pymatgen.analysis.chemenv.utils.math_utils — pymatgen 2020.7.3 documentation

# Source code for pymatgen.analysis.chemenv.utils.math_utils

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

    """
    This module contains some math utils 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"

    from math import sqrt

    import numpy as np
    from scipy.special import erf
    from functools import reduce

    ##############################################################
    # cartesian product of lists ##################################
    ##############################################################

    def _append_es2sequences(sequences, es):
        result = []
        if not sequences:
            for e in es:
                result.append([e])
        else:
            for e in es:
                result += [seq + [e] for seq in sequences]
        return result

    def _cartesian_product(lists):
        """
        given a list of lists,
        returns all the possible combinations taking one element from each list
        The list does not have to be of equal length
        """
        return reduce(_append_es2sequences, lists, [])

    def prime_factors(n):
        """Lists prime factors of a given natural integer, from greatest to smallest
        :param n: Natural integer
        :rtype : list of all prime factors of the given natural n
        """
        i = 2
        while i <= sqrt(n):
            if n % i == 0:
                l = prime_factors(n / i)
                l.append(i)
                return l
            i += 1
        return [n]  # n is prime

    def _factor_generator(n):
        """
        From a given natural integer, returns the prime factors and their multiplicity
        :param n: Natural integer
        :return:
        """
        p = prime_factors(n)
        factors = {}
        for p1 in p:
            try:
                factors[p1] += 1
            except KeyError:
                factors[p1] = 1
        return factors

    def divisors(n):
        """
        From a given natural integer, returns the list of divisors in ascending order
        :param n: Natural integer
        :return: List of divisors of n in ascending order
        """
        factors = _factor_generator(n)
        _divisors = []
        listexponents = [[k ** x for x in range(0, factors[k] + 1)] for k in list(factors.keys())]
        listfactors = _cartesian_product(listexponents)
        for f in listfactors:
            _divisors.append(reduce(lambda x, y: x * y, f, 1))
        _divisors.sort()
        return _divisors

    def get_center_of_arc(p1, p2, radius):
        dx = p2[0] - p1[0]
        dy = p2[1] - p1[1]
        dd = np.sqrt(dx * dx + dy * dy)
        radical = np.power((radius / dd), 2) - 0.25
        if radical < 0:
            raise ValueError("Impossible to find center of arc because the arc is ill-defined")
        tt = np.sqrt(radical)
        if radius > 0:
            tt = -tt
        return (p1[0] + p2[0]) / 2 - tt * dy, (p1[1] + p2[1]) / 2 + tt * dx

    def get_linearly_independent_vectors(vectors_list):
        independent_vectors_list = []
        for vector in vectors_list:
            if np.any(vector != 0):
                if len(independent_vectors_list) == 0:
                    independent_vectors_list.append(np.array(vector))
                elif len(independent_vectors_list) == 1:
                    rank = np.linalg.matrix_rank(np.array([independent_vectors_list[0], vector, [0, 0, 0]]))
                    if rank == 2:
                        independent_vectors_list.append(np.array(vector))
                elif len(independent_vectors_list) == 2:
                    mm = np.array([independent_vectors_list[0], independent_vectors_list[1], vector])
                    if np.linalg.det(mm) != 0:
                        independent_vectors_list.append(np.array(vector))
            if len(independent_vectors_list) == 3:
                break
        return independent_vectors_list

    def scale_and_clamp(xx, edge0, edge1, clamp0, clamp1):
        return np.clip((xx - edge0) / (edge1 - edge0), clamp0, clamp1)

    # Step function based on the cumulative distribution function of the normal law
    def normal_cdf_step(xx, mean, scale):
        return 0.5 * (1.0 + erf((xx - mean) / (np.sqrt(2.0) * scale)))

    # SMOOTH STEP FUNCTIONS
    # Set of smooth step functions that allow to smoothly go from y = 0.0 (1.0) to y = 1.0 (0.0) by changing x
    # from 0.0 to 1.0 respectively when inverse is False (True).
    # (except if edges is given in which case a the values are first scaled and clamped to the interval given by edges)
    # The derivative at x = 0.0 and x = 1.0 have to be 0.0

    def smoothstep(xx, edges=None, inverse=False):
        if edges is None:
            xx_clipped = np.clip(xx, 0.0, 1.0)
            if inverse:
                return 1.0 - xx_clipped * xx_clipped * (3.0 - 2.0 * xx_clipped)
            else:
                return xx_clipped * xx_clipped * (3.0 - 2.0 * xx_clipped)
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return smoothstep(xx_scaled_and_clamped, inverse=inverse)

    def smootherstep(xx, edges=None, inverse=False):
        if edges is None:
            xx_clipped = np.clip(xx, 0.0, 1.0)
            if inverse:
                return 1.0 - xx_clipped * xx_clipped * xx_clipped * (xx_clipped * (xx_clipped * 6 - 15) + 10)
            else:
                return xx_clipped * xx_clipped * xx_clipped * (xx_clipped * (xx_clipped * 6 - 15) + 10)
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return smootherstep(xx_scaled_and_clamped, inverse=inverse)

