Python numpy.issubdtype方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def SetDistribution(self, distinct_values):
        """This is all the values this column will ever see."""
        assert self.all_distinct_values is None
        # pd.isnull returns true for both np.nan and np.datetime64('NaT').
        is_nan = pd.isnull(distinct_values)
        contains_nan = np.any(is_nan)
        dv_no_nan = distinct_values[~is_nan]
        # NOTE: np.sort puts NaT values at beginning, and NaN values at end.
        # For our purposes we always add any null value to the beginning.
        vs = np.sort(np.unique(dv_no_nan))
        if contains_nan and np.issubdtype(distinct_values.dtype, np.datetime64):
            vs = np.insert(vs, 0, np.datetime64('NaT'))
        elif contains_nan:
            vs = np.insert(vs, 0, np.nan)
        if self.distribution_size is not None:
            assert len(vs) == self.distribution_size
        self.all_distinct_values = vs
        self.distribution_size = len(vs)
        return self 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def test_basic_property(self):
        # Check A = L L^H
        shapes = [(1, 1), (2, 2), (3, 3), (50, 50), (3, 10, 10)]
        dtypes = (np.float32, np.float64, np.complex64, np.complex128)

        for shape, dtype in itertools.product(shapes, dtypes):
            np.random.seed(1)
            a = np.random.randn(*shape)
            if np.issubdtype(dtype, np.complexfloating):
                a = a + 1j*np.random.randn(*shape)

            t = list(range(len(shape)))
            t[-2:] = -1, -2

            a = np.matmul(a.transpose(t).conj(), a)
            a = np.asarray(a, dtype=dtype)

            c = np.linalg.cholesky(a)

            b = np.matmul(c, c.transpose(t).conj())
            assert_allclose(b, a,
                            err_msg="{} {}\n{}\n{}".format(shape, dtype, a, c),
                            atol=500 * a.shape[0] * np.finfo(dtype).eps) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def _checksum(fname, buffer_size=512 * 1024, dtype='uint64'):
    # https://github.com/airware/buzzard/pull/39/#discussion_r239071556
    dtype = np.dtype(dtype)
    dtypesize = dtype.itemsize
    assert buffer_size % dtypesize == 0
    assert np.issubdtype(dtype, np.unsignedinteger)

    acc = dtype.type(0)
    with open(fname, "rb") as f:
        with np.warnings.catch_warnings():
            np.warnings.filterwarnings('ignore', r'overflow encountered')

            for chunk in iter(lambda: f.read(buffer_size), b""):
                head = np.frombuffer(chunk, dtype, count=len(chunk) // dtypesize)
                head = np.add.reduce(head, dtype=dtype, initial=acc)
                acc += head

                tailsize = len(chunk) % dtypesize
                if tailsize > 0:
                    # This should only be needed for file's tail
                    tail = chunk[-tailsize:] + b'\0' * (dtypesize - tailsize)
                    tail = np.frombuffer(tail, dtype)
                    acc += tail
        return '{:016x}'.format(acc.item()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def normalize_channels_parameter(channels, channel_count):
    if channels is None:
        if channel_count == 1:
            return [0], True
        else:
            return list(range(channel_count)), False

    indices = np.arange(channel_count)
    indices = indices[channels]
    indices = np.atleast_1d(indices)

    if isinstance(channels, slice):
        return indices.tolist(), False

    channels = np.asarray(channels)
    if not np.issubdtype(channels.dtype, np.number):
        raise TypeError('`channels` should be None or int or slice or list of int')
    if channels.ndim == 0:
        assert len(indices) == 1
        return indices.tolist(), True
    return indices.tolist(), False 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def __call__(self, df_or_series):
        if isinstance(df_or_series, SERIES_TYPE):
            if not np.issubdtype(df_or_series.dtype, np.number):
                raise NotImplementedError('non-numeric type is not supported for now')
            test_series = pd.Series([], dtype=df_or_series.dtype).describe(
                percentiles=self._percentiles, include=self._include, exclude=self._exclude)
            return self.new_series([df_or_series], shape=(len(test_series),),
                                   dtype=test_series.dtype,
                                   index_value=parse_index(test_series.index, store_data=True))
        else:
            test_inp_df = build_empty_df(df_or_series.dtypes)
            test_df = test_inp_df.describe(
                percentiles=self._percentiles, include=self._include, exclude=self._exclude)
            for dtype in test_df.dtypes:
                if not np.issubdtype(dtype, np.number):
                    raise NotImplementedError('non-numeric type is not supported for now')
            return self.new_dataframe([df_or_series], shape=test_df.shape, dtypes=test_df.dtypes,
                                      index_value=parse_index(test_df.index, store_data=True),
                                      columns_value=parse_index(test_df.columns, store_data=True)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def testAstype(self):
        arr = ones((10, 20, 30), chunk_size=3)

        arr2 = arr.astype(np.int32)
        arr2 = arr2.tiles()

        self.assertEqual(arr2.shape, (10, 20, 30))
        self.assertTrue(np.issubdtype(arr2.dtype, np.int32))
        self.assertEqual(arr2.op.casting, 'unsafe')

        with self.assertRaises(TypeError):
            arr.astype(np.int32, casting='safe')

        arr3 = arr.astype(arr.dtype, order='F')
        self.assertTrue(arr3.flags['F_CONTIGUOUS'])
        self.assertFalse(arr3.flags['C_CONTIGUOUS'])

        arr3 = arr3.tiles()

        self.assertEqual(arr3.chunks[0].order.value, 'F') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def _AccumulateHistogram(self, statistics=None, labels=None):
    """Accumulate histogram of binned statistic by label.

