Make quantization optional.

waveprop/simulation.py

@@ -38,6 +38,7 @@ def __init__(
         device_conv="cpu",
         random_shift=False,
         is_torch=False,
+        quantize=True,
         **kwargs
     ):
         """
@@ -63,6 +64,8 @@ def __init__(
             Whether to randomly shift the image, by default False.
         is_torch : bool, optional
             Whether to use pytorch, by default False.
+        quantize : bool, optional
+            Whether to quantize image, by default True.
         """
         if is_torch:
             self.axes = (-2, -1)
@@ -78,6 +81,7 @@ def __init__(
         self.mask2sensor = mask2sensor
         self.sensor = sensor_dict[sensor]
         self.random_shift = random_shift
+        self.quantize = quantize
 
         # for convolution
         if psf is not None:
@@ -162,13 +166,14 @@ def propagate(self, obj, return_object_plane=False):
             if self.snr_db is not None:
                 image_plane = add_shot_noise(image_plane, snr_db=self.snr_db)
 
-            # 5) Quantize as on sensor
-            image_plane = image_plane / image_plane.max()
-            image_plane = image_plane * self.max_val
-            if torch.is_tensor(image_plane):
-                image_plane = image_plane.to(self.output_dtype)
-            else:
-                image_plane = image_plane.astype(self.output_dtype)
+            # 5) (Optionaly) Quantize as on sensor
+            if self.quantize:
+                image_plane = image_plane / image_plane.max()
+                image_plane = image_plane * self.max_val
+                if torch.is_tensor(image_plane):
+                    image_plane = image_plane.to(self.output_dtype)
+                else:
+                    image_plane = image_plane.astype(self.output_dtype)
 
             if return_object_plane:
                 return image_plane, object_plane