def test_linear_wavelength_setting(self):
input_dict = {"binning": {"wavelength": {"type": "Lin",
"start": 1.0,
"stop": 10.0,
"step": 2.0}}}
wav_objs = self._get_wavelength_objs()
WavelengthTomlParser(input_dict).set_wavelength_details(wav_objs)
self._assert_wav_objs(wav_objs, wav_low=1.0, wav_high=10.0,
range_type=RangeStepType.LIN, step_size=2.0)
def test_rangelin_wavelength_setting_legacy(self):
input_dict = {"binning": {"wavelength": {"type": "RangeLog",
"binning": "1,3,7,9",
"step": "0.5"}}}
wav_objs = self._get_wavelength_objs()
WavelengthTomlParser(input_dict).set_wavelength_details(wav_objs)
self._assert_wav_objs(wav_objs, range_type=RangeStepType.RANGE_LOG,
wav_low=1., wav_high=9.)
self.assertEqual([(1., 3.), (3., 7.), (7., 9.), (1., 9.)], wav_objs.wavelength.wavelength_interval.selected_ranges)
self.assertEqual(0.5, wav_objs.wavelength.wavelength_interval.wavelength_step)
def test_rangelin_wavelength_setting(self):
input_dict = {"binning": {"wavelength": {"type": "RangeLin",
"binning": "1-3,4-6",
"step": "0.5"}}}
wav_objs = self._get_wavelength_objs()
WavelengthTomlParser(input_dict).set_wavelength_details(wav_objs)
self._assert_wav_objs(wav_objs, range_type=RangeStepType.RANGE_LIN,
wav_low=1., wav_high=6.)
self.assertEqual([(1., 3.), (4., 6.), (1., 6.0)], wav_objs.wavelength.wavelength_interval.selected_ranges)
self.assertEqual(0.5, wav_objs.wavelength.wavelength_interval.wavelength_step)
def _parse_binning(self):
binning_dict = self.get_val(["binning"])
to_set = DuplicateWavelengthStates(
transmission=self.calculate_transmission,
normalize=self.normalize_to_monitor,
wavelength=self.wavelength,
pixel=self.wavelength_and_pixel)
WavelengthTomlParser(toml_dict=self._input).set_wavelength_details(
state_objs=to_set)
one_d_dict = self.get_val("1d_reduction", binning_dict)
if one_d_dict:
one_d_binning = self.get_val(["1d_reduction", "binning"],
binning_dict)
q_min, q_max = self._get_1d_min_max(one_d_binning)
self.convert_to_q.q_min = q_min
self.convert_to_q.q_1d_rebin_string = one_d_binning
self.convert_to_q.q_max = q_max
self.convert_to_q.radius_cutoff = self.get_val("radius_cut",
one_d_dict,
default=0.0)
self.convert_to_q.wavelength_cutoff = self.get_val(
"wavelength_cut", one_d_dict, default=0.0)
self.convert_to_q.q_xy_max = self.get_val(["2d_reduction", "stop"],
binning_dict)
self.convert_to_q.q_xy_step = self.get_val(["2d_reduction", "step"],
binning_dict)
two_d_step_type = self.get_val(["2d_reduction", "type"], binning_dict)
if two_d_step_type:
self.convert_to_q.q_xy_step_type = RangeStepType(two_d_step_type)
rebin_type = self.get_val(["2d_reduction", "interpolate"],
binning_dict,
default=False)
rebin_type = RebinType.INTERPOLATING_REBIN if rebin_type else RebinType.REBIN
self.calculate_transmission.rebin_type = rebin_type
self.normalize_to_monitor.rebin_type = rebin_type
def test_can_handle_no_wavelength(self):
input_dict = {"binning": {"another_field": None}}
self.assertIsNone(
WavelengthTomlParser(input_dict).set_wavelength_details(
mock.NonCallableMock()))