Python numpy.isfortran方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def test_trirefiner_fortran_contiguous_triangles():
    # github issue 4180.  Test requires two arrays of triangles that are
    # identical except that one is C-contiguous and one is fortran-contiguous.
    triangles1 = np.array([[2, 0, 3], [2, 1, 0]])
    assert not np.isfortran(triangles1)

    triangles2 = np.array(triangles1, copy=True, order='F')
    assert np.isfortran(triangles2)

    x = np.array([0.39, 0.59, 0.43, 0.32])
    y = np.array([33.99, 34.01, 34.19, 34.18])
    triang1 = mtri.Triangulation(x, y, triangles1)
    triang2 = mtri.Triangulation(x, y, triangles2)

    refiner1 = mtri.UniformTriRefiner(triang1)
    refiner2 = mtri.UniformTriRefiner(triang2)

    fine_triang1 = refiner1.refine_triangulation(subdiv=1)
    fine_triang2 = refiner2.refine_triangulation(subdiv=1)

    assert_array_equal(fine_triang1.triangles, fine_triang2.triangles) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def hasFortranFlag(node):
    """Returns node value fortran flag."""
    if node[1] is None:
        return True
    if node[1] == []:
        return True
    if isinstance(node[1], str):
        return True  # link
    if not node[1].shape:
        return True
    if len(node[1].shape) == 1:
        return True
    return numpy.isfortran(node[1])


# -------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def _ndarray_representer(dumper, obj):
    if not (obj.flags['C_CONTIGUOUS'] or obj.flags['F_CONTIGUOUS']):
        obj = np.ascontiguousarray(obj)

    if np.isfortran(obj):
        obj = obj.T
        order = 'F'
    else:
        order = 'C'

    data_b64 = base64.b64encode(obj.tostring())

    out = dict(buffer=data_b64,
               dtype=str(obj.dtype),
               shape=obj.shape,
               order=order)

    return dumper.represent_mapping('!numpy.ndarray', out) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def strides(Z):
    strides = [Z.itemsize]
    
    # Fotran ordered array
    if np.isfortran(Z):
        for i in range(0, Z.ndim-1):
            strides.append(strides[-1] * Z.shape[i])
        return tuple(strides)
    # C ordered array
    else:
        for i in range(Z.ndim-1, 0, -1):
            strides.append(strides[-1] * Z.shape[i])
        return tuple(strides[::-1])

# This work 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def test_polynomial_feature_array_order():
    X = np.arange(10).reshape(5, 2)

    def is_c_contiguous(a):
        return np.isfortran(a.T)

    assert is_c_contiguous(PolynomialFeatures().fit_transform(X))
    assert is_c_contiguous(PolynomialFeatures(order='C').fit_transform(X))
    assert np.isfortran(PolynomialFeatures(order='F').fit_transform(X)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def copyArray(a):
    """Copy a numpy.ndarray with flags"""
    if not isinstance(a, numpy.ndarray):
        return None  # None, []
    if numpy.isfortran(a):
        b = numpy.array(a, order='Fortran', copy=True)
    else:
        b = numpy.array(a, copy=True)
    return b


# -------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def toStringValue(v):
    """ASCII pretty print of one node value."""
    if v is None:
        return None
    ao = 'C'
    if numpy.isfortran(v):
        ao = 'F'
    at = v.dtype.name
    av = v.tolist()
    return "numpy.array(%s,dtype='%s',order='%s')" % (av, at, ao)


# -------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def __init__(self, data, block_length=1, use_blocks=None, offsets=None):
        """
        data can be a numpy array (in C order), a tuple of such arrays, or a list of such tuples or arrays
        """
        self.files = list()
        if isinstance(data, list): #Several files
            for file in data:
                if isinstance(file, tuple):
                    for d in file:
                        assert(isinstance(d, np.ndarray) and not np.isfortran(d))
                    self.files.append(file)
        elif isinstance(data, tuple): #Just one file
            for d in data:
                assert(isinstance(d, np.ndarray) and d.ndim == 2 and not np.isfortran(d))
            self.files.append(data)
        elif isinstance(data, np.ndarray): #One file with one kind of element only (not input-output)
            assert(isinstance(data, np.ndarray) and not np.isfortran(data))
            self.files.append(tuple([data]))
        # Support for block datapoints
        self.block_length = block_length
        if block_length == 1:
            self.block_lengths = [np.int(1)] * self.get_arity()
            self.offsets = [np.int(0)] * self.get_arity()
        elif block_length > 1:
            self.block_lengths = [np.int(block_length) if ub else np.int(1) for ub in use_blocks]  # np.asarray(dtype=np.int) and [np.int(x)] have elements with diff type. Careful!
            self.offsets = [np.int(off) for off in offsets]
            for ub, off in zip(use_blocks, offsets):
                if off != 0 and ub:
                    raise Exception("Can't have both a block size greater than 1 and an offset.")
        else:
            raise Exception("Block size must be positive") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def _impose_f_order(X):
    """Helper Function"""
    # important to access flags instead of calling np.isfortran,
    # this catches corner cases.
    if X.flags.c_contiguous:
        return check_array(X.T, copy=False, order='F'), True
    else:
        return check_array(X, copy=False, order='F'), False 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def info(Z):
    import sys
    import numpy as np
    endianness = {'=': 'native (%s)' % sys.byteorder,
                 '<': 'little',
                 '>': 'big',
                 '|': 'not applicable'}

