def get_parameter_value(nxsfile, plugin_name, plugin_param):
content = Content()
content.fopen(nxsfile)
plist = content.plugin_list.plugin_list
for plugin in plist:
if plugin['name'] == plugin_name:
if plugin_param in plugin['data'].keys():
print plugin_name, plugin['data'][plugin_param]
def create_process_list_from_user_data(data):
"""
Creates a process list from user supplied data.
"""
process_list = Content()
# For each plugin
for i, pl in enumerate(data['plugins']):
pos = str(i + 1)
# Add plugin
process_list.add(pl['name'], pos)
# Set plugin enable state
process_list.on_and_off(
pos,
const.PLUGIN_ENABLED if pl['active'] else const.PLUGIN_DISABLED)
# Set parameter values
for param in pl['parameters']:
if param['name'] == 'preview':
# Don't cast a preview value, since it always comes into
# flask as a string and should always be cast to a string, but
# sometimes it can have a value of '[]' which gets cast to the
# empty list by Content.value() and plugin.utils._dumps()
cast_param_val = param['value']
else:
cast_param_val = process_list.value(param['value'])
process_list.modify(pos, param['name'], cast_param_val, ref=True)
return process_list
def setUp(self):
self.test_folder = tempfile.mkdtemp(suffix='template_test/')
self.tif_folder = os.path.join(self.test_folder, 'tiffs/')
os.mkdir(self.tif_folder)
# copy across the process list to the working directory
self.process_list_path = os.path.join(self.test_folder,
'xrd_template_test.nxs')
shutil.copyfile(tu.get_test_process_path('xrd_template_test.nxs'),
self.process_list_path)
utils.populate_plugins()
self.process_list = Content()
self.process_list.fopen(self.process_list_path, update=False)
for idx in self.process_list.get_positions():
self.process_list.refresh(idx)
self.detX_axis_label = {
'dim': '$idx_detx',
'name': 'detector_x',
'value': None,
'units': 'pixels'
}
self.detY_axis_label = {
'dim': '$idx_dety',
'name': 'detector_y',
'value': None,
'units': 'pixels'
}
self.yaml = OrderedDict()
# now make some standard modifications
self.yaml['inherit'] = [
tu.get_test_data_path(
os.path.join('i18_templates', 'xrd_calibration.yml'))
]
self.yaml['xrd'] = OrderedDict()
self.yaml['xrd']['params'] = {}
self.yaml['xrd']['data'] = {}
self.yaml['xrd']['data']['folder'] = self.tif_folder
self.yaml['xrd']['params']['cfile'] = \
"$h5py.File('%s', 'r')" % tu.get_test_data_path('LaB6_calibration_new.nxs')
self.yaml['xrd']['patterns'] = {}
self.yaml['xrd']['patterns']['DIFFRACTION'] = {
'core_dims':
'$(idx_detx, idx_dety)',
'slice_dims':
'$tuple([d for d in dims if d not in [idx_detx, idx_dety]])'
}
self.yaml['xrd']['axis_labels'] = {}
self.yaml['xrd']['metadata'] = {}
self.data_file_path = 'test_data.nxs'
self.data_file = h5.File(self.data_file_path,
'w') # this will have the axes in.
