Added a minimum plotting interval to model.inspect(dataset) to make it easier to not plot so much without manual intervention

examples/fancy_ptycho.py

@@ -32,9 +32,9 @@ recon = cdtools.reconstructors.AdamReconstructor(model, dataset)
 # The batch size sets the minibatch size
 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)
+    # Because plotting can be expensive, setting a minimum plotting interval
+    # (in seconds) can avoid excessive replots. 
+    model.inspect(dataset, min_interval=5)
 
 # It's common to chain several different reconstruction loops. Here, we
 # started with an aggressive refinement to find the probe in the previous
@@ -42,12 +42,12 @@ for loss in recon.optimize(50, lr=0.02, batch_size=10):
 # 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)
+    model.inspect(dataset, min_interval=5)
 
 # This orthogonalizes the recovered probe modes
 model.tidy_probes()
 
-model.inspect(dataset)
+# Setting replot_all will reopen any windows which were closed earlier
+model.inspect(dataset, replot_all=True)
 model.compare(dataset)
 plt.show()

examples/gold_ball_ptycho.py

@@ -54,13 +54,11 @@ with model.save_on_exception(
     
     for loss in recon.optimize(20, lr=0.005, batch_size=50):
         print(model.report())
-        if model.epoch % 10 == 0:
-            model.inspect(dataset)
+        model.inspect(dataset, min_interval=5)
 
     for loss in recon.optimize(50, lr=0.002, batch_size=100):
         print(model.report())
-        if model.epoch % 10 == 0:
-            model.inspect(dataset)
+        model.inspect(dataset, min_interval=5)
 
     # We can often reset our guess of the probe positions once we have a
     # good guess of probe and object, but in this case it causes the
@@ -71,8 +69,7 @@ with model.save_on_exception(
     # the loss fails to improve after 10 epochs
     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)
+        model.inspect(dataset, min_interval=5)
 
 
 model.tidy_probes()
@@ -80,6 +77,6 @@ model.tidy_probes()
 # This saves the final result
 model.save_to_h5('example_reconstructions/gold_balls.h5', dataset)
 
-model.inspect(dataset)
+model.inspect(dataset, replot_all=True)
 model.compare(dataset)
 plt.show()

examples/near_field_ptycho.py

@@ -40,18 +40,15 @@ recon = cdtools.reconstructors.AdamReconstructor(model, dataset)
 
 for loss in recon.optimize(100, lr=0.04, 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)
+    model.inspect(dataset, min_interval=5)
 
 for loss in recon.optimize(50, lr=0.005, batch_size=50):
     print(model.report())
-    if model.epoch % 10 == 0:
-        model.inspect(dataset)
+    model.inspect(dataset, min_interval=5)
 
 # This orthogonalizes the recovered probe modes
 model.tidy_probes()
 
-model.inspect(dataset)
+model.inspect(dataset, replot_all=True)
 model.compare(dataset)
 plt.show()

examples/simple_ptycho.py

@@ -30,10 +30,8 @@ for loss in model.Adam_optimize(30, dataset, batch_size=10):
     # We print a quick report of the optimization status
     print(model.report())
     # And liveplot the updates to the model as they happen
-    if model.epoch % 10 == 0:
-        model.inspect(dataset)
+    model.inspect(dataset)
 
-# We study the results
-model.inspect(dataset, replot_all=True)
+# We open a comparison of the simulated and measured data
 model.compare(dataset)
 plt.show()

examples/transmission_RPI.py

@@ -30,20 +30,20 @@ if t.cuda.is_available():
 # Note that the inspect step takes the vast majority of the time
 # The regularization is an L2 regularizer that empirically helps accelerate
 # convergence
-for loss in model.Adam_optimize(30, dataset, lr=0.4, regularization_factor=[0.05,0.05]):
-    model.inspect(dataset)
+for loss in model.LBFGS_optimize(30, dataset, lr=0.4, regularization_factor=[0.05,0.05]):
+    model.inspect(dataset, min_interval=5)
     print(model.report())
     
 
 # Now we use the regularizer to damp all but the top modes
 for loss in model.LBFGS_optimize(50, dataset, lr=0.4, regularization_factor=[0.001,0.1]):
-    #model.inspect(dataset)
+    model.inspect(dataset, min_interval=5)
     print(model.report())
 
-# Save results to a python dictionary
-results = model.save_results()
+# Save results to an h5 file
+model.save_to_h5('example_reconstructions/transmission_RPI.h5', dataset)
 
 # Finally, we plot the results
-model.inspect(dataset)
+model.inspect(dataset, replot_all=True)
 model.compare(dataset)
 plt.show()