Update the example codes

examples/fancy_ptycho.py

@@ -19,19 +19,31 @@ device = 'cuda'
 model.to(device=device)
 dataset.get_as(device=device)
 
+# Now, to do the reconstruction we will use the more flexible pattern of
+# creating an explicit reconstructor. This is what is used behind the
+# scenes by the convenience functions model.<algorithm>_optimize. For a long
+# reconstruction script with multiple steps, it is better to create the
+# reconstructor explicitly.
+#
+# The reconstructor will store the model and dataset and create an appropriate
+# optimizer. This allows the optimizer to persist, along with e.g. estimates
+# of the moments of individual parameters between loops
+recon = cdtools.reconstructors.AdamReconstructor(model, dataset)
+
 # The learning rate parameter sets the alpha for Adam.
 # The beta parameters are (0.9, 0.999) by default
 # The batch size sets the minibatch size
-for loss in model.Adam_optimize(50, dataset, lr=0.02, batch_size=10):
+for loss in recon.optimize(50, lr=0.02, batch_size=10):
     print(model.report())
     # Plotting is expensive, so we only do it every tenth epoch
     if model.epoch % 10 == 0:
         model.inspect(dataset)
 
 # It's common to chain several different reconstruction loops. Here, we
-# started with an aggressive refinement to find the probe, and now we
-# polish the reconstruction with a lower learning rate and larger minibatch
-for loss in model.Adam_optimize(50, dataset,  lr=0.005, batch_size=50):
+# started with an aggressive refinement to find the probe in the previous
+# loop, and now we polish the reconstruction with a lower learning rate
+# and larger minibatch
+for loss in recon.optimize(50, lr=0.005, batch_size=50):
     print(model.report())
     if model.epoch % 10 == 0:
         model.inspect(dataset)

examples/gold_ball_ptycho.py

@@ -29,6 +29,7 @@ model = cdtools.models.FancyPtycho.from_dataset(
     probe_fourier_crop=pad 
 )
 
+
 # This is a trick that my grandmother taught me, to combat the raster grid
 # pathology: we randomze the our initial guess of the probe positions.
 # The units here are pixels in the object array.
@@ -42,17 +43,20 @@ device = 'cuda'
 model.to(device=device)
 dataset.get_as(device=device)
 
+# Create the reconstructor
+recon = cdtools.reconstructors.AdamReconstructor(model, dataset)
+
 # This will save out the intermediate results if an exception is thrown
 # during the reconstruction
 with model.save_on_exception(
         'example_reconstructions/gold_balls_earlyexit.h5', dataset):
     
-    for loss in model.Adam_optimize(20, dataset, lr=0.005, batch_size=50):
+    for loss in recon.optimize(20, lr=0.005, batch_size=50):
         print(model.report())
         if model.epoch % 10 == 0:
             model.inspect(dataset)
 
-    for loss in model.Adam_optimize(50, dataset, lr=0.002, batch_size=100):
+    for loss in recon.optimize(50, lr=0.002, batch_size=100):
         print(model.report())
         if model.epoch % 10 == 0:
             model.inspect(dataset)
@@ -64,8 +68,7 @@ with model.save_on_exception(
 
     # Setting schedule=True automatically lowers the learning rate if
     # the loss fails to improve after 10 epochs
-    for loss in model.Adam_optimize(100, dataset, lr=0.001, batch_size=100,
-                                    schedule=True):
+    for loss in recon.optimize(100, lr=0.001, batch_size=100, schedule=True):
         print(model.report())
         if model.epoch % 10 == 0:
             model.inspect(dataset)

examples/gold_ball_split.py

@@ -36,15 +36,18 @@ for label, dataset in zip(labels, datasets):
     model.to(device=device)
     dataset.get_as(device=device)
 
+    # Create the reconstructor
+    recon = cdtools.reconstructors.AdamReconstructor(model, dataset)
+
     # For batched reconstructions like this, there's no need to live-plot
     # the progress
-    for loss in model.Adam_optimize(20, dataset, lr=0.005, batch_size=50):
+    for loss in recon.optimize(20, lr=0.005, batch_size=50):
         print(model.report())
 
-    for loss in model.Adam_optimize(50, dataset, lr=0.002, batch_size=100):
+    for loss in recon.optimize(50, lr=0.002, batch_size=100):
         print(model.report())
 
-    for loss in model.Adam_optimize(100, dataset, lr=0.001, batch_size=100,
+    for loss in recon.optimize(100, lr=0.001, batch_size=100,
                                     schedule=True):
         print(model.report())
 

examples/simple_ptycho.py

@@ -1,3 +1,12 @@
+"""
+Runs a very simple reconstruction using the SimplePtycho model, which was
+designed to be an easy introduction to show how the models are made and used.
+
+For a more realistic example of how to use cdtools for real-world data,
+look at fancy_ptycho.py and gold_ball_ptycho.py, both of which use the
+more powerful FancyPtycho model and include more information on how to
+correct for common sources of error.
+"""
 import cdtools
 from matplotlib import pyplot as plt
 
@@ -13,7 +22,7 @@ device = 'cuda'
 model.to(device=device)
 dataset.get_as(device=device)
 
-# We run the actual reconstruction
+# We run the reconstruction
 for loss in model.Adam_optimize(100, dataset, batch_size=10):
     # We print a quick report of the optimization status
     print(model.report())