API reference

This reference manual details the public classes, modules and functions in diffsims as generated from their docstrings. Some of the docstrings contain examples.

Caution

diffsims is in an alpha stage, and there will likely be breaking changes with each release.


crystallography

Generation of reciprocal lattice points (crystal plane, reflector, g, hkl) for a crystal structure.

class diffsims.crystallography.ReciprocalLatticePoint(phase, hkl)[source]

Bases: object

Reciprocal lattice point (or crystal plane, reflector, g, etc.) with Miller indices, length of the reciprocal lattice vectors and other relevant structure_factor parameters.

property allowed

Return whether planes diffract according to structure_factor selection rules assuming kinematical scattering theory.

calculate_structure_factor(method=None, voltage=None)[source]

Populate self.structure_factor with the structure factor F for each plane.

Parameters
  • method (str, optional) – Either “kinematical” for kinematical X-ray structure factors or “doyleturner” for structure factors using Doyle-Turner atomic scattering factors. If None (default), kinematical structure factors are calculated.

  • voltage (float, optional) – Beam energy in V used when method=doyleturner.

calculate_theta(voltage)[source]

Populate self.theta with the Bragg angle \(theta_B\) for each plane.

Parameters

voltage (float) – Beam energy in V.

property dspacing

Return np.ndarray of direct lattice interplanar spacings.

classmethod from_highest_hkl(phase, highest_hkl)[source]

Create a CrystalPlane object populated by unique Miller indices below, but including, a set of higher indices.

Parameters
  • phase (orix.crystal_map.phase_list.Phase) – A phase container with a crystal structure and a space and point group describing the allowed symmetry operations.

  • highest_hkl (np.ndarray, list, or tuple of int) – Highest Miller indices to consider (including).

classmethod from_min_dspacing(phase, min_dspacing=0.5)[source]

Create a CrystalPlane object populated by unique Miller indices with a direct space interplanar spacing greater than a lower threshold.

Parameters
  • phase (orix.crystal_map.phase_list.Phase) – A phase container with a crystal structure and a space and point group describing the allowed symmetry operations.

  • min_dspacing (float, optional) – Smallest interplanar spacing to consider. Default is 0.5 Å.

property gspacing

Return np.ndarray of reciprocal lattice point spacings.

property h

Return np.ndarray of Miller index h.

property hkl

Return Vector3d of Miller indices.

property k

Return np.ndarray of Miller index k.

property l

Return np.ndarray of Miller index l.

property multiplicity

Return either int or np.ndarray of int.

property scattering_parameter

Return np.ndarray of scattering parameters s.

property shape

Return tuple.

property size

Return int.

property structure_factor

Return np.ndarray of structure factors F or None.

symmetrise(antipodal=True, unique=True, return_multiplicity=False)[source]

Return planes with symmetrically equivalent Miller indices.

Parameters
  • antipodal (bool, optional) – Whether to include antipodal symmetry operations. Default is True.

  • unique (bool, optional) – Whether to return only distinct indices. Default is True. If True, zero-entries, which are assumed to be degenerate, are removed.

  • return_multiplicity (bool, optional) – Whether to return the multiplicity of indices. This option is only available if unique is True. Default is False.

Returns

  • ReciprocalLatticePoint – Planes with Miller indices symmetrically equivalent to the original planes.

  • multiplicity (np.ndarray) – Multiplicity of the original Miller indices. Only returned if return_multiplicity is True.

Notes

Should be the same as EMsoft’s CalcFamily in their symmetry.f90 module, although not entirely sure. Use with care.

property theta

Return np.ndarray of twice the Bragg angle.

unique(use_symmetry=True)[source]

Return planes with unique Miller indices.

Parameters

use_symmetry (bool, optional) – Whether to use symmetry to remove the planes with indices symmetrically equivalent to another set of indices.

Returns

Return type

ReciprocalLatticePoint

diffsims.crystallography.get_equivalent_hkl(hkl, operations, unique=False, return_multiplicity=False)[source]

Return symmetrically equivalent Miller indices.

Parameters
  • hkl (orix.vector.Vector3d, np.ndarray, list or tuple of int) – Miller indices.

  • operations (orix.quaternion.symmetry.Symmetry) – Point group describing allowed symmetry operations.

  • unique (bool, optional) – Whether to return only unique Miller indices. Default is False.

  • return_multiplicity (bool, optional) – Whether to return the multiplicity of the input indices. Default is False.

Returns

  • new_hkl (orix.vector.Vector3d) – The symmetrically equivalent Miller indices.

  • multiplicity (np.ndarray) – Number of symmetrically equivalent indices. Only returned if return_multiplicity is True.

diffsims.crystallography.get_highest_hkl(lattice, min_dspacing=0.5)[source]

Return the highest Miller indices hkl of the plane with a direct space interplanar spacing greater than but closest to a lower threshold.

Parameters
  • lattice (diffpy.structure.Lattice) – Crystal lattice.

  • min_dspacing (float, optional) – Smallest interplanar spacing to consider. Default is 0.5 Å.

Returns

highest_hkl – Highest Miller indices.

Return type

np.ndarray

diffsims.crystallography.get_hkl(highest_hkl)[source]

Return a list of planes from a set of highest Miller indices.

Parameters

highest_hkl (orix.vector.Vector3d, np.ndarray, list, or tuple of int) – Highest Miller indices to consider.

Returns

hkl – An array of Miller indices.

Return type

np.ndarray


generators

Generation of diffraction simulations and libraries, and lists of rotations.

diffraction_generator

Electron diffraction pattern simulation.

class diffsims.generators.diffraction_generator.AtomicDiffractionGenerator(accelerating_voltage, detector, reciprocal_mesh=False)[source]

Bases: object

Computes electron diffraction patterns for an atomic lattice.

Parameters
  • accelerating_voltage (float, 'inf') – The accelerating voltage of the microscope in kV

  • detector (list of 1D float-type arrays) – List of mesh vectors defining the (flat) detector size and sensor positions

  • reciprocal_mesh (bool, optional) – If True then detector is assumed to be a reciprocal grid, else (default) it is assumed to be a real grid.

calculate_ed_data(structure, probe, slice_thickness, probe_centre=None, z_range=200, precessed=False, dtype='float64', ZERO=1e-14, mode='kinematic', **kwargs)[source]

Calculates single electron diffraction image for particular atomic structure and probe.

Parameters
  • structure (Structure) – The structure for upon which to perform the calculation

  • probe (instance of probeFunction) – Function representing 3D shape of beam

  • slice_thickness (float) – Discretisation thickness in the z-axis

  • probe_centre (ndarray (or iterable), shape [3] or [2]) – Translation vector for the probe. Either of the same dimension of the space or the dimension of the detector. default=None focusses the probe at [0,0,0]

  • zrange (float) – z-thickness to discretise. Only required if sample is not thick enough to fully resolve the Ewald-sphere. Default value is 200.

  • precessed (bool, float, or (float, int)) – Dictates whether beam precession is simulated. If False or the float is 0 then no precession is computed. If <precessed> = (alpha, n) then the precession arc of tilt alpha (in degrees) is discretised into n projections. If n is not provided then default of 30 is used.

  • dtype (str or numpy.dtype) – Defines the precision to use whilst computing diffraction image.

  • ZERO (float > 0) – Rounding error permitted in computation of atomic density. This value is the smallest value rounded to 0. Default is 1e-14.

  • mode (str) – Only <mode>=’kinematic’ is currently supported.

  • kwargs (dictionary) – Extra key-word arguments to pass to child simulator. For kinematic: GPU (bool): Flag to use GPU if available, default is True. pointwise (bool): Flag to evaluate charge pointwise on voxels rather than average, default is False.

Returns

Diffraction data to be interpreted as a discretisation on the original detector mesh.

Return type

ndarray

class diffsims.generators.diffraction_generator.DiffractionGenerator(accelerating_voltage, scattering_params='lobato', precession_angle=0, shape_factor_model='lorentzian', approximate_precession=True, minimum_intensity=1e-20, **kwargs)[source]

Bases: object

Computes electron diffraction patterns for a crystal structure.

  1. Calculate reciprocal lattice of structure. Find all reciprocal points within the limiting sphere given by \(\frac{2}{\lambda}\).

  2. For each reciprocal point \(\mathbf{g_{hkl}}\) corresponding to lattice plane \((hkl)\), compute the Bragg condition \(\sin(\theta) = \frac{\lambda}{2d_{hkl}}\)

  3. The intensity of each reflection is then given in the kinematic approximation as the modulus square of the structure factor. \(I_{hkl} = F_{hkl}F_{hkl}^*\)

Parameters
  • accelerating_voltage (float) – The accelerating voltage of the microscope in kV.

  • scattering_params (str) – “lobato” or “xtables”

  • minimum_intensity (float) – Minimum intensity for a peak to be considered visible in the pattern

  • precession_angle (float) – Angle about which the beam is precessed. Default is no precession.

  • approximate_precession (boolean) – When using precession, whether to precisely calculate average excitation errors and intensities or use an approximation. See notes.

  • shape_factor_model (function or string) – A function that takes excitation_error and max_excitation_error (and potentially kwargs) and returns an intensity scaling factor. If None defaults to shape_factor_models.linear. A number of pre-programmed functions are available via strings.

  • kwargs – Keyword arguments passed to shape_factor_model.

Notes

  • A full calculation is much slower and is not recommended for calculating a diffraction library for precession diffraction patterns.

  • When using precession and approximate_precession=True, the shape factor

model defaults to Lorentzian; shape_factor_model is ignored. Only with approximate_precession=False the custom shape_factor_model is used.

calculate_ed_data(structure, reciprocal_radius, rotation=(0, 0, 0), with_direct_beam=True, max_excitation_error=0.01, debye_waller_factors={})[source]

Calculates the Electron Diffraction data for a structure.

Parameters
  • structure (diffpy.structure.structure.Structure) – The structure for which to derive the diffraction pattern. Note that the structure must be rotated to the appropriate orientation and that testing is conducted on unit cells (rather than supercells).

  • reciprocal_radius (float) – The maximum radius of the sphere of reciprocal space to sample, in reciprocal Angstroms.

  • rotation (tuple) – Euler angles, in degrees, in the rzxz convention. Default is (0, 0, 0) which aligns ‘z’ with the electron beam.

  • with_direct_beam (bool) – If True, the direct beam is included in the simulated diffraction pattern. If False, it is not.

