FM / pyarrow

/usr/local/lib64/python3.6/site-packages/pyarrow/

/usr/local/lib64/python3.6/site-packages/pyarrow

Name	Size	Mode	Actions
include/	-	0755	rm
includes/	-	0755	rm
tensorflow/	-	0755	rm
tests/	-	0755	rm
vendored/	-	0755	rm
__pycache__/	-	0755	rm
array.pxi	79609	0644	edit dl rm
benchmark.pxi	869	0644	edit dl rm
benchmark.py	856	0644	edit dl rm
builder.pxi	2688	0644	edit dl rm
cffi.py	2178	0644	edit dl rm
compat.pxi	1810	0644	edit dl rm
compat.py	1076	0644	edit dl rm
compute.py	22837	0644	edit dl rm
config.pxi	2613	0644	edit dl rm
csv.py	962	0644	edit dl rm
cuda.py	1087	0644	edit dl rm
dataset.py	33333	0644	edit dl rm
error.pxi	7807	0644	edit dl rm
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filesystem.py	14468	0644	edit dl rm
flight.py	1794	0644	edit dl rm
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json.py	858	0644	edit dl rm
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libarrow.so.600	48162600	0755	edit dl rm
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memory.pxi	7451	0644	edit dl rm
orc.py	5269	0644	edit dl rm
pandas-shim.pxi	7987	0644	edit dl rm
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plasma-store-server	482776	0755	edit dl rm
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public-api.pxi	12814	0644	edit dl rm
scalar.pxi	29182	0644	edit dl rm
serialization.pxi	19086	0644	edit dl rm
serialization.py	18202	0644	edit dl rm
table.pxi	72277	0644	edit dl rm
tensor.pxi	34311	0644	edit dl rm
types.pxi	79432	0644	edit dl rm
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util.py	5001	0644	edit dl rm
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_json.cpython-36m-x86_64-linux-gnu.so	90616	0755	edit dl rm
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_orc.cpython-36m-x86_64-linux-gnu.so	109776	0755	edit dl rm
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_parquet.cpython-36m-x86_64-linux-gnu.so	507792	0755	edit dl rm
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_s3fs.cpython-36m-x86_64-linux-gnu.so	199496	0755	edit dl rm
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__init__.pxd	2195	0644	edit dl rm
__init__.py	21263	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pyarrow/serialization.py (18202B)

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import collections
import warnings

import numpy as np

import pyarrow as pa
from pyarrow.lib import SerializationContext, py_buffer, builtin_pickle

try:
    import cloudpickle
except ImportError:
    cloudpickle = builtin_pickle

try:
    # This function is available after numpy-0.16.0.
    # See also: https://github.com/numpy/numpy/blob/master/numpy/lib/format.py
    from numpy.lib.format import descr_to_dtype
except ImportError:
    def descr_to_dtype(descr):
        '''
        descr may be stored as dtype.descr, which is a list of (name, format,
        [shape]) tuples where format may be a str or a tuple.  Offsets are not
        explicitly saved, rather empty fields with name, format == '', '|Vn'
        are added as padding.  This function reverses the process, eliminating
        the empty padding fields.
        '''
        if isinstance(descr, str):
            # No padding removal needed
            return np.dtype(descr)
        elif isinstance(descr, tuple):
            # subtype, will always have a shape descr[1]
            dt = descr_to_dtype(descr[0])
            return np.dtype((dt, descr[1]))
        fields = []
        offset = 0
        for field in descr:
            if len(field) == 2:
                name, descr_str = field
                dt = descr_to_dtype(descr_str)
            else:
                name, descr_str, shape = field
                dt = np.dtype((descr_to_dtype(descr_str), shape))

# Ignore padding bytes, which will be void bytes with '' as name
            # Once support for blank names is removed, only "if name == ''"
            # needed)
            is_pad = (name == '' and dt.type is np.void and dt.names is None)
            if not is_pad:
                fields.append((name, dt, offset))

offset += dt.itemsize

names, formats, offsets = zip(*fields)
        # names may be (title, names) tuples
        nametups = (n if isinstance(n, tuple) else (None, n) for n in names)
        titles, names = zip(*nametups)
        return np.dtype({'names': names, 'formats': formats, 'titles': titles,
                         'offsets': offsets, 'itemsize': offset})

def _deprecate_serialization(name):
    msg = (
        "'pyarrow.{}' is deprecated as of 2.0.0 and will be removed in a "
        "future version. Use pickle or the pyarrow IPC functionality instead."
    ).format(name)
    warnings.warn(msg, FutureWarning, stacklevel=3)

