Exemple #1
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def plot_field(eng, mesh, val='u'):
    Ne = mesh.Ne
    it = FM.get_connections(mesh)
    mat = []
    for e in mesh.getElements():
        mat.append(e.getMaterial().getID() + 1)

    xyz = []
    field = []
    for n in mesh.getNodes():
        xyz.append(n.getX().tolist())
    if val == 'u':
        for n in mesh.getNodes():
            field.append(n.getU().tolist())
    elif val == 'v':
        for n in mesh.getNodes():
            field.append(n.getV().tolist())
    elif val == 'a':
        for n in mesh.getNodes():
            field.append(n.getU().tolist())
    else:
        return

    #eng = matlab.engine.start_matlab()
    IT = matlab.int64(it)
    MAT = matlab.int64(mat)
    XYZ = matlab.double(xyz)
    Field = matlab.double(field)
    eng.plot_field(IT, Field, XYZ, Ne, MAT, 0)
    return eng
Exemple #2
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 def convertLabels(self, inLabels):
     convLabels = [] 
     for stateID in inLabels.keys():
         convLabels.append([stateID,'p1',matlab.int64(inLabels[stateID]['p1'][:])])
         convLabels.append([stateID,'p2',matlab.int64(inLabels[stateID]['p2'][:])])
         convLabels.append([stateID,'p3',matlab.int64(inLabels[stateID]['p3'][:])])
         convLabels.append([stateID,'p4',matlab.int64(inLabels[stateID]['p4'][:])])
     return convLabels 
def interleave(eng, arrData_bin, nParam, strMethod=INTRLV_MATRIX):
    """
        interleave data in a block way
    """

    data_intrlv = None
    nPadding = None
    if(strMethod == INTRLV_MATRIX):
        nCols = nParam
        arrData_bin, nPadding = ct.zeroPadding(arrData_bin, nCols)   
        nRows = len(arrData_bin)/nCols
        data = matlab.int64(list(arrData_bin))
        data_intrlv = eng.matintrlv(data, nRows, nCols)
    elif(strMethod == INTRLV_RANDOM):
        nSeed = 4831
        data = matlab.int64(list(arrData_bin))
        data_intrlv = eng.randintrlv(data, nSeed)
    else:
        raise ValueError("Unsupported interleaving method: %s" % strMethod)
        
        
    arrData_intrlv = np.array([i for i in data_intrlv[0]])
    return arrData_intrlv, nPadding
def matlab_SurfStatLinMod(Y, M, surf=None, niter=1, thetalim=0.01, drlim=0.1):

    from term import Term
    from brainspace.mesh.mesh_elements import get_cells
    from brainspace.vtk_interface.wrappers.data_object import BSPolyData

    if isinstance(Y, np.ndarray):
        Y = matlab.double(Y.tolist())
    else:
        Y = surfstat_eng.double(Y)

    if isinstance(M, np.ndarray):
        M = {'matrix': matlab.double(M.tolist())}

    elif isinstance(M, Term):
        M = surfstat_eng.term(matlab.double(M.matrix.values.tolist()),
                              M.matrix.columns.tolist())
    else:  # Random
        M1 = matlab.double(M.mean.matrix.values.tolist())
        V1 = matlab.double(M.variance.matrix.values.tolist())

        M = surfstat_eng.random(V1, M1, surfstat_eng.cell(0),
                                surfstat_eng.cell(0), 1)

    # Only require 'tri' or 'lat'
    if surf is None:
        k = None
        surf = surfstat_eng.cell(0)
    else:
        if isinstance(surf,BSPolyData):
            surf = {'tri': np.array(get_cells(surf))+1}
        k = 'tri' if 'tri' in surf else 'lat'
        s = surf[k]
        surf = {k: matlab.int64(s.tolist())}

    slm = surfstat_eng.SurfStatLinMod(Y, M, surf, niter, thetalim, drlim)
    for key in ['SSE', 'coef']:
        if key not in slm:
            continue
        slm[key] = np.atleast_2d(slm[key])
    slm = {k: v if np.isscalar(v) else np.array(v) for k, v in slm.items()}
   
