""" Msgpack serializer support for reading and writing pandas data structures to disk portions of msgpack_numpy package, by Lev Givon were incorporated into this module (and tests_packers.py) License ======= Copyright (c) 2013, Lev Givon. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Lev Givon nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ from datetime import datetime, date, timedelta from dateutil.parser import parse import os from textwrap import dedent import warnings import numpy as np from pandas import compat from pandas.compat import u, u_safe from pandas.core.dtypes.common import ( is_categorical_dtype, is_object_dtype, needs_i8_conversion, pandas_dtype) from pandas import (Timestamp, Period, Series, DataFrame, # noqa Index, MultiIndex, Float64Index, Int64Index, Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT, Categorical, CategoricalIndex) from pandas.core.sparse.api import SparseSeries, SparseDataFrame from pandas.core.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame from pandas.errors import PerformanceWarning from pandas.io.common import get_filepath_or_buffer, _stringify_path from pandas.core.internals import BlockManager, make_block, _safe_reshape import pandas.core.internals as internals from pandas.io.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType from pandas.util._move import ( BadMove as _BadMove, move_into_mutable_buffer as _move_into_mutable_buffer, ) # check whcih compression libs we have installed try: import zlib def _check_zlib(): pass except ImportError: def _check_zlib(): raise ImportError('zlib is not installed') _check_zlib.__doc__ = dedent( """\ Check if zlib is installed. Raises ------ ImportError Raised when zlib is not installed. """, ) try: import blosc def _check_blosc(): pass except ImportError: def _check_blosc(): raise ImportError('blosc is not installed') _check_blosc.__doc__ = dedent( """\ Check if blosc is installed. Raises ------ ImportError Raised when blosc is not installed. """, ) # until we can pass this into our conversion functions, # this is pretty hacky compressor = None def to_msgpack(path_or_buf, *args, **kwargs): """ msgpack (serialize) object to input file path THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release. Parameters ---------- path_or_buf : string File path, buffer-like, or None if None, return generated string args : an object or objects to serialize encoding: encoding for unicode objects append : boolean whether to append to an existing msgpack (default is False) compress : type of compressor (zlib or blosc), default to None (no compression) """ global compressor compressor = kwargs.pop('compress', None) if compressor: compressor = u(compressor) append = kwargs.pop('append', None) if append: mode = 'a+b' else: mode = 'wb' def writer(fh): for a in args: fh.write(pack(a, **kwargs)) path_or_buf = _stringify_path(path_or_buf) if isinstance(path_or_buf, compat.string_types): with open(path_or_buf, mode) as fh: writer(fh) elif path_or_buf is None: buf = compat.BytesIO() writer(buf) return buf.getvalue() else: writer(path_or_buf) def read_msgpack(path_or_buf, encoding='utf-8', iterator=False, **kwargs): """ Load msgpack pandas object from the specified file path THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release. Parameters ---------- path_or_buf : string File path, BytesIO like or string encoding: Encoding for decoding msgpack str type iterator : boolean, if True, return an iterator to the unpacker (default is False) Returns ------- obj : type of object stored in file """ path_or_buf, _, _ = get_filepath_or_buffer(path_or_buf) if iterator: return Iterator(path_or_buf) def read(fh): l = list(unpack(fh, encoding=encoding, **kwargs)) if len(l) == 1: return l[0] return l # see if we have an actual file if