pyccapt.calibration.reconstructions package

Submodules

pyccapt.calibration.reconstructions.crystal_helper module

pyccapt.calibration.reconstructions.crystal_helper.apply_noise_to_structure(structure, noise_levels=(5, 5, 2), noise_type='correlative')

Applies random displacements to the atomic positions in a structure.

Parameters: structure (Structure): The pymatgen Structure object containing atom positions. noise_levels (tuple of floats): Noise levels for the x, y, and z coordinates in Ångstroms.

For example, (0.5, 0.3, 0.1) will add up to 0.5 Å noise in x, 0.3 Å noise in y, and 0.1 Å noise in z.

noise_type (str): Type of noise to apply. Options are ‘correlative’ or ‘noncorrelative’.

Returns: tuple:

• Structure: A new Structure object with noise applied to each atomic position.

• np.ndarray: The noise array applied to each atom (for debugging or visualization purposes).

pyccapt.calibration.reconstructions.crystal_helper.filter_atoms_in_cone_and_hemisphere(structure, cone_r, cone_L, hemi_z_base)

Filters atoms that are inside a cone and inside/below a hemisphere.

Parameters: structure (Structure): Pymatgen Structure object containing atom positions. cone_r (float): Radius of the cone’s base. cone_L (float): Height of the cone along the z-axis. hemi_z_base (float): Height of the hemisphere’s flat face along the z-axis.

Returns: Structure: Filtered Structure object containing only atoms inside the cone or inside/below the hemisphere. numpy.ndarray: Filtered array of atom coordinates.

pyccapt.calibration.reconstructions.crystal_helper.find_phi_and_theta(sdm_param, theta_min, theta_max, phi_min, phi_max)

Plots noise level based on theta and phi values.

Parameters: - sdm_param (np.ndarray): Array of parameters, where columns 2, 3, 4 represent theta, phi, and noise respectively. - theta_min (float): Minimum theta for plot limits. - theta_max (float): Maximum theta for plot limits. - phi_min (float): Minimum phi for plot limits. - phi_max (float): Maximum phi for plot limits.

Returns: - theta_value (float): Theta value with minimum noise. - phi_value (float): Phi value with minimum noise.

pyccapt.calibration.reconstructions.crystal_helper.project_to_surface(structure)

Projects all atomic positions onto the surface

Parameters: structure (Structure): A pymatgen Structure object with atomic positions.

Returns: Structure: A new Structure object with all atomic positions projected onto the hemisphere surface.

pyccapt.calibration.reconstructions.crystal_helper.pyccapt_to_pymatgen(data, range)

Converts data from pyccapt format to pymatgen format.

Parameters:

• data – the data in pyccapt format

• range – the range of data IN pyccapt format to be converted

Returns:

pymatgen Structure object

pyccapt.calibration.reconstructions.crystal_helper.rotate_structure(structure, theta_deg, phi_deg)

Rotates the atomic coordinates of a pymatgen Structure based on given angles theta (angle from the z-axis) and phi (angle from the x-axis in the xy-plane).

Parameters: - structure (Structure): Pymatgen Structure object containing atomic positions. - theta_deg (float): Angle theta in degrees (from the z-axis). - phi_deg (float): Angle phi in degrees (from the x-axis in the xy-plane).

Returns: - Structure: New pymatgen Structure with rotated atomic coordinates.

pyccapt.calibration.reconstructions.crystal_helper.stereographic_projection(structure, d_z=0)

Projects all atomic positions onto the surface

Parameters: structure (Structure): A pymatgen Structure object with atomic positions. d_z (float): The distance of the projection plane from the origin.

Returns: Structure: A new Structure object with all atomic positions projected onto the hemisphere surface.

pyccapt.calibration.reconstructions.density_map module

pyccapt.calibration.reconstructions.density_map.plot_density_map(x, y, z_weigth=False, log=True, bins=(256, 256), frac=1.0, axis_mode='normal', figure_size=(5, 4), variables=None, composition=None, roi=None, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[], data_crop=False, draw_circle=False, mode_selector='circle', axis=['x', 'y'], save=False, figname='disparity_map', cmap='plasma', normalize=False, normalize_axes=False)

Plot and crop the density map with the option to select a region of interest.

