Source code for pymatgen.analysis.chemenv.utils.coordination_geometry_utils

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.


"""
This module contains some utility functions and classes that are used in the chemenv package.
"""

__author__ = "David Waroquiers"
__copyright__ = "Copyright 2012, The Materials Project"
__credits__ = "Geoffroy Hautier"
__version__ = "2.0"
__maintainer__ = "David Waroquiers"
__email__ = "david.waroquiers@gmail.com"
__date__ = "Feb 20, 2016"

import math

import numpy as np
from numpy.linalg import norm
from scipy.spatial import ConvexHull
from scipy.integrate import quad
from scipy.interpolate import UnivariateSpline

from pymatgen.analysis.chemenv.utils.chemenv_errors import SolidAngleError


[docs]def get_lower_and_upper_f(surface_calculation_options): mindist = surface_calculation_options['distance_bounds']['lower'] maxdist = surface_calculation_options['distance_bounds']['upper'] minang = surface_calculation_options['angle_bounds']['lower'] maxang = surface_calculation_options['angle_bounds']['upper'] if surface_calculation_options['type'] == 'standard_elliptic': lower_and_upper_functions = quarter_ellipsis_functions(xx=(mindist, maxang), yy=(maxdist, minang)) elif surface_calculation_options['type'] == 'standard_diamond': deltadist = surface_calculation_options['distance_bounds']['delta'] deltaang = surface_calculation_options['angle_bounds']['delta'] lower_and_upper_functions = diamond_functions(xx=(mindist, maxang), yy=(maxdist, minang), x_y0=deltadist, y_x0=deltaang) elif surface_calculation_options['type'] == 'standard_spline': lower_points = surface_calculation_options['lower_points'] upper_points = surface_calculation_options['upper_points'] degree = surface_calculation_options['degree'] lower_and_upper_functions = spline_functions(lower_points=lower_points, upper_points=upper_points, degree=degree) else: raise ValueError('Surface calculation of type "{}" ' 'is not implemented'.format(surface_calculation_options['type'])) return lower_and_upper_functions
[docs]def function_comparison(f1, f2, x1, x2, numpoints_check=500): """ Method that compares two functions Args: f1: First function to compare f2: Second function to compare x1: Lower bound of the interval to compare x2: Upper bound of the interval to compare numpoints_check: Number of points used to compare the functions Returns: Whether the function are equal ("="), f1 is always lower than f2 ("<"), f1 is always larger than f2 (">"), f1 is always lower than or equal to f2 ("<"), f1 is always larger than or equal to f2 (">") on the interval [x1, x2]. If the two functions cross, a RuntimeError is thrown (i.e. we expect to compare functions that do not cross...) """ xx = np.linspace(x1, x2, num=numpoints_check) y1 = f1(xx) y2 = f2(xx) if np.all(y1 < y2): return '<' elif np.all(y1 > y2): return '>' elif np.all(y1 == y2): return '=' elif np.all(y1 <= y2): return '<=' elif np.all(y1 >= y2): return '>=' else: raise RuntimeError('Error in comparing functions f1 and f2 ...')