isinstance(path_or_buf, compat.string_types): try: exists = os.path.exists(path_or_buf) except (TypeError, ValueError): exists = False if exists: with open(path_or_buf, 'rb') as fh: return read(fh) # treat as a binary-like if isinstance(path_or_buf, compat.binary_type): fh = None try: fh = compat.BytesIO(path_or_buf) return read(fh) finally: if fh is not None: fh.close() # a buffer like if hasattr(path_or_buf, 'read') and compat.callable(path_or_buf.read): return read(path_or_buf) raise ValueError('path_or_buf needs to be a string file path or file-like') dtype_dict = {21: np.dtype('M8[ns]'), u('datetime64[ns]'): np.dtype('M8[ns]'), u('datetime64[us]'): np.dtype('M8[us]'), 22: np.dtype('m8[ns]'), u('timedelta64[ns]'): np.dtype('m8[ns]'), u('timedelta64[us]'): np.dtype('m8[us]'), # this is platform int, which we need to remap to np.int64 # for compat on windows platforms 7: np.dtype('int64'), 'category': 'category' } def dtype_for(t): """ return my dtype mapping, whether number or name """ if t in dtype_dict: return dtype_dict[t] return np.typeDict.get(t, t) c2f_dict = {'complex': np.float64, 'complex128': np.float64, 'complex64': np.float32} # numpy 1.6.1 compat if hasattr(np, 'float128'): c2f_dict['complex256'] = np.float128 def c2f(r, i, ctype_name): """ Convert strings to complex number instance with specified numpy type. """ ftype = c2f_dict[ctype_name] return np.typeDict[ctype_name](ftype(r) + 1j * ftype(i)) def convert(values): """ convert the numpy values to a list """ dtype = values.dtype if is_categorical_dtype(values): return values elif is_object_dtype(dtype): return values.ravel().tolist() if needs_i8_conversion(dtype): values = values.view('i8') v = values.ravel() if compressor == 'zlib': _check_zlib() # return string arrays like they are if dtype == np.object_: return v.tolist() # convert to a bytes array v = v.tostring() return ExtType(0, zlib.compress(v)) elif compressor == 'blosc': _check_blosc() # return string arrays like they are if dtype == np.object_: return v.tolist() # convert to a bytes array v = v.tostring() return ExtType(0, blosc.compress(v, typesize=dtype.itemsize)) # ndarray (on original dtype) return ExtType(0, v.tostring()) def unconvert(values, dtype, compress=None): as_is_ext = isinstance(values, ExtType) and values.code == 0 if as_is_ext: values = values.data if is_categorical_dtype(dtype): return values elif is_object_dtype(dtype): return np.array(values, dtype=object) dtype = pandas_dtype(dtype).base if not as_is_ext: values = values.encode('latin1') if compress: if compress == u'zlib': _check_zlib() decompress = zlib.decompress elif compress == u'blosc': _check_blosc() decompress = blosc.decompress else: raise ValueError("compress must be one of 'zlib' or 'blosc'") try: return np.frombuffer( _move_into_mutable_buffer(decompress(values)), dtype=dtype, ) except _BadMove as e: # Pull the decompressed data off of the `_BadMove` exception. # We don't just store this in the locals because we want to # minimize the risk of giving users access to a `bytes` object # whose data is also given to a mutable buffer. values = e.args[0] if len(values) > 1: # The empty string and single characters are memoized in many # string creating functions in the capi. This case should not # warn even though we need to make a copy because we are only # copying at most 1 byte. warnings.warn( 'copying data after decompressing; this may mean that' ' decompress is caching its result', PerformanceWarning, ) # fall through to copying `np.fromstring` # Copy the bytes into a numpy array. buf = np.frombuffer(values, dtype=dtype) buf = buf.copy() # required to not mutate the original data buf.flags.writeable = True return buf def encode(obj): """ Data encoder """ tobj = type(obj) if isinstance(obj, Index): if isinstance(obj, RangeIndex): return {u'typ': u'range_index', u'klass': u(obj.