):
-
- """Exception for using rules with incorrect rule type."""
- pass
-
-
-class UnmappedClass(PermissionMapException):
-
- """Exception for classes that are unmapped"""
- pass
-
-
-class UnmappedPermission(PermissionMapException):
-
- """Exception for permissions that are unmapped"""
- pass
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/fsusequery.py b/lib/python2.7/site-packages/setoolsgui/setools/fsusequery.py
deleted file mode 100644
index 6825a45..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/fsusequery.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import contextquery
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor
-
-
-class FSUseQuery(contextquery.ContextQuery):
-
- """
- Query fs_use_* statements.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- ruletype The rule type(s) to match.
- fs The criteria to match the file system type.
- fs_regex If true, regular expression matching
- will be used on the file system type.
- user The criteria to match the context's user.
- user_regex If true, regular expression matching
- will be used on the user.
- role The criteria to match the context's role.
- role_regex If true, regular expression matching
- will be used on the role.
- type_ The criteria to match the context's type.
- type_regex If true, regular expression matching
- will be used on the type.
- range_ The criteria to match the context's range.
- range_subset If true, the criteria will match if it is a subset
- of the context's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the context's range.
- range_superset If true, the criteria will match if it is a superset
- of the context's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- ruletype = None
- fs = CriteriaDescriptor("fs_regex")
- fs_regex = False
-
- def results(self):
- """Generator which yields all matching fs_use_* statements."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Ruletypes: {0.ruletype}".format(self))
- self.log.debug("FS: {0.fs!r}, regex: {0.fs_regex}".format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for fsu in self.policy.fs_uses():
- if self.ruletype and fsu.ruletype not in self.ruletype:
- continue
-
- if self.fs and not self._match_regex(
- fsu.fs,
- self.fs,
- self.fs_regex):
- continue
-
- if not self._match_context(fsu.context):
- continue
-
- yield fsu
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/genfsconquery.py b/lib/python2.7/site-packages/setoolsgui/setools/genfsconquery.py
deleted file mode 100644
index c67dfd6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/genfsconquery.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import contextquery
-from .descriptors import CriteriaDescriptor
-
-
-class GenfsconQuery(contextquery.ContextQuery):
-
- """
- Query genfscon statements.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- fs The criteria to match the file system type.
- fs_regex If true, regular expression matching
- will be used on the file system type.
- path The criteria to match the path.
- path_regex If true, regular expression matching
- will be used on the path.
- user The criteria to match the context's user.
- user_regex If true, regular expression matching
- will be used on the user.
- role The criteria to match the context's role.
- role_regex If true, regular expression matching
- will be used on the role.
- type_ The criteria to match the context's type.
- type_regex If true, regular expression matching
- will be used on the type.
- range_ The criteria to match the context's range.
- range_subset If true, the criteria will match if it is a subset
- of the context's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the context's range.
- range_superset If true, the criteria will match if it is a superset
- of the context's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- filetype = None
- fs = CriteriaDescriptor("fs_regex")
- fs_regex = False
- path = CriteriaDescriptor("path_regex")
- path_regex = False
-
- def results(self):
- """Generator which yields all matching genfscons."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("FS: {0.fs!r}, regex: {0.fs_regex}".format(self))
- self.log.debug("Path: {0.path!r}, regex: {0.path_regex}".format(self))
- self.log.debug("Filetype: {0.filetype!r}".format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for genfs in self.policy.genfscons():
- if self.fs and not self._match_regex(
- genfs.fs,
- self.fs,
- self.fs_regex):
- continue
-
- if self.path and not self._match_regex(
- genfs.path,
- self.path,
- self.path_regex):
- continue
-
- if self.filetype and not self.filetype == genfs.filetype:
- continue
-
- if not self._match_context(genfs.context):
- continue
-
- yield genfs
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/infoflow.py b/lib/python2.7/site-packages/setoolsgui/setools/infoflow.py
deleted file mode 100644
index ea3ec32..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/infoflow.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import itertools
-import logging
-from collections import namedtuple
-
-import networkx as nx
-from networkx.exception import NetworkXError, NetworkXNoPath
-
-from .descriptors import EdgeAttrIntMax, EdgeAttrList
-
-__all__ = ['InfoFlowAnalysis']
-
-# Return values for the analysis
-# are in the following tuple format:
-step_output = namedtuple("step", ["source",
- "target",
- "rules"])
-
-
-class InfoFlowAnalysis(object):
-
- """Information flow analysis."""
-
- def __init__(self, policy, perm_map, min_weight=1, exclude=None):
- """
- Parameters:
- policy The policy to analyze.
- perm_map The permission map or path to the permission map file.
- minweight The minimum permission weight to include in the analysis.
- (default is 1)
- exclude The types excluded from the information flow analysis.
- (default is none)
- """
- self.log = logging.getLogger(self.__class__.__name__)
-
- self.policy = policy
-
- self.min_weight = min_weight
- self.perm_map = perm_map
- self.exclude = exclude
- self.rebuildgraph = True
- self.rebuildsubgraph = True
-
- self.G = nx.DiGraph()
- self.subG = None
-
- @property
- def min_weight(self):
- return self._min_weight
-
- @min_weight.setter
- def min_weight(self, weight):
- if not 1 <= weight <= 10:
- raise ValueError(
- "Min information flow weight must be an integer 1-10.")
-
- self._min_weight = weight
- self.rebuildsubgraph = True
-
- @property
- def perm_map(self):
- return self._perm_map
-
- @perm_map.setter
- def perm_map(self, perm_map):
- self._perm_map = perm_map
- self.rebuildgraph = True
- self.rebuildsubgraph = True
-
- @property
- def exclude(self):
- return self._exclude
-
- @exclude.setter
- def exclude(self, types):
- if types:
- self._exclude = [self.policy.lookup_type(t) for t in types]
- else:
- self._exclude = []
-
- self.rebuildsubgraph = True
-
- def shortest_path(self, source, target):
- """
- Generator which yields one shortest path between the source
- and target types (there may be more).
-
- Parameters:
- source The source type.
- target The target type.
-
- Yield: generator(steps)
-
- steps Yield: tuple(source, target, rules)
-
- source The source type for this step of the information flow.
- target The target type for this step of the information flow.
- rules The list of rules creating this information flow step.
- """
- s = self.policy.lookup_type(source)
- t = self.policy.lookup_type(target)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating one shortest path from {0} to {1}...".format(s, t))
-
- try:
- yield self.__generate_steps(nx.shortest_path(self.subG, s, t))
- except (NetworkXNoPath, NetworkXError):
- # NetworkXError: the type is valid but not in graph, e.g.
- # excluded or disconnected due to min weight
- # NetworkXNoPath: no paths or the target type is
- # not in the graph
- pass
-
- def all_paths(self, source, target, maxlen=2):
- """
- Generator which yields all paths between the source and target
- up to the specified maximum path length. This algorithm
- tends to get very expensive above 3-5 steps, depending
- on the policy complexity.
-
- Parameters:
- source The source type.
- target The target type.
- maxlen Maximum length of paths.
-
- Yield: generator(steps)
-
- steps Yield: tuple(source, target, rules)
-
- source The source type for this step of the information flow.
- target The target type for this step of the information flow.
- rules The list of rules creating this information flow step.
- """
- if maxlen < 1:
- raise ValueError("Maximum path length must be positive.")
-
- s = self.policy.lookup_type(source)
- t = self.policy.lookup_type(target)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating all paths from {0} to {1}, max len {2}...".format(s, t, maxlen))
-
- try:
- for path in nx.all_simple_paths(self.subG, s, t, maxlen):
- yield self.__generate_steps(path)
- except (NetworkXNoPath, NetworkXError):
- # NetworkXError: the type is valid but not in graph, e.g.
- # excluded or disconnected due to min weight
- # NetworkXNoPath: no paths or the target type is
- # not in the graph
- pass
-
- def all_shortest_paths(self, source, target):
- """
- Generator which yields all shortest paths between the source
- and target types.
-
- Parameters:
- source The source type.
- target The target type.
-
- Yield: generator(steps)
-
- steps Yield: tuple(source, target, rules)
-
- source The source type for this step of the information flow.
- target The target type for this step of the information flow.
- rules The list of rules creating this information flow step.
- """
- s = self.policy.lookup_type(source)
- t = self.policy.lookup_type(target)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating all shortest paths from {0} to {1}...".format(s, t))
-
- try:
- for path in nx.all_shortest_paths(self.subG, s, t):
- yield self.__generate_steps(path)
- except (NetworkXNoPath, NetworkXError, KeyError):
- # NetworkXError: the type is valid but not in graph, e.g.
- # excluded or disconnected due to min weight
- # NetworkXNoPath: no paths or the target type is
- # not in the graph
- # KeyError: work around NetworkX bug
- # when the source node is not in the graph
- pass
-
- def infoflows(self, type_, out=True):
- """
- Generator which yields all information flows in/out of a
- specified source type.
-
- Parameters:
- source The starting type.
-
- Keyword Parameters:
- out If true, information flows out of the type will
- be returned. If false, information flows in to the
- type will be returned. Default is true.
-
- Yield: generator(steps)
-
- steps A generator that returns the tuple of
- source, target, and rules for each
- information flow.
- """
- s = self.policy.lookup_type(type_)
-
- if self.rebuildsubgraph:
- self._build_subgraph()
-
- self.log.info("Generating all infoflows out of {0}...".format(s))
-
- if out:
- flows = self.subG.out_edges_iter(s)
- else:
- flows = self.subG.in_edges_iter(s)
-
- try:
- for source, target in flows:
- edge = Edge(self.subG, source, target)
- yield step_output(source, target, edge.rules)
- except NetworkXError:
- # NetworkXError: the type is valid but not in graph, e.g.
- # excluded or disconnected due to min weight
- pass
-
- def get_stats(self): # pragma: no cover
- """
- Get the information flow graph statistics.
-
- Return: tuple(nodes, edges)
-
- nodes The number of nodes (types) in the graph.
- edges The number of edges (information flows between types)
- in the graph.
- """
- return (self.G.number_of_nodes(), self.G.number_of_edges())
-
- #
- # Internal functions follow
- #
-
- def __generate_steps(self, path):
- """
- Generator which returns the source, target, and associated rules
- for each information flow step.
-
- Parameter:
- path A list of graph node names representing an information flow path.
-
- Yield: tuple(source, target, rules)
-
- source The source type for this step of the information flow.
- target The target type for this step of the information flow.
- rules The list of rules creating this information flow step.
- """
- for s in range(1, len(path)):
- edge = Edge(self.subG, path[s - 1], path[s])
- yield step_output(edge.source, edge.target, edge.rules)
-
- #
- #
- # Graph building functions
- #
- #
- # 1. _build_graph determines the flow in each direction for each TE
- # rule and then expands the rule. All information flows are
- # included in this main graph: memory is traded off for efficiency
- # as the main graph should only need to be rebuilt if permission
- # weights change.
- # 2. _build_subgraph derives a subgraph which removes all excluded
- # types (nodes) and edges (information flows) which are below the
- # minimum weight. This subgraph is rebuilt only if the main graph
- # is rebuilt or the minimum weight or excluded types change.
-
- def _build_graph(self):
- self.G.clear()
-
- self.perm_map.map_policy(self.policy)
-
- self.log.info("Building graph from {0}...".format(self.policy))
-
- for rule in self.policy.terules():
- if rule.ruletype != "allow":
- continue
-
- (rweight, wweight) = self.perm_map.rule_weight(rule)
-
- for s, t in itertools.product(rule.source.expand(), rule.target.expand()):
- # only add flows if they actually flow
- # in or out of the source type type
- if s != t:
- if wweight:
- edge = Edge(self.G, s, t, create=True)
- edge.rules.append(rule)
- edge.weight = wweight
-
- if rweight:
- edge = Edge(self.G, t, s, create=True)
- edge.rules.append(rule)
- edge.weight = rweight
-
- self.rebuildgraph = False
- self.rebuildsubgraph = True
- self.log.info("Completed building graph.")
-
- def _build_subgraph(self):
- if self.rebuildgraph:
- self._build_graph()
-
- self.log.info("Building subgraph...")
- self.log.debug("Excluding {0!r}".format(self.exclude))
- self.log.debug("Min weight {0}".format(self.min_weight))
-
- # delete excluded types from subgraph
- nodes = [n for n in self.G.nodes() if n not in self.exclude]
- self.subG = self.G.subgraph(nodes)
-
- # delete edges below minimum weight.
- # no need if weight is 1, since that
- # does not exclude any edges.
- if self.min_weight > 1:
- delete_list = []
- for s, t in self.subG.edges_iter():
- edge = Edge(self.subG, s, t)
- if edge.weight < self.min_weight:
- delete_list.append(edge)
-
- self.subG.remove_edges_from(delete_list)
-
- self.rebuildsubgraph = False
- self.log.info("Completed building subgraph.")
-
-
-class Edge(object):
-
- """
- A graph edge. Also used for returning information flow steps.
-
- Parameters:
- source The source type of the edge.
- target The target type of the edge.
-
- Keyword Parameters:
- create (T/F) create the edge if it does not exist.
- The default is False.
- """
-
- rules = EdgeAttrList('rules')
-
- # use capacity to store the info flow weight so
- # we can use network flow algorithms naturally.
- # The weight for each edge is 1 since each info
- # flow step is no more costly than another
- # (see below add_edge() call)
- weight = EdgeAttrIntMax('capacity')
-
- def __init__(self, graph, source, target, create=False):
- self.G = graph
- self.source = source
- self.target = target
-
- # a bit of a hack to make edges work
- # in NetworkX functions that work on
- # 2-tuples of (source, target)
- # (see __getitem__ below)
- self.st_tuple = (source, target)
-
- if not self.G.has_edge(source, target):
- if create:
- self.G.add_edge(source, target, weight=1)
- self.rules = None
- self.weight = None
- else:
- raise ValueError("Edge does not exist in graph")
-
- def __getitem__(self, key):
- return self.st_tuple[key]
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/initsidquery.py b/lib/python2.7/site-packages/setoolsgui/setools/initsidquery.py
deleted file mode 100644
index 1eb3790..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/initsidquery.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import compquery
-from . import contextquery
-
-
-class InitialSIDQuery(compquery.ComponentQuery, contextquery.ContextQuery):
-
- """
- Initial SID (Initial context) query.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The Initial SID name to match.
- name_regex If true, regular expression matching
- will be used on the Initial SID name.
- user The criteria to match the context's user.
- user_regex If true, regular expression matching
- will be used on the user.
- role The criteria to match the context's role.
- role_regex If true, regular expression matching
- will be used on the role.
- type_ The criteria to match the context's type.
- type_regex If true, regular expression matching
- will be used on the type.
- range_ The criteria to match the context's range.
- range_subset If true, the criteria will match if it is a subset
- of the context's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the context's range.
- range_superset If true, the criteria will match if it is a superset
- of the context's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- def results(self):
- """Generator which yields all matching initial SIDs."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for i in self.policy.initialsids():
- if not self._match_name(i):
- continue
-
- if not self._match_context(i.context):
- continue
-
- yield i
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/mixins.py b/lib/python2.7/site-packages/setoolsgui/setools/mixins.py
deleted file mode 100644
index a31d420..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/mixins.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-# pylint: disable=attribute-defined-outside-init,no-member
-import re
-
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor
-
-
-class MatchAlias(object):
-
- """Mixin for matching an object's aliases."""
-
- alias = CriteriaDescriptor("alias_regex")
- alias_regex = False
-
- def _match_alias(self, obj):
- """
- Match the alias criteria
-
- Parameter:
- obj An object with an alias generator method named "aliases"
- """
-
- if not self.alias:
- # if there is no criteria, everything matches.
- return True
-
- return self._match_in_set(obj.aliases(), self.alias, self.alias_regex)
-
-
-class MatchObjClass(object):
-
- """Mixin for matching an object's class."""
-
- tclass = CriteriaSetDescriptor("tclass_regex", "lookup_class")
- tclass_regex = False
-
- def _match_object_class(self, obj):
- """
- Match the object class criteria
-
- Parameter:
- obj An object with an object class attribute named "tclass"
- """
-
- if not self.tclass:
- # if there is no criteria, everything matches.
- return True
- elif self.tclass_regex:
- return bool(self.tclass.search(str(obj.tclass)))
- else:
- return obj.tclass in self.tclass
-
-
-class MatchPermission(object):
-
- """Mixin for matching an object's permissions."""
-
- perms = CriteriaSetDescriptor("perms_regex")
- perms_equal = False
- perms_regex = False
-
- def _match_perms(self, obj):
- """
- Match the permission criteria
-
- Parameter:
- obj An object with a permission set class attribute named "perms"
- """
-
- if not self.perms:
- # if there is no criteria, everything matches.
- return True
-
- return self._match_regex_or_set(obj.perms, self.perms, self.perms_equal, self.perms_regex)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/mlsrulequery.py b/lib/python2.7/site-packages/setoolsgui/setools/mlsrulequery.py
deleted file mode 100644
index 3a9e1bf..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/mlsrulequery.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import mixins, query
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor, RuletypeDescriptor
-
-
-class MLSRuleQuery(mixins.MatchObjClass, query.PolicyQuery):
-
- """
- Query MLS rules.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- ruletype The list of rule type(s) to match.
- source The name of the source type/attribute to match.
- source_regex If true, regular expression matching will
- be used on the source type/attribute.
- target The name of the target type/attribute to match.
- target_regex If true, regular expression matching will
- be used on the target type/attribute.
- tclass The object class(es) to match.
- tclass_regex If true, use a regular expression for
- matching the rule's object class.
- """
-
- ruletype = RuletypeDescriptor("validate_mls_ruletype")
- source = CriteriaDescriptor("source_regex", "lookup_type_or_attr")
- source_regex = False
- target = CriteriaDescriptor("target_regex", "lookup_type_or_attr")
- target_regex = False
- tclass = CriteriaSetDescriptor("tclass_regex", "lookup_class")
- tclass_regex = False
- default = CriteriaDescriptor(lookup_function="lookup_range")
- default_overlap = False
- default_subset = False
- default_superset = False
- default_proper = False
-
- def results(self):
- """Generator which yields all matching MLS rules."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Ruletypes: {0.ruletype}".format(self))
- self.log.debug("Source: {0.source!r}, regex: {0.source_regex}".format(self))
- self.log.debug("Target: {0.target!r}, regex: {0.target_regex}".format(self))
- self.log.debug("Class: {0.tclass!r}, regex: {0.tclass_regex}".format(self))
- self.log.debug("Default: {0.default!r}, overlap: {0.default_overlap}, "
- "subset: {0.default_subset}, superset: {0.default_superset}, "
- "proper: {0.default_proper}".format(self))
-
- for rule in self.policy.mlsrules():
- #
- # Matching on rule type
- #
- if self.ruletype:
- if rule.ruletype not in self.ruletype:
- continue
-
- #
- # Matching on source type
- #
- if self.source and not self._match_regex(
- rule.source,
- self.source,
- self.source_regex):
- continue
-
- #
- # Matching on target type
- #
- if self.target and not self._match_regex(
- rule.target,
- self.target,
- self.target_regex):
- continue
-
- #
- # Matching on object class
- #
- if not self._match_object_class(rule):
- continue
-
- #
- # Matching on range
- #
- if self.default and not self._match_range(
- rule.default,
- self.default,
- self.default_subset,
- self.default_overlap,
- self.default_superset,
- self.default_proper):
- continue
-
- # if we get here, we have matched all available criteria
- yield rule
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/netifconquery.py b/lib/python2.7/site-packages/setoolsgui/setools/netifconquery.py
deleted file mode 100644
index 30db977..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/netifconquery.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import compquery
-from . import contextquery
-
-
-class NetifconQuery(compquery.ComponentQuery, contextquery.ContextQuery):
-
- """
- Network interface context query.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The name of the network interface to match.
- name_regex If true, regular expression matching will
- be used for matching the name.
- user The criteria to match the context's user.
- user_regex If true, regular expression matching
- will be used on the user.
- role The criteria to match the context's role.
- role_regex If true, regular expression matching
- will be used on the role.
- type_ The criteria to match the context's type.
- type_regex If true, regular expression matching
- will be used on the type.
- range_ The criteria to match the context's range.
- range_subset If true, the criteria will match if it is a subset
- of the context's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the context's range.
- range_superset If true, the criteria will match if it is a superset
- of the context's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- def results(self):
- """Generator which yields all matching netifcons."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for netif in self.policy.netifcons():
- if self.name and not self._match_regex(
- netif.netif,
- self.name,
- self.name_regex):
- continue
-
- if not self._match_context(netif.context):
- continue
-
- yield netif
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/nodeconquery.py b/lib/python2.7/site-packages/setoolsgui/setools/nodeconquery.py
deleted file mode 100644
index eb21d81..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/nodeconquery.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-try:
- import ipaddress
-except ImportError: # pragma: no cover
- pass
-
-import logging
-from socket import AF_INET, AF_INET6
-
-from . import contextquery
-
-
-class NodeconQuery(contextquery.ContextQuery):
-
- """
- Query nodecon statements.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- network The IPv4/IPv6 address or IPv4/IPv6 network address
- with netmask, e.g. 192.168.1.0/255.255.255.0 or
- "192.168.1.0/24".
- network_overlap If true, the net will match if it overlaps with
- the nodecon's network instead of equality.
- ip_version The IP version of the nodecon to match. (socket.AF_INET
- for IPv4 or socket.AF_INET6 for IPv6)
- user The criteria to match the context's user.
- user_regex If true, regular expression matching
- will be used on the user.
- role The criteria to match the context's role.
- role_regex If true, regular expression matching
- will be used on the role.
- type_ The criteria to match the context's type.
- type_regex If true, regular expression matching
- will be used on the type.
- range_ The criteria to match the context's range.
- range_subset If true, the criteria will match if it is a subset
- of the context's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the context's range.
- range_superset If true, the criteria will match if it is a superset
- of the context's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- _network = None
- network_overlap = False
- _ip_version = None
-
- @property
- def ip_version(self):
- return self._ip_version
-
- @ip_version.setter
- def ip_version(self, value):
- if value:
- if not (value == AF_INET or value == AF_INET6):
- raise ValueError(
- "The address family must be {0} for IPv4 or {1} for IPv6.".
- format(AF_INET, AF_INET6))
-
- self._ip_version = value
- else:
- self._ip_version = None
-
- @property
- def network(self):
- return self._network
-
- @network.setter
- def network(self, value):
- if value:
- try:
- self._network = ipaddress.ip_network(value)
- except NameError: # pragma: no cover
- raise RuntimeError("Nodecon IP address/network functions require Python 3.3+.")
- else:
- self._network = None
-
- def results(self):
- """Generator which yields all matching nodecons."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Network: {0.network!r}, overlap: {0.network_overlap}".format(self))
- self.log.debug("IP Version: {0.ip_version}".format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for nodecon in self.policy.nodecons():
-
- if self.network:
- try:
- netmask = ipaddress.ip_address(nodecon.netmask)
- except NameError: # pragma: no cover
- # Should never actually hit this since the self.network
- # setter raises the same exception.
- raise RuntimeError("Nodecon IP address/network functions require Python 3.3+.")
-
- # Python 3.3's IPv6Network constructor does not support
- # expanded netmasks, only CIDR numbers. Convert netmask
- # into CIDR.
- # This is Brian Kernighan's method for counting set bits.
- # If the netmask happens to be invalid, this will
- # not detect it.