  • max_excitation_error (float) – The extinction distance for reflections, in reciprocal Angstroms. Roughly equal to 1/thickness.

  • debye_waller_factors (dict of str:value pairs) – Maps element names to their temperature-dependent Debye-Waller factors.

Returns

The data associated with this structure and diffraction setup.

Return type

diffsims.sims.diffraction_simulation.DiffractionSimulation

calculate_profile_data(structure, reciprocal_radius=1.0, minimum_intensity=0.001, debye_waller_factors={})[source]

Calculates a one dimensional diffraction profile for a structure.

Parameters
  • structure (diffpy.structure.structure.Structure) – The structure for which to calculate the diffraction profile.

  • reciprocal_radius (float) – The maximum radius of the sphere of reciprocal space to sample, in reciprocal angstroms.

  • minimum_intensity (float) – The minimum intensity required for a diffraction peak to be considered real. Deals with numerical precision issues.

  • debye_waller_factors (dict of str:value pairs) – Maps element names to their temperature-dependent Debye-Waller factors.

Returns

The diffraction profile corresponding to this structure and experimental conditions.

Return type

diffsims.sims.diffraction_simulation.ProfileSimulation

library_generator

Diffraction pattern library generator and associated tools.

class diffsims.generators.library_generator.DiffractionLibraryGenerator(electron_diffraction_calculator)[source]

Bases: object

Computes a library of electron diffraction patterns for specified atomic structures and orientations.

get_diffraction_library(structure_library, calibration, reciprocal_radius, half_shape, with_direct_beam=True, max_excitation_error=0.01, debye_waller_factors={})[source]

Calculates a dictionary of diffraction data for a library of crystal structures and orientations.

Each structure in the structure library is rotated to each associated orientation and the diffraction pattern is calculated each time.

Angles must be in the Euler representation (Z,X,Z) and in degrees

Parameters
  • structure_library (difffsims:StructureLibrary Object) – Dictionary of structures and associated orientations for which electron diffraction is to be simulated.

  • calibration (float) – The calibration of experimental data to be correlated with the library, in reciprocal Angstroms per pixel.

  • reciprocal_radius (float) – The maximum g-vector magnitude to be included in the simulations.

  • half_shape (tuple) – The half shape of the target patterns, for 144x144 use (72,72) etc

  • with_direct_beam (bool) – Include the direct beam in the library.

  • max_excitation_error (float) – The extinction distance for reflections, in reciprocal Angstroms.

  • debye_waller_factors (dict of str:value pairs) – Maps element names to their temperature-dependent Debye-Waller factors.

Returns

diffraction_library – Mapping of crystal structure and orientation to diffraction data objects.

Return type

DiffractionLibrary

class diffsims.generators.library_generator.VectorLibraryGenerator(structure_library)[source]

Bases: object

Computes a library of diffraction vectors and pairwise inter-vector angles for a specified StructureLibrary.

get_vector_library(reciprocal_radius)[source]

Calculates a library of diffraction vectors and pairwise inter-vector angles for a library of crystal structures.

Parameters

reciprocal_radius (float) – The maximum g-vector magnitude to be included in the library.

Returns

vector_library – Mapping of phase identifier to phase information in dictionary format.

Return type

DiffractionVectorLibrary

rotation_list_generators

Provides users with a range of gridding functions

diffsims.generators.rotation_list_generators.get_beam_directions_grid(crystal_system, resolution, mesh='spherified_cube_corner')[source]

Produces an array of beam directions, within the stereographic triangle of the relevant crystal system. The way the array is constructed is based on different methods of meshing the sphere [Cajaravelli2015] and can be specified through the mesh argument.

Parameters
  • crystal_system (str) – Allowed are: ‘cubic’,’hexagonal’,’trigonal’,’tetragonal’, ‘orthorhombic’,’monoclinic’,’triclinic’

  • resolution (float) – An angle in degrees representing the worst-case angular distance to a first nearest neighbor grid point.

  • mesh (str) – Type of meshing of the sphere that defines how the grid is created. Options are: uv_sphere, normalized_cube, spherified_cube_corner (default), spherified_cube_edge, icosahedral, random.

Returns

rotation_list

Return type

list of tuples

diffsims.generators.rotation_list_generators.get_fundamental_zone_grid(resolution=2, point_group=None, space_group=None)[source]

Generates an equispaced grid of rotations within a fundamental zone.

Parameters
  • resolution (float, optional) – The characteristic distance between a rotation and its neighbour (degrees)

  • point_group (orix.quaternion.symmetry.Symmetry, optional) – One of the 11 proper point groups, defaults to None

  • space_group (int, optional) – Between 1 and 231, defaults to None

Returns

rotation_list – Grid of rotations lying within the specified fundamental zone

Return type

list of tuples

diffsims.generators.rotation_list_generators.get_grid_around_beam_direction(beam_rotation, resolution, angular_range=(0, 360))[source]

Creates a rotation list of rotations for which the rotation is about given beam direction.

Parameters
  • beam_rotation (tuple) – A desired beam direction as a rotation (rzxz eulers), usually found via get_rotation_from_z_to_direction.

  • resolution (float) – The resolution of the grid (degrees).

  • angular_range (tuple) – The minimum (included) and maximum (excluded) rotation around the beam direction to be included.

Returns

rotation_list

Return type

list of tuples

Examples

>>> from diffsims.generators.zap_map_generator import get_rotation_from_z_to_direction
>>> beam_rotation = get_rotation_from_z_to_direction(structure,[1,1,1])
>>> grid = get_grid_around_beam_direction(beam_rotation,1)
diffsims.generators.rotation_list_generators.get_list_from_orix(grid, rounding=2)[source]

Converts an orix sample to a rotation list

Parameters
Returns

rotation_list – A rotation list

Return type

list of tuples

diffsims.generators.rotation_list_generators.get_local_grid(resolution=2, center=None, grid_width=10)[source]

Generates a grid of rotations about a given rotation

Parameters
  • resolution (float, optional) – The characteristic distance between a rotation and its neighbour (degrees)

  • center (euler angle tuple or orix.quaternion.rotation.Rotation, optional) – The rotation at which the grid is centered. If None (default) uses the identity

  • grid_width (float, optional) – The largest angle of rotation away from center that is acceptable (degrees)

Returns

rotation_list

Return type

list of tuples

sphere_mesh_generators

diffsims.generators.sphere_mesh_generators.beam_directions_grid_to_euler(vectors)[source]

Convert list of vectors representing zones to a list of Euler angles in the bunge convention with the constraint that phi1=0.

Parameters

vectors (numpy.ndarray (N, 3)) – N 3-dimensional vectors to convert to Euler angles

Returns

grid – Euler angles in bunge convention corresponding to each vector in degrees.

Return type

numpy.ndarray (N, 3)

Notes

The Euler angles represent the orientation of the crystal if that particular vector were parallel to the beam direction [001]. The additional constraint of phi1=0 means that this orientation is uniquely defined for most vectors. phi1 represents the rotation of the crystal around the beam direction and can be interpreted as the rotation of a particular diffraction pattern.

diffsims.generators.sphere_mesh_generators.get_cube_mesh_vertices(resolution, grid_type='spherified_corner')[source]

Return the (x, y, z) coordinates of the vertices of a cube mesh on a sphere. To generate the mesh, a cube is made to surround the sphere. The surfaces of the cube are subdivided into a grid. The vectors from the origin to these grid points are normalized to unit length. The grid on the cube can be generated in three ways, see grid_type and reference [Cajaravelli2015].

Parameters
  • resolution (float) – The maximum angle in degrees between first nearest neighbor grid points.

  • grid_type (str) – The type of cube grid, can be either normalized or spherified_edge or spherified_corner (default). For details see notes.

Returns

points_in_cartesian – Rows are x, y, z where z is the 001 pole direction

Return type

numpy.ndarray (N,3)

Notes

The resolution determines the maximum angle between first nearest neighbor grid points, but to get an integer number of points between the cube face center and the edges, the number of grid points is rounded up. In practice this means that resolution is always an upper limit. Additionally, where on the grid this maximum angle will be will depend on the type of grid chosen. Resolution says something about the maximum angle but nothing about the distribution of nearest neighbor angles or the minimum angle - also this is fixed by the chosen grid.

In the normalized grid, the grid on the surface of the cube is linear. The maximum angle between nearest neighbors is found between the <001> directions and the first grid point towards the <011> directions. Points approaching the edges and corners of the cube will have a smaller angular deviation, so orientation space will be oversampled there compared to the cube faces <001>.

In the spherified_edge grid, the grid is constructed so that there are still two sets of perpendicular grid lines parallel to the {100} directions on each cube face, but the spacing of the grid lines is chosen so that the angles between the grid points on the line connecting the face centers (<001>) to the edges (<011>) are equal. The maximum angle is also between the <001> directions and the first grid point towards the <011> edges. This grid slightly oversamples the directions between <011> and <111>

The spherified_corner case is similar to the spherified_edge case, but the spacing of the grid lines is chosen so that the angles between the grid points on the line connecting the face centers to the cube corners (<111>) is equal. The maximum angle in this grid is from the corners to the first grid point towards the cube face centers.

References

Cajaravelli2015(1,2,3,4)

O. S. Cajaravelli, “Four Ways to Create a Mesh for a Sphere,” https://medium.com/game-dev-daily/four-ways-to-create-a-mesh-for-a-sphere-d7956b825db4.

diffsims.generators.sphere_mesh_generators.get_icosahedral_mesh_vertices(resolution)[source]

Return the (x, y, z) coordinates of the vertices of an icosahedral mesh of a cube, see [Cajaravelli2015]. Method was adapted from meshzoo [Meshzoo].

Parameters

resolution (float) – The maximum angle in degrees between neighboring grid points. Since the mesh is generated iteratively, the actual maximum angle in the mesh can be slightly smaller.

Returns

points_in_cartesian – Rows are x, y, z where z is the 001 pole direction

Return type

numpy.ndarray (N,3)

References

Meshzoo

The meshzoo.sphere module, https://github.com/nschloe/meshzoo/blob/master/meshzoo/sphere.py.

diffsims.generators.sphere_mesh_generators.get_random_sphere_vertices(resolution, seed=None)[source]

Create a mesh that randomly samples the surface of a sphere

Parameters
  • resolution (float) – The expected mean angle between nearest neighbor grid points in degrees.