# ----------------------------------------------------------------------
# Set up serialization for numpy with dtype object (primitive types are
# handled efficiently with Arrow's Tensor facilities, see
# python_to_arrow.cc)

def _serialize_numpy_array_list(obj):
    if obj.dtype.str != '|O':
        # Make the array c_contiguous if necessary so that we can call change
        # the view.
        if not obj.flags.c_contiguous:
            obj = np.ascontiguousarray(obj)
        return obj.view('uint8'), np.lib.format.dtype_to_descr(obj.dtype)
    else:
        return obj.tolist(), np.lib.format.dtype_to_descr(obj.dtype)

def _deserialize_numpy_array_list(data):
    if data[1] != '|O':
        assert data[0].dtype == np.uint8
        return data[0].view(descr_to_dtype(data[1]))
    else:
        return np.array(data[0], dtype=np.dtype(data[1]))

def _serialize_numpy_matrix(obj):
    if obj.dtype.str != '|O':
        # Make the array c_contiguous if necessary so that we can call change
        # the view.
        if not obj.flags.c_contiguous:
            obj = np.ascontiguousarray(obj.A)
        return obj.A.view('uint8'), np.lib.format.dtype_to_descr(obj.dtype)
    else:
        return obj.A.tolist(), np.lib.format.dtype_to_descr(obj.dtype)

def _deserialize_numpy_matrix(data):
    if data[1] != '|O':
        assert data[0].dtype == np.uint8
        return np.matrix(data[0].view(descr_to_dtype(data[1])),
                         copy=False)
    else:
        return np.matrix(data[0], dtype=np.dtype(data[1]), copy=False)

# ----------------------------------------------------------------------
# pyarrow.RecordBatch-specific serialization matters

def _serialize_pyarrow_recordbatch(batch):
    output_stream = pa.BufferOutputStream()
    with pa.RecordBatchStreamWriter(output_stream, schema=batch.schema) as wr:
        wr.write_batch(batch)
    return output_stream.getvalue()  # This will also close the stream.

def _deserialize_pyarrow_recordbatch(buf):
    with pa.RecordBatchStreamReader(buf) as reader:
        return reader.read_next_batch()

# ----------------------------------------------------------------------
# pyarrow.Array-specific serialization matters

def _serialize_pyarrow_array(array):
    # TODO(suquark): implement more effcient array serialization.
    batch = pa.RecordBatch.from_arrays([array], [''])
    return _serialize_pyarrow_recordbatch(batch)

def _deserialize_pyarrow_array(buf):
    # TODO(suquark): implement more effcient array deserialization.
    batch = _deserialize_pyarrow_recordbatch(buf)
    return batch.columns[0]

# ----------------------------------------------------------------------
# pyarrow.Table-specific serialization matters

def _serialize_pyarrow_table(table):
    output_stream = pa.BufferOutputStream()
    with pa.RecordBatchStreamWriter(output_stream, schema=table.schema) as wr:
        wr.write_table(table)
    return output_stream.getvalue()  # This will also close the stream.

def _deserialize_pyarrow_table(buf):
    with pa.RecordBatchStreamReader(buf) as reader:
        return reader.read_all()

def _pickle_to_buffer(x):
    pickled = builtin_pickle.dumps(x, protocol=builtin_pickle.HIGHEST_PROTOCOL)
    return py_buffer(pickled)

def _load_pickle_from_buffer(data):
    as_memoryview = memoryview(data)
    return builtin_pickle.loads(as_memoryview)

# ----------------------------------------------------------------------
# pandas-specific serialization matters

def _register_custom_pandas_handlers(context):
    # ARROW-1784, faster path for pandas-only visibility

try:
        import pandas as pd
    except ImportError:
        return

import pyarrow.pandas_compat as pdcompat

sparse_type_error_msg = (
        '{0} serialization is not supported.\n'
        'Note that {0} is planned to be deprecated '
        'in pandas future releases.\n'
        'See https://github.com/pandas-dev/pandas/issues/19239 '
        'for more information.'
    )

def _serialize_pandas_dataframe(obj):
        if (pdcompat._pandas_api.has_sparse and
                isinstance(obj, pd.SparseDataFrame)):
            raise NotImplementedError(
                sparse_type_error_msg.format('SparseDataFrame')
            )

return pdcompat.dataframe_to_serialized_dict(obj)

def _deserialize_pandas_dataframe(data):
        return pdcompat.serialized_dict_to_dataframe(data)

def _serialize_pandas_series(obj):
        if (pdcompat._pandas_api.has_sparse and
                isinstance(obj, pd.SparseSeries)):
            raise NotImplementedError(
                sparse_type_error_msg.format('SparseSeries')
            )

return _serialize_pandas_dataframe(pd.DataFrame({obj.name: obj}))

