Python numpy.generic方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def test_sparse_nd_setitem():
    def check_sparse_nd_setitem(stype, shape, dst):
        x = mx.nd.zeros(shape=shape, stype=stype)
        x[:] = dst
        dst_nd = mx.nd.array(dst) if isinstance(dst, (np.ndarray, np.generic)) else dst
        assert np.all(x.asnumpy() == dst_nd.asnumpy() if isinstance(dst_nd, NDArray) else dst)

    shape = rand_shape_2d()
    for stype in ['row_sparse', 'csr']:
        # ndarray assignment
        check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, 'default'))
        check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, stype))
        # numpy assignment
        check_sparse_nd_setitem(stype, shape, np.ones(shape))
    # scalar assigned to row_sparse NDArray
    check_sparse_nd_setitem('row_sparse', shape, 2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def test_char_radd(self):
        # GH issue 9620, reached gentype_add and raise TypeError
        np_s = np.string_('abc')
        np_u = np.unicode_('abc')
        s = b'def'
        u = u'def'
        assert_(np_s.__radd__(np_s) is NotImplemented)
        assert_(np_s.__radd__(np_u) is NotImplemented)
        assert_(np_s.__radd__(s) is NotImplemented)
        assert_(np_s.__radd__(u) is NotImplemented)
        assert_(np_u.__radd__(np_s) is NotImplemented)
        assert_(np_u.__radd__(np_u) is NotImplemented)
        assert_(np_u.__radd__(s) is NotImplemented)
        assert_(np_u.__radd__(u) is NotImplemented)
        assert_(s + np_s == b'defabc')
        assert_(u + np_u == u'defabc')


        class Mystr(str, np.generic):
            # would segfault
            pass

        ret = s + Mystr('abc')
        assert_(type(ret) is type(s)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def normalization(s: Union[np.ndarray, np.generic],
                  dt: int) -> Union[np.ndarray, np.generic]:
    """"
    Parameters
    ----------
    s : numpy.ndarray
        Scales.
    dt : int
        Time step.

    Returns
    -------
    numpy.ndarray
        Normalized data.
    """

    return np.sqrt((2 * np.pi * s) / dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def _validate_date_like_dtype(dtype):
    """
    Check whether the dtype is a date-like dtype. Raises an error if invalid.

    Parameters
    ----------
    dtype : dtype, type
        The dtype to check.

    Raises
    ------
    TypeError : The dtype could not be casted to a date-like dtype.
    ValueError : The dtype is an illegal date-like dtype (e.g. the
                 the frequency provided is too specific)
    """

    try:
        typ = np.datetime_data(dtype)[0]
    except ValueError as e:
        raise TypeError('{error}'.format(error=e))
    if typ != 'generic' and typ != 'ns':
        msg = '{name!r} is too specific of a frequency, try passing {type!r}'
        raise ValueError(msg.format(name=dtype.name, type=dtype.type.__name__)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def convert_input(X, columns=None, deep=False):
    """
    Unite data into a DataFrame.
    Objects that do not contain column names take the names from the argument.
    Optionally perform deep copy of the data.
    """
    if not isinstance(X, pd.DataFrame):
        if isinstance(X, pd.Series):
            X = pd.DataFrame(X, copy=deep)
        else:
            if columns is not None and np.size(X,1) != len(columns):
                raise ValueError('The count of the column names does not correspond to the count of the columns')
            if isinstance(X, list):
                X = pd.DataFrame(X, columns=columns, copy=deep)  # lists are always copied, but for consistency, we still pass the argument
            elif isinstance(X, (np.generic, np.ndarray)):
                X = pd.DataFrame(X, columns=columns, copy=deep)
            elif isinstance(X, csr_matrix):
                X = pd.DataFrame(X.todense(), columns=columns, copy=deep)
            else:
                raise ValueError('Unexpected input type: %s' % (str(type(X))))
    elif deep:
        X = X.copy(deep=True)

    return X 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def get_numpy_dtype(obj):
    """Return NumPy data type associated to obj
    Return None if NumPy is not available
    or if obj is not a NumPy array or scalar"""
    if ndarray is not FakeObject:
        # NumPy is available
        import numpy as np
        if isinstance(obj, np.generic) or isinstance(obj, np.ndarray):
        # Numpy scalars all inherit from np.generic.
        # Numpy arrays all inherit from np.ndarray.
        # If we check that we are certain we have one of these
        # types then we are less likely to generate an exception below.
            try:
                return obj.dtype.type
            except (AttributeError, RuntimeError):
                #  AttributeError: some NumPy objects have no dtype attribute
                #  RuntimeError: happens with NetCDF objects (Issue 998)
                return


#==============================================================================
# Pandas support
#============================================================================== 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def _infer_dtype(value):
    """Infer the dtype for the features.

