Source code for dynamo.vectorfield.Ao

import numpy as np
from scipy.optimize import least_squares
import scipy.sparse as sp
from import squareform, condensed_idx_to_squareform_idx, timeit
from tqdm import tqdm

# from scPotential import show_landscape

def f_left(X, F):
    """An auxiliary function for fast computation of F.X - (F.X)^T"""
    R =
    return R - R.T

def f_left_jac(q, F):
    Analytical Jacobian of f(Q) = F.Q - (F.Q)^T, where Q is
    an anti-symmetric matrix s.t. Q^T = -Q.
    J = np.zeros((, len(q)))
    for i in range(len(q)):
        jac = np.zeros(F.shape)
        a, b = condensed_idx_to_squareform_idx(len(q), i)
        jac[:, b] = F[:, a]
        jac[:, a] = -F[:, b]
        jac[b, :] -= F[:, a]
        jac[a, :] -= -F[:, b]
        J[:, i] = jac.flatten()
    return J

def solveQ(D, F, q0=None, debug=False, precompute_jac=True, **kwargs):
    """Function to solve for the anti-symmetric Q matrix in the equation:
        F.Q - (F.Q)^T = F.D - (F.D)^T
    using least squares.

        D: :class:`~numpy.ndarray`
            A symmetric diffusion matrix.
        F: :class:`~numpy.ndarray`
            Jacobian of the vector field function evaluated at a particular point.
        debug: bool
            Whether additional info of the solution is returned.
        precompute_jac: bool
            Whether the analytical Jacobian is precomputed for the optimizer.

        Q: :class:`~numpy.ndarray`
            The solved anti-symmetric Q matrix.
        C: :class:`~numpy.ndarray`
            The right-hand side of the equation to be solved.

    n = D.shape[0]
    m = int(n * (n - 1) / 2)
    C = f_left(D, F)
    f_obj = lambda q: (f_left(squareform(q, True), F) - C).flatten()
    q0 = np.ones(m, dtype=float) if q0 is None else q0
    if precompute_jac:
        J = f_left_jac(q0, F)
        f_jac = lambda q: J
        f_jac = "2-point"
    sol = least_squares(f_obj, q0, jac=f_jac, **kwargs)
    Q = squareform(sol.x, True)
    if debug:
        C_left = f_left(Q, F)
        return Q, C, C_left, sol.cost
        return Q, C

[docs]def Ao_pot_map(vecFunc, X, D=None, **kwargs): """Mapping potential landscape with the algorithm developed by Ao method. References: Potential in stochastic differential equations: novel construction. Journal of physics A: mathematical and general, Ao Ping, 2004 Parameters ---------- vecFunc: `function` The vector field function X: :class:`~numpy.ndarray` A n_cell x n_dim matrix of coordinates where the potential function is evaluated. D: None or :class:`~numpy.ndarray` Diffusion matrix. It must be a square matrix with size corresponds to the number of columns (features) in the X matrix. Returns ------- X: :class:`~numpy.ndarray` A matrix storing the x-coordinates on the two-dimesional grid. U: :class:`~numpy.ndarray` A matrix storing the potential value at each position. P: :class:`~numpy.ndarray` Steady state distribution or the Boltzmann-Gibbs distribution for the state variable. vecMat: list List velocity vector at each position from X. S: list List of constant symmetric and semi-positive matrix or friction matrix, corresponding to the divergence part, at each position from X. A: list List of constant antisymmetric matrix or transverse matrix, corresponding to the curl part, at each position from X. """ import numdifftools as nda nobs, ndim = X.shape D = 0.1 * np.eye(ndim) if D is None else D U = np.zeros((nobs, 1)) vecMat, S, A = [None] * nobs, [None] * nobs, [None] * nobs for i in range(nobs): X_s = X[i, :] F = nda.Jacobian(vecFunc)(X_s) Q, _ = solveQ(D, F, **kwargs) H = np.linalg.inv(D + Q).dot(F) U[i] = -0.5 * vecMat[i] = vecFunc(X_s) S[i], A[i] = ( (np.linalg.inv(D + Q) + np.linalg.inv((D + Q).T)) / 2, (np.linalg.inv(D + Q) - np.linalg.inv((D + Q).T)) / 2, ) P = np.exp(-U) P = P / np.sum(P) return X, U, P, vecMat, S, A
def Ao_pot_map_jac(fjac, X, D=None, **kwargs): nobs, ndim = X.shape if D is None: D = 0.1 * np.eye(ndim) elif np.isscalar(D): D = D * np.eye(ndim) U = np.zeros((nobs, 1)) m = int(ndim * (ndim - 1) / 2) q0 = np.ones(m) * np.mean(np.diag(D)) * 1000 for i in tqdm(range(nobs), "Calc Ao potential"): X_s = X[i, :] F = fjac(X_s) Q, _ = solveQ(D, F, q0=q0, **kwargs) H = np.linalg.inv(D + Q).dot(F) U[i] = -0.5 * return U.flatten()