def _deserialize_pandas_series(data):
        deserialized = _deserialize_pandas_dataframe(data)
        return deserialized[deserialized.columns[0]]

context.register_type(
        pd.Series, 'pd.Series',
        custom_serializer=_serialize_pandas_series,
        custom_deserializer=_deserialize_pandas_series)

context.register_type(
        pd.Index, 'pd.Index',
        custom_serializer=_pickle_to_buffer,
        custom_deserializer=_load_pickle_from_buffer)

if hasattr(pd.core, 'arrays'):
        if hasattr(pd.core.arrays, 'interval'):
            context.register_type(
                pd.core.arrays.interval.IntervalArray,
                'pd.core.arrays.interval.IntervalArray',
                custom_serializer=_pickle_to_buffer,
                custom_deserializer=_load_pickle_from_buffer)

if hasattr(pd.core.arrays, 'period'):
            context.register_type(
                pd.core.arrays.period.PeriodArray,
                'pd.core.arrays.period.PeriodArray',
                custom_serializer=_pickle_to_buffer,
                custom_deserializer=_load_pickle_from_buffer)

if hasattr(pd.core.arrays, 'datetimes'):
            context.register_type(
                pd.core.arrays.datetimes.DatetimeArray,
                'pd.core.arrays.datetimes.DatetimeArray',
                custom_serializer=_pickle_to_buffer,
                custom_deserializer=_load_pickle_from_buffer)

context.register_type(
        pd.DataFrame, 'pd.DataFrame',
        custom_serializer=_serialize_pandas_dataframe,
        custom_deserializer=_deserialize_pandas_dataframe)

def register_torch_serialization_handlers(serialization_context):
    # ----------------------------------------------------------------------
    # Set up serialization for pytorch tensors
    _deprecate_serialization("register_torch_serialization_handlers")

try:
        import torch

def _serialize_torch_tensor(obj):
            if obj.is_sparse:
                return pa.SparseCOOTensor.from_numpy(
                    obj._values().detach().numpy(),
                    obj._indices().detach().numpy().T,
                    shape=list(obj.shape))
            else:
                return obj.detach().numpy()

def _deserialize_torch_tensor(data):
            if isinstance(data, pa.SparseCOOTensor):
                return torch.sparse_coo_tensor(
                    indices=data.to_numpy()[1].T,
                    values=data.to_numpy()[0][:, 0],
                    size=data.shape)
            else:
                return torch.from_numpy(data)

for t in [torch.FloatTensor, torch.DoubleTensor, torch.HalfTensor,
                  torch.ByteTensor, torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor, torch.Tensor]:
            serialization_context.register_type(
                t, "torch." + t.__name__,
                custom_serializer=_serialize_torch_tensor,
                custom_deserializer=_deserialize_torch_tensor)
    except ImportError:
        # no torch
        pass

def _register_collections_serialization_handlers(serialization_context):
    def _serialize_deque(obj):
        return list(obj)

def _deserialize_deque(data):
        return collections.deque(data)

serialization_context.register_type(
        collections.deque, "collections.deque",
        custom_serializer=_serialize_deque,
        custom_deserializer=_deserialize_deque)

def _serialize_ordered_dict(obj):
        return list(obj.keys()), list(obj.values())

def _deserialize_ordered_dict(data):
        return collections.OrderedDict(zip(data[0], data[1]))

serialization_context.register_type(
        collections.OrderedDict, "collections.OrderedDict",
        custom_serializer=_serialize_ordered_dict,
        custom_deserializer=_deserialize_ordered_dict)

def _serialize_default_dict(obj):
        return list(obj.keys()), list(obj.values()), obj.default_factory

def _deserialize_default_dict(data):
        return collections.defaultdict(data[2], zip(data[0], data[1]))

serialization_context.register_type(
        collections.defaultdict, "collections.defaultdict",
        custom_serializer=_serialize_default_dict,
        custom_deserializer=_deserialize_default_dict)

def _serialize_counter(obj):
        return list(obj.keys()), list(obj.values())

def _deserialize_counter(data):
        return collections.Counter(dict(zip(data[0], data[1])))

serialization_context.register_type(
        collections.Counter, "collections.Counter",
        custom_serializer=_serialize_counter,
        custom_deserializer=_deserialize_counter)

