def test_write_csv_from_project(project_file_name, csv_filename,
expected_header, num_subruns, num_logs,
startswith, endswith):
"""Test the method to export CSV file
"""
# load project file
if not os.path.exists(project_file_name):
pytest.skip('Project file {} does not exist'.format(project_file_name))
project = HidraProjectFile(project_file_name)
# get information from the project file
peak_tags = project.read_peak_tags()
peak_collections = [
project.read_peak_parameters(tag) for tag in peak_tags
] # all tags
sample_logs = project.read_sample_logs()
assert sample_logs.subruns.size == num_subruns # just as a quick check
# write out the csv file
generator = SummaryGenerator(csv_filename)
generator.setHeaderInformation({'project': '/some/place/random.h5'
}) # only set one value
generator.write_csv(sample_logs, peak_collections)
# testing
assert os.path.exists(csv_filename), '{} was not created'.format(
csv_filename)
# verify the file contents
with open(csv_filename, 'r') as handle:
# read in the file and remove whitespace
contents = [line.strip() for line in handle.readlines()]
# verify exact match on the header
for exp, obs in zip(contents[:len(expected_header)], expected_header):
assert exp == obs
# verify the column headers
assert contents[len(expected_header)].startswith(startswith)
assert contents[len(expected_header)].endswith(endswith)
assert len(contents) == len(
expected_header) + 1 + num_subruns, 'Does not have full body'
# verify that the number of columns is correct
# columns are (subruns, seven logs, parameter values, uncertainties, d_spacing values,
# strain values and uncertainties, chisq)
for line in contents[len(expected_header) +
1:]: # skip past header and constant log
assert len(line.split(',')) == 1 + num_logs + 7 * 2 + (2 * 2) + 1
# cleanup
os.remove(csv_filename)
def load_hidra_project_file(self, filename, direction):
try:
source_project = HidraProjectFile(
filename, mode=HidraProjectFileMode.READONLY)
ws = HidraWorkspace(direction)
ws.load_hidra_project(source_project, False, False)
peaks = dict()
for peak in source_project.read_peak_tags():
peaks[peak] = source_project.read_peak_parameters(peak)
return ws, peaks
except Exception as e:
self.failureMsg.emit(
f"Failed to load {filename}. Check that this is a Hidra Project File",
str(e), traceback.format_exc())
return None, dict()
def test_strain_io():
"""Test PeakCollection writing and reading with *D reference*
Returns
-------
"""
# Generate a unique test file
now = datetime.datetime.now()
test_file_name = 'test_strain_io_{}.h5'.format(now.toordinal())
test_ref_d = 1.23454321
test_ref_d2 = np.array([1.23, 1.24, 1.25])
peak_tag = 'Fake Peak D'
peak_tag_2 = 'Fake Peak D Diff'
# Generate a HiDRA project file
test_project_file = HidraProjectFile(test_file_name,
HidraProjectFileMode.OVERWRITE)
# Create a ND array for output parameters
param_names = PeakShape.PSEUDOVOIGT.native_parameters + BackgroundFunction.LINEAR.native_parameters
data_type = list()
for param_name in param_names:
data_type.append((param_name, np.float32))
test_error_array = np.zeros(3, dtype=data_type)
test_params_array = np.zeros(3, dtype=data_type)
for i in range(3):
# sub run
for j, par_name in enumerate(param_names):
test_params_array[par_name][i] = 2**i + 0.1 * 3**j
test_error_array[par_name][i] = np.sqrt(
abs(test_params_array[par_name][i]))
# END-FOR
chi2_array = np.array([0.323, 0.423, 0.523])
# Add test data to output
peaks = PeakCollection(peak_tag, PeakShape.PSEUDOVOIGT,
BackgroundFunction.LINEAR)
peaks.set_peak_fitting_values(np.array([1, 2, 3]), test_params_array,
test_error_array, chi2_array)
peaks.set_d_reference(test_ref_d)
