honestly, not sure

examples/gold_ball_ptycho.py

@@ -7,6 +7,8 @@ import torch as t
 filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
 dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(filename)
 
+dataset.inspect()
+
 # Next, we create a ptychography model from the dataset
 # Note that we explicitly ask for two incoherent probe modes
 model = cdtools.models.FancyPtycho.from_dataset(
@@ -20,10 +22,10 @@ model = cdtools.models.FancyPtycho.from_dataset(
 
 with model.save_on_exit('example_reconstructions/gold_balls.h5', dataset):
     # Now, we run a short reconstruction from the dataset
-    for loss in model.Adam_optimize(1, dataset, batch_size=50):
+    for loss in model.Adam_optimize(10, dataset, batch_size=50):
         # And we liveplot the updates to the model as they happen
         print(model.report())
-        model.inspect(dataset)
+        #model.inspect(dataset)
 
     # This orthogonalizes the incoherent probe modes
     model.tidy_probes()

examples/lab_bragg_2d_ptycho.py

@@ -18,13 +18,16 @@ dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(filename)
 model = cdtools.models.Bragg2DPtycho.from_dataset(
     dataset,
     probe_support_radius=60,
-    correct_tilt=False
+    correct_tilt=False,
+    units='mm',
 )
 
 #model.to(device='cuda')
 #dataset.get_as(device='cuda')
 
-
+model.inspect(dataset)
+plt.show()
+exit()
 model.translation_offsets.requires_grad = False
 for loss in model.Adam_optimize(100, dataset):
     model.inspect(dataset)