refactor datasets, wrote more docs, added one more test dataset

examples/MIT_BNL_logo.py

@@ -7,7 +7,7 @@ from matplotlib import pyplot as plt
 # This file is too large to be distributed via Github.
 # Please contact Abe Levitan (alevitan@mit) if you would like access
 filename = '/media/Data Bank/CSX_6_17/Processed_CXIs/79511_p.cxi'
-dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 # In this dataset, the edges of the patterns are masked off anyway
 # We can easily just remove this data instead of leaving it to float.

examples/ensemble_reconstruction.py

@@ -5,7 +5,7 @@ import pickle
 
 # Load the data
 filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
-dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 results = []
 

examples/gold_ball_ptycho.py

@@ -6,7 +6,7 @@ import pickle
 
 # First, we load an example dataset from a .cxi file
 filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
-dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 # Next, we create a ptychography model from the dataset
 # Note that we explicitly as for two incoherent probe modes

examples/inspect_dataset.py

@@ -5,7 +5,7 @@ from matplotlib import pyplot as plt
 
 # First, we load an example dataset from a .cxi file
 filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
-dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 # And we take a look at the data
 dataset.inspect()

examples/lab_ptycho_data.py

@@ -0,0 +1,28 @@
+from __future__ import division, print_function, absolute_import
+
+import CDTools
+from matplotlib import pyplot as plt
+import pickle
+
+filename = 'example_data/lab_ptycho_data.cxi'
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
+
+# dataset.inspect()
+# plt.show()
+
+model = CDTools.models.FancyPtycho.from_dataset(dataset, oversampling=2,
+                                                probe_support_radius=90)
+model.to(device='cuda')
+dataset.get_as(device='cuda')
+
+
+model.translation_offsets.requires_grad = False
+for i, loss in enumerate(model.Adam_optimize(50, dataset)):
+    model.inspect(dataset)
+    print(i,loss)
+    
+with open('example_reconstructions/lab_ptycho.pickle', 'wb') as f:
+    pickle.dump(model.save_results(dataset),f)
+
+model.compare(dataset)
+plt.show()

examples/simple_ptycho.py

@@ -5,7 +5,7 @@ from matplotlib import pyplot as plt
 
 # First, we load an example dataset from a .cxi file
 filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
-dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 # Next, we create a ptychography model from the dataset
 model = CDTools.models.SimplePtycho.from_dataset(dataset)

examples/specular_ptycho.py

@@ -7,7 +7,7 @@ from matplotlib import pyplot as plt
 # This file is too large to be distributed via Github.
 # Please contact Abe Levitan (alevitan@mit) if you would like access
 filename = '/media/Data Bank/CSX_10_18/Processed_CXIs/110531_p.cxi'
-dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 
 # This model definition includes lots of tweaks, described below.