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643 lines
22 KiB
Python
643 lines
22 KiB
Python
"""Variation fonts interpolation models."""
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__all__ = [
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"normalizeValue",
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"normalizeLocation",
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"supportScalar",
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"piecewiseLinearMap",
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"VariationModel",
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]
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from fontTools.misc.roundTools import noRound
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from .errors import VariationModelError
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def nonNone(lst):
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return [l for l in lst if l is not None]
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def allNone(lst):
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return all(l is None for l in lst)
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def allEqualTo(ref, lst, mapper=None):
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if mapper is None:
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return all(ref == item for item in lst)
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mapped = mapper(ref)
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return all(mapped == mapper(item) for item in lst)
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def allEqual(lst, mapper=None):
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if not lst:
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return True
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it = iter(lst)
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try:
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first = next(it)
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except StopIteration:
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return True
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return allEqualTo(first, it, mapper=mapper)
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def subList(truth, lst):
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assert len(truth) == len(lst)
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return [l for l, t in zip(lst, truth) if t]
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def normalizeValue(v, triple, extrapolate=False):
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"""Normalizes value based on a min/default/max triple.
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>>> normalizeValue(400, (100, 400, 900))
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0.0
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>>> normalizeValue(100, (100, 400, 900))
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-1.0
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>>> normalizeValue(650, (100, 400, 900))
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0.5
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"""
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lower, default, upper = triple
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if not (lower <= default <= upper):
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raise ValueError(
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f"Invalid axis values, must be minimum, default, maximum: "
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f"{lower:3.3f}, {default:3.3f}, {upper:3.3f}"
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)
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if not extrapolate:
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v = max(min(v, upper), lower)
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if v == default or lower == upper:
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return 0.0
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if (v < default and lower != default) or (v > default and upper == default):
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return (v - default) / (default - lower)
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else:
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assert (v > default and upper != default) or (
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v < default and lower == default
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), f"Ooops... v={v}, triple=({lower}, {default}, {upper})"
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return (v - default) / (upper - default)
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def normalizeLocation(location, axes, extrapolate=False, *, validate=False):
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"""Normalizes location based on axis min/default/max values from axes.
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>>> axes = {"wght": (100, 400, 900)}
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>>> normalizeLocation({"wght": 400}, axes)
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{'wght': 0.0}
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>>> normalizeLocation({"wght": 100}, axes)
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{'wght': -1.0}
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>>> normalizeLocation({"wght": 900}, axes)
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{'wght': 1.0}
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>>> normalizeLocation({"wght": 650}, axes)
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{'wght': 0.5}
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>>> normalizeLocation({"wght": 1000}, axes)
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{'wght': 1.0}
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>>> normalizeLocation({"wght": 0}, axes)
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{'wght': -1.0}
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>>> axes = {"wght": (0, 0, 1000)}
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>>> normalizeLocation({"wght": 0}, axes)
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{'wght': 0.0}
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>>> normalizeLocation({"wght": -1}, axes)
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{'wght': 0.0}
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>>> normalizeLocation({"wght": 1000}, axes)
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{'wght': 1.0}
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>>> normalizeLocation({"wght": 500}, axes)
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{'wght': 0.5}
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>>> normalizeLocation({"wght": 1001}, axes)
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{'wght': 1.0}
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>>> axes = {"wght": (0, 1000, 1000)}
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>>> normalizeLocation({"wght": 0}, axes)
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{'wght': -1.0}
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>>> normalizeLocation({"wght": -1}, axes)
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{'wght': -1.0}
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>>> normalizeLocation({"wght": 500}, axes)
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{'wght': -0.5}
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>>> normalizeLocation({"wght": 1000}, axes)
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{'wght': 0.0}
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>>> normalizeLocation({"wght": 1001}, axes)
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{'wght': 0.0}
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"""
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if validate:
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assert set(location.keys()) <= set(axes.keys()), set(location.keys()) - set(
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axes.keys()
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)
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out = {}
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for tag, triple in axes.items():
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v = location.get(tag, triple[1])
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out[tag] = normalizeValue(v, triple, extrapolate=extrapolate)
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return out
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def supportScalar(location, support, ot=True, extrapolate=False, axisRanges=None):
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"""Returns the scalar multiplier at location, for a master
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with support. If ot is True, then a peak value of zero
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for support of an axis means "axis does not participate". That
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is how OpenType Variation Font technology works.
