Python numpy.promote_types方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def test_promote_types_endian(self):
        # promote_types should always return native-endian types
        assert_equal(np.promote_types('<i8', '<i8'), np.dtype('i8'))
        assert_equal(np.promote_types('>i8', '>i8'), np.dtype('i8'))

        assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21'))
        assert_equal(np.promote_types('<i8', '<U16'), np.dtype('U21'))
        assert_equal(np.promote_types('>U16', '>i8'), np.dtype('U21'))
        assert_equal(np.promote_types('<U16', '<i8'), np.dtype('U21'))

        assert_equal(np.promote_types('<S5', '<U8'), np.dtype('U8'))
        assert_equal(np.promote_types('>S5', '>U8'), np.dtype('U8'))
        assert_equal(np.promote_types('<U8', '<S5'), np.dtype('U8'))
        assert_equal(np.promote_types('>U8', '>S5'), np.dtype('U8'))
        assert_equal(np.promote_types('<U5', '<U8'), np.dtype('U8'))
        assert_equal(np.promote_types('>U8', '>U5'), np.dtype('U8'))

        assert_equal(np.promote_types('<M8', '<M8'), np.dtype('M8'))
        assert_equal(np.promote_types('>M8', '>M8'), np.dtype('M8'))
        assert_equal(np.promote_types('<m8', '<m8'), np.dtype('m8'))
        assert_equal(np.promote_types('>m8', '>m8'), np.dtype('m8')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def __init__(self, endog, exog, offset=None, exposure=None, missing='none',
                 **kwargs):
        super(CountModel, self).__init__(endog, exog, missing=missing,
                                         offset=offset,
                                         exposure=exposure, **kwargs)
        if exposure is not None:
            self.exposure = np.log(self.exposure)
        self._check_inputs(self.offset, self.exposure, self.endog)
        if offset is None:
            delattr(self, 'offset')
        if exposure is None:
            delattr(self, 'exposure')

        # promote dtype to float64 if needed
        dt = np.promote_types(self.endog.dtype, np.float64)
        self.endog = np.asarray(self.endog, dt)
        dt = np.promote_types(self.exog.dtype, np.float64)
        self.exog = np.asarray(self.exog, dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def test_promote_types_endian(self):
        # promote_types should always return native-endian types
        assert_equal(np.promote_types('<i8', '<i8'), np.dtype('i8'))
        assert_equal(np.promote_types('>i8', '>i8'), np.dtype('i8'))

        assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U16'))
        assert_equal(np.promote_types('<i8', '<U16'), np.dtype('U16'))
        assert_equal(np.promote_types('>U16', '>i8'), np.dtype('U16'))
        assert_equal(np.promote_types('<U16', '<i8'), np.dtype('U16'))

        assert_equal(np.promote_types('<S5', '<U8'), np.dtype('U8'))
        assert_equal(np.promote_types('>S5', '>U8'), np.dtype('U8'))
        assert_equal(np.promote_types('<U8', '<S5'), np.dtype('U8'))
        assert_equal(np.promote_types('>U8', '>S5'), np.dtype('U8'))
        assert_equal(np.promote_types('<U5', '<U8'), np.dtype('U8'))
        assert_equal(np.promote_types('>U8', '>U5'), np.dtype('U8'))

        assert_equal(np.promote_types('<M8', '<M8'), np.dtype('M8'))
        assert_equal(np.promote_types('>M8', '>M8'), np.dtype('M8'))
        assert_equal(np.promote_types('<m8', '<m8'), np.dtype('m8'))
        assert_equal(np.promote_types('>m8', '>m8'), np.dtype('m8')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def common_type(*arrays):
    """Return a scalar type which is common to the input arrays.

