def save_fit_result(self, out_file_name=''):
"""Save the fit result, including a copy of the rest of the file if it does not exist at the specified path.
If out_file_name is empty or if it matches the parent's current file, this updates the file.
Otherwise, the parent's file is copied to out_file_name and
then the updated peak fit data is written to the copy.
:param out_file_name: string absolute fill path for the place to save the file
"""
fit_result = self.parent.fit_result
if fit_result is None:
return
if out_file_name is not None and self.parent._curr_file_name != out_file_name:
copyfile(self.parent._curr_file_name, out_file_name)
current_project_file = out_file_name
else:
current_project_file = self.parent._curr_file_name
project_h5_file = HidraProjectFile(current_project_file,
mode=HidraProjectFileMode.READWRITE)
peakcollections = fit_result.peakcollections
for peak in peakcollections:
project_h5_file.write_peak_parameters(peak)
project_h5_file.save(False)
project_h5_file.close()
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