`CHAP.edd.models`¶

Module Contents¶

Classes¶

`BaselineConfig`	Baseline model configuration
`MaterialConfig`	Model for parameters to characterize a sample material.
`MCAElementConfig`	Class representing metadata required to configure a single MCA detector element.
`MCAElementCalibrationConfig`	Class representing metadata required to calibrate a single MCA detector element.
`MCAElementDiffractionVolumeLengthConfig`	Class representing metadata required to perform a diffraction volume length measurement for a single MCA detector element.
`MCAElementStrainAnalysisConfig`	Class representing metadata required to perform a strain analysis fitting for a single MCA detector element.
`MCAScanDataConfig`	Class representing metadata required to locate raw MCA data for a single scan and construct a mask for it.
`DiffractionVolumeLengthConfig`	Class representing metadata required to perform a diffraction volume length calculation for an EDD setup using a steel-foil raster scan.
`MCAEnergyCalibrationConfig`	Class representing metadata required to perform an energy calibration for an MCA detector.
`MCATthCalibrationConfig`	Class representing metadata required to perform a tth calibration for an MCA detector.
`StrainAnalysisConfig`	Class representing input parameters required to perform a strain analysis.

API¶

class CHAP.edd.models.BaselineConfig¶

Bases: pydantic.BaseModel

Baseline model configuration

Variables:

tol – The convergence tolerence, defaults to 1.e-6.
lam – The &lambda (smoothness) parameter (the balance between the residual of the data and the baseline and the smoothness of the baseline). The suggested range is between 100 and 10^8, defaults to 10^6.
max_iter – The maximum number of iterations, defaults to 100.

tol: confloat(gt=0, allow_inf_nan=False)¶: 1e-06

lam: confloat(gt=0, allow_inf_nan=False)¶: 1000000.0

max_iter: conint(gt=0)¶: 100

attrs: Optional[dict]¶: None

class CHAP.edd.models.MaterialConfig¶

Bases: pydantic.BaseModel

Model for parameters to characterize a sample material.

Variables:

material_name – Sample material name.
lattice_parameters – Lattice spacing(s) in angstroms.
sgnum – Space group of the material.

material_name: Optional[constr(strip_whitespace=True, min_length=1)]¶: None

lattice_parameters: Optional[Union[confloat(gt=0), conlist(min_length=1, max_length=6, item_type=confloat(gt=0))]]¶: None

sgnum: Optional[conint(ge=0)]¶: None

_material: Optional[hexrd.material.Material]¶: None

validate_material()¶

Create and validate the private attribute _material.

Returns:: The validated list of class properties.
Return type:: dict

unique_hkls_ds(tth_tol=0.15, tth_max=90.0)¶

Get a list of unique HKLs and their lattice spacings.

Parameters:

tth_tol (float, optional) – Minimum resolvable difference in 2&theta between two unique HKL peaks, defaults to 0.15.
tth_max (float, optional) – Detector rotation about hutch x axis, defaults to 90.0.

Returns:

Unique HKLs and their lattice spacings in angstroms.

Return type:

numpy.ndarray, numpy.ndarray

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.MCAElementConfig¶

Bases: pydantic.BaseModel

Class representing metadata required to configure a single MCA detector element.

Variables:

detector_name – Name of the MCA detector element in the scan, defaults to 'mca1'.
num_bins – Number of MCA channels.

detector_name: constr(strip_whitespace=True, min_length=1)¶: ‘mca1’

num_bins: Optional[conint(gt=0)]¶: None

classmethod validate_detector_name(detector_name)¶

Validate the specified detector name.

Variables:: detector_name – Name of the MCA detector element in the scan.
Raises:: ValueError – Invalid detector_name.
Returns:: detector_name.
Return type:: str

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.MCAElementCalibrationConfig¶

Bases: CHAP.edd.models.MCAElementConfig

Class representing metadata required to calibrate a single MCA detector element.

