Chimie Théorique » QMX

import numpy as np

from math import sqrt, exp

def tri2full(H_nn, UL='L'):

    """Fill in values of hermitian matrix.

    Fill values in lower or upper triangle of H_nn based on the opposite

    triangle, such that the resulting matrix is symmetric/hermitian.

    UL='U' will copy (conjugated) values from upper triangle into the

    lower triangle.

    UL='L' will copy (conjugated) values from lower triangle into the

    upper triangle.

    """

    N, tmp = H_nn.shape

    assert N == tmp, 'Matrix must be square'

    #assert np.isreal(H_nn.diagonal()).all(), 'Diagonal should be real'

    if UL != 'L':

        H_nn = H_nn.T

    for n in range(N - 1):

        H_nn[n, n + 1:] = H_nn[n + 1:, n].conj()

def dagger(matrix):

    return np.conj(matrix.T)

def rotate_matrix(h, u):

    return np.dot(u.T.conj(), np.dot(h, u))

def get_subspace(matrix, index):

    """Get the subspace spanned by the basis function listed in index"""

    assert matrix.ndim == 2 and matrix.shape[0] == matrix.shape[1]

    return matrix.take(index, 0).take(index, 1)

permute_matrix = get_subspace

def normalize(matrix, S=None):

    """Normalize column vectors.

    ::

      <matrix[:,i]| S |matrix[:,i]> = 1

    """

    for col in matrix.T:

        if S is None:

            col /= np.linalg.norm(col)

        else:

            col /= np.sqrt(np.dot(col.conj(), np.dot(S, col)))

def subdiagonalize(h_ii, s_ii, index_j):

    nb = h_ii.shape[0]

    nb_sub = len(index_j)

    h_sub_jj = get_subspace(h_ii, index_j)

    s_sub_jj = get_subspace(s_ii, index_j)

    e_j, v_jj = np.linalg.eig(np.linalg.solve(s_sub_jj, h_sub_jj))

    normalize(v_jj, s_sub_jj) # normalize: <v_j|s|v_j> = 1

    permute_list = np.argsort(e_j.real)

    e_j = np.take(e_j, permute_list)

    v_jj = np.take(v_jj, permute_list, axis=1)

    #setup transformation matrix

    c_ii = np.identity(nb, complex)

    for i in xrange(nb_sub):

        for j in xrange(nb_sub):

            c_ii[index_j[i], index_j[j]] = v_jj[i, j]

    h1_ii = rotate_matrix(h_ii, c_ii)

    s1_ii = rotate_matrix(s_ii, c_ii)

    return h1_ii, s1_ii, c_ii, e_j

def cutcoupling(h, s, index_n):

    for i in index_n:

        s[:, i] = 0.0

        s[i, :] = 0.0

        s[i, i] = 1.0

        Ei = h[i, i]

        h[:, i] = 0.0

        h[i, :] = 0.0

        h[i, i] = Ei

def fermidistribution(energy, kt):

    #fermi level is fixed to zero

    return 1.0 / (1.0 + np.exp(energy / kt) )

def fliplr(a):

    length=len(a)

    b = [0] * length

    for i in range(length):

        b[i] = a[length - i - 1]

    return b

def plot_path(energy):

    import pylab

    pylab.plot(np.real(energy), np.imag(energy), 'b--o')

    pylab.show()

def function_integral(function, calcutype):

    #return the integral of the 'function' on 'intrange'

    #the function can be a value or a matrix, arg1,arg2 are the possible

    #parameters of the function

    intctrl = function.intctrl

    if calcutype == 'eqInt':

        intrange = intctrl.eqintpath

        tol = intctrl.eqinttol

        if hasattr(function.intctrl, 'eqpath_radius'):

            radius = function.intctrl.eqpath_radius

        else:

            radius = -1

        if hasattr(function.intctrl, 'eqpath_origin'):

            origin = function.intctrl.eqpath_origin

        else:

            origin = 1000

    elif calcutype == 'neInt':

        intrange = intctrl.neintpath

        tol = intctrl.neinttol

        radius = -1

        origin = 1000

    elif calcutype == 'locInt':

        intrange = intctrl.locintpath

        tol = intctrl.locinttol

        if hasattr(function.intctrl, 'locpath_radius'):

            radius = function.intctrl.locpath_radius

        else:

            radius = -1

        if hasattr(function.intctrl, 'locpath_origin'):

            origin = function.intctrl.locpath_origin

        else:

            origin = 1000

    trace = 0

    a = 0.

    b = 1.

