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How to use _supported_types method in pandera

Best Python code snippet using pandera_python

check_utils.py

Source:check_utils.py

...11        ("multiindex_types", Tuple[type, ...]),12    ),13)14@lru_cache(maxsize=None)15def _supported_types():16    # pylint: disable=import-outside-toplevel17    table_types = [pd.DataFrame]18    field_types = [pd.Series]19    index_types = [pd.Index]20    multiindex_types = [pd.MultiIndex]21    try:22        import databricks.koalas as ks23        table_types.append(ks.DataFrame)24        field_types.append(ks.Series)25        index_types.append(ks.Index)26        multiindex_types.append(ks.MultiIndex)27    except ImportError:28        pass29    try:  # pragma: no cover30        import modin.pandas as mpd31        table_types.append(mpd.DataFrame)32        field_types.append(mpd.Series)33        index_types.append(mpd.Index)34        multiindex_types.append(mpd.MultiIndex)35    except ImportError:36        pass37    try:38        import dask.dataframe as dd39        table_types.append(dd.DataFrame)40        field_types.append(dd.Series)41        index_types.append(dd.Index)42    except ImportError:43        pass44    return SupportedTypes(45        tuple(table_types),46        tuple(field_types),47        tuple(index_types),48        tuple(multiindex_types),49    )50def is_table(obj):51    """Verifies whether an object is table-like.52    Where a table is a 2-dimensional data matrix of rows and columns, which53    can be indexed in multiple different ways.54    """55    return isinstance(obj, _supported_types().table_types)56def is_field(obj):57    """Verifies whether an object is field-like.58    Where a field is a columnar representation of data in a table-like59    data structure.60    """61    return isinstance(obj, _supported_types().field_types)62def is_index(obj):63    """Verifies whether an object is a table index."""64    return isinstance(obj, _supported_types().index_types)65def is_multiindex(obj):66    """Verifies whether an object is a multi-level table index."""67    return isinstance(obj, _supported_types().multiindex_types)68def is_supported_check_obj(obj):69    """Verifies whether an object is table- or field-like."""70    return is_table(obj) or is_field(obj)71def prepare_series_check_output(72    check_obj: Union[pd.Series, pd.DataFrame],73    check_output: pd.Series,74    ignore_na: bool = True,75    n_failure_cases: Optional[int] = None,76) -> Tuple[pd.Series, pd.Series]:77    """Prepare the check output and failure cases for a Series check output.78    check_obj can be a dataframe, since a check function can potentially return79    a Series resulting from applying some check function that outputs a Series.80    """81    if ignore_na:...

aggregate_ops_test.py

Source:aggregate_ops_test.py

...26  # after which it adds the remaining (N - M) tensors 8 at a time in a loop.27  # Test N in [1, 10] so we check each special-case from 1 to 9 and one28  # iteration of the loop.29  _MAX_N = 1030  def _supported_types(self):31    if test.is_gpu_available():32      return [dtypes.float16, dtypes.float32, dtypes.float64, dtypes.complex64,33              dtypes.complex128]34    return [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,35            dtypes.float16, dtypes.float32, dtypes.float64, dtypes.complex64,36            dtypes.complex128]37  def _buildData(self, shape, dtype):38    data = np.random.randn(*shape).astype(dtype.as_numpy_dtype)39    # For complex types, add an index-dependent imaginary component so we can40    # tell we got the right value.41    if dtype.is_complex:42      return data + 10j * data43    return data44  def testAddN(self):45    np.random.seed(12345)46    with self.test_session(use_gpu=True) as sess:47      for dtype in self._supported_types():48        for count in range(1, self._MAX_N + 1):49          data = [self._buildData((2, 2), dtype) for _ in range(count)]50          actual = sess.run(math_ops.add_n(data))51          expected = np.sum(np.vstack(52              [np.expand_dims(d, 0) for d in data]), axis=0)53          tol = 5e-3 if dtype == dtypes.float16 else 5e-754          self.assertAllClose(expected, actual, rtol=tol, atol=tol)55  def testUnknownShapes(self):56    np.random.seed(12345)57    with self.test_session(use_gpu=True) as sess:58      for dtype in self._supported_types():59        data = self._buildData((2, 2), dtype)60        for count in range(1, self._MAX_N + 1):61          data_ph = array_ops.placeholder(dtype=dtype)62          actual = sess.run(math_ops.add_n([data_ph] * count), {data_ph: data})63          expected = np.sum(np.vstack([np.expand_dims(data, 0)] * count),64                            axis=0)65          tol = 5e-3 if dtype == dtypes.float16 else 5e-766          self.assertAllClose(expected, actual, rtol=tol, atol=tol)67if __name__ == "__main__":...

_serialize.py

Source:_serialize.py

1"""2packages data into the raw format the clearvolume client expects3author: Martin Weigert4email: mweigert@mpi-cbg.de5"""6from __future__ import absolute_import7from __future__ import print_function8import numpy as np9import six10from six.moves import zip11DEFAULT_METADATA = {12    "index": 0,13    "time": 0,14    "channel": 0,15    "channelname": "python source",16    "viewmatrix": "1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.",17    "dim": 3,18    "color": (1., 1., 1., 1.),19    "type": "UnsignedShort",20    "bytespervoxel": 2,21    "elementsize": 1,22    "voxelwidth": 1,23    "voxelheight": 1,24    "voxeldepth": 1,25    "realunit": 126}27_SUPPORTED_TYPES = {np.uint8: "UnsignedByte",28                    np.uint16: "UnsignedShort"}29def _serialize_data(data, meta=DEFAULT_METADATA):30    """returns serialized version of data for clearvolume data viewer"""31    if not isinstance(data, np.ndarray):32        raise TypeError("data should be a numpy array (but is %s)" % type(data))33    if not data.dtype.type in _SUPPORTED_TYPES:34        raise ValueError("data type should be in (%s) (but is %s)" % (list(_SUPPORTED_TYPES.keys()), data.dtype))35    LenInt64 = len(np.int64(1).tostring())36    Ns = data.shape37    metaData = DEFAULT_METADATA.copy()38    # prepare header....39    metaData["type"] = _SUPPORTED_TYPES[data.dtype.type]40    for attrName, N in zip(["width", "height", "depth"], Ns[::-1]):41        metaData[attrName] = meta.get(attrName, N)42    for key, val in six.iteritems(meta):43        if key not in metaData:44            raise KeyError(" '%s' (= %s) as is an unknown property!" % (key, val))45        else:46            metaData[key] = val47    print(metaData)48    keyValPairs = [str(key) + ":" + str(val) for key, val in six.iteritems(metaData)]49    headerStr = ",".join(keyValPairs)50    headerStr = "[" + headerStr + "]"51    # headerStr = str(metaData).replace("{","[").replace("}","]").replace("'",'')#.replace(" ",'')52    headerLength = len(headerStr)53    dataStr = data.tostring()54    dataLength = len(dataStr)55    neededBufferLength = 3 * LenInt64 + headerLength + dataLength56    return "%s%s%s%s%s" % (np.int64(neededBufferLength).tostring(), np.int64(headerLength).tostring(), headerStr,57                           np.int64(dataLength).tostring(), dataStr)58if __name__ == '__main__':59    Ns = [1, 2, 3]60    d = (123 * np.linspace(0, 200, np.prod(Ns))).reshape(Ns).astype(np.uint8)61    # dStr = _serialize_data(d,{"width": 5.,"color":"1. .5 .2 1."})62    dStr = _serialize_data(d, {"width": "5", "color": "1. .5 .2 1."})...

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