__class__.__name__), u'name': getattr(obj, 'name', None), u'start': getattr(obj, '_start', None), u'stop': getattr(obj, '_stop', None), u'step': getattr(obj, '_step', None)} elif isinstance(obj, PeriodIndex): return {u'typ': u'period_index', u'klass': u(obj.__class__.__name__), u'name': getattr(obj, 'name', None), u'freq': u_safe(getattr(obj, 'freqstr', None)), u'dtype': u(obj.dtype.name), u'data': convert(obj.asi8), u'compress': compressor} elif isinstance(obj, DatetimeIndex): tz = getattr(obj, 'tz', None) # store tz info and data as UTC if tz is not None: tz = u(tz.zone) obj = obj.tz_convert('UTC') return {u'typ': u'datetime_index', u'klass': u(obj.__class__.__name__), u'name': getattr(obj, 'name', None), u'dtype': u(obj.dtype.name), u'data': convert(obj.asi8), u'freq': u_safe(getattr(obj, 'freqstr', None)), u'tz': tz, u'compress': compressor} elif isinstance(obj, MultiIndex): return {u'typ': u'multi_index', u'klass': u(obj.__class__.__name__), u'names': getattr(obj, 'names', None), u'dtype': u(obj.dtype.name), u'data': convert(obj.values), u'compress': compressor} else: return {u'typ': u'index', u'klass': u(obj.__class__.__name__), u'name': getattr(obj, 'name', None), u'dtype': u(obj.dtype.name), u'data': convert(obj.values), u'compress': compressor} elif isinstance(obj, Categorical): return {u'typ': u'category', u'klass': u(obj.__class__.__name__), u'name': getattr(obj, 'name', None), u'codes': obj.codes, u'categories': obj.categories, u'ordered': obj.ordered, u'compress': compressor} elif isinstance(obj, Series): if isinstance(obj, SparseSeries): raise NotImplementedError( 'msgpack sparse series is not implemented' ) # d = {'typ': 'sparse_series', # 'klass': obj.__class__.__name__, # 'dtype': obj.dtype.name, # 'index': obj.index, # 'sp_index': obj.sp_index, # 'sp_values': convert(obj.sp_values), # 'compress': compressor} # for f in ['name', 'fill_value', 'kind']: # d[f] = getattr(obj, f, None) # return d else: return {u'typ': u'series', u'klass': u(obj.__class__.__name__), u'name': getattr(obj, 'name', None), u'index': obj.index, u'dtype': u(obj.dtype.name), u'data': convert(obj.values), u'compress': compressor} elif issubclass(tobj, NDFrame): if isinstance(obj, SparseDataFrame): raise NotImplementedError( 'msgpack sparse frame is not implemented' ) # d = {'typ': 'sparse_dataframe', # 'klass': obj.__class__.__name__, # 'columns': obj.columns} # for f in ['default_fill_value', 'default_kind']: # d[f] = getattr(obj, f, None) # d['data'] = dict([(name, ss) # for name, ss in compat.iteritems(obj)]) # return d else: data = obj._data if not data.is_consolidated(): data = data.consolidate() # the block manager return {u'typ': u'block_manager', u'klass': u(obj.__class__.__name__), u'axes': data.axes, u'blocks': [{u'locs': b.mgr_locs.as_array, u'values': convert(b.values), u'shape': b.values.shape, u'dtype': u(b.dtype.name), u'klass': u(b.__class__.__name__), u'compress': compressor} for b in data.blocks] } elif isinstance(obj, (datetime, date, np.datetime64, timedelta, np.timedelta64)) or obj is NaT: if isinstance(obj, Timestamp): tz = obj.tzinfo if tz is not None: tz = u(tz.zone) freq = obj.freq if freq is not None: freq = u(freq.freqstr) return {u'typ': u'timestamp', u'value': obj.value, u'freq': freq, u'tz': tz} if obj is NaT: return {u'typ': u'nat'} elif isinstance(obj, np.timedelta64): return {u'typ': u'timedelta64', u'data': obj.view('i8')} elif isinstance(obj, timedelta): return {u'typ': u'timedelta', u'data': (obj.days, obj.seconds, obj.microseconds)} elif isinstance(obj, np.datetime64): return {u'typ': u'datetime64', u'data': u(str(obj))} elif isinstance(obj, datetime): return {u'typ': u'datetime', u'data': u(obj.isoformat())} elif isinstance(obj, date): return {u'typ': u'date', u'data': u(obj.isoformat())} raise Exception("cannot encode this datetimelike object: %s" % obj) elif isinstance(obj, Period): return {u'typ': u'period', u'ordinal': obj.ordinal, u'freq': u(obj.freq)} elif isinstance(obj, BlockIndex): return {u'typ': u'block_index', u'klass': u(obj.