source: issm/trunk/src/py3/miscellaneous/MatlabFuncs.py@ 20500

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

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

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

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

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

        return b

def det(a):
        import numpy

        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

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

        a=numpy.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

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

        return y