Parameters:

• x – x-axis data

• y – y-axis data

• z_weigth – z weight data

• log – Flag to choose whether to plot the log of the data

• bins – Number of bins for the histogram in tuple or bin size in nm as float

• frac – Fraction of the data to be plotted

• axis_mode – Flag to choose whether to plot axis or scalebar: ‘normal’ or ‘scalebar’

• variables – Variables object

• composition – list of elements

• roi – Region of interest

• range_sequence – Range of sequence as list or percentage

• range_mc – Range of mc

• range_detx – Range of detx

• range_dety – Range of dety

• range_x – Range of x-axis

• range_y – Range of y-axis

• range_z – Range of z-axis

• range_vol – Range of voltage

• figure_size – Size of the plot

• draw_circle – Flag to enable circular region of interest selection

• mode_selector – Mode of selection (circle or ellipse)

• axis – Axes to be plotted

• save – Flag to choose whether to save the plot or not

• data_crop – Flag to control whether only the plot is shown or cropping functionality is enabled

• figname – Name of the figure

• cmap – Colormap for the plot

• normalize – Flag to normalize the histogram (default False).

• normalize_axes – Flag to normalize the axis limits (default False).

Returns:

None

pyccapt.calibration.reconstructions.fft module

pyccapt.calibration.reconstructions.fft.fft(particles, d, variables=None, normalize=False, reference_point=None, box_dimensions=None, plot=False, save=False, figure_size=(6, 6), figname='fft', fft_type='1d', axes=None)

Compute histogram-based FFT spectra for 1D or 2D coordinate profiles.

pyccapt.calibration.reconstructions.iso_surface module

pyccapt.calibration.reconstructions.iso_surface.bin_vectors_from_distance(dist, bin_values, mode='distance')

Create a set of grid vectors to be used in nD binning. The bounds are calculated such that they don’t go beyond the size of the dataset.

Parameters:

• dist (numpy.ndarray) – The distance variable to be binned. One column per dimension. It is the generalized distance.

• bin_values (list or numpy.ndarray) – The bin ‘distance’ per bin in either a distance metric or a count. Non-isometric bins are possible.

• mode (str) – Mode can be ‘distance’ (constant distance) or ‘count’ (constant count). Default is ‘distance’.

Returns:

• bin_centers (list of numpy.ndarray): The bin centers of each bin.

• bin_edges (list of numpy.ndarray): The edges of each bin.

Return type:

tuple

pyccapt.calibration.reconstructions.iso_surface.build_element_mask(variables, element_name, base_mask=None, pure_only=False)

Build a boolean mask for ranged ions matching a given element selector.

pyccapt.calibration.reconstructions.iso_surface.build_range_mask(variables, range_sequence=None, range_mc=None, range_detx=None, range_dety=None, range_x=None, range_y=None, range_z=None, range_vol=None, verbose=False)

Build a boolean mask for the requested reconstruction sub-range.

pyccapt.calibration.reconstructions.iso_surface.calculate_element_isosurface(variables, element_name, bin_values, base_mask=None, smoothing_sigma=1.0, min_atoms_per_voxel=10, min_vertices=20, fig_name=None, pure_only=False, manual_iso_value=None)

Create a filtered isosurface mesh for a single interface element.

pyccapt.calibration.reconstructions.iso_surface.calculate_iso_value(conc, save_path=None, fig_name=None)

Calculate the optimal iso value from a 3D array and save the histogram plot.

Parameters:

• conc (numpy.ndarray) – 3D array of concentration values.

• save_path (str) – Directory to save the histogram plot.

Returns:

Optimal iso value.

Return type:

float

pyccapt.calibration.reconstructions.iso_surface.filter_isosurface_by_size(isosurf, min_vertices=100, largest_n=None)

Remove small disconnected isosurface components.

pyccapt.calibration.reconstructions.iso_surface.format_ion(elements, complexities)

pyccapt.calibration.reconstructions.iso_surface.isosurface(gridVec, data, isovalue)

Extract isosurface using pyvista for a custom 3D grid.

Parameters:

• gridVec (list of np.ndarray) – List of 3 arrays representing the grid points in x, y, and z.

• data (np.ndarray) – 3D scalar field (same shape as the meshgrid defined by gridVec).

• isovalue (float) – Scalar value to extract the isosurface.

Returns:

Isosurface with faces and vertices.

Return type:

pyvista.PolyData

pyccapt.calibration.reconstructions.iso_surface.plotly_fig2array(fig)

convert Plotly fig to an array

Parameters:

fig (plotly.graph_objects.Figure) – The base figure.

Returns:

The array representation of the figure.

