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jsoncodec.py
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jsoncodec.py
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"""
Defines JSON-format encoding and decoding functions
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed 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 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
# XXX this module should certainly be rewritten as a custom `json.JSONEncoder`
import base64 as _base64
import collections as _collections
import importlib as _importlib
import types as _types
import uuid as _uuid
import numpy as _np
import scipy.sparse as _sps
def _class_hasattr(instance, attr):
"""
Helper function for checking if `instance.__class__` has an attribute
Parameters
----------
instance : obj
instance to check
attr : str
attribute name
Returns
-------
bool
"""
return hasattr(instance.__class__, attr)
def encode_obj(py_obj, binary):
"""
Returns JSON-compatible version of `py_obj`.
Constructs in-memory a JSON-format-compatible copy of the Python object
`py_obj`, handling pyGSTi objects appropriately. When `binary=False`,
the output must contain only ASCII-compatible strings (no 'bytes'),
otherwise the output is allowed to contain non-ASCII string values (OK for
binary formats like MSGPACK and BSON).
Parameters
----------
py_obj : object
The object to encode.
binary : bool
Whether the output is allowed to have binary-mode strings or not.
Returns
-------
object
A JSON-format compatible object. Usually a dict, list, or string.
"""
#print("ENCODING ", str(type(py_obj)))
is_pygsti_obj = hasattr(py_obj, '__class__') and \
hasattr(py_obj.__class__, '__module__') and \
py_obj.__class__.__module__.startswith('pygsti')
is_pygsti_class = isinstance(py_obj, type) and hasattr(py_obj, '__module__') \
and py_obj.__module__.startswith('pygsti')
is_plotly_fig = hasattr(py_obj, '__class__') and \
hasattr(py_obj.__class__, '__module__') and \
py_obj.__class__.__module__ == 'plotly.graph_objs._figure' and \
py_obj.__class__.__name__ == "Figure"
# just needed for v3 plotly where figures aren't dicts...
# Pygsti class encoding
if is_pygsti_class: # or _class_hasattr(py_obj, '__pygsti_getstate__')
return {'__pygsticlass__': (py_obj.__module__, py_obj.__name__)}
# Pygsti object encoding
elif is_pygsti_obj: # or _class_hasattr(py_obj, '__pygsti_getstate__')
#Get State (and/or init args)
if _class_hasattr(py_obj, '__pygsti_reduce__'):
red = py_obj.__pygsti_reduce__() # returns class, construtor_args, state
assert(callable(red[0]))
init_args = red[1] if len(red) > 1 else []
state = red[2] if len(red) > 2 else ()
if state is None: state = ()
if not isinstance(state, dict): state = {'__state_obj__': state} # when state is, e.g, a tuple
if red[0] is not py_obj.__class__:
state['__init_fn__'] = (red[0].__module__, red[0].__name__) # Note: 'object' type has module == None
state.update({'__init_args__': init_args})
elif _class_hasattr(py_obj, '__pygsti_getstate__'):
state = py_obj.__pygsti_getstate__() # must return a dict
elif _class_hasattr(py_obj, '__getstate__'):
state = py_obj.__getstate__()
elif hasattr(py_obj, '__dict__'):
state = py_obj.__dict__ # take __dict__ as state
elif _class_hasattr(py_obj, '__reduce__'):
red = py_obj.__reduce__() # returns class, construtor_args, state
if red[0] is not py_obj.__class__:
state = None # weird reducing can happen, for instance, for namedtuples - just punt
else:
init_args = red[1] if len(red) > 1 else []
state = red[2] if len(red) > 2 else {}
if state is None: state = {}
state.update({'__init_args__': init_args})
else:
state = None
if state is None: # Note: __dict__ and __getstate__ may *return* None (python 2.7)
if hasattr(py_obj, '_asdict'): # named tuples
state = {'__init_args__': list(py_obj._asdict().values())}
# values will be ordered as per __init__ so no need for keys
else:
raise ValueError("Can't get state of %s object" % type(py_obj))
d = {k: encode_obj(v, binary) for k, v in state.items()}
#DEBUG (instead of above line)
#import json as _json
#d = {}
#print("DB: Encoding state for pyGSTi %s object:" % type(py_obj))
#for k,v in state.items():
# print(">>> Encoding key: ",k)
# d[k] = encode_obj(v,binary)
# print("<<< Done encoding key ",k)
# try: _json.dumps(d[k])
# except Exception as e:
# print("Cannot JSON %s key: " % k, d[k])
# raise e
d.update({'__pygstiobj__': (py_obj.__class__.__module__,
py_obj.__class__.__name__)})
#Currently, don't add standard-base-class state
#if we know how to __init__, since we'll assume this
# should initialize the entire (base class included) instance
encode_std_base = bool('__init_args__' not in d)