__class__.__name__), u'blocs': obj.blocs, u'blengths': obj.blengths, u'length': obj.length} elif isinstance(obj, IntIndex): return {u'typ': u'int_index', u'klass': u(obj.__class__.__name__), u'indices': obj.indices, u'length': obj.length} elif isinstance(obj, np.ndarray): return {u'typ': u'ndarray', u'shape': obj.shape, u'ndim': obj.ndim, u'dtype': u(obj.dtype.name), u'data': convert(obj), u'compress': compressor} elif isinstance(obj, np.number): if np.iscomplexobj(obj): return {u'typ': u'np_scalar', u'sub_typ': u'np_complex', u'dtype': u(obj.dtype.name), u'real': u(obj.real.__repr__()), u'imag': u(obj.imag.__repr__())} else: return {u'typ': u'np_scalar', u'dtype': u(obj.dtype.name), u'data': u(obj.__repr__())} elif isinstance(obj, complex): return {u'typ': u'np_complex', u'real': u(obj.real.__repr__()), u'imag': u(obj.imag.__repr__())} return obj def decode(obj): """ Decoder for deserializing numpy data types. """ typ = obj.get(u'typ') if typ is None: return obj elif typ == u'timestamp': freq = obj[u'freq'] if 'freq' in obj else obj[u'offset'] return Timestamp(obj[u'value'], tz=obj[u'tz'], freq=freq) elif typ == u'nat': return NaT elif typ == u'period': return Period(ordinal=obj[u'ordinal'], freq=obj[u'freq']) elif typ == u'index': dtype = dtype_for(obj[u'dtype']) data = unconvert(obj[u'data'], dtype, obj.get(u'compress')) return globals()[obj[u'klass']](data, dtype=dtype, name=obj[u'name']) elif typ == u'range_index': return globals()[obj[u'klass']](obj[u'start'], obj[u'stop'], obj[u'step'], name=obj[u'name']) elif typ == u'multi_index': dtype = dtype_for(obj[u'dtype']) data = unconvert(obj[u'data'], dtype, obj.get(u'compress')) data = [tuple(x) for x in data] return globals()[obj[u'klass']].from_tuples(data, names=obj[u'names']) elif typ == u'period_index': data = unconvert(obj[u'data'], np.int64, obj.get(u'compress')) d = dict(name=obj[u'name'], freq=obj[u'freq']) return globals()[obj[u'klass']]._from_ordinals(data, **d) elif typ == u'datetime_index': data = unconvert(obj[u'data'], np.int64, obj.get(u'compress')) d = dict(name=obj[u'name'], freq=obj[u'freq'], verify_integrity=False) result = globals()[obj[u'klass']](data, **d) tz = obj[u'tz'] # reverse tz conversion if tz is not None: result = result.tz_localize('UTC').tz_convert(tz) return result elif typ == u'category': from_codes = globals()[obj[u'klass']].from_codes return from_codes(codes=obj[u'codes'], categories=obj[u'categories'], ordered=obj[u'ordered']) elif typ == u'series': dtype = dtype_for(obj[u'dtype']) pd_dtype = pandas_dtype(dtype) index = obj[u'index'] result = globals()[obj[u'klass']](unconvert(obj[u'data'], dtype, obj[u'compress']), index=index, dtype=pd_dtype, name=obj[u'name']) return result elif typ == u'block_manager': axes = obj[u'axes'] def create_block(b): values = _safe_reshape(unconvert( b[u'values'], dtype_for(b[u'dtype']), b[u'compress']), b[u'shape']) # locs handles duplicate column names, and should be used instead # of items; see GH 9618 if u'locs' in b: placement = b[u'locs'] else: placement = axes[0].get_indexer(b[u'items']) return make_block(values=values, klass=getattr(internals, b[u'klass']), placement=placement, dtype=b[u'dtype']) blocks = [create_block(b) for b in obj[u'blocks']] return globals()[obj[u'klass']](BlockManager(blocks, axes)) elif typ == u'datetime': return parse(obj[u'data']) elif typ == u'datetime64': return np.datetime64(parse(obj[u'data'])) elif typ == u'date': return parse(obj[u'data']).date() elif typ == u'timedelta': return timedelta(*obj[u'data']) elif