Return type:

array

pyccapt.calibration.reconstructions.iso_surface.pos_to_voxel(data, grid_vec, species=None)

Creates a voxelization of the data in ‘pos’ based on the bin centers in ‘grid_vec’ for the atoms/ions in the specified species.

Args:

data (pyccapt DataFrame): The data to be voxelized. when input species is given, ranges must be allocated.

% A decomposed DataFrame file is also possible. Use range_to_pyccapt to decompose the data.

grid_vec (list of numpy.ndarray): Grid vectors for the voxel grid. These are the bin centers. species (list, str, or numpy.ndarray, optional): The species to filter by. Can be:

• List of species names (e.g., [‘Fe’, ‘Mn’]).

• Boolean array matching the length of pos.

• None, to include all atoms/ions.

Returns:

numpy.ndarray: A 3D array representing the voxelized data.

pyccapt.calibration.reconstructions.iso_surface.reconstruction_plot(variables, element_percentage, opacity, rotary_fig_save, figname, save, make_gif=False, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[], ions_individually_plots=False, max_num_ions=None, min_num_ions=None, isosurface_dic=None, detailed_isotope_charge=False, only_iso=False, cluster_result=None, smoothing_sigma=1.0, min_atoms_per_voxel=10, min_isosurface_vertices=20, pure_element_only=False, manual_iso_value=None, cluster_display_mode='overlay')

Generate a 3D plot for atom probe reconstruction data.

Parameters:

• variables (DataFrame) – variables object contains daraframe with the data.

• element_percentage (list) – Percentage of each element to plot.

• opacity (float) – Opacity of the ions.

• rotary_fig_save (bool) – Whether to save the rotary figure.

• figname (str) – Name of the figure.

• save (bool) – Whether to save the figure.

• make_gif (bool) – Whether to make a GIF.

• range_sequence (list) – Sequence of the range data.

• range_mc (list) – Mass-to-charge ratio of the range data.

• range_detx (list) – Detector x-coordinate of the range data.

• range_dety (list) – Detector y-coordinate of the range data.

• range_x (list) – x-coordinate of the range data.

• range_y (list) – y-coordinate of the range data.

• range_z (list) – z-coordinate of the range data.

• range_vol (list) – Volume of the range data.

• ions_individually_plots (bool) – Whether to plot each ion individually.

• max_num_ions (int) – Maximum number of ions to plot.

• min_num_ions (int) – Minimum number of ions to plot.

• isosurface_dic (dic) – Dictionary with the isosurface elements and their values.

• detailed_isotope_charge (bool) – Whether to plot the range of each isotopes and charge state.

• only_iso (bool) – Whether to plot only the isosurface.

• cluster_result – Optional Min-Max precipitate segmentation overlay.

• cluster_display_mode (str) – overlay or clusters-only.

Returns:

None

pyccapt.calibration.reconstructions.iso_surface.rotary_fig(fig, variables, rotary_fig_save, make_gif, figname)

Generate a rotating figure using Plotly.

Parameters:

• fig (plotly.graph_objects.Figure) – The base figure.

• rotary_fig_save (bool) – Whether to save the rotary figure.

• make_gif (bool) – Whether to make a GIF.

• figname (str) – The name of the figure.

Returns:

None

pyccapt.calibration.reconstructions.iso_surface.rotate_z(x, y, z, theta)

Rotate coordinates around the z-axis.

Parameters:

• x (float) – x-coordinate.

• y (float) – y-coordinate.

• z (float) – z-coordinate.

• theta (float) – Rotation angle.

Returns:

Rotated coordinates (x, y, z).

Return type:

tuple

pyccapt.calibration.reconstructions.rdf module

pyccapt.calibration.reconstructions.rdf.rdf(particles, dr, variables=None, rho=None, rcutoff=0.9, eps=1e-15, normalize=True, reference_point=None, box_dimensions=None, plot=False, save=False, figure_size=(6, 6), figname='rdf')

Computes 2D or 3D radial distribution function g(r) of a set of particle coordinates of shape (N, d). Particle must be placed in a 2D or 3D cuboidal box of dimensions [width x height (x depth)].

Parameters:

• particles ((N, d) np.array) – Set of particle from which to compute the radial distribution function g(r). Must be of shape (N, 2) or (N, 3) for 2D and 3D coordinates repsectively.

• dr (float) – Delta r. Determines the spacing between successive radii over which g(r) is computed.