- CIDR = 0
- int_netmask = int(netmask)
- while int_netmask:
- int_netmask &= int_netmask - 1
- CIDR += 1
-
- net = ipaddress.ip_network('{0}/{1}'.format(nodecon.address, CIDR))
-
- if self.network_overlap:
- if not self.network.overlaps(net):
- continue
- else:
- if not net == self.network:
- continue
-
- if self.ip_version and self.ip_version != nodecon.ip_version:
- continue
-
- if not self._match_context(nodecon.context):
- continue
-
- yield nodecon
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/objclassquery.py b/lib/python2.7/site-packages/setoolsgui/setools/objclassquery.py
deleted file mode 100644
index 8f40df8..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/objclassquery.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import compquery
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor
-from .policyrep.exception import NoCommon
-
-
-class ObjClassQuery(compquery.ComponentQuery):
-
- """
- Query object classes.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The name of the object set to match.
- name_regex If true, regular expression matching will
- be used for matching the name.
- common The name of the inherited common to match.
- common_regex If true, regular expression matching will
- be used for matching the common name.
- perms The permissions to match.
- perms_equal If true, only commons with permission sets
- that are equal to the criteria will
- match. Otherwise, any intersection
- will match.
- perms_regex If true, regular expression matching
- will be used on the permission names instead
- of set logic.
- comparison will not be used.
- perms_indirect If false, permissions inherited from a common
- permission set not will be evaluated. Default
- is true.
- """
-
- common = CriteriaDescriptor("common_regex", "lookup_common")
- common_regex = False
- perms = CriteriaSetDescriptor("perms_regex")
- perms_equal = False
- perms_indirect = True
- perms_regex = False
-
- def results(self):
- """Generator which yields all matching object classes."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Common: {0.common!r}, regex: {0.common_regex}".format(self))
- self.log.debug("Perms: {0.perms}, regex: {0.perms_regex}, "
- "eq: {0.perms_equal}, indirect: {0.perms_indirect}".format(self))
-
- for class_ in self.policy.classes():
- if not self._match_name(class_):
- continue
-
- if self.common:
- try:
- if not self._match_regex(
- class_.common,
- self.common,
- self.common_regex):
- continue
- except NoCommon:
- continue
-
- if self.perms:
- perms = class_.perms
-
- if self.perms_indirect:
- try:
- perms |= class_.common.perms
- except NoCommon:
- pass
-
- if not self._match_regex_or_set(
- perms,
- self.perms,
- self.perms_equal,
- self.perms_regex):
- continue
-
- yield class_
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/permmap.py b/lib/python2.7/site-packages/setoolsgui/setools/permmap.py
deleted file mode 100644
index 54cd9f9..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/permmap.py
+++ /dev/null
@@ -1,363 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import sys
-import logging
-from errno import ENOENT
-
-from . import exception
-from . import policyrep
-
-
-class PermissionMap(object):
-
- """Permission Map for information flow analysis."""
-
- valid_infoflow_directions = ["r", "w", "b", "n", "u"]
- min_weight = 1
- max_weight = 10
-
- def __init__(self, permmapfile=None):
- """
- Parameter:
- permmapfile The path to the permission map to load.
- """
- self.log = logging.getLogger(self.__class__.__name__)
-
- if permmapfile:
- self.load(permmapfile)
- else:
- for path in ["data/", sys.prefix + "/share/setools/"]:
- try:
- self.load(path + "perm_map")
- break
- except (IOError, OSError) as err:
- if err.errno != ENOENT:
- raise
- else:
- raise RuntimeError("Unable to load default permission map.")
-
- def load(self, permmapfile):
- """
- Parameter:
- permmapfile The path to the permission map to load.
- """
- self.log.info("Opening permission map \"{0}\"".format(permmapfile))
-
- # state machine
- # 1 = read number of classes
- # 2 = read class name and number of perms
- # 3 = read perms
- with open(permmapfile, "r") as mapfile:
- class_count = 0
- num_classes = 0
- state = 1
-
- self.permmap = dict()
-
- for line_num, line in enumerate(mapfile, start=1):
- entry = line.split()
-
- if len(entry) == 0 or entry[0][0] == '#':
- continue
-
- if state == 1:
- try:
- num_classes = int(entry[0])
- except ValueError:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Invalid number of classes: {2}".
- format(permmapfile, line_num, entry[0]))
-
- if num_classes < 1:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Number of classes must be positive: {2}".
- format(permmapfile, line_num, entry[0]))
-
- state = 2
-
- elif state == 2:
- if len(entry) != 3 or entry[0] != "class":
- raise exception.PermissionMapParseError(
- "{0}:{1}:Invalid class declaration: {2}".
- format(permmapfile, line_num, entry))
-
- class_name = str(entry[1])
-
- try:
- num_perms = int(entry[2])
- except ValueError:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Invalid number of permissions: {2}".
- format(permmapfile, line_num, entry[2]))
-
- if num_perms < 1:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Number of permissions must be positive: {2}".
- format(permmapfile, line_num, entry[2]))
-
- class_count += 1
- if class_count > num_classes:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Extra class found: {2}".
- format(permmapfile, line_num, class_name))
-
- self.permmap[class_name] = dict()
- perm_count = 0
- state = 3
-
- elif state == 3:
- perm_name = str(entry[0])
-
- flow_direction = str(entry[1])
- if flow_direction not in self.valid_infoflow_directions:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Invalid information flow direction: {2}".
- format(permmapfile, line_num, entry[1]))
-
- try:
- weight = int(entry[2])
- except ValueError:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Invalid permission weight: {2}".
- format(permmapfile, line_num, entry[2]))
-
- if not self.min_weight <= weight <= self.max_weight:
- raise exception.PermissionMapParseError(
- "{0}:{1}:Permission weight must be {3}-{4}: {2}".
- format(permmapfile, line_num, entry[2],
- self.min_weight, self.max_weight))
-
- self.permmap[class_name][perm_name] = {'direction': flow_direction,
- 'weight': weight,
- 'enabled': True}
-
- perm_count += 1
- if perm_count >= num_perms:
- state = 2
-
- def exclude_class(self, class_):
- """
- Exclude all permissions in an object class for calculating rule weights.
-
- Parameter:
- class_ The object class to exclude.
-
- Exceptions:
- UnmappedClass The specified object class is not mapped.
- """
-
- classname = str(class_)
-
- try:
- for perm in self.permmap[classname]:
- self.permmap[classname][perm]['enabled'] = False
- except KeyError:
- raise exception.UnmappedClass("{0} is not mapped.".format(classname))
-
- def exclude_permission(self, class_, permission):
- """
- Exclude a permission for calculating rule weights.
-
- Parameter:
- class_ The object class of the permission.
- permission The permission name to exclude.
-
- Exceptions:
- UnmappedClass The specified object class is not mapped.
- UnmappedPermission The specified permission is not mapped for the object class.
- """
- classname = str(class_)
-
- if classname not in self.permmap:
- raise exception.UnmappedClass("{0} is not mapped.".format(classname))
-
- try:
- self.permmap[classname][permission]['enabled'] = False
- except KeyError:
- raise exception.UnmappedPermission("{0}:{1} is not mapped.".
- format(classname, permission))
-
- def include_class(self, class_):
- """
- Include all permissions in an object class for calculating rule weights.
-
- Parameter:
- class_ The object class to include.
-
- Exceptions:
- UnmappedClass The specified object class is not mapped.
- """
-
- classname = str(class_)
-
- try:
- for perm in self.permmap[classname]:
- self.permmap[classname][perm]['enabled'] = True
- except KeyError:
- raise exception.UnmappedClass("{0} is not mapped.".format(classname))
-
- def include_permission(self, class_, permission):
- """
- Include a permission for calculating rule weights.
-
- Parameter:
- class_ The object class of the permission.
- permission The permission name to include.
-
- Exceptions:
- UnmappedClass The specified object class is not mapped.
- UnmappedPermission The specified permission is not mapped for the object class.
- """
-
- classname = str(class_)
-
- if classname not in self.permmap:
- raise exception.UnmappedClass("{0} is not mapped.".format(classname))
-
- try:
- self.permmap[classname][permission]['enabled'] = True
- except KeyError:
- raise exception.UnmappedPermission("{0}:{1} is not mapped.".
- format(classname, permission))
-
- def map_policy(self, policy):
- """Create mappings for all classes and permissions in the specified policy."""
- for class_ in policy.classes():
- class_name = str(class_)
-
- if class_name not in self.permmap:
- self.log.info("Adding unmapped class {0} from {1}".format(class_name, policy))
- self.permmap[class_name] = dict()
-
- perms = class_.perms
-
- try:
- perms |= class_.common.perms
- except policyrep.exception.NoCommon:
- pass
-
- for perm_name in perms:
- if perm_name not in self.permmap[class_name]:
- self.log.info("Adding unmapped permission {0} in {1} from {2}".
- format(perm_name, class_name, policy))
- self.permmap[class_name][perm_name] = {'direction': 'u',
- 'weight': 1,
- 'enabled': True}
-
- def rule_weight(self, rule):
- """
- Get the type enforcement rule's information flow read and write weights.
-
- Parameter:
- rule A type enforcement rule.
-
- Return: Tuple(read_weight, write_weight)
- read_weight The type enforcement rule's read weight.
- write_weight The type enforcement rule's write weight.
- """
-
- write_weight = 0
- read_weight = 0
- class_name = str(rule.tclass)
-
- if rule.ruletype != 'allow':
- raise exception.RuleTypeError("{0} rules cannot be used for calculating a weight".
- format(rule.ruletype))
-
- if class_name not in self.permmap:
- raise exception.UnmappedClass("{0} is not mapped.".format(class_name))
-
- # iterate over the permissions and determine the
- # weight of the rule in each direction. The result
- # is the largest-weight permission in each direction
- for perm_name in rule.perms:
- try:
- mapping = self.permmap[class_name][perm_name]
- except KeyError:
- raise exception.UnmappedPermission("{0}:{1} is not mapped.".
- format(class_name, perm_name))
-
- if not mapping['enabled']:
- continue
-
- if mapping['direction'] == "r":
- read_weight = max(read_weight, mapping['weight'])
- elif mapping['direction'] == "w":
- write_weight = max(write_weight, mapping['weight'])
- elif mapping['direction'] == "b":
- read_weight = max(read_weight, mapping['weight'])
- write_weight = max(write_weight, mapping['weight'])
-
- return (read_weight, write_weight)
-
- def set_direction(self, class_, permission, direction):
- """
- Set the information flow direction of a permission.
-
- Parameter:
- class_ The object class of the permission.
- permission The permission name.
- direction The information flow direction the permission (r/w/b/n).
-
- Exceptions:
- UnmappedClass The specified object class is not mapped.
- UnmappedPermission The specified permission is not mapped for the object class.
- """
-
- if direction not in self.valid_infoflow_directions:
- raise ValueError("Invalid information flow direction: {0}".format(direction))
-
- classname = str(class_)
-
- if classname not in self.permmap:
- raise exception.UnmappedClass("{0} is not mapped.".format(classname))
-
- try:
- self.permmap[classname][permission]['direction'] = direction
- except KeyError:
- raise exception.UnmappedPermission("{0}:{1} is not mapped.".
- format(classname, permission))
-
- def set_weight(self, class_, permission, weight):
- """
- Set the weight of a permission.
-
- Parameter:
- class_ The object class of the permission.
- permission The permission name.
- weight The weight of the permission (1-10).
-
- Exceptions:
- UnmappedClass The specified object class is not mapped.
- UnmappedPermission The specified permission is not mapped for the object class.
- """
-
- if not self.min_weight <= weight <= self.max_weight:
- raise ValueError("Permission weights must be 1-10: {0}".format(weight))
-
- classname = str(class_)
-
- if classname not in self.permmap:
- raise exception.UnmappedClass("{0} is not mapped.".format(classname))
-
- try:
- self.permmap[classname][permission]['weight'] = weight
- except KeyError:
- raise exception.UnmappedPermission("{0}:{1} is not mapped.".
- format(classname, permission))
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/polcapquery.py b/lib/python2.7/site-packages/setoolsgui/setools/polcapquery.py
deleted file mode 100644
index e024b05..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/polcapquery.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import compquery
-
-
-class PolCapQuery(compquery.ComponentQuery):
-
- """
- Query SELinux policy capabilities
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The name of the policy capability to match.
- name_regex If true, regular expression matching will
- be used for matching the name.
- """
-
- def results(self):
- """Generator which yields all matching policy capabilities."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
-
- for cap in self.policy.polcaps():
- if not self._match_name(cap):
- continue
-
- yield cap
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/__init__.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/__init__.py
deleted file mode 100644
index b03e524..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/__init__.py
+++ /dev/null
@@ -1,568 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-# pylint: disable=too-many-public-methods
-#
-# Create a Python representation of the policy.
-# The idea is that this is module provides convenient
-# abstractions and methods for accessing the policy
-# structures.
-import logging
-from itertools import chain
-from errno import ENOENT
-
-try:
- import selinux
-except ImportError:
- pass
-
-from . import qpol
-
-# The libqpol SWIG class is not quite natural for
-# Python the policy is repeatedly referenced in the
-# function calls, which makes sense for C code
-# but not for python code, so each object keeps
-# a reference to the policy for internal use.
-# This also makes sense since an object would only
-# be valid for the policy it comes from.
-
-# Exceptions
-from . import exception
-
-# Components
-from . import boolcond
-from . import default
-from . import mls
-from . import objclass
-from . import polcap
-from . import role
-from . import typeattr
-from . import user
-
-# Rules
-from . import mlsrule
-from . import rbacrule
-from . import terule
-
-# Constraints
-from . import constraint
-
-# In-policy Labeling
-from . import fscontext
-from . import initsid
-from . import netcontext
-
-
-class SELinuxPolicy(object):
-
- """The complete SELinux policy."""
-
- def __init__(self, policyfile=None):
- """
- Parameter:
- policyfile Path to a policy to open.
- """
-
- self.log = logging.getLogger(self.__class__.__name__)
- self.policy = None
- self.filename = None
-
- if policyfile:
- self._load_policy(policyfile)
- else:
- try:
- self._load_running_policy()
- except NameError:
- raise RuntimeError("Loading the running policy requires libselinux Python bindings")
-
- def __repr__(self):
- return "<SELinuxPolicy(\"{0}\")>".format(self.filename)
-
- def __str__(self):
- return self.filename
-
- def __deepcopy__(self, memo):
- # shallow copy as all of the members are immutable
- newobj = SELinuxPolicy.__new__(SELinuxPolicy)
- newobj.policy = self.policy
- newobj.filename = self.filename
- memo[id(self)] = newobj
- return newobj
-
- #
- # Policy loading functions
- #
-
- def _load_policy(self, filename):
- """Load the specified policy."""
- self.log.info("Opening SELinux policy \"{0}\"".format(filename))
-
- try:
- self.policy = qpol.qpol_policy_factory(str(filename))
- except SyntaxError as err:
- raise exception.InvalidPolicy("Error opening policy file \"{0}\": {1}".
- format(filename, err))
-
- self.log.info("Successfully opened SELinux policy \"{0}\"".format(filename))
- self.filename = filename
-
- @staticmethod
- def _potential_policies():
- """Generate a list of potential policies to use."""
- # Start with binary policies in the standard location
- base_policy_path = selinux.selinux_binary_policy_path()
- for version in range(qpol.QPOL_POLICY_MAX_VERSION, qpol.QPOL_POLICY_MIN_VERSION-1, -1):
- yield "{0}.{1}".format(base_policy_path, version)
-
- # Last chance, try selinuxfs. This is not first, to avoid
- # holding kernel memory for a long time
- if selinux.selinuxfs_exists():
- yield selinux.selinux_current_policy_path()
-
- def _load_running_policy(self):
- """Try to load the current running policy."""
- self.log.info("Attempting to locate current running policy.")
-
- for filename in self._potential_policies():
- try:
- self._load_policy(filename)
- except OSError as err:
- if err.errno != ENOENT:
- raise
- else:
- break
- else:
- raise RuntimeError("Unable to locate an SELinux policy to load.")
-
- #
- # Policy properties
- #
- @property
- def handle_unknown(self):
- """The handle unknown permissions setting (allow,deny,reject)"""
- return self.policy.handle_unknown()
-
- @property
- def mls(self):
- """(T/F) The policy has MLS enabled."""
- return mls.enabled(self.policy)
-
- @property
- def version(self):
- """The policy database version (e.g. v29)"""
- return self.policy.version()
-
- #
- # Policy statistics
- #
-
- @property
- def allow_count(self):
- """The number of (type) allow rules."""
- return self.policy.avrule_allow_count()
-
- @property
- def auditallow_count(self):
- """The number of auditallow rules."""
- return self.policy.avrule_auditallow_count()
-
- @property
- def boolean_count(self):
- """The number of Booleans."""
- return self.policy.bool_count()
-
- @property
- def category_count(self):
- """The number of categories."""
- return sum(1 for _ in self.categories())
-
- @property
- def class_count(self):
- """The number of object classes."""
- return self.policy.class_count()
-
- @property
- def common_count(self):
- """The number of common permission sets."""
- return self.policy.common_count()
-
- @property
- def conditional_count(self):
- """The number of conditionals."""
- return self.policy.cond_count()
-
- @property
- def constraint_count(self):
- """The number of standard constraints."""
- return sum(1 for c in self.constraints() if c.ruletype == "constrain")
-
- @property
- def dontaudit_count(self):
- """The number of dontaudit rules."""
- return self.policy.avrule_dontaudit_count()
-
- @property
- def fs_use_count(self):
- """fs_use_* statements."""
- return self.policy.fs_use_count()
-
- @property
- def genfscon_count(self):
- """The number of genfscon statements."""
- return self.policy.genfscon_count()
-
- @property
- def initialsids_count(self):
- """The number of initial sid statements."""
- return self.policy.isid_count()
-
- @property
- def level_count(self):
- """The number of levels."""
- return sum(1 for _ in self.levels())
-
- @property
- def mlsconstraint_count(self):
- """The number of MLS constraints."""
- return sum(1 for c in self.constraints() if c.ruletype == "mlsconstrain")
-
- @property
- def mlsvalidatetrans_count(self):
- """The number of MLS validatetrans."""
- return sum(1 for v in self.constraints() if v.ruletype == "mlsvalidatetrans")
-
- @property
- def netifcon_count(self):
- """The number of netifcon statements."""
- return self.policy.netifcon_count()
-
- @property
- def neverallow_count(self):
- """The number of neverallow rules."""
- return self.policy.avrule_neverallow_count()
-
- @property
- def nodecon_count(self):
- """The number of nodecon statements."""
- return self.policy.nodecon_count()
-
- @property
- def permission_count(self):
- """The number of permissions."""
- return sum(len(c.perms) for c in chain(self.commons(), self.classes()))
-
- @property
- def permissives_count(self):
- """The number of permissive types."""
- return self.policy.permissive_count()
-
- @property
- def polcap_count(self):
- """The number of policy capabilities."""
- return self.policy.polcap_count()
-
- @property
- def portcon_count(self):
- """The number of portcon statements."""
- return self.policy.portcon_count()
-
- @property
- def range_transition_count(self):
- """The number of range_transition rules."""
- return self.policy.range_trans_count()
-
- @property
- def role_count(self):
- """The number of roles."""
- return self.policy.role_count()
-
- @property
- def role_allow_count(self):
- """The number of (role) allow rules."""
- return self.policy.role_allow_count()
-
- @property
- def role_transition_count(self):
- """The number of role_transition rules."""
- return self.policy.role_trans_count()
-
- @property
- def type_attribute_count(self):
- """The number of (type) attributes."""
- return sum(1 for _ in self.typeattributes())
-
- @property
- def type_count(self):
- """The number of types."""
- return sum(1 for _ in self.types())
-
- @property
- def type_change_count(self):
- """The number of type_change rules."""
- return self.policy.terule_change_count()
-
- @property
- def type_member_count(self):
- """The number of type_member rules."""
- return self.policy.terule_member_count()
-
- @property
- def type_transition_count(self):
- """The number of type_transition rules."""
- return self.policy.terule_trans_count() + self.policy.filename_trans_count()
-
- @property
- def user_count(self):
- """The number of users."""
- return self.policy.user_count()
-
- @property
- def validatetrans_count(self):
- """The number of validatetrans."""
- return sum(1 for v in self.constraints() if v.ruletype == "validatetrans")
-
- #
- # Policy components lookup functions
- #
- def lookup_boolean(self, name):
- """Look up a Boolean."""
- return boolcond.boolean_factory(self.policy, name)
-
- def lookup_class(self, name):
- """Look up an object class."""
- return objclass.class_factory(self.policy, name)
-
- def lookup_common(self, name):
- """Look up a common permission set."""
- return objclass.common_factory(self.policy, name)
-
- def lookup_initialsid(self, name):
- """Look up an initial sid."""
- return initsid.initialsid_factory(self.policy, name)
-
- def lookup_level(self, level):
- """Look up a MLS level."""
- return mls.level_factory(self.policy, level)
-
- def lookup_sensitivity(self, name):
- """Look up a MLS sensitivity by name."""
- return mls.sensitivity_factory(self.policy, name)
-
- def lookup_range(self, range_):
- """Look up a MLS range."""
- return mls.range_factory(self.policy, range_)
-
- def lookup_role(self, name):
- """Look up a role by name."""
- return role.role_factory(self.policy, name)
-
- def lookup_type(self, name):
- """Look up a type by name."""
- return typeattr.type_factory(self.policy, name, deref=True)
-
- def lookup_type_or_attr(self, name):
- """Look up a type or type attribute by name."""
- return typeattr.type_or_attr_factory(self.policy, name, deref=True)
-
- def lookup_typeattr(self, name):
- """Look up a type attribute by name."""
- return typeattr.attribute_factory(self.policy, name)
-
- def lookup_user(self, name):
- """Look up a user by name."""
- return user.user_factory(self.policy, name)
-
- #
- # Policy components generators
- #
-
- def bools(self):
- """Generator which yields all Booleans."""
- for bool_ in self.policy.bool_iter():
- yield boolcond.boolean_factory(self.policy, bool_)
-
- def categories(self):
- """Generator which yields all MLS categories."""
- for cat in self.policy.cat_iter():
- try:
- yield mls.category_factory(self.policy, cat)
- except TypeError:
- # libqpol unfortunately iterates over aliases too
- pass
-
- def classes(self):
- """Generator which yields all object classes."""
- for class_ in self.policy.class_iter():
- yield objclass.class_factory(self.policy, class_)
-
- def commons(self):
- """Generator which yields all commons."""
- for common in self.policy.common_iter():
- yield objclass.common_factory(self.policy, common)
-
- def defaults(self):
- """Generator which yields all default_* statements."""
- for default_ in self.policy.default_iter():
- try:
- for default_obj in default.default_factory(self.policy, default_):
- yield default_obj
- except exception.NoDefaults:
- # qpol iterates over all classes. Handle case
- # where a class has no default_* settings.
- pass
-
- def levels(self):
- """Generator which yields all level declarations."""
- for level in self.policy.level_iter():
-
- try:
- yield mls.level_decl_factory(self.policy, level)
- except TypeError:
- # libqpol unfortunately iterates over levels and sens aliases
- pass
-
- def polcaps(self):
- """Generator which yields all policy capabilities."""
- for cap in self.policy.polcap_iter():
- yield polcap.polcap_factory(self.policy, cap)
-
- def roles(self):
- """Generator which yields all roles."""
- for role_ in self.policy.role_iter():
- yield role.role_factory(self.policy, role_)
-
- def sensitivities(self):
- """Generator which yields all sensitivities."""
- # see mls.py for more info on why level_iter is used here.
- for sens in self.policy.level_iter():
- try:
- yield mls.sensitivity_factory(self.policy, sens)
- except TypeError:
- # libqpol unfortunately iterates over sens and aliases
- pass
-
- def types(self):
- """Generator which yields all types."""
- for type_ in self.policy.type_iter():
- try:
- yield typeattr.type_factory(self.policy, type_)
- except TypeError:
- # libqpol unfortunately iterates over attributes and aliases
- pass
-
- def typeattributes(self):
- """Generator which yields all (type) attributes."""