    #Initialize with 13 function evaluations.

    c = (a + b) / 2

    h = (b - a) / 2

    realmin = 2e-17

    s = [.942882415695480, sqrt(2.0/3),

         .641853342345781, 1/sqrt(5.0), .236383199662150]

    s1 = [0] * len(s)

    s2 = [0] * len(s)

    for i in range(len(s)):

        s1[i] = c - s[i] * h

        s2[i] = c + fliplr(s)[i] * h

    x0 = [a] + s1 + [c] + s2 + [b]

    s0 = [.0158271919734802, .094273840218850, .155071987336585,

          .188821573960182,  .199773405226859, .224926465333340]

    w0 = s0 + [.242611071901408] + fliplr(s0)

    w1 = [1, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 0, 1]

    w2 = [77, 0, 432, 0, 625, 0, 672, 0, 625, 0, 432, 0, 77]

    for i in range(len(w1)):

        w1[i] = w1[i] / 6.0

        w2[i] = w2[i] / 1470.0

    dZ = [intrange[:len(intrange) - 1], intrange[1:]]

    hmin = [0] * len(dZ[1])

    path_type = []

    for i in range(len(intrange) - 1):

        rs = np.abs(dZ[0][i] - origin)

        re = np.abs(dZ[1][i] - origin)

        if abs(rs - radius) < 1.0e-8 and abs(re - radius) < 1.0e-8:

            path_type.append('half_circle')

        else:

            path_type.append('line')

    for i in range(len(dZ[1])):

        if path_type[i] == 'half_circle':

            dZ[0][i] = 0

            dZ[1][i] = np.pi

    for i in range(len(dZ[1])):

        dZ[1][i] = dZ[1][i] - dZ[0][i]

        hmin[i] = realmin / 1024 * abs(dZ[1][i])

    temp = np.array([[1] * 13, x0]).transpose()

    Zx = np.dot(temp, np.array(dZ))

    Zxx = []

    for i in range(len(intrange) - 1):

        for j in range(13):

            Zxx.append(Zx[j][i])

    ns = 0

    ne = 12

    if path_type[0] == 'line':

        yns = function.calgfunc(Zxx[ns], calcutype)

    elif path_type[0] == 'half_circle':

        energy = origin + radius * np.exp((np.pi - Zxx[ns + i]) * 1.j)

        yns = -1.j * radius * np.exp(-1.j* Zxx[ns +i])* function.calgfunc(energy, calcutype)

    fcnt = 0

    for n in range(len(intrange)-1):

        # below evaluate the integral and adjust the tolerance

        Q1pQ0 = yns * (w1[0] - w0[0])

        Q2pQ0 = yns * (w2[0] - w0[0])

        fcnt = fcnt + 12

        for i in range(1,12):

            if path_type[n] == 'line':

                yne = function.calgfunc(Zxx[ns + i], calcutype)

            elif path_type[n] == 'half_circle':

                energy = origin + radius * np.exp((np.pi -Zxx[ns + i]) * 1.j)

                yne = -1.j * radius * np.exp(-1.j * Zxx[ns + i])* function.calgfunc(energy, calcutype)

            Q1pQ0 += yne * (w1[i] - w0[i])

            Q2pQ0 += yne * (w2[i] - w0[i])

        # Increase the tolerance if refinement appears to be effective

        r = np.abs(Q2pQ0) / (np.abs(Q1pQ0) + np.abs(realmin))

        dim = np.product(r.shape)

        r = np.sum(r) / dim

        if r > 0 and r < 1:

            thistol = tol / r

        else:

            thistol = tol

        if path_type[n] == 'line':

            yne = function.calgfunc(Zxx[ne], calcutype)

        elif path_type[n] == 'half_circle':

            energy = origin + radius * np.exp((np.pi -Zxx[ne]) * 1.j)

            yne = -1.j * radius * np.exp(-1.j * Zxx[ne])* function.calgfunc(energy, calcutype)

