blob: e0080129c643ffa39291650f467dd72f7fc63232 [file] [log] [blame]
from fontTools.varLib.models import (
normalizeLocation,
supportScalar,
VariationModel,
VariationModelError,
)
import pytest
def test_normalizeLocation():
axes = {"wght": (100, 400, 900)}
assert normalizeLocation({"wght": 400}, axes) == {"wght": 0.0}
assert normalizeLocation({"wght": 100}, axes) == {"wght": -1.0}
assert normalizeLocation({"wght": 900}, axes) == {"wght": 1.0}
assert normalizeLocation({"wght": 650}, axes) == {"wght": 0.5}
assert normalizeLocation({"wght": 1000}, axes) == {"wght": 1.0}
assert normalizeLocation({"wght": 0}, axes) == {"wght": -1.0}
axes = {"wght": (0, 0, 1000)}
assert normalizeLocation({"wght": 0}, axes) == {"wght": 0.0}
assert normalizeLocation({"wght": -1}, axes) == {"wght": 0.0}
assert normalizeLocation({"wght": 1000}, axes) == {"wght": 1.0}
assert normalizeLocation({"wght": 500}, axes) == {"wght": 0.5}
assert normalizeLocation({"wght": 1001}, axes) == {"wght": 1.0}
axes = {"wght": (0, 1000, 1000)}
assert normalizeLocation({"wght": 0}, axes) == {"wght": -1.0}
assert normalizeLocation({"wght": -1}, axes) == {"wght": -1.0}
assert normalizeLocation({"wght": 500}, axes) == {"wght": -0.5}
assert normalizeLocation({"wght": 1000}, axes) == {"wght": 0.0}
assert normalizeLocation({"wght": 1001}, axes) == {"wght": 0.0}
def test_supportScalar():
assert supportScalar({}, {}) == 1.0
assert supportScalar({"wght": 0.2}, {}) == 1.0
assert supportScalar({"wght": 0.2}, {"wght": (0, 2, 3)}) == 0.1
assert supportScalar({"wght": 2.5}, {"wght": (0, 2, 4)}) == 0.75
assert supportScalar({"wght": 4}, {"wght": (0, 2, 2)}) == 0.0
assert supportScalar({"wght": 4}, {"wght": (0, 2, 2)}, extrapolate=True) == 2.0
assert supportScalar({"wght": 4}, {"wght": (0, 2, 3)}, extrapolate=True) == 2.0
assert supportScalar({"wght": 2}, {"wght": (0, 0.75, 1)}, extrapolate=True) == -4.0
@pytest.mark.parametrize(
"numLocations, numSamples",
[
pytest.param(127, 509, marks=pytest.mark.slow),
(31, 251),
],
)
def test_modeling_error(numLocations, numSamples):
# https://github.com/fonttools/fonttools/issues/2213
locations = [{"axis": float(i) / numLocations} for i in range(numLocations)]
masterValues = [100.0 if i else 0.0 for i in range(numLocations)]
model = VariationModel(locations)
for i in range(numSamples):
loc = {"axis": float(i) / numSamples}
scalars = model.getScalars(loc)
deltas_float = model.getDeltas(masterValues)
deltas_round = model.getDeltas(masterValues, round=round)
expected = model.interpolateFromDeltasAndScalars(deltas_float, scalars)
actual = model.interpolateFromDeltasAndScalars(deltas_round, scalars)
err = abs(actual - expected)
assert err <= 0.5, (i, err)
# This is how NOT to round deltas.
# deltas_late_round = [round(d) for d in deltas_float]
# bad = model.interpolateFromDeltasAndScalars(deltas_late_round, scalars)
# err_bad = abs(bad - expected)
# if err != err_bad:
# print("{:d} {:.2} {:.2}".format(i, err, err_bad))
class VariationModelTest(object):
@pytest.mark.parametrize(
"locations, axisOrder, sortedLocs, supports, deltaWeights",
[
(
[
{"wght": 0.55, "wdth": 0.0},
{"wght": -0.55, "wdth": 0.0},
{"wght": -1.0, "wdth": 0.0},
{"wght": 0.0, "wdth": 1.0},
{"wght": 0.66, "wdth": 1.0},
{"wght": 0.66, "wdth": 0.66},
{"wght": 0.0, "wdth": 0.0},
{"wght": 1.0, "wdth": 1.0},
{"wght": 1.0, "wdth": 0.0},
],
["wght"],
[
{},
{"wght": -0.55},
{"wght": -1.0},
{"wght": 0.55},
{"wght": 1.0},
