266 lines
9.1 KiB
Python
266 lines
9.1 KiB
Python
from fontTools.varLib.models import (
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normalizeLocation,
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supportScalar,
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VariationModel,
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VariationModelError,
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)
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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": 0.2}, {}) == 1.0
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assert supportScalar({"wght": 0.2}, {"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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[
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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 = [100.0 if i else 0.0 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 <= 0.5, (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, 4: 1.0, 5: 1.0},
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{0: 1.0, 3: 0.7555555555555555, 4: 0.24444444444444444, 5: 1.0},
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{0: 1.0, 3: 0.7555555555555555, 4: 0.24444444444444444, 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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{},
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{"foo": 0.25},
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{"foo": 0.5},
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{"foo": 0.75},
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{"foo": 1.0},
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{"bar": 0.25},
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{"bar": 0.75},
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{"bar": 1.0},
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],
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None,
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[
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{},
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{"bar": 0.25},
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{"bar": 0.75},
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{"bar": 1.0},
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{"foo": 0.25},
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{"foo": 0.5},
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{"foo": 0.75},
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{"foo": 1.0},
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],
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[
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{},
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{"bar": (0.0, 0.25, 0.75)},
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{"bar": (0.25, 0.75, 1.0)},
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{"bar": (0.75, 1.0, 1.0)},
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{"foo": (0.0, 0.25, 0.5)},
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{"foo": (0.25, 0.5, 0.75)},
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{"foo": (0.5, 0.75, 1.0)},
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{"foo": (0.75, 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},
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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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],
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),
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(
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[
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{},
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{"foo": 0.25},
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{"foo": 0.5},
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{"foo": 0.75},
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{"foo": 1.0},
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{"bar": 0.25},
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{"bar": 0.75},
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{"bar": 1.0},
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],
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None,
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[
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{},
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{"bar": 0.25},
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{"bar": 0.75},
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{"bar": 1.0},
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{"foo": 0.25},
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{"foo": 0.5},
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{"foo": 0.75},
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{"foo": 1.0},
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],
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[
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{},
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{"bar": (0, 0.25, 0.75)},
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{"bar": (0.25, 0.75, 1.0)},
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{"bar": (0.75, 1.0, 1.0)},
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{"foo": (0, 0.25, 0.5)},
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{"foo": (0.25, 0.5, 0.75)},
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{"foo": (0.5, 0.75, 1.0)},
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{"foo": (0.75, 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},
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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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],
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),
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],
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)
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def test_init(self, locations, axisOrder, sortedLocs, supports, deltaWeights):
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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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