    return slm
Exemple #5
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def matlab_SurfStatLinMod(Y, M, surf=None, niter=1, thetalim=0.01, drlim=0.1):

    from term import Term

    if isinstance(Y, np.ndarray):
        Y = matlab.double(Y.tolist())
    else:
        Y = surfstat_eng.double(Y)

    if isinstance(M, np.ndarray):
        M = {'matrix': matlab.double(M.tolist())}

    elif isinstance(M, Term):
        M = surfstat_eng.term(matlab.double(M.matrix.values.tolist()),
                              M.matrix.columns.tolist())
    else:  # Random
        M1 = matlab.double(M.mean.matrix.values.tolist())
        V1 = matlab.double(M.variance.matrix.values.tolist())

        M = surfstat_eng.random(V1, M1, surfstat_eng.cell(0),
                                surfstat_eng.cell(0), 1)

    # Only require 'tri' or 'lat'
    if surf is None or ('tri' not in surf and 'lat' not in surf):
        k = None
        surf = surfstat_eng.cell(0)
    else:
        k = 'tri' if 'tri' in surf else 'lat'
        s = surf[k]

        surf = {k: matlab.int64(s.tolist())}

    slm = surfstat_eng.SurfStatLinMod(Y, M, surf, niter, thetalim, drlim)
    for key in ['SSE', 'coef']:
        if key not in slm:
            continue
        slm[key] = np.atleast_2d(slm[key])
    slm = {k: v if np.isscalar(v) else np.array(v) for k, v in slm.items()}

    return slm
Exemple #6
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def ndarray_to_matlab(numpy_array):
    """Conversion of a numpy array to matlab mlarray

    The conversion is realised without copy for real data. First an empty initialization is realized.
    Then the numpy array is affected to the _data field. Thus the data field is not really an
    array.array but a numpy array. Matlab doesn't see anything...
    For complex data, the strides seems to not work properly with matlab.double.

    Paramerters
    -----------
    numpy_array : numpy array
      the array to convert
    Returns
    -------
    matlab_array : mlarray
      the converted array that can be passe to matlab
    Examples
    --------
    >>> npi=numpy.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]],dtype=numpy.int64,order='C')
    >>> ndarray_to_matlab(npi)
    matlab.int64([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
    >>> npif=numpy.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]],dtype=numpy.int64,order='F')
    >>> ndarray_to_matlab(npif)
    matlab.int64([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])

    >>> npcf=numpy.array([[1,2+0.2j,3],[4,5,6],[7,8,9],[10+0.1j,11,12]],dtype=numpy.complex,order='F')
    >>> ndarray_to_matlab(npcf)
    matlab.double([[(1+0j),(2+0.2j),(3+0j)],[(4+0j),(5+0j),(6+0j)],[(7+0j),(8+0j),(9+0j)],[(10+0.1j),(11+0j),(12+0j)]], is_complex=True)

    References
    -----------
    https://scipy-lectures.org/advanced/advanced_numpy/ (strides)
    """

    if "ndarray" not in str(type(numpy_array)):  # check numpy
        raise TypeError(f"Expect  numpy.ndarray. Got {type(numpy_array)}")

    shape = numpy_array.shape  # get shape

    num_elements = numpy.prod(shape)  # number of elements

    if numpy_array.flags.f_contiguous:  # compute strides (real case)
        strides = get_strides_f(shape)
        order = "F"
    else:
        strides = get_strides_c(shape)
        order = "C"

    if numpy.iscomplexobj(numpy_array):  # complex case

        matlab_array = matlab.double(
            initializer=None, size=(1, num_elements), is_complex=True
        )  # create empty matlab.mlarray
        # associate the data
        """
# associate the data (no copy), works on 2D array only...
matlab_array._real=numpy_array.ravel(order=order) # allow to map real and imaginary part continuously!
"""
        cpx = numpy_array.ravel(order="F")  # copy except for fortran like array
        matlab_array._real = (
            cpx.real.ravel()
        )  # second ravel to correct the strides 18->8
        matlab_array._imag = cpx.imag.ravel()
        matlab_array.reshape(shape)
        # matlab_array._strides=strides