  • seed (int, optional) – passed to np.random.default_rng(), defaults to None which will give a “new” random result each time

Returns

points_in_cartesian – Rows are x, y, z where z is the 001 pole direction

Return type

numpy.ndarray (N,3)

References

https://mathworld.wolfram.com/SpherePointPicking.html

diffsims.generators.sphere_mesh_generators.get_uv_sphere_mesh_vertices(resolution)[source]

Return the vertices of a UV (spherical coordinate) mesh on a unit sphere [Cajaravelli2015]. The mesh vertices are defined by the parametrization:

\[ \begin{align}\begin{aligned}x = sin(u)cos(v)\\y = sin(u)sin(v)\\z = cos(u)\end{aligned}\end{align} \]
Parameters

resolution (float) – An angle in degrees. The maximum angle between nearest neighbor grid points. In this mesh this occurs on the equator of the sphere. All elevation grid lines are separated by at most resolution. The step size of u and v are rounded up to get an integer number of elevation and azimuthal grid lines with equal spacing.

Returns

points_in_cartesian – Rows are x, y, z where z is the 001 pole direction

Return type

numpy.ndarray (N,3)

zap_map_generator

diffsims.generators.zap_map_generator.corners_to_centroid_and_edge_centers(corners)[source]

Produces the midpoints and center of a trio of corners

Parameters

corners (list of lists) – Three corners of a streographic triangle

Returns

list_of_corners – Length 7, elements ca, cb, cc, mean, cab, cbc, cac where naming is such that ca is the first corner of the input, and cab is the midpoint between corner a and corner b.

Return type

list

diffsims.generators.zap_map_generator.generate_directional_simulations(structure, simulator, direction_list, reciprocal_radius=1, **kwargs)[source]

Produces simulation of a structure aligned with certain axes

Parameters
  • structure (diffpy.structure.structure.Structure) – The structure from which simulations need to be produced.

  • simulator (DiffractionGenerator) – The diffraction generator object used to produce the simulations

  • direction_list (list of lists) – A list of [UVW] indices, eg. [[1,0,0],[1,1,0]]

  • reciprocal_radius (float) – Default to 1

Returns

direction_dictionary – Keys are zone axes, values are simulations

Return type

dict

diffsims.generators.zap_map_generator.generate_zap_map(structure, simulator, system='cubic', reciprocal_radius=1, density='7', **kwargs)[source]

Produces a number of zone axis patterns for a structure

Parameters
  • structure (diffpy.structure.structure.Structure) – The structure to be simulated.

  • simulator (DiffractionGenerator) – The simulator used to generate the simulations

  • system (str) – ‘cubic’, ‘hexagonal’, ‘trigonal’, ‘tetragonal’, ‘orthorhombic’, ‘monoclinic’. Defaults to ‘cubic’.

  • reciprocal_radius (float) – The range of reciprocal lattice spots to be included. Default to 1.

  • density (str) – ‘3’ for the corners or ‘7’ (corners + midpoints + centroids). Defaults to 7.

  • kwargs – Keyword arguments to be passed to simulator.calculate_ed_data().

Returns

zap_dictionary – Keys are zone axes, values are simulations

Return type

dict

Examples

Plot all of the patterns that you have generated

>>> zap_map = generate_zap_map(structure,simulator,'hexagonal',density='3')
>>> for k in zap_map.keys():
>>>     pattern = zap_map[k]
>>>     pattern.calibration = 4e-3
>>>     plt.figure()
>>>     plt.imshow(pattern.get_diffraction_pattern(),vmax=0.02)
diffsims.generators.zap_map_generator.get_rotation_from_z_to_direction(structure, direction)[source]

Finds the rotation that takes [001] to a given zone axis.

Parameters
  • structure (diffpy.structure.structure.Structure) – The structure for which a rotation needs to be found.

  • direction (array like) – [UVW] direction that the ‘z’ axis should end up point down.

Returns

euler_angles – ‘rzxz’ in degrees.

Return type

tuple

Notes

This implementation works with an axis arrangement that has +x as left to right, +y as bottom to top and +z as out of the plane of a page. Rotations are counter clockwise as you look from the tip of the axis towards the origin


libraries

Diffraction, structure and vector libraries.

diffraction_library

class diffsims.libraries.diffraction_library.DiffractionLibrary(*args, **kwargs)[source]

Bases: dict

Maps crystal structure (phase) and orientation to simulated diffraction data.

identifiers

A list of phase identifiers referring to different atomic structures.

Type

list of strings/ints

structures

A list of diffpy.structure.Structure objects describing the atomic structure associated with each phase in the library.

Type

list of diffpy.structure.Structure objects.

diffraction_generator

Diffraction generator used to generate this library.

Type

DiffractionGenerator

reciprocal_radius

Maximum g-vector magnitude for peaks in the library.

Type

float

with_direct_beam

Whether the direct beam included in the library or not.

Type

bool

get_library_entry(phase=None, angle=None)[source]

Extracts a single DiffractionLibrary entry.

Parameters
  • phase (str) – Key for the phase of interest. If unspecified the choice is random.

  • angle (tuple) – The orientation of interest as a tuple of Euler angles following the Bunge convention [z, x, z] in degrees. If unspecified the choise is random (the first hit).

Returns

library_entries – Dictionary containing the simulation associated with the specified phase and orientation with associated properties.

Return type

dict

pickle_library(filename)[source]

Saves a diffraction library in the pickle format.

Parameters

filename (str) – The location in which to save the file

diffsims.libraries.diffraction_library.load_DiffractionLibrary(filename, safety=False)[source]

Loads a previously saved diffraction library.

Parameters
  • filename (str) – The location of the file to be loaded

  • safety (bool (defaults to False)) – Unpickling is risky, this variable requires you to acknowledge this.

Returns

Previously saved Library

Return type

DiffractionLibrary

structure_library

class diffsims.libraries.structure_library.StructureLibrary(identifiers, structures, orientations)[source]

Bases: object

A dictionary containing all the structures and their associated rotations in the .struct_lib attribute.

identifiers

A list of phase identifiers referring to different atomic structures.

Type

list of strings/ints

structures

A list of diffpy.structure.Structure objects describing the atomic structure associated with each phase in the library.

Type

list of diffpy.structure.Structure objects.

orientations

A list over identifiers of lists of euler angles (as tuples) in the rzxz convention and in degrees.

Type

list

classmethod from_crystal_systems(identifiers, structures, systems, resolution, equal='angle')[source]

Creates a structure library from crystal system derived orientation lists

Parameters
  • identifiers (list of strings/ints) – A list of phase identifiers referring to different atomic structures.

  • structures (list of diffpy.structure.Structure objects.) – A list of diffpy.structure.Structure objects describing the atomic structure associated with each phase in the library.

  • systems (list) – A list over indentifiers of crystal systems

  • resolution (float) – resolution in degrees

  • equal (str) – Default is ‘angle’

Raises

NotImplementedError: – “This function has been removed in version 0.3.0, in favour of creation from orientation lists”

classmethod from_orientation_lists(identifiers, structures, orientations)[source]

Creates a structure library from “manual” orientation lists

Parameters
  • identifiers (list of strings/ints) – A list of phase identifiers referring to different atomic structures.

  • structures (list of diffpy.structure.Structure objects.) – A list of diffpy.structure.Structure objects describing the atomic structure associated with each phase in the library.

  • orientations (list of lists of tuples) – A list over identifiers of lists of euler angles (as tuples) in the rzxz convention and in degrees.

Returns

Return type

StructureLibrary

get_library_size(to_print=False)[source]

Returns the the total number of orientations in the current StructureLibrary object. Will also print the number of orientations for each identifier in the library if the to_print==True

Parameters

to_print (bool) – Default is ‘False’

Returns

size_library – Total number of entries in the current StructureLibrary object.

Return type

int

vector_library

class diffsims.libraries.vector_library.DiffractionVectorLibrary(*args, **kwargs)[source]

Bases: dict

Maps crystal structure (phase) to diffraction vectors.

The library is a dictionary mapping from a phase name to phase information. The phase information is stored as a dictionary with the following entries:

‘indices’np.array

List of peak indices [hkl1, hkl2] as a 2D array.

‘measurements’np.array

List of vector measurements [len1, len2, angle] in the same order as the indices. Lengths in reciprocal Angstrom and angles in radians.

identifiers

A list of phase identifiers referring to different atomic structures.

Type

list of strings/ints

structures

A list of diffpy.structure.Structure objects describing the atomic structure associated with each phase in the library.

Type

list of diffpy.structure.Structure objects.

reciprocal_radius

Maximum reciprocal radius used when generating the library.

Type

float

pickle_library(filename)[source]

Saves a vector library in the pickle format.

Parameters

filename (str) – The location in which to save the file

diffsims.libraries.vector_library.load_VectorLibrary(filename, safety=False)[source]

Loads a previously saved vectorlibrary.

Parameters
  • filename (str) – The location of the file to be loaded

  • safety (bool (defaults to False)) – Unpickling is risky, this variable requires you to acknowledge this.

Returns

Previously saved Library

Return type

VectorLibrary

See also

VectorLibrary.pickle_library


sims

Diffraction simulations.

diffraction_simulation

class diffsims.sims.diffraction_simulation.DiffractionSimulation(coordinates=None, indices=None, intensities=None, calibration=1.0, offset=(0.0, 0.0), with_direct_beam=False)[source]

Bases: object

Holds the result of a kinematic diffraction pattern simulation.

Parameters
  • coordinates (array-like, shape [n_points, 2]) – The x-y coordinates of points in reciprocal space.

  • indices (array-like, shape [n_points, 3]) – The indices of the reciprocal lattice points that intersect the Ewald sphere.

  • intensities (array-like, shape [n_points, ]) – The intensity of the reciprocal lattice points.

  • calibration (float or tuple of float, optional) – The x- and y-scales of the pattern, with respect to the original reciprocal angstrom coordinates.

  • offset (tuple of float, optional) – The x-y offset of the pattern in reciprocal angstroms. Defaults to zero in each direction.

property calibrated_coordinates

Coordinates converted into pixel space.

Type

ndarray

property calibration

The x- and y-scales of the pattern, with respect to the original reciprocal angstrom coordinates.

Type

tuple of float

property coordinates

The coordinates of all unmasked points.

