Improve plotting infrastructure and fix various bugs

- Switch panel plots to use subfigures with constrained_layout for better layout management
- Add plot_loss_history method to CDIModel base class
- Fix matplotlib compatibility for older versions (interactive backend detection)
- Make CUDA usage conditional in examples
- Add panel_plot_mode and plot_level params to FancyPtycho constructor
- Default plot_level filtering to 1 instead of 0

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

examples/fancy_ptycho.py

@@ -1,4 +1,5 @@
 import cdtools
+import torch as t
 from matplotlib import pyplot as plt
 
 filename = 'example_data/lab_ptycho_data.cxi'
@@ -12,12 +13,15 @@ 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,
+    exponentiate_obj=False,
+    panel_plot_mode=True,
+    plot_level=2,
 )
 
-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')
 
 # For this script, we use a slightly different pattern where we explicitly
 # create a `Reconstructor` class to orchestrate the reconstruction. The
@@ -26,13 +30,14 @@ dataset.get_as(device=device)
 # e.g. estimates of the moments of individual parameters
 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 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:
+    if model.epoch % 2 == 0:
         model.inspect(dataset)
 
 # It's common to chain several different reconstruction loops. Here, we

examples/simple_ptycho.py

@@ -8,6 +8,7 @@ more powerful FancyPtycho model and include more information on how to
 correct for common sources of error.
 """
 import cdtools
+import torch as t
 from matplotlib import pyplot as plt
 
 # We load an example dataset from a .cxi file
@@ -15,12 +16,12 @@ filename = 'example_data/lab_ptycho_data.cxi'
 dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(filename)
 
 # We create a ptychography model from the dataset
-model = cdtools.models.SimplePtycho.from_dataset(dataset, panel_plot_mode=True)
+model = cdtools.models.SimplePtycho.from_dataset(dataset)
 
-# We move the model to the GPU
-device = 'cuda'
-model.to(device=device)
-dataset.get_as(device=device)
+# We move the model to the GPU, if possible
+if t.cuda.is_available():
+    model.to(device='cuda')
+    dataset.get_as(device='cuda')
 
 model.inspect(dataset)
 print('hi')
@@ -32,7 +33,6 @@ for loss in model.Adam_optimize(30, dataset, batch_size=10):
     if model.epoch % 10 == 0:
         model.inspect(dataset)
 
-
 # We study the results
 model.inspect(dataset, replot_all=True)
 model.compare(dataset)