    def cosinus_step(xx, edges=None, inverse=False):
        if edges is None:
            xx_clipped = np.clip(xx, 0.0, 1.0)
            if inverse:
                return (np.cos(xx_clipped * np.pi) + 1.0) / 2.0
            else:
                return 1.0 - (np.cos(xx_clipped * np.pi) + 1.0) / 2.0
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return cosinus_step(xx_scaled_and_clamped, inverse=inverse)

    def power3_step(xx, edges=None, inverse=False):
        return smoothstep(xx, edges=edges, inverse=inverse)

    def powern_parts_step(xx, edges=None, inverse=False, nn=2):
        if edges is None:
            aa = np.power(0.5, 1.0 - nn)
            xx_clipped = np.clip(xx, 0.0, 1.0)
            if np.mod(nn, 2) == 0:
                if inverse:
                    return 1.0 - np.where(xx_clipped < 0.5, aa * np.power(xx_clipped, nn),
                                          1.0 - aa * np.power(xx_clipped - 1.0, nn))
                else:
                    return np.where(xx_clipped < 0.5, aa * np.power(xx_clipped, nn),
                                    1.0 - aa * np.power(xx_clipped - 1.0, nn))
            else:
                if inverse:
                    return 1.0 - np.where(xx_clipped < 0.5, aa * np.power(xx_clipped, nn),
                                          1.0 + aa * np.power(xx_clipped - 1.0, nn))
                else:
                    return np.where(xx_clipped < 0.5, aa * np.power(xx_clipped, nn),
                                    1.0 + aa * np.power(xx_clipped - 1.0, nn))
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return powern_parts_step(xx_scaled_and_clamped, inverse=inverse, nn=nn)

    # FINITE DECREASING FUNCTIONS
    # Set of decreasing functions that allow to smoothly go from y = 1.0 to y = 0.0 by changing x from 0.0 to 1.0
    # The derivative at x = 1.0 has to be 0.0

    def powern_decreasing(xx, edges=None, nn=2):
        if edges is None:
            aa = 1.0 / np.power(-1.0, nn)
            return aa * np.power(xx - 1.0, nn)
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return powern_decreasing(xx_scaled_and_clamped, nn=nn)

    def power2_decreasing_exp(xx, edges=None, alpha=1.0):
        if edges is None:
            aa = 1.0 / np.power(-1.0, 2)
            return aa * np.power(xx - 1.0, 2) * np.exp(-alpha * xx)
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return power2_decreasing_exp(xx_scaled_and_clamped, alpha=alpha)

    # INFINITE TO FINITE DECREASING FUNCTIONS
    # Set of decreasing functions that allow to smoothly go from y = + Inf to y = 0.0 by changing x from 0.0 to 1.0
    # The derivative at x = 1.0 has to be 0.0

    def power2_tangent_decreasing(xx, edges=None, prefactor=None):
        if edges is None:
            if prefactor is None:
                aa = 1.0 / np.power(-1.0, 2)
            else:
                aa = prefactor
            return -aa * np.power(xx - 1.0, 2) * np.tan((xx - 1.0) * np.pi / 2.0)
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return power2_tangent_decreasing(xx_scaled_and_clamped, prefactor=prefactor)

    def power2_inverse_decreasing(xx, edges=None, prefactor=None):
        if edges is None:
            if prefactor is None:
                aa = 1.0 / np.power(-1.0, 2)
            else:
                aa = prefactor
            return np.where(np.isclose(xx, 0.0), aa * float("inf"), aa * np.power(xx - 1.0, 2) / xx)
            # return aa * np.power(xx-1.0, 2) / xx if xx != 0 else aa * float("inf")
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return power2_inverse_decreasing(xx_scaled_and_clamped, prefactor=prefactor)

    def power2_inverse_power2_decreasing(xx, edges=None, prefactor=None):
        if edges is None:
            if prefactor is None:
                aa = 1.0 / np.power(-1.0, 2)
            else:
                aa = prefactor
            return np.where(np.isclose(xx, 0.0), aa * float("inf"), aa * np.power(xx - 1.0, 2) / xx ** 2.0)
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return power2_inverse_power2_decreasing(xx_scaled_and_clamped, prefactor=prefactor)

    def power2_inverse_powern_decreasing(xx, edges=None, prefactor=None, powern=2.0):
        if edges is None:
            if prefactor is None:
                aa = 1.0 / np.power(-1.0, 2)
            else:
                aa = prefactor
            return aa * np.power(xx - 1.0, 2) / xx ** powern
        else:
            xx_scaled_and_clamped = scale_and_clamp(xx, edges[0], edges[1], 0.0, 1.0)
            return power2_inverse_powern_decreasing(xx_scaled_and_clamped, prefactor=prefactor, powern=powern)