Variables:

tth_max – Detector rotation about lab frame x axis, defaults to 90.
hkl_tth_tol – Minimum resolvable difference in 2&theta between two unique Bragg peaks, defaults to 0.15.
energy_calibration_coeffs – Detector channel index to energy polynomial conversion coefficients ([a, b, c] with E_i = ai^2 + bi + c), defaults to [0, 0, 1].
background – Background model for peak fitting.
baseline – Automated baseline subtraction configuration, defaults to False.
tth_initial_guess – Initial guess for 2&theta, defaults to 5.0.
tth_calibrated – Calibrated value for 2&theta.
include_energy_ranges – List of MCA channel energy ranges in keV whose data should be included after applying a mask (bounds are inclusive), defaults to [[50, 150]]

tth_max: confloat(gt=0, allow_inf_nan=False)¶: 90.0

hkl_tth_tol: confloat(gt=0, allow_inf_nan=False)¶: 0.15

energy_calibration_coeffs: conlist(min_length=3, max_length=3, item_type=confloat(allow_inf_nan=False))¶: [0, 0, 1]

background: Optional[Union[str, list]]¶: None

baseline: Optional[Union[bool, CHAP.edd.models.BaselineConfig]]¶: False

tth_initial_guess: confloat(gt=0, le=tth_max, allow_inf_nan=False)¶: 5.0

tth_calibrated: Optional[confloat(gt=0, allow_inf_nan=False)]¶: None

include_energy_ranges: typing_extensions.Annotated[conlist(min_length=1, item_type=conlist(min_length=2, max_length=2, item_type=confloat(ge=25))), Field(validate_default=True)]¶: [[50, 150]]

_hkl_indices: list¶: ‘PrivateAttr(…)’

classmethod validate_include_energy_range(include_energy_ranges, info)¶

Ensure that no energy ranges are outside the boundary of the detector.

Parameters:

include_energy_ranges (dict) – The value of include_energy_ranges to validate.
info (pydantic_core._pydantic_core.ValidationInfo) – Pydantic validator info object.

Return type:

dict

property energies¶: Return calibrated bin energies.

property include_bin_ranges¶: Return the value of include_energy_ranges represented in terms of channel indices instead of channel energies.

property hkl_indices¶: Return the hkl_indices consistent with the selected energy ranges (include_energy_ranges).

get_include_energy_ranges(include_bin_ranges)¶

Given a list of channel index ranges, return the corresponding list of channel energy ranges.

Parameters:: include_bin_ranges (list[list[int]]) – A list of channel bin ranges to convert to energy ranges.
Returns:: Energy ranges
Return type:: list[list[float]]

mca_mask()¶

Get a boolean mask array to use on this MCA element’s data. Note that the bounds of self.include_energy_ranges are inclusive.

Returns:: Boolean mask array.
Return type:: numpy.ndarray

set_hkl_indices(hkl_indices)¶: Set the private attribute hkl_indices.

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.MCAElementDiffractionVolumeLengthConfig¶

Bases: CHAP.edd.models.MCAElementConfig

Class representing metadata required to perform a diffraction volume length measurement for a single MCA detector element.

Variables:

include_bin_ranges – List of MCA channel index ranges whose data is included in the measurement.
measurement_mode – Placeholder for recording whether the measured DVL value was obtained through the automated calculation or a manual selection, defaults to 'auto'.
sigma_to_dvl_factor – The DVL is obtained by fitting a reduced form of the MCA detector data. sigma_to_dvl_factor is a scalar value that converts the standard deviation of the gaussian fit to the measured DVL, defaults to 3.5.
dvl_measured – Placeholder for the measured diffraction volume length before writing the data to file.
fit_amplitude – Placeholder for amplitude of the gaussian fit.
fit_center – Placeholder for center of the gaussian fit.
fit_sigma – Placeholder for sigma of the gaussian fit.

include_bin_ranges: Optional[conlist(min_length=1, item_type=conlist(min_length=2, max_length=2, item_type=conint(ge=0)))]¶: None

measurement_mode: Optional[Literal[manual, auto]]¶: ‘auto’

sigma_to_dvl_factor: Optional[Literal[3.5, 2.0, 4.0]]¶: 3.5

dvl_measured: Optional[confloat(gt=0)]¶: None

fit_amplitude: Optional[float]¶: None

fit_center: Optional[float]¶: None

fit_sigma: Optional[float]¶: None

mca_mask()¶

Get a boolean mask array to use on this MCA element’s data. Note that the bounds of self.include_energy_ranges are inclusive.

Returns:: Boolean mask array.
Return type:: numpy.ndarray

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file. Exclude sigma_to_dvl_factor from the dict representation if measurement_mode is 'manual'.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.MCAElementStrainAnalysisConfig¶

Bases: CHAP.edd.models.MCAElementConfig

Class representing metadata required to perform a strain analysis fitting for a single MCA detector element.

Parameters:

tth_max (float, optional) – Detector rotation about hutch x axis, defaults to 90.0.

