Separate computing delta weights
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07a5e2de62
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@ -281,7 +281,6 @@ class VariationModel(object):
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def _computeMasterSupports(self, axisPoints):
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supports = []
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deltaWeights = []
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locations = self.locations
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# Compute min/max across each axis, use it as total range.
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# TODO Take this as input from outside?
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@ -291,6 +290,7 @@ class VariationModel(object):
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for k,v in l.items():
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minV[k] = min(v, minV.get(k, v))
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maxV[k] = max(v, maxV.get(k, v))
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for i,loc in enumerate(locations):
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box = {}
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for axis,locV in loc.items():
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@ -346,16 +346,19 @@ class VariationModel(object):
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for axis,triple in bestAxes.items ():
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box[axis] = triple
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supports.append(box)
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self.supports = supports
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self._computeDeltaWeights()
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def _computeDeltaWeights(self):
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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,m in enumerate(locations[:i]):
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scalar = supportScalar(loc, supports[j])
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for j,m in enumerate(self.locations[:i]):
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scalar = supportScalar(loc, self.supports[j])
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if scalar:
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deltaWeight[j] = scalar
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deltaWeights.append(deltaWeight)
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self.supports = supports
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self.deltaWeights = deltaWeights
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def getDeltas(self, masterValues):
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