- for type_ in self.policy.type_iter():
- try:
- yield typeattr.attribute_factory(self.policy, type_)
- except TypeError:
- # libqpol unfortunately iterates over attributes and aliases
- pass
-
- def users(self):
- """Generator which yields all users."""
- for user_ in self.policy.user_iter():
- yield user.user_factory(self.policy, user_)
-
- #
- # Policy rules generators
- #
- def mlsrules(self):
- """Generator which yields all MLS rules."""
- for rule in self.policy.range_trans_iter():
- yield mlsrule.mls_rule_factory(self.policy, rule)
-
- def rbacrules(self):
- """Generator which yields all RBAC rules."""
- for rule in chain(self.policy.role_allow_iter(),
- self.policy.role_trans_iter()):
- yield rbacrule.rbac_rule_factory(self.policy, rule)
-
- def terules(self):
- """Generator which yields all type enforcement rules."""
- for rule in chain(self.policy.avrule_iter(),
- self.policy.terule_iter(),
- self.policy.filename_trans_iter()):
- yield terule.te_rule_factory(self.policy, rule)
-
- #
- # Policy rule type validators
- #
- @staticmethod
- def validate_constraint_ruletype(types):
- """Validate constraint types."""
- constraint.validate_ruletype(types)
-
- @staticmethod
- def validate_mls_ruletype(types):
- """Validate MLS rule types."""
- mlsrule.validate_ruletype(types)
-
- @staticmethod
- def validate_rbac_ruletype(types):
- """Validate RBAC rule types."""
- rbacrule.validate_ruletype(types)
-
- @staticmethod
- def validate_te_ruletype(types):
- """Validate type enforcement rule types."""
- terule.validate_ruletype(types)
-
- #
- # Constraints generators
- #
-
- def constraints(self):
- """Generator which yields all constraints (regular and MLS)."""
- for constraint_ in chain(self.policy.constraint_iter(),
- self.policy.validatetrans_iter()):
-
- yield constraint.constraint_factory(self.policy, constraint_)
-
- #
- # In-policy Labeling statement generators
- #
- def fs_uses(self):
- """Generator which yields all fs_use_* statements."""
- for fs_use in self.policy.fs_use_iter():
- yield fscontext.fs_use_factory(self.policy, fs_use)
-
- def genfscons(self):
- """Generator which yields all genfscon statements."""
- for fscon in self.policy.genfscon_iter():
- yield fscontext.genfscon_factory(self.policy, fscon)
-
- def initialsids(self):
- """Generator which yields all initial SID statements."""
- for sid in self.policy.isid_iter():
- yield initsid.initialsid_factory(self.policy, sid)
-
- def netifcons(self):
- """Generator which yields all netifcon statements."""
- for ifcon in self.policy.netifcon_iter():
- yield netcontext.netifcon_factory(self.policy, ifcon)
-
- def nodecons(self):
- """Generator which yields all nodecon statements."""
- for node in self.policy.nodecon_iter():
- yield netcontext.nodecon_factory(self.policy, node)
-
- def portcons(self):
- """Generator which yields all portcon statements."""
- for port in self.policy.portcon_iter():
- yield netcontext.portcon_factory(self.policy, port)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/_qpol.so b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/_qpol.so
deleted file mode 100755
index aaccf28..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/_qpol.so
+++ /dev/null
Binary files differ
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/boolcond.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/boolcond.py
deleted file mode 100644
index c3c0608..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/boolcond.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import symbol
-
-
-def boolean_factory(policy, name):
- """Factory function for creating Boolean statement objects."""
-
- if isinstance(name, Boolean):
- assert name.policy == policy
- return name
- elif isinstance(name, qpol.qpol_bool_t):
- return Boolean(policy, name)
-
- try:
- return Boolean(policy, qpol.qpol_bool_t(policy, str(name)))
- except ValueError:
- raise exception.InvalidBoolean("{0} is not a valid Boolean".format(name))
-
-
-def condexpr_factory(policy, name):
- """Factory function for creating conditional expression objects."""
-
- if not isinstance(name, qpol.qpol_cond_t):
- raise TypeError("Conditional expressions cannot be looked up.")
-
- return ConditionalExpr(policy, name)
-
-
-class Boolean(symbol.PolicySymbol):
-
- """A Boolean."""
-
- @property
- def state(self):
- """The default state of the Boolean."""
- return bool(self.qpol_symbol.state(self.policy))
-
- def statement(self):
- """The policy statement."""
- return "bool {0} {1};".format(self, str(self.state).lower())
-
-
-class ConditionalExpr(symbol.PolicySymbol):
-
- """A conditional policy expression."""
-
- _cond_expr_val_to_text = {
- qpol.QPOL_COND_EXPR_NOT: "!",
- qpol.QPOL_COND_EXPR_OR: "||",
- qpol.QPOL_COND_EXPR_AND: "&&",
- qpol.QPOL_COND_EXPR_XOR: "^",
- qpol.QPOL_COND_EXPR_EQ: "==",
- qpol.QPOL_COND_EXPR_NEQ: "!="}
-
- _cond_expr_val_to_precedence = {
- qpol.QPOL_COND_EXPR_NOT: 5,
- qpol.QPOL_COND_EXPR_OR: 1,
- qpol.QPOL_COND_EXPR_AND: 3,
- qpol.QPOL_COND_EXPR_XOR: 2,
- qpol.QPOL_COND_EXPR_EQ: 4,
- qpol.QPOL_COND_EXPR_NEQ: 4}
-
- def __contains__(self, other):
- for expr_node in self.qpol_symbol.expr_node_iter(self.policy):
- expr_node_type = expr_node.expr_type(self.policy)
-
- if expr_node_type == qpol.QPOL_COND_EXPR_BOOL and other == \
- boolean_factory(self.policy, expr_node.get_boolean(self.policy)):
- return True
-
- return False
-
- def __str__(self):
- # qpol representation is in postfix notation. This code
- # converts it to infix notation. Parentheses are added
- # to ensure correct expressions, though they may end up
- # being overused. Set previous operator at start to the
- # highest precedence (NOT) so if there is a single binary
- # operator, no parentheses are output
- stack = []
- prev_op_precedence = self._cond_expr_val_to_precedence[qpol.QPOL_COND_EXPR_NOT]
- for expr_node in self.qpol_symbol.expr_node_iter(self.policy):
- expr_node_type = expr_node.expr_type(self.policy)
-
- if expr_node_type == qpol.QPOL_COND_EXPR_BOOL:
- # append the boolean name
- nodebool = boolean_factory(
- self.policy, expr_node.get_boolean(self.policy))
- stack.append(str(nodebool))
- elif expr_node_type == qpol.QPOL_COND_EXPR_NOT: # unary operator
- operand = stack.pop()
- operator = self._cond_expr_val_to_text[expr_node_type]
- op_precedence = self._cond_expr_val_to_precedence[expr_node_type]
-
- # NOT is the highest precedence, so only need
- # parentheses if the operand is a subexpression
- if isinstance(operand, list):
- subexpr = [operator, "(", operand, ")"]
- else:
- subexpr = [operator, operand]
-
- stack.append(subexpr)
- prev_op_precedence = op_precedence
- else:
- operand1 = stack.pop()
- operand2 = stack.pop()
- operator = self._cond_expr_val_to_text[expr_node_type]
- op_precedence = self._cond_expr_val_to_precedence[expr_node_type]
-
- if prev_op_precedence > op_precedence:
- # if previous operator is of higher precedence
- # no parentheses are needed.
- subexpr = [operand1, operator, operand2]
- else:
- subexpr = ["(", operand1, operator, operand2, ")"]
-
- stack.append(subexpr)
- prev_op_precedence = op_precedence
-
- return self.__unwind_subexpression(stack)
-
- def __unwind_subexpression(self, expr):
- ret = []
-
- # do a string.join on sublists (subexpressions)
- for i in expr:
- if isinstance(i, list):
- ret.append(self.__unwind_subexpression(i))
- else:
- ret.append(i)
-
- return ' '.join(ret)
-
- @property
- def booleans(self):
- """The set of Booleans in the expression."""
- bools = set()
-
- for expr_node in self.qpol_symbol.expr_node_iter(self.policy):
- expr_node_type = expr_node.expr_type(self.policy)
-
- if expr_node_type == qpol.QPOL_COND_EXPR_BOOL:
- bools.add(boolean_factory(self.policy, expr_node.get_boolean(self.policy)))
-
- return bools
-
- def statement(self):
- raise exception.NoStatement
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/constraint.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/constraint.py
deleted file mode 100644
index 9994c5b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/constraint.py
+++ /dev/null
@@ -1,297 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import role
-from . import symbol
-from . import objclass
-from . import typeattr
-from . import user
-
-
-def _is_mls(policy, sym):
- """Determine if this is a regular or MLS constraint/validatetrans."""
- # this can only be determined by inspecting the expression.
- for expr_node in sym.expr_iter(policy):
- sym_type = expr_node.sym_type(policy)
- expr_type = expr_node.expr_type(policy)
-
- if expr_type == qpol.QPOL_CEXPR_TYPE_ATTR and sym_type >= qpol.QPOL_CEXPR_SYM_L1L2:
- return True
-
- return False
-
-
-def validate_ruletype(types):
- """Validate constraint rule types."""
- for t in types:
- if t not in ["constrain", "mlsconstrain", "validatetrans", "mlsvalidatetrans"]:
- raise exception.InvalidConstraintType("{0} is not a valid constraint type.".format(t))
-
-
-def constraint_factory(policy, sym):
- """Factory function for creating constraint objects."""
-
- try:
- if _is_mls(policy, sym):
- if isinstance(sym, qpol.qpol_constraint_t):
- return Constraint(policy, sym, "mlsconstrain")
- else:
- return Validatetrans(policy, sym, "mlsvalidatetrans")
- else:
- if isinstance(sym, qpol.qpol_constraint_t):
- return Constraint(policy, sym, "constrain")
- else:
- return Validatetrans(policy, sym, "validatetrans")
-
- except AttributeError:
- raise TypeError("Constraints cannot be looked-up.")
-
-
-class BaseConstraint(symbol.PolicySymbol):
-
- """Base class for constraint rules."""
-
- _expr_type_to_text = {
- qpol.QPOL_CEXPR_TYPE_NOT: "not",
- qpol.QPOL_CEXPR_TYPE_AND: "and",
- qpol.QPOL_CEXPR_TYPE_OR: "\n\tor"}
-
- _expr_op_to_text = {
- qpol.QPOL_CEXPR_OP_EQ: "==",
- qpol.QPOL_CEXPR_OP_NEQ: "!=",
- qpol.QPOL_CEXPR_OP_DOM: "dom",
- qpol.QPOL_CEXPR_OP_DOMBY: "domby",
- qpol.QPOL_CEXPR_OP_INCOMP: "incomp"}
-
- _sym_to_text = {
- qpol.QPOL_CEXPR_SYM_USER: "u1",
- qpol.QPOL_CEXPR_SYM_ROLE: "r1",
- qpol.QPOL_CEXPR_SYM_TYPE: "t1",
- qpol.QPOL_CEXPR_SYM_USER + qpol.QPOL_CEXPR_SYM_TARGET: "u2",
- qpol.QPOL_CEXPR_SYM_ROLE + qpol.QPOL_CEXPR_SYM_TARGET: "r2",
- qpol.QPOL_CEXPR_SYM_TYPE + qpol.QPOL_CEXPR_SYM_TARGET: "t2",
- qpol.QPOL_CEXPR_SYM_USER + qpol.QPOL_CEXPR_SYM_XTARGET: "u3",
- qpol.QPOL_CEXPR_SYM_ROLE + qpol.QPOL_CEXPR_SYM_XTARGET: "r3",
- qpol.QPOL_CEXPR_SYM_TYPE + qpol.QPOL_CEXPR_SYM_XTARGET: "t3",
- qpol.QPOL_CEXPR_SYM_L1L2: "l1",
- qpol.QPOL_CEXPR_SYM_L1H2: "l1",
- qpol.QPOL_CEXPR_SYM_H1L2: "h1",
- qpol.QPOL_CEXPR_SYM_H1H2: "h1",
- qpol.QPOL_CEXPR_SYM_L1H1: "l1",
- qpol.QPOL_CEXPR_SYM_L2H2: "l2",
- qpol.QPOL_CEXPR_SYM_L1L2 + qpol.QPOL_CEXPR_SYM_TARGET: "l2",
- qpol.QPOL_CEXPR_SYM_L1H2 + qpol.QPOL_CEXPR_SYM_TARGET: "h2",
- qpol.QPOL_CEXPR_SYM_H1L2 + qpol.QPOL_CEXPR_SYM_TARGET: "l2",
- qpol.QPOL_CEXPR_SYM_H1H2 + qpol.QPOL_CEXPR_SYM_TARGET: "h2",
- qpol.QPOL_CEXPR_SYM_L1H1 + qpol.QPOL_CEXPR_SYM_TARGET: "h1",
- qpol.QPOL_CEXPR_SYM_L2H2 + qpol.QPOL_CEXPR_SYM_TARGET: "h2"}
-
- # Boolean operators
- _expr_type_to_precedence = {
- qpol.QPOL_CEXPR_TYPE_NOT: 3,
- qpol.QPOL_CEXPR_TYPE_AND: 2,
- qpol.QPOL_CEXPR_TYPE_OR: 1}
-
- # Logical operators have the same precedence
- _logical_op_precedence = 4
-
- def __init__(self, policy, qpol_symbol, ruletype):
- symbol.PolicySymbol.__init__(self, policy, qpol_symbol)
- self.ruletype = ruletype
-
- def __str__(self):
- raise NotImplementedError
-
- def _build_expression(self):
- # qpol representation is in postfix notation. This code
- # converts it to infix notation. Parentheses are added
- # to ensure correct expressions, though they may end up
- # being overused. Set previous operator at start to the
- # highest precedence (op) so if there is a single binary
- # operator, no parentheses are output
-
- stack = []
- prev_op_precedence = self._logical_op_precedence
- for expr_node in self.qpol_symbol.expr_iter(self.policy):
- op = expr_node.op(self.policy)
- sym_type = expr_node.sym_type(self.policy)
- expr_type = expr_node.expr_type(self.policy)
-
- if expr_type == qpol.QPOL_CEXPR_TYPE_ATTR:
- # logical operator with symbol (e.g. u1 == u2)
- operand1 = self._sym_to_text[sym_type]
- operand2 = self._sym_to_text[sym_type + qpol.QPOL_CEXPR_SYM_TARGET]
- operator = self._expr_op_to_text[op]
-
- stack.append([operand1, operator, operand2])
-
- prev_op_precedence = self._logical_op_precedence
- elif expr_type == qpol.QPOL_CEXPR_TYPE_NAMES:
- # logical operator with type or attribute list (e.g. t1 == { spam_t eggs_t })
- operand1 = self._sym_to_text[sym_type]
- operator = self._expr_op_to_text[op]
-
- names = list(expr_node.names_iter(self.policy))
-
- if not names:
- operand2 = "<empty set>"
- elif len(names) == 1:
- operand2 = names[0]
- else:
- operand2 = "{{ {0} }}".format(' '.join(names))
-
- stack.append([operand1, operator, operand2])
-
- prev_op_precedence = self._logical_op_precedence
- elif expr_type == qpol.QPOL_CEXPR_TYPE_NOT:
- # unary operator (not)
- operand = stack.pop()
- operator = self._expr_type_to_text[expr_type]
-
- stack.append([operator, "(", operand, ")"])
-
- prev_op_precedence = self._expr_type_to_precedence[expr_type]
- else:
- # binary operator (and/or)
- operand1 = stack.pop()
- operand2 = stack.pop()
- operator = self._expr_type_to_text[expr_type]
- op_precedence = self._expr_type_to_precedence[expr_type]
-
- # if previous operator is of higher precedence
- # no parentheses are needed.
- if op_precedence < prev_op_precedence:
- stack.append([operand1, operator, operand2])
- else:
- stack.append(["(", operand1, operator, operand2, ")"])
-
- prev_op_precedence = op_precedence
-
- return self.__unwind_subexpression(stack)
-
- def _get_symbols(self, syms, factory):
- """
- Internal generator for getting users/roles/types in a constraint
- expression. Symbols will be yielded multiple times if they appear
- in the expression multiple times.
-
- Parameters:
- syms List of qpol symbol types.
- factory The factory function related to these symbols.
- """
- for expr_node in self.qpol_symbol.expr_iter(self.policy):
- sym_type = expr_node.sym_type(self.policy)
- expr_type = expr_node.expr_type(self.policy)
-
- if expr_type == qpol.QPOL_CEXPR_TYPE_NAMES and sym_type in syms:
- for s in expr_node.names_iter(self.policy):
- yield factory(self.policy, s)
-
- def __unwind_subexpression(self, expr):
- ret = []
-
- # do a string.join on sublists (subexpressions)
- for i in expr:
- if isinstance(i, list):
- ret.append(self.__unwind_subexpression(i))
- else:
- ret.append(i)
-
- return ' '.join(ret)
-
- # There is no levels function as specific
- # levels cannot be used in expressions, only
- # the l1, h1, etc. symbols
-
- @property
- def roles(self):
- """The roles used in the expression."""
- role_syms = [qpol.QPOL_CEXPR_SYM_ROLE,
- qpol.QPOL_CEXPR_SYM_ROLE + qpol.QPOL_CEXPR_SYM_TARGET,
- qpol.QPOL_CEXPR_SYM_ROLE + qpol.QPOL_CEXPR_SYM_XTARGET]
-
- return set(self._get_symbols(role_syms, role.role_factory))
-
- @property
- def perms(self):
- raise NotImplementedError
-
- def statement(self):
- return str(self)
-
- @property
- def tclass(self):
- """Object class for this constraint."""
- return objclass.class_factory(self.policy, self.qpol_symbol.object_class(self.policy))
-
- @property
- def types(self):
- """The types and type attributes used in the expression."""
- type_syms = [qpol.QPOL_CEXPR_SYM_TYPE,
- qpol.QPOL_CEXPR_SYM_TYPE + qpol.QPOL_CEXPR_SYM_TARGET,
- qpol.QPOL_CEXPR_SYM_TYPE + qpol.QPOL_CEXPR_SYM_XTARGET]
-
- return set(self._get_symbols(type_syms, typeattr.type_or_attr_factory))
-
- @property
- def users(self):
- """The users used in the expression."""
- user_syms = [qpol.QPOL_CEXPR_SYM_USER,
- qpol.QPOL_CEXPR_SYM_USER + qpol.QPOL_CEXPR_SYM_TARGET,
- qpol.QPOL_CEXPR_SYM_USER + qpol.QPOL_CEXPR_SYM_XTARGET]
-
- return set(self._get_symbols(user_syms, user.user_factory))
-
-
-class Constraint(BaseConstraint):
-
- """A constraint rule (constrain/mlsconstrain)."""
-
- def __str__(self):
- rule_string = "{0.ruletype} {0.tclass} ".format(self)
-
- perms = self.perms
- if len(perms) > 1:
- rule_string += "{{ {0} }} (\n".format(' '.join(perms))
- else:
- # convert to list since sets cannot be indexed
- rule_string += "{0} (\n".format(list(perms)[0])
-
- rule_string += "\t{0}\n);".format(self._build_expression())
-
- return rule_string
-
- @property
- def perms(self):
- """The constraint's permission set."""
- return set(self.qpol_symbol.perm_iter(self.policy))
-
-
-class Validatetrans(BaseConstraint):
-
- """A validatetrans rule (validatetrans/mlsvalidatetrans)."""
-
- def __str__(self):
- return "{0.ruletype} {0.tclass}\n\t{1}\n);".format(self, self._build_expression())
-
- @property
- def perms(self):
- raise exception.ConstraintUseError("{0} rules do not have permissions.".
- format(self.ruletype))
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/context.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/context.py
deleted file mode 100644
index f2f3fc7..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/context.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import symbol
-from . import user
-from . import role
-from . import typeattr
-from . import mls
-
-
-def context_factory(policy, name):
- """Factory function for creating context objects."""
-
- if not isinstance(name, qpol.qpol_context_t):
- raise TypeError("Contexts cannot be looked-up.")
-
- return Context(policy, name)
-
-
-class Context(symbol.PolicySymbol):
-
- """A SELinux security context/security attribute."""
-
- def __str__(self):
- try:
- return "{0.user}:{0.role}:{0.type_}:{0.range_}".format(self)
- except exception.MLSDisabled:
- return "{0.user}:{0.role}:{0.type_}".format(self)
-
- @property
- def user(self):
- """The user portion of the context."""
- return user.user_factory(self.policy, self.qpol_symbol.user(self.policy))
-
- @property
- def role(self):
- """The role portion of the context."""
- return role.role_factory(self.policy, self.qpol_symbol.role(self.policy))
-
- @property
- def type_(self):
- """The type portion of the context."""
- return typeattr.type_factory(self.policy, self.qpol_symbol.type_(self.policy))
-
- @property
- def range_(self):
- """The MLS range of the context."""
- return mls.range_factory(self.policy, self.qpol_symbol.range(self.policy))
-
- def statement(self):
- raise exception.NoStatement
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/default.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/default.py
deleted file mode 100644
index 175b709..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/default.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import symbol
-from . import objclass
-from . import qpol
-
-
-def default_factory(policy, sym):
- """Factory generator for creating default_* statement objects."""
-
- # The low level policy groups default_* settings by object class.
- # Since each class can have up to four default_* statements,
- # this factory function is a generator which yields up to
- # four Default objects.
-
- if not isinstance(sym, qpol.qpol_default_object_t):
- raise NotImplementedError
-
- # qpol will essentially iterate over all classes
- # and emit None for classes that don't set a default
- if not sym.object_class(policy):
- raise exception.NoDefaults
-
- if sym.user_default(policy):
- yield UserDefault(policy, sym)
-
- if sym.role_default(policy):
- yield RoleDefault(policy, sym)
-
- if sym.type_default(policy):
- yield TypeDefault(policy, sym)
-
- if sym.range_default(policy):
- yield RangeDefault(policy, sym)
-
-
-class Default(symbol.PolicySymbol):
-
- """Base class for default_* statements."""
-
- def __str__(self):
- raise NotImplementedError
-
- @property
- def object_class(self):
- """The object class."""
- return objclass.class_factory(self.policy, self.qpol_symbol.object_class(self.policy))
-
- @property
- def default(self):
- raise NotImplementedError
-
- def statement(self):
- return str(self)
-
-
-class UserDefault(Default):
-
- """A default_user statement."""
-
- def __str__(self):
- return "default_user {0.object_class} {0.default};".format(self)
-
- @property
- def default(self):
- """The default user location (source/target)."""
- return self.qpol_symbol.user_default(self.policy)
-
-
-class RoleDefault(Default):
-
- """A default_role statement."""
-
- def __str__(self):
- return "default_role {0.object_class} {0.default};".format(self)
-
- @property
- def default(self):
- """The default role location (source/target)."""
- return self.qpol_symbol.role_default(self.policy)
-
-
-class TypeDefault(Default):
-
- """A default_type statement."""
-
- def __str__(self):
- return "default_type {0.object_class} {0.default};".format(self)
-
- @property
- def default(self):
- """The default type location (source/target)."""
- return self.qpol_symbol.type_default(self.policy)
-
-
-class RangeDefault(Default):
-
- """A default_range statement."""
-
- def __str__(self):
- return "default_range {0.object_class} {0.default} {0.default_range};".format(self)
-
- @property
- def default(self):
- """The default range location (source/target)."""
- return self.qpol_symbol.range_default(self.policy).split()[0]
-
- @property
- def default_range(self):
- """The default range setting (low/high/low_high)."""