• variables (variables object)

• rho (float, optional) – Number density. If left as None, box dimensions will be inferred from the particles and the number density will be calculated accordingly.

• rcutoff (float) – radii cutoff value between 0 and 1. The default value of 0.9 means the independent variable (radius) over which the RDF is computed will range from 0 to 0.9*r_max. This removes the noise that occurs at r values close to r_max, due to fewer valid particles available to compute the RDF from at these r values.

• eps (float, optional) – Epsilon value used to find particles less than or equal to a distance in KDTree.

• normalize (bool, optional) – Option to normalize the RDF. If True, the RDF values are normalized.

• reference_point ((d,) np.array or list, optional) – The center of the box. If left as None, there is no data cropping and calculate the rdf for the whole data.

• box_dimensions ((d,) np.array or list, optional) – The dimensions of the box. If left as None, the box dimensions will be inferred from the particles.

• plot (bool, optional) – Option to plot the RDF. If True, the RDF is plotted.

• save (bool, optional) – Option to save the RDF. If True, the RDF is saved.

• figure_size ((float, float), optional) – The size of the figure in inches.

• figname (str, optional) – The name of the figure.

Returns:

• g_r ((n_radii) np.array) – radial distribution function values g(r).

• radii ((n_radii) np.array) – radii over which g(r) is computed

pyccapt.calibration.reconstructions.reconstruction module

pyccapt.calibration.reconstructions.reconstruction.atom_probe_recons_Bas_et_al(detx, dety, hv, flight_path_length, kf, det_eff, icf, field_evap, avg_dens)

Perform atom probe reconstruction using Bas et al.’s method.

Parameters:

• detx (float) – Hit position on the detector (x-coordinate).

• dety (float) – Hit position on the detector (y-coordinate).

• hv (float) – High voltage.

• flight_path_length (float) – Distance between detector and sample.

• kf (float) – Field reduction factor.

• det_eff (float) – Efficiency of the detector.

• icf (float) – Image compression factor due to sample imperfections.

• field_evap (float) – Evaporation field in V/nm.

• avg_dens (float) – Atomic density in atoms/nm^3.

Returns:

x-coordinates of reconstructed atom positions in nm. float: y-coordinates of reconstructed atom positions in nm. float: z-coordinates of reconstructed atom positions in nm.

Return type:

float

pyccapt.calibration.reconstructions.reconstruction.atom_probe_recons_from_detector_Geiser_et_al(detx, dety, hv, flight_path_length, kf, det_eff, icf, field_evap, avg_dens)

Perform atom probe reconstruction using Geiser et al.’s method.

Parameters:

• detx (float) – Hit position on the detector (x-coordinate).

• dety (float) – Hit position on the detector (y-coordinate).

• hv (float) – High voltage.

• flight_path_length (float) – Distance between detector and sample.

• kf (float) – Field reduction factor.

• det_eff (float) – Efficiency of the detector.

• icf (float) – Image compression factor due to sample imperfections.

• field_evap (float) – Evaporation field in V/nm.

• avg_dens (float) – Atomic density in atoms/nm^3.

Returns:

x-coordinates of reconstructed atom positions in nm. float: y-coordinates of reconstructed atom positions in nm. float: z-coordinates of reconstructed atom positions in nm.

Return type:

float

pyccapt.calibration.reconstructions.reconstruction.cart2pol(x, y)

Convert Cartesian coordinates to polar coordinates.

Parameters:

• x (float) – x-coordinate.

• y (float) – y-coordinate.

Returns:

rho, the radial distance from the origin. float: phi, the angle in radians.

Return type:

float

pyccapt.calibration.reconstructions.reconstruction.detector_animation(variables, points_per_frame, ranged, selected_area_specially, selected_area_temporally, figure_name, figure_sie, save)

Generate a animated heatmap based on the provided data.

Parameters:

• variables (object) – The variables object.

• points_per_frame (int) – The number of points per frame.

• ranged (bool) – True if the data is ranged, False otherwise.

• selected_area_specially (bool) – True if a specific area is selected, False otherwise.

• selected_area_temporally (bool) – True if a specific area is selected, False otherwise.

• figure_name (str) – The name of the figure.

• figure_sie – The size of the figure.

• save (bool) – True to save the plot, False to display it.

Returns:

None

pyccapt.calibration.reconstructions.reconstruction.draw_qube(fig, range, col=None, row=None)

pyccapt.calibration.reconstructions.reconstruction.heatmap(variables, element_percentage, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[], figure_name='hetmap', figure_sie=(5, 5), save=False)

Generate a heatmap based on the provided data.