    else:  # real case     # create empty matlab.mlarray
        if numpy_array.dtype == numpy.float64:
            matlab_array = matlab.double(
                initializer=None, size=(1, num_elements), is_complex=False
            )
        elif numpy_array.dtype == numpy.int64:
            matlab_array = matlab.int64(initializer=None, size=(1, num_elements))
        elif numpy_array.dtype == numpy.bool:
            matlab_array = matlab.logical(initializer=None, size=(1, num_elements))
        else:
            raise TypeError(f"Type {numpy_array.dtype} is missing")

        matlab_array._data = numpy_array.ravel(order=order)  # associate the data
        # print(matlab_array._data.flags,matlab_array._data,'\n') # control owner

        matlab_array.reshape(shape)  # back to original shape
        if (
            len(shape) > 1
        ):  # array strides are in number of cell (numpy strides are in bytes)
            # if len(shape)==1 no need to change. Format pb because _stride expect (1,1) and stride = (1,)
            matlab_array._strides = (
                strides  # change stride (matlab use 'F' order ie [nc,1] )
            )

    return matlab_array
Exemple #7
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    def decompose(self, r, method='tensorly') -> 'DecomposedFactor':
        """
        Decompose this factor into r rank-1 components using non-negative CP decomposition. In certain trivial cases,
        returns a decomposition with one component rather than r.
        :param method: Library to use to compute CP decomposition, either 'tensorly' or 'matlab' (see note about MATLAB
        support at the top of this file).
        """

        assert method in ['tensorly', 'matlab']
        if method == 'matlab':
            assert with_matlab, 'MATLAB Python API not detected'

        # Variables with cardinality 1 cause problems with tensorly non_negative_decomp, so we only decompose the
        # nontrivial dimensions of the table (and set the matrices for trivial dimensions to [[1, ..., 1]]).
        table_nt = np.squeeze(self.table)

        if table_nt.ndim == 1:
            # If the nontrivial table has just one variable, the resulting decomposition has just one term (not r terms)
            # and we don't call the decomposition algorithm at all
            n_terms = 1
            weights = np.ones(n_terms)
            # Reshape is to change np.array([1,2,3]) to np.array([[1],[2],[3]])
            matrices_nt = [table_nt.reshape(table_nt.shape[0], 1)]
        else:
            n_terms = r

            if method == 'tensorly':

                if 'tensorly' not in sys.modules:
                    if verbosity == 3:
                        import_tensorly()
                    else:
                        with SuppressOutput():
                            import_tensorly()

                weights = np.ones(n_terms)
                matrices_nt = None
                table_nt_orig = table_nt
                i = 0
                while not matrices_nt:
                    try:
                        matrices_nt = tensorly.decomposition.non_negative_parafac(
                            table_nt, n_terms)
                    except LinAlgError:
                        i += 1
                        status(
                            "Warning: singular matrix encountered, perturbing tensor and attempting decomposition "
                            "again ({})".format(i), 3)
                        table_nt = perturb(table_nt_orig)

            elif method == 'matlab':

                # Initialise MATLAB
                global eng
                if not eng:
                    eng = matlab.engine.start_matlab()
                    eng.addpath(MATLAB_PATH)

                w, T = eng.cp_kolda(matlab.double(
                    table_nt.flatten(order='F').tolist()),
                                    matlab.int64(table_nt.shape),
                                    r,
                                    nargout=2)
                matrices_nt = [np.array(t) for t in T]
                weights = np.array(w).flatten()

        # Add the trivial dimensions back in
        matrices = []
        j = 0
        for i in range(self.n_vars):
            if self.cardinalities[i] == 1:
                matrices.append(np.ones((1, n_terms)))
            else:
                matrices.append(matrices_nt[j])
                j += 1
            assert matrices[i].shape == (self.cardinalities[i], n_terms)
        assert j == len(matrices_nt)

        df = DecomposedFactor(self.vars, weights, matrices)