    print("------------------------------")
    print("Interface (item)")
    print("  shape:      ", Z.shape)
    print("  dtype:      ", Z.dtype)
    print("  length:     ", len(Z))
    print("  size:       ", Z.size)
    print("  endianness: ", endianness[Z.dtype.byteorder])
    if np.isfortran(Z):
        print("  order:       ☐ C  ☑ Fortran")
    else:
        print("  order:       ☑ C  ☐ Fortran")
    print("")
    print("Memory (byte)")
    print("  item size:  ", Z.itemsize)
    print("  array size: ", Z.size*Z.itemsize)
    print("  strides:    ", Z.strides)
    print("")
    print("Properties")
    if Z.flags["OWNDATA"]:
        print("  own data:    ☑ Yes  ☐ No")
    else:
        print("  own data:    ☐ Yes  ☑ No")
    if Z.flags["WRITEABLE"]:
        print("  writeable:   ☑ Yes  ☐ No")
    else:
        print("  writeable:   ☐ Yes  ☑ No")
    if np.isfortran(Z) and Z.flags["F_CONTIGUOUS"]:
        print("  contiguous:  ☑ Yes  ☐ No")
    elif not np.isfortran(Z) and Z.flags["C_CONTIGUOUS"]:
        print("  contiguous:  ☑ Yes  ☐ No")
    else:
        print("  contiguous:  ☐ Yes  ☑ No")
    if Z.flags["ALIGNED"]:
        print("  aligned:     ☑ Yes  ☐ No")
    else:
        print("  aligned:     ☐ Yes  ☑ No")
    print("------------------------------")
    print() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def isfortran(a):
    """
    Returns True if the array is Fortran contiguous but *not* C contiguous.

    This function is obsolete and, because of changes due to relaxed stride
    checking, its return value for the same array may differ for versions
    of NumPy >= 1.10.0 and previous versions. If you only want to check if an
    array is Fortran contiguous use ``a.flags.f_contiguous`` instead.

    Parameters
    ----------
    a : ndarray
        Input array.


    Examples
    --------

    np.array allows to specify whether the array is written in C-contiguous
    order (last index varies the fastest), or FORTRAN-contiguous order in
    memory (first index varies the fastest).

    >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
    >>> a
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> np.isfortran(a)
    False

    >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')
    >>> b
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> np.isfortran(b)
    True


    The transpose of a C-ordered array is a FORTRAN-ordered array.

    >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
    >>> a
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> np.isfortran(a)
    False
    >>> b = a.T
    >>> b
    array([[1, 4],
           [2, 5],
           [3, 6]])
    >>> np.isfortran(b)
    True

    C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.

    >>> np.isfortran(np.array([1, 2], order='FORTRAN'))
    False

    """
    return a.flags.fnc 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def test_as_float_array():
    # Test function for as_float_array
    X = np.ones((3, 10), dtype=np.int32)
    X = X + np.arange(10, dtype=np.int32)
    X2 = as_float_array(X, copy=False)
    assert_equal(X2.dtype, np.float32)
    # Another test
    X = X.astype(np.int64)
    X2 = as_float_array(X, copy=True)
    # Checking that the array wasn't overwritten
    assert as_float_array(X, False) is not X
    assert_equal(X2.dtype, np.float64)
    # Test int dtypes <= 32bit
    tested_dtypes = [np.bool,
                     np.int8, np.int16, np.int32,
                     np.uint8, np.uint16, np.uint32]
    for dtype in tested_dtypes:
        X = X.astype(dtype)
        X2 = as_float_array(X)
        assert_equal(X2.dtype, np.float32)

    # Test object dtype
    X = X.astype(object)
    X2 = as_float_array(X, copy=True)
    assert_equal(X2.dtype, np.float64)

    # Here, X is of the right type, it shouldn't be modified
    X = np.ones((3, 2), dtype=np.float32)
    assert as_float_array(X, copy=False) is X
    # Test that if X is fortran ordered it stays
    X = np.asfortranarray(X)
    assert np.isfortran(as_float_array(X, copy=True))

    # Test the copy parameter with some matrices
    matrices = [
        np.matrix(np.arange(5)),
        sp.csc_matrix(np.arange(5)).toarray(),
        sparse_random_matrix(10, 10, density=0.10).toarray()
    ]
    for M in matrices:
        N = as_float_array(M, copy=True)
        N[0, 0] = np.nan
        assert not np.isnan(M).any() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def isfortran(a):
    """Returns True if the array is Fortran contiguous but *not* C contiguous.

    If you only want to check if an array is Fortran contiguous use
    ``a.flags.f_contiguous`` instead.

    Args:
        a (cupy.ndarray): Input array.

    Returns:
        bool: The return value, True if ``a`` is Fortran contiguous but not C
        contiguous.

