"""Variation fonts interpolation models.""" from __future__ import print_function, division, absolute_import from fontTools.misc.py23 import * __all__ = ['nonNone', 'allNone', 'allEqual', 'allEqualTo', 'subList', 'normalizeValue', 'normalizeLocation', 'supportScalar', 'VariationModel'] def nonNone(lst): return [l for l in lst if l is not None] def allNone(lst): return all(l is None for l in lst) def allEqualTo(ref, lst, mapper=None): if mapper is None: return all(ref == item for item in lst) else: mapped = mapper(ref) return all(mapped == mapper(item) for item in lst) def allEqual(lst, mapper=None): if not lst: return True it = iter(lst) first = next(it) return allEqualTo(first, it, mapper=mapper) def subList(truth, lst): assert len(truth) == len(lst) return [l for l,t in zip(lst,truth) if t] def normalizeValue(v, triple): """Normalizes value based on a min/default/max triple. >>> normalizeValue(400, (100, 400, 900)) 0.0 >>> normalizeValue(100, (100, 400, 900)) -1.0 >>> normalizeValue(650, (100, 400, 900)) 0.5 """ lower, default, upper = triple assert lower <= default <= upper, "invalid axis values: %3.3f, %3.3f %3.3f"%(lower, default, upper) v = max(min(v, upper), lower) if v == default: v = 0. elif v < default: v = (v - default) / (default - lower) else: v = (v - default) / (upper - default) return v def normalizeLocation(location, axes): """Normalizes location based on axis min/default/max values from axes. >>> axes = {"wght": (100, 400, 900)} >>> normalizeLocation({"wght": 400}, axes) {'wght': 0.0} >>> normalizeLocation({"wght": 100}, axes) {'wght': -1.0} >>> normalizeLocation({"wght": 900}, axes) {'wght': 1.0} >>> normalizeLocation({"wght": 650}, axes) {'wght': 0.5} >>> normalizeLocation({"wght": 1000}, axes) {'wght': 1.0} >>> normalizeLocation({"wght": 0}, axes) {'wght': -1.0} >>> axes = {"wght": (0, 0, 1000)} >>> normalizeLocation({"wght": 0}, axes) {'wght': 0.0} >>> normalizeLocation({"wght": -1}, axes) {'wght': 0.0} >>> normalizeLocation({"wght": 1000}, axes) {'wght': 1.0} >>> normalizeLocation({"wght": 500}, axes) {'wght': 0.5} >>> normalizeLocation({"wght": 1001}, axes) {'wght': 1.0} >>> axes = {"wght": (0, 1000, 1000)} >>> normalizeLocation({"wght": 0}, axes) {'wght': -1.0} >>> normalizeLocation({"wght": -1}, axes) {'wght': -1.0} >>> normalizeLocation({"wght": 500}, axes) {'wght': -0.5} >>> normalizeLocation({"wght": 1000}, axes) {'wght': 0.0} >>> normalizeLocation({"wght": 1001}, axes) {'wght': 0.0} """ out = {} for tag,triple in axes.items(): v = location.get(tag, triple[1]) out[tag] = normalizeValue(v, triple) return out def supportScalar(location, support, ot=True): """Returns the scalar multiplier at location, for a master with support. If ot is True, then a peak value of zero for support of an axis means "axis does not participate". That is how OpenType Variation Font technology works. >>> supportScalar({}, {}) 1.0 >>> supportScalar({'wght':.2}, {}) 1.0 >>> supportScalar({'wght':.2}, {'wght':(0,2,3)}) 0.1 >>> supportScalar({'wght':2.5}, {'wght':(0,2,4)}) 0.75 >>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}) 0.75 >>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}, ot=False) 0.375 >>> supportScalar({'wght':2.5, 'wdth':0}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}) 0.75 >>> supportScalar({'wght':2.5, 'wdth':.5}, {'wght':(0,2,4), 'wdth':(-1,0,+1)}) 0.75 """ scalar = 1. for axis,(lower,peak,upper) in support.items(): if ot: # OpenType-specific case handling if peak == 0.: continue if lower > peak or peak > upper: continue if lower < 0. and upper > 0.: continue v = location.get(axis, 0.) else: assert axis in location v = location[axis] if v == peak: continue if v <= lower or upper <= v: scalar = 0. break; if v < peak: scalar *= (v - lower) / (peak - lower) else: # v > peak scalar *= (v - upper) / (peak - upper) return scalar class VariationModel(object): """ Locations must be in normalized space. Ie. base master is at origin (0). >>> from pprint import pprint >>> locations = [ \ {'wght':100}, \ {'wght':-100}, \ {'wght':-180}, \ {'wdth':+.3}, \ {'wght':+120,'wdth':.3}, \ {'wght':+120,'wdth':.2}, \ {}, \ {'wght':+180,'wdth':.3}, \ {'wght':+180}, \ ] >>> model = VariationModel(locations, axisOrder=['wght']) >>> pprint(model.locations) [{}, {'wght': -100}, {'wght': -180}, {'wght': 100}, {'wght': 180}, {'wdth': 0.3}, {'wdth': 0.3, 'wght': 180}, {'wdth': 0.3, 'wght': 120}, {'wdth': 0.2, 'wght': 120}] >>> pprint(model.deltaWeights) [{}, {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: 0.75, 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): if len(set(tuple(sorted(l.items())) for l in locations)) != len(locations): raise ValueError("locations must be unique") self.origLocations = locations self.axisOrder = axisOrder if axisOrder is not None else [] locations = [{k:v for k,v in loc.items() if v != 0.} for loc in locations] keyFunc = self.getMasterLocationsSortKeyFunc(locations, axisOrder=self.axisOrder) self.locations = sorted(locations, key=keyFunc) # Mapping from user's master order to our master order self.mapping = [self.locations.index(l) for l in locations] self.reverseMapping = [locations.index(l) for l in self.locations] self._computeMasterSupports(keyFunc.axisPoints) self._subModels = {} def getSubModel(self, items): if None not in items: return self, items key = tuple(v is not None for v in items) subModel = self._subModels.get(key) if subModel is None: subModel = VariationModel(subList(key, self.origLocations), self.axisOrder) self._subModels[key] = subModel return subModel, subList(key, items) @staticmethod def getMasterLocationsSortKeyFunc(locations, axisOrder=[]): assert {} in locations, "Base master not found." axisPoints = {} for loc in locations: if len(loc) != 1: continue axis = next(iter(loc)) value = loc[axis] if axis not in axisPoints: axisPoints[axis] = {0.} assert value not in axisPoints[axis], ( 'Value "%s" in axisPoints["%s"] --> %s' % (value, axis, axisPoints) ) axisPoints[axis].add(value) def getKey(axisPoints, axisOrder): def sign(v): return -1 if v < 0 else +1 if v > 0 else 0 def key(loc): rank = len(loc) onPointAxes = [axis for axis,value in loc.items() if value in axisPoints[axis]] orderedAxes = [axis for axis in axisOrder if axis in loc] orderedAxes.extend([axis for axis in sorted(loc.keys()) if axis not in axisOrder]) return ( rank, # First, order by increasing rank -len(onPointAxes), # Next, by decreasing number of onPoint axes tuple(axisOrder.index(axis) if axis in axisOrder else 0x10000 for axis in orderedAxes), # Next, by known axes tuple(orderedAxes), # Next, by all axes tuple(sign(loc[axis]) for axis in orderedAxes), # Next, by signs of axis values tuple(abs(loc[axis]) for axis in orderedAxes), # Next, by absolute value of axis values ) return key ret = getKey(axisPoints, axisOrder) ret.axisPoints = axisPoints return ret def reorderMasters(self, master_list, mapping): # For changing the master data order without # recomputing supports and deltaWeights. new_list = [master_list[idx] for idx in mapping] self.origLocations = [self.origLocations[idx] for idx in mapping] locations = [{k:v for k,v in loc.items() if v != 0.