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