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Merge pull request #2765 from fonttools/revert-narrow-tents

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Revert "[varLib.models] Generate narrower tents"
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Cosimo Lupo 2022-08-23 17:43:12 +01:00 committed by GitHub
commit e25ee4b606
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 520 additions and 486 deletions
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53
Lib/fontTools/varLib/models.py
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@ -9,7 +9,6 @@ __all__ = [
from fontTools.misc.roundTools import noRound
from .errors import VariationModelError
from collections import defaultdict
def nonNone(lst):
@ -225,8 +224,13 @@ class VariationModel(object):
{0: 1.0},
{0: 1.0},
{0: 1.0, 4: 1.0, 5: 1.0},
{0: 1.0, 3: 0.75, 4: 0.25, 5: 1.0},
{0: 1.0, 3: 0.75, 4: 0.25, 5: 0.6666666666666667}]
{0: 1.0, 3: 0.75, 4: 0.25, 5: 1.0, 6: 0.6666666666666666},
{0: 1.0,
3: 0.75,
4: 0.25,
5: 0.6666666666666667,
6: 0.4444444444444445,
7: 0.6666666666666667}]
"""
def __init__(self, locations, axisOrder=None, extrapolate=False):
@ -385,48 +389,23 @@ class VariationModel(object):
def _locationsToRegions(self):
locations = self.locations
# Compute min/max across each axis, use it as total range.
# TODO Take this as input from outside?
minV = {}
maxV = {}
axes = set()
for l in locations:
for axis in l.keys():
axes.add(axis)
for axis in axes:
minV[axis] = 0.0
maxV[axis] = 0.0
for l in locations:
for axis, v in l.items():
minV[axis] = min(v, minV.get(axis, v))
maxV[axis] = max(v, maxV.get(axis, v))
axisPoints = defaultdict(lambda: defaultdict(lambda: {0.0}))
for loc in locations:
for axis, value in loc.items():
offAxisLoc = loc.copy()
offAxisLoc.pop(axis)
offAxisLoc = tuple(sorted(offAxisLoc.items()))
axisPoints[axis][offAxisLoc].add(value)
for k, v in l.items():
minV[k] = min(v, minV.get(k, v))
maxV[k] = max(v, maxV.get(k, v))
regions = []
for loc in locations:
region = {}
for axis, peak in loc.items():
assert peak != 0
offAxisLoc = loc.copy()
offAxisLoc.pop(axis)
offAxisLoc = tuple(sorted(offAxisLoc.items()))
points = axisPoints[axis][offAxisLoc]
points.add(minV[axis])
points.add(maxV[axis])
points = sorted(points)
peakIndex = points.index(peak)
lower = peak if peakIndex == 0 else points[peakIndex - 1]
upper = peak if peakIndex == len(points) - 1 else points[peakIndex + 1]
region[axis] = (lower, peak, upper)
for axis, locV in loc.items():
if locV > 0:
region[axis] = (0, locV, maxV[axis])
else:
region[axis] = (minV[axis], locV, 0)
regions.append(region)
return regions

864
Tests/varLib/data/test_results/TestSparseCFF2VF.ttx
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File diff suppressed because it is too large Load Diff

89
Tests/varLib/models_test.py
View File

@ -39,7 +39,7 @@ def test_supportScalar():
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, .75, 1)}, extrapolate=True) == -4.0
assert supportScalar({"wght": 2}, {"wght": (0, 0.75, 1)}, extrapolate=True) == -4.0
@pytest.mark.parametrize(
@ -112,8 +112,8 @@ class VariationModelTest(object):
{"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": (0.66, 1.0, 1.0)},
{"wdth": (0.66, 1.0, 1.0), "wght": (0, 0.66, 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)},
],
[
@ -124,8 +124,21 @@ class VariationModelTest(object):
{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},
{0: 1.0, 3: 0.7555555555555555, 4: 0.24444444444444444, 5: 0.66},
{
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,
},
],
),
(
@ -187,22 +200,22 @@ class VariationModelTest(object):
],
[
{},
{"bar": (0.0, 0.25, 0.75)},
{"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, 0.5)},
{"foo": (0.25, 0.5, 0.75)},
{"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},
{0: 1.0},
{0: 1.0},
{0: 1.0, 4: 0.6666666666666666},
{0: 1.0, 4: 0.3333333333333333, 5: 0.5},
{0: 1.0},
],
),
@ -230,22 +243,22 @@ class VariationModelTest(object):
],
[
{},
{"bar": (0, 0.25, 0.75)},
{"bar": (0, 0.25, 1.0)},
{"bar": (0.25, 0.75, 1.0)},
{"bar": (0.75, 1.0, 1.0)},
{"foo": (0, 0.25, 0.5)},
{"foo": (0.25, 0.5, 0.75)},
{"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},
{0: 1.0},
{0: 1.0},
{0: 1.0, 4: 0.6666666666666666},
{0: 1.0, 4: 0.3333333333333333, 5: 0.5},
{0: 1.0},
],
),
@ -267,3 +280,45 @@ class VariationModelTest(object):
{"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