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lint conftest.py
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gnzng committed
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+66
-66
@@ -47,6 +47,7 @@ def pytest_collection_modifyitems(config, items):
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if "slow" in item.keywords:
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item.add_marker(skip_slow)
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@pytest.fixture
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def reconstruction_device(request):
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return request.config.getoption("--reconstruction_device")
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@@ -57,7 +58,6 @@ def show_plot(request):
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return request.config.getoption("--plot")
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@pytest.fixture(scope='module')
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def ptycho_cxi_1():
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"""Creates an example file for CXI ptychography. This file is defined
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@@ -67,11 +67,11 @@ def ptycho_cxi_1():
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"""
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expected = {}
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f = h5py.File('ptycho_cxi_1','w',driver='core',backing_store=False)
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f = h5py.File('ptycho_cxi_1', 'w', driver='core', backing_store=False)
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# Start by defining the basic structure
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f.create_dataset('cxi_version', data=150)
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f.create_dataset('number_of_entries',data=1)
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f.create_dataset('number_of_entries', data=1)
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# Then define a bunch of metadata for entry_1
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e1f = f.create_group('entry_1')
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@@ -104,19 +104,19 @@ def ptycho_cxi_1():
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s1f['concentration'] = s1e['concentration']
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s1e['mass'] = np.float32(np.random.rand())
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s1f['mass'] = s1e['mass']
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s1e['temperature'] = np.float32(np.random.rand()*100)
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s1e['temperature'] = np.float32(np.random.rand() * 100)
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s1f['temperature'] = s1e['temperature']
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s1e['thickness'] = np.float32(np.random.rand()*1e-7)
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s1e['thickness'] = np.float32(np.random.rand() * 1e-7)
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s1f['thickness'] = s1e['thickness']
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s1e['unit_cell_volume'] = np.float32(np.random.rand() * 1e-27)
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s1f['unit_cell_volume'] = s1e['unit_cell_volume']
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s1e['unit_cell'] = np.array([1,1,1,90,90,90]).astype(np.float32)
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s1f.create_dataset('unit_cell',data = s1e['unit_cell'])
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s1e['unit_cell'] = np.array([1, 1, 1, 90, 90, 90]).astype(np.float32)
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s1f.create_dataset('unit_cell', data=s1e['unit_cell'])
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i1f = e1f.create_group('instrument_1')
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source1f = i1f.create_group('source_1')
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energy = np.float32(1.3618e-16) #Joules, = 850 eV
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energy = np.float32(1.3618e-16) # Joules, = 850 eV
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source1f['energy'] = energy
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expected['wavelength'] = np.float32(1.9864459e-25) / energy
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source1f['wavelength'] = expected['wavelength']
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@@ -126,48 +126,48 @@ def ptycho_cxi_1():
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d1e = expected['detector']
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d1e['distance'] = np.float32(0.3)
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d1f['distance'] = d1e['distance']
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d1e['basis'] = np.array([[0,-30e-6,0],
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[-20e-6,0,0]]).astype(np.float32).transpose()
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d1f.create_dataset('basis_vectors',data=d1e['basis'])
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d1e['basis'] = np.array([[0, -30e-6, 0],
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[-20e-6, 0, 0]]).astype(np.float32).transpose()
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d1f.create_dataset('basis_vectors', data=d1e['basis'])
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d1f['x_pixel_size'] = np.float32(20e-6)
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d1f['y_pixel_size'] = np.float32(30e-6)
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d1e['corner'] = np.array((2550e-6,3825e-6,0.3)).astype(np.float32)
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d1e['corner'] = np.array((2550e-6, 3825e-6, 0.3)).astype(np.float32)
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d1f.create_dataset('corner_position', data=d1e['corner'])
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# Remember the format for the CXI file differs from the format used
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# internally
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mask = np.zeros((256,256)).astype(np.int32)
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mask[5,8] = 1
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expected['mask'] = np.ones((256,256)).astype(bool)
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expected['mask'][5,8] = 0
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d1f.create_dataset('mask',data=mask)
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mask = np.zeros((256, 256)).astype(np.int32)
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mask[5, 8] = 1
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expected['mask'] = np.ones((256, 256)).astype(bool)
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expected['mask'][5, 8] = 0
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d1f.create_dataset('mask', data=mask)
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# There is no specification for this in the CXI file format :(
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qe_mask = np.ones((256,256)).astype(np.float32)
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qe_mask = np.ones((256, 256)).astype(np.float32)
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expected['qe_mask'] = qe_mask
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d1f.create_dataset('qe_mask',data=qe_mask)
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d1f.create_dataset('qe_mask', data=qe_mask)
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# Create an initial background
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dark = np.ones((256,256)) * 0.01
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dark = np.ones((256, 256)) * 0.01
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expected['dark'] = dark
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d1f.create_dataset('data_dark', data=dark)