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If extrapolate is True, axisRanges must be a dict that maps axis
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names to (axisMin, axisMax) tuples.
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>>> supportScalar({}, {})
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1.0
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>>> supportScalar({'wght':.2}, {})
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1.0
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>>> supportScalar({'wght':.2}, {'wght':(0,2,3)})
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0.1
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>>> supportScalar({'wght':2.5}, {'wght':(0,2,4)})
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0.75
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>>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
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0.75
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>>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}, ot=False)
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0.375
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>>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
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0.75
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>>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)})
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0.75
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>>> supportScalar({'wght':3}, {'wght':(0,1,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
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-1.0
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>>> supportScalar({'wght':-1}, {'wght':(0,1,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
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-1.0
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>>> supportScalar({'wght':3}, {'wght':(0,2,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
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1.5
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>>> supportScalar({'wght':-1}, {'wght':(0,2,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
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-0.5
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"""
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if extrapolate and axisRanges is None:
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raise TypeError("axisRanges must be passed when extrapolate is True")
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scalar = 1.0
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for axis, (lower, peak, upper) in support.items():
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if ot:
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# OpenType-specific case handling
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if peak == 0.0:
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continue
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if lower > peak or peak > upper:
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continue
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if lower < 0.0 and upper > 0.0:
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continue
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v = location.get(axis, 0.0)
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else:
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assert axis in location
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v = location[axis]
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if v == peak:
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continue
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if extrapolate:
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axisMin, axisMax = axisRanges[axis]
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if v < axisMin and lower <= axisMin:
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if peak <= axisMin and peak < upper:
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scalar *= (v - upper) / (peak - upper)
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continue
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elif axisMin < peak:
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scalar *= (v - lower) / (peak - lower)
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continue
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elif axisMax < v and axisMax <= upper:
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if axisMax <= peak and lower < peak:
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scalar *= (v - lower) / (peak - lower)
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continue
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elif peak < axisMax:
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scalar *= (v - upper) / (peak - upper)
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continue
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if v <= lower or upper <= v:
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scalar = 0.0
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break
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if v < peak:
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scalar *= (v - lower) / (peak - lower)
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else: # v > peak
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scalar *= (v - upper) / (peak - upper)
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return scalar
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class VariationModel(object):
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"""Locations must have the base master at the origin (ie. 0).
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If axis-ranges are not provided, values are assumed to be normalized to
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the range [-1, 1].
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If the extrapolate argument is set to True, then values are extrapolated
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outside the axis range.
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>>> from pprint import pprint
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>>> axisRanges = {'wght': (-180, +180), 'wdth': (-1, +1)}
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>>> locations = [ \
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{'wght':100}, \
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{'wght':-100}, \
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{'wght':-180}, \
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{'wdth':+.3}, \
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{'wght':+120,'wdth':.3}, \
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{'wght':+120,'wdth':.2}, \
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{}, \
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{'wght':+180,'wdth':.3}, \
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{'wght':+180}, \
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]
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>>> model = VariationModel(locations, axisOrder=['wght'], axisRanges=axisRanges)
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>>> pprint(model.locations)
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[{},
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{'wght': -100},
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{'wght': -180},
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{'wght': 100},
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{'wght': 180},
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{'wdth': 0.3},
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{'wdth': 0.3, 'wght': 180},
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{'wdth': 0.3, 'wght': 120},
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{'wdth': 0.2, 'wght': 120}]
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>>> pprint(model.deltaWeights)
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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: 0.75, 4: 0.25, 5: 1.0, 6: 0.6666666666666666},
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{0: 1.0,
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3: 0.75,
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4: 0.25,
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5: 0.6666666666666667,
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6: 0.4444444444444445,
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7: 0.6666666666666667}]
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"""
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def __init__(
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self, locations, axisOrder=None, extrapolate=False, *, axisRanges=None
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):
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if len(set(tuple(sorted(l.items())) for l in locations)) != len(locations):
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raise VariationModelError("Locations must be unique.")