[docs]def quarter_ellipsis_functions(xx, yy): """ Method that creates two quarter-ellipse functions based on points xx and yy. The ellipsis is supposed to be aligned with the axes. The two ellipsis pass through the two points xx and yy. Args: xx: First point yy: Second point Returns: A dictionary with the lower and upper quarter ellipsis functions. """ npxx = np.array(xx) npyy = np.array(yy) if np.any(npxx == npyy): raise RuntimeError('Invalid points for quarter_ellipsis_functions') if np.all(npxx < npyy) or np.all(npxx > npyy): if npxx[0] < npyy[0]: p1 = npxx p2 = npyy else: p1 = npyy p2 = npxx c_lower = np.array([p1[0], p2[1]]) c_upper = np.array([p2[0], p1[1]]) b2 = (p2[1] - p1[1]) ** 2 else: if npxx[0] < npyy[0]: p1 = npxx p2 = npyy else: p1 = npyy p2 = npxx c_lower = np.array([p2[0], p1[1]]) c_upper = np.array([p1[0], p2[1]]) b2 = (p1[1] - p2[1]) ** 2 b2overa2 = b2 / (p2[0] - p1[0]) ** 2 def lower(x): return c_lower[1] - np.sqrt(b2 - b2overa2 * (x - c_lower[0]) ** 2) def upper(x): return c_upper[1] + np.sqrt(b2 - b2overa2 * (x - c_upper[0]) ** 2) return {'lower': lower, 'upper': upper}
[docs]def spline_functions(lower_points, upper_points, degree=3): """ Method that creates two (upper and lower) spline functions based on points lower_points and upper_points. Args: lower_points: Points defining the lower function. upper_points: Points defining the upper function. degree: Degree for the spline function Returns: A dictionary with the lower and upper spline functions. """ lower_xx = np.array([pp[0] for pp in lower_points]) lower_yy = np.array([pp[1] for pp in lower_points]) upper_xx = np.array([pp[0] for pp in upper_points]) upper_yy = np.array([pp[1] for pp in upper_points]) lower_spline = UnivariateSpline(lower_xx, lower_yy, k=degree, s=0) upper_spline = UnivariateSpline(upper_xx, upper_yy, k=degree, s=0) def lower(x): return lower_spline(x) def upper(x): return upper_spline(x) return {'lower': lower, 'upper': upper}
[docs]def diamond_functions(xx, yy, y_x0, x_y0): r""" Method that creates two upper and lower functions based on points xx and yy as well as intercepts defined by y_x0 and x_y0. The resulting functions form kind of a distorted diamond-like structure aligned from point xx to point yy. Schematically : xx is symbolized by x, yy is symbolized by y, y_x0 is equal to the distance from x to a, x_y0 is equal to the distance from x to b, the lines a-p and b-q are parallel to the line x-y such that points p and q are obtained automatically. In case of an increasing diamond the lower function is x-b-q and the upper function is a-p-y while in case of a decreasing diamond, the lower function is a-p-y and the upper function is x-b-q. Increasing diamond | Decreasing diamond p--y x----b / /| |\ \ / / | | \ q / / | a \ | a / | \ \ | | / q \ \ | |/ / \ \| x----b p--y Args: xx: First point yy: Second point Returns: A dictionary with the lower and upper diamond functions. """ npxx = np.array(xx) npyy = np.array(yy) if np.any(npxx == npyy): raise RuntimeError('Invalid points for diamond_functions') if np.all(npxx < npyy) or np.all(npxx > npyy): if npxx[0] < npyy[0]: p1 = npxx p2 = npyy else: p1 = npyy p2 = npxx else: if npxx[0] < npyy[0]: p1 = npxx p2 = npyy else: p1 = npyy p2 = npxx slope = (p2[1] - p1[1]) / (p2[0] - p1[0]) if slope > 0.0: x_bpoint = p1[0] + x_y0 myy = p1[1] bq_intercept = myy - slope * x_bpoint myx = p1[0] myy = p1[1] + y_x0 ap_intercept = myy - slope * myx x_ppoint = (p2[1] - ap_intercept) / slope def lower(x): return np.where(x <= x_bpoint, p1[1] * np.ones_like(x), slope * x + bq_intercept) def upper(x): return np.where(x >= x_ppoint, p2[1] * np.ones_like(x), slope * x + ap_intercept) else: x_bpoint = p1[0] + x_y0 myy = p1[1] bq_intercept = myy - slope * x_bpoint myx = p1[0] myy = p1[1] - y_x0 ap_intercept = myy - slope * myx x_ppoint = (p2[1] - ap_intercept) / slope def lower(x): return np.where(x >= x_ppoint, p2[1] * np.ones_like(x), slope * x + ap_intercept) def upper(x): return np.where(x <= x_bpoint, p1[1] * np.ones_like(x), slope * x + bq_intercept) return {'lower': lower, 'upper': upper}