def save_yaml_and_change_process_list(self):
self.yml_path = os.path.join(self.test_folder, 'xrd_tiff.yml')
with open(self.yml_path, 'w') as f:
yu.dump_yaml(self.yaml, f)
# now set the template path in the process list
# uses the parameter name instead of number
# because the number can change between runs
self.process_list = Content()
self.process_list.fopen(self.process_list_path, update=False)
self.process_list.modify('1', 'yaml_file', self.yml_path)
self.process_list.save(self.process_list_path)
def get_parameter_value(nxsfile, plugin_name, plugin_param):
content = Content()
content.fopen(nxsfile)
plist = content.plugin_list.plugin_list
found_params = []
for plugin in plist:
if plugin['name'] == plugin_name:
if plugin_param in list(plugin['data'].keys()):
found_params.append(plugin['data'][plugin_param])
return found_params
def _refresh_process_file(self, path):
content = Content()
# open
content.fopen(path, update=True)
# refresh
positions = content.get_positions()
for pos_str in positions:
content.refresh(pos_str)
# save
content.save(content.filename)
def setUp(self):
self.test_folder = tempfile.mkdtemp(suffix='template_test/')
self.tif_folder = os.path.join(self.test_folder, 'tiffs/')
os.mkdir(self.tif_folder)
# copy across the process list to the working directory
self.process_list_path = os.path.join(self.test_folder, 'xrd_template_test.nxs')
shutil.copyfile(tu.get_test_process_path('xrd_template_test.nxs'),
self.process_list_path)
utils.populate_plugins()
self.process_list = Content()
self.process_list.fopen(self.process_list_path, update=False)
for idx in self.process_list.get_positions():
self.process_list.refresh(idx)
self.detX_axis_label = {'dim': '$idx_detx', 'name': 'detector_x', 'value': None, 'units': 'pixels'}
self.detY_axis_label = {'dim': '$idx_dety', 'name': 'detector_y', 'value': None, 'units': 'pixels'}
self.yaml = OrderedDict()
# now make some standard modifications
self.yaml['inherit'] = [tu.get_test_data_path(os.path.join('i18_templates', 'xrd_calibration.yml'))]
self.yaml['xrd'] = OrderedDict()
self.yaml['xrd']['params'] = {}
self.yaml['xrd']['data'] = {}
self.yaml['xrd']['data']['folder'] = self.tif_folder
self.yaml['xrd']['params']['cfile'] = \
"$h5py.File('%s', 'r')" % tu.get_test_data_path('LaB6_calibration_new.nxs')
self.yaml['xrd']['patterns'] = {}
self.yaml['xrd']['patterns']['DIFFRACTION'] = {'core_dims': '$(idx_detx, idx_dety)',
'slice_dims': '$tuple([d for d in dims if d not in [idx_detx, idx_dety]])'}
self.yaml['xrd']['axis_labels'] = {}
self.yaml['xrd']['metadata'] = {}
self.data_file_path = 'test_data.nxs'
self.data_file = h5.File(self.data_file_path, 'w') # this will have the axes in.
def _refresh_process_file(self, path):
content = Content()
# open
content.fopen(path, update=True)
# refresh
positions = content.get_positions()
for pos_str in positions:
content.refresh(pos_str)
# save
content.save(content.filename)
def process_list_info():
"""
Return the contents of a specific process list
.. :quickref: ProcessList; Get the contents of a process list
:query string filename: the absolute path to a process list file on the filesystem
"""
fname = const.ROOT_MOUNT_POINT + request.args.get(const.KEY_FILENAME)
# Ensure file is a valid process list
file_validation = validate_file(fname, is_file_a_process_list)
if not file_validation['is_valid']:
abort(404, file_validation['error_message'])
# Open process list
process_list = Content()
process_list.fopen(fname)
# Refresh all plugins in the process list once it has been opened
positions = process_list.get_positions()
for pos_str in positions:
process_list.refresh(pos_str)
# Format plugin list
plugins = [plugin_list_entry_to_dict(p) for \
p in process_list.plugin_list.plugin_list]
data = {const.KEY_FILENAME: fname, const.KEY_PLUGINS: plugins}
validation.process_list_list_filename_schema(data)
return jsonify(data)
def process_list_list():
# Listing process list files in a given search directory
if urlparams.KEY_PATH in request.args:
# Get the absolute path being searched
user_path = request.args.get(urlparams.KEY_PATH)
abs_path = os.path.abspath(os.path.expanduser(user_path))
data = {
urlparams.KEY_PATH:
abs_path,
urlparams.KEY_FILES:
list(find_files_recursive(abs_path, is_file_a_process_list)),
}
validation.filename_listing_schema(data)
return jsonify(data)
# Listing details of a specific process list
elif urlparams.KEY_FILENAME in request.args:
fname = request.args.get(urlparams.KEY_FILENAME)
# Ensure file is a valid process list
if not validate_file(fname, is_file_a_process_list):
abort(status.HTTP_404_NOT_FOUND)
# Open process list
process_list = Content()
process_list.fopen(fname)
# Format plugin list
plugins = [plugin_list_entry_to_dict(p) for \
p in process_list.plugin_list.plugin_list]
data = {
urlparams.KEY_FILENAME: fname,
urlparams.KEY_PLUGINS: plugins
