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249 lines
8.4 KiB
249 lines
8.4 KiB
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from datetime import datetime, date, time, timedelta
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from fractions import Fraction
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from importlib import import_module
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from collections import OrderedDict
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from decimal import Decimal
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from logging import warning
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from json_tricks import NoPandasException, NoNumpyException
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class DuplicateJsonKeyException(Exception):
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""" Trying to load a json map which contains duplicate keys, but allow_duplicates is False """
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class TricksPairHook(object):
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"""
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Hook that converts json maps to the appropriate python type (dict or OrderedDict)
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and then runs any number of hooks on the individual maps.
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"""
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def __init__(self, ordered=True, obj_pairs_hooks=None, allow_duplicates=True):
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"""
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:param ordered: True if maps should retain their ordering.
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:param obj_pairs_hooks: An iterable of hooks to apply to elements.
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"""
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self.map_type = OrderedDict
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if not ordered:
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self.map_type = dict
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self.obj_pairs_hooks = []
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if obj_pairs_hooks:
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self.obj_pairs_hooks = list(obj_pairs_hooks)
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self.allow_duplicates = allow_duplicates
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def __call__(self, pairs):
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if not self.allow_duplicates:
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known = set()
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for key, value in pairs:
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if key in known:
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raise DuplicateJsonKeyException(('Trying to load a json map which contains a' +
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' duplicate key "{0:}" (but allow_duplicates is False)').format(key))
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known.add(key)
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map = self.map_type(pairs)
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for hook in self.obj_pairs_hooks:
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map = hook(map)
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return map
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def json_date_time_hook(dct):
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"""
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Return an encoded date, time, datetime or timedelta to it's python representation, including optional timezone.
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:param dct: (dict) json encoded date, time, datetime or timedelta
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:return: (date/time/datetime/timedelta obj) python representation of the above
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"""
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def get_tz(dct):
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if not 'tzinfo' in dct:
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return None
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try:
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import pytz
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except ImportError as err:
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raise ImportError(('Tried to load a json object which has a timezone-aware (date)time. '
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'However, `pytz` could not be imported, so the object could not be loaded. '
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'Error: {0:}').format(str(err)))
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return pytz.timezone(dct['tzinfo'])
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if isinstance(dct, dict):
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if '__date__' in dct:
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return date(year=dct.get('year', 0), month=dct.get('month', 0), day=dct.get('day', 0))
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elif '__time__' in dct:
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tzinfo = get_tz(dct)
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return time(hour=dct.get('hour', 0), minute=dct.get('minute', 0), second=dct.get('second', 0),
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microsecond=dct.get('microsecond', 0), tzinfo=tzinfo)
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elif '__datetime__' in dct:
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tzinfo = get_tz(dct)
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return datetime(year=dct.get('year', 0), month=dct.get('month', 0), day=dct.get('day', 0),
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hour=dct.get('hour', 0), minute=dct.get('minute', 0), second=dct.get('second', 0),
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microsecond=dct.get('microsecond', 0), tzinfo=tzinfo)
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elif '__timedelta__' in dct:
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return timedelta(days=dct.get('days', 0), seconds=dct.get('seconds', 0),
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microseconds=dct.get('microseconds', 0))
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return dct
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def json_complex_hook(dct):
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"""
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Return an encoded complex number to it's python representation.
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:param dct: (dict) json encoded complex number (__complex__)
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:return: python complex number
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"""
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if isinstance(dct, dict):
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if '__complex__' in dct:
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parts = dct['__complex__']
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assert len(parts) == 2
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return parts[0] + parts[1] * 1j
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return dct
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def numeric_types_hook(dct):
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if isinstance(dct, dict):
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if '__decimal__' in dct:
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return Decimal(dct['__decimal__'])
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if '__fraction__' in dct:
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return Fraction(numerator=dct['numerator'], denominator=dct['denominator'])
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return dct
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class ClassInstanceHook(object):
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"""
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This hook tries to convert json encoded by class_instance_encoder back to it's original instance.
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It only works if the environment is the same, e.g. the class is similarly importable and hasn't changed.
