186 lines
6.6 KiB
Python
186 lines
6.6 KiB
Python
from fontTools.varLib.models import (
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normalizeLocation, supportScalar, VariationModel, VariationModelError)
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import pytest
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def test_normalizeLocation():
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axes = {"wght": (100, 400, 900)}
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assert normalizeLocation({"wght": 400}, axes) == {'wght': 0.0}
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assert normalizeLocation({"wght": 100}, axes) == {'wght': -1.0}
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assert normalizeLocation({"wght": 900}, axes) == {'wght': 1.0}
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assert normalizeLocation({"wght": 650}, axes) == {'wght': 0.5}
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assert normalizeLocation({"wght": 1000}, axes) == {'wght': 1.0}
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assert normalizeLocation({"wght": 0}, axes) == {'wght': -1.0}
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axes = {"wght": (0, 0, 1000)}
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assert normalizeLocation({"wght": 0}, axes) == {'wght': 0.0}
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assert normalizeLocation({"wght": -1}, axes) == {'wght': 0.0}
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assert normalizeLocation({"wght": 1000}, axes) == {'wght': 1.0}
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assert normalizeLocation({"wght": 500}, axes) == {'wght': 0.5}
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assert normalizeLocation({"wght": 1001}, axes) == {'wght': 1.0}
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axes = {"wght": (0, 1000, 1000)}
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assert normalizeLocation({"wght": 0}, axes) == {'wght': -1.0}
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assert normalizeLocation({"wght": -1}, axes) == {'wght': -1.0}
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assert normalizeLocation({"wght": 500}, axes) == {'wght': -0.5}
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assert normalizeLocation({"wght": 1000}, axes) == {'wght': 0.0}
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assert normalizeLocation({"wght": 1001}, axes) == {'wght': 0.0}
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def test_supportScalar():
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assert supportScalar({}, {}) == 1.0
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assert supportScalar({'wght':.2}, {}) == 1.0
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assert supportScalar({'wght':.2}, {'wght':(0,2,3)}) == 0.1
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assert supportScalar({'wght':2.5}, {'wght':(0,2,4)}) == 0.75
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@pytest.mark.parametrize(
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"numLocations, numSamples", [
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pytest.param(127, 509, marks=pytest.mark.slow),
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(31, 251),
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]
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)
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def test_modeling_error(numLocations, numSamples):
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# https://github.com/fonttools/fonttools/issues/2213
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locations = [{'axis': float(i)/numLocations} for i in range(numLocations)]
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masterValues = [100. if i else 0. for i in range(numLocations)]
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model = VariationModel(locations)
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for i in range(numSamples):
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loc = {'axis': float(i)/numSamples}
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scalars = model.getScalars(loc)
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deltas_float = model.getDeltas(masterValues)
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deltas_round = model.getDeltas(masterValues, round=round)
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expected = model.interpolateFromDeltasAndScalars(deltas_float, scalars)
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actual = model.interpolateFromDeltasAndScalars(deltas_round, scalars)
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err = abs(actual - expected)
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assert err <= .5, (i, err)
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# This is how NOT to round deltas.
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#deltas_late_round = [round(d) for d in deltas_float]
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#bad = model.interpolateFromDeltasAndScalars(deltas_late_round, scalars)
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#err_bad = abs(bad - expected)
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#if err != err_bad:
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# print("{:d} {:.2} {:.2}".format(i, err, err_bad))
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class VariationModelTest(object):
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@pytest.mark.parametrize(
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"locations, axisOrder, sortedLocs, supports, deltaWeights",
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[
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(
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[
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{'wght': 0.55, 'wdth': 0.0},
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{'wght': -0.55, 'wdth': 0.0},
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{'wght': -1.0, 'wdth': 0.0},
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{'wght': 0.0, 'wdth': 1.0},
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{'wght': 0.66, 'wdth': 1.0},
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{'wght': 0.66, 'wdth': 0.66},
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{'wght': 0.0, 'wdth': 0.0},
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{'wght': 1.0, 'wdth': 1.0},
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{'wght': 1.0, 'wdth': 0.0},
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],
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["wght"],
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[
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{},
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{'wght': -0.55},
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{'wght': -1.0},
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{'wght': 0.55},
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{'wght': 1.0},
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{'wdth': 1.0},
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{'wdth': 1.0, 'wght': 1.0},
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{'wdth': 1.0, 'wght': 0.66},
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{'wdth': 0.66, 'wght': 0.66}
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],
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[
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{},
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{'wght': (-1.0, -0.55, 0)},
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{'wght': (-1.0, -1.0, -0.55)},
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{'wght': (0, 0.55, 1.0)},
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{'wght': (0.55, 1.0, 1.0)},
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{'wdth': (0, 1.0, 1.0)},
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{'wdth': (0, 1.0, 1.0), 'wght': (0.66, 1.0, 1.0)},
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{'wdth': (0.66, 1.0, 1.0), 'wght': (0, 0.66, 1.0)},
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{'wdth': (0, 0.66, 1.0), 'wght': (0, 0.66, 1.0)}
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],
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[
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{},
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{0: 1.0},
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{0: 1.0},
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{0: 1.0},
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{0: 1.0},
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{0: 1.0},
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{0: 1.0,
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4: 1.0,
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5: 1.0},
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{0: 1.0,
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3: 0.7555555555555555,
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4: 0.24444444444444444,
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5: 1.0},
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{0: 1.0,
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3: 0.7555555555555555,
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4: 0.24444444444444444,
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5: 0.66}
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]
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),
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(
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[
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{},
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{'bar': 0.5},
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{'bar': 1.0},
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{'foo': 1.0},
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{'bar': 0.5, 'foo': 1.0},
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{'bar': 1.0, 'foo': 1.0},
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],
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None,
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[
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{},
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{'bar': 0.5},
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{'bar': 1.0},
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{'foo': 1.0},
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{'bar': 0.5, 'foo': 1.0},
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{'bar': 1.0, 'foo': 1.0},
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],
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[
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{},
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{'bar': (0, 0.5, 1.0)},
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{'bar': (0.5, 1.0, 1.0)},
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{'foo': (0, 1.0, 1.0)},
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{'bar': (0, 0.5, 1.0), 'foo': (0, 1.0, 1.0)},
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{'bar': (0.5, 1.0, 1.0), 'foo': (0, 1.0, 1.0)},
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],
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[
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{},
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{0: 1.0},
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{0: 1.0},
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{0: 1.0},
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{0: 1.0, 1: 1.0, 3: 1.0},
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{0: 1.0, 2: 1.0, 3: 1.0},
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],
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)
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]
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)
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def test_init(
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self, locations, axisOrder, sortedLocs, supports, deltaWeights
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):
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model = VariationModel(locations, axisOrder=axisOrder)
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assert model.locations == sortedLocs
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assert model.supports == supports
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assert model.deltaWeights == deltaWeights
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def test_init_duplicate_locations(self):
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with pytest.raises(VariationModelError, match="Locations must be unique."):
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VariationModel(
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[
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{"foo": 0.0, "bar": 0.0},
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{"foo": 1.0, "bar": 1.0},
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{"bar": 1.0, "foo": 1.0},
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]
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)
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