Finally finally finally add an RPI model. Makes it soooo much easier...

examples/transmission_RPI.py

@@ -0,0 +1,48 @@
+from __future__ import division, print_function, absolute_import
+
+import CDTools
+from matplotlib import pyplot as plt
+import pickle
+from torch.utils.data import Subset
+
+# First, we load an example dataset from a .cxi file
+
+ss_filename = 'example_data/Optical_Data_ss.cxi'
+
+with open('example_data/Optical_ptycho.pickle', 'rb') as f:
+    ptycho_results = pickle.load(f)
+
+probe = ptycho_results['probe']
+background = ptycho_results['background']
+
+dataset = CDTools.datasets.Ptycho2DDataset.from_cxi(ss_filename)
+
+# Next, we create a ptychography model from the dataset
+# Note that we explicitly as for two incoherent probe modes
+model = CDTools.models.RPI.from_dataset(dataset, probe, [800,800],
+                                        background=background, n_modes=2)
+
+
+# Let's do this reconstruction on the GPU, shall we? 
+model.to(device='cuda')
+dataset.get_as(device='cuda')
+
+# Note that the inspect step takes the vast majority of the time
+# The regularization is an L2 regularizer that empirically helps accelerate
+# convergence
+for i, loss in enumerate(model.LBFGS_optimize(30, dataset, lr=0.4, regularization_factor=[0.05,0.05])):#0.1)):
+    model.inspect(dataset)
+    print(i,loss)
+
+# Now we use the regularizer to damp all but the top modes
+for i, loss in enumerate(model.LBFGS_optimize(20, dataset, lr=0.4, regularization_factor=[0.001,0.1])):
+    model.inspect(dataset)
+    print(i,loss)
+
+results = model.save_results()
+
+
+# Finally, we plot the results
+model.inspect(dataset)
+model.compare(dataset)
+plt.show()