# Add 2nd peak
peaks2 = PeakCollection(peak_tag_2, PeakShape.PSEUDOVOIGT,
BackgroundFunction.LINEAR)
peaks2.set_peak_fitting_values(np.array([1, 2, 3]), test_params_array,
test_error_array, chi2_array)
peaks2.set_d_reference(test_ref_d2)
# Write
test_project_file.write_peak_parameters(peaks)
test_project_file.write_peak_parameters(peaks2)
# Save
test_project_file.save(verbose=False)
# Verify
assert os.path.exists(test_file_name), 'Test project file for peak fitting result {} cannot be found.' \
''.format(test_file_name)
# import
verify_project_file = HidraProjectFile(test_file_name,
HidraProjectFileMode.READONLY)
# check tags
peak_tags = verify_project_file.read_peak_tags()
assert peak_tag in peak_tags and peak_tag_2 in peak_tags
assert len(peak_tags) == 2
# Get d-reference of peak 1 to check
peak_info = verify_project_file.read_peak_parameters(peak_tag)
verify_d_ref = peak_info.get_d_reference()
gold_ref_d = np.array([test_ref_d] * 3)
np.testing.assert_allclose(verify_d_ref, gold_ref_d)
# Get d-reference of peak 2 to check
peak_info2 = verify_project_file.read_peak_parameters(peak_tag_2)
verify_d_ref_2 = peak_info2.get_d_reference()
np.testing.assert_allclose(verify_d_ref_2, test_ref_d2)
# Clean
os.remove(test_file_name)
return
def test_peak_fitting_result_io():
"""Test peak fitting result's writing and reading
Returns
-------
"""
# Generate a unique test file
now = datetime.datetime.now()
test_file_name = 'test_peak_io_{}.hdf'.format(now.toordinal())
# Generate a HiDRA project file
test_project_file = HidraProjectFile(test_file_name,
HidraProjectFileMode.OVERWRITE)
# Create a ND array for output parameters
param_names = PeakShape.PSEUDOVOIGT.native_parameters + BackgroundFunction.LINEAR.native_parameters
data_type = list()
for param_name in param_names:
data_type.append((param_name, np.float32))
test_error_array = np.zeros(3, dtype=data_type)
test_params_array = np.zeros(3, dtype=data_type)
for i in range(3):
# sub run
for j, par_name in enumerate(param_names):
test_params_array[par_name][i] = 2**i + 0.1 * 3**j
test_error_array[par_name][i] = np.sqrt(
abs(test_params_array[par_name][i]))
# END-FOR
chi2_array = np.array([0.323, 0.423, 0.523])
# Add test data to output
peaks = PeakCollection('test fake', PeakShape.PSEUDOVOIGT,
BackgroundFunction.LINEAR)
peaks.set_peak_fitting_values(np.array([1, 2, 3]), test_params_array,
test_error_array, chi2_array)
test_project_file.write_peak_parameters(peaks)
test_project_file.save(False)
# Check
assert os.path.exists(test_file_name), 'Test project file for peak fitting result {} cannot be found.' \
''.format(test_file_name)
print('[INFO] Peak parameter test project file: {}'.format(test_file_name))
# Import
verify_project_file = HidraProjectFile(test_file_name,
HidraProjectFileMode.READONLY)
# get the tags
peak_tags = verify_project_file.read_peak_tags()
assert 'test fake' in peak_tags
assert len(peak_tags) == 1
# get the parameter of certain
peak_info = verify_project_file.read_peak_parameters('test fake')
# peak profile
assert peak_info.peak_profile == str(PeakShape.PSEUDOVOIGT)
assert peak_info.background_type == str(BackgroundFunction.LINEAR)
# sub runs
assert np.allclose(peak_info.sub_runs, np.array([1, 2, 3]))
# parameter values
# print('DEBUG:\n Expected: {}\n Found: {}'.format(test_params_array, peak_info[3]))
peak_values, peak_errors = peak_info.get_native_params()
assert_allclose_structured_numpy_arrays(test_params_array, peak_values)
# np.testing.assert_allclose(peak_info[3], test_params_array, atol=1E-12)
# parameter values
# assert np.allclose(peak_info[4], test_error_array, 1E-12)
assert_allclose_structured_numpy_arrays(test_error_array, peak_errors)
# Clean
os.remove(test_file_name)
return