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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