Python operator.index方法代碼示例

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def bcd_decode(value):
    try:
        # Far faster than my routine for scalars
        return int('{:x}'.format(index(value)))
    except TypeError as exc:  # Might still be an array
        try:
            assert value.dtype.kind in 'iu'
        except Exception:
            raise exc

    d = np.arange(value.itemsize * 2)
    digits = (value[:, np.newaxis] >> d*4) & 0xf
    if digits.max() > 9:
        bad = value[(digits > 9).any(1)][0]
        raise ValueError("invalid BCD encoded value {0}={1}."
                         .format(bad, hex(bad)))
    return (digits * 10**d).sum(1) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def check(self, stream):
        """Check that the CRC at the end of athe stream is correct.

        Parameters
        ----------
        stream : int or array of unsigned int
            For an integer, the value is the stream to check the CRC for.
            For arrays, the dimension is treated as the index into the bits.
            A single stream would thus be of type `bool`. Unsigned integers
            represent multiple streams. E.g., for a 64-track Mark 4 header,
            the stream would be an array of ``np.uint64`` words.

        Returns
        -------
        ok : bool
             `True` if the calculated CRC is all zero (which should be the
             case if the CRC at the end of the stream is correct).
        """
        return self._crc(stream) == 0 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def test_same_kind_index_casting(self):
        # Indexes should be cast with same-kind and not safe, even if that
        # is somewhat unsafe. So test various different code paths.
        index = np.arange(5)
        u_index = index.astype(np.uintp)
        arr = np.arange(10)

        assert_array_equal(arr[index], arr[u_index])
        arr[u_index] = np.arange(5)
        assert_array_equal(arr, np.arange(10))

        arr = np.arange(10).reshape(5, 2)
        assert_array_equal(arr[index], arr[u_index])

        arr[u_index] = np.arange(5)[:,None]
        assert_array_equal(arr, np.arange(5)[:,None].repeat(2, axis=1))

        arr = np.arange(25).reshape(5, 5)
        assert_array_equal(arr[u_index, u_index], arr[index, index]) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def test_broken_sequence_not_nd_index(self):
        # See gh-5063:
        # If we have an object which claims to be a sequence, but fails
        # on item getting, this should not be converted to an nd-index (tuple)
        # If this object happens to be a valid index otherwise, it should work
        # This object here is very dubious and probably bad though:
        class SequenceLike(object):
            def __index__(self):
                return 0

            def __len__(self):
                return 1

            def __getitem__(self, item):
                raise IndexError('Not possible')

        arr = np.arange(10)
        assert_array_equal(arr[SequenceLike()], arr[SequenceLike(),])

        # also test that field indexing does not segfault
        # for a similar reason, by indexing a structured array
        arr = np.zeros((1,), dtype=[('f1', 'i8'), ('f2', 'i8')])
        assert_array_equal(arr[SequenceLike()], arr[SequenceLike(),]) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def test_boolean_index_cast_assign(self):
        # Setup the boolean index and float arrays.
        shape = (8, 63)
        bool_index = np.zeros(shape).astype(bool)
        bool_index[0, 1] = True
        zero_array = np.zeros(shape)

        # Assigning float is fine.
        zero_array[bool_index] = np.array([1])
        assert_equal(zero_array[0, 1], 1)

        # Fancy indexing works, although we get a cast warning.
        assert_warns(np.ComplexWarning,
                     zero_array.__setitem__, ([0], [1]), np.array([2 + 1j]))
        assert_equal(zero_array[0, 1], 2)  # No complex part

        # Cast complex to float, throwing away the imaginary portion.
        assert_warns(np.ComplexWarning,
                     zero_array.__setitem__, bool_index, np.array([1j]))
        assert_equal(zero_array[0, 1], 0) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def _check_multi_index(self, arr, index):
        """Check a multi index item getting and simple setting.

        Parameters
        ----------
        arr : ndarray
            Array to be indexed, must be a reshaped arange.
        index : tuple of indexing objects
            Index being tested.
        """
        # Test item getting
        try:
            mimic_get, no_copy = self._get_multi_index(arr, index)
        except Exception as e:
            if HAS_REFCOUNT:
                prev_refcount = sys.getrefcount(arr)
            assert_raises(type(e), arr.__getitem__, index)
            assert_raises(type(e), arr.__setitem__, index, 0)
            if HAS_REFCOUNT:
                assert_equal(prev_refcount, sys.getrefcount(arr))
            return

        self._compare_index_result(arr, index, mimic_get, no_copy) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def _check_single_index(self, arr, index):
        """Check a single index item getting and simple setting.

