def test_regenerate_powder_map_data(_):
# Read from existing file first to allow option of only replacing for
# certain test cases or keys
try:
with open(powder_map_output_file, 'r') as json_file:
json_data = json.load(json_file)
except FileNotFoundError:
json_data = {}
for powder_map_param in powder_map_params:
# Generate current figure for us to retrieve with gcf
euphonic.cli.powder_map.main(powder_map_param)
# Retrieve with gcf and write to file
image_data = get_current_plot_image_data()
# Optionally only write certain keys
keys_to_replace = []
if len(keys_to_replace) > 0:
for key in keys_to_replace:
json_data[args_to_key(powder_map_param)][key] = image_data[key]
else:
json_data[args_to_key(powder_map_param)] = image_data
with open(powder_map_output_file, 'w+') as json_file:
json.dump(json_data, json_file, indent=4)
def test_regenerate_disp_data(_):
json_data = {}
for disp_param in disp_params:
# Generate current figure for us to retrieve with gcf
euphonic.cli.dispersion.main(disp_param)
# Retrieve with gcf and write to file
json_data[args_to_key(disp_param)] = get_current_plot_line_data()
with open(disp_output_file, 'w+') as json_file:
json.dump(json_data, json_file, indent=4)
def test_regenerate_intensity_map_data(_):
json_data = {}
for intensity_map_param in intensity_map_params:
# Generate current figure for us to retrieve with gcf
euphonic.cli.intensity_map.main(intensity_map_param)
# Retrieve with gcf and write to file
json_data[args_to_key(
intensity_map_param)] = get_current_plot_image_data()
with open(intensity_map_output_file, 'w+') as json_file:
json.dump(json_data, json_file, indent=4)
def test_plots_produce_expected_image(self, inject_mocks, powder_map_args):
euphonic.cli.powder_map.main(powder_map_args)
image_data = get_current_plot_image_data()
with open(powder_map_output_file, 'r') as expected_data_file:
expected_image_data = json.load(expected_data_file)[args_to_key(
powder_map_args)]
for key, value in image_data.items():
if key == 'extent':
# Lower bound of y-data (energy) varies by up to ~2e-6 on
# different systems when --asr is used, compared to
# the upper bound of 100s of meV this is effectively zero,
# so increase tolerance to allow for this
npt.assert_allclose(value, expected_image_data[key], atol=2e-6)
elif isinstance(value, list) and isinstance(value[0], float):
# Errors of 2-4 epsilon seem to be common when using
# broadening, so slightly increase tolerance
npt.assert_allclose(value,
expected_image_data[key],
atol=1e-14)
else:
assert value == expected_image_data[key]
def test_plots_contain_expected_data(self, inject_mocks, dispersion_args):
euphonic.cli.dispersion.main(dispersion_args)
line_data = get_current_plot_line_data()
with open(disp_output_file, 'r') as f:
expected_line_data = json.load(f)[args_to_key(dispersion_args)]
# Increase tolerance if asr present - can give slightly
# different results with different libs
if any(['--asr' in arg for arg in dispersion_args]):
atol = 1.5e-6
else:
atol = sys.float_info.epsilon
for key, value in line_data.items():
if key == 'xy_data':
# numpy can only auto convert 2D lists - xy_data has
# dimensions (n_lines, 2, n_points) so check in a loop
for idx, line in enumerate(value):
npt.assert_allclose(line,
expected_line_data[key][idx],
atol=atol)
else:
assert value == expected_line_data[key]