[docs]def rectangle_surface_intersection(rectangle, f_lower, f_upper, bounds_lower=None, bounds_upper=None, check=True, numpoints_check=500): """ Method to calculate the surface of the intersection of a rectangle (aligned with axes) and another surface defined by two functions f_lower and f_upper. Args: rectangle: Rectangle defined as : ((x1, x2), (y1, y2)). f_lower: Function defining the lower bound of the surface. f_upper: Function defining the upper bound of the surface. bounds_lower: Interval in which the f_lower function is defined. bounds_upper: Interval in which the f_upper function is defined. check: Whether to check if f_lower is always lower than f_upper. numpoints_check: Number of points used to check whether f_lower is always lower than f_upper Returns: The surface of the intersection of the rectangle and the surface defined by f_lower and f_upper. """ x1 = np.min(rectangle[0]) x2 = np.max(rectangle[0]) y1 = np.min(rectangle[1]) y2 = np.max(rectangle[1]) # Check that f_lower is allways lower than f_upper between x1 and x2 if no bounds are given or between the bounds # of the f_lower and f_upper functions if they are given. if check: if bounds_lower is not None: if bounds_upper is not None: if not all(np.array(bounds_lower) == np.array(bounds_upper)): raise ValueError('Bounds should be identical for both f_lower and f_upper') if '<' not in function_comparison(f1=f_lower, f2=f_upper, x1=bounds_lower[0], x2=bounds_lower[1], numpoints_check=numpoints_check): raise RuntimeError('Function f_lower is not allways lower or equal to function f_upper within ' 'the domain defined by the functions bounds.') else: raise ValueError('Bounds are given for f_lower but not for f_upper') elif bounds_upper is not None: if bounds_lower is None: raise ValueError('Bounds are given for f_upper but not for f_lower') else: if '<' not in function_comparison(f1=f_lower, f2=f_upper, x1=bounds_lower[0], x2=bounds_lower[1], numpoints_check=numpoints_check): raise RuntimeError('Function f_lower is not allways lower or equal to function f_upper within ' 'the domain defined by the functions bounds.') else: if '<' not in function_comparison(f1=f_lower, f2=f_upper, x1=x1, x2=x2, numpoints_check=numpoints_check): raise RuntimeError('Function f_lower is not allways lower or equal to function f_upper within ' 'the domain defined by x1 and x2.') if bounds_lower is None: raise NotImplementedError('Bounds should be given right now ...') else: if x2 < bounds_lower[0] or x1 > bounds_lower[1]: return (0.0, 0.0) if x1 < bounds_lower[0]: xmin = bounds_lower[0] else: xmin = x1 if x2 > bounds_lower[1]: xmax = bounds_lower[1] else: xmax = x2 def diff(x): flwx = f_lower(x) fupx = f_upper(x) minup = np.min([fupx, y2 * np.ones_like(fupx)], axis=0) maxlw = np.max([flwx, y1 * np.ones_like(flwx)], axis=0) zeros = np.zeros_like(fupx) upper = np.where(y2 >= flwx, np.where(y1 <= fupx, minup, zeros), zeros) lower = np.where(y1 <= fupx, np.where(y2 >= flwx, maxlw, zeros), zeros) return upper - lower return quad(diff, xmin, xmax)
[docs]def my_solid_angle(center, coords): """ Helper method to calculate the solid angle of a set of coords from the center. Args: center: Center to measure solid angle from. coords: List of coords to determine solid angle. Returns: The solid angle. """ o = np.array(center) r = [np.array(c) - o for c in coords] r.append(r[0]) n = [np.cross(r[i + 1], r[i]) for i in range(len(r) - 1)] n.append(np.cross(r[1], r[0])) phi = 0.0 for i in range(len(n) - 1): try: value = math.acos(-np.dot(n[i], n[i + 1]) / (np.linalg.norm(n[i]) * np.linalg.norm(n[i + 1]))) except ValueError: mycos = -np.dot(n[i], n[i + 1]) / (np.linalg.norm(n[i]) * np.linalg.norm(n[i + 1])) if 0.999999999999 < mycos < 1.000000000001: value = math.acos(1.0) elif -0.999999999999 > mycos > -1.000000000001: value = math.acos(-1.0) else: raise SolidAngleError(mycos) phi += value return phi + (3 - len(r)) * math.pi
[docs]def vectorsToMatrix(aa, bb): """ Performs the vector multiplication of the elements of two vectors, constructing the 3x3 matrix. :param aa: One vector of size 3 :param bb: Another vector of size 3 :return: A 3x3 matrix M composed of the products of the elements of aa and bb : M_ij = aa_i * bb_j """ MM = np.zeros([3, 3], np.float) for ii in range(3): for jj in range(3): MM[ii, jj] = aa[ii] * bb[jj] return MM
