| """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) |
| try: |
| first = next(it) |
| except StopIteration: |
| return True |
| 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) |