Type

ndarray

property direct_beam_mask

If with_direct_beam is True, returns a True array for all points. If with_direct_beam is False, returns a True array with False in the position of the direct beam.

Type

ndarray

get_diffraction_pattern(size=512, sigma=10)[source]

Returns the diffraction data as a numpy array with two-dimensional Gaussians representing each diffracted peak. Should only be used for qualitative work.

Parameters
  • size (int) – The size of a side length (in pixels)

  • sigma (float) – Standard deviation of the Gaussian function to be plotted (in pixels).

Returns

diffraction-pattern – The simulated electron diffraction pattern, normalised.

Return type

numpy.array

Notes

If don’t know the exact calibration of your diffraction signal using 1e-2 produces reasonably good patterns when the lattice parameters are on the order of 0.5nm and a the default size and sigma are used.

property intensities

The intensities of all unmasked points.

Type

ndarray

plot(size_factor=1, units='real', **kwargs)[source]

A quick-plot function for a simulation of spots

Parameters
  • size_factor (float, optional) – linear spot size scaling, default to 1

  • units (str, optional) – ‘real’ or ‘pixel’, only changes scalebars, falls back on ‘real’, the default

  • **kwargs – passed to ax.scatter() method

Returns

Return type

ax,sp

Notes

spot size scales with the square root of the intensity.

class diffsims.sims.diffraction_simulation.ProfileSimulation(magnitudes, intensities, hkls)[source]

Bases: object

Holds the result of a given kinematic simulation of a diffraction profile.

Parameters
  • magnitudes (array-like, shape [n_peaks, 1]) – Magnitudes of scattering vectors.

  • intensities (array-like, shape [n_peaks, 1]) – The kinematic intensity of the diffraction peaks.

  • hkls ([{(h, k, l): mult}] {(h, k, l): mult} is a dict of Miller) – indices for all diffracted lattice facets contributing to each intensity.

get_plot(annotate_peaks=True, with_labels=True, fontsize=12)[source]
Plots the diffraction profile simulation for the

calculate_profile_data method in DiffractionGenerator.

Parameters
  • annotate_peaks (boolean) – If True, peaks are annotaed with hkl information.

  • with_labels (boolean) – If True, xlabels and ylabels are added to the plot.

  • fontsize (integer) – Fontsize for peak labels.


structure_factor

Calculation of scattering factors and structure factors.

diffsims.structure_factor.find_asymmetric_positions(positions, space_group)[source]

Return the asymmetric atom positions among a set of positions when considering symmetry operations defined by a space group.

Parameters
Returns

Asymmetric atom positions.

Return type

numpy.ndarray

diffsims.structure_factor.get_atomic_scattering_parameters(element, unit=None)[source]

Return the eight atomic scattering parameters a_1-4, b_1-4 for elements with atomic numbers Z = 1-98 from Table 12.1 in [DeGraef2007], which are themselves from [Doyle1968] and [Smith1962].

Parameters
  • element (int or str) – Element to return scattering parameters for. Either one-two letter string or integer atomic number.

  • unit (str, optional) – Either “nm” or “Å”/”A”. Whether to return parameters in terms of Å^-2 or nm^-2. If None (default), Å^-2 is used.

Returns

  • a (numpy.ndarray) – The four atomic scattering parameters a_1-4.

  • b (numpy.ndarray) – The four atomic scattering parameters b_1-4.

References

DeGraef2007
  1. De Graef, M. E. McHenry, “Structure of Materials,” Cambridge University Press (2007).

Doyle1968(1,2,3)

P. A. Doyle, P. S. Turner, “Relativistic Hartree-Fock X-ray and electron scattering factors,” Acta Cryst. 24 (1968), doi: https://doi.org/10.1107/S0567739468000756.

Smith1962

G. Smith, R. Burge, “The analytical representation of atomic scattering amplitudes for electrons,” Acta Cryst. A15 (1962), doi: https://doi.org/10.1107/S0365110X62000481.

diffsims.structure_factor.get_doyleturner_atomic_scattering_factor(atom, scattering_parameter, unit_cell_volume)[source]

Return the atomic scattering factor f for a certain atom and scattering parameter using Doyle-Turner atomic scattering parameters [Doyle1968].

Assumes structure’s Debye-Waller factors are expressed in Ångströms.

This function is adapted from EMsoft.

Parameters
  • atom (diffpy.structure.atom.Atom) – Atom with element type, Debye-Waller factor and occupancy number.

  • scattering_parameter (float) – The scattering parameter s for these Miller indices describing the crystal plane in which the atom lies.

  • unit_cell_volume (float) – Volume of the unit cell.

Returns

f – Scattering factor for this atom on this plane.

Return type

float

diffsims.structure_factor.get_doyleturner_structure_factor(phase, hkl, scattering_parameter, voltage, return_parameters=False)[source]

Return the structure factor for a given family of Miller indices using Doyle-Turner atomic scattering parameters [Doyle1968].

Assumes structure’s lattice parameters and Debye-Waller factors are expressed in Ångströms.

This function is adapted from EMsoft.

Parameters
  • phase (orix.crystal_map.phase_list.Phase) – A phase container with a crystal structure and a space and point group describing the allowed symmetry operations.

  • hkl (numpy.ndarray or list) – Miller indices.

  • scattering_parameter (float) – Scattering parameter for these Miller indices.

  • voltage (float) – Beam energy in V.

  • return_parameters (bool, optional) – Whether to return a set of parameters derived from the calculation as a dictionary. Default is False.

Returns

  • structure_factor (float) – Structure factor F.

  • params (dict) – A dictionary with (key, item) (str, float) of parameters derived from the calculation. Only returned if return_parameters=True.

diffsims.structure_factor.get_element_id_from_string(element_str)[source]

Get periodic element ID for elements Z = 1-98 from one-two letter string.

Parameters

element_str (str) – One-two letter string.

Returns

element_id – Integer ID in the periodic table of elements.

Return type

int

diffsims.structure_factor.get_kinematical_atomic_scattering_factor(atom, scattering_parameter)[source]

Return the kinematical (X-ray) atomic scattering factor f for a certain atom and scattering parameter.

Assumes structure’s Debye-Waller factors are expressed in Ångströms.

This function is adapted from EMsoft.

Parameters
  • atom (diffpy.structure.atom.Atom) – Atom with element type, Debye-Waller factor and occupancy number.

  • scattering_parameter (float) – The scattering parameter s for these Miller indices describing the crystal plane in which the atom lies.

Returns

f – Scattering factor for this atom on this plane.

Return type

float

diffsims.structure_factor.get_kinematical_structure_factor(phase, hkl, scattering_parameter)[source]

Return the kinematical (X-ray) structure factor for a given family of Miller indices.

Assumes structure’s lattice parameters and Debye-Waller factors are expressed in Ångströms.

This function is adapted from EMsoft.

Parameters
  • phase (orix.crystal_map.phase_list.Phase) – A phase container with a crystal structure and a space and point group describing the allowed symmetry operations.

  • hkl (numpy.ndarray or list) – Miller indices.

  • scattering_parameter (float) – Scattering parameter for these Miller indices.

Returns

structure_factor – Structure factor F.

Return type

float

diffsims.structure_factor.get_refraction_corrected_wavelength(phase, voltage)[source]

Return the refraction corrected relativistic electron wavelength in Ångströms for a given crystal structure and beam energy in V.

This function is adapted from EMsoft.

Parameters
  • phase (orix.crystal_map.phase_list.Phase) – A phase container with a crystal structure and a space and point group describing the allowed symmetry operations.

  • voltage (float) – Beam energy in V.

Returns

wavelength – Refraction corrected relativistic electron wavelength in Ångströms.

Return type

float


pattern

detector_functions

diffsims.pattern.detector_functions.add_dead_pixels(pattern, n=None, fraction=None, seed=None)[source]

Adds randomly placed dead pixels onto a pattern

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector

  • n (int) – The number of dead pixels, defaults to None

  • fraction (float) – The fraction of dead pixels, defaults to None

  • seed (int or None) – seed value for the random number generator

Returns

corrupted_pattern – The pattern, with dead pixels included

Return type

numpy.ndarray

diffsims.pattern.detector_functions.add_detector_offset(pattern, offset)[source]

Adds/subtracts a fixed offset value from a pattern

Parameters
Returns

corrupted_pattern – The pattern, with offset applied, pixels that would have been negative are instead 0.

Return type

np.ndarray

diffsims.pattern.detector_functions.add_gaussian_noise(pattern, sigma, seed=None)[source]

Applies gaussian noise at each pixel within the pattern

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector

  • sigma (float) – The (absolute) deviation of the gaussian errors

  • seed (int or None) – seed value for the random number generator

Returns

Return type

corrupted_pattern

diffsims.pattern.detector_functions.add_gaussian_point_spread(pattern, sigma)[source]

Blurs intensities across space with a gaussian function

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector

  • sigma (float) – The standard deviation of the gaussian blur, in pixels

Returns

blurred_pattern – The blurred pattern (deterministic)

Return type

numpy.ndarray

diffsims.pattern.detector_functions.add_linear_detector_gain(pattern, gain)[source]

Multiplies the pattern by a gain (which is not a function of the pattern)

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector

  • gain (float or numpy.ndarray) – Multiplied through the pattern, broadcasting applies

Returns

corrupted_pattern – The pattern, with gain applied

Return type

numpy.ndarray

diffsims.pattern.detector_functions.add_shot_and_point_spread(pattern, sigma, shot_noise=True, seed=None)[source]

Adds shot noise (optional) and gaussian point spread (via a convolution) to a pattern

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector

  • sigma (float) – The standard deviation of the gaussian blur, in pixels

  • shot_noise (bool) – Whether to include shot noise in the original signal, default True

  • seed (int or None) – seed value for the random number generator (effects the shot noise only)

Returns

detector_pattern – A single sample of the pattern after accounting for detector properties

Return type

numpy.ndarray

See also

add_shot_noise

adds only shot noise

add_gaussian_point_spread

adds only point spread

diffsims.pattern.detector_functions.add_shot_noise(pattern, seed=None)[source]

Applies shot noise to a pattern

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector

  • seed (int or None) – seed value for the random number generator

Returns

shotted_pattern – A single sample of the pattern after accounting for shot noise

Return type

numpy.ndarray

Notes

This function will (as it should) behave differently depending on the pattern intensity, so be mindful to put your intensities in physical units

diffsims.pattern.detector_functions.constrain_to_dynamic_range(pattern, detector_max=None)[source]

Force the values within pattern to lie between [0,detector_max]

Parameters
  • pattern (numpy.ndarray) – The diffraction pattern at the detector after corruption

  • detector_max (float) – The maximum allowed value at the detector

Returns

within_range_pattern – The pattern, with values >=0 and =< detector_max

Return type

numpy.ndarray


utils

Diffraction utilities used by the other modules.

atomic_diffraction_generator_utils

Back end for computing diffraction patterns with a kinematic model.

diffsims.utils.atomic_diffraction_generator_utils.get_diffraction_image(coordinates, species, probe, x, wavelength, precession, GPU=True, pointwise=False, **kwargs)[source]

Return kinematically simulated diffraction pattern

Parameters
  • coordinates (numpy.ndarray [float], (n_atoms, 3)) – List of atomic coordinates

  • species (numpy.ndarray [int], (n_atoms,)) – List of atomic numbers

  • probe (diffsims.ProbeFunction) – Function representing 3D shape of beam

  • x (list [numpy.ndarray [float] ], of shapes [(nx,), (ny,), (nz,)]) – Mesh on which to compute the volume density

  • wavelength (float) – Wavelength of electron beam

  • precession (a pair (float, int)) – The float dictates the angle of precession and the int how many points are used to discretise the integration.

  • dtype ((str, str)) – tuple of floating/complex datatypes to cast outputs to

  • ZERO (float > 0, optional) – Rounding error permitted in computation of atomic density. This value is the smallest value rounded to 0.

  • GPU (bool, optional) – Flag whether to use GPU or CPU discretisation. Default (if available) is True

  • pointwise (bool, optional) – Optional parameter whether atomic intensities are computed point-wise at the centre of a voxel or an integral over the voxel. default=False

Returns

DP – The two-dimensional diffraction pattern evaluated on the reciprocal grid corresponding to the first two vectors of x.

Return type

numpy.ndarray [dtype[0]], (nx, ny, nz)

diffsims.utils.atomic_diffraction_generator_utils.grid2sphere(arr, x, dx, C)[source]

Projects 3d array onto a sphere

Parameters
  • arr (np.ndarray [float], (nx, ny, nz)) – Input function to be projected

  • x (list [np.ndarray [float]], of shapes [(nx,), (ny,), (nz,)]) – Vectors defining mesh of <arr>

  • dx (list [np.ndarray [float]], of shapes [(3,), (3,), (3,)]) – Basis in which to orient sphere. Centre of sphere will be at C*dx[2] and mesh of output array will be defined by the first two vectors

  • C (float) – Radius of sphere

Returns

out – If y is the point on the line between i*dx[0]+j*dx[1] and C*dx[2] which also lies on the sphere of radius C from C*dx[2] then: out[i,j] = arr(y). Interpolation on arr is linear.

Return type

np.ndarray [float], (nx, ny)

diffsims.utils.atomic_diffraction_generator_utils.normalise(arr)[source]
diffsims.utils.atomic_diffraction_generator_utils.precess_mat(alpha, theta)[source]

Generates rotation matrices for precession curves.

Parameters
  • alpha (float) – Angle (in degrees) of precession tilt

  • theta (float) – Angle (in degrees) along precession curve

Returns

R – Rotation matrix associated to the tilt of alpha away from the vertical axis and a rotation of theta about the vertical axis.

Return type

numpy.ndarray [float], (3, 3)

atomic_scattering_params

discretise_utils

Utils for converting lists of atoms to a discretised volume

diffsims.utils.discretise_utils.do_binning(x, loc, Rmax, d, GPU)[source]

Utility function which takes in a mesh, atom locations, atom radius and minimal grid-spacing and returns a binned array of atom indices.

Parameters
  • x (list [np.ndarray [float]], of shape [(nx,), (ny,), ..]) – Dictates the range of the box over which to bin atoms.

  • loc (np.ndarray, (n, 3)) – Atoms to bin.

  • Rmax (float > 3) – Maximum radius of an atom (rounded up to 3).

  • d (list of float > 0) – The finest permitted binning.

  • GPU (bool) – If True then constrains to memory of GPU rather than RAM.

Returns

  • subList (np.ndarray [int]) – subList[i0,i1,i2] is a list of indices [j0, j1, …, jn, -1,…] such that the only atoms which are contained in the box: [x[0].min()+i0*r,x[0].min(),+(i0+1)*r] x [x[1].min()+i1*r,x[1].min(),+(i1+1)*r]…

  • r (np.ndarray [float]) – Size of each bin.

  • mem (int) – Upper limit of memory in bytes.

diffsims.utils.discretise_utils.get_atoms(Z, returnFunc=True, dtype='f8')[source]

This function returns an approximation of the atom with atomic number Z using a list of Gaussians.

Parameters
  • Z (int) – Atomic number of atom

  • returnFunc (bool, optional) – If True (default) then returns functions for real/reciprocal space discretisation else returns the vectorial representation of the approximating Gaussians.

Returns

obj1, obj2 – Continuous atom is represented by: .. math:: ymapsto sum_i a[i]*exp(-b[i]*|y|^2)

Return type

numpy.ndarray or function

This is data table 3 from ‘Robust Parameterization of Elastic and Absorptive Electron Atomic Scattering Factors’ by L.-M. Peng, G. Ren, S. L. Dudarev and M. J. Whelan, 1996

diffsims.utils.discretise_utils.get_discretisation(loc, Z, x, GPU=False, ZERO=None, dtype=('f8', 'c16'), pointwise=False, FT=False, **kwargs)[source]
Parameters
  • loc (numpy.ndarray, (n, 3)) – Atoms to bin

  • Z (str, int, or numpy.ndarray [str or int], (n,)) – atom labels, either string or atomic masses.

  • x (list [numpy.ndarray [float]]) – Dictates mesh over which to discretise. Volume will be discretised at points [x[0][i],x[1][j],…]

  • GPU (bool, optional) – If True (default) then attempts to use the GPU.

  • ZERO (float > 0) – Approximation threshold

  • dtype ((str, str), optional) – Real and complex data precisions to use, default=(‘float64’, ‘complex128’)

  • pointwise (bool, optional) – If True (default) then computes pointwise atomic potentials on mesh points, else averages the potential over cube of same size as the discretisation.

  • FT (bool, optional) – If True then computes the Fourier transform directly on the reciprocal mesh, otherwise (default) computes the volume potential

Returns

out – Discretisation of atoms defined by loc/Z on mesh defined by x.

Return type

numpy.ndarray, (x[0].size, x[1].size, x[2].size)

diffsims.utils.discretise_utils.rebin(x, loc, r, k, mem)[source]

Bins each location into a grid subject to memory constraints.

Parameters
  • x (list [np.ndarray [float]], of shape [(nx,), (ny,), ..]) – Dictates the range of the box over which to bin atoms.

  • loc (np.ndarray, (n, 3)) – Atoms to bin.

  • r (float or [float, float, float]) – Mesh size (in each direction).

  • k (int) – Integer such that the radius of the atom is <= k*r. Consequently, each atom will appear in approximately 8k^3 bins.

  • mem (int) – Upper limit of number of bytes permitted for mesh. If not possible then raises a MemoryError.

Returns

subListsubList[i0,i1,i2] is a list of indices [j0, j1, …, jn, -1,…] such that the only atoms which are contained in the box: [x[0].min()+i0*r,x[0].min(),+(i0+1)*r] x [x[1].min()+i1*r,x[1].min(),+(i1+1)*r]… are the atoms with locations loc[j0], …, loc[jn].

Return type

np.ndarray [int]

fourier_transform

Created on 31 Oct 2019

Module provides optimised fft and Fourier transform approximation.

@author: Rob Tovey

diffsims.utils.fourier_transform.convolve(arr1, arr2, dx=None, axes=None)[source]

Performs a centred convolution of input arrays

Parameters
  • arr1 (numpy.ndarray) – Arrays to be convolved. If dimensions are not equal then 1s are appended to the lower dimensional array. Otherwise, arrays must be broadcastable.

  • arr2 (numpy.ndarray) – Arrays to be convolved. If dimensions are not equal then 1s are appended to the lower dimensional array. Otherwise, arrays must be broadcastable.

  • dx (float > 0, list of float, or None , optional) – Grid spacing of input arrays. Output is scaled by dx**max(arr1.ndim, arr2.ndim). default=`None` applies no scaling

  • axes (tuple of ints or None, optional) – Choice of axes to convolve. default=`None` convolves all axes

diffsims.utils.fourier_transform.fast_abs(x, y=None)[source]

Fast computation of abs of an array

Parameters
  • x (numpy.ndarray) – Input

  • y (numpy.ndarray or None, optional) – If y is not None, used as preallocated output

Returns

y – Array equal to abs(x)

Return type

numpy.ndarray

diffsims.utils.fourier_transform.fast_fft_len(n)[source]

Returns the smallest integer greater than input such that the fft can be computed efficiently at this size

Parameters

n (int) – minimum size

Returns

N – smallest integer greater than n which permits efficient ffts.

Return type

int

diffsims.utils.fourier_transform.fftn(a, s=None, axes=None, norm=None, **_)[source]
diffsims.utils.fourier_transform.fftshift_phase(x)[source]

Fast implementation of fft_shift: fft(fftshift_phase(x)) = fft_shift(fft(x))

Note two things: - this is an in-place manipulation of the (3D) input array - the input array must have even side lengths. This is softly guaranteed by fast_fft_len but will raise error if not true.

diffsims.utils.fourier_transform.from_recip(y)[source]

Converts Fourier frequencies to spatial coordinates.

Parameters

y (list [numpy.ndarray [float]], of shape [(nx,), (ny,), …]) – List (or equivalent) of vectors which define a mesh in the dimension equal to the length of x

Returns

x – List of vectors defining a mesh such that for a function, f, defined on the mesh given by y, ifft(f) is defined on the mesh given by x. 0 will be in the middle of x.

Return type

list [numpy.ndarray [float]], of shape [(nx,), (ny,), …]

diffsims.utils.fourier_transform.get_DFT(X=None, Y=None)[source]

Returns discrete analogues for the Fourier/inverse Fourier transform pair defined from grid X to grid Y and back again.

