flake8 for fourier.py

theano/sandbox/fourier.py

 """Provides Ops for FFT and DCT.
 
 """
-
-from theano.gof import Op, Apply, generic
-from theano import tensor
-
-import numpy.fft
 import numpy
+import numpy.fft
+
 from six.moves import xrange
 
+from theano import tensor
+from theano.gof import Op, Apply, generic
+
 
 class GradTodo(Op):
     def make_node(self, x):
         return Apply(self, [x], [x.type()])
+
     def perform(self, node, inputs, outputs):
         raise NotImplementedError('TODO')
 grad_todo = GradTodo()
@@ -45,8 +46,9 @@ class FFT(Op):
         self.inverse = inverse
 
     def __eq__(self, other):
-        return type(self) == type(other) and (self.half == other.half) and (self.inverse ==
-                other.inverse)
+        return (type(self) == type(other) and
+                self.half == other.half and
+                self.inverse == other.inverse)
 
     def __hash__(self):
         return hash(type(self)) ^ hash(self.half) ^ 9828743 ^ (self.inverse)
@@ -83,15 +85,16 @@ class FFT(Op):
             if axis == 0:
                 if (M % 2):
                     raise ValueError('halfFFT on odd-length vectors is undefined')
-                spectrogram[0] = fft[0:M/2, :]
+                spectrogram[0] = fft[0:M / 2, :]
             elif axis == 1:
                 if (N % 2):
                     raise ValueError('halfFFT on odd-length vectors is undefined')
-                spectrogram[0] = fft[:, 0:N/2]
+                spectrogram[0] = fft[:, 0:N / 2]
             else:
                 raise NotImplementedError()
         else:
             spectrogram[0] = fft
+
     def grad(self, inp, out):
         frames, n, axis = inp
         g_spectrogram, g_buf = out
@@ -112,9 +115,9 @@ def dct_matrix(rows, cols, unitary=True):
     """
     rval = numpy.zeros((rows, cols))
     col_range = numpy.arange(cols)
-    scale = numpy.sqrt(2.0/cols)
+    scale = numpy.sqrt(2.0 / cols)
     for i in xrange(rows):
-        rval[i] = numpy.cos(i * (col_range*2+1)/(2.0 * cols) * numpy.pi) * scale
+        rval[i] = numpy.cos(i * (col_range * 2 + 1) / (2.0 * cols) * numpy.pi) * scale
 
     if unitary:
         rval[0] *= numpy.sqrt(0.5)

theano/tests/test_flake8.py

@@ -137,7 +137,6 @@ whitelist_flake8 = [
     "sandbox/rng_mrg.py",
     "sandbox/theano_object.py",
     "sandbox/scan.py",
-    "sandbox/fourier.py",
     "sandbox/test_multinomial.py",
     "sandbox/minimal.py",
     "sandbox/test_rng_mrg.py",