        # Make sure the decomposition is close to the original factor
        # diff = df.expand().table - self.table
        # print("Reconstruction error: norm=%s, largest=%s" % (
        #     np.linalg.norm(diff),
        #     np.max(diff),
        # ))

        return df
Exemple #8
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def asiduj_test():
    """
    Test of the module

    It runs the doctest and create other tests with matlab engine calls."""
    import scipy.linalg as spl

    print("Run matlab engine...")
    if len(matlab.engine.find_matlab()) == 0:
        # si aucune session share, run
        eng = matlab.engine.start_matlab()
    else:
        # connect to a session
        eng = matlab.engine.connect_matlab(matlab.engine.find_matlab()[0])
        print("connected...")

    print("Further tests....\n")

    # create matlab data
    # ------------------------------------------------------------------------
    mf = eng.rand(3)
    mc = matlab.double([[1 + 1j, 0.3, 1j], [1.2j - 1, 0, 1 + 1j]],
                       is_complex=True)
    mi64 = matlab.int64([1, 2, 3])
    mi8 = matlab.int8([1, 2, 3])
    mb = matlab.logical([True, True, False])

    # Test conversion from matlab to numpy
    # ------------------------------------------------------------------------
    npf = matlab_to_ndarray(mf)  # no copy, if mf is changed, npf change!
    npc = matlab_to_ndarray(mc)  # still copy for complex (only)
    npi64 = matlab_to_ndarray(mi64)
    npi8 = matlab_to_ndarray(mi8)
    npb = matlab_to_ndarray(mb)

    # Test conversion from numpy to matlab
    # ------------------------------------------------------------------------
    npi = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
                      dtype=numpy.int64,
                      order="F")
    mi = ndarray_to_matlab(npi)
    mc2 = ndarray_to_matlab(npc)
    mf2 = ndarray_to_matlab(
        npf)  # copy, because npf has 'F' order (comes from mlarray)
    mi64_2 = ndarray_to_matlab(npi)
    mb2 = ndarray_to_matlab(npb)

    # test orientation in the matlab workspace
    # ------------------------------------------------------------------------
    eng.workspace["mi"] = mi64_2
    eng.workspace["mc2"] = mc2

    # check results
    # ------------------------------------------------------------------------
    npcc = numpy.array(
        [
            [1.0, 1.1 + 1j],
            [
                1.12 + 0.13j,
                22.1,
            ],
        ],
        dtype=numpy.complex,
    )  # assume C
    mcc = ndarray_to_matlab(npcc)
    npcc_inv = spl.inv(npcc)
    mcc_inv = eng.inv(mcc)
    print("Are the inverse of matrix equal ?")
    print(mcc_inv)
    print(npcc_inv)

    #    # no copy check
    #    # ------------------------------------------------------------------------
    #    # complex
    #
    #    npcc[0,0]=0.25
    #    print("Are the data reuse ?", ", OWNDATA =", mcc._real.flags.owndata,
    #          "same base =", mcc._real.base is npcc,
    #          ', If one is modified, the other is modified =', mcc._real[0]==npcc[0,0])
    #

    # test sparse matrix requiert Recast4py.m
    K1, K2 = eng.sptest(3.0, nargout=2)
    Ksp1 = dict_to_sparse(K1)
    Ksp2 = dict_to_sparse(K2)
Exemple #9
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def np2mlarray(npa):
    """ Conversion of a numpy array to matlab mlarray
    
    The conversion is realised without copy for real data. First an empty initialization is realized.
    Then the numpy array is affected to the _data field. Thus the data field is not really an 
    array.array but a numpy array. Matlab doesn't see anything...
    For complex data, the strides seems to not work properly with matlab.double.
    
