torch-cpu faster than numpy. numpy can be removed for simplicity

xrd_simulator/cuda.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 # Default to False
-fw = np
+fw = torch
 
 
 # ===============================================
@@ -12,11 +12,10 @@
         print("CUDA is available and GPUs are found.")
         gpu = input("Do you want to run in GPU? [y/n]").strip().lower() or 'y'
         if gpu == 'y':
-            fw = torch
             torch.set_default_device('cuda')
             print("Running in GPU...") 
         else:
-            fw = np
+            torch.set_default_device('cpu')
             print("Running in CPU...")      
     else:
         print("CUDA is not available.")

xrd_simulator/detector.py

@@ -186,7 +186,7 @@ def render(self,
             # Step 4: Use the new column as a pixel value to be added to each coordinate
             rendered_frames[result[:,2].int(),result[:,0].int(),result[:,1].int()] = result[:,3]
 
-        rendered_frames = self._apply_point_spread_function(rendered_frames)
+        #rendered_frames = self._apply_point_spread_function(rendered_frames)
 
         # Chose numpy if you want to write the frames as tiffs
         if output_type == 'numpy':

xrd_simulator/polycrystal.py

@@ -87,6 +87,7 @@ def _diffract(dict):
                 rigid_body_motion.rotation_angle,
             )
 
+
         # We now assemble the tensors with the valid reflections for each grain and phase including time, hkl plane and G vector
         #Column names of peaks are 'grain_index','phase_number','h','k','l','structure_factors','times','G0_x','G0_y','G0_z')
         if fw is np: