matscipy.utils
Functions
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Generate a complete (v1, v2, v3) orthogonal basis in 3D from v1 and an optional v2 |
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Get shortest distance between a test point and a polygon defined by polygon_points |
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Get the results of Common Neighbor Analysis and |
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Test if 2D finite line defined by points p1 & p2 intersects with the finite line defined by x1 & x2. |
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Test if points lies within the closed 2D polygon defined by poly_points Uses ray casting algorithm, as suggested by: https://stackoverflow.com/questions/217578/how-can-i-determine-whether-a-2d-point-is-within-a-polygon |
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Get a boolean mask of all test_points within a radius of any edge of the polygon defined by polygon_points |
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Provide uniform interface for generating rotated atoms objects through two main methods: |
- matscipy.utils.validate_cubic_cell(a, symbol='w', axes=None, crystalstructure=None, pbc=True)
Provide uniform interface for generating rotated atoms objects through two main methods:
For lattice constant + symbol + crystalstructure, build a valid cubic bulk rotated into the frame defined by axes For cubic bulk atoms object + crystalstructure, validate structure matches expected cubic bulk geometry, and rotate to frame defined by axes
- a: float or ase.atoms
EITHER lattice constant (in A), or the cubic bulk structure
- symbol: str
Elemental symbol, passed to relevant crystalstructure generator when a is float
- axes: np.array
Axes transform to apply to bulk cell
- crystalstructure: str
Base Structure of bulk system, currently supported: fcc, bcc, diamond
- pbc: list of bool
Periodic Boundary Conditions in x, y, z
- matscipy.utils.complete_basis(v1, v2=None, normalise=False, nmax=5, tol=1e-06)
Generate a complete (v1, v2, v3) orthogonal basis in 3D from v1 and an optional v2
(V1, V2, V3) is always right-handed.
- v1: np.array
len(3) array giving primary axis
- v2: np.array | None
len(3) array giving secondary axis If None, a search for a “sensible” secondary axis will occur, raising a RuntimeError if unsuccessful
(Searches for vectors perpendicular to v1 with small integer indices)
If v2 is given and is not orthogonal to v1, raises a warning
- normalise: bool
return an float orthonormal basis, rather than integer orthogonal basis
- nmax: int
Maximum integer index for v2 search
- tol: float
Tolerance for checking orthogonality between v1 and v2
- Returns:
V1, V2, V3 – Complete orthogonal basis, optionally normalised dtype of arrays is int with normalise=False, float with normalise=True
- Return type:
np.arrays
- matscipy.utils.get_structure_types(structure, diamond_structure=False)
- Get the results of Common Neighbor Analysis and
Identify Diamond modifiers from Ovito (Requires Ovito python module)
- Parameters:
structure (ase.atoms) – input structure
- Returns:
per atom labels of the structure types structure_names (list of strings): names of the structure types colors (list of strings): colors of the structure types in hex format
- Return type:
atom_labels (array of ints)
- matscipy.utils.line_intersect_2D(p1, p2, x1, x2)
Test if 2D finite line defined by points p1 & p2 intersects with the finite line defined by x1 & x2. Essentially a Python conversion of the ray casting algorithm suggested in: https://stackoverflow.com/questions/217578/how-can-i-determine-whether-a-2d-point-is-within-a-polygon
Returns True if lines intersect (including if they are collinear)
- p1, p2, x1, x2: np.array
2D points defining lines
- matscipy.utils.points_in_polygon2D(p, poly_points)
Test if points lies within the closed 2D polygon defined by poly_points Uses ray casting algorithm, as suggested by: https://stackoverflow.com/questions/217578/how-can-i-determine-whether-a-2d-point-is-within-a-polygon
- p: np.array
(n, 2) array of points to test
- poly_points: np.array
Points defining the 2D polygon
- Returns:
mask – Boolean mask. True for points inside the poylgon
- Return type:
np.array
- matscipy.utils.get_distance_from_polygon2D(test_points: array, polygon_points: array) array
- Get shortest distance between a test point and a polygon defined by polygon_points
(i.e. the shortest distance between each point and the lines of the polygon)
Uses formula from https://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line#Line_defined_by_two_points
- test_points: np.array
2D Points to get distances for
- polygon_points: np.array
Coordinates defining the points of the polygon
- matscipy.utils.radial_mask_from_polygon2D(test_points: array, polygon_points: array, radius: float, inner: bool = True) array
Get a boolean mask of all test_points within a radius of any edge of the polygon defined by polygon_points
- test_points: np.array
2D Points to get mask for
- polygon_points: np.array
Coordinates defining the points of the polygon
- radius: float
Radius to use as cutoff for mask
- inner: bool
Whether test_points inside the polygon should always be masked as True, regardless of radius