Parameters:

• variables (object) – The variables object.

• element_percentage (str) – Element percentage information.

• range_sequence – Range of sequence

• range_mc – Range of mc

• range_detx – Range of detx

• range_dety – Range of dety

• range_x – Range of x-axis

• range_y – Range of y-axis

• range_z – Range of z-axis

• range_vol – Range of volume

• figure_name (str) – The name of the figure.

• figure_sie – The size of the figure.

• save (bool) – True to save the plot, False to display it.

Returns:

None

pyccapt.calibration.reconstructions.reconstruction.pol2cart(rho, phi)

Convert polar coordinates to Cartesian coordinates.

Parameters:

• rho (float) – radial distance from the origin.

• phi (float) – angle in radians.

Returns:

x-coordinate. float: y-coordinate.

Return type:

float

pyccapt.calibration.reconstructions.reconstruction.projection(variables, element_percentage, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[], x_or_y='x', figname='projection', figure_size=(5, 5), save=False)

Generate a projection plot based on the provided data.

Parameters:

• variables (object) – The variables object.

• element_percentage (str) – Element percentage information.

• range_sequence – Range of sequence

• range_mc – Range of mc

• range_detx – Range of detx

• range_dety – Range of dety

• range_x – Range of x-axis

• range_y – Range of y-axis

• range_z – Range of z-axis

• range_vol – Range of volume

• x_or_y (str) – Either ‘x’ or ‘y’ indicating the axis to plot.

• figname (str) – The name of the figure.

Returns:

None

pyccapt.calibration.reconstructions.reconstruction.reconstruction_2d_histogram(variables, x, y, bins, percentage, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[], xlabel='X-axis', ylabel='Y-axis', save=False, figure_name=None, figure_size=None)

Generate a 2D histogram based on the provided data.

Parameters:

• variables (object) – The variables object.

• x (array) – The x-axis data.

• y (array) – The y-axis data.

• bins (int or tuple) – The number of bins.

• percentage (float) – percent of data to be plotted.

• range_sequence – Range of sequence

• range_mc – Range of mc

• range_detx – Range of detx

• range_dety – Range of dety

• range_x – Range of x-axis

• range_y – Range of y-axis

• range_z – Range of z-axis

• range_vol – Range of volume

• xlabel (str) – The label of the x-axis.

• ylabel (str) – The label of the y-axis.

• save (bool) – True to save the plot, False to display it.

• figure_name (str) – The name of the figure.

• figure_size (tuple) – The size of the figure.

Returns:

None

pyccapt.calibration.reconstructions.reconstruction.reconstruction_plot(variables, element_percentage, opacity, rotary_fig_save, figname, save, make_gif=False, make_evaporation_gif=False, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[], ions_individually_plots=False, detailed_isotope_charge=False, colab=False, cluster_result=None, cluster_display_mode='overlay', cluster_opacity_override=None, element_alpha=None)

Generate a 3D plot for atom probe reconstruction data.

Parameters:

• variables (object) – Variables object.

• element_percentage (str) – Percentage of elements to display.

• opacity (float) – Default opacity of the markers.

• rotary_fig_save (bool) – Whether to save the rotary figure.

• figname (str) – Name of the figure.

• save (bool) – Whether to save the figure.

• make_gif (bool) – Whether to make a GIF.

• make_evaporation_gif (bool) – Whether to make an evaporation GIF.

• range_sequence (list) – Range of sequence

• range_mc – Range of mc

• range_detx – Range of detx

• range_dety – Range of dety

• range_x – Range of x-axis

• range_y – Range of y-axis

• range_z – Range of z-axis

• range_vol – Range of volume

• ions_individually_plots (bool) – Whether to plot ions individually.

• detailed_isotope_charge (bool) – Whether to plot detailed isotope and charge information.

• colab (bool) – Whether to run in Google Colab.

• cluster_result – Optional Min-Max segmentation result for precipitate overlays.

• cluster_display_mode (str) – overlay or clusters-only.

• element_alpha (list[float] | None) – Optional per-ion opacity overrides aligned with the range table order.

Returns:

None

pyccapt.calibration.reconstructions.reconstruction.scatter_plot(data, range_data, variables, element_percentage, selected_area, x_or_y, figname, figure_size, save=False)

Generate a scatter plot based on the provided data.