}
validation.process_list_list_filename_schema(data)
return jsonify(data)
else:
abort(status.HTTP_400_BAD_REQUEST)
def add_plugin_to_process_list(plugin_name, plugin_index):
"""
Add a plugin to the current process list, and return a dict representation
of the newly added plugin
.. :quickref: Plugin; Add plugin to process list
:param string plugin_name: the name of the plugin to add
:param int plugin_index: the index in the current process list to add the
plugin to
"""
if plugin_name not in pu.plugins:
abort(status.HTTP_404_NOT_FOUND)
# create a process list with a single plugin in it (an instance of the
# plugin desired to be added to the process list editor) so then plugin
# data of the desired format can be passed to plugin_list_entry_to_dict()
process_list = Content()
dummy_pos = "1"
process_list.add(plugin_name, dummy_pos)
process_list.on_and_off(dummy_pos, const.PLUGIN_ENABLED)
plugin = process_list.plugin_list.plugin_list[0]
data = plugin_list_entry_to_dict(plugin)
# update with the desired pos value
data['pos'] = plugin_index
validation.process_list_entry_schema(data)
return jsonify(data)
def process_list_list():
# Listing process list files in a given search directory
if const.KEY_PATH in request.args:
# Get the absolute path being searched
user_path = request.args.get(const.KEY_PATH)
abs_path = os.path.abspath(os.path.expanduser(user_path))
data = {
const.KEY_PATH: abs_path,
const.KEY_FILES: list(
find_files_recursive(abs_path, is_file_a_process_list)),
}
validation.filename_listing_schema(data)
return jsonify(data)
# Listing details of a specific process list
elif const.KEY_FILENAME in request.args:
fname = const.ROOT_MOUNT_POINT + request.args.get(const.KEY_FILENAME)
# Ensure file is a valid process list
if not validate_file(fname, is_file_a_process_list):
abort(status.HTTP_404_NOT_FOUND)
# Open process list
process_list = Content()
process_list.fopen(fname)
# Refresh all plugins in the process list once it has been opened
positions = process_list.get_positions()
for pos_str in positions:
process_list.refresh(pos_str)
# Format plugin list
plugins = [plugin_list_entry_to_dict(p) for \
p in process_list.plugin_list.plugin_list]
data = {
const.KEY_FILENAME: fname,
const.KEY_PLUGINS: plugins
}
validation.process_list_list_filename_schema(data)
return jsonify(data)
else:
abort(status.HTTP_400_BAD_REQUEST)
def create_process_list_from_user_data(data):
"""
Creates a process list from user supplied data.
"""
process_list = Content()
# For each plugin
for i, pl in enumerate(data['plugins']):
pos = str(i + 1)
# Add plugin
process_list.add(pl['name'], pos)
# Set plugin enable state
process_list.on_and_off(
pos,
const.PLUGIN_ENABLED if pl['active'] else const.PLUGIN_DISABLED)
# Set parameter values
for param in pl['parameters']:
cast_param_val = process_list.value(param['value'])
process_list.modify(pos, param['name'], cast_param_val, ref=True)
return process_list
def add_plugin_to_process_list():
request_data = request.get_json()
if request_data['pluginName'] not in pu.plugins:
abort(status.HTTP_404_NOT_FOUND)
# create a process list with a single plugin in it (an instance of the
# plugin desired to be added to the process list editor) so then plugin
# data of the desired format can be passed to plugin_list_entry_to_dict()
process_list = Content()
dummy_pos = "1"
process_list.add(request_data['pluginName'], dummy_pos)
process_list.on_and_off(dummy_pos, const.PLUGIN_ENABLED)
plugin = process_list.plugin_list.plugin_list[0]
data = plugin_list_entry_to_dict(plugin)
# update with the desired pos value
data['pos'] = request_data['pluginIndex']
validation.process_list_entry_schema(data)
return jsonify(data)
class TemplateLoaderTest(unittest.TestCase):
def test_1D_data(self):
A = 11
X = 10
Y = 13
expected_output_shape = (A, X, Y)
expected_top_corners = np.arange(A, dtype=np.float32)
self.create_N_tiffs(A, frame_size=(X, Y))
# create a data file with the axis information
self.data_file['entry/A'] = np.arange(A)
self.data_file.close()
# edit and save the yaml
self.yaml['xrd']['data']['shape'] = '$(len(A_vals),)'
self.yaml['xrd']['params'].update({
'idx_A': 0,
'idx_detx': 1,
'idx_dety': 2,
'A_vals': "$dfile['entry/A'].value",
'dims': "$range(0, 3)"
})
# add some axis labels
self.yaml['xrd']['axis_labels'] = {
0: {
'dim': '$idx_A',
'name': 'A',
'value': '$A_vals',
'units': 'pixels'
},
1: self.detX_axis_label,
2: self.detY_axis_label
}
self.save_yaml_and_change_process_list()
run_protected_plugin_runner(
tu.set_options(self.data_file_path,
process_file=self.process_list_path,
out_path=self.test_folder))
# now check the result
result = h5.File(os.path.join(self.test_folder, 'test_processed.nxs'),
'r')['entry/final_result_xrd/data']
np.testing.assert_array_equal(
result.shape,
expected_output_shape,
err_msg='The output shape is not as expected.')
