A few small changes to revert unimportant edits and fix linting issues

examples/fancy_ptycho.py

@@ -13,7 +13,7 @@ model = cdtools.models.FancyPtycho.from_dataset(
     probe_support_radius=120, # Force the probe to 0 outside a radius of 120 pix
     propagation_distance=5e-3, # Propagate the initial probe guess by 5 mm
     units='mm', # Set the units for the live plots
-    obj_view_crop=-50, # Expands the field of view in the object plot by 50 pix,
+    obj_view_crop=-50, # Expands the field of view in the object plot by 50 pix
 )
 
 if t.cuda.is_available():

examples/near_field_ptycho.py

@@ -27,7 +27,7 @@ model = cdtools.models.FancyPtycho.from_dataset(
     propagation_distance=3.65e-3, # 3.65 downstream from focus
     units='um', # Set the units for the live plots
     obj_view_crop=-35,
-    panel_plot_mode=True,
+    panel_plot_mode=True, # Set to False to get individual figures
 )
 
 if t.cuda.is_available():

examples/simple_ptycho.py

@@ -26,7 +26,7 @@ if t.cuda.is_available():
 model.inspect(dataset)
 
 # We run the reconstruction
-for loss in model.Adam_optimize(30, dataset, batch_size=10):
+for loss in model.Adam_optimize(100, 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

src/cdtools/models/fancy_ptycho.py

@@ -7,8 +7,6 @@ from cdtools.tools import analysis
 from matplotlib import pyplot as plt
 from datetime import datetime
 import numpy as np
-from scipy import linalg as sla
-from copy import copy
 
 __all__ = ['FancyPtycho']
 
@@ -557,7 +555,7 @@ class FancyPtycho(CDIModel):
         else:
             try:
                 Ws = t.ones(len(index)) # I'm positive this introduced a bug
-            except:
+            except TypeError:
                 Ws = 1
 
         if self.weights is None or len(self.weights[0].shape) == 0:
@@ -782,7 +780,6 @@ class FancyPtycho(CDIModel):
         # First we treat the incoherent but stable  case, where the weights are
         # just one per-shot overall weight
         if self.weights.dim() == 1:
-            probe = self.probe.detach().cpu().numpy()
             ortho_probes = analysis.orthogonalize_probes(self.probe.detach())
             self.probe.data = ortho_probes
             return