source: issm/trunk/src/m/miscellaneous/MatlabFuncs.py@ 24313

def oshostname():
    import socket

    return socket.gethostname()


def ispc():
    import platform

    if 'Windows' in platform.system():
        return True
    else:
        return False


def ismac():
    import platform

    if 'Darwin' in platform.system():
        return True
    else:
        return False


def strcmp(s1, s2):

    if s1 == s2:
        return True
    else:
        return False


def strncmp(s1, s2, n):

    if s1[0:n] == s2[0:n]:
        return True
    else:
        return False


def strcmpi(s1, s2):

    if s1.lower() == s2.lower():
        return True
    else:
        return False


def strncmpi(s1, s2, n):

    if s1.lower()[0:n] == s2.lower()[0:n]:
        return True
    else:
        return False


def ismember(a, s):
    import numpy as np

    if not isinstance(s, (tuple, list, dict, np.ndarray)):
        s = [s]

    if not isinstance(a, (tuple, list, dict, np.ndarray)):
        a = [a]

    if not isinstance(a, np.ndarray):
        b = [item in s for item in a]

    else:
        if not isinstance(s, np.ndarray):
            b = np.empty_like(a)
            for i, item in enumerate(a.flat):
                b.flat[i] = item in s
        else:
            b = np.in1d(a.flat, s.flat).reshape(a.shape)

    return b


def det(a):

    if a.shape == (1, ):
        return a[0]
    elif a.shape == (1, 1):
        return a[0, 0]
    elif a.shape == (2, 2):
        return a[0, 0] * a[1, 1] - a[0, 1] * a[1, 0]
    else:
        raise TypeError("MatlabFunc.det only implemented for shape (2, 2), not for shape %s." % str(a.shape))


def sparse(ivec, jvec, svec, m=0, n=0, nzmax=0):
    import numpy as np

    if not m:
        m = np.max(ivec)
    if not n:
        n = np.max(jvec)

    a = np.zeros((m, n))

    for i, j, s in zip(ivec.reshape(-1, order='F'), jvec.reshape(-1, order='F'), svec.reshape(-1, order='F')):
        a[i - 1, j - 1] += s

    return a


def heaviside(x):
    import numpy as np

    y = np.zeros_like(x)
    y[np.nonzero(x > 0.)] = 1.
    y[np.nonzero(x == 0.)] = 0.5

    return y