Parameters:

• data (pandas.DataFrame) – The input data.

• range_data (pandas.DataFrame) – Data containing range information for different elements.

• variables (object) – The variables object.

• element_percentage (str) – Element percentage information.

• selected_area (bool) – True if a specific area is selected, False otherwise.

• x_or_y (str) – Either ‘x’ or ‘y’ indicating the axis to plot.

• figname (str) – The name of the figure.

Returns:

None

pyccapt.calibration.reconstructions.reconstruction.x_y_z_calculation_and_plot(variables, element_percentage, kf, det_eff, icf, field_evap, avg_dens, flight_path_length, rotary_fig_save, mode, opacity, figname, save, colab=False, cluster_result=None)

Calculate the x, y, z coordinates of the atoms and plot them.

Args:

variables (object): The variables object. element_percentage (str): Element percentage information. kf (float): The kinetic energy of the ions. det_eff (float): The detector efficiency. icf (float): The image compression factor. field_evap (float): The field evaporation efficiency. avg_dens (float): The average density of the atoms. flight_path_length (float): The flight path length. pulse_mode (str): rotary_fig_save (bool): True to save the rotary plot, False to display it. mode (str): The reconstruction mode. opacity (float): The opacity of the markers. figname (str): The name of the figure. save (bool): True to save the plot, False to display it. colab (bool): True if the code is running in Google Colab, False otherwise. cluster_result: Optional Min-Max precipitate segmentation overlay.

Returns:

None

pyccapt.calibration.reconstructions.sdm module

pyccapt.calibration.reconstructions.sdm.sdm(particles, bin_size, variables=None, roi=[0, 0, 0.5], z_cut=True, normalize=False, plot_mode='bar', plot=False, save=False, figure_size=(6, 6), figname='sdm', histogram_type='1d', axes=None, i_composition=None, j_composition=None, plot_roi=False, theta_x=0, phi_y=0, log=False, frac=1.0, range_sequence=[], range_mc=[], range_detx=[], range_dety=[], range_x=[], range_y=[], range_z=[], range_vol=[])

Computes 1D or 2D histograms for a set of particle coordinates.

particles(N, 3) np.array

Set of particle coordinates for which to compute the SDM.

bin_sizefloat

Bin size for each histogram.

variables : variables object normalize : bool, optional

Option to normalize the histograms. If True, the histogram values are normalized.

roilist, optional

Region of interest for the SDM. Default is [0, 0, 1].

z_cutbool, optional

Cut the z distances over 1 nm

plot_modestr, optional

The plot mode for the histograms. Options are ‘bar’ or ‘line’.

plotbool, optional

Option to plot the histograms. If True, the histograms are plotted.

savebool, optional

Option to save the histograms. If True, the histograms are saved.

figure_size(float, float), optional

The size of the figure in inches.

fignamestr, optional

The name of the figure.

histogram_typestr, optional

Type of histogram. Options are ‘1D’ or ‘2D’ or ‘3D’.

axeslist or None, optional

Specifies the axes for 1D or 2D histograms. For ‘1d’, provide a list like [‘x’], [‘y’], or [‘z’]. For ‘2d’, provide a list like [‘x’, ‘y’], [‘y’, ‘z’], or [‘x’, ‘z’] or [‘x’, ‘y’, ‘z’].

i_compositionlist, optional

Composition of the first element in the SDM.

j_compositionlist, optional

Composition of the second element in the SDM.

plot_roibool, optional

Option to plot the region of interest. If True, the region of interest is plotted.

theta_xfloat, optional

Rotation angle around the x-axis.

phi_yfloat, optional

Rotation angle around the y-axis.

logbool, optional

Option to plot the SDM in log scale. If True, the SDM is plotted in log scale. frac : float, optional

Fraction of the second element in the SDM.

range_sequencelist, optional

Sequence range for the SDM.

range_mclist, optional

Mass-to-charge range for the SDM.

range_detxlist, optional

Detector x-coordinate range for the SDM.

range_detylist, optional

Detector y-coordinate range for the SDM.

range_xlist, optional

X-coordinate range for the SDM.

range_ylist, optional

Y-coordinate range for the SDM.

range_zlist, optional

Z-coordinate range for the SDM.

range_vollist, optional

Volume range for the SDM.

histogramslist of np.array

List of 1D or 2D histograms based on user preferences.

edgeslist of np.array

Bin edges for each histogram.

Module contents

Reconstruction and visualization utilities for calibrated datasets.