{"wdth": 1.0},
{"wdth": 1.0, "wght": 1.0},
{"wdth": 1.0, "wght": 0.66},
{"wdth": 0.66, "wght": 0.66},
],
[
{},
{"wght": (-1.0, -0.55, 0)},
{"wght": (-1.0, -1.0, -0.55)},
{"wght": (0, 0.55, 1.0)},
{"wght": (0.55, 1.0, 1.0)},
{"wdth": (0, 1.0, 1.0)},
{"wdth": (0, 1.0, 1.0), "wght": (0, 1.0, 1.0)},
{"wdth": (0, 1.0, 1.0), "wght": (0, 0.66, 1.0)},
{"wdth": (0, 0.66, 1.0), "wght": (0, 0.66, 1.0)},
],
[
{},
{0: 1.0},
{0: 1.0},
{0: 1.0},
{0: 1.0},
{0: 1.0},
{0: 1.0, 4: 1.0, 5: 1.0},
{
0: 1.0,
3: 0.7555555555555555,
4: 0.24444444444444444,
5: 1.0,
6: 0.66,
},
{
0: 1.0,
3: 0.7555555555555555,
4: 0.24444444444444444,
5: 0.66,
6: 0.43560000000000004,
7: 0.66,
},
],
),
(
[
{},
{"bar": 0.5},
{"bar": 1.0},
{"foo": 1.0},
{"bar": 0.5, "foo": 1.0},
{"bar": 1.0, "foo": 1.0},
],
None,
[
{},
{"bar": 0.5},
{"bar": 1.0},
{"foo": 1.0},
{"bar": 0.5, "foo": 1.0},
{"bar": 1.0, "foo": 1.0},
],
[
{},
{"bar": (0, 0.5, 1.0)},
{"bar": (0.5, 1.0, 1.0)},
{"foo": (0, 1.0, 1.0)},
{"bar": (0, 0.5, 1.0), "foo": (0, 1.0, 1.0)},
{"bar": (0.5, 1.0, 1.0), "foo": (0, 1.0, 1.0)},
],
[
{},
{0: 1.0},
{0: 1.0},
{0: 1.0},
{0: 1.0, 1: 1.0, 3: 1.0},
{0: 1.0, 2: 1.0, 3: 1.0},
],
),
(
[
{},
{"foo": 0.25},
{"foo": 0.5},
{"foo": 0.75},
{"foo": 1.0},
{"bar": 0.25},
{"bar": 0.75},
{"bar": 1.0},
],
None,
[
{},
{"bar": 0.25},
{"bar": 0.75},
{"bar": 1.0},
{"foo": 0.25},
{"foo": 0.5},
{"foo": 0.75},
{"foo": 1.0},
],
[
{},
{"bar": (0.0, 0.25, 1.0)},
{"bar": (0.25, 0.75, 1.0)},
{"bar": (0.75, 1.0, 1.0)},
{"foo": (0.0, 0.25, 1.0)},
{"foo": (0.25, 0.5, 1.0)},
{"foo": (0.5, 0.75, 1.0)},
{"foo": (0.75, 1.0, 1.0)},
],
[
{},
{0: 1.0},
{0: 1.0, 1: 0.3333333333333333},
{0: 1.0},
{0: 1.0},
{0: 1.0, 4: 0.6666666666666666},
{0: 1.0, 4: 0.3333333333333333, 5: 0.5},
{0: 1.0},
],
),
(
[
{},
{"foo": 0.25},
{"foo": 0.5},
{"foo": 0.75},
{"foo": 1.0},
{"bar": 0.25},
{"bar": 0.75},
{"bar": 1.0},
],
None,
[
{},
{"bar": 0.25},
{"bar": 0.75},
{"bar": 1.0},
{"foo": 0.25},
{"foo": 0.5},
{"foo": 0.75},
{"foo": 1.0},
],
[
{},
{"bar": (0, 0.25, 1.0)},
{"bar": (0.25, 0.75, 1.0)},
{"bar": (0.75, 1.0, 1.0)},
{"foo": (0, 0.25, 1.0)},
{"foo": (0.25, 0.5, 1.0)},
{"foo": (0.5, 0.75, 1.0)},
{"foo": (0.75, 1.0, 1.0)},
],
[
{},
{0: 1.0},
{0: 1.0, 1: 0.3333333333333333},
{0: 1.0},
{0: 1.0},
{0: 1.0, 4: 0.6666666666666666},
{0: 1.0, 4: 0.3333333333333333, 5: 0.5},
{0: 1.0},
],
),
],
)
def test_init(self, locations, axisOrder, sortedLocs, supports, deltaWeights):
model = VariationModel(locations, axisOrder=axisOrder)
assert model.locations == sortedLocs
assert model.supports == supports
assert model.deltaWeights == deltaWeights
def test_init_duplicate_locations(self):
with pytest.raises(VariationModelError, match="Locations must be unique."):
VariationModel(
[
{"foo": 0.0, "bar": 0.0},
{"foo": 1.0, "bar": 1.0},
{"bar": 1.0, "foo": 1.0},
]
)
@pytest.mark.parametrize(
"locations, axisOrder, masterValues, instanceLocation, expectedValue",
[
(
[
{},
{"axis_A": 1.0},
{"axis_B": 1.0},
{"axis_A": 1.0, "axis_B": 1.0},
{"axis_A": 0.5, "axis_B": 1.0},
{"axis_A": 1.0, "axis_B": 0.5},
],
["axis_A", "axis_B"],
[
0,
10,
20,
70,
50,
60,
],
{
"axis_A": 0.5,
"axis_B": 0.5,
},
37.5,
),
],
)
def test_interpolation(
self,
locations,
axisOrder,
masterValues,
instanceLocation,
expectedValue,
):
model = VariationModel(locations, axisOrder=axisOrder)
interpolatedValue = model.interpolateFromMasters(instanceLocation, masterValues)
assert interpolatedValue == expectedValue