- return self.qpol_symbol.range_default(self.policy).split()[1]
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/exception.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/exception.py
deleted file mode 100644
index ce367c0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/exception.py
+++ /dev/null
@@ -1,248 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from ..exception import SEToolsException
-
-#
-# Policyrep base exception
-#
-
-
-class PolicyrepException(SEToolsException):
-
- """Base class for all policyrep exceptions."""
- pass
-
-
-#
-# General Policyrep exceptions
-#
-
-
-class InvalidPolicy(SyntaxError, PolicyrepException):
-
- """Exception for invalid policy."""
- pass
-
-
-class MLSDisabled(PolicyrepException):
-
- """
- Exception when MLS is disabled.
- """
- pass
-
-
-#
-# Invalid component exceptions
-#
-class InvalidSymbol(ValueError, PolicyrepException):
-
- """
- Base class for invalid symbols. Typically this is attempting to
- look up an object in the policy, but it does not exist.
- """
- pass
-
-
-class InvalidBoolean(InvalidSymbol):
-
- """Exception for invalid Booleans."""
- pass
-
-
-class InvalidCategory(InvalidSymbol):
-
- """Exception for invalid MLS categories."""
- pass
-
-
-class InvalidClass(InvalidSymbol):
-
- """Exception for invalid object classes."""
- pass
-
-
-class InvalidCommon(InvalidSymbol):
-
- """Exception for invalid common permission sets."""
- pass
-
-
-class InvalidInitialSid(InvalidSymbol):
-
- """Exception for invalid initial sids."""
- pass
-
-
-class InvalidLevel(InvalidSymbol):
-
- """
- Exception for an invalid level.
- """
- pass
-
-
-class InvalidLevelDecl(InvalidSymbol):
-
- """
- Exception for an invalid level declaration.
- """
- pass
-
-
-class InvalidRange(InvalidSymbol):
-
- """
- Exception for an invalid range.
- """
- pass
-
-
-class InvalidRole(InvalidSymbol):
-
- """Exception for invalid roles."""
- pass
-
-
-class InvalidSensitivity(InvalidSymbol):
-
- """
- Exception for an invalid sensitivity.
- """
- pass
-
-
-class InvalidType(InvalidSymbol):
-
- """Exception for invalid types and attributes."""
- pass
-
-
-class InvalidUser(InvalidSymbol):
-
- """Exception for invalid users."""
- pass
-
-#
-# Rule type exceptions
-#
-
-
-class InvalidRuleType(InvalidSymbol):
-
- """Exception for invalid rule types."""
- pass
-
-
-class InvalidConstraintType(InvalidSymbol):
-
- """Exception for invalid constraint types."""
- # This is not a rule but is similar.
- pass
-
-
-class InvalidMLSRuleType(InvalidRuleType):
-
- """Exception for invalid MLS rule types."""
- pass
-
-
-class InvalidRBACRuleType(InvalidRuleType):
-
- """Exception for invalid RBAC rule types."""
- pass
-
-
-class InvalidTERuleType(InvalidRuleType):
-
- """Exception for invalid TE rule types."""
- pass
-
-
-#
-# Object use errors
-#
-class SymbolUseError(PolicyrepException):
-
- """
- Base class for incorrectly using an object. Typically this is
- for classes with strong similarities, but with slight variances in
- functionality, e.g. allow vs type_transition rules.
- """
- pass
-
-
-class RuleUseError(SymbolUseError):
-
- """
- Base class for incorrect parameters for a rule. For
- example, trying to get the permissions of a rule that has no
- permissions.
- """
- pass
-
-
-class ConstraintUseError(SymbolUseError):
-
- """Exception when getting permissions from a validatetrans."""
- pass
-
-
-class NoStatement(SymbolUseError):
-
- """
- Exception for objects that have no inherent statement, such
- as conditional expressions and MLS ranges.
- """
- pass
-
-
-#
-# Other exceptions
-#
-class NoCommon(PolicyrepException):
-
- """
- Exception when a class does not inherit a common permission set.
- """
- pass
-
-
-class NoDefaults(InvalidSymbol):
-
- """Exception for classes that have no default_* statements."""
- pass
-
-
-class RuleNotConditional(PolicyrepException):
-
- """
- Exception when getting the conditional expression for rules
- that are unconditional (not conditional).
- """
- pass
-
-
-class TERuleNoFilename(PolicyrepException):
-
- """
- Exception when getting the file name of a
- type_transition rule that has no file name.
- """
- pass
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/fscontext.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/fscontext.py
deleted file mode 100644
index a17b0bc..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/fscontext.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import stat
-
-from . import qpol
-from . import symbol
-from . import context
-
-
-def fs_use_factory(policy, name):
- """Factory function for creating fs_use_* objects."""
-
- if not isinstance(name, qpol.qpol_fs_use_t):
- raise TypeError("fs_use_* cannot be looked-up.")
-
- return FSUse(policy, name)
-
-
-def genfscon_factory(policy, name):
- """Factory function for creating genfscon objects."""
-
- if not isinstance(name, qpol.qpol_genfscon_t):
- raise TypeError("Genfscons cannot be looked-up.")
-
- return Genfscon(policy, name)
-
-
-class FSContext(symbol.PolicySymbol):
-
- """Base class for in-policy labeling rules."""
-
- def __str__(self):
- raise NotImplementedError
-
- @property
- def fs(self):
- """The filesystem type for this statement."""
- return self.qpol_symbol.name(self.policy)
-
- @property
- def context(self):
- """The context for this statement."""
- return context.context_factory(self.policy, self.qpol_symbol.context(self.policy))
-
- def statement(self):
- return str(self)
-
-
-class Genfscon(FSContext):
-
- """A genfscon statement."""
-
- _filetype_to_text = {
- 0: "",
- stat.S_IFBLK: "-b",
- stat.S_IFCHR: "-c",
- stat.S_IFDIR: "-d",
- stat.S_IFIFO: "-p",
- stat.S_IFREG: "--",
- stat.S_IFLNK: "-l",
- stat.S_IFSOCK: "-s"}
-
- def __str__(self):
- return "genfscon {0.fs} {0.path} {1} {0.context}".format(
- self, self._filetype_to_text[self.filetype])
-
- def __eq__(self, other):
- # Libqpol allocates new C objects in the
- # genfscons iterator, so pointer comparison
- # in the PolicySymbol object doesn't work.
- try:
- return (self.fs == other.fs and
- self.path == other.path and
- self.filetype == other.filetype and
- self.context == other.context)
- except AttributeError:
- return str(self) == str(other)
-
- @property
- def filetype(self):
- """The file type (e.g. stat.S_IFBLK) for this genfscon statement."""
- return self.qpol_symbol.object_class(self.policy)
-
- @property
- def path(self):
- """The path for this genfscon statement."""
- return self.qpol_symbol.path(self.policy)
-
-
-class FSUse(FSContext):
-
- """A fs_use_* statement."""
-
- # there are more rule types, but modern SELinux
- # only supports these three.
- _ruletype_to_text = {
- qpol.QPOL_FS_USE_XATTR: 'fs_use_xattr',
- qpol.QPOL_FS_USE_TRANS: 'fs_use_trans',
- qpol.QPOL_FS_USE_TASK: 'fs_use_task'}
-
- def __str__(self):
- return "{0.ruletype} {0.fs} {0.context};".format(self)
-
- @property
- def ruletype(self):
- """The rule type for this fs_use_* statement."""
- return self._ruletype_to_text[self.qpol_symbol.behavior(self.policy)]
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/initsid.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/initsid.py
deleted file mode 100644
index 0197c74..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/initsid.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import symbol
-from . import context
-
-
-def initialsid_factory(policy, name):
- """Factory function for creating initial sid objects."""
-
- if isinstance(name, InitialSID):
- assert name.policy == policy
- return name
- elif isinstance(name, qpol.qpol_isid_t):
- return InitialSID(policy, name)
-
- try:
- return InitialSID(policy, qpol.qpol_isid_t(policy, name))
- except ValueError:
- raise exception.InvalidInitialSid("{0} is not a valid initial sid".format(name))
-
-
-class InitialSID(symbol.PolicySymbol):
-
- """An initial SID statement."""
-
- @property
- def context(self):
- """The context for this initial SID."""
- return context.context_factory(self.policy, self.qpol_symbol.context(self.policy))
-
- def statement(self):
- return "sid {0} {0.context}".format(self)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/mls.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/mls.py
deleted file mode 100644
index 2541704..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/mls.py
+++ /dev/null
@@ -1,463 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-# pylint: disable=protected-access
-import itertools
-
-from . import exception
-from . import qpol
-from . import symbol
-
-# qpol does not expose an equivalent of a sensitivity declaration.
-# qpol_level_t is equivalent to the level declaration:
-# level s0:c0.c1023;
-
-# qpol_mls_level_t represents a level as used in contexts,
-# such as range_transitions or labeling statements such as
-# portcon and nodecon.
-
-# Here qpol_level_t is also used for MLSSensitivity
-# since it has the sensitivity name, dominance, and there
-# is a 1:1 correspondence between the sensitivity declarations
-# and level declarations.
-
-# Hashing has to be handled below because the qpol references,
-# normally used for a hash key, are not the same for multiple
-# instances of the same object (except for level decl).
-
-
-def enabled(policy):
- """Determine if MLS is enabled."""
- return policy.capability(qpol.QPOL_CAP_MLS)
-
-
-def category_factory(policy, sym):
- """Factory function for creating MLS category objects."""
-
- if not enabled(policy):
- raise exception.MLSDisabled
-
- if isinstance(sym, Category):
- assert sym.policy == policy
- return sym
- elif isinstance(sym, qpol.qpol_cat_t):
- if sym.isalias(policy):
- raise TypeError("{0} is an alias".format(sym.name(policy)))
-
- return Category(policy, sym)
-
- try:
- return Category(policy, qpol.qpol_cat_t(policy, str(sym)))
- except ValueError:
- raise exception.InvalidCategory("{0} is not a valid category".format(sym))
-
-
-def sensitivity_factory(policy, sym):
- """Factory function for creating MLS sensitivity objects."""
-
- if not enabled(policy):
- raise exception.MLSDisabled
-
- if isinstance(sym, Sensitivity):
- assert sym.policy == policy
- return sym
- elif isinstance(sym, qpol.qpol_level_t):
- if sym.isalias(policy):
- raise TypeError("{0} is an alias".format(sym.name(policy)))
-
- return Sensitivity(policy, sym)
-
- try:
- return Sensitivity(policy, qpol.qpol_level_t(policy, str(sym)))
- except ValueError:
- raise exception.InvalidSensitivity("{0} is not a valid sensitivity".format(sym))
-
-
-def level_factory(policy, sym):
- """
- Factory function for creating MLS level objects (e.g. levels used
- in contexts of labeling statements)
- """
-
- if not enabled(policy):
- raise exception.MLSDisabled
-
- if isinstance(sym, Level):
- assert sym.policy == policy
- return sym
- elif isinstance(sym, qpol.qpol_mls_level_t):
- return Level(policy, sym)
-
- sens_split = str(sym).split(":")
-
- sens = sens_split[0]
- try:
- semantic_level = qpol.qpol_semantic_level_t(policy, sens)
- except ValueError:
- raise exception.InvalidLevel("{0} is invalid ({1} is not a valid sensitivity)".
- format(sym, sens))
-
- try:
- cats = sens_split[1]
- except IndexError:
- pass
- else:
- for group in cats.split(","):
- catrange = group.split(".")
-
- if len(catrange) == 2:
- try:
- semantic_level.add_cats(policy, catrange[0], catrange[1])
- except ValueError:
- raise exception.InvalidLevel(
- "{0} is invalid ({1} is not a valid category range)".format(sym, group))
- elif len(catrange) == 1:
- try:
- semantic_level.add_cats(policy, catrange[0], catrange[0])
- except ValueError:
- raise exception.InvalidLevel("{0} is invalid ({1} is not a valid category)".
- format(sym, group))
- else:
- raise exception.InvalidLevel("{0} is invalid (level parsing error)".format(sym))
-
- # convert to level object
- try:
- policy_level = qpol.qpol_mls_level_t(policy, semantic_level)
- except ValueError:
- raise exception.InvalidLevel(
- "{0} is invalid (one or more categories are not associated with the sensitivity)".
- format(sym))
-
- return Level(policy, policy_level)
-
-
-def level_decl_factory(policy, sym):
- """
- Factory function for creating MLS level declaration objects.
- (level statements) Lookups are only by sensitivity name.
- """
-
- if not enabled(policy):
- raise exception.MLSDisabled
-
- if isinstance(sym, LevelDecl):
- assert sym.policy == policy
- return sym
- elif isinstance(sym, qpol.qpol_level_t):
- if sym.isalias(policy):
- raise TypeError("{0} is an alias".format(sym.name(policy)))
-
- return LevelDecl(policy, sym)
-
- try:
- return LevelDecl(policy, qpol.qpol_level_t(policy, str(sym)))
- except ValueError:
- raise exception.InvalidLevelDecl("{0} is not a valid sensitivity".format(sym))
-
-
-def range_factory(policy, sym):
- """Factory function for creating MLS range objects."""
-
- if not enabled(policy):
- raise exception.MLSDisabled
-
- if isinstance(sym, Range):
- assert sym.policy == policy
- return sym
- elif isinstance(sym, qpol.qpol_mls_range_t):
- return Range(policy, sym)
-
- # build range:
- levels = str(sym).split("-")
-
- # strip() levels to handle ranges with spaces in them,
- # e.g. s0:c1 - s0:c0.c255
- try:
- low = level_factory(policy, levels[0].strip())
- except exception.InvalidLevel as ex:
- raise exception.InvalidRange("{0} is not a valid range ({1}).".format(sym, ex))
-
- try:
- high = level_factory(policy, levels[1].strip())
- except exception.InvalidLevel as ex:
- raise exception.InvalidRange("{0} is not a valid range ({1}).".format(sym, ex))
- except IndexError:
- high = low
-
- # convert to range object
- try:
- policy_range = qpol.qpol_mls_range_t(policy, low.qpol_symbol, high.qpol_symbol)
- except ValueError:
- raise exception.InvalidRange("{0} is not a valid range ({1} is not dominated by {2})".
- format(sym, low, high))
-
- return Range(policy, policy_range)
-
-
-class BaseMLSComponent(symbol.PolicySymbol):
-
- """Base class for sensitivities and categories."""
-
- @property
- def _value(self):
- """
- The value of the component.
-
- This is a low-level policy detail exposed for internal use only.
- """
- return self.qpol_symbol.value(self.policy)
-
- def aliases(self):
- """Generator that yields all aliases for this category."""
-
- for alias in self.qpol_symbol.alias_iter(self.policy):
- yield alias
-
-
-class Category(BaseMLSComponent):
-
- """An MLS category."""
-
- def statement(self):
- aliases = list(self.aliases())
- stmt = "category {0}".format(self)
- if aliases:
- if len(aliases) > 1:
- stmt += " alias {{ {0} }}".format(' '.join(aliases))
- else:
- stmt += " alias {0}".format(aliases[0])
- stmt += ";"
- return stmt
-
-
-class Sensitivity(BaseMLSComponent):
-
- """An MLS sensitivity"""
-
- def __eq__(self, other):
- try:
- return self._value == other._value
- except AttributeError:
- return str(self) == str(other)
-
- def __ge__(self, other):
- return self._value >= other._value
-
- def __gt__(self, other):
- return self._value > other._value
-
- def __le__(self, other):
- return self._value <= other._value
-
- def __lt__(self, other):
- return self._value < other._value
-
- def statement(self):
- aliases = list(self.aliases())
- stmt = "sensitivity {0}".format(self)
- if aliases:
- if len(aliases) > 1:
- stmt += " alias {{ {0} }}".format(' '.join(aliases))
- else:
- stmt += " alias {0}".format(aliases[0])
- stmt += ";"
- return stmt
-
-
-class BaseMLSLevel(symbol.PolicySymbol):
-
- """Base class for MLS levels."""
-
- def __str__(self):
- lvl = str(self.sensitivity)
-
- # sort by policy declaration order
- cats = sorted(self.categories(), key=lambda k: k._value)
-
- if cats:
- # generate short category notation
- shortlist = []
- for _, i in itertools.groupby(cats, key=lambda k,
- c=itertools.count(): k._value - next(c)):
- group = list(i)
- if len(group) > 1:
- shortlist.append("{0}.{1}".format(group[0], group[-1]))
- else:
- shortlist.append(str(group[0]))
-
- lvl += ":" + ','.join(shortlist)
-
- return lvl
-
- @property
- def sensitivity(self):
- raise NotImplementedError
-
- def categories(self):
- """
- Generator that yields all individual categories for this level.
- All categories are yielded, not a compact notation such as
- c0.c255
- """
-
- for cat in self.qpol_symbol.cat_iter(self.policy):
- yield category_factory(self.policy, cat)
-
-
-class LevelDecl(BaseMLSLevel):
-
- """
- The declaration statement for MLS levels, e.g:
-
- level s7:c0.c1023;
- """
- # below comparisons are only based on sensitivity
- # dominance since, in this context, the allowable
- # category set is being defined for the level.
- # object type is asserted here because this cannot
- # be compared to a Level instance.
-
- def __eq__(self, other):
- assert not isinstance(other, Level), "Levels cannot be compared to level declarations"
-
- try:
- return self.sensitivity == other.sensitivity
- except AttributeError:
- return str(self) == str(other)
-
- def __ge__(self, other):
- assert not isinstance(other, Level), "Levels cannot be compared to level declarations"
- return self.sensitivity >= other.sensitivity
-
- def __gt__(self, other):
- assert not isinstance(other, Level), "Levels cannot be compared to level declarations"
- return self.sensitivity > other.sensitivity
-
- def __le__(self, other):
- assert not isinstance(other, Level), "Levels cannot be compared to level declarations"
- return self.sensitivity <= other.sensitivity
-
- def __lt__(self, other):
- assert not isinstance(other, Level), "Levels cannot be compared to level declarations"
- return self.sensitivity < other.sensitivity
-
- @property
- def sensitivity(self):
- """The sensitivity of the level."""
- # since the qpol symbol for levels is also used for
- # MLSSensitivity objects, use self's qpol symbol
- return sensitivity_factory(self.policy, self.qpol_symbol)
-
- def statement(self):
- return "level {0};".format(self)
-
-
-class Level(BaseMLSLevel):
-
- """An MLS level used in contexts."""
-
- def __hash__(self):
- return hash(str(self))
-
- def __eq__(self, other):
- try:
- othercats = set(other.categories())
- except AttributeError:
- return str(self) == str(other)
- else:
- selfcats = set(self.categories())
- return self.sensitivity == other.sensitivity and selfcats == othercats
-
- def __ge__(self, other):
- """Dom operator."""
- selfcats = set(self.categories())
- othercats = set(other.categories())
- return self.sensitivity >= other.sensitivity and selfcats >= othercats
-
- def __gt__(self, other):
- selfcats = set(self.categories())
- othercats = set(other.categories())
- return ((self.sensitivity > other.sensitivity and selfcats >= othercats) or
- (self.sensitivity >= other.sensitivity and selfcats > othercats))
-
- def __le__(self, other):
- """Domby operator."""
- selfcats = set(self.categories())
- othercats = set(other.categories())
- return self.sensitivity <= other.sensitivity and selfcats <= othercats
-
- def __lt__(self, other):
- selfcats = set(self.categories())
- othercats = set(other.categories())
- return ((self.sensitivity < other.sensitivity and selfcats <= othercats) or
- (self.sensitivity <= other.sensitivity and selfcats < othercats))
-
- def __xor__(self, other):
- """Incomp operator."""
- return not (self >= other or self <= other)
-
- @property
- def sensitivity(self):
- """The sensitivity of the level."""
- return sensitivity_factory(self.policy, self.qpol_symbol.sens_name(self.policy))
-
- def statement(self):
- raise exception.NoStatement
-
-
-class Range(symbol.PolicySymbol):
-
- """An MLS range"""
-
- def __str__(self):
- high = self.high
- low = self.low
- if high == low:
- return str(low)
-
- return "{0} - {1}".format(low, high)
-
- def __hash__(self):
- return hash(str(self))
-
- def __eq__(self, other):
- try:
- return self.low == other.low and self.high == other.high
- except AttributeError:
- # remove all spaces in the string representations
- # to handle cases where the other object does not
- # have spaces around the '-'
- other_str = str(other).replace(" ", "")
- self_str = str(self).replace(" ", "")
- return self_str == other_str
-
- def __contains__(self, other):
- return self.low <= other <= self.high
-
- @property
- def high(self):
- """The high end/clearance level of this range."""
- return level_factory(self.policy, self.qpol_symbol.high_level(self.policy))
-
- @property
- def low(self):
- """The low end/current level of this range."""
- return level_factory(self.policy, self.qpol_symbol.low_level(self.policy))
-
- def statement(self):
- raise exception.NoStatement
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/mlsrule.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/mlsrule.py
deleted file mode 100644
index 5c91c59..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/mlsrule.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import rule
-from . import typeattr
-from . import mls
-
-
-def mls_rule_factory(policy, symbol):
- """Factory function for creating MLS rule objects."""
- if not isinstance(symbol, qpol.qpol_range_trans_t):
- raise TypeError("MLS rules cannot be looked-up.")
-
- return MLSRule(policy, symbol)
-
-
-def validate_ruletype(types):
- """Validate MLS rule types."""
- for t in types:
- if t not in ["range_transition"]:
- raise exception.InvalidMLSRuleType("{0} is not a valid MLS rule type.".format(t))
-
-
-class MLSRule(rule.PolicyRule):
-
- """An MLS rule."""
-
- def __str__(self):
- # TODO: If we ever get more MLS rules, fix this format.
- return "range_transition {0.source} {0.target}:{0.tclass} {0.default};".format(self)
-
- @property
- def source(self):
- """The rule's source type/attribute."""
- return typeattr.type_or_attr_factory(self.policy, self.qpol_symbol.source_type(self.policy))
-
- @property
- def target(self):
- """The rule's target type/attribute."""
- return typeattr.type_or_attr_factory(self.policy, self.qpol_symbol.target_type(self.policy))
-
- @property
- def default(self):
- """The rule's default range."""
- return mls.range_factory(self.policy, self.qpol_symbol.range(self.policy))
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/netcontext.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/netcontext.py
deleted file mode 100644
index 5aeed5c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/netcontext.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import socket
-from collections import namedtuple
-
-from . import qpol
-from . import symbol
-from . import context
-
-port_range = namedtuple("port_range", ["low", "high"])
-
-
-def netifcon_factory(policy, name):
- """Factory function for creating netifcon objects."""
-
- if not isinstance(name, qpol.qpol_netifcon_t):
- raise NotImplementedError
-
- return Netifcon(policy, name)
-
-
-def nodecon_factory(policy, name):
- """Factory function for creating nodecon objects."""
-
- if not isinstance(name, qpol.qpol_nodecon_t):
- raise NotImplementedError
-
- return Nodecon(policy, name)
-
-
-def portcon_factory(policy, name):
- """Factory function for creating portcon objects."""
-
- if not isinstance(name, qpol.qpol_portcon_t):
- raise NotImplementedError
-
- return Portcon(policy, name)
-
-
-class NetContext(symbol.PolicySymbol):
-
- """Base class for in-policy network labeling rules."""
-
- def __str__(self):
- raise NotImplementedError
-
- @property
- def context(self):
- """The context for this statement."""
- return context.context_factory(self.policy, self.qpol_symbol.context(self.policy))
-
- def statement(self):
- return str(self)
-
-
-class Netifcon(NetContext):
-
- """A netifcon statement."""
-
- def __str__(self):
- return "netifcon {0.netif} {0.context} {0.packet}".format(self)
-
- @property
- def netif(self):
- """The network interface name."""
- return self.qpol_symbol.name(self.policy)
-
- @property
- def context(self):
- """The context for the interface."""