    .. seealso::
       :func:`~numpy.isfortran`

    Examples
    --------

    cupy.array allows to specify whether the array is written in C-contiguous
    order (last index varies the fastest), or FORTRAN-contiguous order in
    memory (first index varies the fastest).

    >>> a = cupy.array([[1, 2, 3], [4, 5, 6]], order='C')
    >>> a
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> cupy.isfortran(a)
    False

    >>> b = cupy.array([[1, 2, 3], [4, 5, 6]], order='F')
    >>> b
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> cupy.isfortran(b)
    True

    The transpose of a C-ordered array is a FORTRAN-ordered array.

    >>> a = cupy.array([[1, 2, 3], [4, 5, 6]], order='C')
    >>> a
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> cupy.isfortran(a)
    False
    >>> b = a.T
    >>> b
    array([[1, 4],
           [2, 5],
           [3, 6]])
    >>> cupy.isfortran(b)
    True

    C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.

    >>> cupy.isfortran(np.array([1, 2], order='F'))
    False

    """
    return a.flags.f_contiguous and not a.flags.c_contiguous 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def convert_to_ctypes(args, func):
    """ Converts an argument list to a ctype compatible list for our launcher function """

    # pass numpy buffers using ctypes
    cargs = []
    try:

        # Iterate over the args and convert
        for arg in args:

            # Check for supported types
            if isinstance(arg, np.ndarray):

                # Check for the array type
                if not arg.dtype == np.float32:
                    raise ValueError(
                        'Input array of type {0} detected, Not supported.'.format(arg.dtype))

                # Otherwise add the bounds
                if len(arg.shape) > 4:
                    raise ValueError(
                        'Detected {0} dimensions. Halide supports only up to 4.'.format(
                            len(arg.shape)))

                # Check if fortran array
                if len(arg.shape) > 1 and not np.isfortran(arg):
                    print('Arg ', arg)
                    # Much faster and more natural halide code
                    raise ValueError('Currently supports only Fortran order')

                # Add ctype
                cargs.append(arg.ctypes.data_as(ctypes.c_void_p))

                # Add bound w,h,x,y ...
                for s in [1, 0, 2, 3]:
                    if s < len(arg.shape):
                        cargs.append(ctypes.c_int(np.int32(arg.shape[s])))
                    else:
                        cargs.append(ctypes.c_int(np.int32(1)))

            elif isinstance(arg, float) or isinstance(arg, np.float32):
                cargs.append(ctypes.c_float(arg))

            elif isinstance(arg, int) or isinstance(arg, np.int32):
                cargs.append(ctypes.c_int(arg))

            else:
                raise ValueError('Unsupported type.')

    except Exception as e:
        print('Error argument conversion: {0} in func {1}'.format(e.message, func), file=sys.stderr)
        exit()

    return cargs 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfortran [as 別名]
def concordance_td(durations, events, surv, surv_idx, method='adj_antolini'):
    """Time dependent concorance index from
    Antolini, L.; Boracchi, P.; and Biganzoli, E. 2005. A timedependent discrimination
    index for survival data. Statistics in Medicine 24:3927–3944.

    If 'method' is 'antolini', the concordance from Antolini et al. is computed.
    
    If 'method' is 'adj_antolini' (default) we have made a small modifications
    for ties in predictions and event times.
    We have followed step 3. in Sec 5.1. in Random Survial Forests paper, except for the last
    point with "T_i = T_j, but not both are deaths", as that doesn't make much sense.
    See '_is_concordant'.

    Arguments:
        durations {np.array[n]} -- Event times (or censoring times.)
        events {np.array[n]} -- Event indicators (0 is censoring).
        surv {np.array[n_times, n]} -- Survival function (each row is a duraratoin, and each col
            is an individual).
        surv_idx {np.array[n_test]} -- Mapping of survival_func s.t. 'surv_idx[i]' gives index in
            'surv' corresponding to the event time of individual 'i'.

    Keyword Arguments:
        method {str} -- Type of c-index 'antolini' or 'adj_antolini' (default {'adj_antolini'}).

    Returns:
        float -- Time dependent concordance index.
    """
    if np.isfortran(surv):
        surv = np.array(surv, order='C')
    assert durations.shape[0] == surv.shape[1] == surv_idx.shape[0] == events.shape[0]
    assert type(durations) is type(events) is type(surv) is type(surv_idx) is np.ndarray
    if events.dtype in ('float', 'float32'):
        events = events.astype('int32')
    if method == 'adj_antolini':
        is_concordant = _is_concordant
        is_comparable = _is_comparable
        return (_sum_concordant_disc(surv, durations, events, surv_idx, is_concordant) /
                _sum_comparable(durations, events, is_comparable))
    elif method == 'antolini':
        is_concordant = _is_concordant_antolini
        is_comparable = _is_comparable_antolini
        return (_sum_concordant_disc(surv, durations, events, surv_idx, is_concordant) /
                _sum_comparable(durations, events, is_comparable))
    return ValueError(f"Need 'method' to be e.g. 'antolini', got '{method}'.")