Do a final review of all the examples to ensure they work and are well structured

.flake8

@@ -1,2 +1,2 @@
 [flake8]
-ignore = E501, W503
+ignore = E501, W503, E731

examples/gold_ball_split.py

@@ -32,9 +32,9 @@ for label, dataset in zip(labels, datasets):
     
     model.weights.requires_grad = False
     
-    device = 'cuda'
-    model.to(device=device)
-    dataset.get_as(device=device)
+    if t.cuda.is_available():
+        model.to(device='cuda')
+        dataset.get_as(device='cuda')
 
     # Create the reconstructor
     recon = cdtools.reconstructors.AdamReconstructor(model, dataset)

examples/gold_ball_synthesize.py

@@ -5,11 +5,11 @@ import numpy as np
 
 # We load all three reconstructions
 half_1 = cdtools.tools.data.h5_to_nested_dict(
-    f'example_reconstructions/gold_balls_half_1.h5')
+    'example_reconstructions/gold_balls_half_1.h5')
 half_2 = cdtools.tools.data.h5_to_nested_dict(
-    f'example_reconstructions/gold_balls_half_2.h5')
+    'example_reconstructions/gold_balls_half_2.h5')
 full = cdtools.tools.data.h5_to_nested_dict(
-    f'example_reconstructions/gold_balls_full.h5')
+    'example_reconstructions/gold_balls_full.h5')
 
 # This defines the region of recovered object to use for the analysis.
 pad = 260

examples/near_field_ptycho.py

@@ -6,7 +6,6 @@ filename = 'example_data/PETRAIII_P25_Near_Field_Ptycho.cxi'
 dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 dataset.inspect()
-plt.show()
 
 # Setting near_field equal to True uses an angular spectrum propagator in
 # lieu of the default Fourier-transform propagator for far-field ptychography.

examples/simple_ptycho.py

@@ -24,7 +24,7 @@ if t.cuda.is_available():
     dataset.get_as(device='cuda')
 
 model.inspect(dataset)
-print('hi')
+
 # We run the reconstruction
 for loss in model.Adam_optimize(30, dataset, batch_size=10):
     # We print a quick report of the optimization status

examples/tutorial_finale.py

@@ -1,5 +1,6 @@
 from tutorial_basic_ptycho_dataset import BasicPtychoDataset
 from tutorial_simple_ptycho import SimplePtycho
+import torch as t
 from h5py import File
 from matplotlib import pyplot as plt
 
@@ -11,8 +12,9 @@ dataset.inspect()
 
 model = SimplePtycho.from_dataset(dataset)
 
-model.to(device='cuda')
-dataset.get_as(device='cuda')
+if t.cuda.is_available():
+    model.to(device='cuda')
+    dataset.get_as(device='cuda')
 
 for loss in model.Adam_optimize(10, dataset):
     model.inspect(dataset)

examples/tutorial_simple_ptycho.py

@@ -108,14 +108,26 @@ class SimplePtycho(CDIModel):
 
     # This lists all the plots to display on a call to model.inspect()
     plot_list = [
-        ('Probe Amplitude',
-         lambda self, fig: p.plot_amplitude(self.probe, fig=fig, basis=self.probe_basis)),
-        ('Probe Phase',
-         lambda self, fig: p.plot_phase(self.probe, fig=fig, basis=self.probe_basis)),
-        ('Object Amplitude',
-         lambda self, fig: p.plot_amplitude(self.obj, fig=fig, basis=self.probe_basis)),
-        ('Object Phase',
-         lambda self, fig: p.plot_phase(self.obj, fig=fig, basis=self.probe_basis))
+        {
+            'title': 'Probe Amplitude',
+            'plot_func': lambda self, fig:
+                p.plot_amplitude(self.probe, fig, basis=self.probe_basis),
+        },
+        {
+            'title': 'Probe Phase',
+            'plot_func': lambda self, fig:
+                p.plot_phase(self.probe, fig, basis=self.probe_basis)
+        },
+        {
+            'title': 'Object Amplitude',
+            'plot_func': lambda self, fig:
+                p.plot_amplitude(self.obj, fig, basis=self.probe_basis)
+        },
+        {
+            'title': 'Object Phase',
+            'plot_func': lambda self, fig:
+                p.plot_phase(self.obj, fig, basis=self.probe_basis)
+        },
     ]
     
     def save_results(self, dataset):

pyproject.toml

@@ -50,4 +50,5 @@ docs = [
 ]
 
 [tool.ruff]
-line-length = 79
+line-length = 79
+ignore = ["E501", "E731"]

src/cdtools/models/base.py

@@ -61,6 +61,16 @@ class CDIModel(t.nn.Module):
     """
 
     def __init__(self, panel_plot_mode=False, plot_level=np.inf):
+        """Initializes the CDIModel base class.
+
+        Parameters
+        ----------
+        panel_plot_mode : bool, default: False
+            If True, plot_panel_list entries are rendered as multi-subplot
+            figures. If False, each subplot is rendered as its own figure.
+        plot_level : float, default: np.inf
+            Only plots whose plot_level <= this value are shown.
+        """
         super(CDIModel, self).__init__()
 
         self.loss_history = []
@@ -640,6 +650,7 @@ class CDIModel(t.nn.Module):
     def _is_backend_interactive(
             self
     ):
+        """Returns True if the current matplotlib backend is interactive."""
         backend = matplotlib.get_backend().lower()
         try:
             # matplotlib >= 3.9

src/cdtools/models/fancy_ptycho.py

@@ -848,6 +848,15 @@ class FancyPtycho(CDIModel):
 
 
     def get_probe_intensities(self):
+        """Returns the effective probe intensity at each scan position.
+
+        Handles both the simple (1D weights) and OPRP (2D weights) cases.
+
+        Returns
+        -------
+        probe_intensities : np.ndarray
+            Array of probe intensities, one per scan position.
+        """
         if not hasattr(self, 'weights'):
             raise NotImplementedError(
                 "I don't know how to handle having no weights")
@@ -920,6 +929,7 @@ class FancyPtycho(CDIModel):
 
 
     def plot_illumination_intensity(self, fig, dataset):
+        """Plots the probe intensity nanomap. Only used to make a plot for the plot list."""
         p.plot_nanomap(
             self.corrected_translations(dataset),
             self.get_probe_intensities(),

src/cdtools/models/simple_ptycho.py

@@ -110,28 +110,23 @@ class SimplePtycho(CDIModel):
     plot_list = [
         {
             'title': 'Probe Amplitude',
-            'subplot' : (0, 0),
             'plot_func': lambda self, fig:
-                p.plot_amplitude(self.probe, fig,
-                    basis=self.probe_basis),
-        }, {
+                p.plot_amplitude(self.probe, fig, basis=self.probe_basis),
+        },
+        {
             'title': 'Probe Phase',
-            'subplot' : (0, 1),
             'plot_func': lambda self, fig:
-                p.plot_phase(self.probe, fig,
-                    basis=self.probe_basis)
-        }, {
+                p.plot_phase(self.probe, fig, basis=self.probe_basis)
+        },
+        {
             'title': 'Object Amplitude',
-            'subplot' : (1, 0),
             'plot_func': lambda self, fig:
-                p.plot_amplitude(self.obj, fig,
-                    basis=self.probe_basis)
-        }, {
+                p.plot_amplitude(self.obj, fig, basis=self.probe_basis)
+        },
+        {
             'title': 'Object Phase',
-            'subplot' : (1, 1),
             'plot_func': lambda self, fig:
-                p.plot_phase(self.obj, fig,
-                    basis=self.probe_basis)
+                p.plot_phase(self.obj, fig, basis=self.probe_basis)
         },
     ]