replace ** with clearer operators where possible

nifty/field.py

@@ -409,7 +409,7 @@ class Field(Loggable, Versionable, object):
 
     def _calculate_power_spectrum(self, x, pindex, rho, axes=None):
         fieldabs = abs(x)
-        fieldabs **= 2
+        fieldabs *= fieldabs
 
         if axes is not None:
             pindex = self._shape_up_pindex(
@@ -425,7 +425,7 @@ class Field(Loggable, Versionable, object):
             rho = rho.reshape(new_rho_shape)
         power_spectrum /= rho
 
-        power_spectrum **= 0.5
+        power_spectrum = sqrt(power_spectrum)
         return power_spectrum
 
     def _shape_up_pindex(self, pindex, target_shape, target_strategy, axes):

nifty/minimization/line_searching/line_search_strong_wolfe.py

@@ -65,7 +65,6 @@ class LineSearchStrongWolfe(LineSearch):
                  max_step_size=50, max_iterations=10,
                  max_zoom_iterations=10):
 
-
         super(LineSearchStrongWolfe, self).__init__()
 
         self.c1 = np.float(c1)
@@ -308,7 +307,8 @@ class LineSearchStrongWolfe(LineSearch):
         """Estimating the minimum with cubic interpolation.
 
         Finds the minimizer for a cubic polynomial that goes through the
-        points ( a,f(a) ), ( b,f(b) ), and ( c,f(c) ) with derivative at point a of fpa.
+        points ( a,f(a) ), ( b,f(b) ), and ( c,f(c) ) with derivative at point
+        a of fpa.
         f(x) = A *(x-a)^3 + B*(x-a)^2 + C*(x-a) + D
         If no minimizer can be found return None
 
@@ -341,12 +341,12 @@ class LineSearchStrongWolfe(LineSearch):
                 C = fpa
                 db = b - a
                 dc = c - a
-                denom = (db * dc) ** 2 * (db - dc)
+                denom = db * db * dc * dc * (db - dc)
                 d1 = np.empty((2, 2))
-                d1[0, 0] = dc ** 2
-                d1[0, 1] = -db ** 2
-                d1[1, 0] = -dc ** 3
-                d1[1, 1] = db ** 3
+                d1[0, 0] = dc * dc
+                d1[0, 1] = -(db*db)
+                d1[1, 0] = -(dc*dc*dc)
+                d1[1, 1] = db*db*db
                 [A, B] = np.dot(d1, np.asarray([fb - fa - C * db,
                                                 fc - fa - C * dc]).flatten())
                 A /= denom

nifty/operators/fft_operator/transformations/rgrgtransformation.py

@@ -118,7 +118,7 @@ class RGRGTransformation(Transformation):
             np.absolute(np.array(domain.shape) *
                         np.array(domain.distances) *
                         np.array(codomain.distances) - 1) <
-                10**-7):
+                1e-7):
             raise AttributeError("The grid-distances of domain and codomain "
                                  "do not match.")
 

nifty/spaces/hp_space/hp_space.py

@@ -93,11 +93,11 @@ class HPSpace(Space):
 
     @property
     def shape(self):
-        return (np.int(12 * self.nside ** 2),)
+        return (np.int(12 * self.nside * self.nside),)
 
     @property
     def dim(self):
-        return np.int(12 * self.nside ** 2)
+        return np.int(12 * self.nside * self.nside)
 
     @property
     def total_volume(self):
@@ -108,7 +108,7 @@ class HPSpace(Space):
 
     def weight(self, x, power=1, axes=None, inplace=False):
 
-        weight = ((4 * np.pi) / (12 * self.nside**2)) ** np.float(power)
+        weight = ((4*np.pi) / (12*self.nside*self.nside)) ** np.float(power)
 
         if inplace:
             x *= weight

nifty/spaces/lm_space/lm_space.py

@@ -130,7 +130,7 @@ class LMSpace(Space):
         # dim = (((2*(l+1)-1)+1)**2/4 - 2 * (l-m)(l-m+1)/2
         # dim = np.int((l+1)**2 - (l-m)*(l-m+1.))
         # We fix l == m
-        return np.int((l+1)**2)
+        return np.int((l+1)*(l+1))
 
     @property
     def total_volume(self):
@@ -166,7 +166,7 @@ class LMSpace(Space):
 
     def get_fft_smoothing_kernel_function(self, sigma):
         # FIXME why x(x+1) ? add reference to paper!
-        return lambda x: np.exp(-0.5 * x * (x + 1) * sigma**2)
+        return lambda x: np.exp(-0.5 * x * (x + 1) * sigma*sigma)
 
     # ---Added properties and methods---
 

nifty/spaces/rg_space/rg_space.py

@@ -267,14 +267,16 @@ class RGSpace(Space):
 
         cords = np.ogrid[inds]
 
-        dists = ((cords[0] - shape[0]//2)*dk[0])**2
+        dists = (cords[0] - shape[0]//2)*dk[0]
+        dists *= dists
         # apply zerocenterQ shift
         if not self.zerocenter[0]:
             dists = np.fft.ifftshift(dists)
         # only save the individual slice
         dists = dists[slice_of_first_dimension]
         for ii in range(1, len(shape)):
-            temp = ((cords[ii] - shape[ii] // 2) * dk[ii])**2
+            temp = (cords[ii] - shape[ii] // 2) * dk[ii]
+            temp *= temp
             if not self.zerocenter[ii]:
                 temp = np.fft.ifftshift(temp)
             dists = dists + temp
@@ -282,7 +284,7 @@ class RGSpace(Space):
         return dists
 
     def get_fft_smoothing_kernel_function(self, sigma):
-        return lambda x: np.exp(-0.5 * np.pi**2 * x**2 * sigma**2)
+        return lambda x: np.exp(-0.5 * np.pi*np.pi * x*x * sigma*sigma)
 
     # ---Added properties and methods---
 

nifty/sugar.py

@@ -54,7 +54,7 @@ def create_power_operator(domain, power_spectrum, dtype=None,
                               distribution_strategy=distribution_strategy)
     fp = Field(power_domain, val=power_spectrum, dtype=dtype,
                distribution_strategy=distribution_strategy)
-    fp **= 2
+    fp *= fp
     f = fp.power_synthesize(mean=1, std=0, real_signal=False)
 
     return DiagonalOperator(domain, diagonal=f, bare=True)