[docs]def matrixTimesVector(MM, aa): """ :param MM: A matrix of size 3x3 :param aa: A vector of size 3 :return: A vector of size 3 which is the product of the matrix by the vector """ bb = np.zeros(3, np.float) for ii in range(3): bb[ii] = np.sum(MM[ii, :] * aa) return bb
[docs]def rotateCoords(coords, R): """ Rotate the list of points using rotation matrix R :param coords: List of points to be rotated :param R: Rotation matrix :return: List of rotated points """ newlist = list() for pp in coords: rpp = matrixTimesVector(R, pp) newlist.append(rpp) return newlist
[docs]def rotateCoordsOpt(coords, R): """ Rotate the list of points using rotation matrix R :param coords: List of points to be rotated :param R: Rotation matrix :return: List of rotated points """ return [np.dot(R, pp) for pp in coords]
[docs]def changebasis(uu, vv, nn, pps): """ For a list of points given in standard coordinates (in terms of e1, e2 and e3), returns the same list expressed in the basis (uu, vv, nn), which is supposed to be orthonormal. :param uu: First vector of the basis :param vv: Second vector of the basis :param nn: Third vector of the bais :param pps: List of points in basis (e1, e2, e3) :return: List of points in basis (uu, vv, nn) """ MM = np.zeros([3, 3], np.float) for ii in range(3): MM[ii, 0] = uu[ii] MM[ii, 1] = vv[ii] MM[ii, 2] = nn[ii] PP = np.linalg.inv(MM) newpps = list() for pp in pps: newpps.append(matrixTimesVector(PP, pp)) return newpps
[docs]def collinear(p1, p2, p3=None, tolerance=0.25): """ Checks if the three points p1, p2 and p3 are collinear or not within a given tolerance. The collinearity is checked by computing the area of the triangle defined by the three points p1, p2 and p3. If the area of this triangle is less than (tolerance x largest_triangle), then the three points are considered collinear. The largest_triangle is defined as the right triangle whose legs are the two smallest distances between the three points ie, its area is : 0.5 x (min(|p2-p1|,|p3-p1|,|p3-p2|) x secondmin(|p2-p1|,|p3-p1|,|p3-p2|)) :param p1: First point :param p2: Second point :param p3: Third point (origin [0.0, 0.0, 0.0 if not given]) :param tolerance: Area tolerance for the collinearity test (0.25 gives about 0.125 deviation from the line) :return: True if the three points are considered as collinear within the given tolerance, False otherwise """ if p3 is None: triangle_area = 0.5 * np.linalg.norm(np.cross(p1, p2)) dist = np.sort([np.linalg.norm(p2 - p1), np.linalg.norm(p1), np.linalg.norm(p2)]) else: triangle_area = 0.5 * np.linalg.norm(np.cross(p1 - p3, p2 - p3)) dist = np.sort([np.linalg.norm(p2 - p1), np.linalg.norm(p3 - p1), np.linalg.norm(p3 - p2)]) largest_triangle_area = 0.5 * dist[0] * dist[1] return triangle_area < tolerance * largest_triangle_area
[docs]def anticlockwise_sort(pps): """ Sort a list of 2D points in anticlockwise order :param pps: List of points to be sorted :return: Sorted list of points """ newpps = list() angles = np.zeros(len(pps), np.float) for ipp, pp in enumerate(pps): angles[ipp] = np.arctan2(pp[1], pp[0]) iisorted = np.argsort(angles) for ii in range(len(pps)): newpps.append(pps[iisorted[ii]]) return newpps
[docs]def anticlockwise_sort_indices(pps): """ Returns the indices that would sort a list of 2D points in anticlockwise order :param pps: List of points to be sorted :return: Indices of the sorted list of points """ angles = np.zeros(len(pps), np.float) for ipp, pp in enumerate(pps): angles[ipp] = np.arctan2(pp[1], pp[0]) return np.argsort(angles)
[docs]def sort_separation(separation): if len(separation[0]) > len(separation[2]): return [sorted(separation[2]), sorted(separation[1]), sorted(separation[0])] return [sorted(separation[0]), sorted(separation[1]), sorted(separation[2])]
[docs]def sort_separation_tuple(separation): if len(separation[0]) > len(separation[2]): return (tuple(sorted(separation[2])), tuple(sorted(separation[1])), tuple(sorted(separation[0]))) return (tuple(sorted(separation[0])), tuple(sorted(separation[1])), tuple(sorted(separation[2])))
[docs]def separation_in_list(separation_indices, separation_indices_list): """ Checks if the separation indices of a plane are already in the list :param separation_indices: list of separation indices (three arrays of integers) :param separation_indices_list: list of the list of separation indices to be compared to :return: True if the separation indices are already in the list, False otherwise """ sorted_separation = sort_separation(separation_indices) for sep in separation_indices_list: if len(sep[1]) == len(sorted_separation[1]) and np.allclose(sorted_separation[1], sep[1]): return True return False
[docs]def is_anion_cation_bond(valences, ii, jj): """ Checks if two given sites are an anion and a cation. :param valences: list of site valences :param ii: index of a site :param jj: index of another site :return: True if one site is an anion and the other is a cation (from the valences) """ if valences == 'undefined': return True if valences[ii] == 0 or valences[jj] == 0: return True return (valences[ii] > 0 > valences[jj]) or (valences[jj] > 0 > valences[ii])
[docs]class Plane: """ Class used to describe a plane """ TEST_2D_POINTS = [np.array([0, 0], np.float), np.array([1, 0], np.float), np.array([0, 1], np.float), np.array([-1, 0], np.float), np.array([0, -1], np.float), np.array([0, 2], np.float), np.array([2, 0], np.float), np.array([0, -2], np.float), np.array([-2, 0], np.float), np.array([1, 1], np.float), np.array([2, 2], np.float), np.array([-1, -1], np.float), np.array([-2, -2], np.float), np.array([1, 2], np.float), np.array([1, -2], np.float), np.array([-1, 2], np.float), np.array([-1, -2], np.float), np.array([2, 1], np.float), np.array([2, -1], np.float), np.array([-2, 1], np.float), np.array([-2, -1], np.float)] def __init__(self, coefficients, p1=None, p2=None, p3=None): """ Initializes a plane from the 4 coefficients a, b, c and d of ax + by + cz + d = 0 :param coefficients: abcd coefficients of the plane """ # Initializes the normal vector self.normal_vector = np.array([coefficients[0], coefficients[1], coefficients[2]], np.float) normv = np.linalg.norm(self.normal_vector) self.normal_vector /= normv nonzeros = np.argwhere(self.normal_vector != 0.0).flatten() zeros = list(set(range(3)) - set(nonzeros)) if len(nonzeros) == 0: raise ValueError("Normal vector is equal to 0.0") if self.normal_vector[nonzeros[0]] < 0.0: self.normal_vector = -self.normal_vector dd = -np.float(coefficients[3]) / normv else: dd = np.float(coefficients[3]) / normv self._coefficients = np.array([self.normal_vector[0], self.normal_vector[1], self.normal_vector[2], dd], np.float) self._crosses_origin = np.isclose(dd, 0.0, atol=1e-7, rtol=0.0) self.p1 = p1 self.p2 = p2 self.p3 = p3 # Initializes 3 points belonging to the plane (useful for some methods) if self.p1 is None: self.init_3points(nonzeros, zeros) self.vector_to_origin = dd * self.normal_vector self.e1 = None self.e2 = None self.e3 = self.normal_vector
[docs] def init_3points(self, nonzeros, zeros): if len(nonzeros) == 3: self.p1 = np.array([-self.d / self.a, 0.0, 0.0], np.float) self.p2 = np.array([0.0, -self.d / self.b, 0.0], np.float) self.p3 = np.array([0.0, 0.0, -self.d / self.c], np.float) elif len(nonzeros) == 2: self.p1 = np.zeros(3, np.float) self.p1[nonzeros[1]] = -self.d / self.coefficients[nonzeros[1]] self.p2 = np.array(self.p1) self.p2[zeros[0]] = 1.0 self.p3 = np.zeros(3, np.float) self.p3[nonzeros[0]] = -self.d / self.coefficients[nonzeros[0]] elif len(nonzeros) == 1: self.p1 = np.zeros(3, np.float) self.p1[nonzeros[0]] = -self.d / self.coefficients[nonzeros[0]] self.p2 = np.array(self.p1) self.p2[zeros[0]] = 1.0 self.p3 = np.array(self.p1) self.p3[zeros[1]] = 1.0
def __str__(self): """ String representation of the Plane object :return: String representation of the Plane object """ outs = ['Plane object'] outs.append(' => Normal vector : {nn}'.format(nn=self.normal_vector)) outs.append(' => Equation of the plane ax + by + cz + d = 0') outs.append(' with a = {v}'.format(v=self._coefficients[0])) outs.append(' b = {v}'.format(v=self._coefficients[1])) outs.append(' c = {v}'.format(v=self._coefficients[2])) outs.append(' d = {v}'.format(v=self._coefficients[3])) return '\n'.join(outs)
[docs] def is_in_plane(self, pp, dist_tolerance): """ Determines if point pp is in the plane within the tolerance dist_tolerance :param pp: point to be tested :param dist_tolerance: tolerance on the distance to the plane within which point pp is considered in the plane :return: True if pp is in the plane, False otherwise """ return np.abs(np.dot(self.normal_vector, pp) + self._coefficients[3]) <= dist_tolerance
[docs] def is_same_plane_as(self, plane): """ Checks whether the plane is identical to another Plane "plane" :param plane: Plane to be compared to :return: True if the two facets are identical, False otherwise """ return np.allclose(self._coefficients, plane.coefficients)
[docs] def is_in_list(self, plane_list): """ Checks whether the plane is identical to one of the Planes in the plane_list list of Planes :param plane_list: List of Planes to be compared to :return: True if the plane is in the list, False otherwise """ for plane in plane_list: if self.is_same_plane_as(plane): return True return False
[docs] def indices_separate(self, points, dist_tolerance): """ Returns three lists containing the indices of the points lying on one side of the plane, on the plane and on the other side of the plane. The dist_tolerance parameter controls the tolerance to which a point is considered to lie on the plane or not (distance to the plane) :param points: list of points :param dist_tolerance: tolerance to which a point is considered to lie on the plane or not (distance to the plane) :return: The lists of indices of the points on one side of the plane, on the plane and on the other side of the plane """ side1 = list() inplane = list() side2 = list() for ip, pp in enumerate(points): if self.is_in_plane(pp, dist_tolerance): inplane.append(ip) else: if np.dot(pp + self.vector_to_origin, self.normal_vector) < 0.0: side1.append(ip) else: side2.append(ip) return [side1, inplane, side2]
[docs] def distance_to_point(self, point): """ Computes the absolute distance from the plane to the point :param point: Point for which distance is computed :return: Distance between the plane and the point """ return np.abs(np.dot(self.normal_vector, point) + self.d)
[docs] def distances(self, points): """ Computes the distances from the plane to each of the points. Positive distances are on the side of the normal of the plane while negative distances are on the other side :param points: Points for which distances are computed :return: Distances from the plane to the points (positive values on the side of the normal to the plane, negative values on the other side) """ return [np.dot(self.normal_vector, pp) + self.d for pp in points]
[docs] def distances_indices_sorted(self, points, sign=False): """ Computes the distances from the plane to each of the points. Positive distances are on the side of the normal of the plane while negative distances are on the other side. Indices sorting the points from closest to furthest is also computed. :param points: Points for which distances are computed :param sign: Whether to add sign information in the indices sorting the points distances :return: Distances from the plane to the points (positive values on the side of the normal to the plane, negative values on the other side), as well as indices of the points from closest to furthest. For the latter, when the sign parameter is True, items of the sorting list are given as tuples of (index, sign). """ distances = [np.dot(self.normal_vector, pp) + self.d for pp in points] indices = sorted(range(len(distances)), key=lambda k: np.abs(distances[k])) if sign: indices = [(ii, int(np.sign(distances[ii]))) for ii in indices] return distances, indices
[docs] def distances_indices_groups(self, points, delta=None, delta_factor=0.05, sign=False): """ Computes the distances from the plane to each of the points. Positive distances are on the side of the normal of the plane while negative distances are on the other side. Indices sorting the points from closest to furthest is also computed. Grouped indices are also given, for which indices of the distances that are separated by less than delta are grouped together. The delta parameter is either set explictly or taken as a fraction (using the delta_factor parameter) of the maximal point distance. :param points: Points for which distances are computed :param delta: Distance interval for which two points are considered in the same group. :param delta_factor: If delta is None, the distance interval is taken as delta_factor times the maximal point distance. :param sign: Whether to add sign information in the indices sorting the points distances :return: Distances from the plane to the points (positive values on the side of the normal to the plane, negative values on the other side), as well as indices of the points from closest to furthest and grouped indices of distances separated by less than delta. For the sorting list and the grouped indices, when the sign parameter is True, items are given as tuples of (index, sign). """ distances, indices = self.distances_indices_sorted(points=points) if delta is None: delta = delta_factor * np.abs(distances[indices[-1]]) iends = [ii for ii, idist in enumerate(indices, start=1) if ii == len(distances) or (np.abs(distances[indices[ii]]) - np.abs(distances[idist]) > delta)] if sign: indices = [(ii, int(np.sign(distances[ii]))) for ii in indices] grouped_indices = [indices[iends[ii - 1]:iend] if ii > 0 else indices[:iend] for ii, iend in enumerate(iends)] return distances, indices, grouped_indices
[docs] def projectionpoints(self, pps): """ Projects each points in the point list pps on plane and returns the list of projected points :param pps: List of points to project on plane :return: List of projected point on plane """ return [pp - np.dot(pp - self.p1, self.normal_vector) * self.normal_vector for pp in pps]
[docs] def orthonormal_vectors(self): """ Returns a list of three orthogonal vectors, the two first being parallel to the plane and the third one is the normal vector of the plane :return: List of orthogonal vectors :raise: ValueError if all the coefficients are zero or if there is some other strange error """ if self.e1 is None: diff = self.p2 - self.p1 self.e1 = diff / norm(diff) self.e2 = np.cross(self.e3, self.e1) return [self.e1, self.e2, self.e3]
[docs] def orthonormal_vectors_old(self): """ Returns a list of three orthogonal vectors, the two first being parallel to the plane and the third one is the normal vector of the plane :return: List of orthogonal vectors :raise: ValueError if all the coefficients are zero or if there is some other strange error """ if self.e1 is None: imax = np.argmax(np.abs(self.normal_vector)) if imax == 0: self.e1 = np.array([self.e3[1], -self.e3[0], 0.0]) / np.sqrt(self.e3[0] ** 2 + self.e3[1] ** 2) elif imax == 1: self.e1 = np.array([0.0, self.e3[2], -self.e3[1]]) / np.sqrt(self.e3[1] ** 2 + self.e3[2] ** 2) elif imax == 2: self.e1 = np.array([-self.e3[2], 0.0, self.e3[0]]) / np.sqrt(self.e3[0] ** 2 + self.e3[2] ** 2) else: raise ValueError('Only three values in the normal vector, should not be here ...') self.e2 = np.cross(self.e3, self.e1) return [self.e1, self.e2, self.e3]
[docs] def project_and_to2dim_ordered_indices(self, pps, plane_center='mean'): """ Projects each points in the point list pps on plane and returns the indices that would sort the list of projected points in anticlockwise order :param pps: List of points to project on plane :return: List of indices that would sort the list of projected points """ pp2d = self.project_and_to2dim(pps, plane_center) return anticlockwise_sort_indices(pp2d)
[docs] def project_and_to2dim(self, pps, plane_center): """ Projects the list of points pps to the plane and changes the basis from 3D to the 2D basis of the plane :param pps: List of points to be projected :return: :raise: """ proj = self.projectionpoints(pps) [u1, u2, u3] = self.orthonormal_vectors() PP = np.array([[u1[0], u2[0], u3[0]], [u1[1], u2[1], u3[1]], [u1[2], u2[2], u3[2]]]) xypps = list() for pp in proj: xyzpp = np.dot(pp, PP) xypps.append(xyzpp[0:2]) if str(plane_center) == str('mean'): mean = np.zeros(2, np.float) for pp in xypps: mean += pp mean /= len(xypps) xypps = [pp - mean for pp in xypps] elif plane_center is not None: projected_plane_center = self.projectionpoints([plane_center])[0] xy_projected_plane_center = np.dot(projected_plane_center, PP)[0:2] xypps = [pp - xy_projected_plane_center for pp in xypps] return xypps