Variables:

hkl_tth_tol – Minimum resolvable difference in 2&theta between two unique HKL peaks, defaults to 0.15.
hkl_indices – List of unique HKL indices to fit peaks for in the calibration routine, defaults to [].
background – Background model for peak fitting.
baseline – Automated baseline subtraction configuration, defaults to False.
num_proc – Number of processors used for peak fitting.
peak_models – Peak model for peak fitting, defaults to 'gaussian'.
fwhm_min – Minimum FWHM for peak fitting, defaults to 0.25.
fwhm_max – Maximum FWHM for peak fitting, defaults to 2.0.
centers_range – Peak centers range for peak fitting. The allowed range the peak centers will be the initial values ± centers_range. Defaults to 2.0.
rel_height_cutoff – Relative peak height cutoff for peak fitting (any peak with a height smaller than rel_height_cutoff times the maximum height of all peaks gets removed from the fit model), defaults to None.
tth_calibrated – Calibrated value for 2&theta.
energy_calibration_coeffs – Detector channel index to energy polynomial conversion coefficients ([a, b, c] with E_i = ai^2 + bi + c), defaults to [0, 0, 1].
calibration_bin_ranges – List of MCA channel index ranges whose data is included in the calibration.
tth_file – Path to the file with the 2&theta map.
tth_map – Map of the 2&theta values.
include_energy_ranges – List of MCA channel energy ranges in keV whose data should be included after applying a mask (bounds are inclusive), defaults to [[50, 150]]

tth_max: confloat(gt=0, allow_inf_nan=False)¶: 90.0

hkl_tth_tol: confloat(gt=0, allow_inf_nan=False)¶: 0.15

hkl_indices: Optional[conlist(item_type=conint(ge=0))]¶: []

background: Optional[Union[str, list]]¶: None

baseline: Optional[Union[bool, CHAP.edd.models.BaselineConfig]]¶: False

num_proc: Optional[conint(gt=0)]¶: ‘cpu_count(…)’

peak_models: Union[conlist(min_length=1, item_type=Literal['gaussian', 'lorentzian']), Literal[gaussian, lorentzian]]¶: ‘gaussian’

fwhm_min: confloat(gt=0, allow_inf_nan=False)¶: 0.25

fwhm_max: confloat(gt=0, allow_inf_nan=False)¶: 2.0

centers_range: confloat(gt=0, allow_inf_nan=False)¶: 2.0

rel_height_cutoff: Optional[confloat(gt=0, lt=1.0, allow_inf_nan=False)]¶: None

tth_calibrated: Optional[confloat(gt=0, allow_inf_nan=False)]¶: None

energy_calibration_coeffs: conlist(min_length=3, max_length=3, item_type=confloat(allow_inf_nan=False))¶: [0, 0, 1]

calibration_bin_ranges: Optional[conlist(min_length=1, item_type=conlist(min_length=2, max_length=2, item_type=conint(ge=0)))]¶: None

tth_file: Optional[pydantic.FilePath]¶: None

tth_map: Optional[numpy.ndarray]¶: None

include_energy_ranges: conlist(min_length=1, item_type=conlist(min_length=2, max_length=2, item_type=confloat(ge=25)))¶: [[50, 150]]

classmethod validate_hkl_indices(hkl_indices)¶

class Config¶

arbitrary_types_allowed¶: True

property energies¶: Return calibrated bin energies.

property include_bin_ranges¶: Return the value of include_energy_ranges represented in terms of channel indices instead of channel energies.

get_include_energy_ranges(include_bin_ranges)¶

Given a list of channel index ranges, return the corresponding list of channel energy ranges.

Parameters:: include_bin_ranges (list[list[int]]) – A list of channel bin ranges to convert to energy ranges.
Returns:: Energy ranges
Return type:: list[list[float]]

mca_mask()¶

Get a boolean mask array to use on this MCA element’s data. Note that the bounds of self.include_energy_ranges are inclusive.

Returns:: Boolean mask array.
Return type:: numpy.ndarray

add_calibration(calibration)¶

Finalize values for some fields using a completed MCAElementCalibrationConfig that corresponds to the same detector.

Parameters:: calibration (MCAElementCalibrationConfig) – Existing calibration configuration to use by MCAElementStrainAnalysisConfig.
Returns:: None

get_tth_map(map_shape)¶

Return the map of 2&theta values to use – may vary at each point in the map.