- return context.context_factory(self.policy, self.qpol_symbol.if_con(self.policy))
-
- @property
- def packet(self):
- """The context for the packets."""
- return context.context_factory(self.policy, self.qpol_symbol.msg_con(self.policy))
-
-
-class Nodecon(NetContext):
-
- """A nodecon statement."""
-
- def __str__(self):
- return "nodecon {0.address} {0.netmask} {0.context}".format(self)
-
- def __eq__(self, other):
- # Libqpol allocates new C objects in the
- # nodecons iterator, so pointer comparison
- # in the PolicySymbol object doesn't work.
- try:
- return (self.address == other.address and
- self.netmask == other.netmask and
- self.context == other.context)
- except AttributeError:
- return (str(self) == str(other))
-
- @property
- def ip_version(self):
- """
- The IP version for the nodecon (socket.AF_INET or
- socket.AF_INET6).
- """
- return self.qpol_symbol.protocol(self.policy)
-
- @property
- def address(self):
- """The network address for the nodecon."""
- return self.qpol_symbol.addr(self.policy)
-
- @property
- def netmask(self):
- """The network mask for the nodecon."""
- return self.qpol_symbol.mask(self.policy)
-
-
-class Portcon(NetContext):
-
- """A portcon statement."""
-
- _proto_to_text = {socket.IPPROTO_TCP: 'tcp',
- socket.IPPROTO_UDP: 'udp'}
-
- def __str__(self):
- low, high = self.ports
- proto = self._proto_to_text[self.protocol]
-
- if low == high:
- return "portcon {0} {1} {2}".format(proto, low, self.context)
- else:
- return "portcon {0} {1}-{2} {3}".format(proto, low, high, self.context)
-
- @property
- def protocol(self):
- """
- The protocol number for the portcon (socket.IPPROTO_TCP
- or socket.IPPROTO_UDP).
- """
- return self.qpol_symbol.protocol(self.policy)
-
- @property
- def ports(self):
- """
- The port range for this portcon.
-
- Return: Tuple(low, high)
- low The low port of the range.
- high The high port of the range.
- """
- low = self.qpol_symbol.low_port(self.policy)
- high = self.qpol_symbol.high_port(self.policy)
- return port_range(low, high)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/objclass.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/objclass.py
deleted file mode 100644
index bf9a553..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/objclass.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import symbol
-from . import qpol
-
-
-def common_factory(policy, name):
- """Factory function for creating common permission set objects."""
-
- if isinstance(name, Common):
- assert name.policy == policy
- return name
- elif isinstance(name, qpol.qpol_common_t):
- return Common(policy, name)
-
- try:
- return Common(policy, qpol.qpol_common_t(policy, str(name)))
- except ValueError:
- raise exception.InvalidCommon("{0} is not a valid common".format(name))
-
-
-def class_factory(policy, name):
- """Factory function for creating object class objects."""
-
- if isinstance(name, ObjClass):
- assert name.policy == policy
- return name
- elif isinstance(name, qpol.qpol_class_t):
- return ObjClass(policy, name)
-
- try:
- return ObjClass(policy, qpol.qpol_class_t(policy, str(name)))
- except ValueError:
- raise exception.InvalidClass("{0} is not a valid object class".format(name))
-
-
-class Common(symbol.PolicySymbol):
-
- """A common permission set."""
-
- def __contains__(self, other):
- return other in self.perms
-
- @property
- def perms(self):
- """The list of the common's permissions."""
- return set(self.qpol_symbol.perm_iter(self.policy))
-
- def statement(self):
- return "common {0}\n{{\n\t{1}\n}}".format(self, '\n\t'.join(self.perms))
-
-
-class ObjClass(Common):
-
- """An object class."""
-
- def __contains__(self, other):
- try:
- if other in self.common.perms:
- return True
- except exception.NoCommon:
- pass
-
- return other in self.perms
-
- @property
- def common(self):
- """
- The common that the object class inherits.
-
- Exceptions:
- NoCommon The object class does not inherit a common.
- """
-
- try:
- return common_factory(self.policy, self.qpol_symbol.common(self.policy))
- except ValueError:
- raise exception.NoCommon("{0} does not inherit a common.".format(self))
-
- def statement(self):
- stmt = "class {0}\n".format(self)
-
- try:
- stmt += "inherits {0}\n".format(self.common)
- except exception.NoCommon:
- pass
-
- # a class that inherits may not have additional permissions
- perms = self.perms
- if len(perms) > 0:
- stmt += "{{\n\t{0}\n}}".format('\n\t'.join(perms))
-
- return stmt
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/polcap.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/polcap.py
deleted file mode 100644
index 8ab164d..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/polcap.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import qpol
-from . import symbol
-
-
-def polcap_factory(policy, name):
- """Factory function for creating policy capability objects."""
-
- if isinstance(name, PolicyCapability):
- assert name.policy == policy
- return name
- elif isinstance(name, qpol.qpol_polcap_t):
- return PolicyCapability(policy, name)
- else:
- raise TypeError("Policy capabilities cannot be looked up.")
-
-
-class PolicyCapability(symbol.PolicySymbol):
-
- """A policy capability."""
-
- def statement(self):
- return "policycap {0};".format(self)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/qpol.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/qpol.py
deleted file mode 100644
index 97e602b..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/qpol.py
+++ /dev/null
@@ -1,1114 +0,0 @@
-# This file was automatically generated by SWIG (http://www.swig.org).
-# Version 2.0.11
-#
-# Do not make changes to this file unless you know what you are doing--modify
-# the SWIG interface file instead.
-
-
-
-
-
-from sys import version_info
-if version_info >= (2,6,0):
- def swig_import_helper():
- from os.path import dirname
- import imp
- fp = None
- try:
- fp, pathname, description = imp.find_module('_qpol', [dirname(__file__)])
- except ImportError:
- import _qpol
- return _qpol
- if fp is not None:
- try:
- _mod = imp.load_module('_qpol', fp, pathname, description)
- finally:
- fp.close()
- return _mod
- _qpol = swig_import_helper()
- del swig_import_helper
-else:
- import _qpol
-del version_info
-try:
- _swig_property = property
-except NameError:
- pass # Python < 2.2 doesn't have 'property'.
-def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
- if (name == "thisown"): return self.this.own(value)
- if (name == "this"):
- if type(value).__name__ == 'SwigPyObject':
- self.__dict__[name] = value
- return
- method = class_type.__swig_setmethods__.get(name,None)
- if method: return method(self,value)
- if (not static):
- self.__dict__[name] = value
- else:
- raise AttributeError("You cannot add attributes to %s" % self)
-
-def _swig_setattr(self,class_type,name,value):
- return _swig_setattr_nondynamic(self,class_type,name,value,0)
-
-def _swig_getattr(self,class_type,name):
- if (name == "thisown"): return self.this.own()
- method = class_type.__swig_getmethods__.get(name,None)
- if method: return method(self)
- raise AttributeError(name)
-
-def _swig_repr(self):
- try: strthis = "proxy of " + self.this.__repr__()
- except: strthis = ""
- return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
-
-try:
- _object = object
- _newclass = 1
-except AttributeError:
- class _object : pass
- _newclass = 0
-
-
-
-def to_str(*args):
- return _qpol.to_str(*args)
-to_str = _qpol.to_str
-import logging
-from functools import wraps
-
-def QpolGenerator(cast):
- """
- A decorator which converts qpol iterators into Python generators.
-
- Qpol iterators use void* to be generic about their contents.
- The purpose of the _from_void functions below is to wrap
- the pointer casting, hence the "cast" variable name here.
-
- Decorator parameter:
- cast A wrapper function which casts the qpol iterator return pointer
- to the proper C data type pointer. The Python function
- reference to the C Python extension is used, for example:
-
- @QpolGenerator(_qpol.qpol_type_from_void)
- """
-
- def decorate(func):
- @wraps(func)
- def wrapper(*args, **kwargs):
- qpol_iter = func(*args)
- while not qpol_iter.isend():
- yield cast(qpol_iter.item())
- qpol_iter.next_()
-
- return wrapper
- return decorate
-
-def qpol_logger(level, msg):
- """Log qpol messages via Python logging."""
- logging.getLogger("libqpol").debug(msg)
-
-def qpol_policy_factory(path):
- """Factory function for qpol policy objects."""
- # The main purpose here is to hook in the
- # above logger callback.
- return qpol_policy_t(path, 0, qpol_logger)
-
-QPOL_POLICY_OPTION_NO_NEVERALLOWS = _qpol.QPOL_POLICY_OPTION_NO_NEVERALLOWS
-QPOL_POLICY_OPTION_NO_RULES = _qpol.QPOL_POLICY_OPTION_NO_RULES
-QPOL_POLICY_OPTION_MATCH_SYSTEM = _qpol.QPOL_POLICY_OPTION_MATCH_SYSTEM
-QPOL_POLICY_MAX_VERSION = _qpol.QPOL_POLICY_MAX_VERSION
-QPOL_POLICY_MIN_VERSION = _qpol.QPOL_POLICY_MIN_VERSION
-class qpol_policy_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_policy_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_policy_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_policy_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_policy_t
- __del__ = lambda self : None;
- def version(self): return _qpol.qpol_policy_t_version(self)
- def handle_unknown(self): return _qpol.qpol_policy_t_handle_unknown(self)
- def capability(self, *args): return _qpol.qpol_policy_t_capability(self, *args)
- @QpolGenerator(_qpol.qpol_type_from_void)
- def type_iter(self): return _qpol.qpol_policy_t_type_iter(self)
- def type_count(self): return _qpol.qpol_policy_t_type_count(self)
- @QpolGenerator(_qpol.qpol_role_from_void)
- def role_iter(self): return _qpol.qpol_policy_t_role_iter(self)
- def role_count(self): return _qpol.qpol_policy_t_role_count(self)
- @QpolGenerator(_qpol.qpol_level_from_void)
- def level_iter(self): return _qpol.qpol_policy_t_level_iter(self)
- def level_count(self): return _qpol.qpol_policy_t_level_count(self)
- @QpolGenerator(_qpol.qpol_cat_from_void)
- def cat_iter(self): return _qpol.qpol_policy_t_cat_iter(self)
- def cat_count(self): return _qpol.qpol_policy_t_cat_count(self)
- @QpolGenerator(_qpol.qpol_user_from_void)
- def user_iter(self): return _qpol.qpol_policy_t_user_iter(self)
- def user_count(self): return _qpol.qpol_policy_t_user_count(self)
- @QpolGenerator(_qpol.qpol_bool_from_void)
- def bool_iter(self): return _qpol.qpol_policy_t_bool_iter(self)
- def bool_count(self): return _qpol.qpol_policy_t_bool_count(self)
- @QpolGenerator(_qpol.qpol_class_from_void)
- def class_iter(self, perm=None): return _qpol.qpol_policy_t_class_iter(self, perm)
- def class_count(self): return _qpol.qpol_policy_t_class_count(self)
- @QpolGenerator(_qpol.qpol_common_from_void)
- def common_iter(self, perm=None): return _qpol.qpol_policy_t_common_iter(self, perm)
- def common_count(self): return _qpol.qpol_policy_t_common_count(self)
- @QpolGenerator(_qpol.qpol_fs_use_from_void)
- def fs_use_iter(self): return _qpol.qpol_policy_t_fs_use_iter(self)
- def fs_use_count(self): return _qpol.qpol_policy_t_fs_use_count(self)
- @QpolGenerator(_qpol.qpol_genfscon_from_void)
- def genfscon_iter(self): return _qpol.qpol_policy_t_genfscon_iter(self)
- def genfscon_count(self): return _qpol.qpol_policy_t_genfscon_count(self)
- @QpolGenerator(_qpol.qpol_isid_from_void)
- def isid_iter(self): return _qpol.qpol_policy_t_isid_iter(self)
- def isid_count(self): return _qpol.qpol_policy_t_isid_count(self)
- @QpolGenerator(_qpol.qpol_netifcon_from_void)
- def netifcon_iter(self): return _qpol.qpol_policy_t_netifcon_iter(self)
- def netifcon_count(self): return _qpol.qpol_policy_t_netifcon_count(self)
- @QpolGenerator(_qpol.qpol_nodecon_from_void)
- def nodecon_iter(self): return _qpol.qpol_policy_t_nodecon_iter(self)
- def nodecon_count(self): return _qpol.qpol_policy_t_nodecon_count(self)
- @QpolGenerator(_qpol.qpol_portcon_from_void)
- def portcon_iter(self): return _qpol.qpol_policy_t_portcon_iter(self)
- def portcon_count(self): return _qpol.qpol_policy_t_portcon_count(self)
- @QpolGenerator(_qpol.qpol_constraint_from_void)
- def constraint_iter(self): return _qpol.qpol_policy_t_constraint_iter(self)
- def constraint_count(self): return _qpol.qpol_policy_t_constraint_count(self)
- @QpolGenerator(_qpol.qpol_validatetrans_from_void)
- def validatetrans_iter(self): return _qpol.qpol_policy_t_validatetrans_iter(self)
- def validatetrans_count(self): return _qpol.qpol_policy_t_validatetrans_count(self)
- @QpolGenerator(_qpol.qpol_role_allow_from_void)
- def role_allow_iter(self): return _qpol.qpol_policy_t_role_allow_iter(self)
- def role_allow_count(self): return _qpol.qpol_policy_t_role_allow_count(self)
- @QpolGenerator(_qpol.qpol_role_trans_from_void)
- def role_trans_iter(self): return _qpol.qpol_policy_t_role_trans_iter(self)
- def role_trans_count(self): return _qpol.qpol_policy_t_role_trans_count(self)
- @QpolGenerator(_qpol.qpol_range_trans_from_void)
- def range_trans_iter(self): return _qpol.qpol_policy_t_range_trans_iter(self)
- def range_trans_count(self): return _qpol.qpol_policy_t_range_trans_count(self)
- @QpolGenerator(_qpol.qpol_avrule_from_void)
- def avrule_iter(self): return _qpol.qpol_policy_t_avrule_iter(self)
- def avrule_allow_count(self): return _qpol.qpol_policy_t_avrule_allow_count(self)
- def avrule_auditallow_count(self): return _qpol.qpol_policy_t_avrule_auditallow_count(self)
- def avrule_neverallow_count(self): return _qpol.qpol_policy_t_avrule_neverallow_count(self)
- def avrule_dontaudit_count(self): return _qpol.qpol_policy_t_avrule_dontaudit_count(self)
- @QpolGenerator(_qpol.qpol_terule_from_void)
- def terule_iter(self): return _qpol.qpol_policy_t_terule_iter(self)
- def terule_trans_count(self): return _qpol.qpol_policy_t_terule_trans_count(self)
- def terule_change_count(self): return _qpol.qpol_policy_t_terule_change_count(self)
- def terule_member_count(self): return _qpol.qpol_policy_t_terule_member_count(self)
- def cond_iter(self): return _qpol.qpol_policy_t_cond_iter(self)
- def cond_count(self): return _qpol.qpol_policy_t_cond_count(self)
- @QpolGenerator(_qpol.qpol_filename_trans_from_void)
- def filename_trans_iter(self): return _qpol.qpol_policy_t_filename_trans_iter(self)
- def filename_trans_count(self): return _qpol.qpol_policy_t_filename_trans_count(self)
- @QpolGenerator(_qpol.qpol_type_from_void)
- def permissive_iter(self): return _qpol.qpol_policy_t_permissive_iter(self)
- def permissive_count(self): return _qpol.qpol_policy_t_permissive_count(self)
- def typebounds_iter(self): return _qpol.qpol_policy_t_typebounds_iter(self)
- def typebounds_count(self): return _qpol.qpol_policy_t_typebounds_count(self)
- @QpolGenerator(_qpol.qpol_polcap_from_void)
- def polcap_iter(self): return _qpol.qpol_policy_t_polcap_iter(self)
- def polcap_count(self): return _qpol.qpol_policy_t_polcap_count(self)
- @QpolGenerator(_qpol.qpol_default_object_from_void)
- def default_iter(self): return _qpol.qpol_policy_t_default_iter(self)
-qpol_policy_t_swigregister = _qpol.qpol_policy_t_swigregister
-qpol_policy_t_swigregister(qpol_policy_t)
-
-QPOL_CAP_ATTRIB_NAMES = _qpol.QPOL_CAP_ATTRIB_NAMES
-QPOL_CAP_SYN_RULES = _qpol.QPOL_CAP_SYN_RULES
-QPOL_CAP_LINE_NUMBERS = _qpol.QPOL_CAP_LINE_NUMBERS
-QPOL_CAP_CONDITIONALS = _qpol.QPOL_CAP_CONDITIONALS
-QPOL_CAP_MLS = _qpol.QPOL_CAP_MLS
-QPOL_CAP_MODULES = _qpol.QPOL_CAP_MODULES
-QPOL_CAP_RULES_LOADED = _qpol.QPOL_CAP_RULES_LOADED
-QPOL_CAP_SOURCE = _qpol.QPOL_CAP_SOURCE
-QPOL_CAP_NEVERALLOW = _qpol.QPOL_CAP_NEVERALLOW
-QPOL_CAP_POLCAPS = _qpol.QPOL_CAP_POLCAPS
-QPOL_CAP_BOUNDS = _qpol.QPOL_CAP_BOUNDS
-QPOL_CAP_DEFAULT_OBJECTS = _qpol.QPOL_CAP_DEFAULT_OBJECTS
-QPOL_CAP_DEFAULT_TYPE = _qpol.QPOL_CAP_DEFAULT_TYPE
-QPOL_CAP_PERMISSIVE = _qpol.QPOL_CAP_PERMISSIVE
-QPOL_CAP_FILENAME_TRANS = _qpol.QPOL_CAP_FILENAME_TRANS
-QPOL_CAP_ROLETRANS = _qpol.QPOL_CAP_ROLETRANS
-class qpol_iterator_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_iterator_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_iterator_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_iterator_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_iterator_t
- __del__ = lambda self : None;
- def item(self): return _qpol.qpol_iterator_t_item(self)
- def next_(self): return _qpol.qpol_iterator_t_next_(self)
- def isend(self): return _qpol.qpol_iterator_t_isend(self)
- def size(self): return _qpol.qpol_iterator_t_size(self)
-qpol_iterator_t_swigregister = _qpol.qpol_iterator_t_swigregister
-qpol_iterator_t_swigregister(qpol_iterator_t)
-
-class qpol_type_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_type_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_type_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_type_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_type_t
- __del__ = lambda self : None;
- def name(self, *args): return _qpol.qpol_type_t_name(self, *args)
- def value(self, *args): return _qpol.qpol_type_t_value(self, *args)
- def isalias(self, *args): return _qpol.qpol_type_t_isalias(self, *args)
- def isattr(self, *args): return _qpol.qpol_type_t_isattr(self, *args)
- def ispermissive(self, *args): return _qpol.qpol_type_t_ispermissive(self, *args)
- @QpolGenerator(_qpol.qpol_type_from_void)
- def type_iter(self, *args): return _qpol.qpol_type_t_type_iter(self, *args)
- @QpolGenerator(_qpol.qpol_type_from_void)
- def attr_iter(self, *args): return _qpol.qpol_type_t_attr_iter(self, *args)
- @QpolGenerator(_qpol.to_str)
- def alias_iter(self, *args): return _qpol.qpol_type_t_alias_iter(self, *args)
-qpol_type_t_swigregister = _qpol.qpol_type_t_swigregister
-qpol_type_t_swigregister(qpol_type_t)
-
-
-def qpol_type_from_void(*args):
- return _qpol.qpol_type_from_void(*args)
-qpol_type_from_void = _qpol.qpol_type_from_void
-class qpol_role_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_role_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_role_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_role_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_role_t
- __del__ = lambda self : None;
- def value(self, *args): return _qpol.qpol_role_t_value(self, *args)
- def name(self, *args): return _qpol.qpol_role_t_name(self, *args)
- @QpolGenerator(_qpol.qpol_type_from_void)
- def type_iter(self, *args): return _qpol.qpol_role_t_type_iter(self, *args)
- def dominate_iter(self, *args): return _qpol.qpol_role_t_dominate_iter(self, *args)
-qpol_role_t_swigregister = _qpol.qpol_role_t_swigregister
-qpol_role_t_swigregister(qpol_role_t)
-
-
-def qpol_role_from_void(*args):
- return _qpol.qpol_role_from_void(*args)
-qpol_role_from_void = _qpol.qpol_role_from_void
-class qpol_level_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_level_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_level_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_level_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_level_t
- __del__ = lambda self : None;
- def isalias(self, *args): return _qpol.qpol_level_t_isalias(self, *args)
- def value(self, *args): return _qpol.qpol_level_t_value(self, *args)
- def name(self, *args): return _qpol.qpol_level_t_name(self, *args)
- @QpolGenerator(_qpol.qpol_cat_from_void)
- def cat_iter(self, *args): return _qpol.qpol_level_t_cat_iter(self, *args)
- @QpolGenerator(_qpol.to_str)
- def alias_iter(self, *args): return _qpol.qpol_level_t_alias_iter(self, *args)
-qpol_level_t_swigregister = _qpol.qpol_level_t_swigregister
-qpol_level_t_swigregister(qpol_level_t)
-
-
-def qpol_level_from_void(*args):
- return _qpol.qpol_level_from_void(*args)
-qpol_level_from_void = _qpol.qpol_level_from_void
-class qpol_cat_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_cat_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_cat_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_cat_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_cat_t
- __del__ = lambda self : None;
- def isalias(self, *args): return _qpol.qpol_cat_t_isalias(self, *args)
- def value(self, *args): return _qpol.qpol_cat_t_value(self, *args)
- def name(self, *args): return _qpol.qpol_cat_t_name(self, *args)
- @QpolGenerator(_qpol.to_str)
- def alias_iter(self, *args): return _qpol.qpol_cat_t_alias_iter(self, *args)
-qpol_cat_t_swigregister = _qpol.qpol_cat_t_swigregister
-qpol_cat_t_swigregister(qpol_cat_t)
-
-
-def qpol_cat_from_void(*args):
- return _qpol.qpol_cat_from_void(*args)
-qpol_cat_from_void = _qpol.qpol_cat_from_void
-class qpol_mls_range_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_mls_range_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_mls_range_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_mls_range_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_mls_range_t
- __del__ = lambda self : None;
- def high_level(self, *args): return _qpol.qpol_mls_range_t_high_level(self, *args)
- def low_level(self, *args): return _qpol.qpol_mls_range_t_low_level(self, *args)
-qpol_mls_range_t_swigregister = _qpol.qpol_mls_range_t_swigregister
-qpol_mls_range_t_swigregister(qpol_mls_range_t)
-
-
-def qpol_mls_range_from_void(*args):
- return _qpol.qpol_mls_range_from_void(*args)
-qpol_mls_range_from_void = _qpol.qpol_mls_range_from_void
-class qpol_semantic_level_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_semantic_level_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_semantic_level_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_semantic_level_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_semantic_level_t
- __del__ = lambda self : None;
- def add_cats(self, *args): return _qpol.qpol_semantic_level_t_add_cats(self, *args)
-qpol_semantic_level_t_swigregister = _qpol.qpol_semantic_level_t_swigregister
-qpol_semantic_level_t_swigregister(qpol_semantic_level_t)
-
-class qpol_mls_level_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_mls_level_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_mls_level_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_mls_level_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_mls_level_t
- __del__ = lambda self : None;
- def sens_name(self, *args): return _qpol.qpol_mls_level_t_sens_name(self, *args)
- @QpolGenerator(_qpol.qpol_cat_from_void)
- def cat_iter(self, *args): return _qpol.qpol_mls_level_t_cat_iter(self, *args)
-qpol_mls_level_t_swigregister = _qpol.qpol_mls_level_t_swigregister
-qpol_mls_level_t_swigregister(qpol_mls_level_t)
-
-
-def qpol_mls_level_from_void(*args):
- return _qpol.qpol_mls_level_from_void(*args)
-qpol_mls_level_from_void = _qpol.qpol_mls_level_from_void
-class qpol_user_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_user_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_user_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_user_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_user_t
- __del__ = lambda self : None;
- def value(self, *args): return _qpol.qpol_user_t_value(self, *args)
- @QpolGenerator(_qpol.qpol_role_from_void)
- def role_iter(self, *args): return _qpol.qpol_user_t_role_iter(self, *args)
- def range(self, *args): return _qpol.qpol_user_t_range(self, *args)
- def name(self, *args): return _qpol.qpol_user_t_name(self, *args)
- def dfltlevel(self, *args): return _qpol.qpol_user_t_dfltlevel(self, *args)
-qpol_user_t_swigregister = _qpol.qpol_user_t_swigregister
-qpol_user_t_swigregister(qpol_user_t)
-
-
-def qpol_user_from_void(*args):
- return _qpol.qpol_user_from_void(*args)
-qpol_user_from_void = _qpol.qpol_user_from_void
-class qpol_bool_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_bool_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_bool_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_bool_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_bool_t
- __del__ = lambda self : None;
- def value(self, *args): return _qpol.qpol_bool_t_value(self, *args)
- def state(self, *args): return _qpol.qpol_bool_t_state(self, *args)
- def name(self, *args): return _qpol.qpol_bool_t_name(self, *args)
-qpol_bool_t_swigregister = _qpol.qpol_bool_t_swigregister
-qpol_bool_t_swigregister(qpol_bool_t)
-
-
-def qpol_bool_from_void(*args):
- return _qpol.qpol_bool_from_void(*args)
-qpol_bool_from_void = _qpol.qpol_bool_from_void
-class qpol_context_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_context_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_context_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_context_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_context_t
- __del__ = lambda self : None;
- def user(self, *args): return _qpol.qpol_context_t_user(self, *args)
- def role(self, *args): return _qpol.qpol_context_t_role(self, *args)
- def type_(self, *args): return _qpol.qpol_context_t_type_(self, *args)
- def range(self, *args): return _qpol.qpol_context_t_range(self, *args)
-qpol_context_t_swigregister = _qpol.qpol_context_t_swigregister
-qpol_context_t_swigregister(qpol_context_t)
-
-
-def qpol_context_from_void(*args):
- return _qpol.qpol_context_from_void(*args)
