updated test.py

test.py

@@ -0,0 +1,29 @@
+from ds1 import PatchDataset
+from torch.utils.data import DataLoader
+from torchvision.transforms import Normalize
+import numpy as np
+import torch 
+
+ffile = '/Users/dennistrujillo/Downloads/park_sam_cx5b_s0_ff_with_Pb_000402.ge3'
+dfile = '/Users/dennistrujillo/Downloads/dark_before_000384.ge3'
+
+bs = 1
+dataset = PatchDataset(ffile, dfile, nFrames=1440)
+#dataloader   = DataLoader(dataset, batch_size=bs, shuffle=True)
+patches_array = np.array(dataset.patches)
+fnum_array    = np.array(dataset.f_nums)
+
+patches_tensor = torch.from_numpy(patches_array)
+torch.save(patches_tensor, 'patches_array.pt')
+
+fnum_tensor = torch.from_numpy(fnum_array)
+torch.save(fnum_tensor, 'fnum_array.pt')
+
+# Initialize an empty array to store the patches
+#patches_tensor = torch.empty(size=dataloader.size)
+
+# Concatenate the patches from each iteration of the dataloader
+#for i, (patches, xy_positions) in enumerate(dataloader):
+#    patches_tensor = torch.cat((patches_tensor, patches))
+
+#torch.save(patches_tensor, 'patches_tensor.pt')