def assert_checks(fit_result, exp_params, obs_params, number_of_peakCollection,
peak_profile_type=PeakShape.GAUSSIAN):
"""verified the fitted parameters
Parameters
----------
fit_result: object
results of the fitting
exp_params: dict(float)
parameters of original shape
obs_params:List(float)
parameter values of fitting
number_of_peakCollection:Integer
number of fitted peaks
peak_profile_type : str
type of peak profile
Returns
-------
"""
assert len(fit_result.peakcollections) == number_of_peakCollection, 'Only one PeakCollection'
assert fit_result.fitted
assert fit_result.difference
peak_profile_type = PeakShape.getShape(peak_profile_type)
if peak_profile_type == PeakShape.GAUSSIAN:
np.testing.assert_allclose(obs_params['PeakCentre'], exp_params['peak_center'], rtol=50.)
# np.testing.assert_allclose(obs_params['Intensity'], exp_params['peak_intensity'], rtol=50.)
# np.testing.assert_allclose(obs_params['FWHM'], exp_params['peak_FWHM'], rtol=50.)
elif peak_profile_type == PeakShape.PSEUDOVOIGT:
np.testing.assert_allclose(obs_params['Intensity'], exp_params['peak_intensity'], rtol=50.)
np.testing.assert_allclose(obs_params['PeakCentre'], exp_params['peak_center'], rtol=50.)
np.testing.assert_allclose(obs_params['FWHM'], exp_params['peak_FWHM'], rtol=50.)
def __init__(self, peak_tag, peak_profile, background_type, wavelength=np.nan, d_reference=np.nan):
"""Initialization
Parameters
----------
peak_tag : str
tag for the peak such as 'Si111'
peak_profile : str
Peak profile
background_type : str
Background type
"""
# Init variables from input
self._tag = peak_tag
# Init other parameters
self._peak_profile = PeakShape.getShape(peak_profile)
self._background_type = BackgroundFunction.getFunction(background_type)
self._wavelength = wavelength
self._d_reference = d_reference
# sub run numbers: 1D array
self._sub_run_array = SubRuns()
# parameter values: numpy structured array
self._params_value_array = None
# parameter fitting error: numpy structured array
self._params_error_array = None
# fitting cost (chi2): numpy 1D array
self._fit_cost_array = None
# status messages: list of strings
self._fit_status = None
def check_peak_shape_enum(peak_shape, num_native_params):
"""check peak shape enum
Parameters
----------
peak_shape : str
Peak shape
num_native_params : integer
number of native parameters
Returns
-------
"""
assert PeakShape.getShape(peak_shape) == PeakShape[peak_shape.upper()]
assert PeakShape.getShape(
peak_shape.upper()) == PeakShape[peak_shape.upper()]
with pytest.raises(KeyError):
PeakShape.getShape('non-existant-peak-shape')
assert len(
PeakShape.getShape(peak_shape).native_parameters) == num_native_params
def __init__(self,
peak_tag: str,
peak_profile,
background_type,
wavelength: float = np.nan,
d_reference: Union[float, np.ndarray] = np.nan,
d_reference_error: Union[float, np.ndarray] = 0.,
projectfilename: str = '',
runnumber: int = -1) -> None:
"""Initialization
Parameters
----------
peak_tag : str
tag for the peak such as 'Si111'
peak_profile : str
Peak profile
background_type : str
Background type
"""
# Init variables from input
self._tag = peak_tag
self._filename: str = ''
self.projectfilename = projectfilename # use the setter
self._runnumber: int = runnumber
# Init other parameters
self._peak_profile = PeakShape.getShape(peak_profile)
self._background_type = BackgroundFunction.getFunction(background_type)
self._wavelength = wavelength
# sub run numbers: 1D array
self._sub_run_array = SubRuns()
# parameter values: numpy structured array
self._params_value_array = None
# parameter fitting error: numpy structured array
self._params_error_array = None
# fitting cost (chi2): numpy 1D array
self._fit_cost_array = None
# status messages: list of strings
self._fit_status = None
# must happen after the sub_run array is set
self._d_reference: Optional[unumpy.uarray]
self.set_d_reference(d_reference, d_reference_error)
def __init__(self, hidraworkspace, peak_function_name,
background_function_name, wavelength, out_of_plane_angle):
'''It is not expected that any subclass will need to implement this method. It is
designed to unpack all of the information necessary for fitting.'''
# configure logging for this class
self._log = Logger(__name__)
if out_of_plane_angle:
# TODO variable should go into creating the mantid workspace
raise NotImplementedError(
'Do not currently support out_of_plane_angle')
# TODO generate_mantid_workspace should not use `mask_id`
self._mtd_wksp = mantid_helper.generate_mantid_workspace(
hidraworkspace, hidraworkspace.name, None)
self._subruns = hidraworkspace.get_sub_runs()
# create a
self._peak_function = PeakShape.getShape(peak_function_name)
self._background_function = BackgroundFunction.getFunction(
background_function_name)
self._wavelength = wavelength