# ----------------------------------------------------------------------
# Set up serialization for scipy sparse matrices. Primitive types are handled
# efficiently with Arrow's SparseTensor facilities, see numpy_convert.cc)

def _register_scipy_handlers(serialization_context):
    try:
        from scipy.sparse import (csr_matrix, csc_matrix, coo_matrix,
                                  isspmatrix_coo, isspmatrix_csr,
                                  isspmatrix_csc, isspmatrix)

def _serialize_scipy_sparse(obj):
            if isspmatrix_coo(obj):
                return 'coo', pa.SparseCOOTensor.from_scipy(obj)

elif isspmatrix_csr(obj):
                return 'csr', pa.SparseCSRMatrix.from_scipy(obj)

elif isspmatrix_csc(obj):
                return 'csc', pa.SparseCSCMatrix.from_scipy(obj)

elif isspmatrix(obj):
                return 'csr', pa.SparseCOOTensor.from_scipy(obj.to_coo())

else:
                raise NotImplementedError(
                    "Serialization of {} is not supported.".format(obj[0]))

def _deserialize_scipy_sparse(data):
            if data[0] == 'coo':
                return data[1].to_scipy()

elif data[0] == 'csr':
                return data[1].to_scipy()

elif data[0] == 'csc':
                return data[1].to_scipy()

else:
                return data[1].to_scipy()

serialization_context.register_type(
            coo_matrix, 'scipy.sparse.coo.coo_matrix',
            custom_serializer=_serialize_scipy_sparse,
            custom_deserializer=_deserialize_scipy_sparse)

serialization_context.register_type(
            csr_matrix, 'scipy.sparse.csr.csr_matrix',
            custom_serializer=_serialize_scipy_sparse,
            custom_deserializer=_deserialize_scipy_sparse)

serialization_context.register_type(
            csc_matrix, 'scipy.sparse.csc.csc_matrix',
            custom_serializer=_serialize_scipy_sparse,
            custom_deserializer=_deserialize_scipy_sparse)

except ImportError:
        # no scipy
        pass

# ----------------------------------------------------------------------
# Set up serialization for pydata/sparse tensors.

def _register_pydata_sparse_handlers(serialization_context):
    try:
        import sparse

def _serialize_pydata_sparse(obj):
            if isinstance(obj, sparse.COO):
                return 'coo', pa.SparseCOOTensor.from_pydata_sparse(obj)
            else:
                raise NotImplementedError(
                    "Serialization of {} is not supported.".format(sparse.COO))

def _deserialize_pydata_sparse(data):
            if data[0] == 'coo':
                data_array, coords = data[1].to_numpy()
                return sparse.COO(
                    data=data_array[:, 0],
                    coords=coords.T, shape=data[1].shape)

serialization_context.register_type(
            sparse.COO, 'sparse.COO',
            custom_serializer=_serialize_pydata_sparse,
            custom_deserializer=_deserialize_pydata_sparse)

except ImportError:
        # no pydata/sparse
        pass

def _register_default_serialization_handlers(serialization_context):

# ----------------------------------------------------------------------
    # Set up serialization for primitive datatypes

# TODO(pcm): This is currently a workaround until arrow supports
    # arbitrary precision integers. This is only called on long integers,
    # see the associated case in the append method in python_to_arrow.cc
    serialization_context.register_type(
        int, "int",
        custom_serializer=lambda obj: str(obj),
        custom_deserializer=lambda data: int(data))

serialization_context.register_type(
        type(lambda: 0), "function",
        pickle=True)

serialization_context.register_type(type, "type", pickle=True)

serialization_context.register_type(
        np.matrix, 'np.matrix',
        custom_serializer=_serialize_numpy_matrix,
        custom_deserializer=_deserialize_numpy_matrix)

serialization_context.register_type(
        np.ndarray, 'np.array',
        custom_serializer=_serialize_numpy_array_list,
        custom_deserializer=_deserialize_numpy_array_list)

serialization_context.register_type(
        pa.Array, 'pyarrow.Array',
        custom_serializer=_serialize_pyarrow_array,
        custom_deserializer=_deserialize_pyarrow_array)

serialization_context.register_type(
        pa.RecordBatch, 'pyarrow.RecordBatch',
        custom_serializer=_serialize_pyarrow_recordbatch,
        custom_deserializer=_deserialize_pyarrow_recordbatch)

serialization_context.register_type(
        pa.Table, 'pyarrow.Table',
        custom_serializer=_serialize_pyarrow_table,
        custom_deserializer=_deserialize_pyarrow_table)

_register_collections_serialization_handlers(serialization_context)
    _register_custom_pandas_handlers(serialization_context)
    _register_scipy_handlers(serialization_context)
    _register_pydata_sparse_handlers(serialization_context)

def register_default_serialization_handlers(serialization_context):
    _deprecate_serialization("register_default_serialization_handlers")
    _register_default_serialization_handlers(serialization_context)

def default_serialization_context():
    _deprecate_serialization("default_serialization_context")
    context = SerializationContext()
    _register_default_serialization_handlers(context)
    return context

cmd: