Add licensed near-field ptycho data to enable testing

examples/example_data/LICENSE.txt

@@ -11,3 +11,14 @@ The dataset contained in the file:
 - AuBalls_700ms_30nmStep_3_6SS_filter.cxi
 
 is sourced from https://cxidb.org/id-65.html, and was made available by the original authors under the CC0 Public Domain Dedication Waiver. This data was deposited into the CXIDB by Stefano Marchesini.
+
+The dataset contained in the file:
+
+- PETRAIII_P25_Near_Field_Ptycho.cxi
+
+is sourced from from [this](http://dx.doi.org/10.5281/zenodo.17899482) Zenodo upload, and was collected at the P25 beamline of the PETRA III light source at DESY. The following list of experiment participants were involved:
+
+
+Nazanin Samadi,  Aknur Karabay, Pengju Sheng, Canrong Qiu, Kathryn Spiers, Wenhui Xu, Abraham Levitan, and Manuel Guizar-Sicairos.
+
+The dataset is made available under a CC BY 4.0 License, defined at https://creativecommons.org/licenses/by/4.0/.

examples/near_field_ptycho.py

@@ -0,0 +1,56 @@
+import cdtools
+from matplotlib import pyplot as plt
+
+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.
+#
+# If propagation_distance is not set, it assumes that the geometry is
+# a standard near-field geometry with flat illumination wavefronts, and
+# pulls the sample to detector distance from dataset.distance
+#
+# If propagation_distance is set, it assumes a Fresnel scaling theorem
+# geometry with:
+#
+# - distance (from the dataset): The sample-to-detector distance
+# - propagation_distance: The focus-to-sample distance
+# 
+model = cdtools.models.FancyPtycho.from_dataset(
+    dataset,
+    n_modes=1,
+    near_field=True,
+    propagation_distance=3.65e-3, # 3.65 downstream from focus
+    units='um', # Set the units for the live plots
+    obj_view_crop=-35,
+)
+
+device = 'cuda'
+model.to(device=device)
+dataset.get_as(device=device)
+
+model.inspect(dataset)
+
+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)
+
+for loss in recon.optimize(50, lr=0.005, batch_size=50):
+    print(model.report())
+    if model.epoch % 10 == 0:
+        model.inspect(dataset)
+
+# This orthogonalizes the recovered probe modes
+model.tidy_probes()
+
+model.inspect(dataset)
+model.compare(dataset)
+plt.show()

tests/conftest.py

@@ -379,6 +379,11 @@ def lab_ptycho_cxi(pytestconfig):
     return str(pytestconfig.rootpath) + \
         '/examples/example_data/lab_ptycho_data.cxi'
 
+@pytest.fixture(scope='module')
+def near_field_ptycho_cxi(pytestconfig):
+    return str(pytestconfig.rootpath) + \
+        '/examples/example_data/PETRAIII_P25_Near_Field_Ptycho.cxi'
+
 
 @pytest.fixture(scope='module')
 def optical_data_ss_cxi(pytestconfig):

tests/models/test_fancy_ptycho.py

@@ -98,3 +98,40 @@ def test_lab_ptycho(lab_ptycho_cxi, reconstruction_device, show_plot):
     # If this fails, the reconstruction has gotten worse
     assert model.loss_history[-1] < 0.0013
 
+
+@pytest.mark.slow
+def test_near_field_ptycho(near_field_ptycho_cxi, reconstruction_device, show_plot):
+
+    print('\nTesting performance on the standard transmission ptycho dataset')
+    dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(near_field_ptycho_cxi)
+    
+    model = cdtools.models.FancyPtycho.from_dataset(
+        dataset,
+        n_modes=1,
+        near_field=True,
+        propagation_distance=3.65e-3, # 3.65 downstream from focus
+    )
+
+    print('Running reconstruction on provided reconstruction_device,',
+          reconstruction_device)
+    model.to(device=reconstruction_device)
+    dataset.get_as(device=reconstruction_device)
+
+    for loss in model.Adam_optimize(100, dataset, lr=0.04, batch_size=10):
+        print(model.report())
+        if show_plot and model.epoch % 10 == 0:
+            model.inspect(dataset)
+
+    for loss in model.Adam_optimize(50, dataset, lr=0.005, batch_size=50):
+        print(model.report())
+        if show_plot and model.epoch % 10 == 0:
+            model.inspect(dataset)
+
+    model.tidy_probes()
+
+    if show_plot:
+        model.inspect(dataset)
+        model.compare(dataset)
+
+    # If this fails, the reconstruction has gotten worse
+    assert model.loss_history[-1] < 0.005