added log time scale capability for spectral results
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6163896531
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@ -95,6 +95,7 @@ class MPIEspectral_result: # mimic py_post result object
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self._increments = self._keyedPackedArray('increments',count=self.N_loadcases,type='i')
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self._increments[0] -= 1 # delete zero'th entry
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self._times = self._keyedPackedArray('times',count=self.N_loadcases,type='d',default=0.0)
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self._logscales = self._keyedPackedArray('logscales',count=self.N_loadcases,type='i',default=0)
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self.dimension = self._keyedPackedArray('dimension',count=3,type='d')
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self.resolution = self._keyedPackedArray('resolution',count=3,type='i')
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self.N_nodes = (self.resolution[0]+1)*(self.resolution[1]+1)*(self.resolution[2]+1)
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@ -171,8 +172,14 @@ class MPIEspectral_result: # mimic py_post result object
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self.increment += self._increments[l]
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self.time += self._times[l]
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p -= self._increments[l]//self._frequencies[l]
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self.increment += self._frequencies[l] * p
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self.time += self._times[l]/self._increments[l] * self._frequencies[l] * p
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if self._logscales[l] > 0: # logarithmic time scale
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if l == 0: self.time = 2**(self._increments[l] - (1+self._frequencies[l]*p)) * self._times[l] # first loadcase
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else: self.time *= ((self.time + self._times[l])/self.time)**((1+self._frequencies[l]*p)/self._increments[l]) # any subsequent loadcase
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else: # linear time scale
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self.time += self._times[l]/self._increments[l] * self._frequencies[l] * p
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def extrapolation(self,value):
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self.extrapolate = value
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