typ == u'timedelta64': return np.timedelta64(int(obj[u'data'])) # elif typ == 'sparse_series': # dtype = dtype_for(obj['dtype']) # return globals()[obj['klass']]( # unconvert(obj['sp_values'], dtype, obj['compress']), # sparse_index=obj['sp_index'], index=obj['index'], # fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name']) # elif typ == 'sparse_dataframe': # return globals()[obj['klass']]( # obj['data'], columns=obj['columns'], # default_fill_value=obj['default_fill_value'], # default_kind=obj['default_kind'] # ) # elif typ == 'sparse_panel': # return globals()[obj['klass']]( # obj['data'], items=obj['items'], # default_fill_value=obj['default_fill_value'], # default_kind=obj['default_kind']) elif typ == u'block_index': return globals()[obj[u'klass']](obj[u'length'], obj[u'blocs'], obj[u'blengths']) elif typ == u'int_index': return globals()[obj[u'klass']](obj[u'length'], obj[u'indices']) elif typ == u'ndarray': return unconvert(obj[u'data'], np.typeDict[obj[u'dtype']], obj.get(u'compress')).reshape(obj[u'shape']) elif typ == u'np_scalar': if obj.get(u'sub_typ') == u'np_complex': return c2f(obj[u'real'], obj[u'imag'], obj[u'dtype']) else: dtype = dtype_for(obj[u'dtype']) try: return dtype(obj[u'data']) except: return dtype.type(obj[u'data']) elif typ == u'np_complex': return complex(obj[u'real'] + u'+' + obj[u'imag'] + u'j') elif isinstance(obj, (dict, list, set)): return obj else: return obj def pack(o, default=encode, encoding='utf-8', unicode_errors='strict', use_single_float=False, autoreset=1, use_bin_type=1): """ Pack an object and return the packed bytes. """ return Packer(default=default, encoding=encoding, unicode_errors=unicode_errors, use_single_float=use_single_float, autoreset=autoreset, use_bin_type=use_bin_type).pack(o) def unpack(packed, object_hook=decode, list_hook=None, use_list=False, encoding='utf-8', unicode_errors='strict', object_pairs_hook=None, max_buffer_size=0, ext_hook=ExtType): """ Unpack a packed object, return an iterator Note: packed lists will be returned as tuples """ return Unpacker(packed, object_hook=object_hook, list_hook=list_hook, use_list=use_list, encoding=encoding, unicode_errors=unicode_errors, object_pairs_hook=object_pairs_hook, max_buffer_size=max_buffer_size, ext_hook=ext_hook) class Packer(_Packer): def __init__(self, default=encode, encoding='utf-8', unicode_errors='strict', use_single_float=False, autoreset=1, use_bin_type=1): super(Packer, self).__init__(default=default, encoding=encoding, unicode_errors=unicode_errors, use_single_float=use_single_float, autoreset=autoreset, use_bin_type=use_bin_type) class Unpacker(_Unpacker): def __init__(self, file_like=None, read_size=0, use_list=False, object_hook=decode, object_pairs_hook=None, list_hook=None, encoding='utf-8', unicode_errors='strict', max_buffer_size=0, ext_hook=ExtType): super(Unpacker, self).__init__(file_like=file_like, read_size=read_size, use_list=use_list, object_hook=object_hook, object_pairs_hook=object_pairs_hook, list_hook=list_hook, encoding=encoding, unicode_errors=unicode_errors, max_buffer_size=max_buffer_size, ext_hook=ext_hook) class Iterator(object): """ manage the unpacking iteration, close the file on completion """ def __init__(self, path, **kwargs): self.path = path self.kwargs = kwargs def __iter__(self): needs_closing = True try: # see if we have an actual file if isinstance(self.path, compat.string_types): try: path_exists = os.path.exists(self.path) except TypeError: path_exists = False if path_exists: fh = open(self.path, 'rb') else: fh = compat.BytesIO(self.path) else: if not hasattr(self.path, 'read'): fh = compat.BytesIO(self.path) else: # a file-like needs_closing = False fh = self.path unpacker = unpack(fh) for o in unpacker: yield o finally: if needs_closing: fh.close()