        #Call the recursive core integrator

        Qk, xpk, wpk, fcnt, warn = quadlstep(function, Zxx[ns],

                                            Zxx[ne], yns, yne,

                                            thistol, trace, fcnt,

                                            hmin[n], calcutype, path_type[n],

                                            origin, radius)

        if n == 0:

            Q = np.copy(Qk)

            Xp = xpk[:]

            Wp = wpk[:]

        else:

            Q += Qk

            Xp = Xp[:-1] + xpk

            Wp = Wp[:-1] + [Wp[-1] + wpk[0]] + wpk[1:]

        if warn == 1:

            print 'warning: Minimum step size reached,singularity possible'

        elif warn == 2:

            print 'warning: Maximum function count excced; singularity likely'

        elif warn == 3:

            print 'warning: Infinite or Not-a-Number function value encountered'

        else:

            pass

        ns += 13

        ne += 13

        yns = np.copy(yne)

    return Q,Xp,Wp,fcnt

def quadlstep(f, Za, Zb, fa, fb, tol, trace, fcnt, hmin, calcutype,

                                                   path_type, origin, radius):

    #Gaussian-Lobatto and Kronrod method

    #QUADLSTEP Recursive core routine for integral

    #input parameters:

    #      f      ----------   function, here we just use the module calgfunc

    #                          to return the value, if wanna use it for

    #                          another one, change it

    #     Za, Zb  ----------   the start and end point of the integral

    #     fa, fb  ----------   the function value on Za and Zb

    #     fcnt    ----------   the number of the funtion recalled till now

    #output parameters:

    #      Q      ----------   integral

    #     Xp      ----------   selected points

    #     Wp      ----------   weight

    #    fcnt     ----------   the number of the function recalled till now

    maxfcnt = 10000

    # Evaluate integrand five times in interior of subintrval [a,b]

    Zh = (Zb - Za) / 2.0

    if abs(Zh) < hmin:

        # Minimun step size reached; singularity possible

        Q = Zh * (fa + fb)

        if path_type == 'line':

            Xp = [Za, Zb]

        elif path_type == 'half_circle':

            Xp = [origin + radius * np.exp((np.pi - Za) * 1.j),

                                      origin + radius * np.exp((np.pi - Zb) * 1.j)]

        Wp = [Zh, Zh]

        warn = 1

        return Q, Xp, Wp, fcnt, warn

    fcnt += 5

    if fcnt > maxfcnt:

        #Maximum function count exceed; singularity likely

        Q = Zh * (fa + fb)

        if path_type == 'line':

            Xp = [Za, Zb]

        elif path_type == 'half_circle':

            Xp = [origin + radius * np.exp((np.pi - Za) * 1.j),

                                      origin + radius * np.exp((np.pi - Zb) * 1.j)]

        Wp = [Zh, Zh]

        warn = 2

        return Q, Xp, Wp, fcnt, warn

    x = [0.18350341907227,   0.55278640450004,   1.0,

         1.44721359549996,   1.81649658092773];

    Zx = [0] * len(x)

    y = [0] * len(x)

    for i in range(len(x)):

        x[i] *= 0.5

        Zx[i] = Za + (Zb - Za) * x[i]

        if path_type == 'line':

            y[i] = f.calgfunc(Zx[i], calcutype)

        elif path_type == 'half_circle':

            energy = origin + radius * np.exp((np.pi - Zx[i]) * 1.j)

            y[i] = f.calgfunc(energy, calcutype)

    #Four point Lobatto quadrature

    s1 = [1.0, 0.0, 5.0, 0.0, 5.0, 0.0, 1.0]

    s2 = [77.0, 432.0, 625.0, 672.0, 625.0, 432.0, 77.0]

    Wk = [0] * 7

    Wp = [0] * 7

    for i in range(7):

        Wk[i] = (Zh / 6.0) * s1[i]