    .. seealso:: :func:`numpy.common_type`
    """
    if len(arrays) == 0:
        return numpy.float16

    default_float_dtype = numpy.dtype('float64')
    dtypes = []
    for a in arrays:
        if a.dtype.kind == 'b':
            raise TypeError('can\'t get common type for non-numeric array')
        elif a.dtype.kind in 'iu':
            dtypes.append(default_float_dtype)
        else:
            dtypes.append(a.dtype)

    return functools.reduce(numpy.promote_types, dtypes).type 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def promote_types(dtype1, dtype2):
    """
    Get the smallest type to which the given scalar types can be cast.

    Args:
        dtype1 (builtin):
        dtype2 (builtin):

    Returns:
        A builtin datatype or None.
    """
    nptype1 = nptype_from_builtin(dtype1)
    nptype2 = nptype_from_builtin(dtype2)
    # Circumvent the undesirable np type promotion:
    # >> np.promote_types(np.float32, np.int)
    # dtype('float64')
    if np.issubdtype(nptype1, np.floating) and np.issubdtype(nptype2, np.signedinteger):
        nppromoted = nptype1
    elif np.issubdtype(nptype2, np.floating) and np.issubdtype(
        nptype1, np.signedinteger
    ):
        nppromoted = nptype2
    else:
        nppromoted = np.promote_types(nptype1, nptype2)
    return numpy_type_to_builtin_type(nppromoted) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def get_volume_pixeldata(sorted_slices):
    """
    the slice and intercept calculation can cause the slices to have different dtypes
    we should get the correct dtype that can cover all of them
    
    :type sorted_slices: list of slices
    :param sorted_slices: sliced sored in the correct order to create volume
    """
    slices = []
    combined_dtype = None
    for slice_ in sorted_slices:
        slice_data = _get_slice_pixeldata(slice_)
        slice_data = slice_data[numpy.newaxis, :, :]
        slices.append(slice_data)
        if combined_dtype is None:
            combined_dtype = slice_data.dtype
        else:
            combined_dtype = numpy.promote_types(combined_dtype, slice_data.dtype)

    # create the new volume with with the correct data
    vol = numpy.concatenate(slices, axis=0)

    # Done
    vol = numpy.transpose(vol, (2, 1, 0))
    return vol 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def dot(self, other):
        """Matrix/vector multiplication with another LookupArray"""
        if not isinstance(other, LookupArray):
            assert(self.shape[-1] == other.shape[0])
            new_lookup = self.lookup[:-1]+[None,]*(other.ndim-1)
            new_shape = self.shape[:-1]+other.shape[1:]
        elif compatible_quantity_part(self.lookup[-2:], other.lookup[:2]):
            new_lookup = self.lookup[:-2]+other.lookup[2:]
            new_shape = self.shape[:-2]+other.shape[2:]
            other = other.simple_view()
        else:
            raise NotImplementedError

        if len(new_shape) == 0:
            # handle the case of a scalar result
            new_array = np.dot(self.simple_view(), other)
        else:
            new_array = LookupArray(lookup=new_lookup, shape=new_shape,
                                    dtype=np.promote_types(self.dtype, other.dtype))
            new_array.simple_view()[:] = np.dot(self.simple_view(), other)
        return new_array 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def _set_or_promote_dtype(self, column_dtypes, c, dtype):
        existing_dtype = column_dtypes.get(c)
        if existing_dtype is None or existing_dtype != dtype:
            # Promote ints to floats - as we can't easily represent NaNs
            if np.issubdtype(dtype, int):
                dtype = np.dtype('f8')
            column_dtypes[c] = np.promote_types(column_dtypes.get(c, dtype), dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def _promote_struct_dtypes(dtype1, dtype2):
    if not set(dtype1.names).issuperset(set(dtype2.names)):
        raise Exception("Removing columns from dtype not handled")

