models

Variation fonts interpolation models.

class fontTools.varLib.models.VariationModel(locations, axisOrder=None, extrapolate=False)

Locations must have the base master at the origin (ie. 0).

If the extrapolate argument is set to True, then values are extrapolated outside the axis range.

>>> 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}]

static computeAxisRanges(locations)

getDeltas(masterValues, *, round=<function noRound>)

getDeltasAndSupports(items, *, round=<function noRound>)

static getMasterLocationsSortKeyFunc(locations, axisOrder=[])

getScalars(loc)

getSubModel(items)

interpolateFromDeltas(loc, deltas)

static interpolateFromDeltasAndScalars(deltas, scalars)

interpolateFromMasters(loc, masterValues, *, round=<function noRound>)

interpolateFromMastersAndScalars(masterValues, scalars, *, round=<function noRound>)

reorderMasters(master_list, mapping)

fontTools.varLib.models.normalizeLocation(location, axes, extrapolate=False)

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}

fontTools.varLib.models.normalizeValue(v, triple, extrapolate=False)

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

fontTools.varLib.models.piecewiseLinearMap(v, mapping)

fontTools.varLib.models.supportScalar(location, support, ot=True, extrapolate=False, axisRanges=None)

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.

If extrapolate is True, axisRanges must be a dict that maps axis names to (axisMin, axisMax) tuples.

>>> 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
>>> supportScalar({'wght':3}, {'wght':(0,1,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
-1.0
>>> supportScalar({'wght':-1}, {'wght':(0,1,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
-1.0
>>> supportScalar({'wght':3}, {'wght':(0,2,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
1.5
>>> supportScalar({'wght':-1}, {'wght':(0,2,2)}, extrapolate=True, axisRanges={'wght':(0, 2)})
-0.5