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self.origLocations = locations
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self.axisOrder = axisOrder if axisOrder is not None else []
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self.extrapolate = extrapolate
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if axisRanges is None:
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if extrapolate:
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axisRanges = self.computeAxisRanges(locations)
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else:
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allAxes = {axis for loc in locations for axis in loc.keys()}
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axisRanges = {axis: (-1, 1) for axis in allAxes}
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self.axisRanges = axisRanges
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locations = [{k: v for k, v in loc.items() if v != 0.0} for loc in locations]
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keyFunc = self.getMasterLocationsSortKeyFunc(
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locations, axisOrder=self.axisOrder
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)
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self.locations = sorted(locations, key=keyFunc)
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# Mapping from user's master order to our master order
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self.mapping = [self.locations.index(l) for l in locations]
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self.reverseMapping = [locations.index(l) for l in self.locations]
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self._computeMasterSupports()
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self._subModels = {}
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def getSubModel(self, items):
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"""Return a sub-model and the items that are not None.
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The sub-model is necessary for working with the subset
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of items when some are None.
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The sub-model is cached."""
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if None not in items:
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return self, items
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key = tuple(v is not None for v in items)
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subModel = self._subModels.get(key)
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if subModel is None:
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subModel = VariationModel(subList(key, self.origLocations), self.axisOrder)
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self._subModels[key] = subModel
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return subModel, subList(key, items)
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@staticmethod
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def computeAxisRanges(locations):
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axisRanges = {}
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allAxes = {axis for loc in locations for axis in loc.keys()}
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for loc in locations:
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for axis in allAxes:
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value = loc.get(axis, 0)
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axisMin, axisMax = axisRanges.get(axis, (value, value))
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axisRanges[axis] = min(value, axisMin), max(value, axisMax)
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return axisRanges
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@staticmethod
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def getMasterLocationsSortKeyFunc(locations, axisOrder=[]):
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if {} not in locations:
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raise VariationModelError("Base master not found.")
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axisPoints = {}
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for loc in locations:
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if len(loc) != 1:
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continue
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axis = next(iter(loc))
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value = loc[axis]
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if axis not in axisPoints:
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axisPoints[axis] = {0.0}
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assert (
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value not in axisPoints[axis]
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), 'Value "%s" in axisPoints["%s"] --> %s' % (value, axis, axisPoints)
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axisPoints[axis].add(value)
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def getKey(axisPoints, axisOrder):
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def sign(v):
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return -1 if v < 0 else +1 if v > 0 else 0
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def key(loc):
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rank = len(loc)
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onPointAxes = [
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axis
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for axis, value in loc.items()
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if axis in axisPoints and value in axisPoints[axis]
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]
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orderedAxes = [axis for axis in axisOrder if axis in loc]
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orderedAxes.extend(
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[axis for axis in sorted(loc.keys()) if axis not in axisOrder]
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)
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return (
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rank, # First, order by increasing rank
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-len(onPointAxes), # Next, by decreasing number of onPoint axes
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tuple(
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axisOrder.index(axis) if axis in axisOrder else 0x10000
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for axis in orderedAxes
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), # Next, by known axes
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tuple(orderedAxes), # Next, by all axes
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tuple(
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sign(loc[axis]) for axis in orderedAxes
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), # Next, by signs of axis values
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tuple(
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abs(loc[axis]) for axis in orderedAxes
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), # Next, by absolute value of axis values
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)
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return key
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ret = getKey(axisPoints, axisOrder)
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return ret
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def reorderMasters(self, master_list, mapping):
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# For changing the master data order without
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# recomputing supports and deltaWeights.
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new_list = [master_list[idx] for idx in mapping]
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self.origLocations = [self.origLocations[idx] for idx in mapping]
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locations = [
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{k: v for k, v in loc.items() if v != 0.0} for loc in self.origLocations
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]
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self.mapping = [self.locations.index(l) for l in locations]
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self.reverseMapping = [locations.index(l) for l in self.locations]
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self._subModels = {}
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return new_list
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def _computeMasterSupports(self):
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self.supports = []
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regions = self._locationsToRegions()
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for i, region in enumerate(regions):
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locAxes = set(region.keys())
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# Walk over previous masters now
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for prev_region in regions[:i]:
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# Master with different axes do not participte
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if set(prev_region.keys()) != locAxes:
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continue
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# If it's NOT in the current box, it does not participate
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relevant = True
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for axis, (lower, peak, upper) in region.items():
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if not (
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prev_region[axis][1] == peak
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or lower < prev_region[axis][1] < upper
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):
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relevant = False
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break
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if not relevant:
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continue
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# Split the box for new master; split in whatever direction
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# that has largest range ratio.