-qpol_context_from_void = _qpol.qpol_context_from_void
-class qpol_class_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_class_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_class_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_class_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_class_t
- __del__ = lambda self : None;
- def value(self, *args): return _qpol.qpol_class_t_value(self, *args)
- def common(self, *args): return _qpol.qpol_class_t_common(self, *args)
- @QpolGenerator(_qpol.to_str)
- def perm_iter(self, *args): return _qpol.qpol_class_t_perm_iter(self, *args)
- @QpolGenerator(_qpol.qpol_constraint_from_void)
- def constraint_iter(self, *args): return _qpol.qpol_class_t_constraint_iter(self, *args)
- @QpolGenerator(_qpol.qpol_validatetrans_from_void)
- def validatetrans_iter(self, *args): return _qpol.qpol_class_t_validatetrans_iter(self, *args)
- def name(self, *args): return _qpol.qpol_class_t_name(self, *args)
-qpol_class_t_swigregister = _qpol.qpol_class_t_swigregister
-qpol_class_t_swigregister(qpol_class_t)
-
-
-def qpol_class_from_void(*args):
- return _qpol.qpol_class_from_void(*args)
-qpol_class_from_void = _qpol.qpol_class_from_void
-class qpol_common_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_common_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_common_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_common_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_common_t
- __del__ = lambda self : None;
- def value(self, *args): return _qpol.qpol_common_t_value(self, *args)
- @QpolGenerator(_qpol.to_str)
- def perm_iter(self, *args): return _qpol.qpol_common_t_perm_iter(self, *args)
- def name(self, *args): return _qpol.qpol_common_t_name(self, *args)
-qpol_common_t_swigregister = _qpol.qpol_common_t_swigregister
-qpol_common_t_swigregister(qpol_common_t)
-
-
-def qpol_common_from_void(*args):
- return _qpol.qpol_common_from_void(*args)
-qpol_common_from_void = _qpol.qpol_common_from_void
-QPOL_FS_USE_XATTR = _qpol.QPOL_FS_USE_XATTR
-QPOL_FS_USE_TRANS = _qpol.QPOL_FS_USE_TRANS
-QPOL_FS_USE_TASK = _qpol.QPOL_FS_USE_TASK
-QPOL_FS_USE_GENFS = _qpol.QPOL_FS_USE_GENFS
-QPOL_FS_USE_NONE = _qpol.QPOL_FS_USE_NONE
-QPOL_FS_USE_PSID = _qpol.QPOL_FS_USE_PSID
-class qpol_fs_use_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_fs_use_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_fs_use_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_fs_use_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_fs_use_t
- __del__ = lambda self : None;
- def name(self, *args): return _qpol.qpol_fs_use_t_name(self, *args)
- def behavior(self, *args): return _qpol.qpol_fs_use_t_behavior(self, *args)
- def context(self, *args): return _qpol.qpol_fs_use_t_context(self, *args)
-qpol_fs_use_t_swigregister = _qpol.qpol_fs_use_t_swigregister
-qpol_fs_use_t_swigregister(qpol_fs_use_t)
-
-
-def qpol_fs_use_from_void(*args):
- return _qpol.qpol_fs_use_from_void(*args)
-qpol_fs_use_from_void = _qpol.qpol_fs_use_from_void
-QPOL_CLASS_ALL = _qpol.QPOL_CLASS_ALL
-QPOL_CLASS_BLK_FILE = _qpol.QPOL_CLASS_BLK_FILE
-QPOL_CLASS_CHR_FILE = _qpol.QPOL_CLASS_CHR_FILE
-QPOL_CLASS_DIR = _qpol.QPOL_CLASS_DIR
-QPOL_CLASS_FIFO_FILE = _qpol.QPOL_CLASS_FIFO_FILE
-QPOL_CLASS_FILE = _qpol.QPOL_CLASS_FILE
-QPOL_CLASS_LNK_FILE = _qpol.QPOL_CLASS_LNK_FILE
-QPOL_CLASS_SOCK_FILE = _qpol.QPOL_CLASS_SOCK_FILE
-class qpol_genfscon_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_genfscon_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_genfscon_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_genfscon_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_genfscon_t
- __del__ = lambda self : None;
- def name(self, *args): return _qpol.qpol_genfscon_t_name(self, *args)
- def path(self, *args): return _qpol.qpol_genfscon_t_path(self, *args)
- def object_class(self, *args): return _qpol.qpol_genfscon_t_object_class(self, *args)
- def context(self, *args): return _qpol.qpol_genfscon_t_context(self, *args)
-qpol_genfscon_t_swigregister = _qpol.qpol_genfscon_t_swigregister
-qpol_genfscon_t_swigregister(qpol_genfscon_t)
-
-
-def qpol_genfscon_from_void(*args):
- return _qpol.qpol_genfscon_from_void(*args)
-qpol_genfscon_from_void = _qpol.qpol_genfscon_from_void
-class qpol_isid_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_isid_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_isid_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_isid_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_isid_t
- __del__ = lambda self : None;
- def name(self, *args): return _qpol.qpol_isid_t_name(self, *args)
- def context(self, *args): return _qpol.qpol_isid_t_context(self, *args)
-qpol_isid_t_swigregister = _qpol.qpol_isid_t_swigregister
-qpol_isid_t_swigregister(qpol_isid_t)
-
-
-def qpol_isid_from_void(*args):
- return _qpol.qpol_isid_from_void(*args)
-qpol_isid_from_void = _qpol.qpol_isid_from_void
-class qpol_netifcon_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_netifcon_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_netifcon_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_netifcon_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_netifcon_t
- __del__ = lambda self : None;
- def name(self, *args): return _qpol.qpol_netifcon_t_name(self, *args)
- def msg_con(self, *args): return _qpol.qpol_netifcon_t_msg_con(self, *args)
- def if_con(self, *args): return _qpol.qpol_netifcon_t_if_con(self, *args)
-qpol_netifcon_t_swigregister = _qpol.qpol_netifcon_t_swigregister
-qpol_netifcon_t_swigregister(qpol_netifcon_t)
-
-
-def qpol_netifcon_from_void(*args):
- return _qpol.qpol_netifcon_from_void(*args)
-qpol_netifcon_from_void = _qpol.qpol_netifcon_from_void
-QPOL_IPV4 = _qpol.QPOL_IPV4
-QPOL_IPV6 = _qpol.QPOL_IPV6
-class qpol_nodecon_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_nodecon_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_nodecon_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_nodecon_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_nodecon_t
- __del__ = lambda self : None;
- def addr(self, *args): return _qpol.qpol_nodecon_t_addr(self, *args)
- def mask(self, *args): return _qpol.qpol_nodecon_t_mask(self, *args)
- def protocol(self, *args): return _qpol.qpol_nodecon_t_protocol(self, *args)
- def context(self, *args): return _qpol.qpol_nodecon_t_context(self, *args)
-qpol_nodecon_t_swigregister = _qpol.qpol_nodecon_t_swigregister
-qpol_nodecon_t_swigregister(qpol_nodecon_t)
-
-
-def qpol_nodecon_from_void(*args):
- return _qpol.qpol_nodecon_from_void(*args)
-qpol_nodecon_from_void = _qpol.qpol_nodecon_from_void
-IPPROTO_TCP = _qpol.IPPROTO_TCP
-IPPROTO_UDP = _qpol.IPPROTO_UDP
-class qpol_portcon_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_portcon_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_portcon_t, name)
- __repr__ = _swig_repr
- def __init__(self, *args):
- this = _qpol.new_qpol_portcon_t(*args)
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_portcon_t
- __del__ = lambda self : None;
- def low_port(self, *args): return _qpol.qpol_portcon_t_low_port(self, *args)
- def high_port(self, *args): return _qpol.qpol_portcon_t_high_port(self, *args)
- def protocol(self, *args): return _qpol.qpol_portcon_t_protocol(self, *args)
- def context(self, *args): return _qpol.qpol_portcon_t_context(self, *args)
-qpol_portcon_t_swigregister = _qpol.qpol_portcon_t_swigregister
-qpol_portcon_t_swigregister(qpol_portcon_t)
-
-
-def qpol_portcon_from_void(*args):
- return _qpol.qpol_portcon_from_void(*args)
-qpol_portcon_from_void = _qpol.qpol_portcon_from_void
-class qpol_constraint_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_constraint_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_constraint_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_constraint_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_constraint_t
- __del__ = lambda self : None;
- def object_class(self, *args): return _qpol.qpol_constraint_t_object_class(self, *args)
- @QpolGenerator(_qpol.to_str)
- def perm_iter(self, *args): return _qpol.qpol_constraint_t_perm_iter(self, *args)
- @QpolGenerator(_qpol.qpol_constraint_expr_node_from_void)
- def expr_iter(self, *args): return _qpol.qpol_constraint_t_expr_iter(self, *args)
-qpol_constraint_t_swigregister = _qpol.qpol_constraint_t_swigregister
-qpol_constraint_t_swigregister(qpol_constraint_t)
-
-
-def qpol_constraint_from_void(*args):
- return _qpol.qpol_constraint_from_void(*args)
-qpol_constraint_from_void = _qpol.qpol_constraint_from_void
-class qpol_validatetrans_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_validatetrans_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_validatetrans_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_validatetrans_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_validatetrans_t
- __del__ = lambda self : None;
- def object_class(self, *args): return _qpol.qpol_validatetrans_t_object_class(self, *args)
- @QpolGenerator(_qpol.qpol_constraint_expr_node_from_void)
- def expr_iter(self, *args): return _qpol.qpol_validatetrans_t_expr_iter(self, *args)
-qpol_validatetrans_t_swigregister = _qpol.qpol_validatetrans_t_swigregister
-qpol_validatetrans_t_swigregister(qpol_validatetrans_t)
-
-
-def qpol_validatetrans_from_void(*args):
- return _qpol.qpol_validatetrans_from_void(*args)
-qpol_validatetrans_from_void = _qpol.qpol_validatetrans_from_void
-QPOL_CEXPR_TYPE_NOT = _qpol.QPOL_CEXPR_TYPE_NOT
-QPOL_CEXPR_TYPE_AND = _qpol.QPOL_CEXPR_TYPE_AND
-QPOL_CEXPR_TYPE_OR = _qpol.QPOL_CEXPR_TYPE_OR
-QPOL_CEXPR_TYPE_ATTR = _qpol.QPOL_CEXPR_TYPE_ATTR
-QPOL_CEXPR_TYPE_NAMES = _qpol.QPOL_CEXPR_TYPE_NAMES
-QPOL_CEXPR_SYM_USER = _qpol.QPOL_CEXPR_SYM_USER
-QPOL_CEXPR_SYM_ROLE = _qpol.QPOL_CEXPR_SYM_ROLE
-QPOL_CEXPR_SYM_TYPE = _qpol.QPOL_CEXPR_SYM_TYPE
-QPOL_CEXPR_SYM_TARGET = _qpol.QPOL_CEXPR_SYM_TARGET
-QPOL_CEXPR_SYM_XTARGET = _qpol.QPOL_CEXPR_SYM_XTARGET
-QPOL_CEXPR_SYM_L1L2 = _qpol.QPOL_CEXPR_SYM_L1L2
-QPOL_CEXPR_SYM_L1H2 = _qpol.QPOL_CEXPR_SYM_L1H2
-QPOL_CEXPR_SYM_H1L2 = _qpol.QPOL_CEXPR_SYM_H1L2
-QPOL_CEXPR_SYM_H1H2 = _qpol.QPOL_CEXPR_SYM_H1H2
-QPOL_CEXPR_SYM_L1H1 = _qpol.QPOL_CEXPR_SYM_L1H1
-QPOL_CEXPR_SYM_L2H2 = _qpol.QPOL_CEXPR_SYM_L2H2
-QPOL_CEXPR_OP_EQ = _qpol.QPOL_CEXPR_OP_EQ
-QPOL_CEXPR_OP_NEQ = _qpol.QPOL_CEXPR_OP_NEQ
-QPOL_CEXPR_OP_DOM = _qpol.QPOL_CEXPR_OP_DOM
-QPOL_CEXPR_OP_DOMBY = _qpol.QPOL_CEXPR_OP_DOMBY
-QPOL_CEXPR_OP_INCOMP = _qpol.QPOL_CEXPR_OP_INCOMP
-class qpol_constraint_expr_node_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_constraint_expr_node_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_constraint_expr_node_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_constraint_expr_node_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_constraint_expr_node_t
- __del__ = lambda self : None;
- def expr_type(self, *args): return _qpol.qpol_constraint_expr_node_t_expr_type(self, *args)
- def sym_type(self, *args): return _qpol.qpol_constraint_expr_node_t_sym_type(self, *args)
- def op(self, *args): return _qpol.qpol_constraint_expr_node_t_op(self, *args)
- @QpolGenerator(_qpol.to_str)
- def names_iter(self, *args): return _qpol.qpol_constraint_expr_node_t_names_iter(self, *args)
-qpol_constraint_expr_node_t_swigregister = _qpol.qpol_constraint_expr_node_t_swigregister
-qpol_constraint_expr_node_t_swigregister(qpol_constraint_expr_node_t)
-
-
-def qpol_constraint_expr_node_from_void(*args):
- return _qpol.qpol_constraint_expr_node_from_void(*args)
-qpol_constraint_expr_node_from_void = _qpol.qpol_constraint_expr_node_from_void
-class qpol_role_allow_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_role_allow_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_role_allow_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_role_allow_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_role_allow_t
- __del__ = lambda self : None;
- def rule_type(self,policy):
- return "allow"
-
- def source_role(self, *args): return _qpol.qpol_role_allow_t_source_role(self, *args)
- def target_role(self, *args): return _qpol.qpol_role_allow_t_target_role(self, *args)
-qpol_role_allow_t_swigregister = _qpol.qpol_role_allow_t_swigregister
-qpol_role_allow_t_swigregister(qpol_role_allow_t)
-
-
-def qpol_role_allow_from_void(*args):
- return _qpol.qpol_role_allow_from_void(*args)
-qpol_role_allow_from_void = _qpol.qpol_role_allow_from_void
-class qpol_role_trans_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_role_trans_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_role_trans_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_role_trans_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_role_trans_t
- __del__ = lambda self : None;
- def rule_type(self,policy):
- return "role_transition"
-
- def source_role(self, *args): return _qpol.qpol_role_trans_t_source_role(self, *args)
- def target_type(self, *args): return _qpol.qpol_role_trans_t_target_type(self, *args)
- def object_class(self, *args): return _qpol.qpol_role_trans_t_object_class(self, *args)
- def default_role(self, *args): return _qpol.qpol_role_trans_t_default_role(self, *args)
-qpol_role_trans_t_swigregister = _qpol.qpol_role_trans_t_swigregister
-qpol_role_trans_t_swigregister(qpol_role_trans_t)
-
-
-def qpol_role_trans_from_void(*args):
- return _qpol.qpol_role_trans_from_void(*args)
-qpol_role_trans_from_void = _qpol.qpol_role_trans_from_void
-class qpol_range_trans_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_range_trans_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_range_trans_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_range_trans_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_range_trans_t
- __del__ = lambda self : None;
- def rule_type(self,policy):
- return "range_transition"
-
- def source_type(self, *args): return _qpol.qpol_range_trans_t_source_type(self, *args)
- def target_type(self, *args): return _qpol.qpol_range_trans_t_target_type(self, *args)
- def object_class(self, *args): return _qpol.qpol_range_trans_t_object_class(self, *args)
- def range(self, *args): return _qpol.qpol_range_trans_t_range(self, *args)
-qpol_range_trans_t_swigregister = _qpol.qpol_range_trans_t_swigregister
-qpol_range_trans_t_swigregister(qpol_range_trans_t)
-
-
-def qpol_range_trans_from_void(*args):
- return _qpol.qpol_range_trans_from_void(*args)
-qpol_range_trans_from_void = _qpol.qpol_range_trans_from_void
-QPOL_RULE_ALLOW = _qpol.QPOL_RULE_ALLOW
-QPOL_RULE_NEVERALLOW = _qpol.QPOL_RULE_NEVERALLOW
-QPOL_RULE_AUDITALLOW = _qpol.QPOL_RULE_AUDITALLOW
-QPOL_RULE_DONTAUDIT = _qpol.QPOL_RULE_DONTAUDIT
-class qpol_avrule_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_avrule_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_avrule_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_avrule_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_avrule_t
- __del__ = lambda self : None;
- def rule_type(self, *args): return _qpol.qpol_avrule_t_rule_type(self, *args)
- def source_type(self, *args): return _qpol.qpol_avrule_t_source_type(self, *args)
- def target_type(self, *args): return _qpol.qpol_avrule_t_target_type(self, *args)
- def object_class(self, *args): return _qpol.qpol_avrule_t_object_class(self, *args)
- @QpolGenerator(_qpol.to_str)
- def perm_iter(self, *args): return _qpol.qpol_avrule_t_perm_iter(self, *args)
- def cond(self, *args): return _qpol.qpol_avrule_t_cond(self, *args)
- def is_enabled(self, *args): return _qpol.qpol_avrule_t_is_enabled(self, *args)
- def which_list(self, *args): return _qpol.qpol_avrule_t_which_list(self, *args)
- def syn_avrule_iter(self, *args): return _qpol.qpol_avrule_t_syn_avrule_iter(self, *args)
-qpol_avrule_t_swigregister = _qpol.qpol_avrule_t_swigregister
-qpol_avrule_t_swigregister(qpol_avrule_t)
-
-
-def qpol_avrule_from_void(*args):
- return _qpol.qpol_avrule_from_void(*args)
-qpol_avrule_from_void = _qpol.qpol_avrule_from_void
-QPOL_RULE_TYPE_TRANS = _qpol.QPOL_RULE_TYPE_TRANS
-QPOL_RULE_TYPE_CHANGE = _qpol.QPOL_RULE_TYPE_CHANGE
-QPOL_RULE_TYPE_MEMBER = _qpol.QPOL_RULE_TYPE_MEMBER
-class qpol_terule_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_terule_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_terule_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_terule_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_terule_t
- __del__ = lambda self : None;
- def rule_type(self, *args): return _qpol.qpol_terule_t_rule_type(self, *args)
- def source_type(self, *args): return _qpol.qpol_terule_t_source_type(self, *args)
- def target_type(self, *args): return _qpol.qpol_terule_t_target_type(self, *args)
- def object_class(self, *args): return _qpol.qpol_terule_t_object_class(self, *args)
- def default_type(self, *args): return _qpol.qpol_terule_t_default_type(self, *args)
- def cond(self, *args): return _qpol.qpol_terule_t_cond(self, *args)
- def is_enabled(self, *args): return _qpol.qpol_terule_t_is_enabled(self, *args)
- def which_list(self, *args): return _qpol.qpol_terule_t_which_list(self, *args)
- def syn_terule_iter(self, *args): return _qpol.qpol_terule_t_syn_terule_iter(self, *args)
-qpol_terule_t_swigregister = _qpol.qpol_terule_t_swigregister
-qpol_terule_t_swigregister(qpol_terule_t)
-
-
-def qpol_terule_from_void(*args):
- return _qpol.qpol_terule_from_void(*args)
-qpol_terule_from_void = _qpol.qpol_terule_from_void
-class qpol_cond_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_cond_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_cond_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_cond_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_cond_t
- __del__ = lambda self : None;
- @QpolGenerator(_qpol.qpol_cond_expr_node_from_void)
- def expr_node_iter(self, *args): return _qpol.qpol_cond_t_expr_node_iter(self, *args)
- def av_true_iter(self, *args): return _qpol.qpol_cond_t_av_true_iter(self, *args)
- def av_false_iter(self, *args): return _qpol.qpol_cond_t_av_false_iter(self, *args)
- def te_true_iter(self, *args): return _qpol.qpol_cond_t_te_true_iter(self, *args)
- def te_false_iter(self, *args): return _qpol.qpol_cond_t_te_false_iter(self, *args)
- def evaluate(self, *args): return _qpol.qpol_cond_t_evaluate(self, *args)
-qpol_cond_t_swigregister = _qpol.qpol_cond_t_swigregister
-qpol_cond_t_swigregister(qpol_cond_t)
-
-
-def qpol_cond_from_void(*args):
- return _qpol.qpol_cond_from_void(*args)
-qpol_cond_from_void = _qpol.qpol_cond_from_void
-QPOL_COND_EXPR_BOOL = _qpol.QPOL_COND_EXPR_BOOL
-QPOL_COND_EXPR_NOT = _qpol.QPOL_COND_EXPR_NOT
-QPOL_COND_EXPR_OR = _qpol.QPOL_COND_EXPR_OR
-QPOL_COND_EXPR_AND = _qpol.QPOL_COND_EXPR_AND
-QPOL_COND_EXPR_XOR = _qpol.QPOL_COND_EXPR_XOR
-QPOL_COND_EXPR_EQ = _qpol.QPOL_COND_EXPR_EQ
-QPOL_COND_EXPR_NEQ = _qpol.QPOL_COND_EXPR_NEQ
-class qpol_cond_expr_node_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_cond_expr_node_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_cond_expr_node_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_cond_expr_node_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_cond_expr_node_t
- __del__ = lambda self : None;
- def expr_type(self, *args): return _qpol.qpol_cond_expr_node_t_expr_type(self, *args)
- def get_boolean(self, *args): return _qpol.qpol_cond_expr_node_t_get_boolean(self, *args)
-qpol_cond_expr_node_t_swigregister = _qpol.qpol_cond_expr_node_t_swigregister
-qpol_cond_expr_node_t_swigregister(qpol_cond_expr_node_t)
-
-
-def qpol_cond_expr_node_from_void(*args):
- return _qpol.qpol_cond_expr_node_from_void(*args)
-qpol_cond_expr_node_from_void = _qpol.qpol_cond_expr_node_from_void
-class qpol_filename_trans_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_filename_trans_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_filename_trans_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_filename_trans_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_filename_trans_t
- __del__ = lambda self : None;
- def rule_type(self,policy):
- return "type_transition"
-
- def source_type(self, *args): return _qpol.qpol_filename_trans_t_source_type(self, *args)
- def target_type(self, *args): return _qpol.qpol_filename_trans_t_target_type(self, *args)
- def object_class(self, *args): return _qpol.qpol_filename_trans_t_object_class(self, *args)
- def default_type(self, *args): return _qpol.qpol_filename_trans_t_default_type(self, *args)
- def filename(self, *args): return _qpol.qpol_filename_trans_t_filename(self, *args)
-qpol_filename_trans_t_swigregister = _qpol.qpol_filename_trans_t_swigregister
-qpol_filename_trans_t_swigregister(qpol_filename_trans_t)
-
-
-def qpol_filename_trans_from_void(*args):
- return _qpol.qpol_filename_trans_from_void(*args)
-qpol_filename_trans_from_void = _qpol.qpol_filename_trans_from_void
-class qpol_polcap_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_polcap_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_polcap_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_polcap_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_polcap_t
- __del__ = lambda self : None;
- def name(self, *args): return _qpol.qpol_polcap_t_name(self, *args)
-qpol_polcap_t_swigregister = _qpol.qpol_polcap_t_swigregister
-qpol_polcap_t_swigregister(qpol_polcap_t)
-
-
-def qpol_polcap_from_void(*args):
- return _qpol.qpol_polcap_from_void(*args)
-qpol_polcap_from_void = _qpol.qpol_polcap_from_void
-class qpol_typebounds_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_typebounds_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_typebounds_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_typebounds_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_typebounds_t
- __del__ = lambda self : None;
- def parent_name(self, *args): return _qpol.qpol_typebounds_t_parent_name(self, *args)
- def child_name(self, *args): return _qpol.qpol_typebounds_t_child_name(self, *args)
-qpol_typebounds_t_swigregister = _qpol.qpol_typebounds_t_swigregister
-qpol_typebounds_t_swigregister(qpol_typebounds_t)
-
-
-def qpol_typebounds_from_void(*args):
- return _qpol.qpol_typebounds_from_void(*args)
-qpol_typebounds_from_void = _qpol.qpol_typebounds_from_void
-class qpol_rolebounds_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_rolebounds_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_rolebounds_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_rolebounds_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_rolebounds_t
- __del__ = lambda self : None;
- def parent_name(self, *args): return _qpol.qpol_rolebounds_t_parent_name(self, *args)
- def child_name(self, *args): return _qpol.qpol_rolebounds_t_child_name(self, *args)
-qpol_rolebounds_t_swigregister = _qpol.qpol_rolebounds_t_swigregister
-qpol_rolebounds_t_swigregister(qpol_rolebounds_t)
-
-
-def qpol_rolebounds_from_void(*args):
- return _qpol.qpol_rolebounds_from_void(*args)
-qpol_rolebounds_from_void = _qpol.qpol_rolebounds_from_void
-class qpol_userbounds_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_userbounds_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_userbounds_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_userbounds_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_userbounds_t
- __del__ = lambda self : None;
- def parent_name(self, *args): return _qpol.qpol_userbounds_t_parent_name(self, *args)
- def child_name(self, *args): return _qpol.qpol_userbounds_t_child_name(self, *args)
-qpol_userbounds_t_swigregister = _qpol.qpol_userbounds_t_swigregister
-qpol_userbounds_t_swigregister(qpol_userbounds_t)
-
-
-def qpol_userbounds_from_void(*args):
- return _qpol.qpol_userbounds_from_void(*args)
-qpol_userbounds_from_void = _qpol.qpol_userbounds_from_void
-class qpol_default_object_t(_object):
- __swig_setmethods__ = {}
- __setattr__ = lambda self, name, value: _swig_setattr(self, qpol_default_object_t, name, value)
- __swig_getmethods__ = {}
- __getattr__ = lambda self, name: _swig_getattr(self, qpol_default_object_t, name)
- __repr__ = _swig_repr
- def __init__(self):
- this = _qpol.new_qpol_default_object_t()
- try: self.this.append(this)
- except: self.this = this
- __swig_destroy__ = _qpol.delete_qpol_default_object_t
- __del__ = lambda self : None;
- def object_class(self, *args): return _qpol.qpol_default_object_t_object_class(self, *args)
- def user_default(self, *args): return _qpol.qpol_default_object_t_user_default(self, *args)
- def role_default(self, *args): return _qpol.qpol_default_object_t_role_default(self, *args)
- def type_default(self, *args): return _qpol.qpol_default_object_t_type_default(self, *args)
- def range_default(self, *args): return _qpol.qpol_default_object_t_range_default(self, *args)
-qpol_default_object_t_swigregister = _qpol.qpol_default_object_t_swigregister
-qpol_default_object_t_swigregister(qpol_default_object_t)
-
-
-def qpol_default_object_from_void(*args):
- return _qpol.qpol_default_object_from_void(*args)
-qpol_default_object_from_void = _qpol.qpol_default_object_from_void
-# This file is compatible with both classic and new-style classes.