Parameters
  • X (list [numpy.ndarray [float]], of shape [(nx,), (ny,), …], optional) – Mesh on real space

  • Y (list [numpy.ndarray [float]], of shape [(nx,), (ny,), …], optional) – Corresponding mesh on Fourier space

  • either X or Y is None then it is inferred from the other (If) –

Returns

  • DFT (function(f, axes=None)) – If f is a function on X then DFT(f) is the Fourier transform of f on Y. axes parameter can be used to specify which axes to transform.

  • iDFT (function(f, axes=None)) – If f is a function on Y then iDFT(f) is the inverse Fourier transform of f on X. axes parameter can be used to specify which axes to transform.

diffsims.utils.fourier_transform.get_recip_points(ndim, n=None, dX=inf, rX=0, dY=inf, rY=1e-16)[source]

Returns a minimal pair of real and Fourier grids which satisfy each given requirement.

Parameters
  • ndim (int) – Dimension of domain

  • n (int, list of length ndim, or None , optional) – Sugested number of pixels (per dimension). default=`None` infers this from other parameters. If enough other constraints are given to define a discretisation then this will be shrunk if possible.

  • dX (float > 0 or list of float of length ndim, optional) – Maximum grid spacing (per dimension). default=`numpy.inf` infers this from other parameters

  • rX (float > 0 or list of float of length ndim, optional) – Minimum grid range (per dimension). default=`None` infers this from other parameters. In this case, range is maximal span, i.e. diameter.

  • dY (float > 0 or list of float of length ndim) – Maximum grid spacing (per dimension) in Fourier domain. default=`None` infers this from other parameters

  • rY (float > 0 or list of float of length ndim) – Minimum grid range (per dimension) in Fourier domain. default=`None` infers this from other parameters. In this case, range is maximal span, i.e. diameter.

Returns

  • x (list [numpy.ndarray [float]], of shape [(nx,), (ny,), …]) – Real mesh of points, centred at 0 with at least n pixels, resolution higher than dX, and range greater than rX.

  • y (list [numpy.ndarray [float]], of shape [(nx,), (ny,), …]) – Fourier mesh of points, centred at 0 with at least n pixels, resolution higher than dY, and range greater than rY.

diffsims.utils.fourier_transform.ifftn(a, s=None, axes=None, norm=None, **_)[source]
diffsims.utils.fourier_transform.plan_fft(A, n=None, axis=None, norm=None, **_)[source]

Plans an fft for repeated use. Parameters are the same as for pyfftw’s fftn which are, where possible, the same as the numpy equivalents. Note that some functionality is only possible when using the pyfftw backend.

Parameters
  • A (numpy.ndarray, of dimension d) – Array of same shape to be input for the fft

  • n (iterable or None, len(n) == d, optional) – The output shape of fft (default=`None` is same as A.shape)

  • axis (int, iterable length d, or None, optional) – The axis (or axes) to transform (default=`None` is all axes)

  • overwrite (bool, optional) – Whether the input array can be overwritten during computation (default=False)

  • planner ({0, 1, 2, 3}, optional) – Amount of effort put into optimising Fourier transform where 0 is low and 3 is high (default=`1`).

  • threads (int, None) – Number of threads to use (default=`None` is all threads)

  • auto_align_input (bool, optional) – If True then may re-align input (default=`True`)

  • auto_contiguous (bool, optional) – If True then may re-order input (default=`True`)

  • avoid_copy (bool, optional) – If True then may over-write initial input (default=`False`)

  • norm ({None, 'ortho'}, optional) – Indicate whether fft is normalised (default=`None`)

Returns

  • plan (function) – Returns the Fourier transform of B, plan() == fftn(B)

  • B (numpy.ndarray, A.shape) – Array which should be modified inplace for fft to be computed. If possible, B is A.

Example

A = numpy.zeros((8,16)) plan, B = plan_fft(A)

B[:,:] = numpy.random.rand(8,16) numpy.fft.fftn(B) == plan()

B = numpy.random.rand(8,16) numpy.fft.fftn(B) != plan()

diffsims.utils.fourier_transform.plan_ifft(A, n=None, axis=None, norm=None, **_)[source]

Plans an ifft for repeated use. Parameters are the same as for pyfftw’s ifftn which are, where possible, the same as the numpy equivalents. Note that some functionality is only possible when using the pyfftw backend.

Parameters
  • A (numpy.ndarray, of dimension d) – Array of same shape to be input for the ifft

  • n (iterable or None, len(n) == d, optional) – The output shape of ifft (default=`None` is same as A.shape)

  • axis (int, iterable length d, or None, optional) – The axis (or axes) to transform (default=`None` is all axes)

  • overwrite (bool, optional) – Whether the input array can be overwritten during computation (default=False)

  • planner ({0, 1, 2, 3}, optional) – Amount of effort put into optimising Fourier transform where 0 is low and 3 is high (default=`1`).

  • threads (int, None) – Number of threads to use (default=`None` is all threads)

  • auto_align_input (bool, optional) – If True then may re-align input (default=`True`)

  • auto_contiguous (bool, optional) – If True then may re-order input (default=`True`)

  • avoid_copy (bool, optional) – If True then may over-write initial input (default=`False`)

  • norm ({None, 'ortho'}, optional) – Indicate whether ifft is normalised (default=`None`)

Returns

  • plan (function) – Returns the inverse Fourier transform of B, plan() == ifftn(B)

  • B (numpy.ndarray, A.shape) – Array which should be modified inplace for ifft to be computed. If possible, B is A.

diffsims.utils.fourier_transform.to_recip(x)[source]

Converts spatial coordinates to Fourier frequencies.

Parameters

x (list [numpy.ndarray [float]], of shape [(nx,), (ny,), …]) – List (or equivalent) of vectors which define a mesh in the dimension equal to the length of x

Returns

y – List of vectors defining a mesh such that for a function, f, defined on the mesh given by x, fft(f) is defined on the mesh given by y

Return type

list [numpy.ndarray [float]], of shape [(nx,), (ny,), …]

generic_utils

Created on 31 Oct 2019

Generic tools for all areas of code.

@author: Rob Tovey

class diffsims.utils.generic_utils.GLOBAL_BOOL(val)[source]

Bases: object

An object which behaves like a bool but can be changed in-place by set or by calling as a function.

set(val)[source]
diffsims.utils.generic_utils.get_grid(sz, tpb=None)[source]
diffsims.utils.generic_utils.to_mesh(x, dx=None, dtype=None)[source]
Generates dense meshes from grid vectors, broadly:

to_mesh(x)[i,j,…] = (x[0][i], x[1][j], …)

Parameters
  • x (list [numpy.ndarray], of shape [(nx,), (ny,), …]) – List of grid vectors

  • dx (list [numpy.ndarray] or None, optional) – Basis in which to expand mesh, default is the canonical basis

  • dtype (str or None, optional) – String representing the numpy type of output, default inherits from x

Returns

X – X[i,j,…, k] = x[0][i]*dx[0][k] + x[1][j]*dx[1][k] + …

Return type

numpy.ndarray [dtype], (x[0].size, x[1].size, …, len(x))

kinematic_simulation_utils

Created on 1 Nov 2019

Back end for computing diffraction patterns with a kinematic model.

@author: Rob Tovey

diffsims.utils.kinematic_simulation_utils.get_diffraction_image(coordinates, species, probe, x, wavelength, precession, GPU=True, pointwise=False, **kwargs)[source]

Return kinematically simulated diffraction pattern

Parameters
  • coordinates (numpy.ndarray [float], (n_atoms, 3)) – List of atomic coordinates

  • species (numpy.ndarray [int], (n_atoms,)) – List of atomic numbers

  • probe (diffsims.ProbeFunction) – Function representing 3D shape of beam

  • x (list [numpy.ndarray [float] ], of shapes [(nx,), (ny,), (nz,)]) – Mesh on which to compute the volume density

  • wavelength (float) – Wavelength of electron beam

  • precession (a pair (float, int)) – The float dictates the angle of precession and the int how many points are used to discretise the integration.

  • dtype ((str, str)) – tuple of floating/complex datatypes to cast outputs to

  • ZERO (float > 0, optional) – Rounding error permitted in computation of atomic density. This value is the smallest value rounded to 0.

  • GPU (bool, optional) – Flag whether to use GPU or CPU discretisation. Default (if available) is True

  • pointwise (bool, optional) – Optional parameter whether atomic intensities are computed point-wise at the centre of a voxel or an integral over the voxel. default=False

Returns

DP – The two-dimensional diffraction pattern evaluated on the reciprocal grid corresponding to the first two vectors of x.

Return type

numpy.ndarray [dtype[0]], (nx, ny, nz)

diffsims.utils.kinematic_simulation_utils.grid2sphere(arr, x, dx, C)[source]

Projects 3d array onto a sphere.

Parameters
  • arr (np.ndarray [float], (nx, ny, nz)) – Input function to be projected

  • x (list [np.ndarray [float]], of shapes [(nx,), (ny,), (nz,)]) – Vectors defining mesh of <arr>

  • dx (list [np.ndarray [float]], of shapes [(3,), (3,), (3,)]) – Basis in which to orient sphere. Centre of sphere will be at C*dx[2] and mesh of output array will be defined by the first two vectors.

  • C (float) – Radius of sphere.

Returns

out – If y is the point on the line between i*dx[0]+j*dx[1] and C*dx[2] which also lies on the sphere of radius C from C*dx[2] then: out[i,j] = arr(y). Interpolation on arr is linear.

Return type

np.ndarray [float], (nx, ny)

diffsims.utils.kinematic_simulation_utils.normalise(arr)[source]
diffsims.utils.kinematic_simulation_utils.precess_mat(alpha, theta)[source]

Generates rotation matrices for precession curves.

Parameters
  • alpha (float) – Angle (in degrees) of precession tilt

  • theta (float) – Angle (in degrees) along precession curve

Returns

R – Rotation matrix associated to the tilt of alpha away from the vertical axis and a rotation of theta about the vertical axis.

Return type

numpy.ndarray [float], (3, 3)

lobato_scattering_params

probe_utils

Created on 5 Nov 2019

@author: Rob Tovey

class diffsims.utils.probe_utils.ProbeFunction(func=None)[source]

Bases: object

Object representing a probe function.

Parameters

func (function) – Function which takes in an array, r, of shape [nx, ny, nz, 3] and returns an array of shape [nx, ny, nz]. r[…,0] corresponds to the x coordinate, r[…, 1] to y etc. If not provided (or None) then the __call__ and FT methods must be overrided.