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data1f = e1f.create_group('data_1')
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data = np.random.rand(100,256,256).astype(np.float32)
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data = np.random.rand(100, 256, 256).astype(np.float32)
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expected['data'] = data
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d1f.create_dataset('data',data=data)
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d1f.create_dataset('data', data=data)
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data1f['data'] = h5py.SoftLink('/entry_1/instrument_1/detector_1/data')
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d1f['data'].attrs['axes'] = np.bytes_('translation:y:x')
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expected['axes'] = ['translation','y','x']
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expected['axes'] = ['translation', 'y', 'x']
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g1f = s1f.create_group('geometry_1')
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orientation = np.array([1.,0,0,0,1,0])
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orientation = np.array([1., 0, 0, 0, 1, 0])
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g1f.create_dataset('orientation', data=orientation)
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s1e['orientation'] = np.array([[1.,0,0],[0,1,0],[0,0,1]])
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translations = np.arange(300).reshape((100,3)).astype(np.float32)
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g1f.create_dataset('translation',data=translations)
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s1e['orientation'] = np.array([[1., 0, 0], [0, 1, 0], [0, 0, 1]])
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translations = np.arange(300).reshape((100, 3)).astype(np.float32)
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g1f.create_dataset('translation', data=translations)
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data1f['translation'] = h5py.SoftLink('/entry_1/sample_1/geometry_1/translation')
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d1f['translation'] = h5py.SoftLink('/entry_1/sample_1/geometry_1/translation')
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expected['translations'] = -translations
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@@ -193,11 +193,11 @@ def ptycho_cxi_2():
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"""
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expected = {}
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f = h5py.File('ptycho_cxi_2','w',driver='core',backing_store=False)
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f = h5py.File('ptycho_cxi_2', 'w', driver='core', backing_store=False)
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# Start by defining the basic structure
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f.create_dataset('cxi_version', data=150)
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f.create_dataset('number_of_entries',data=1)
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f.create_dataset('number_of_entries', data=1)
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# Then define a bunch of metadata for entry_1
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e1f = f.create_group('entry_1')
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@@ -210,13 +210,13 @@ def ptycho_cxi_2():
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s1f = e1f.create_group('sample_1')
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expected['sample info'] = {}
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s1e = expected['sample info']
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s1e['temperature'] = np.float32(np.random.rand()*100)
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s1e['temperature'] = np.float32(np.random.rand() * 100)
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s1f['temperature'] = s1e['temperature']
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i1f = e1f.create_group('instrument_1')
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source1f = i1f.create_group('source_1')
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energy = np.float32(1.3618e-16) #Joules, = 850 eV
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energy = np.float32(1.3618e-16) # Joules, = 850 eV
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expected['wavelength'] = np.float32(1.9864459e-25) / energy
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source1f['wavelength'] = expected['wavelength']
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@@ -224,11 +224,11 @@ def ptycho_cxi_2():
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expected['detector'] = {}
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d1e = expected['detector']
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d1e['distance'] = np.float32(0.3)
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d1e['basis'] = np.array([[0,-30e-6,0],
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[-20e-6,0,0]]).astype(np.float32).transpose()
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d1e['basis'] = np.array([[0, -30e-6, 0],
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[-20e-6, 0, 0]]).astype(np.float32).transpose()
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d1f['x_pixel_size'] = np.float32(20e-6)
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d1f['y_pixel_size'] = np.float32(30e-6)
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d1e['corner'] = np.array((2550e-6,3825e-6,0.3)).astype(np.float32)
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d1e['corner'] = np.array((2550e-6, 3825e-6, 0.3)).astype(np.float32)
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d1f.create_dataset('corner_position', data=d1e['corner'])
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# Remember the format for the CXI file differs from the format used
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@@ -236,24 +236,23 @@ def ptycho_cxi_2():
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expected['mask'] = None
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expected['qe_mask'] = None
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# Test with a set of dark images
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dark = np.ones((10,256,256)) * 0.01
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expected['dark'] = np.nanmean(dark,axis=0)
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dark = np.ones((10, 256, 256)) * 0.01
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expected['dark'] = np.nanmean(dark, axis=0)
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d1f.create_dataset('data_dark', data=dark)
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e1f.create_group('data_1')
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data1f = e1f.create_group('data_1')
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data = np.random.rand(100,256,256).astype(np.float32)
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data = np.random.rand(100, 256, 256).astype(np.float32)
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expected['data'] = data
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d1f.create_dataset('data',data=data)
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d1f.create_dataset('data', data=data)
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expected['axes'] = None
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g1f = s1f.create_group('geometry_1')
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translations = np.arange(300).reshape((100,3)).astype(np.float32)
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g1f.create_dataset('translation',data=translations)
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translations = np.arange(300).reshape((100, 3)).astype(np.float32)
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g1f.create_dataset('translation', data=translations)