result_corners = result[..., -1, -1]
np.testing.assert_equal(
result_corners.dtype,
expected_top_corners.dtype,
err_msg='The array does not output the correct type.')
np.testing.assert_array_equal(
result_corners,
expected_top_corners,
err_msg="The output values are not as expected")
def test_2D_data(self):
A = 3
B = 4
X = 10
Y = 13
expected_output_shape = (A, B, X, Y)
expected_top_corners = np.arange(A * B, dtype=np.float32).reshape(
(A, B))
self.create_N_tiffs(A * B, frame_size=(X, Y))
# create a data file with the axis information
self.data_file['entry/A'] = np.arange(A)
self.data_file['entry/B'] = np.arange(B)
self.data_file.close()
# edit and save the yaml
self.yaml['xrd']['data']['shape'] = '$(len(A_vals), len(B_vals))'
self.yaml['xrd']['params'].update({
'idx_A': 0,
'idx_B': 1,
'idx_detx': 2,
'idx_dety': 3,
'A_vals': "$dfile['entry/A'].value",
'B_vals': "$dfile['entry/B'].value",
'dims': "$range(0, 4)"
})
self.yaml['xrd']['axis_labels'] = {
0: {
'dim': '$idx_A',
'name': 'A',
'value': '$A_vals',
'units': 'pixels'
},
1: {
'dim': '$idx_B',
'name': 'B',
'value': '$B_vals',
'units': 'pixels'
},
2: self.detX_axis_label,
3: self.detY_axis_label
}
self.save_yaml_and_change_process_list()
run_protected_plugin_runner(
tu.set_options(self.data_file_path,
process_file=self.process_list_path,
out_path=self.test_folder))
result = h5.File(os.path.join(self.test_folder, 'test_processed.nxs'),
'r')['entry/final_result_xrd/data']
np.testing.assert_array_equal(
result.shape,
expected_output_shape,
err_msg='The output shape is not as expected.')
result_corners = result[..., -1, -1]
np.testing.assert_equal(
result_corners.dtype,
expected_top_corners.dtype,
err_msg='The array does not output the correct type.')
np.testing.assert_array_equal(
result_corners,
expected_top_corners,
err_msg="The output values are not as expected")
def test_3D_data(self):
A = 3
B = 4
C = 5
X = 10
Y = 13
expected_output_shape = (A, B, C, X, Y)
expected_top_corners = np.arange(A * B * C, dtype=np.float32).reshape(
(A, B, C))
self.create_N_tiffs(A * B * C, frame_size=(X, Y))
# create a data file with the axis information
self.data_file['entry/A'] = np.arange(A)
self.data_file['entry/B'] = np.arange(B)
self.data_file['entry/C'] = np.arange(C)
self.data_file.close()
# edit and save the yaml
self.yaml['xrd']['data'][
'shape'] = '$(len(A_vals), len(B_vals), len(C_vals))'
self.yaml['xrd']['params'].update({
'idx_A': 0,
'idx_B': 1,
'idx_C': 2,
'idx_detx': 3,
'idx_dety': 4,
'A_vals': "$dfile['entry/A'].value",
'B_vals': "$dfile['entry/B'].value",
'C_vals': "$dfile['entry/C'].value",
'dims': "$range(0, 4)"
})
self.yaml['xrd']['axis_labels'] = {
0: {
'dim': '$idx_A',
'name': 'A',
'value': '$A_vals',
'units': 'pixels'
},
1: {
'dim': '$idx_B',
'name': 'B',
'value': '$B_vals',
'units': 'pixels'
},
2: {
'dim': '$idx_C',
'name': 'C',
'value': '$C_vals',
'units': 'pixels'
},
3: self.detX_axis_label,
4: self.detY_axis_label
}
self.save_yaml_and_change_process_list()
run_protected_plugin_runner(
tu.set_options(self.data_file_path,
process_file=self.process_list_path,
out_path=self.test_folder))
result = h5.File(os.path.join(self.test_folder, 'test_processed.nxs'),
'r')['entry/final_result_xrd/data']
np.testing.assert_array_equal(
result.shape,
expected_output_shape,
err_msg='The output shape is not as expected.')