    Paramerters
    -----------
    npa : numpy array
        the array to convert
    Returns 
    -------
    ma : mlarray
        the converted array that can be passe to matlab

    Examples
    --------
    >>> npi=np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]],dtype=np.int64,order='C')
    >>> np2mlarray(npi)
    matlab.int64([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
    >>> npif=np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]],dtype=np.int64,order='F')
    >>> np2mlarray(npif)
    matlab.int64([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
    
    >>> npcf=np.array([[1,2+0.2j,3],[4,5,6],[7,8,9],[10+0.1j,11,12]],dtype=np.complex,order='F')
    >>> np2mlarray(npcf)
    matlab.double([[(1+0j),(2+0.2j),(3+0j)],[(4+0j),(5+0j),(6+0j)],[(7+0j),(8+0j),(9+0j)],[(10+0.1j),(11+0j),(12+0j)]], is_complex=True)


    
    References
    -----------
    https://scipy-lectures.org/advanced/advanced_numpy/ (strides)
    
    """
    # check numpy
    if 'ndarray' not in str(type(npa)):
        raise TypeError('Expect  numpy.ndarray. Got %s' % type(npa))

    # get shape
    shape = npa.shape
    # number of elements
    N = np.prod(shape)
    # compute strides (real case)
    if npa.flags.f_contiguous == True:
        strides = _getStridesF(shape)  # pour la sortie
        order = 'F'
    else:
        strides = _getStridesC(shape)  # ok, garde le même
        order = 'C'

    # complex case
    if npa.dtype in (np.complex128, np.complex):
        #  create empty matlab.mlarray
        ma = matlab.double(initializer=None, size=(1, N), is_complex=True)
        # associate the data
        """
         # associate the data (no copy), works on 2D array only...
         ma._real=npa.ravel(order=order) # allow to map real and imaginary part continuously!
         """
        cpx = npa.ravel(order='F')  # copy except for fortran like array
        ma._real = cpx.real.ravel(
        )  # second ravel to correct the strides 18->8
        ma._imag = cpx.imag.ravel()
        ma.reshape(shape)
        # ma._strides=strides
    # real case
    else:
        # create empty matlab.mlarray
        if npa.dtype == np.float64:
            ma = matlab.double(initializer=None, size=(1, N), is_complex=False)
        elif npa.dtype == np.int64:
            ma = matlab.int64(initializer=None, size=(1, N))
        elif npa.dtype == np.bool:
            ma = matlab.logical(initializer=None, size=(1, N))
        else:
            raise TypeError('Type %s is missing' % npa.dtype)

        # associate the data
        ma._data = npa.ravel(order=order)
        # print(ma._data.flags,ma._data,'\n') # control owner

        # back to original shape
        ma.reshape(shape)
        # array strides are in number of cell (numpy strides are in bytes)
        # if len(shape)==1 no need to change. Format pb because _stride expect (1,1) and stride = (1,)
        if len(shape) > 1:
            ma._strides = strides  # change stride (matlab use 'F' order ie [nc,1] )

    return ma
Exemple #10
0
            eng = matlab.engine.start_matlab()
        else:
            # connect to a session
            eng = matlab.engine.connect_matlab(matlab.engine.find_matlab()[0])
            print('connected...')
    else:
        print('Matlab engine is already runnig...')

    print('Further tests....\n')

    # create matlab data
    # ------------------------------------------------------------------------
    mf = eng.rand(3)
    mc = matlab.double([[1 + 1j, 0.3, 1j], [1.2j - 1, 0, 1 + 1j]],
                       is_complex=True)
    mi64 = matlab.int64([1, 2, 3])
    mi8 = matlab.int8([1, 2, 3])
    mb = matlab.logical([True, True, False])

    # Test conversion from matlab to numpy
    # ------------------------------------------------------------------------
    npf = mlarray2np(mf)  # no copy, if mf is changed, npf change!
    npc = mlarray2np(mc)  # still copy for complex (only)
    npi64 = mlarray2np(mi64)
    npi8 = mlarray2np(mi8)
    npb = mlarray2np(mb)

    # Test conversion from numpy to matlab
    # ------------------------------------------------------------------------
    npi = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
                   dtype=np.int64,