    Args:
      statistics: int32 np.array of shape [K, 1] of binned statistic
      labels: int32 np.array of shape [K, 1] of labels

    Returns:
      nothing
    """
    assert np.issubdtype(statistics.dtype, int)
    if not statistics.size:
      return
    p = self.params
    assert np.max(statistics) < self._histogram.shape[0], (
        'Histogram shape too small %d vs %d' %
        (np.max(statistics), self._histogram.shape[0]))
    for l in range(p.metadata.NumClasses()):
      indices = np.where(labels == l)[0]
      for s in statistics[indices]:
        self._histogram[s, l] += 1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def __init__(self, arr=None, metadata=None, missing_id='<missing>',
                 groupings=None, substitute=True, weights=None, name=None):
        super(self.__class__, self).__init__(arr, metadata, missing_id=missing_id, weights=weights, name=name)
        self._nan = np.array([np.nan]).astype(int)[0]

        if substitute and metadata is None:
            self.arr, self.orig_type = self.substitute_values(self.arr)
        elif substitute and metadata and not np.issubdtype(self.arr.dtype, np.integer):
            # custom metadata has been passed in from external source, and must be converted to int
            self.arr = self.arr.astype(int)
            self.metadata = { int(k):v for k, v in metadata.items() }
            self.metadata[self._nan] = missing_id

        self._groupings = {}
        if groupings is None:
            for x in np.unique(self.arr):
                self._groupings[x] = [x, x + 1, False]
        else:
            for x in np.unique(self.arr):
                self._groupings[x] = list(groupings[x])
        self._possible_groups = None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def to_java_array(m):
    '''
    to_java_array(m) yields to_java_ints(m) if m is an array of integers and to_java_doubles(m) if
    m is anything else. The numpy array m is tested via numpy.issubdtype(m.dtype, numpy.int64).
    '''
    if not hasattr(m, '__iter__'): return m
    m = np.asarray(m)
    if np.issubdtype(m.dtype, np.dtype(int).type) or all(isinstance(x, num.Integral) for x in m):
        return to_java_ints(m)
    else:
        return to_java_doubles(m) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def parse_type(self, hdat, dataobj=None):
        dtype = super(MGHImageType, self).parse_type(hdat, dataobj=dataobj)
        if   np.issubdtype(dtype, np.floating): dtype = np.float32
        elif np.issubdtype(dtype, np.int8):     dtype = np.int8
        elif np.issubdtype(dtype, np.int16):    dtype = np.int16
        elif np.issubdtype(dtype, np.integer):  dtype = np.int32
        else: raise ValueError('Could not deduce appropriate MGH type for dtype %s' % dtype)
        return dtype 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def output_indices(ii):
        ii = flattest(ii)
        if (np.issubdtype(ii.dtype, np.dtype('bool').type)): ii = np.where(ii)[0]
        return pimms.imm_array(ii) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
        dt = cls._dtype
        if dt is int or np.issubdtype(dt, np.integer):
            items = {"type": "number", "multipleOf": 1.0}
        elif dt is float or np.issubdtype(dt, np.floating):
            items = {"type": "number"}
        elif dt is str or np.issubdtype(dt, np.string_):
            items = {"type": "string"}
        elif dt is bool or np.issubdtype(dt, np.bool_):
            items = {"type": "boolean"}
        field_schema.update(type="array", items=items) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def test_simple():
    tree_data, tree_clusters = phate.tree.gen_dla(n_branch=3)
    phate_operator = phate.PHATE(knn=15, t=100, verbose=False)
    tree_phate = phate_operator.fit_transform(tree_data)
    assert tree_phate.shape == (tree_data.shape[0], 2)
    clusters = phate.cluster.kmeans(phate_operator, n_clusters='auto')
    assert np.issubdtype(clusters.dtype, np.signedinteger)
    assert len(np.unique(clusters)) >= 2
    assert len(clusters.shape) == 1
    assert len(clusters) == tree_data.shape[0]
    clusters = phate.cluster.kmeans(phate_operator, n_clusters=3)
    assert np.issubdtype(clusters.dtype, np.signedinteger)
    assert len(np.unique(clusters)) == 3
    assert len(clusters.shape) == 1
    assert len(clusters) == tree_data.shape[0]
    phate_operator.fit(phate_operator.graph)
    G = graphtools.Graph(
        phate_operator.graph.kernel,
        precomputed="affinity",
        use_pygsp=True,
        verbose=False,
    )
    phate_operator.fit(G)
    G = pygsp.graphs.Graph(G.W)
    phate_operator.fit(G)
    phate_operator.fit(anndata.AnnData(tree_data))
    with assert_raises_message(TypeError, "Expected phate_op to be of type PHATE. Got 1"):
        phate.cluster.kmeans(1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import issubdtype [as 別名]
def _name_arms(self, pred):
        if self.choice_names is None:
            return pred
        else:
            if not np.issubdtype(pred.dtype, np.integer):
                pred = pred.astype(int)
            return self.choice_names[pred]