result_corners = result[..., -1, -1]
np.testing.assert_equal(
result_corners.dtype,
expected_top_corners.dtype,
err_msg='The array does not output the correct type.')
np.testing.assert_array_equal(
result_corners,
expected_top_corners,
err_msg="The output values are not as expected")
def setUp(self):
self.test_folder = tempfile.mkdtemp(suffix='template_test/')
self.tif_folder = os.path.join(self.test_folder, 'tiffs/')
os.mkdir(self.tif_folder)
# copy across the process list to the working directory
self.process_list_path = os.path.join(self.test_folder,
'xrd_template_test.nxs')
shutil.copyfile(tu.get_test_process_path('xrd_template_test.nxs'),
self.process_list_path)
utils.populate_plugins()
self.process_list = Content()
self.process_list.fopen(self.process_list_path, update=False)
for idx in self.process_list.get_positions():
self.process_list.refresh(idx)
self.detX_axis_label = {
'dim': '$idx_detx',
'name': 'detector_x',
'value': None,
'units': 'pixels'
}
self.detY_axis_label = {
'dim': '$idx_dety',
'name': 'detector_y',
'value': None,
'units': 'pixels'
}
self.yaml = OrderedDict()
# now make some standard modifications
self.yaml['inherit'] = [
tu.get_test_data_path(
os.path.join('i18_templates', 'xrd_calibration.yml'))
]
self.yaml['xrd'] = OrderedDict()
self.yaml['xrd']['params'] = {}
self.yaml['xrd']['data'] = {}
self.yaml['xrd']['data']['folder'] = self.tif_folder
self.yaml['xrd']['params']['cfile'] = \
"$h5py.File('%s', 'r')" % tu.get_test_data_path('LaB6_calibration_new.nxs')
self.yaml['xrd']['patterns'] = {}
self.yaml['xrd']['patterns']['DIFFRACTION'] = {
'core_dims':
'$(idx_detx, idx_dety)',
'slice_dims':
'$tuple([d for d in dims if d not in [idx_detx, idx_dety]])'
}
self.yaml['xrd']['axis_labels'] = {}
self.yaml['xrd']['metadata'] = {}
self.data_file_path = 'test_data.nxs'
self.data_file = h5.File(self.data_file_path,
'w') # this will have the axes in.
def tearDown(self):
shutil.rmtree(self.test_folder)
def create_N_tiffs(self, N, file_pattern='%04d.tif', frame_size=(10, 13)):
'''
Creates a stack of N tiffs with given frame_size and file pattern in self.tif_folder.
:param N: The number of tiffs in the stack
:param file_pattern: The printf pattern for the file names.
:param frame_size: The shape of the frames. Default: (10,13).
'''
data = np.ones(frame_size, dtype=np.float)
for idx in range(N):
file_path = os.path.join(self.tif_folder, (file_pattern % idx))
tifffile.imsave(file_path, data * idx)
def save_yaml_and_change_process_list(self):
yml_path = os.path.join(self.test_folder, 'xrd_tiff.yml')
with open(yml_path, 'w') as f:
yu.dump_yaml(self.yaml, f)
# now set the template path in the process list
self.process_list.modify('1', '2', yml_path)
self.process_list.save(self.process_list_path)
class TemplateLoaderTest(unittest.TestCase):
def test_1D_data(self):
A = 11
X = 10
Y = 13
expected_output_shape = (A, X, Y)
expected_top_corners = np.arange(A, dtype=np.float32)
self.create_N_tiffs(A, frame_size=(X, Y))
# create a data file with the axis information
self.data_file['entry/A'] = np.arange(A)
self.data_file.close()
# edit and save the yaml
self.yaml['xrd']['data']['shape'] = '$(len(A_vals),)'
self.yaml['xrd']['params'].update({'idx_A': 0,
'idx_detx': 1,
'idx_dety': 2,
'A_vals': "$dfile['entry/A'].value",
'dims': "$range(0, 3)"})
# add some axis labels
self.yaml['xrd']['axis_labels'] = {0: {'dim': '$idx_A', 'name': 'A', 'value': '$A_vals', 'units': 'pixels'},
1: self.detX_axis_label,
2: self.detY_axis_label}
self.save_yaml_and_change_process_list()
run_protected_plugin_runner(tu.set_options(self.data_file_path,
process_file=self.process_list_path,
out_path=self.test_folder))
# now check the result
result = h5.File(os.path.join(self.test_folder, 'test_processed.nxs'), 'r')['entry/final_result_xrd/data']
np.testing.assert_array_equal(result.shape, expected_output_shape,
err_msg='The output shape is not as expected.')
result_corners = result[..., -1, -1]
np.testing.assert_equal(result_corners.dtype, expected_top_corners.dtype,
err_msg='The array does not output the correct type.')
np.testing.assert_array_equal(result_corners, expected_top_corners,
err_msg="The output values are not as expected")
def test_2D_data(self):
A = 3
B = 4
X = 10
Y = 13
expected_output_shape = (A, B, X, Y)
expected_top_corners = np.arange(A*B, dtype=np.float32).reshape((A, B))
self.create_N_tiffs(A*B, frame_size=(X, Y))
# create a data file with the axis information
self.data_file['entry/A'] = np.arange(A)
self.data_file['entry/B'] = np.arange(B)
self.data_file.close()
# edit and save the yaml
self.yaml['xrd']['data']['shape'] = '$(len(A_vals), len(B_vals))'
self.yaml['xrd']['params'].update({'idx_A': 0,
'idx_B': 1,
'idx_detx': 2,
'idx_dety': 3,
'A_vals': "$dfile['entry/A'].value",
'B_vals': "$dfile['entry/B'].value",
'dims': "$range(0, 4)"})
self.yaml['xrd']['axis_labels'] = {0: {'dim': '$idx_A', 'name': 'A', 'value': '$A_vals', 'units': 'pixels'},
1: {'dim': '$idx_B', 'name': 'B', 'value': '$B_vals', 'units': 'pixels'},
2: self.detX_axis_label,
3: self.detY_axis_label}
self.save_yaml_and_change_process_list()
run_protected_plugin_runner(tu.set_options(self.data_file_path,
process_file=self.process_list_path,
out_path=self.test_folder))
result = h5.File(os.path.join(self.test_folder, 'test_processed.nxs'), 'r')['entry/final_result_xrd/data']
np.testing.assert_array_equal(result.shape, expected_output_shape,
err_msg='The output shape is not as expected.')
result_corners = result[..., -1, -1]
np.testing.assert_equal(result_corners.dtype, expected_top_corners.dtype,
err_msg='The array does not output the correct type.')
