def test_exclude_subruns(nexusfile, projectfile):
"""Test converting NeXus to project and convert to diffraction pattern
Note: project file cannot be the same as NeXus file as the output file will be
removed by pytest
Parameters
----------
nexusfile
projectfile
Returns
-------
"""
sub_runs = [2, 4, 5]
# convert the nexus file to a project file and do the "simple" checks
converter = NeXusConvertingApp(nexusfile, None)
hidra_ws = converter.convert()
reducer = ReductionApp()
reducer.load_hidra_workspace(hidra_ws)
reducer.reduce_data(instrument_file=None,
calibration_file=None,
mask=None,
sub_runs=sub_runs,
van_file=None)
reducer.save_diffraction_data(projectfile)
reduced_ws = HidraWorkspace('test_powder_pattern')
reduced_project = HidraProjectFile(projectfile)
reduced_ws.load_hidra_project(reduced_project, load_raw_counts=False, load_reduced_diffraction=True)
assert sub_runs == reduced_ws.get_sub_runs()
reducer.reduce_data(instrument_file=None,
calibration_file=None,
mask=None,
sub_runs=[],
van_file=None)
for sub_run in sub_runs:
np.testing.assert_allclose(reducer.get_diffraction_data(sub_run),
reduced_ws.get_reduced_diffraction_data(sub_run))
# cleanup
reduced_project.close()
os.remove(projectfile)
def test_powder_pattern_service(project_file_name, mask_file_name, gold_file):
"""Test the powder pattern calculator (service) with HB2B-specific reduction routine
Parameters
----------
project_file_name
mask_file_name
gold_file
Returns
-------
"""
if mask_file_name is not None:
pytest.skip('Not Ready Yet for Masking')
# load gold file
gold_data_dict = parse_gold_file(gold_file)
# Parse input file
test_ws = HidraWorkspace('test_powder_pattern')
test_project = HidraProjectFile(project_file_name)
test_ws.load_hidra_project(test_project,
load_raw_counts=True,
load_reduced_diffraction=False)
test_project.close()
# Start reduction service
pyrs_service = HB2BReductionManager()
pyrs_service.init_session(session_name='test_powder', hidra_ws=test_ws)
# Reduce raw counts
pyrs_service.reduce_diffraction_data('test_powder',
False,
1000,
sub_run_list=None,
mask=mask_file_name,
mask_id=None,
vanadium_counts=None,
normalize_by_duration=False)
# Get sub runs
sub_runs = test_ws.get_sub_runs()
for index, sub_run_i in enumerate(sub_runs):
# Get gold data of pattern (i).
gold_data_i = gold_data_dict[str(sub_run_i)]
# Get powder data of pattern (i).
pattern = pyrs_service.get_reduced_diffraction_data(
'test_powder', sub_run_i)
# data_dict[str(sub_run_i)] = pattern
# validate correct two-theta reduction
np.testing.assert_allclose(pattern[0],
gold_data_dict[str(sub_run_i)][0],
rtol=1E-8)
# remove NaN intensity arrays
pattern[1][np.where(np.isnan(pattern[1]))] = 0.
gold_data_i[1][np.where(np.isnan(gold_data_i[1]))] = 0.
# validate correct intesnity reduction
np.testing.assert_allclose(pattern[1],
gold_data_i[1],
rtol=1E-8,
equal_nan=True)
def test_powder_pattern_engine(project_file_name, mask_file_name, gold_file):
"""Test the powder pattern calculator (service) with HB2B-specific reduction routine
Parameters
----------
project_file_name
mask_file_name
gold_file
Returns
-------
"""
if mask_file_name is not None:
pytest.skip('Masking is not implemented yet')
# Parse input file
test_ws = HidraWorkspace('test_powder_pattern')
test_project = HidraProjectFile(project_file_name)
test_ws.load_hidra_project(test_project,
load_raw_counts=True,
load_reduced_diffraction=False)
test_project.close()
# Sub runs
sub_runs = test_ws.get_sub_runs()
# Import gold file
gold_pattern = parse_gold_file(gold_file)
data_dict = dict()
# Start reduction service
pyrs_service = HB2BReductionManager()
pyrs_service.init_session(session_name='test_powder', hidra_ws=test_ws)
# Reduce raw counts
pyrs_service.reduce_diffraction_data('test_powder',
False,
1000,
sub_run_list=None,
mask=mask_file_name,
mask_id=None,
vanadium_counts=None,
normalize_by_duration=False)
for index, sub_run_i in enumerate(sub_runs):
# Get gold data of pattern (i).
gold_data_i = gold_pattern[str(sub_run_i)]
# Get powder data of pattern (i).
pattern = pyrs_service.get_reduced_diffraction_data(
'test_powder', sub_run_i)
# ensure NaN are removed
gold_data_i[1][np.where(np.isnan(gold_data_i[1]))] = 0.
pattern[1][np.where(np.isnan(pattern[1]))] = 0.
# Verify
np.testing.assert_allclose(pattern[1], gold_data_i[1], rtol=1E-8)
data_dict[str(sub_run_i)] = pattern
# if mask_file_name:
# name = 'data/HB2B_1017_Mask_Gold.h5'
# else:
# name = 'data/HB2B_1017_NoMask_Gold.h5'
# write_gold_file(name, data_dict)
return