Parameters:: map_shape – The shape of the suplied 2&theta map.
Returns:: Map of 2&theta values.
Return type:: numpy.ndarray

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.MCAScanDataConfig¶

Bases: pydantic.BaseModel

Class representing metadata required to locate raw MCA data for a single scan and construct a mask for it.

Variables:

inputdir – Input directory, used only if any file in the configuration is not an absolute path.
spec_file – Path to the SPEC file containing the scan.
scan_number – Number of the scan in spec_file.
par_file – Path to the par file associated with the scan.
scan_column – Required column name in par_file.
detectors – List of MCA detector element metadata configurations.

inputdir: Optional[pydantic.DirectoryPath]¶: None

spec_file: Optional[pydantic.FilePath]¶: None

scan_number: Optional[conint(gt=0)]¶: None

par_file: Optional[pydantic.FilePath]¶: None

scan_column: Optional[str]¶: None

detectors: conlist(min_length=1, item_type=MCAElementConfig)¶: None

_parfile: Optional[CHAP.utils.parfile.ParFile]¶: None

_scanparser: Optional[chess_scanparsers.SMBMCAScanParser]¶: None

classmethod validate_scan(data)¶

Finalize file paths for spec_file and par_file.

Parameters:: data (MCAScanDataConfig, pydantic_core._pydantic_core.ValidationInfo) – Pydantic validator data object.
Raises:: ValueError – Invalid SPEC or par file.
Returns:: The validated list of class properties.
Return type:: dict

validate_detectors()¶

Fill in values for _scanparser / _parfile (if applicable). Fill in each detector’s num_bins field, if needed. Check each detector’s include_energy_ranges field against the flux file, if available.

Raises:: ValueError – Unable to obtain a value for num_bins.
Returns:: The validated list of class properties.
Return type:: dict

property scanparser¶: Return the scanparser.

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.DiffractionVolumeLengthConfig¶

Bases: CHAP.edd.models.MCAScanDataConfig

Class representing metadata required to perform a diffraction volume length calculation for an EDD setup using a steel-foil raster scan.

Variables:

sample_thickness – Thickness of scanned foil sample. Quantity must be provided in the same units as the values of the scanning motor.
detectors – Individual detector element DVL measurement configurations

sample_thickness: float¶: None

detectors: conlist(min_length=1, item_type=MCAElementDiffractionVolumeLengthConfig)¶: None

property scanned_vals¶

Return the list of values visited by the scanning motor over the course of the raster scan.

Returns:: List of scanned motor values
Return type:: numpy.ndarray

class CHAP.edd.models.MCAEnergyCalibrationConfig¶

Bases: pydantic.BaseModel

Class representing metadata required to perform an energy calibration for an MCA detector.

Variables:

inputdir – Input directory, used only if any file in the configuration is not an absolute path.
scan_step_indices – Optional scan step indices to use for the calibration. If not specified, the calibration will be performed on the average of all MCA spectra for the scan.
detectors – List of individual MCA detector element calibration configurations.
flux_file – File name of the csv flux file containing station beam energy in eV (column 0) versus flux (column 1).
material – Material configuration for the calibration, defaults to Ceria.
peak_energies – Theoretical locations of peaks in keV to use for calibrating the MCA channel energies. It is strongly recommended to use fluorescence peaks for the energy calibration.
max_peak_index – Index of the peak in peak_energies with the highest amplitude.
fit_index_ranges – Explicit ranges of uncalibrated MCA channel index ranges to include during energy calibration when the given peaks are fitted to the provied MCA spectrum. Use this parameter or select it interactively by running a pipeline with config.interactive: True.

inputdir: Optional[pydantic.DirectoryPath]¶: None

scan_step_indices: Optional[typing_extensions.Annotated[conlist(min_length=1, item_type=conint(ge=0)), Field(validate_default=True)]]¶: None

detectors: Optional[conlist(item_type=MCAElementCalibrationConfig)]¶: None

flux_file: Optional[pydantic.FilePath]¶: None

material: Optional[CHAP.edd.models.MaterialConfig]¶: ‘MaterialConfig(…)’

peak_energies: conlist(min_length=2, item_type=confloat(gt=0))¶: None

max_peak_index: conint(ge=0)¶: None

fit_index_ranges: Optional[conlist(min_length=1, item_type=conlist(min_length=2, max_length=2, item_type=conint(ge=0)))]¶: None

classmethod validate_config(data)¶

Ensure that a valid configuration was provided and finalize flux_file filepath.