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#
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# For symmetry, we actually cut across multiple axes
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# if they have the largest, equal, ratio.
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# https://github.com/fonttools/fonttools/commit/7ee81c8821671157968b097f3e55309a1faa511e#commitcomment-31054804
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bestAxes = {}
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bestRatio = -1
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for axis in prev_region.keys():
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val = prev_region[axis][1]
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assert axis in region
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lower, locV, upper = region[axis]
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newLower, newUpper = lower, upper
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if val < locV:
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newLower = val
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ratio = (val - locV) / (lower - locV)
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elif locV < val:
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newUpper = val
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ratio = (val - locV) / (upper - locV)
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else: # val == locV
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# Can't split box in this direction.
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continue
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if ratio > bestRatio:
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bestAxes = {}
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bestRatio = ratio
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if ratio == bestRatio:
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bestAxes[axis] = (newLower, locV, newUpper)
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for axis, triple in bestAxes.items():
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region[axis] = triple
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self.supports.append(region)
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self._computeDeltaWeights()
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def _locationsToRegions(self):
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locations = self.locations
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axisRanges = self.axisRanges
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regions = []
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for loc in locations:
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region = {}
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for axis, locV in loc.items():
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if locV > 0:
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region[axis] = (0, locV, axisRanges[axis][1])
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else:
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region[axis] = (axisRanges[axis][0], locV, 0)
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regions.append(region)
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return regions
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def _computeDeltaWeights(self):
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self.deltaWeights = []
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for i, loc in enumerate(self.locations):
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deltaWeight = {}
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# Walk over previous masters now, populate deltaWeight
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for j, support in enumerate(self.supports[:i]):
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scalar = supportScalar(loc, support)
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if scalar:
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deltaWeight[j] = scalar
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self.deltaWeights.append(deltaWeight)
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def getDeltas(self, masterValues, *, round=noRound):
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assert len(masterValues) == len(self.deltaWeights), (
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len(masterValues),
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len(self.deltaWeights),
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)
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mapping = self.reverseMapping
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out = []
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for i, weights in enumerate(self.deltaWeights):
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delta = masterValues[mapping[i]]
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for j, weight in weights.items():
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if weight == 1:
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delta -= out[j]
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else:
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delta -= out[j] * weight
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out.append(round(delta))
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return out
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def getDeltasAndSupports(self, items, *, round=noRound):
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model, items = self.getSubModel(items)
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return model.getDeltas(items, round=round), model.supports
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def getScalars(self, loc):
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"""Return scalars for each delta, for the given location.
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If interpolating many master-values at the same location,
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this function allows speed up by fetching the scalars once
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and using them with interpolateFromMastersAndScalars()."""
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return [
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supportScalar(
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loc, support, extrapolate=self.extrapolate, axisRanges=self.axisRanges
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)
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for support in self.supports
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]
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def getMasterScalars(self, targetLocation):
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"""Return multipliers for each master, for the given location.
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If interpolating many master-values at the same location,
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this function allows speed up by fetching the scalars once
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and using them with interpolateFromValuesAndScalars().
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|
|
Note that the scalars used in interpolateFromMastersAndScalars(),
|
|
are *not* the same as the ones returned here. They are the result
|
|
of getScalars()."""
|
|
out = self.getScalars(targetLocation)
|
|
for i, weights in reversed(list(enumerate(self.deltaWeights))):
|
|
for j, weight in weights.items():
|
|
out[j] -= out[i] * weight
|
|
|
|
out = [out[self.mapping[i]] for i in range(len(out))]
|
|
return out
|
|
|
|
@staticmethod
|
|
def interpolateFromValuesAndScalars(values, scalars):
|
|
"""Interpolate from values and scalars coefficients.
|
|
|
|
If the values are master-values, then the scalars should be
|
|
fetched from getMasterScalars().
|
|
|
|
If the values are deltas, then the scalars should be fetched
|
|
from getScalars(); in which case this is the same as
|
|
interpolateFromDeltasAndScalars().