    It is required as the input is usually array of objects before padding.
    """
    while isinstance(value, (list, tuple)) and len(value) > 0:
        value = value[0]

    if not isinstance(value, Iterable):
        return np.array(value).dtype

    if value is not None and len(value) > 0 and np.issubdtype(
            np.array(value).dtype, np.generic):
        dtype = np.array(value[0]).dtype
    else:
        dtype = value.dtype

    # Single Precision
    if dtype == np.double:
        dtype = np.float32

    return dtype 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def cast_if_numpy_array(xp, array, chx_expected_dtype):
    """Casts NumPy result array to match the dtype of ChainerX's corresponding
    result.

    This function receives result arrays for both NumPy and ChainerX and only
    converts dtype of the NumPy array.
    """
    if xp is chainerx:
        assert isinstance(array, chainerx.ndarray)
        return array

    if xp is numpy:
        assert isinstance(array, (numpy.ndarray, numpy.generic))
        # Dtype conversion to allow comparing the correctnesses of the values.
        return array.astype(chx_expected_dtype, copy=False)

    assert False 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def fix_by_image_dimensions(self, height, width=None):
        if isinstance(height, (tuple, list)):
            assert width is None
            height, width = height[0], height[1]
        elif isinstance(height, (np.ndarray, np.generic)):
            assert width is None
            height, width = height.shape[0], height.shape[1]
        else:
            assert width is not None
            assert isinstance(height, int)
            assert isinstance(width, int)

        self.x1 = int(np.clip(self.x1, 0, width-1))
        self.x2 = int(np.clip(self.x2, 0, width-1))
        self.y1 = int(np.clip(self.y1, 0, height-1))
        self.y2 = int(np.clip(self.y2, 0, height-1))

        if self.x1 > self.x2:
            self.x1, self.x2 = self.x2, self.x1
        if self.y1 > self.y2:
            self.y1, self.y2 = self.y2, self.y1

        if self.x1 == self.x2:
            if self.x1 > 0:
                self.x1 = self.x1 - 1
            else:
                self.x2 = self.x2 + 1

        if self.y1 == self.y2:
            if self.y1 > 0:
                self.y1 = self.y1 - 1
            else:
                self.y2 = self.y2 + 1

        #self.width = self.x2 - self.x1
        #self.height = self.y2 - self.y1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def _bytesArrayToList(data):
    if type(data) in (bytes, str):
        is_list = False
    elif isinstance(data, (np.ndarray, np.generic)):
        if len(data.shape) == 0:
            is_list = False
            data = data.tolist()  # tolist will return a scalar in this case
            if type(data) in (list, tuple):
                is_list = True
            else:
                is_list = False
        else:
            is_list = True
    elif type(data) in (list, tuple):
        is_list = True
    else:
        is_list = False

    if is_list:
        out = []
        for item in data:
            out.append(_bytesArrayToList(item)) # recursive call
    elif type(data) is bytes:
        out = data.decode("utf-8")
    else:
        out = data

    return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def bytesArrayToList(data):
    if type(data) in (bytes, str):
        is_list = False
    elif isinstance(data, (np.ndarray, np.generic)):
        if len(data.shape) == 0:
            is_list = False
            data = data.tolist()  # tolist will return a scalar in this case
            if type(data) in (list, tuple):
                is_list = True
            else:
                is_list = False
        else:
            is_list = True
    elif type(data) in (list, tuple):
        is_list = True
    else:
        is_list = False

    if is_list:
        out = []
        for item in data:
            out.append(bytesArrayToList(item)) # recursive call
    elif type(data) is bytes:
        out = data.decode("utf-8")
    else:
        out = data

    return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def test_oddfeatures_3(self):
        # Tests some generic features
        atest = array([10], mask=True)
        btest = array([20])
        idx = atest.mask
        atest[idx] = btest[idx]
        assert_equal(atest, [20]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def test_tolist_specialcase(self):
        # Test mvoid.tolist: make sure we return a standard Python object
        a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
        # w/o mask: each entry is a np.void whose elements are standard Python
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))
        # w/ mask: each entry is a ma.void whose elements should be
        # standard Python
        a.mask[0] = (0, 1)
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def test_masked_where_oddities(self):
        # Tests some generic features.
        atest = ones((10, 10, 10), dtype=float)
        btest = zeros(atest.shape, MaskType)
        ctest = masked_where(btest, atest)
        assert_equal(atest, ctest) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import generic [as 別名]
def is_timedelta64_ns_dtype(arr_or_dtype):
    """
    Check whether the provided array or dtype is of the timedelta64[ns] dtype.

    This is a very specific dtype, so generic ones like `np.timedelta64`
    will return False if passed into this function.

    Parameters
    ----------
    arr_or_dtype : array-like
        The array or dtype to check.

    Returns
    -------
    boolean : Whether or not the array or dtype is of the
              timedelta64[ns] dtype.

    Examples
    --------
    >>> is_timedelta64_ns_dtype(np.dtype('m8[ns]'))
    True
    >>> is_timedelta64_ns_dtype(np.dtype('m8[ps]'))  # Wrong frequency
    False
    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))
    True
    >>> is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))
    False
    """
    return _is_dtype(arr_or_dtype, lambda dtype: dtype == _TD_DTYPE)