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expected['translations'] = -translations
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yield f, expected
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@@ -276,11 +275,11 @@ def ptycho_cxi_3():
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"""
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expected = {}
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f = h5py.File('ptycho_cxi_3','w',driver='core',backing_store=False)
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f = h5py.File('ptycho_cxi_3', 'w', driver='core', backing_store=False)
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# Start by defining the basic structure
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f.create_dataset('cxi_version', data=150)
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f.create_dataset('number_of_entries',data=1)
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f.create_dataset('number_of_entries', data=1)
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# Then define a bunch of metadata for entry_1
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e1f = f.create_group('entry_1')
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@@ -297,7 +296,7 @@ def ptycho_cxi_3():
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i1f = e1f.create_group('instrument_1')
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source1f = i1f.create_group('source_1')
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energy = np.float32(1.3618e-16) #Joules, = 850 eV
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energy = np.float32(1.3618e-16) # Joules, = 850 eV
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source1f['energy'] = energy
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expected['wavelength'] = np.float32(1.9864459e-25) / energy
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@@ -306,41 +305,41 @@ def ptycho_cxi_3():
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d1e = expected['detector']
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d1e['distance'] = np.float32(0.3)
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d1f['distance'] = d1e['distance']
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d1e['basis'] = np.array([[0,-30e-6,0],
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[-20e-6,0,0]]).astype(np.float32).transpose()
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d1f.create_dataset('basis_vectors',data=d1e['basis'])
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d1e['basis'] = np.array([[0, -30e-6, 0],
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[-20e-6, 0, 0]]).astype(np.float32).transpose()
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d1f.create_dataset('basis_vectors', data=d1e['basis'])
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d1e['corner'] = None
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# Remember the format for the CXI file differs from the format used
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# internally
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mask = np.ones((256,256)).astype(np.uint32) * 0x00001000
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mask[15,47] = 38
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expected['mask'] = np.ones((256,256)).astype(bool)
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expected['mask'][15,47] = 0
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d1f.create_dataset('mask',data=mask)
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mask = np.ones((256, 256)).astype(np.uint32) * 0x00001000
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mask[15, 47] = 38
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expected['mask'] = np.ones((256, 256)).astype(bool)
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expected['mask'][15, 47] = 0
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d1f.create_dataset('mask', data=mask)
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expected['qe_mask'] = None
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expected['dark'] = None
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data1f = e1f.create_group('data_1')
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data = np.random.rand(100,256,256).astype(np.float32)
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data = np.random.rand(100, 256, 256).astype(np.float32)
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expected['data'] = data
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data1f.create_dataset('data',data=data)
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data1f.create_dataset('data', data=data)
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data1f['data'].attrs['axes'] = np.bytes_('translation:y:x')
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expected['axes'] = ['translation','y','x']
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expected['axes'] = ['translation', 'y', 'x']
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translations = np.arange(300).reshape((100,3)).astype(np.float32)
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data1f.create_dataset('translation',data=translations)
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translations = np.arange(300).reshape((100, 3)).astype(np.float32)
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data1f.create_dataset('translation', data=translations)
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expected['translations'] = -translations
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yield f, expected
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f.close()
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@pytest.fixture(scope='module')
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def polarized_ptycho_cxi(ptycho_cxi_1):
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f, expected = ptycho_cxi_1
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@@ -352,7 +351,7 @@ def polarized_ptycho_cxi(ptycho_cxi_1):
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data1f.create_dataset('polarizer_angle', data=expected['polarizer_angle'])
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yield f, expected
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# As specific issues start to crop up with loading CXI files from different
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# beamlines, put a fixture here that replicates the issue so that we can
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@@ -374,6 +373,7 @@ def gold_ball_cxi(pytestconfig):
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return str(pytestconfig.rootpath) + \
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'/examples/example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
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@pytest.fixture(scope='module')
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def lab_ptycho_cxi(pytestconfig):
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return str(pytestconfig.rootpath) + \
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@@ -382,8 +382,8 @@ def lab_ptycho_cxi(pytestconfig):
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@pytest.fixture(scope='module')
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def example_nested_dicts(pytestconfig):
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example_tensor = t.as_tensor(np.array([1,4.5,7]))
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example_array = np.ones([10,20,30])
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example_tensor = t.as_tensor(np.array([1, 4.5, 7]))
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example_array = np.ones([10, 20, 30])
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example_scalar = 4.5
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example_single_element_array = np.array([0.3])
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example_string = 'testing'
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