-
-
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/rbacrule.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/rbacrule.py
deleted file mode 100644
index aa6a0d0..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/rbacrule.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import rule
-from . import role
-from . import typeattr
-
-
-def rbac_rule_factory(policy, name):
- """Factory function for creating RBAC rule objects."""
-
- if isinstance(name, qpol.qpol_role_allow_t):
- return RoleAllow(policy, name)
- elif isinstance(name, qpol.qpol_role_trans_t):
- return RoleTransition(policy, name)
- else:
- raise TypeError("RBAC rules cannot be looked up.")
-
-
-def validate_ruletype(types):
- """Validate RBAC rule types."""
- for t in types:
- if t not in ["allow", "role_transition"]:
- raise exception.InvalidRBACRuleType("{0} is not a valid RBAC rule type.".format(t))
-
-
-class RoleAllow(rule.PolicyRule):
-
- """A role allow rule."""
-
- def __str__(self):
- return "allow {0.source} {0.target};".format(self)
-
- @property
- def source(self):
- """The rule's source role."""
- return role.role_factory(self.policy, self.qpol_symbol.source_role(self.policy))
-
- @property
- def target(self):
- """The rule's target role."""
- return role.role_factory(self.policy, self.qpol_symbol.target_role(self.policy))
-
- @property
- def tclass(self):
- """The rule's object class."""
- raise exception.RuleUseError("Role allow rules do not have an object class.")
-
- @property
- def default(self):
- """The rule's default role."""
- raise exception.RuleUseError("Role allow rules do not have a default role.")
-
-
-class RoleTransition(rule.PolicyRule):
-
- """A role_transition rule."""
-
- def __str__(self):
- return "role_transition {0.source} {0.target}:{0.tclass} {0.default};".format(self)
-
- @property
- def source(self):
- """The rule's source role."""
- return role.role_factory(self.policy, self.qpol_symbol.source_role(self.policy))
-
- @property
- def target(self):
- """The rule's target type/attribute."""
- return typeattr.type_or_attr_factory(self.policy, self.qpol_symbol.target_type(self.policy))
-
- @property
- def default(self):
- """The rule's default role."""
- return role.role_factory(self.policy, self.qpol_symbol.default_role(self.policy))
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/role.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/role.py
deleted file mode 100644
index 1d9fbe1..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/role.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import symbol
-from . import typeattr
-
-
-def role_factory(qpol_policy, name):
- """Factory function for creating Role objects."""
-
- if isinstance(name, Role):
- assert name.policy == qpol_policy
- return name
- elif isinstance(name, qpol.qpol_role_t):
- return Role(qpol_policy, name)
-
- try:
- return Role(qpol_policy, qpol.qpol_role_t(qpol_policy, str(name)))
- except ValueError:
- raise exception.InvalidRole("{0} is not a valid role".format(name))
-
-
-class BaseRole(symbol.PolicySymbol):
-
- """Role/role attribute base class."""
-
- def expand(self):
- raise NotImplementedError
-
- def types(self):
- raise NotImplementedError
-
-
-class Role(BaseRole):
-
- """A role."""
-
- def expand(self):
- """Generator that expands this into its member roles."""
- yield self
-
- def types(self):
- """Generator which yields the role's set of types."""
-
- for type_ in self.qpol_symbol.type_iter(self.policy):
- yield typeattr.type_or_attr_factory(self.policy, type_)
-
- def statement(self):
- types = list(str(t) for t in self.types())
- stmt = "role {0}".format(self)
- if types:
- if (len(types) > 1):
- stmt += " types {{ {0} }}".format(' '.join(types))
- else:
- stmt += " types {0}".format(types[0])
- stmt += ";"
- return stmt
-
-
-class RoleAttribute(BaseRole):
-
- """A role attribute."""
-
- pass
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/rule.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/rule.py
deleted file mode 100644
index 73fc812..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/rule.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import symbol
-from . import objclass
-
-
-class PolicyRule(symbol.PolicySymbol):
-
- """This is base class for policy rules."""
-
- def __str__(self):
- raise NotImplementedError
-
- @property
- def ruletype(self):
- """The rule type for the rule."""
- return self.qpol_symbol.rule_type(self.policy)
-
- @property
- def source(self):
- """
- The source for the rule. This should be overridden by
- subclasses.
- """
- raise NotImplementedError
-
- @property
- def target(self):
- """
- The target for the rule. This should be overridden by
- subclasses.
- """
- raise NotImplementedError
-
- @property
- def tclass(self):
- """The object class for the rule."""
- return objclass.class_factory(self.policy, self.qpol_symbol.object_class(self.policy))
-
- @property
- def default(self):
- """
- The default for the rule. This should be overridden by
- subclasses.
- """
- raise NotImplementedError
-
- @property
- def conditional(self):
- """The conditional expression for this rule."""
- # Most rules cannot be conditional.
- raise exception.RuleNotConditional
-
- def statement(self):
- return str(self)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/symbol.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/symbol.py
deleted file mode 100644
index 4712d7f..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/symbol.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-
-
-class PolicySymbol(object):
-
- """This is a base class for all policy objects."""
-
- def __init__(self, policy, qpol_symbol):
- """
- Parameters:
- policy The low-level policy object.
- qpol_symbol The low-level policy symbol object.
- """
-
- assert qpol_symbol
-
- self.policy = policy
- self.qpol_symbol = qpol_symbol
-
- def __str__(self):
- return self.qpol_symbol.name(self.policy)
-
- def __hash__(self):
- return hash(self.qpol_symbol.name(self.policy))
-
- def __eq__(self, other):
- try:
- return self.qpol_symbol.this == other.qpol_symbol.this
- except AttributeError:
- return str(self) == str(other)
-
- def __ne__(self, other):
- return not self == other
-
- def __lt__(self, other):
- """Comparison used by Python sorting functions."""
- return str(self) < str(other)
-
- def __repr__(self):
- return "<{0.__class__.__name__}(<qpol_policy_t id={1}>,\"{0}\")>".format(
- self, id(self.policy))
-
- def __deepcopy__(self, memo):
- # shallow copy as all of the members are immutable
- cls = self.__class__
- newobj = cls.__new__(cls)
- newobj.policy = self.policy
- newobj.qpol_symbol = self.qpol_symbol
- memo[id(self)] = newobj
- return newobj
-
- def statement(self):
- """
- A rendering of the policy statement. This should be
- overridden by subclasses.
- """
- raise NotImplementedError
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/terule.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/terule.py
deleted file mode 100644
index d8a9e94..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/terule.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import rule
-from . import typeattr
-from . import boolcond
-
-
-def te_rule_factory(policy, symbol):
- """Factory function for creating TE rule objects."""
-
- if isinstance(symbol, qpol.qpol_avrule_t):
- return AVRule(policy, symbol)
- elif isinstance(symbol, (qpol.qpol_terule_t, qpol.qpol_filename_trans_t)):
- return TERule(policy, symbol)
- else:
- raise TypeError("TE rules cannot be looked-up.")
-
-
-def validate_ruletype(types):
- """Validate TE Rule types."""
- for t in types:
- if t not in ["allow", "auditallow", "dontaudit", "neverallow",
- "type_transition", "type_member", "type_change"]:
- raise exception.InvalidTERuleType("{0} is not a valid TE rule type.".format(t))
-
-
-class BaseTERule(rule.PolicyRule):
-
- """A type enforcement rule."""
-
- @property
- def source(self):
- """The rule's source type/attribute."""
- return typeattr.type_or_attr_factory(self.policy, self.qpol_symbol.source_type(self.policy))
-
- @property
- def target(self):
- """The rule's target type/attribute."""
- return typeattr.type_or_attr_factory(self.policy, self.qpol_symbol.target_type(self.policy))
-
- @property
- def filename(self):
- raise NotImplementedError
-
- @property
- def conditional(self):
- """The rule's conditional expression."""
- try:
- return boolcond.condexpr_factory(self.policy, self.qpol_symbol.cond(self.policy))
- except (AttributeError, ValueError):
- # AttributeError: name filetrans rules cannot be conditional
- # so no member function
- # ValueError: The rule is not conditional
- raise exception.RuleNotConditional
-
-
-class AVRule(BaseTERule):
-
- """An access vector type enforcement rule."""
-
- def __str__(self):
- rule_string = "{0.ruletype} {0.source} {0.target}:{0.tclass} ".format(
- self)
-
- perms = self.perms
-
- # allow/dontaudit/auditallow/neverallow rules
- if len(perms) > 1:
- rule_string += "{{ {0} }};".format(' '.join(perms))
- else:
- # convert to list since sets cannot be indexed
- rule_string += "{0};".format(list(perms)[0])
-
- try:
- rule_string += " [ {0} ]".format(self.conditional)
- except exception.RuleNotConditional:
- pass
-
- return rule_string
-
- @property
- def perms(self):
- """The rule's permission set."""
- return set(self.qpol_symbol.perm_iter(self.policy))
-
- @property
- def default(self):
- """The rule's default type."""
- raise exception.RuleUseError("{0} rules do not have a default type.".format(self.ruletype))
-
- @property
- def filename(self):
- raise exception.RuleUseError("{0} rules do not have file names".format(self.ruletype))
-
-
-class TERule(BaseTERule):
-
- """A type_* type enforcement rule."""
-
- def __str__(self):
- rule_string = "{0.ruletype} {0.source} {0.target}:{0.tclass} {0.default}".format(self)
-
- try:
- rule_string += " \"{0}\";".format(self.filename)
- except (exception.TERuleNoFilename, exception.RuleUseError):
- # invalid use for type_change/member
- rule_string += ";"
-
- try:
- rule_string += " [ {0} ]".format(self.conditional)
- except exception.RuleNotConditional:
- pass
-
- return rule_string
-
- @property
- def perms(self):
- """The rule's permission set."""
- raise exception.RuleUseError(
- "{0} rules do not have a permission set.".format(self.ruletype))
-
- @property
- def default(self):
- """The rule's default type."""
- return typeattr.type_factory(self.policy, self.qpol_symbol.default_type(self.policy))
-
- @property
- def filename(self):
- """The type_transition rule's file name."""
- try:
- return self.qpol_symbol.filename(self.policy)
- except AttributeError:
- if self.ruletype == "type_transition":
- raise exception.TERuleNoFilename
- else:
- raise exception.RuleUseError("{0} rules do not have file names".
- format(self.ruletype))
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/typeattr.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/typeattr.py
deleted file mode 100644
index a52c69a..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/typeattr.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import symbol
-
-
-def _symbol_lookup(qpol_policy, name):
- """Look up the low-level qpol policy reference"""
- if isinstance(name, qpol.qpol_type_t):
- return name
-
- try:
- return qpol.qpol_type_t(qpol_policy, str(name))
- except ValueError:
- raise exception.InvalidType("{0} is not a valid type/attribute".format(name))
-
-
-def attribute_factory(qpol_policy, name):
- """Factory function for creating attribute objects."""
-
- if isinstance(name, TypeAttribute):
- assert name.policy == qpol_policy
- return name
-
- qpol_symbol = _symbol_lookup(qpol_policy, name)
-
- if not qpol_symbol.isattr(qpol_policy):
- raise TypeError("{0} is a type".format(qpol_symbol.name(qpol_policy)))
-
- return TypeAttribute(qpol_policy, qpol_symbol)
-
-
-def type_factory(qpol_policy, name, deref=False):
- """Factory function for creating type objects."""
-
- if isinstance(name, Type):
- assert name.policy == qpol_policy
- return name
-
- qpol_symbol = _symbol_lookup(qpol_policy, name)
-
- if qpol_symbol.isattr(qpol_policy):
- raise TypeError("{0} is an attribute".format(qpol_symbol.name(qpol_policy)))
- elif qpol_symbol.isalias(qpol_policy) and not deref:
- raise TypeError("{0} is an alias.".format(qpol_symbol.name(qpol_policy)))
-
- return Type(qpol_policy, qpol_symbol)
-
-
-def type_or_attr_factory(qpol_policy, name, deref=False):
- """Factory function for creating type or attribute objects."""
-
- if isinstance(name, (Type, TypeAttribute)):
- assert name.policy == qpol_policy
- return name
-
- qpol_symbol = _symbol_lookup(qpol_policy, name)
-
- if qpol_symbol.isalias(qpol_policy) and not deref:
- raise TypeError("{0} is an alias.".format(qpol_symbol.name(qpol_policy)))
-
- if qpol_symbol.isattr(qpol_policy):
- return TypeAttribute(qpol_policy, qpol_symbol)
- else:
- return Type(qpol_policy, qpol_symbol)
-
-
-class BaseType(symbol.PolicySymbol):
-
- """Type/attribute base class."""
-
- @property
- def ispermissive(self):
- raise NotImplementedError
-
- def expand(self):
- """Generator that expands this attribute into its member types."""
- raise NotImplementedError
-
- def attributes(self):
- """Generator that yields all attributes for this type."""
- raise NotImplementedError
-
- def aliases(self):
- """Generator that yields all aliases for this type."""
- raise NotImplementedError
-
-
-class Type(BaseType):
-
- """A type."""
-
- @property
- def ispermissive(self):
- """(T/F) the type is permissive."""
- return self.qpol_symbol.ispermissive(self.policy)
-
- def expand(self):
- """Generator that expands this into its member types."""
- yield self
-
- def attributes(self):
- """Generator that yields all attributes for this type."""
- for attr in self.qpol_symbol.attr_iter(self.policy):
- yield attribute_factory(self.policy, attr)
-
- def aliases(self):
- """Generator that yields all aliases for this type."""
- for alias in self.qpol_symbol.alias_iter(self.policy):
- yield alias
-
- def statement(self):
- attrs = list(self.attributes())
- aliases = list(self.aliases())
- stmt = "type {0}".format(self)
- if aliases:
- if len(aliases) > 1:
- stmt += " alias {{ {0} }}".format(' '.join(aliases))
- else:
- stmt += " alias {0}".format(aliases[0])
- for attr in attrs:
- stmt += ", {0}".format(attr)
- stmt += ";"
- return stmt
-
-
-class TypeAttribute(BaseType):
-
- """An attribute."""
-
- def __contains__(self, other):
- for type_ in self.expand():
- if other == type_:
- return True
-
- return False
-
- def expand(self):
- """Generator that expands this attribute into its member types."""
- for type_ in self.qpol_symbol.type_iter(self.policy):
- yield type_factory(self.policy, type_)
-
- def attributes(self):
- """Generator that yields all attributes for this type."""
- raise TypeError("{0} is an attribute, thus does not have attributes.".format(self))
-
- def aliases(self):
- """Generator that yields all aliases for this type."""
- raise TypeError("{0} is an attribute, thus does not have aliases.".format(self))
-
- @property
- def ispermissive(self):
- """(T/F) the type is permissive."""
- raise TypeError("{0} is an attribute, thus cannot be permissive.".format(self))
-
- def statement(self):
- return "attribute {0};".format(self)
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/user.py b/lib/python2.7/site-packages/setoolsgui/setools/policyrep/user.py
deleted file mode 100644
index 94f81bc..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/policyrep/user.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright 2014, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-from . import exception
-from . import qpol
-from . import role
-from . import mls
-from . import symbol
-
-
-def user_factory(qpol_policy, name):
- """Factory function for creating User objects."""
-
- if isinstance(name, User):
- assert name.policy == qpol_policy
- return name
- elif isinstance(name, qpol.qpol_user_t):
- return User(qpol_policy, name)
-
- try:
- return User(qpol_policy, qpol.qpol_user_t(qpol_policy, str(name)))
- except ValueError:
- raise exception.InvalidUser("{0} is not a valid user".format(name))
-
-
-class User(symbol.PolicySymbol):
-
- """A user."""
-
- @property
- def roles(self):
- """The user's set of roles."""
-
- roleset = set()
-
- for role_ in self.qpol_symbol.role_iter(self.policy):
- item = role.role_factory(self.policy, role_)
-
- # object_r is implicitly added to all roles by the compiler.
- # technically it is incorrect to skip it, but policy writers
- # and analysts don't expect to see it in results, and it
- # will confuse, especially for role set equality user queries.
- if item != "object_r":
- roleset.add(item)
-
- return roleset
-
- @property
- def mls_level(self):
- """The user's default MLS level."""
- return mls.level_factory(self.policy, self.qpol_symbol.dfltlevel(self.policy))
-
- @property
- def mls_range(self):
- """The user's MLS range."""
- return mls.range_factory(self.policy, self.qpol_symbol.range(self.policy))
-
- def statement(self):
- roles = list(str(r) for r in self.roles)
- stmt = "user {0} roles ".format(self)
- if len(roles) > 1:
- stmt += "{{ {0} }}".format(' '.join(roles))
- else:
- stmt += roles[0]
-
- try:
- stmt += " level {0.mls_level} range {0.mls_range};".format(self)
- except exception.MLSDisabled:
- stmt += ";"
-
- return stmt
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/portconquery.py b/lib/python2.7/site-packages/setoolsgui/setools/portconquery.py
deleted file mode 100644
index 798a828..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/portconquery.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-from socket import IPPROTO_TCP, IPPROTO_UDP
-
-from . import contextquery
-from .policyrep.netcontext import port_range
-
-
-class PortconQuery(contextquery.ContextQuery):
-
- """
- Port context query.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- protocol The protocol to match (socket.IPPROTO_TCP for
- TCP or socket.IPPROTO_UDP for UDP)
-
- ports A 2-tuple of the port range to match. (Set both to
- the same value for a single port)
- ports_subset If true, the criteria will match if it is a subset
- of the portcon's range.
- ports_overlap If true, the criteria will match if it overlaps
- any of the portcon's range.
- ports_superset If true, the criteria will match if it is a superset
- of the portcon's range.
- ports_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
-
- user The criteria to match the context's user.
- user_regex If true, regular expression matching
- will be used on the user.
-
- role The criteria to match the context's role.
- role_regex If true, regular expression matching
- will be used on the role.
-
- type_ The criteria to match the context's type.
- type_regex If true, regular expression matching
- will be used on the type.
-
- range_ The criteria to match the context's range.
- range_subset If true, the criteria will match if it is a subset
- of the context's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the context's range.
- range_superset If true, the criteria will match if it is a superset
- of the context's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- _protocol = None
- _ports = None
- ports_subset = False
- ports_overlap = False
- ports_superset = False
- ports_proper = False
-
- @property
- def ports(self):
- return self._ports
-
- @ports.setter
- def ports(self, value):
- pending_ports = port_range(*value)
-
- if all(pending_ports):
- if pending_ports.low < 1 or pending_ports.high < 1:
- raise ValueError("Port numbers must be positive: {0.low}-{0.high}".
