polishing

demos/demo.py

@@ -41,3 +41,12 @@ if __name__ == '__main__':
         #plotting on logarithmic scale
         plt.imsave('diffuse_component.png', Starblade.s.val, vmin=vmin, vmax=vmax)
         plt.imsave('pointlike_component.png', Starblade.u.val, vmin=vmin, vmax=vmax)
+        plt.figure()
+        k_lenghts = Starblade.power_spectrum.domain[0].k_lengths
+        plt.plot(k_lenghts, Starblade.power_spectrum.val)
+        plt.title('power spectrum')
+        plt.yscale('log')
+        plt.xscale('log')
+        plt.ylabel('power')
+        plt.xscale('harmonic mode')
+        plt.savefig('power_spectrum.png')

demos/multichannel_demo.py

@@ -29,9 +29,7 @@ if __name__ == '__main__':
     data = data.astype(float)
     data = data.clip(0.0001)
     alpha = 1.3
-    MultiStarblade = sb.build_multi_starblade(data, alpha, newton_iterations=1)
-    # power = MultiStarblade[0].power_spectrum / np.arange(len(MultiStarblade[0].power_spectrum.val))**0
-    # MultiStarblade = sb.build_multi_starblade(data,alpha,newton_iterations=1, manual_power_spectrum=power)
+    MultiStarblade = sb.build_multi_starblade(data, alpha=alpha)
 
     for i in range(50):
         MultiStarblade = sb.multi_starblade_iteration(MultiStarblade, processes=1)
@@ -46,10 +44,4 @@ if __name__ == '__main__':
 
         plt.imsave('rgb_diffuse.jpg',diffuse/255.)
         plt.imsave('rgb_point.jpg',point/255.)
-        plt.figure()
 
-        plt.plot(MultiStarblade[0].power_spectrum.val)
-        plt.yscale('log')
-        plt.xscale('log')
-        plt.savefig('power.jpg')
-        plt.close()