Fix Python3 incompatibility in PatternGenerator

imagen/__init__.py

@@ -1387,7 +1387,7 @@ def _create_frequency_indices(self):
 
         # calculate the discrete frequencies possible for the given sample rate.
         sample_rate = self.signal.sample_rate
-        available_frequency_range = np.fft.fftfreq(sample_rate, d=1.0/sample_rate)[0:sample_rate/2]
+        available_frequency_range = np.fft.fftfreq(sample_rate, d=1.0/sample_rate)[0:sample_rate//2]
 
         if not available_frequency_range.min() <= self.min_frequency or not available_frequency_range.max() >= self.max_frequency:
             raise ValueError("Specified frequency interval [%s:%s] is unavailable, available range is [%s:%s]. Adjust to these frequencies or modify the sample rate of the TimeSeries object." %(self.min_frequency, self.max_frequency, available_frequency_range.min(), available_frequency_range.max()))
@@ -1435,7 +1435,7 @@ def _get_row_amplitudes(self):
         else:
             smoothed_window = signal_window[0:sample_rate]
 
-        amplitudes = (np.abs(np.fft.rfft(smoothed_window))[0:sample_rate/2] + self.offset) * self.scale
+        amplitudes = (np.abs(np.fft.rfft(smoothed_window))[0:sample_rate//2] + self.offset) * self.scale
 
         for index in range(0, self._sheet_dimensions[0]-2):
             start_frequency = self.frequency_spacing[index]

imagen/patterngenerator.py

@@ -174,7 +174,7 @@ def __getitem__(self, coords):
             value_dims = {'value_dimensions':[self.z]} if self.z else value_dims
         elif self.num_channels() in [3,4]:
             raster = RGB
-            data = np.dstack(self.channels().values()[1:])
+            data = np.dstack(list(self.channels().values())[1:])
 
         image = raster(data, bounds=self.bounds,
                        **dict(group=self.group,
@@ -216,7 +216,7 @@ def _setup_xy(self,bounds,xdensity,ydensity,x,y,orientation):
         density (or rows and cols), and transforms them according to
         x, y, and orientation.
         """
-        self.debug("bounds=%s, xdensity=%s, ydensity=%s, x=%s, y=%s, orientation=%s",bounds,xdensity,ydensity,x,y,orientation)
+        self.param.debug("bounds=%s, xdensity=%s, ydensity=%s, x=%s, y=%s, orientation=%s",bounds,xdensity,ydensity,x,y,orientation)
         # Generate vectors representing coordinates at which the pattern
         # will be sampled.
 
@@ -402,7 +402,7 @@ def pil(self, **params_to_override):
 
         elif nchans in [3,4]:
             mode = 'RGB' if nchans==3 else 'RGBA'
-            arr = np.dstack(self.channels(**params_to_override).values()[1:])
+            arr = np.dstack(list(self.channels(**params_to_override).values())[1:])
             arr = (255.0*arr).astype(np.uint8)
 
         else:
@@ -413,7 +413,7 @@ def pil(self, **params_to_override):
 
 # Override class type; must be set here rather than when mask_shape is declared,
 # to avoid referring to class not yet constructed
-PatternGenerator.params('mask_shape').class_=PatternGenerator
+PatternGenerator.param.params('mask_shape').class_=PatternGenerator
 
 
 

imagen/random.py

@@ -279,8 +279,8 @@ def __call__(self,**params_to_override):
         for i in range(ndots):
             bigimage[y1[i]:y2[i]+1,x1[i]:x2[i]+1] = col[i]
 
-        result = p.offset + p.scale*bigimage[ (ysize/2)+ydisparity:(3*ysize/2)+ydisparity ,
-                                              (xsize/2)+xdisparity:(3*xsize/2)+xdisparity ]
+        result = p.offset + p.scale*bigimage[ (ysize//2)+ydisparity:(3*ysize//2)+ydisparity ,
+                                              (xsize//2)+xdisparity:(3*xsize//2)+xdisparity ]
 
         for of in p.output_fns:
             of(result)