- format(pending_ports))
-
- if pending_ports.low > pending_ports.high:
- raise ValueError(
- "The low port must be smaller than the high port: {0.low}-{0.high}".
- format(pending_ports))
-
- self._ports = pending_ports
- else:
- self._ports = None
-
- @property
- def protocol(self):
- return self._protocol
-
- @protocol.setter
- def protocol(self, value):
- if value:
- if not (value == IPPROTO_TCP or value == IPPROTO_UDP):
- raise ValueError(
- "The protocol must be {0} for TCP or {1} for UDP.".
- format(IPPROTO_TCP, IPPROTO_UDP))
-
- self._protocol = value
- else:
- self._protocol = None
-
- def results(self):
- """Generator which yields all matching portcons."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Ports: {0.ports}, overlap: {0.ports_overlap}, "
- "subset: {0.ports_subset}, superset: {0.ports_superset}, "
- "proper: {0.ports_proper}".format(self))
- self.log.debug("User: {0.user!r}, regex: {0.user_regex}".format(self))
- self.log.debug("Role: {0.role!r}, regex: {0.role_regex}".format(self))
- self.log.debug("Type: {0.type_!r}, regex: {0.type_regex}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for portcon in self.policy.portcons():
-
- if self.ports and not self._match_range(
- portcon.ports,
- self.ports,
- self.ports_subset,
- self.ports_overlap,
- self.ports_superset,
- self.ports_proper):
- continue
-
- if self.protocol and self.protocol != portcon.protocol:
- continue
-
- if not self._match_context(portcon.context):
- continue
-
- yield portcon
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/query.py b/lib/python2.7/site-packages/setoolsgui/setools/query.py
deleted file mode 100644
index 358a095..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/query.py
+++ /dev/null
@@ -1,192 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-
-class PolicyQuery(object):
-
- """Base class for SELinux policy queries."""
-
- def __init__(self, policy, **kwargs):
- self.log = logging.getLogger(self.__class__.__name__)
-
- self.policy = policy
-
- # keys are sorted in reverse order so regex settings
- # are set before the criteria, e.g. name_regex
- # is set before name. This ensures correct behavior
- # since the criteria descriptors are sensitve to
- # regex settings.
- for name in sorted(kwargs.keys(), reverse=True):
- attr = getattr(self, name, None) # None is not callable
- if callable(attr):
- raise ValueError("Keyword parameter {0} conflicts with a callable.".format(name))
-
- setattr(self, name, kwargs[name])
-
- @staticmethod
- def _match_regex(obj, criteria, regex):
- """
- Match the object with optional regular expression.
-
- Parameters:
- obj The object to match.
- criteria The criteria to match.
- regex If regular expression matching should be used.
- """
-
- if regex:
- return bool(criteria.search(str(obj)))
- else:
- return obj == criteria
-
- @staticmethod
- def _match_set(obj, criteria, equal):
- """
- Match the object (a set) with optional set equality.
-
- Parameters:
- obj The object to match. (a set)
- criteria The criteria to match. (a set)
- equal If set equality should be used. Otherwise
- any set intersection will match.
- """
-
- if equal:
- return obj == criteria
- else:
- return bool(obj.intersection(criteria))
-
- @staticmethod
- def _match_in_set(obj, criteria, regex):
- """
- Match if the criteria is in the list, with optional
- regular expression matching.
-
- Parameters:
- obj The object to match.
- criteria The criteria to match.
- regex If regular expression matching should be used.
- """
-
- if regex:
- return [m for m in obj if criteria.search(str(m))]
- else:
- return criteria in obj
-
- @staticmethod
- def _match_indirect_regex(obj, criteria, indirect, regex):
- """
- Match the object with optional regular expression and indirection.
-
- Parameters:
- obj The object to match.
- criteria The criteria to match.
- regex If regular expression matching should be used.
- indirect If object indirection should be used, e.g.
- expanding an attribute.
- """
-
- if indirect:
- return PolicyQuery._match_in_set((obj.expand()), criteria, regex)
- else:
- return PolicyQuery._match_regex(obj, criteria, regex)
-
- @staticmethod
- def _match_regex_or_set(obj, criteria, equal, regex):
- """
- Match the object (a set) with either set comparisons
- (equality or intersection) or by regex matching of the
- set members. Regular expression matching will override
- the set equality option.
-
- Parameters:
- obj The object to match. (a set)
- criteria The criteria to match.
- equal If set equality should be used. Otherwise
- any set intersection will match. Ignored
- if regular expression matching is used.
- regex If regular expression matching should be used.
- """
-
- if regex:
- return [m for m in obj if criteria.search(str(m))]
- else:
- return PolicyQuery._match_set(obj, set(criteria), equal)
-
- @staticmethod
- def _match_range(obj, criteria, subset, overlap, superset, proper):
- """
- Match ranges of objects.
-
- obj An object with attributes named "low" and "high", representing the range.
- criteria An object with attributes named "low" and "high", representing the criteria.
- subset If true, the criteria will match if it is a subset obj's range.
- overlap If true, the criteria will match if it overlaps any of the obj's range.
- superset If true, the criteria will match if it is a superset of the obj's range.
- proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- if overlap:
- return ((obj.low <= criteria.low <= obj.high) or (
- obj.low <= criteria.high <= obj.high) or (
- criteria.low <= obj.low and obj.high <= criteria.high))
- elif subset:
- if proper:
- return ((obj.low < criteria.low and criteria.high <= obj.high) or (
- obj.low <= criteria.low and criteria.high < obj.high))
- else:
- return obj.low <= criteria.low and criteria.high <= obj.high
- elif superset:
- if proper:
- return ((criteria.low < obj.low and obj.high <= criteria.high) or (
- criteria.low <= obj.low and obj.high < criteria.high))
- else:
- return (criteria.low <= obj.low and obj.high <= criteria.high)
- else:
- return criteria.low == obj.low and obj.high == criteria.high
-
- @staticmethod
- def _match_level(obj, criteria, dom, domby, incomp):
- """
- Match the an MLS level.
-
- obj The level to match.
- criteria The criteria to match. (a level)
- dom If true, the criteria will match if it dominates obj.
- domby If true, the criteria will match if it is dominated by obj.
- incomp If true, the criteria will match if it is incomparable to obj.
- """
-
- if dom:
- return (criteria >= obj)
- elif domby:
- return (criteria <= obj)
- elif incomp:
- return (criteria ^ obj)
- else:
- return (criteria == obj)
-
- def results(self):
- """
- Generator which returns the matches for the query. This method
- should be overridden by subclasses.
- """
- raise NotImplementedError
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/rbacrulequery.py b/lib/python2.7/site-packages/setoolsgui/setools/rbacrulequery.py
deleted file mode 100644
index 240b921..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/rbacrulequery.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import mixins, query
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor, RuletypeDescriptor
-from .policyrep.exception import InvalidType, RuleUseError
-
-
-class RBACRuleQuery(mixins.MatchObjClass, query.PolicyQuery):
-
- """
- Query the RBAC rules.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- ruletype The list of rule type(s) to match.
- source The name of the source role/attribute to match.
- source_indirect If true, members of an attribute will be
- matched rather than the attribute itself.
- source_regex If true, regular expression matching will
- be used on the source role/attribute.
- Obeys the source_indirect option.
- target The name of the target role/attribute to match.
- target_indirect If true, members of an attribute will be
- matched rather than the attribute itself.
- target_regex If true, regular expression matching will
- be used on the target role/attribute.
- Obeys target_indirect option.
- tclass The object class(es) to match.
- tclass_regex If true, use a regular expression for
- matching the rule's object class.
- default The name of the default role to match.
- default_regex If true, regular expression matching will
- be used on the default role.
- """
-
- ruletype = RuletypeDescriptor("validate_rbac_ruletype")
- source = CriteriaDescriptor("source_regex", "lookup_role")
- source_regex = False
- source_indirect = True
- _target = None
- target_regex = False
- target_indirect = True
- tclass = CriteriaSetDescriptor("tclass_regex", "lookup_class")
- tclass_regex = False
- default = CriteriaDescriptor("default_regex", "lookup_role")
- default_regex = False
-
- @property
- def target(self):
- return self._target
-
- @target.setter
- def target(self, value):
- if not value:
- self._target = None
- elif self.target_regex:
- self._target = re.compile(value)
- else:
- try:
- self._target = self.policy.lookup_type_or_attr(value)
- except InvalidType:
- self._target = self.policy.lookup_role(value)
-
- def results(self):
- """Generator which yields all matching RBAC rules."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Ruletypes: {0.ruletype}".format(self))
- self.log.debug("Source: {0.source!r}, indirect: {0.source_indirect}, "
- "regex: {0.source_regex}".format(self))
- self.log.debug("Target: {0.target!r}, indirect: {0.target_indirect}, "
- "regex: {0.target_regex}".format(self))
- self.log.debug("Class: {0.tclass!r}, regex: {0.tclass_regex}".format(self))
- self.log.debug("Default: {0.default!r}, regex: {0.default_regex}".format(self))
-
- for rule in self.policy.rbacrules():
- #
- # Matching on rule type
- #
- if self.ruletype:
- if rule.ruletype not in self.ruletype:
- continue
-
- #
- # Matching on source role
- #
- if self.source and not self._match_indirect_regex(
- rule.source,
- self.source,
- self.source_indirect,
- self.source_regex):
- continue
-
- #
- # Matching on target type (role_transition)/role(allow)
- #
- if self.target and not self._match_indirect_regex(
- rule.target,
- self.target,
- self.target_indirect,
- self.target_regex):
- continue
-
- #
- # Matching on object class
- #
- try:
- if not self._match_object_class(rule):
- continue
- except RuleUseError:
- continue
-
- #
- # Matching on default role
- #
- if self.default:
- try:
- if not self._match_regex(
- rule.default,
- self.default,
- self.default_regex):
- continue
- except RuleUseError:
- continue
-
- # if we get here, we have matched all available criteria
- yield rule
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/rolequery.py b/lib/python2.7/site-packages/setoolsgui/setools/rolequery.py
deleted file mode 100644
index e95dfa6..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/rolequery.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import compquery
-from .descriptors import CriteriaSetDescriptor
-
-
-class RoleQuery(compquery.ComponentQuery):
-
- """
- Query SELinux policy roles.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The role name to match.
- name_regex If true, regular expression matching
- will be used on the role names.
- types The type to match.
- types_equal If true, only roles with type sets
- that are equal to the criteria will
- match. Otherwise, any intersection
- will match.
- types_regex If true, regular expression matching
- will be used on the type names instead
- of set logic.
- """
-
- types = CriteriaSetDescriptor("types_regex", "lookup_type")
- types_equal = False
- types_regex = False
-
- def results(self):
- """Generator which yields all matching roles."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Types: {0.types!r}, regex: {0.types_regex}, "
- "eq: {0.types_equal}".format(self))
-
- for r in self.policy.roles():
- if r == "object_r":
- # all types are implicitly added to object_r by the compiler.
- # technically it is incorrect to skip it, but policy writers
- # and analysts don't expect to see it in results, and it
- # will confuse, especially for set equality type queries.
- continue
-
- if not self._match_name(r):
- continue
-
- if self.types and not self._match_regex_or_set(
- set(r.types()),
- self.types,
- self.types_equal,
- self.types_regex):
- continue
-
- yield r
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/sensitivityquery.py b/lib/python2.7/site-packages/setoolsgui/setools/sensitivityquery.py
deleted file mode 100644
index a102836..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/sensitivityquery.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright 2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-
-from . import compquery
-from . import mixins
-from .descriptors import CriteriaDescriptor
-
-
-class SensitivityQuery(mixins.MatchAlias, compquery.ComponentQuery):
-
- """
- Query MLS Sensitivities
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The name of the category to match.
- name_regex If true, regular expression matching will
- be used for matching the name.
- alias The alias name to match.
- alias_regex If true, regular expression matching
- will be used on the alias names.
- sens The criteria to match the sensitivity by dominance.
- sens_dom If true, the criteria will match if it dominates
- the sensitivity.
- sens_domby If true, the criteria will match if it is dominated
- by the sensitivity.
- """
-
- sens = CriteriaDescriptor(lookup_function="lookup_sensitivity")
- sens_dom = False
- sens_domby = False
-
- def results(self):
- """Generator which yields all matching sensitivities."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Alias: {0.alias}, regex: {0.alias_regex}".format(self))
- self.log.debug("Sens: {0.sens!r}, dom: {0.sens_dom}, domby: {0.sens_domby}".format(self))
-
- for s in self.policy.sensitivities():
- if not self._match_name(s):
- continue
-
- if not self._match_alias(s):
- continue
-
- if self.sens and not self._match_level(
- s,
- self.sens,
- self.sens_dom,
- self.sens_domby,
- False):
- continue
-
- yield s
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/terulequery.py b/lib/python2.7/site-packages/setoolsgui/setools/terulequery.py
deleted file mode 100644
index 7f3eccf..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/terulequery.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import mixins, query
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor, RuletypeDescriptor
-from .policyrep.exception import RuleUseError, RuleNotConditional
-
-
-class TERuleQuery(mixins.MatchObjClass, mixins.MatchPermission, query.PolicyQuery):
-
- """
- Query the Type Enforcement rules.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- ruletype The list of rule type(s) to match.
- source The name of the source type/attribute to match.
- source_indirect If true, members of an attribute will be
- matched rather than the attribute itself.
- Default is true.
- source_regex If true, regular expression matching will
- be used on the source type/attribute.
- Obeys the source_indirect option.
- Default is false.
- target The name of the target type/attribute to match.
- target_indirect If true, members of an attribute will be
- matched rather than the attribute itself.
- Default is true.
- target_regex If true, regular expression matching will
- be used on the target type/attribute.
- Obeys target_indirect option.
- Default is false.
- tclass The object class(es) to match.
- tclass_regex If true, use a regular expression for
- matching the rule's object class.
- Default is false.
- perms The set of permission(s) to match.
- perms_equal If true, the permission set of the rule
- must exactly match the permissions
- criteria. If false, any set intersection
- will match.
- Default is false.
- perms_regex If true, regular expression matching will be used
- on the permission names instead of set logic.
- default The name of the default type to match.
- default_regex If true, regular expression matching will be
- used on the default type.
- Default is false.
- boolean The set of boolean(s) to match.
- boolean_regex If true, regular expression matching will be
- used on the booleans.
- Default is false.
- boolean_equal If true, the booleans in the conditional
- expression of the rule must exactly match the
- criteria. If false, any set intersection
- will match. Default is false.
- """
-
- ruletype = RuletypeDescriptor("validate_te_ruletype")
- source = CriteriaDescriptor("source_regex", "lookup_type_or_attr")
- source_regex = False
- source_indirect = True
- target = CriteriaDescriptor("target_regex", "lookup_type_or_attr")
- target_regex = False
- target_indirect = True
- default = CriteriaDescriptor("default_regex", "lookup_type")
- default_regex = False
- boolean = CriteriaSetDescriptor("boolean_regex", "lookup_boolean")
- boolean_regex = False
- boolean_equal = False
-
- def results(self):
- """Generator which yields all matching TE rules."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Ruletypes: {0.ruletype}".format(self))
- self.log.debug("Source: {0.source!r}, indirect: {0.source_indirect}, "
- "regex: {0.source_regex}".format(self))
- self.log.debug("Target: {0.target!r}, indirect: {0.target_indirect}, "
- "regex: {0.target_regex}".format(self))
- self.log.debug("Class: {0.tclass!r}, regex: {0.tclass_regex}".format(self))
- self.log.debug("Perms: {0.perms!r}, regex: {0.perms_regex}, eq: {0.perms_equal}".
- format(self))
- self.log.debug("Default: {0.default!r}, regex: {0.default_regex}".format(self))
- self.log.debug("Boolean: {0.boolean!r}, eq: {0.boolean_equal}, "
- "regex: {0.boolean_regex}".format(self))
-
- for rule in self.policy.terules():
- #
- # Matching on rule type
- #
- if self.ruletype:
- if rule.ruletype not in self.ruletype:
- continue
-
- #
- # Matching on source type
- #
- if self.source and not self._match_indirect_regex(
- rule.source,
- self.source,
- self.source_indirect,
- self.source_regex):
- continue
-
- #
- # Matching on target type
- #
- if self.target and not self._match_indirect_regex(
- rule.target,
- self.target,
- self.target_indirect,
- self.target_regex):
- continue
-
- #
- # Matching on object class
- #
- if not self._match_object_class(rule):
- continue
-
- #
- # Matching on permission set
- #
- try:
- if not self._match_perms(rule):
- continue
- except RuleUseError:
- continue
-
- #
- # Matching on default type
- #
- if self.default:
- try:
- if not self._match_regex(
- rule.default,
- self.default,
- self.default_regex):
- continue
- except RuleUseError:
- continue
-
- #
- # Match on Boolean in conditional expression
- #
- if self.boolean:
- try:
- if not self._match_regex_or_set(
- rule.conditional.booleans,
- self.boolean,
- self.boolean_equal,
- self.boolean_regex):
- continue
- except RuleNotConditional:
- continue
-
- # if we get here, we have matched all available criteria
- yield rule
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/typeattrquery.py b/lib/python2.7/site-packages/setoolsgui/setools/typeattrquery.py
deleted file mode 100644
index a91026c..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/typeattrquery.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import compquery
-from .descriptors import CriteriaSetDescriptor
-
-
-class TypeAttributeQuery(compquery.ComponentQuery):
-
- """
- Query SELinux policy type attributes.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The type name to match.
- name_regex If true, regular expression matching
- will be used on the type names.
- types The type to match.
- types_equal If true, only attributes with type sets
- that are equal to the criteria will
- match. Otherwise, any intersection
- will match.
- types_regex If true, regular expression matching
- will be used on the type names instead
- of set logic.
- """
-
- types = CriteriaSetDescriptor("types_regex", "lookup_type")
- types_equal = False
- types_regex = False
-
- def results(self):
- """Generator which yields all matching types."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Types: {0.types!r}, regex: {0.types_regex}, "
- "eq: {0.types_equal}".format(self))
-
- for attr in self.policy.typeattributes():
- if not self._match_name(attr):
- continue
-
- if self.types and not self._match_regex_or_set(
- set(attr.expand()),
- self.types,
- self.types_equal,
- self.types_regex):
- continue
-
- yield attr
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/typequery.py b/lib/python2.7/site-packages/setoolsgui/setools/typequery.py
deleted file mode 100644
index 6634f76..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/typequery.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import compquery
-from . import mixins
-from .descriptors import CriteriaSetDescriptor
-
-
-class TypeQuery(mixins.MatchAlias, compquery.ComponentQuery):
-
- """
- Query SELinux policy types.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The type name to match.
- name_regex If true, regular expression matching
- will be used on the type names.
- alias The alias name to match.
- alias_regex If true, regular expression matching
- will be used on the alias names.
- attrs The attribute to match.
- attrs_equal If true, only types with attribute sets
- that are equal to the criteria will
- match. Otherwise, any intersection
- will match.
- attrs_regex If true, regular expression matching
- will be used on the attribute names instead
- of set logic.
- permissive The permissive state to match. If this
- is None, the state is not matched.
- """
-
- attrs = CriteriaSetDescriptor("attrs_regex", "lookup_typeattr")
- attrs_regex = False
- attrs_equal = False
- _permissive = None
-
- @property
- def permissive(self):
- return self._permissive
-
- @permissive.setter
- def permissive(self, value):
- if value is None:
- self._permissive = None
- else:
- self._permissive = bool(value)
-
- def results(self):
- """Generator which yields all matching types."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Alias: {0.alias}, regex: {0.alias_regex}".format(self))
- self.log.debug("Attrs: {0.attrs!r}, regex: {0.attrs_regex}, "
- "eq: {0.attrs_equal}".format(self))
- self.log.debug("Permissive: {0.permissive}".format(self))
-
- for t in self.policy.types():
- if not self._match_name(t):
- continue
-
- if not self._match_alias(t):
- continue
-
- if self.attrs and not self._match_regex_or_set(
- set(t.attributes()),
- self.attrs,
- self.attrs_equal,
- self.attrs_regex):
- continue
-
- if self.permissive is not None and t.ispermissive != self.permissive:
- continue
-
- yield t
diff --git a/lib/python2.7/site-packages/setoolsgui/setools/userquery.py b/lib/python2.7/site-packages/setoolsgui/setools/userquery.py
deleted file mode 100644
index 00910cf..0000000
--- a/lib/python2.7/site-packages/setoolsgui/setools/userquery.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright 2014-2015, Tresys Technology, LLC
-#
-# This file is part of SETools.
-#
-# SETools is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as
-# published by the Free Software Foundation, either version 2.1 of
-# the License, or (at your option) any later version.
-#
-# SETools is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with SETools. If not, see
-# <http://www.gnu.org/licenses/>.
-#
-import logging
-import re
-
-from . import compquery
-from .descriptors import CriteriaDescriptor, CriteriaSetDescriptor
-
-
-class UserQuery(compquery.ComponentQuery):
-
- """
- Query SELinux policy users.
-
- Parameter:
- policy The policy to query.
-
- Keyword Parameters/Class attributes:
- name The user name to match.
- name_regex If true, regular expression matching
- will be used on the user names.
- roles The attribute to match.
- roles_equal If true, only types with role sets
- that are equal to the criteria will
- match. Otherwise, any intersection
- will match.
- roles_regex If true, regular expression matching
- will be used on the role names instead
- of set logic.
- level The criteria to match the user's default level.
- level_dom If true, the criteria will match if it dominates
- the user's default level.
- level_domby If true, the criteria will match if it is dominated
- by the user's default level.
- level_incomp If true, the criteria will match if it is incomparable
- to the user's default level.
- range_ The criteria to match the user's range.
- range_subset If true, the criteria will match if it is a subset
- of the user's range.
- range_overlap If true, the criteria will match if it overlaps
- any of the user's range.
- range_superset If true, the criteria will match if it is a superset
- of the user's range.
- range_proper If true, use proper superset/subset operations.
- No effect if not using set operations.
- """
-
- level = CriteriaDescriptor(lookup_function="lookup_level")
- level_dom = False
- level_domby = False
- level_incomp = False
- range_ = CriteriaDescriptor(lookup_function="lookup_range")
- range_overlap = False
- range_subset = False
- range_superset = False
- range_proper = False
- roles = CriteriaSetDescriptor("roles_regex", "lookup_role")
- roles_equal = False
- roles_regex = False
-
- def results(self):
- """Generator which yields all matching users."""
- self.log.info("Generating results from {0.policy}".format(self))
- self.log.debug("Name: {0.name!r}, regex: {0.name_regex}".format(self))
- self.log.debug("Roles: {0.roles!r}, regex: {0.roles_regex}, "
- "eq: {0.roles_equal}".format(self))
- self.log.debug("Level: {0.level!r}, dom: {0.level_dom}, domby: {0.level_domby}, "
- "incomp: {0.level_incomp}".format(self))
- self.log.debug("Range: {0.range_!r}, subset: {0.range_subset}, overlap: {0.range_overlap}, "
- "superset: {0.range_superset}, proper: {0.range_proper}".format(self))
-
- for user in self.policy.users():
- if not self._match_name(user):
- continue
-
- if self.roles and not self._match_regex_or_set(
- user.roles,
- self.roles,
- self.roles_equal,
- self.roles_regex):
- continue
-
- if self.level and not self._match_level(
- user.mls_level,
- self.level,
- self.level_dom,
- self.level_domby,
- self.level_incomp):
- continue
-
- if self.range_ and not self._match_range(
- user.mls_range,
- self.range_,
- self.range_subset,
- self.range_overlap,
- self.range_superset,
- self.range_proper):
- continue
-
- yield user
