def evaluate_expansion_all(self,
input_array,
output_array,
fast_transform=True):
if fast_transform is False:
SpectralBase.evaluate_expansion_all(self, input_array,
output_array, False)
else:
self.backward.xfftn(normalise_idft=False)
def evaluate_expansion_all(self, input_array, output_array, fast_transform=True):
if fast_transform is False:
SpectralBase.evaluate_expansion_all(self, input_array, output_array, False)
return
w_hat = work[(input_array, 0)]
self.set_factor_arrays(input_array)
w_hat = self.set_w_hat(w_hat, input_array, self._factor1, self._factor2)
self.CT.backward(w_hat)
assert input_array is self.backward.input_array
assert output_array is self.backward.output_array
def evaluate_expansion_all(self, input_array, output_array, fast_transform=True):
if fast_transform is False:
SpectralBase.evaluate_expansion_all(self, input_array, output_array, False)
return
w_hat = work[(input_array, 0)]
self.set_factor_array(input_array)
s0 = self.sl(slice(0, -2))
s1 = self.sl(slice(2, None))
w_hat[s0] = input_array[s0]
w_hat[s1] -= self._factor*input_array[s0]
self.CT.backward(w_hat)
assert output_array is self.CT.backward.output_array
def evaluate_expansion_all(self, input_array, output_array, fast_transform=True):
if fast_transform is False:
SpectralBase.evaluate_expansion_all(self, input_array, output_array, False)
return
w_hat = work[(input_array, 0)]
s0 = self.sl(slice(0, -2))
s1 = self.sl(slice(2, None))
w_hat[s0] = input_array[s0]
w_hat[s1] -= input_array[s0]
self.bc.apply_before(w_hat, False, (0.5, 0.5))
self.CT.backward(w_hat)
assert output_array is self.CT.backward.output_array
def evaluate_expansion_all(self, input_array, output_array, fast_transform=True):
if fast_transform is False:
SpectralBase.evaluate_expansion_all(self, input_array, output_array, False)
return
output_array = self.backward.xfftn()
s0 = self.sl(slice(0, 1))
if self.quad == "GC":
output_array *= 0.5
output_array += input_array[s0]/2
elif self.quad == "GL":
output_array *= 0.5
output_array += input_array[s0]/2
s0[self.axis] = slice(-1, None)
s2 = self.sl(slice(0, None, 2))
output_array[s2] += input_array[s0]/2
s2[self.axis] = slice(1, None, 2)
output_array[s2] -= input_array[s0]/2
