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Added heirarchy of surface_normal definition in Bragg2DPtycho from_dataset
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@@ -258,7 +258,7 @@ class Bragg2DPtycho(CDIModel):
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obj_padding=200,
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obj_view_crop=None,
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units='um',
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surface_normal=np.array([0., 0., 1.])
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surface_normal=None
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):
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wavelength = dataset.wavelength
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det_basis = dataset.detector_geometry['basis']
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@@ -280,20 +280,35 @@ class Bragg2DPtycho(CDIModel):
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oversampling=oversampling)
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# Now we define the surface normal
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if scattering_mode in {'t', 'transmission'}:
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surface_normal = np.array([0.,0.,1.])
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elif scattering_mode in {'r', 'reflection'}:
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outgoing_dir = np.cross(det_basis[:,0], det_basis[:,1])
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outgoing_dir /= np.linalg.norm(outgoing_dir)
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surface_normal = outgoing_dir + np.array([0.,0.,1.])
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surface_normal /= np.linalg.norm(outgoing_dir)
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else:
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# If the scattering_mode has not been defined, we grab
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# this from the cxi file if its present.
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if hasattr(dataset, 'sample_info') and \
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# The surface normal definition is based on the following heirarchy:
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# manual surface_normal definition > scattering_mode
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# > dataset.sample_info['orientation'] > transmission geometry
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# Guard against any surface_normal entries that are not numpy vectors
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if surface_normal is not None and \
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not isinstance(surface_normal, np.ndarray) or \
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(isinstance(surface_normal, np.ndarray)
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and not surface_normal.shape == (3,)):
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raise RuntimeError(
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'surface_normal needs to be a numpy vector with 3 elements.'
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)
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if surface_normal is None:
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if scattering_mode in {'t', 'transmission'}:
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surface_normal = np.array([0.,0.,1.])
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elif scattering_mode in {'r', 'reflection'}:
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outgoing_dir = np.cross(det_basis[:,0], det_basis[:,1])
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outgoing_dir /= np.linalg.norm(outgoing_dir)
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surface_normal = outgoing_dir + np.array([0.,0.,1.])
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surface_normal /= np.linalg.norm(outgoing_dir)
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elif hasattr(dataset, 'sample_info') and \
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dataset.sample_info is not None and \
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'orientation' in dataset.sample_info:
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# If the scattering_mode has not been defined, we grab
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# this from the cxi file if its present.
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surface_normal = dataset.sample_info['orientation'][2]
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else:
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surface_normal = np.array([0., 0., 1.])
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# and we use that to generate the probe basis
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