Python numpy.can_cast方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_lonlat_to_healpix_shape():
    healpix_index = lonlat_to_healpix(2 * u.deg, 3 * u.deg, 8)
    assert np.can_cast(healpix_index, np.int64)

    lon, lat = np.ones((2, 4)) * u.deg, np.zeros((2, 4)) * u.deg
    healpix_index = lonlat_to_healpix(lon, lat, 8)
    assert healpix_index.shape == (2, 4)

    healpix_index, dx, dy = lonlat_to_healpix(2 * u.deg, 3 * u.deg, 8, return_offsets=True)
    assert np.can_cast(healpix_index, np.int64)
    assert isinstance(dx, float)
    assert isinstance(dy, float)

    lon, lat = np.ones((2, 4)) * u.deg, np.zeros((2, 4)) * u.deg
    healpix_index, dx, dy = lonlat_to_healpix(lon, lat, 8, return_offsets=True)
    assert healpix_index.shape == (2, 4)
    assert dx.shape == (2, 4)
    assert dy.shape == (2, 4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_can_cast_values(self):
        # gh-5917
        for dt in np.sctypes['int'] + np.sctypes['uint']:
            ii = np.iinfo(dt)
            assert_(np.can_cast(ii.min, dt))
            assert_(np.can_cast(ii.max, dt))
            assert_(not np.can_cast(ii.min - 1, dt))
            assert_(not np.can_cast(ii.max + 1, dt))

        for dt in np.sctypes['float']:
            fi = np.finfo(dt)
            assert_(np.can_cast(fi.min, dt))
            assert_(np.can_cast(fi.max, dt))


# Custom exception class to test exception propagation in fromiter 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def check_out_param(out, t, casting):
    from .base import broadcast_to

    if not hasattr(out, 'shape'):
        raise TypeError('return arrays must be a tensor')

    try:
        broadcast_to(t, out.shape)
    except ValueError:
        raise ValueError("operands could not be broadcast together "
                         "with shapes ({0}) ({1})".format(','.join(str(s) for s in t.shape),
                                                          ','.join(str(s) for s in out.shape)))

    if not np.can_cast(t.dtype, out.dtype, casting):
        raise TypeError("output (typecode '{0}') could not be coerced "
                        "to provided output paramter (typecode '{1}') "
                        "according to the casting rule ''{2}''".format(t.dtype.char, out.dtype.char, casting)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_can_cast_values(self):
        # gh-5917
        for dt in np.sctypes['int'] + np.sctypes['uint']:
            ii = np.iinfo(dt)
            assert_(np.can_cast(ii.min, dt))
            assert_(np.can_cast(ii.max, dt))
            assert_(not np.can_cast(ii.min - 1, dt))
            assert_(not np.can_cast(ii.max + 1, dt))

        for dt in np.sctypes['float']:
            fi = np.finfo(dt)
            assert_(np.can_cast(fi.min, dt))
            assert_(np.can_cast(fi.max, dt))
            

# Custom exception class to test exception propagation in fromiter 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def set_data(self, A):
        """
        Set the image array

        ACCEPTS: numpy/PIL Image A
        """
        # check if data is PIL Image without importing Image
        if hasattr(A, 'getpixel'):
            self._A = pil_to_array(A)
        else:
            self._A = cbook.safe_masked_invalid(A)

        if (self._A.dtype != np.uint8 and
            not np.can_cast(self._A.dtype, np.float)):
            raise TypeError("Image data can not convert to float")

        if (self._A.ndim not in (2, 3) or
            (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):
            raise TypeError("Invalid dimensions for image data")

        self._imcache = None
        self._rgbacache = None
        self._oldxslice = None
        self._oldyslice = None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def _can_cast_samekind(dtype1, dtype2):
    """Compatibility function for numpy 1.5.1; `casting` kw is numpy >=1.6.x

    default for casting kw is 'safe', which gives the same result as in 1.5.x
    and a strict subset of 'same_kind'.  So for 1.5.x we just skip the cases
    where 'safe' is False and 'same_kind' True.
    """
    if np.__version__[:3] == '1.5':
        return np.can_cast(dtype1, dtype2)
    else:
        return np.can_cast(dtype1, dtype2, casting='same_kind')


#------------------------------------------------------------------------------
# Generic tests
#------------------------------------------------------------------------------

# TODO check that spmatrix( ... , copy=X ) is respected
# TODO test prune
# TODO test has_sorted_indices 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_imul_scalar(self):
        def check(dtype):
            dat = self.dat_dtypes[dtype]
            datsp = self.datsp_dtypes[dtype]