} for loc in self.origLocations] self.mapping = [self.locations.index(l) for l in locations] self.reverseMapping = [locations.index(l) for l in self.locations] self._subModels = {} return new_list def _computeMasterSupports(self, axisPoints): supports = [] deltaWeights = [] locations = self.locations # Compute min/max across each axis, use it as total range. # TODO Take this as input from outside? minV = {} maxV = {} for l in locations: for k,v in l.items(): minV[k] = min(v, minV.get(k, v)) maxV[k] = max(v, maxV.get(k, v)) for i,loc in enumerate(locations): box = {} for axis,locV in loc.items(): if locV > 0: box[axis] = (0, locV, maxV[axis]) else: box[axis] = (minV[axis], locV, 0) locAxes = set(loc.keys()) # Walk over previous masters now for j,m in enumerate(locations[:i]): # Master with extra axes do not participte if not set(m.keys()).issubset(locAxes): continue # If it's NOT in the current box, it does not participate relevant = True for axis, (lower,peak,upper) in box.items(): if axis not in m or not (m[axis] == peak or lower < m[axis] < upper): relevant = False break if not relevant: continue # Split the box for new master; split in whatever direction # that has largest range ratio. # # For symmetry, we actually cut across multiple axes # if they have the largest, equal, ratio. # https://github.com/fonttools/fonttools/commit/7ee81c8821671157968b097f3e55309a1faa511e#commitcomment-31054804 bestAxes = {} bestRatio = -1 for axis in m.keys(): val = m[axis] assert axis in box lower,locV,upper = box[axis] newLower, newUpper = lower, upper if val < locV: newLower = val ratio = (val - locV) / (lower - locV) elif locV < val: newUpper = val ratio = (val - locV) / (upper - locV) else: # val == locV # Can't split box in this direction. continue if ratio > bestRatio: bestAxes = {} bestRatio = ratio if ratio == bestRatio: bestAxes[axis] = (newLower, locV, newUpper) for axis,triple in bestAxes.items (): box[axis] = triple supports.append(box) deltaWeight = {} # Walk over previous masters now, populate deltaWeight for j,m in enumerate(locations[:i]): scalar = supportScalar(loc, supports[j]) if scalar: deltaWeight[j] = scalar deltaWeights.append(deltaWeight) self.supports = supports self.deltaWeights = deltaWeights def getDeltas(self, masterValues): assert len(masterValues) == len(self.deltaWeights) mapping = self.reverseMapping out = [] for i,weights in enumerate(self.deltaWeights): delta = masterValues[mapping[i]] for j,weight in weights.items(): delta -= out[j] * weight out.append(delta) return out def getDeltasAndSupports(self, items): model, items = self.getSubModel(items) return model.getDeltas(items), model.supports def getScalars(self, loc): return [supportScalar(loc, support) for support in self.supports] @staticmethod def interpolateFromDeltasAndScalars(deltas, scalars): v = None assert len(deltas) == len(scalars) for i,(delta,scalar) in enumerate(zip(deltas, scalars)): if not scalar: continue contribution = delta * scalar if v is None: v = contribution else: v += contribution return v def interpolateFromDeltas(self, loc, deltas): scalars = self.getScalars(loc) return self.interpolateFromDeltasAndScalars(deltas, scalars) def interpolateFromMasters(self, loc, masterValues): deltas = self.getDeltas(masterValues) return self.interpolateFromDeltas(loc, deltas) def interpolateFromMastersAndScalars(self, masterValues, scalars): deltas = self.getDeltas(masterValues) 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): from fontTools import configLogger args = args[1:] # TODO: allow user to configure logging via command-line options configLogger(level="INFO") if len(args) < 1: print("usage: fonttools varLib.models source.designspace", file=sys.stderr) print(" or") print("usage: fonttools varLib.models location1 location2 ...", file=sys.stderr) sys.exit(1) from pprint import pprint if len(args) == 1 and args[0].endswith('.designspace'): from fontTools.designspaceLib import DesignSpaceDocument doc = DesignSpaceDocument() doc.read(args[0]) 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] 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.argv)) sys.exit(doctest.testmod().failed)