    def _promote(type1, type2):
        if type2 is None:
            return type1
        if type1.shape is not None:
            if not type1.shape == type2.shape:
                raise Exception("We do not handle changes to dtypes that have shape")
            return np.promote_types(type1.base, type2.base), type1.shape
        return np.promote_types(type1, type2)
    return np.dtype([(n, _promote(dtype1.fields[n][0], dtype2.fields.get(n, (None,))[0])) for n in dtype1.names]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def _promote_types(self, dtype, dtype_str):
        if dtype_str == str(dtype):
            return dtype
        prev_dtype = self._dtype(dtype_str)
        if dtype.names is None:
            rtn = np.promote_types(dtype, prev_dtype)
        else:
            rtn = _promote_struct_dtypes(dtype, prev_dtype)
        rtn = np.dtype(rtn, metadata=dict(dtype.metadata or {}))
        return rtn 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def test_promote_types2(library):
    ndarr = np.array(np.arange(1000), dtype=[('abc', 'float64')])
    library.write('MYARR', ndarr[:800])
    library.append('MYARR', ndarr[-200:].astype([('abc', 'int64')]))
    saved_arr = library.read('MYARR').data
    assert np.all(ndarr.astype([('abc', np.promote_types('float64', 'int64'))]) == saved_arr) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
    print('+', end=' ')
    for char in ntypes:
        print(char, end=' ')
    print()
    for row in ntypes:
        if row == 'O':
            rowtype = GenericObject
        else:
            rowtype = np.obj2sctype(row)

        print(row, end=' ')
        for col in ntypes:
            if col == 'O':
                coltype = GenericObject
            else:
                coltype = np.obj2sctype(col)
            try:
                if firstarray:
                    rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
                else:
                    rowvalue = rowtype(inputfirstvalue)
                colvalue = coltype(inputsecondvalue)
                if use_promote_types:
                    char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
                else:
                    value = np.add(rowvalue, colvalue)
                    if isinstance(value, np.ndarray):
                        char = value.dtype.char
                    else:
                        char = np.dtype(type(value)).char
            except ValueError:
                char = '!'
            except OverflowError:
                char = '@'
            except TypeError:
                char = '#'
            print(char, end=' ')
        print() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def test_dtype_promotion(self):
        # datetime <op> datetime computes the metadata gcd
        # timedelta <op> timedelta computes the metadata gcd
        for mM in ['m', 'M']:
            assert_equal(
                np.promote_types(np.dtype(mM+'8[2Y]'), np.dtype(mM+'8[2Y]')),
                np.dtype(mM+'8[2Y]'))
            assert_equal(
                np.promote_types(np.dtype(mM+'8[12Y]'), np.dtype(mM+'8[15Y]')),
                np.dtype(mM+'8[3Y]'))
            assert_equal(
                np.promote_types(np.dtype(mM+'8[62M]'), np.dtype(mM+'8[24M]')),
                np.dtype(mM+'8[2M]'))
            assert_equal(
                np.promote_types(np.dtype(mM+'8[1W]'), np.dtype(mM+'8[2D]')),
                np.dtype(mM+'8[1D]'))
            assert_equal(
                np.promote_types(np.dtype(mM+'8[W]'), np.dtype(mM+'8[13s]')),
                np.dtype(mM+'8[s]'))
            assert_equal(
                np.promote_types(np.dtype(mM+'8[13W]'), np.dtype(mM+'8[49s]')),
                np.dtype(mM+'8[7s]'))
        # timedelta <op> timedelta raises when there is no reasonable gcd
        assert_raises(TypeError, np.promote_types,
                            np.dtype('m8[Y]'), np.dtype('m8[D]'))
        assert_raises(TypeError, np.promote_types,
                            np.dtype('m8[M]'), np.dtype('m8[W]'))