        Parameters
        ----------
        arr : ndarray
            Array to be indexed, must be an arange.
        index : indexing object
            Index being tested. Must be a single index and not a tuple
            of indexing objects (see also `_check_multi_index`).
        """
        try:
            mimic_get, no_copy = self._get_multi_index(arr, (index,))
        except Exception as e:
            if HAS_REFCOUNT:
                prev_refcount = sys.getrefcount(arr)
            assert_raises(type(e), arr.__getitem__, index)
            assert_raises(type(e), arr.__setitem__, index, 0)
            if HAS_REFCOUNT:
                assert_equal(prev_refcount, sys.getrefcount(arr))
            return

        self._compare_index_result(arr, index, mimic_get, no_copy) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def hash_dataframe_on(df, on, size, level=None):
    if on is None:
        idx = df.index
        if level is not None:
            idx = idx.to_frame(False)[level]
        hashed_label = pd.util.hash_pandas_object(idx, categorize=False)
    elif callable(on):
        # todo optimization can be added, if ``on`` is a numpy ufunc or sth can be vectorized
        hashed_label = pd.util.hash_pandas_object(df.index.map(on), categorize=False)
    else:
        if isinstance(on, list):
            to_concat = []
            for v in on:
                if isinstance(v, pd.Series):
                    to_concat.append(v)
                else:
                    to_concat.append(df[v])
            data = pd.concat(to_concat, axis=1)
        else:
            data = df[on]
        hashed_label = pd.util.hash_pandas_object(data, index=False, categorize=False)
    idx_to_grouped = df.index.groupby(hashed_label % size)
    return [idx_to_grouped.get(i, pd.Index([])).unique() for i in range(size)] 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def build_split_idx_to_origin_idx(splits, increase=True):
    # splits' len is equal to the original chunk size on a specified axis,
    # splits is sth like [[(0, True, 2, True), (2, False, 3, True)]]
    # which means there is one input chunk, and will be split into 2 out chunks
    # in this function, we want to build a new dict from the out chunk index to
    # the original chunk index and the inner position, like {0: (0, 0), 1: (0, 1)}
    if increase is False:
        splits = list(reversed(splits))
    out_idx = itertools.count(0)
    res = dict()
    for origin_idx, _ in enumerate(splits):
        for pos in range(len(splits[origin_idx])):
            if increase is False:
                o_idx = len(splits) - origin_idx - 1
            else:
                o_idx = origin_idx
            res[next(out_idx)] = o_idx, pos
    return res 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def build_series(series_obj, fill_value=1, size=1):
    empty_series = build_empty_series(series_obj.dtype, index=series_obj.index_value.to_pandas()[:0])
    record = _generate_value(series_obj.dtype, fill_value)
    if isinstance(empty_series.index, pd.MultiIndex):
        index = tuple(_generate_value(level.dtype, fill_value) for level in empty_series.index.levels)
        empty_series.loc[index, ] = record
    else:
        if isinstance(empty_series.index.dtype, pd.CategoricalDtype):
            index = None
        else:
            index = _generate_value(empty_series.index.dtype, fill_value)
        empty_series.loc[index] = record

    empty_series = pd.concat([empty_series] * size)
    # make sure dtype correct for MultiIndex
    empty_series = empty_series.astype(series_obj.dtype, copy=False)
    return empty_series 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def build_concatenated_rows_frame(df):
    from ..core import OutputType
    from .merge.concat import DataFrameConcat

    # When the df isn't splitted along the column axis, return the df directly.
    if df.chunk_shape[1] == 1:
        return df

    columns = concat_index_value([df.cix[0, idx].columns_value for idx in range(df.chunk_shape[1])],
                                 store_data=True)
    columns_size = columns.to_pandas().size

    out_chunks = []
    for idx in range(df.chunk_shape[0]):
        out_chunk = DataFrameConcat(axis=1, output_types=[OutputType.dataframe]).new_chunk(
            [df.cix[idx, k] for k in range(df.chunk_shape[1])], index=(idx, 0),
            shape=(df.cix[idx, 0].shape[0], columns_size), dtypes=df.dtypes,
            index_value=df.cix[idx, 0].index_value, columns_value=columns)
        out_chunks.append(out_chunk)

    return DataFrameConcat(axis=1, output_types=[OutputType.dataframe]).new_dataframe(
        [df], chunks=out_chunks, nsplits=((chunk.shape[0] for chunk in out_chunks), (df.shape[1],)),
        shape=df.shape, dtypes=df.dtypes,
        index_value=df.index_value, columns_value=df.columns_value) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def infer_index_value(left_index_value, right_index_value):
    from .core import IndexValue

    if isinstance(left_index_value.value, IndexValue.RangeIndex) and \
            isinstance(right_index_value.value, IndexValue.RangeIndex):
        if left_index_value.value.slice == right_index_value.value.slice:
            return left_index_value
        return parse_index(pd.Int64Index([]), left_index_value, right_index_value)

    # when left index and right index is identical, and both of them are elements unique,
    # we can infer that the out index should be identical also
    if left_index_value.is_unique and right_index_value.is_unique and \
            left_index_value.key == right_index_value.key:
        return left_index_value

    left_index = left_index_value.to_pandas()
    right_index = right_index_value.to_pandas()
    out_index = pd.Index([], dtype=find_common_type([left_index.dtype, right_index.dtype]))
    return parse_index(out_index, left_index_value, right_index_value) 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def validate_axis(axis, tileable=None):
    if axis == 'index':
        axis = 0
    elif axis == 'columns':
        axis = 1

    illegal = False
    try:
        axis = operator.index(axis)
        if axis < 0 or (tileable is not None and axis >= tileable.ndim):
            illegal = True
    except TypeError:
        illegal = True

    if illegal:
        raise ValueError('No axis named {} for '
                         'object type {}'.format(axis, type(tileable)))
    return axis 

# 需要導入模塊: import operator [as 別名]
# 或者: from operator import index [as 別名]
def __index__(self):
        return operator.index(self.__wrapped__)