__call__(x, out=None, scale=None)[source]

Returns func(x)*scale. If out is provided then it is used as preallocated storage. If scale is not provided then it is assumed to be 1. If x is a list of arrays it is converted into a mesh first.

Parameters
  • x (numpy.ndarray, (nx, ny, nz, 3) or list of arrays of shape) – [(nx,), (ny,), (nz,)] Mesh points at which to evaluate the probe density.

  • out (numpy.ndarray, (nx, ny, nz), optional) – If provided then computation is performed inplace.

  • scale (numpy.ndarray, (nx, ny, nz), optional) – If provided then the probe density is scaled by this before being returned.

Returns

out – An array equal to probe(x)*scale.

Return type

numpy.ndarray, (nx, ny, nz)

FT(y, out=None)[source]

Returns the Fourier transform of func on the mesh y. Again, if out is provided then computation is inplace. If y is a list of arrays then it is converted into a mesh first. If this function is not overridden then an approximation is made using func and the fft.

Parameters
  • y (numpy.ndarray, (nx, ny, nz, 3) or list of arrays of shape) – [(nx,), (ny,), (nz,)] Mesh of Fourier coordinates at which to evaluate the probe density.

  • out (numpy.ndarray, (nx, ny, nz), optional) – If provided then computation is performed inplace.

Returns

out – An array equal to FourierTransform(probe)(y).

Return type

numpy.ndarray, (nx, ny, nz)

class diffsims.utils.probe_utils.BesselProbe(r)[source]

Bases: diffsims.utils.probe_utils.ProbeFunction

Probe function given by a radially scaled Bessel function of the first kind.

Parameters

r (float) – Width of probe at the surface of the sample. More specifically, the smallest 0 of the probe.

__call__(x, out=None, scale=None)[source]

If X = sqrt(x[…,0]**2+x[…,1]**2)/r then returns J_1(X)/X*scale. If out is provided then this is computed inplace. If scale is not provided then it is assumed to be 1. If x is a list of arrays it is converted into a mesh first.

Parameters
  • x (numpy.ndarray, (nx, ny, nz, 3) or list of arrays of shape) – [(nx,), (ny,), (nz,)] Mesh points at which to evaluate the probe density. As a plotting utility, if a lower dimensional mesh is provided then the remaining coordinates are assumed to be 0 and so only the respective 1D/2D slice of the probe is returned.

  • out (numpy.ndarray, (nx, ny, nz), optional) – If provided then computation is performed inplace.

  • scale (numpy.ndarray, (nx, ny, nz), optional) – If provided then the probe density is scaled by this before being returned.

Returns

out – An array equal to probe(x)*scale. If ny=0 or nz=0 then array is of smaller dimension.

Return type

numpy.ndarray, (nx, ny, nz)

FT(y, out=None)[source]

If Y = sqrt(y[…,0]**2 + y[…,1]**2)*r then returns an indicator function on the disc Y < 1, y[2]==0. Again, if out is provided then computation is inplace. If y is a list of arrays then it is converted into a mesh first.

Parameters
  • y (numpy.ndarray, (nx, ny, nz, 3) or list of arrays of shape) – [(nx,), (ny,), (nz,)] Mesh of Fourier coordinates at which to evaluate the probe density. As a plotting utility, if a lower dimensional mesh is provided then the remaining coordinates are assumed to be 0 and so only the respective 1D/2D slice of the probe is returned.

  • out (numpy.ndarray, (nx, ny, nz), optional) – If provided then computation is performed inplace.

Returns

out – An array equal to FourierTransform(probe)(y). If ny=0 or nz=0 then array is of smaller dimension.

Return type

numpy.ndarray, (nx, ny, nz)

scattering_params

Scattering Paramaters as Tabulated in “Advanced Computing in Electron Microscopy - Second Edition (2010) - Earl.J.Kirkland” ISBN 978-1-4419-6532-5 Pages 253-260 Appendix C

This transcription comes from scikit-ued (MIT license) - https://pypi.python.org/pypi/scikit-ued

shape_factor_models

diffsims.utils.shape_factor_models.atanc(excitation_error, max_excitation_error, minima_number=5)[source]

Intensity with arctan(s)/s profile that closely follows sin(s)/s but is smooth for s!=0.

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

  • minima_number (int) – The minima_number’th minima in the corresponding sinx/x lies at max_excitation_error from 0

Returns

intensity

Return type

array-like or float

diffsims.utils.shape_factor_models.binary(excitation_error, max_excitation_error)[source]

Returns a unit intensity for all reflections

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

Returns

intensities

Return type

array-like or float

diffsims.utils.shape_factor_models.linear(excitation_error, max_excitation_error)[source]

Returns an intensity linearly scaled with by the excitation error

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

Returns

intensities

Return type

array-like or float

diffsims.utils.shape_factor_models.lorentzian(excitation_error, max_excitation_error)[source]

Lorentzian intensity profile that should approximate the two-beam rocking curve. This is equation (6) in reference [1].

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

Returns

intensity_factor – Vector representing the rel-rod factor for each reflection

Return type

array-like or float

References

[1] L. Palatinus, P. Brázda, M. Jelínek, J. Hrdá, G. Steciuk, M. Klementová, Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0, Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater. 75 (2019) 512–522. doi:10.1107/S2052520619007534.

diffsims.utils.shape_factor_models.lorentzian_precession(excitation_error, max_excitation_error, r_spot, precession_angle)[source]

Intensity profile factor for a precessed beam assuming a Lorentzian intensity profile for the un-precessed beam. This is equation (10) in reference [1].

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

  • r_spot (array-like or float) – The distance (reciprocal) from each reflection to the origin

  • precession_angle (float) – The beam precession angle in degrees; the angle the beam makes with the optical axis.

Returns

intensity_factor – Vector representing the rel-rod factor for each reflection

Return type

array-like or float

References

[1] L. Palatinus, P. Brázda, M. Jelínek, J. Hrdá, G. Steciuk, M. Klementová, Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0, Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater. 75 (2019) 512–522. doi:10.1107/S2052520619007534.

diffsims.utils.shape_factor_models.sin2c(excitation_error, max_excitation_error, minima_number=5)[source]

Intensity with sin^2(s)/s^2 profile, after Howie-Whelan rel-rod

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

  • minima_number (int) – The minima_number’th minima lies at max_excitation_error from 0

Returns

intensity

Return type

array-like or float

diffsims.utils.shape_factor_models.sinc(excitation_error, max_excitation_error, minima_number=5)[source]

Returns an intensity with a sinc profile

Parameters
  • excitation_error (array-like or float) – The distance (reciprocal) from a reflection to the Ewald sphere

  • max_excitation_error (float) – The distance at which a reflection becomes extinct

  • minima_number (int) – The minima_number’th minima lies at max_excitation_error from 0

Returns

intensity

Return type

array-like or float

sim_utils

diffsims.utils.sim_utils.acceleration_voltage_to_relativistic_mass(acceleration_voltage)[source]

Get relativistic mass of electron as function of acceleration voltage.

Parameters

acceleration_voltage (float) – In Volt

Returns

mr – Relativistic electron mass

Return type

float

Example

>>> import diffsims.utils.sim_utils as sim_utils
>>> mr = sim_utils.acceleration_voltage_to_relativistic_mass(200000) # 200 kV
diffsims.utils.sim_utils.acceleration_voltage_to_velocity(acceleration_voltage)[source]

Get relativistic velocity of electron from acceleration voltage.

Parameters

acceleration_voltage (float) – In Volt

Returns

v – In m/s

Return type

float

Example

>>> import diffsims.utils.sim_utils as sim_utils
>>> v = sim_utils.acceleration_voltage_to_velocity(200000) # 200 kV
>>> round(v)
208450035
diffsims.utils.sim_utils.acceleration_voltage_to_wavelength(acceleration_voltage)[source]

Get electron wavelength from the acceleration voltage.

Parameters

acceleration_voltage (float or array-like) – In Volt

Returns

wavelength – In meters

Return type

float or array-like

diffsims.utils.sim_utils.beta_to_bst(beam_deflection, acceleration_voltage)[source]

Calculate Bs * t values from beam deflection (beta).

Parameters
  • beam_deflection (NumPy array) – In radians

  • acceleration_voltage (float) – In Volts

Returns

bst – In Tesla * meter

Return type

NumPy array

Examples

>>> import numpy as np
>>> import diffsims.utils.sim_utils as sim_utils
>>> data = np.random.random((100, 100))  # In radians
>>> acceleration_voltage = 200000  # 200 kV (in Volt)
>>> bst = sim_utils.beta_to_bst(data, 200000)
diffsims.utils.sim_utils.bst_to_beta(bst, acceleration_voltage)[source]

Calculate beam deflection (beta) values from Bs * t.

Parameters
  • bst (NumPy array) – Saturation induction Bs times thickness t of the sample. In Tesla*meter

  • acceleration_voltage (float) – In Volts

Returns

beta – Beam deflection in radians

Return type

NumPy array

Examples

>>> import numpy as np
>>> import diffsims.utils.sim_utils as sim_utils
>>> data = np.random.random((100, 100))  # In Tesla*meter
>>> acceleration_voltage = 200000  # 200 kV (in Volt)
>>> beta = sim_utils.bst_to_beta(data, acceleration_voltage)
diffsims.utils.sim_utils.diffraction_scattering_angle(acceleration_voltage, lattice_size, miller_index)[source]

Get electron scattering angle from a crystal lattice.

Returns the total scattering angle, as measured from the middle of the direct beam (0, 0, 0) to the given Miller index.

Miller index: h, k, l = miller_index Interplanar distance: d = a / (h**2 + k**2 + l**2)**0.5 Bragg’s law: theta = arcsin(electron_wavelength / (2 * d)) Total scattering angle (phi): phi = 2 * theta

Parameters
  • acceleration_voltage (float) – In Volt

  • lattice_size (float or array-like) – In meter

  • miller_index (tuple) – (h, k, l)

Returns

angle – Scattering angle in radians.

Return type

float

diffsims.utils.sim_utils.et_to_beta(et, acceleration_voltage)[source]

Calculate beam deflection (beta) values from E * t.

Parameters
  • et (NumPy array) – Electric field times thickness t of the sample.