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"""
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def __init__(self, cls_lookup_map=None):
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self.cls_lookup_map = cls_lookup_map or {}
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def __call__(self, dct):
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if isinstance(dct, dict) and '__instance_type__' in dct:
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mod, name = dct['__instance_type__']
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attrs = dct['attributes']
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if mod is None:
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try:
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Cls = getattr((__import__('__main__')), name)
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except (ImportError, AttributeError) as err:
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if not name in self.cls_lookup_map:
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raise ImportError(('class {0:s} seems to have been exported from the main file, which means '
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'it has no module/import path set; you need to provide cls_lookup_map which maps names '
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'to classes').format(name))
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Cls = self.cls_lookup_map[name]
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else:
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imp_err = None
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try:
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module = import_module('{0:}'.format(mod, name))
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except ImportError as err:
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imp_err = ('encountered import error "{0:}" while importing "{1:}" to decode a json file; perhaps '
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'it was encoded in a different environment where {1:}.{2:} was available').format(err, mod, name)
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else:
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if not hasattr(module, name):
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imp_err = 'imported "{0:}" but could find "{1:}" inside while decoding a json file (found {2:}'.format(
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module, name, ', '.join(attr for attr in dir(module) if not attr.startswith('_')))
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Cls = getattr(module, name)
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if imp_err:
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if 'name' in self.cls_lookup_map:
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Cls = self.cls_lookup_map[name]
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else:
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raise ImportError(imp_err)
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try:
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obj = Cls.__new__(Cls)
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except TypeError:
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raise TypeError(('problem while decoding instance of "{0:s}"; this instance has a special '
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'__new__ method and can\'t be restored').format(name))
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if hasattr(obj, '__json_decode__'):
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obj.__json_decode__(**attrs)
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else:
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obj.__dict__ = dict(attrs)
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return obj
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return dct
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def json_set_hook(dct):
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"""
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Return an encoded set to it's python representation.
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"""
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if isinstance(dct, dict):
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if '__set__' in dct:
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return set((tuple(item) if isinstance(item, list) else item) for item in dct['__set__'])
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return dct
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def pandas_hook(dct):
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if '__pandas_dataframe__' in dct or '__pandas_series__' in dct:
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# todo: this is experimental
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if not getattr(pandas_hook, '_warned', False):
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pandas_hook._warned = True
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warning('Pandas loading support in json-tricks is experimental and may change in future versions.')
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if '__pandas_dataframe__' in dct:
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try:
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from pandas import DataFrame
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except ImportError:
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raise NoPandasException('Trying to decode a map which appears to represent a pandas data structure, but pandas appears not to be installed.')
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from numpy import dtype, array
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meta = dct.pop('__pandas_dataframe__')
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indx = dct.pop('index') if 'index' in dct else None
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dtypes = dict((colname, dtype(tp)) for colname, tp in zip(meta['column_order'], meta['types']))
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data = OrderedDict()
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for name, col in dct.items():
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data[name] = array(col, dtype=dtypes[name])
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return DataFrame(
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data=data,
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index=indx,
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columns=meta['column_order'],
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# mixed `dtypes` argument not supported, so use duct of numpy arrays
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)
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elif '__pandas_series__' in dct:
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from pandas import Series
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from numpy import dtype, array
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meta = dct.pop('__pandas_series__')
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indx = dct.pop('index') if 'index' in dct else None
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return Series(
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data=dct['data'],
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index=indx,
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name=meta['name'],
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dtype=dtype(meta['type']),
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)
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return dct
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def nopandas_hook(dct):
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if isinstance(dct, dict) and ('__pandas_dataframe__' in dct or '__pandas_series__' in dct):
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raise NoPandasException(('Trying to decode a map which appears to represent a pandas '
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'data structure, but pandas support is not enabled, perhaps it is not installed.'))
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return dct
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def json_numpy_obj_hook(dct):
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"""
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Replace any numpy arrays previously encoded by NumpyEncoder to their proper
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shape, data type and data.
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:param dct: (dict) json encoded ndarray
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:return: (ndarray) if input was an encoded ndarray
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"""
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if isinstance(dct, dict) and '__ndarray__' in dct:
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try:
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from numpy import asarray
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import numpy as nptypes
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except ImportError:
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raise NoNumpyException('Trying to decode a map which appears to represent a numpy '
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'array, but numpy appears not to be installed.')
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order = 'A'
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if 'Corder' in dct:
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order = 'C' if dct['Corder'] else 'F'
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if dct['shape']:
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return asarray(dct['__ndarray__'], dtype=dct['dtype'], order=order)
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else:
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dtype = getattr(nptypes, dct['dtype'])
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return dtype(dct['__ndarray__'])
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return dct
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def json_nonumpy_obj_hook(dct):
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"""
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This hook has no effect except to check if you're trying to decode numpy arrays without support, and give you a useful message.
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"""
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if isinstance(dct, dict) and '__ndarray__' in dct:
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raise NoNumpyException(('Trying to decode a map which appears to represent a numpy array, '
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'but numpy support is not enabled, perhaps it is not installed.'))
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return dct
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