            # Avoid implicit casting.
            if np.can_cast(type(2), dtype, casting='same_kind'):
                a = datsp.copy()
                a *= 2
                b = dat.copy()
                b *= 2
                assert_array_equal(b, a.todense())

            if np.can_cast(type(17.3), dtype, casting='same_kind'):
                a = datsp.copy()
                a *= 17.3
                b = dat.copy()
                b *= 17.3
                assert_array_equal(b, a.todense())

        for dtype in self.math_dtypes:
            check(dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_idiv_scalar(self):
        def check(dtype):
            dat = self.dat_dtypes[dtype]
            datsp = self.datsp_dtypes[dtype]

            if np.can_cast(type(2), dtype, casting='same_kind'):
                a = datsp.copy()
                a /= 2
                b = dat.copy()
                b /= 2
                assert_array_equal(b, a.todense())

            if np.can_cast(type(17.3), dtype, casting='same_kind'):
                a = datsp.copy()
                a /= 17.3
                b = dat.copy()
                b /= 17.3
                assert_array_equal(b, a.todense())

        for dtype in self.math_dtypes:
            # /= should only be used with float dtypes to avoid implicit
            # casting.
            if not np.can_cast(dtype, np.int_):
                check(dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def set_data(self, A):
        """
        Set the image array

        ACCEPTS: numpy/PIL Image A
        """

        self._full_res = A
        self._A = A

        if (self._A.dtype != np.uint8 and
                not np.can_cast(self._A.dtype, np.float)):
            raise TypeError("Image data can not convert to float")

        if (self._A.ndim not in (2, 3) or
                (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):
            raise TypeError("Invalid dimensions for image data")

        self._imcache = None
        self._rgbacache = None
        self._oldxslice = None
        self._oldyslice = None
        self._sx, self._sy = None, None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def _pad_and_fix_dtypes(self, cols, column_dtypes):
        # Pad out Nones with empty arrays of appropriate dtypes
        rtn = {}
        index = cols[INDEX]
        full_length = len(index)
        for k, v in iteritems(cols):
            if k != INDEX and k != 'SYMBOL':
                col_len = len(v)
                if col_len < full_length:
                    v = ([None, ] * (full_length - col_len)) + v
                    assert len(v) == full_length
                for i, arr in enumerate(v):
                    if arr is None:
                        #  Replace Nones with appropriate-length empty arrays
                        v[i] = self._empty(len(index[i]), column_dtypes.get(k))
                    else:
                        # Promote to appropriate dtype only if we can safely cast all the values
                        # This avoids the case with strings where None is cast as 'None'.
                        # Casting the object to a string is not worthwhile anyway as Pandas changes the
                        # dtype back to objectS
                        if (i == 0 or v[i].dtype != v[i - 1].dtype) and np.can_cast(v[i].dtype, column_dtypes[k],
                                                                                    casting='safe'):
                            v[i] = v[i].astype(column_dtypes[k], casting='safe')

            rtn[k] = v
        return rtn 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_nested_ring_shape(function):
    index = function(1, 8)
    assert np.can_cast(index, np.int64)

    index = function([[1, 2, 3], [2, 3, 4]], 8)
    assert index.shape == (2, 3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_ang2pix_shape():
    ipix = hp_compat.ang2pix(8, 1., 2.)
    assert np.can_cast(ipix, np.int64)

    ipix = hp_compat.ang2pix(8, [[1., 2.], [3., 4.]], [[1., 2.], [3., 4.]])
    assert ipix.shape == (2, 2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def test_vec2pix_shape():
    ipix = hp_compat.vec2pix(8, 1., 2., 3.)
    assert np.can_cast(ipix, np.int64)

    ipix = hp_compat.vec2pix(8, [[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]])
    assert ipix.shape == (2, 2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def print_cancast_table(ntypes):
    print('X', end=' ')
    for char in ntypes:
        print(char, end=' ')
    print()
    for row in ntypes:
        print(row, end=' ')
        for col in ntypes:
            print(int(np.can_cast(row, col)), end=' ')
        print() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import can_cast [as 別名]
def replace(self, to_replace, value, inplace=False, filter=None,
                regex=False, convert=True):
        inplace = validate_bool_kwarg(inplace, 'inplace')
        to_replace_values = np.atleast_1d(to_replace)
        if not np.can_cast(to_replace_values, bool):
            return self
        return super(BoolBlock, self).replace(to_replace, value,
                                              inplace=inplace, filter=filter,
                                              regex=regex, convert=convert)