np.testing.assert_array_equal(result_corners, expected_top_corners,
err_msg="The output values are not as expected")
def test_3D_data(self):
A = 3
B = 4
C = 5
X = 10
Y = 13
expected_output_shape = (A, B, C, X, Y)
expected_top_corners = np.arange(A*B*C, dtype=np.float32).reshape((A, B, C))
self.create_N_tiffs(A*B*C, frame_size=(X, Y))
# create a data file with the axis information
self.data_file['entry/A'] = np.arange(A)
self.data_file['entry/B'] = np.arange(B)
self.data_file['entry/C'] = np.arange(C)
self.data_file.close()
# edit and save the yaml
self.yaml['xrd']['data']['shape'] = '$(len(A_vals), len(B_vals), len(C_vals))'
self.yaml['xrd']['params'].update({'idx_A': 0,
'idx_B': 1,
'idx_C': 2,
'idx_detx': 3,
'idx_dety': 4,
'A_vals': "$dfile['entry/A'].value",
'B_vals': "$dfile['entry/B'].value",
'C_vals': "$dfile['entry/C'].value",
'dims': "$range(0, 4)"})
self.yaml['xrd']['axis_labels'] = {0: {'dim': '$idx_A', 'name': 'A', 'value': '$A_vals', 'units': 'pixels'},
1: {'dim': '$idx_B', 'name': 'B', 'value': '$B_vals', 'units': 'pixels'},
2: {'dim': '$idx_C', 'name': 'C', 'value': '$C_vals', 'units': 'pixels'},
3: self.detX_axis_label,
4: self.detY_axis_label}
self.save_yaml_and_change_process_list()
run_protected_plugin_runner(tu.set_options(self.data_file_path,
process_file=self.process_list_path,
out_path=self.test_folder))
result = h5.File(os.path.join(self.test_folder, 'test_processed.nxs'), 'r')['entry/final_result_xrd/data']
np.testing.assert_array_equal(result.shape, expected_output_shape,
err_msg='The output shape is not as expected.')
result_corners = result[..., -1, -1]
np.testing.assert_equal(result_corners.dtype, expected_top_corners.dtype,
err_msg='The array does not output the correct type.')
np.testing.assert_array_equal(result_corners, expected_top_corners,
err_msg="The output values are not as expected")
def setUp(self):
self.test_folder = tempfile.mkdtemp(suffix='template_test/')
self.tif_folder = os.path.join(self.test_folder, 'tiffs/')
os.mkdir(self.tif_folder)
# copy across the process list to the working directory
self.process_list_path = os.path.join(self.test_folder, 'xrd_template_test.nxs')
shutil.copyfile(tu.get_test_process_path('xrd_template_test.nxs'),
self.process_list_path)
utils.populate_plugins()
self.process_list = Content()
self.process_list.fopen(self.process_list_path, update=False)
for idx in self.process_list.get_positions():
self.process_list.refresh(idx)
self.detX_axis_label = {'dim': '$idx_detx', 'name': 'detector_x', 'value': None, 'units': 'pixels'}
self.detY_axis_label = {'dim': '$idx_dety', 'name': 'detector_y', 'value': None, 'units': 'pixels'}
self.yaml = OrderedDict()
# now make some standard modifications
self.yaml['inherit'] = [tu.get_test_data_path(os.path.join('i18_templates', 'xrd_calibration.yml'))]
self.yaml['xrd'] = OrderedDict()
self.yaml['xrd']['params'] = {}
self.yaml['xrd']['data'] = {}
self.yaml['xrd']['data']['folder'] = self.tif_folder
self.yaml['xrd']['params']['cfile'] = \
"$h5py.File('%s', 'r')" % tu.get_test_data_path('LaB6_calibration_new.nxs')
self.yaml['xrd']['patterns'] = {}
self.yaml['xrd']['patterns']['DIFFRACTION'] = {'core_dims': '$(idx_detx, idx_dety)',
'slice_dims': '$tuple([d for d in dims if d not in [idx_detx, idx_dety]])'}
self.yaml['xrd']['axis_labels'] = {}
self.yaml['xrd']['metadata'] = {}
self.data_file_path = 'test_data.nxs'
self.data_file = h5.File(self.data_file_path, 'w') # this will have the axes in.
def tearDown(self):
shutil.rmtree(self.test_folder)
def create_N_tiffs(self, N, file_pattern='%04d.tif', frame_size=(10, 13)):
'''
Creates a stack of N tiffs with given frame_size and file pattern in self.tif_folder.
:param N: The number of tiffs in the stack
:param file_pattern: The printf pattern for the file names.
:param frame_size: The shape of the frames. Default: (10,13).
'''
data = np.ones(frame_size, dtype=np.float)
for idx in range(N):
file_path = os.path.join(self.tif_folder, (file_pattern % idx))
tifffile.imsave(file_path, data*idx)
def save_yaml_and_change_process_list(self):
yml_path = os.path.join(self.test_folder, 'xrd_tiff.yml')
with open(yml_path, 'w') as f:
yu.dump_yaml(self.yaml, f)
# now set the template path in the process list
self.process_list.modify('1', '2', yml_path)
self.process_list.save(self.process_list_path)