  • acceleration_voltage (float) – In Volts

Returns

beta – Beam deflection in radians

Return type

NumPy array

Examples

>>> import numpy as np
>>> import diffsims.utils.sim_utils as sim_utils
>>> data = np.random.random((100, 100))
>>> acceleration_voltage = 200000  # 200 kV (in Volt)
>>> beta = sim_utils.et_to_beta(data, acceleration_voltage)
diffsims.utils.sim_utils.get_atomic_scattering_factors(g_hkl_sq, coeffs, scattering_params)[source]

Calculate atomic scattering factors for n atoms.

Parameters
  • g_hkl_sq (ndarray) – One-dimensional array of g-vector lengths squared.

  • coeffs (ndarray) – Three-dimensional array [n, 5, 2] of coefficients corresponding to the n atoms.

  • scattering_params (string) – Type of scattering factor calculation to use. One of ‘lobato’, ‘xtables’.

Returns

scattering_factors – The calculated atomic scattering parameters.

Return type

ndarray

diffsims.utils.sim_utils.get_electron_wavelength(accelerating_voltage)[source]

Calculates the (relativistic) electron wavelength in Angstroms for a given accelerating voltage in kV.

Parameters

accelerating_voltage (float or 'inf') – The accelerating voltage in kV. Values numpy.inf and ‘inf’ are also accepted.

Returns

wavelength – The relativistic electron wavelength in Angstroms.

Return type

float

diffsims.utils.sim_utils.get_holz_angle(electron_wavelength, lattice_parameter)[source]

Converts electron wavelength and lattice paramater to holz angle :param electron_wavelength: In nanometers :type electron_wavelength: scalar :param lattice_parameter: In nanometers :type lattice_parameter: scalar

Returns

scattering_angle – Scattering angle in radians

Return type

scalar

Examples

>>> import diffsims.utils.sim_utils as sim_utils
>>> lattice_size = 0.3905 # STO-(001) in nm
>>> wavelength = 2.51/1000 # Electron wavelength for 200 kV
>>> angle = sim_utils.get_holz_angle(wavelength, lattice_size)
diffsims.utils.sim_utils.get_intensities_params(reciprocal_lattice, reciprocal_radius)[source]

Calculates the variables needed for get_kinematical_intensities

Parameters
  • reciprocal_lattice (diffpy.Structure.Lattice) – The reciprocal crystal lattice for the structure of interest.

  • reciprocal_radius (float) – The radius of the sphere in reciprocal space (units of reciprocal Angstroms) within which reciprocal lattice points are returned.

Returns

  • unique_hkls (array-like) – The unique plane families which lie in the given reciprocal sphere.

  • multiplicites (array-like) – The multiplicites of the given unqiue planes in the sphere.

  • g_hkls (list) – The g vector length of the given hkl in the sphere.

diffsims.utils.sim_utils.get_interaction_constant(accelerating_voltage)[source]

Calculates the interaction constant, sigma, for a given acelerating voltage.

Parameters

accelerating_voltage (float) – The accelerating voltage in V.

Returns

sigma – The relativistic electron wavelength in m.

Return type

float

diffsims.utils.sim_utils.get_kinematical_intensities(structure, g_indices, g_hkls_array, debye_waller_factors={}, scattering_params='lobato', prefactor=1)[source]

Calculates peak intensities.

The peak intensity is a combination of the structure factor for a given peak and the position the Ewald sphere intersects the relrod. In this implementation, the intensity scales linearly with proximity.

Parameters
  • structure (Structure) – The structure for which to derive the structure factors.

  • g_indices (array-like) – Indicies of spots to be considered

  • g_hkls_array (array-like) – coordinates of spots to be considered

  • debye_waller_factors (dict of str:value pairs) – Maps element names to their temperature-dependent Debye-Waller factors.

  • scattering_params (str) – “lobato” or “xtables”

  • prefactor (array-like) – multiplciation factor for structure factor

Returns

peak_intensities – The intensities of the peaks.

Return type

array-like

diffsims.utils.sim_utils.get_points_in_sphere(reciprocal_lattice, reciprocal_radius)[source]

Finds all reciprocal lattice points inside a given reciprocal sphere. Utilised within the DiffractionGenerator.

Parameters
  • reciprocal_lattice (diffpy.Structure.Lattice) – The reciprocal crystal lattice for the structure of interest.

  • reciprocal_radius (float) – The radius of the sphere in reciprocal space (units of reciprocal Angstroms) within which reciprocal lattice points are returned.

Returns

  • spot_indices (numpy.array) – Miller indices of reciprocal lattice points in sphere.

  • cartesian_coordinates (numpy.array) – Cartesian coordinates of reciprocal lattice points in sphere.

  • spot_distances (numpy.array) – Distance of reciprocal lattice points in sphere from the origin.

diffsims.utils.sim_utils.get_scattering_params_dict(scattering_params)[source]

Get scattering parameter dictionary from name.

Parameters

scattering_params (string) – Name of scattering factors. One of ‘lobato’, ‘xtables’.

Returns

scattering_params_dict – Dictionary of scattering parameters mapping from element name.

Return type

dict

diffsims.utils.sim_utils.get_unique_families(hkls)[source]

Returns unique families of Miller indices, which must be permutations of each other.

Parameters

hkls (list) – List of Miller indices ([h, k, l])

Returns

pretty_unique – A dict with unique hkl and multiplicity {hkl: multiplicity}.

Return type

dictionary

diffsims.utils.sim_utils.get_vectorized_list_for_atomic_scattering_factors(structure, debye_waller_factors, scattering_params)[source]

Create a flattened array of coeffs, fcoords and occus for vectorized computation of atomic scattering factors.

Note: The dimensions of the returned objects are not necessarily the same size as the number of atoms in the structure as each partially occupied specie occupies its own position in the flattened array.

Parameters
  • structure (diffpy.structure) – The atomic structure for which scattering factors are required.

  • debye_waller_factors (list) – List of Debye-Waller factors for atoms in structure.

Returns

  • coeffs (np.array()) – Coefficients of atomic scattering factor parameterization for each atom.

  • fcoords (np.array()) – Fractional coordinates of each atom in structure.

  • occus (np.array()) – Occupancy of each atomic site.

  • dwfactors (np.array()) – Debye-Waller factors for each atom in the structure.

diffsims.utils.sim_utils.is_lattice_hexagonal(latt)[source]

Determines if a diffpy lattice is hexagonal or trigonal. :param latt: The diffpy lattice object to be determined as hexagonal or not. :type latt: diffpy.Structure.lattice

Returns

is_true – True if hexagonal or trigonal.

Return type

bool

diffsims.utils.sim_utils.scattering_angle_to_lattice_parameter(electron_wavelength, angle)[source]

Convert scattering angle data to lattice parameter sizes.

Parameters
  • electron_wavelength (float) – Wavelength of the electrons in the electron beam. In nm. For 200 kV electrons: 0.00251 (nm)

  • angle (NumPy array) – Scattering angle, in radians.

Returns

lattice_parameter – Lattice parameter, in nanometers

Return type

NumPy array

Examples

>>> import diffsims.utils.sim_utils as sim_utils
>>> angle_list = [0.1, 0.1, 0.1, 0.1] # in radians
>>> wavelength = 2.51/1000 # Electron wavelength for 200 kV
>>> lattice_size = sim_utils.scattering_angle_to_lattice_parameter(
...     wavelength, angle_list)
diffsims.utils.sim_utils.simulate_kinematic_scattering(atomic_coordinates, element, accelerating_voltage, simulation_size=256, max_k=1.5, illumination='plane_wave', sigma=20, scattering_params='lobato')[source]

Simulate electron scattering from arrangement of atoms comprising one elemental species.

Parameters
  • atomic_coordinates (array) – Array specifying atomic coordinates in structure.

  • element (string) – Element symbol, e.g. “C”.

  • accelerating_voltage (float) – Accelerating voltage in keV.

  • simulation_size (int) – Simulation size, n, specifies the n x n array size for the simulation calculation.

  • max_k (float) – Maximum scattering vector magnitude in reciprocal angstroms.

  • illumination (string) – Either ‘plane_wave’ or ‘gaussian_probe’ illumination

  • sigma (float) – Gaussian probe standard deviation, used when illumination == ‘gaussian_probe’

  • scattering_params (string) – Type of scattering factor calculation to use. One of ‘lobato’, ‘xtables’.

Returns

simulation – ElectronDiffraction simulation.

Return type

ElectronDiffraction

diffsims.utils.sim_utils.tesla_to_am(data)[source]

Convert data from Tesla to A/m

Parameters

data (NumPy array) – Data in Tesla

Returns

output_data – In A/m

Return type

NumPy array

Examples

>>> import numpy as np
>>> import diffsims.utils.sim_utils as sim_utils
>>> data_T = np.random.random((100, 100))  # In tesla
>>> data_am = sim_utils.tesla_to_am(data_T)
diffsims.utils.sim_utils.uvtw_to_uvw(uvtw)[source]

Convert 4-index direction to a 3-index direction.

Parameters

uvtw (array-like with 4 floats) –

Returns

uvw

Return type

tuple of 4 floats

vector_utils

diffsims.utils.vector_utils.get_angle_cartesian(a, b)[source]

Compute the angle between two vectors in a cartesian coordinate system.

Parameters
  • a (array-like with 3 floats) – The two directions to compute the angle between.

  • b (array-like with 3 floats) – The two directions to compute the angle between.

Returns

angle – Angle between a and b in radians.

Return type

float

diffsims.utils.vector_utils.get_angle_cartesian_vec(a, b)[source]

Compute the angles between two lists of vectors in a cartesian coordinate system.

Parameters
  • a (np.array()) – The two lists of directions to compute the angle between in Nx3 float arrays.

  • b (np.array()) – The two lists of directions to compute the angle between in Nx3 float arrays.

Returns

angles – List of angles between a and b in radians.

Return type

np.array()

diffsims.utils.vector_utils.vectorised_spherical_polars_to_cartesians(z)[source]

Converts an array of spherical polars into an array of (x,y,z) = r(cos(psi)sin(theta),sin(psi)sin(theta),cos(theta))

Parameters

z (np.array) – With rows of r : the radius value, r = sqrt(x**2+y**2+z**2) psi : The azimuthal angle generally (0,2pi]) theta : The elevation angle generally (0,pi)

Returns

xyz – With rows of x,y,z

Return type

np.array