[docs] def fit_error(self, points, fit='least_square_distance'): if fit == 'least_square_distance': return self.fit_least_square_distance_error(points) if fit == 'maximum_distance': return self.fit_maximum_distance_error(points)
[docs] def fit_least_square_distance_error(self, points): return np.sum([self.distance_to_point(pp) ** 2.0 for pp in points])
[docs] def fit_maximum_distance_error(self, points): return np.max([self.distance_to_point(pp) for pp in points])
@property def coefficients(self): return np.copy(self._coefficients) @property def abcd(self): return self._coefficients[0], self._coefficients[1], self._coefficients[2], self._coefficients[3] @property def a(self): return self._coefficients[0] @property def b(self): return self._coefficients[1] @property def c(self): return self._coefficients[2] @property def d(self): return self._coefficients[3] @property def distance_to_origin(self): return self._coefficients[3] @property def crosses_origin(self): return self._crosses_origin
[docs] @classmethod def from_2points_and_origin(cls, p1, p2): return cls.from_3points(p1, p2, np.zeros(3))
[docs] @classmethod def from_3points(cls, p1, p2, p3): nn = np.cross(p1 - p3, p2 - p3) normal_vector = nn / norm(nn) nonzeros = np.argwhere(normal_vector != 0.0) if normal_vector[nonzeros[0, 0]] < 0.0: normal_vector = -normal_vector dd = - np.dot(normal_vector, p1) coefficients = np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], np.float) return cls(coefficients, p1=p1, p2=p2, p3=p3)
[docs] @classmethod def from_npoints(cls, points, best_fit='least_square_distance'): if len(points) == 2: return cls.from_2points_and_origin(points[0], points[1]) if len(points) == 3: return cls.from_3points(points[0], points[1], points[2]) if best_fit == 'least_square_distance': return cls.from_npoints_least_square_distance(points) elif best_fit == 'maximum_distance': return cls.from_npoints_maximum_distance(points)
[docs] @classmethod def from_npoints_least_square_distance(cls, points): mean_point = np.array([sum([pp[ii] for pp in points]) for ii in range(3)], np.float) mean_point /= len(points) AA = np.zeros((len(points), 3), np.float) for ii, pp in enumerate(points): for jj in range(3): AA[ii, jj] = pp[jj] - mean_point[jj] [UU, SS, Vt] = np.linalg.svd(AA) imin = np.argmin(SS) normal_vector = Vt[imin] nonzeros = np.argwhere(normal_vector != 0.0) if normal_vector[nonzeros[0, 0]] < 0.0: normal_vector = -normal_vector dd = - np.dot(normal_vector, mean_point) coefficients = np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], np.float) return cls(coefficients)
[docs] @classmethod def perpendicular_bisector(cls, p1, p2): middle_point = 0.5 * (p1 + p2) normal_vector = p2 - p1 dd = -np.dot(normal_vector, middle_point) return cls(np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], np.float))
[docs] @classmethod def from_npoints_maximum_distance(cls, points): convex_hull = ConvexHull(points) heights = [] ipoints_heights = [] for isimplex, simplex in enumerate(convex_hull.simplices): cc = convex_hull.equations[isimplex] plane = Plane.from_coefficients(cc[0], cc[1], cc[2], cc[3]) distances = [plane.distance_to_point(pp) for pp in points] ipoint_height = np.argmax(distances) heights.append(distances[ipoint_height]) ipoints_heights.append(ipoint_height) imin_height = np.argmin(heights) normal_vector = convex_hull.equations[imin_height, 0:3] cc = convex_hull.equations[imin_height] highest_point = points[ipoints_heights[imin_height]] middle_point = (Plane.from_coefficients(cc[0], cc[1], cc[2], cc[3]).projectionpoints([highest_point])[0] + highest_point) / 2 dd = - np.dot(normal_vector, middle_point) return cls(np.array([normal_vector[0], normal_vector[1], normal_vector[2], dd], np.float))
[docs] @classmethod def from_coefficients(cls, a, b, c, d): return cls(np.array([a, b, c, d], np.float))