        # timedelta <op> timedelta may overflow with big unit ranges
        assert_raises(OverflowError, np.promote_types,
                            np.dtype('m8[W]'), np.dtype('m8[fs]'))
        assert_raises(OverflowError, np.promote_types,
                            np.dtype('m8[s]'), np.dtype('m8[as]')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def test_promote_types_strings(self):
        assert_equal(np.promote_types('bool', 'S'), np.dtype('S5'))
        assert_equal(np.promote_types('b', 'S'), np.dtype('S4'))
        assert_equal(np.promote_types('u1', 'S'), np.dtype('S3'))
        assert_equal(np.promote_types('u2', 'S'), np.dtype('S5'))
        assert_equal(np.promote_types('u4', 'S'), np.dtype('S10'))
        assert_equal(np.promote_types('u8', 'S'), np.dtype('S20'))
        assert_equal(np.promote_types('i1', 'S'), np.dtype('S4'))
        assert_equal(np.promote_types('i2', 'S'), np.dtype('S6'))
        assert_equal(np.promote_types('i4', 'S'), np.dtype('S11'))
        assert_equal(np.promote_types('i8', 'S'), np.dtype('S21'))
        assert_equal(np.promote_types('bool', 'U'), np.dtype('U5'))
        assert_equal(np.promote_types('b', 'U'), np.dtype('U4'))
        assert_equal(np.promote_types('u1', 'U'), np.dtype('U3'))
        assert_equal(np.promote_types('u2', 'U'), np.dtype('U5'))
        assert_equal(np.promote_types('u4', 'U'), np.dtype('U10'))
        assert_equal(np.promote_types('u8', 'U'), np.dtype('U20'))
        assert_equal(np.promote_types('i1', 'U'), np.dtype('U4'))
        assert_equal(np.promote_types('i2', 'U'), np.dtype('U6'))
        assert_equal(np.promote_types('i4', 'U'), np.dtype('U11'))
        assert_equal(np.promote_types('i8', 'U'), np.dtype('U21'))
        assert_equal(np.promote_types('bool', 'S1'), np.dtype('S5'))
        assert_equal(np.promote_types('bool', 'S30'), np.dtype('S30'))
        assert_equal(np.promote_types('b', 'S1'), np.dtype('S4'))
        assert_equal(np.promote_types('b', 'S30'), np.dtype('S30'))
        assert_equal(np.promote_types('u1', 'S1'), np.dtype('S3'))
        assert_equal(np.promote_types('u1', 'S30'), np.dtype('S30'))
        assert_equal(np.promote_types('u2', 'S1'), np.dtype('S5'))
        assert_equal(np.promote_types('u2', 'S30'), np.dtype('S30'))
        assert_equal(np.promote_types('u4', 'S1'), np.dtype('S10'))
        assert_equal(np.promote_types('u4', 'S30'), np.dtype('S30'))
        assert_equal(np.promote_types('u8', 'S1'), np.dtype('S20'))
        assert_equal(np.promote_types('u8', 'S30'), np.dtype('S30')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import promote_types [as 別名]
def _eig_worker(hermitian, a, sort, UPLO='L'):
    """Worker for ``eig``, ``eigh``"""
    if a.rank != 2 or a.shape[0] != a.shape[1]:
        raise ValueError("expect a square matrix!")
    a.legs[0].test_contractible(a.legs[1])
    if np.any(a.qtotal != a.chinfo.make_valid()):
        raise ValueError("Non-trivial qtotal -> Nilpotent. Not diagonizable!?")

    piped_axes, a = a.as_completely_blocked()  # ensure complete blocking

    dtype = np.float if hermitian else np.complex
    resw = np.zeros(a.shape[0], dtype=dtype)
    resv = diag(1., a.legs[0], dtype=np.promote_types(dtype, a.dtype))
    # w, v now default to 0 and the Identity
    for qindices, block in zip(a._qdata, a._data):  # non-zero blocks on the diagonal
        if hermitian:
            rw, rv = np.linalg.eigh(block, UPLO)
        else:
            rw, rv = np.linalg.eig(block)
        if sort is not None:  # apply sorting options
            perm = argsort(rw, sort)
            rw = np.take(rw, perm)
            rv = np.take(rv, perm, axis=1)
        qi = qindices[0]  # both `a` and `resv` are sorted and share the same qindices
        resv._data[qi] = rv  # replace idendity block
        resw[a.legs[0].get_slice(qi)] = rw  # replace eigenvalues
    if len(piped_axes) > 0:
        resv = resv.split_legs(0)  # the 'outer' facing leg is permuted back.
    return resw, resv