|
|
"""
|
|
v = None
|
|
assert len(values) == len(scalars)
|
|
for value, scalar in zip(values, scalars):
|
|
if not scalar:
|
|
continue
|
|
contribution = value * scalar
|
|
if v is None:
|
|
v = contribution
|
|
else:
|
|
v += contribution
|
|
return v
|
|
|
|
@staticmethod
|
|
def interpolateFromDeltasAndScalars(deltas, scalars):
|
|
"""Interpolate from deltas and scalars fetched from getScalars()."""
|
|
return VariationModel.interpolateFromValuesAndScalars(deltas, scalars)
|
|
|
|
def interpolateFromDeltas(self, loc, deltas):
|
|
"""Interpolate from deltas, at location loc."""
|
|
scalars = self.getScalars(loc)
|
|
return self.interpolateFromDeltasAndScalars(deltas, scalars)
|
|
|
|
def interpolateFromMasters(self, loc, masterValues, *, round=noRound):
|
|
"""Interpolate from master-values, at location loc."""
|
|
scalars = self.getMasterScalars(loc)
|
|
return self.interpolateFromValuesAndScalars(masterValues, scalars)
|
|
|
|
def interpolateFromMastersAndScalars(self, masterValues, scalars, *, round=noRound):
|
|
"""Interpolate from master-values, and scalars fetched from
|
|
getScalars(), which is useful when you want to interpolate
|
|
multiple master-values with the same location."""
|
|
deltas = self.getDeltas(masterValues, round=round)
|
|
return self.interpolateFromDeltasAndScalars(deltas, scalars)
|
|
|
|
|
|
def piecewiseLinearMap(v, mapping):
|
|
keys = mapping.keys()
|
|
if not keys:
|
|
return v
|
|
if v in keys:
|
|
return mapping[v]
|
|
k = min(keys)
|
|
if v < k:
|
|
return v + mapping[k] - k
|
|
k = max(keys)
|
|
if v > k:
|
|
return v + mapping[k] - k
|
|
# Interpolate
|
|
a = max(k for k in keys if k < v)
|
|
b = min(k for k in keys if k > v)
|
|
va = mapping[a]
|
|
vb = mapping[b]
|
|
return va + (vb - va) * (v - a) / (b - a)
|
|
|
|
|
|
def main(args=None):
|
|
"""Normalize locations on a given designspace"""
|
|
from fontTools import configLogger
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(
|
|
"fonttools varLib.models",
|
|
description=main.__doc__,
|
|
)
|
|
parser.add_argument(
|
|
"--loglevel",
|
|
metavar="LEVEL",
|
|
default="INFO",
|
|
help="Logging level (defaults to INFO)",
|
|
)
|
|
|
|
group = parser.add_mutually_exclusive_group(required=True)
|
|
group.add_argument("-d", "--designspace", metavar="DESIGNSPACE", type=str)
|
|
group.add_argument(
|
|
"-l",
|
|
"--locations",
|
|
metavar="LOCATION",
|
|
nargs="+",
|
|
help="Master locations as comma-separate coordinates. One must be all zeros.",
|
|
)
|
|
|
|
args = parser.parse_args(args)
|
|
|
|
configLogger(level=args.loglevel)
|
|
from pprint import pprint
|
|
|
|
if args.designspace:
|
|
from fontTools.designspaceLib import DesignSpaceDocument
|
|
|
|
doc = DesignSpaceDocument()
|
|
doc.read(args.designspace)
|
|
locs = [s.location for s in doc.sources]
|
|
print("Original locations:")
|
|
pprint(locs)
|
|
doc.normalize()
|
|
print("Normalized locations:")
|
|
locs = [s.location for s in doc.sources]
|
|
pprint(locs)
|
|
else:
|
|
axes = [chr(c) for c in range(ord("A"), ord("Z") + 1)]
|
|
locs = [
|
|
dict(zip(axes, (float(v) for v in s.split(",")))) for s in args.locations
|
|
]
|
|
|
|
model = VariationModel(locs)
|
|
print("Sorted locations:")
|
|
pprint(model.locations)
|
|
print("Supports:")
|
|
pprint(model.supports)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest, sys
|
|
|
|
if len(sys.argv) > 1:
|
|
sys.exit(main())
|
|
|
|
sys.exit(doctest.testmod().failed)
|