        Wp[i] = (Zh / 1470.0) * s2[i]

    if path_type == 'line':

        Xp = [Za] + Zx + [Zb]

    elif path_type == 'half_circle':

        Xp = [Za] + Zx + [Zb]

        for i in range(7):

            factor = -1.j * radius * np.exp(1.j * (np.pi - Xp[i]))

            Wk[i] *= factor

            Wp[i] *= factor

            Xp[i] = origin + radius * np.exp((np.pi - Xp[i]) * 1.j)

    Qk = fa * Wk[0] + fb * Wk[6]

    Q = fa * Wp[0] + fb * Wp[6]

    for i in range(1, 6):

        Qk += y[i-1] * Wk[i]

        Q  += y[i-1] * Wp[i]

    if np.isinf(np.max(np.abs(Q))):

        Q = Zh * (fa + fb)

        if path_type == 'line':

            Xp = [Za, Zb]

        elif path_type == 'half_circle':

            Xp = [origin + radius * np.exp((np.pi - Za) * 1.j),

                                      origin + radius * np.exp((np.pi - Zb) * 1.j)]

        Wp = [Zh, Zh]

        warn = 3

        return Qk, Xp, Wp, fcnt, warn

    else:

        pass

    if trace:

        print fcnt, real(Za), imag(Za), abs(Zh)

    #Check accurancy of integral over this subinterval

    XXk = [Xp[0], Xp[2], Xp[4], Xp[6]]

    WWk = [Wk[0], Wk[2], Wk[4], Wk[6]]

    YYk = [fa, y[1], y[3], fb]

    if np.max(np.abs(Qk - Q)) <= tol:

        warn = 0

        return Q, XXk, WWk, fcnt, warn

    #Subdivide into six subintevals

    else:

        Q, Xk, Wk, fcnt, warn = quadlstep(f, Za, Zx[1], fa, YYk[1],

                                           tol, trace, fcnt, hmin,

                                               calcutype, path_type,

                                                origin, radius)

        Qk, xkk, wkk, fcnt, warnk = quadlstep(f, Zx[1],

                          Zx[3], YYk[1], YYk[2], tol, trace, fcnt, hmin,

                                             calcutype, path_type,

                                             origin, radius)

        Q += Qk

        Xk = Xk[:-1] + xkk

        Wk = Wk[:-1] + [Wk[-1] + wkk[0]] + wkk[1:]

        warn = max(warn, warnk)

        Qk, xkk, wkk, fcnt, warnk = quadlstep(f, Zx[3], Zb, YYk[2], fb,

                                           tol, trace, fcnt, hmin,

                                                   calcutype, path_type,

                                                   origin, radius)

        Q += Qk

        Xk = Xk[:-1] + xkk

        Wk = Wk[:-1] + [Wk[-1] + wkk[0]] + wkk[1:]

        warn = max(warn, warnk)

    return Q, Xk, Wk, fcnt, warn

def mytextread0(filename):

    num = 0

    df = file(filename)

    df.seek(0)

    for line in df:

        if num == 0:

            dim = line.strip().split(' ')

            row = int(dim[0])

            col = int(dim[1])

            mat = np.empty([row, col])

        else:

            data = line.strip().split(' ')

            if len(data) == 0 or len(data)== 1:

                break

            else:

                for i in range(len(data)):

                    mat[num - 1, i] = float(data[i])

        num += 1

    return mat

def mytextread1(filename):

    num = 0

    df = file(filename)

    df.seek(0)

    data = []

    for line in df:

        tmp = line.strip()

        if len(tmp) != 0:

            data.append(float(tmp))

        else:

            break

    dim = int(sqrt(len(data)))

    mat = np.empty([dim, dim])

    for i in range(dim):

        for j in range(dim):

            mat[i, j] = data[num]

            num += 1

    return mat

def mytextwrite1(filename, mat):

    num = 0

    df = open(filename,'w')

    df.seek(0)

    dim = mat.shape[0]

    if dim != mat.shape[1]:

        print 'matwirte, matrix is not square'

    for i in range(dim):

        for j in range(dim):

            df.write('%20.20e\n'% mat[i, j])

    df.close()