Parameters:: data (MCAEnergyCalibrationConfig, pydantic_core._pydantic_core.ValidationInfo) – Pydantic validator data object.
Returns:: The currently validated list of class properties.
Return type:: dict

classmethod validate_scan_step_indices(scan_step_indices)¶

Validate the specified list of scan numbers.

Variables:: scan_step_indices – Optional scan step indices to use for the calibration. If not specified, the calibration will be performed on the average of all MCA spectra for the scan.
Raises:: ValueError – Invalid experiment type.
Returns:: List of step indices.
Return type:: list of int

classmethod validate_max_peak_index(max_peak_index, info)¶

Validate the specified index of the XRF peak with the highest amplitude.

Variables:: max_peak_index – The index of the XRF peak with the highest amplitude.
Parameters:: info (pydantic_core._pydantic_core.ValidationInfo) – Pydantic validator info object.
Raises:: ValueError – Invalid max_peak_index.
Returns:: The validated value of max_peak_index.
Return type:: int

flux_file_energy_range()¶

Get the energy range in the flux corection file.

Returns:: The energy range in the flux corection file.
Return type:: tuple(float, float)

flux_correction_interpolation_function()¶

Get an interpolation function to correct MCA data for the relative energy flux of the incident beam.

Returns:: Energy flux correction interpolation function.
Return type:: scipy.interpolate._polyint._Interpolator1D

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

class CHAP.edd.models.MCATthCalibrationConfig¶

Bases: CHAP.edd.models.MCAEnergyCalibrationConfig

Class representing metadata required to perform a tth calibration for an MCA detector.

Variables:

calibration_method – Type of calibration method, defaults to 'direct_fit_residual'.
max_iter – Maximum number of iterations of the calibration routine (only used for 'iterate_tth'), defaults to 10.
tune_tth_tol – Cutoff error for tuning 2&theta (only used for 'iterate_tth'). Stop iterating the calibration routine after an iteration produces a change in the tuned value of 2&theta that is smaller than this cutoff, defaults to 1e-8.
tune_tth_tol – float, optional

calibration_method: Optional[Literal[direct_fit_residual, direct_fit_peak_energies, direct_fit_combined, iterate_tth]]¶: ‘iterate_tth’

max_iter: conint(gt=0)¶: 10

tune_tth_tol: confloat(ge=0)¶: 1e-08

flux_file_energy_range()¶

Get the energy range in the flux corection file.

Returns:: The energy range in the flux corection file.
Return type:: tuple(float, float)

class CHAP.edd.models.StrainAnalysisConfig¶

Bases: pydantic.BaseModel

Class representing input parameters required to perform a strain analysis.

Variables:

inputdir – Input directory, used only if any file in the configuration is not an absolute path.
detectors – List of individual detector element strain analysis configurations, defaults to None (use all detectors).
materials – Sample material configurations.
flux_file – File name of the csv flux file containing station beam energy in eV (column 0) versus flux (column 1).
sum_axes – Whether to sum over the fly axis or not for EDD scan types not 0, defaults to True.
oversampling – FIX

inputdir: Optional[pydantic.DirectoryPath]¶: None

detectors: Optional[conlist(min_length=1, item_type=MCAElementStrainAnalysisConfig)]¶: None

materials: list[CHAP.edd.models.MaterialConfig]¶: None

flux_file: Optional[pydantic.FilePath]¶: None

sum_axes: Optional[bool]¶: True

oversampling: Optional[typing_extensions.Annotated[dict, Field(validate_default=True)]]¶: None

classmethod validate_config(data)¶

Ensure that a valid configuration was provided and finalize flux_file filepath.

Parameters:: data (MCAEnergyCalibrationConfig, pydantic_core._pydantic_core.ValidationInfo) – Pydantic validator data object.
Returns:: The currently validated list of class properties.
Return type:: dict

classmethod validate_tth_file(detectors, info)¶: Finalize value for tth_file for each detector

classmethod validate_oversampling(oversampling, info)¶

Validate the oversampling field.

Parameters:

oversampling (dict) – The value of oversampling to validate.
info (StrainAnalysisConfig, pydantic_core._pydantic_core.ValidationInfo) – Pydantic validator info object.

Returns:

The validated value for oversampling.

Return type:

bool

dict(*args, **kwargs)¶

Return a representation of this configuration in a dictionary that is suitable for dumping to a YAML file.

Returns:: Dictionary representation of the configuration.
Return type:: dict

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