Start moving the example code to datasets which can fit on the git server

CDTools/models/__init__.py

@@ -236,6 +236,7 @@ class CDIModel(t.nn.Module):
         Args:
             dataset (CDataset) : A dataset containing the simulated diffraction patterns to compare agains
         """
+        
         fig, axes = plt.subplots(1,3,figsize=(12,5.3))
         fig.tight_layout(rect=[0.02, 0.09, 0.98, 0.96])
         axslider = plt.axes([0.15,0.06,0.75,0.03])
@@ -324,8 +325,6 @@ class CDIModel(t.nn.Module):
         fig.canvas.mpl_connect('scroll_event',on_action)
         update(0)
         
-        
-        pass
 
 
 

examples/gold_ball_ptycho.py

@@ -1,44 +1,25 @@
 from __future__ import division, print_function, absolute_import
 
 import CDTools
-from CDTools.tools.plotting import *
-from CDTools.tools.cmath import *
-from CDTools.tools import interactions
 import h5py
 import numpy as np
-from matplotlib import pyplot as plt
-import torch as t
 import pickle
+from matplotlib import pyplot as plt
 
-filename = '../../../Downloads/AuBalls_700ms_30nmStep_3_3SS_filter.cxi'
-#filename = '/media/Data Bank/CSX_3_19/Processed_CXIs/115195_p.cxi'
+filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
 
 with h5py.File(filename,'r') as f:
     dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(f)
-    #darks = np.array(f['entry_1/instrument_1/detector_1/data_dark'])
-
-#old_patterns = dataset.patterns.clone()
-#dataset.patterns -= t.tensor(np.nanmean(darks,axis=0))
-#dataset.patterns = t.clamp(dataset.patterns,min=0)
 
 model = CDTools.models.FancyPtycho.from_dataset(dataset,n_modes=3,randomize_ang=0.1*np.pi)
-#dataset.patterns = old_patterns
+
 
 # default is CPU with 32-bit floats
 model.to(device='cuda')
 dataset.get_as(device='cuda')
 
-#model.translation_offsets.requires_grad = False
 
-for i, loss in enumerate(model.Adam_optimize(30, dataset, batch_size=100)):
-    model.inspect(dataset)
-    print(i,loss)
-
-for i, loss in enumerate(model.Adam_optimize(30, dataset, batch_size=100, lr=0.001)):
-    model.inspect(dataset)
-    print(i,loss)
-
-for i, loss in enumerate(model.Adam_optimize(30, dataset, batch_size=100, lr=0.0001)):
+for i, loss in enumerate(model.Adam_optimize(3, dataset, batch_size=100)):
     model.inspect(dataset)
     print(i,loss)
 
@@ -49,4 +30,3 @@ for i, loss in enumerate(model.Adam_optimize(30, dataset, batch_size=100, lr=0.0
 model.inspect(dataset)
 model.compare(dataset)
 plt.show()
-exit()