if encode_std_base:
std_encode = _encode_std_obj(py_obj, binary)
if std_encode is not py_obj: # if there's something to encode
# this pygsti object is also a standard-object instance
assert(isinstance(std_encode, dict))
d['__std_base__'] = std_encode
#try:
# _json.dumps(d)
#except Exception as e:
# print("Cannot JSON ",type(py_obj))
# raise e
return d
#Special case: a plotly Figure object - these need special help being serialized
elif is_plotly_fig and hasattr(py_obj, 'to_dict'):
return {'__plotlyfig__': _encode_std_obj(py_obj.to_dict(), binary)}
else:
return _encode_std_obj(py_obj, binary)
def _encode_std_obj(py_obj, binary):
"""
Helper to :func:`encode_obj` that encodes only "standard" (non-pyGSTi) types
Parameters
----------
py_obj : object
standard Python object to encode
binary : bool
whether to use binary-mode strings
Returns
-------
dict
"""
# Other builtin or standard object encoding
#print("Encoding std type: ",str(type(py_obj)))
if isinstance(py_obj, tuple):
return {'__tuple__': [encode_obj(v, binary) for v in py_obj]}
elif isinstance(py_obj, list):
return {'__list__': [encode_obj(v, binary) for v in py_obj]}
elif isinstance(py_obj, set):
return {'__set__': [encode_obj(v, binary) for v in py_obj]}
elif isinstance(py_obj, slice):
return {'__slice__': [encode_obj(py_obj.start, binary),
encode_obj(py_obj.stop, binary),
encode_obj(py_obj.step, binary)]}
elif isinstance(py_obj, range):
return {'__range__': (py_obj.start, py_obj.stop, py_obj.step)}
elif isinstance(py_obj, _collections.OrderedDict):
return {'__odict__': [(encode_obj(k, binary), encode_obj(v, binary))
for k, v in py_obj.items()]}
elif isinstance(py_obj, _collections.Counter):
return {'__counter__': [(encode_obj(k, binary), encode_obj(v, binary))
for k, v in dict(py_obj).items()]}
elif isinstance(py_obj, dict):
return {'__ndict__': [(encode_obj(k, binary), encode_obj(v, binary))
for k, v in py_obj.items()]}
elif isinstance(py_obj, _uuid.UUID):
return {'__uuid__': str(py_obj.hex)}
elif isinstance(py_obj, complex):
rep = py_obj.__repr__() # a string
data = _tobin(rep) if binary else rep # binary if need be
return {'__complex__': data}
elif not binary and isinstance(py_obj, bytes):
return {'__bytes__': _tostr(_base64.b64encode(py_obj))}
elif binary and isinstance(py_obj, str):
return {'__string__': _tobin(py_obj)}
#Numpy encoding
elif isinstance(py_obj, _np.ndarray):
# If the dtype is structured, store the interface description;
# otherwise, store the corresponding array protocol type string:
if py_obj.dtype.kind == 'V':
kind = 'V'
descr = _tobin(py_obj.dtype.descr) if binary else _tostr(py_obj.dtype.descr)
else:
kind = py_obj.dtype.kind # can be '' or 'O' (for object types)
descr = _tobin(py_obj.dtype.str) if binary else _tostr(py_obj.dtype.str)
if kind == 'O':
#Special case of object arrays: store flattened array data
data = [encode_obj(el, binary) for el in py_obj.flat]
assert(len(data) == _np.prod(py_obj.shape))
else:
data = py_obj.tobytes() if binary else _tostr(_base64.b64encode(py_obj.tobytes()))
return {'__ndarray__': data,
'dtype': descr,
'kind': kind,
'shape': py_obj.shape}
#Scipy sparse matrix encoding
elif isinstance(py_obj, _sps.csr_matrix):
# If the dtype is structured, store the interface description;
# otherwise, store the corresponding array protocol type string:
if py_obj.dtype.kind == 'V':
kind = 'V'
descr = _tobin(py_obj.dtype.descr) if binary else _tostr(py_obj.dtype.descr)
else:
kind = py_obj.dtype.kind # can be '' or 'O' (for object types)
descr = _tobin(py_obj.dtype.str) if binary else _tostr(py_obj.dtype.str)
if kind == 'O':
raise TypeError("Cannot serialize sparse matrices of *objects*!")
return {'__scipy_csrmatrix__': encode_obj(py_obj.data, binary),
'indices': encode_obj(py_obj.indices, binary),
'indptr': encode_obj(py_obj.indptr, binary),
'dtype': descr,
'kind': kind,
'shape': py_obj.shape}
elif isinstance(py_obj, (_np.bool_, _np.number)):
data = py_obj.tobytes() if binary else _tostr(_base64.b64encode(py_obj.tobytes()))
return {'__npgeneric__': data,
'dtype': _tostr(py_obj.dtype.str)}
elif isinstance(py_obj, _types.FunctionType): # functions
# OLD: elif callable(py_obj): #incorrectly includes pygsti classes w/__call__ (e.g. AutoGator)
return {'__function__': (py_obj.__module__, py_obj.__name__)}
return py_obj # assume the bare py_obj is json-able
def decode_obj(json_obj, binary):
"""
Inverse of :func:`encode_obj`.
Decodes the JSON-compatible `json_obj` object into the original Python
object that was encoded.
Parameters
----------
json_obj : object
The JSON-compabtible object to decode. Note that this is NOT a JSON
string, but rather the object that would be decoded from such a string
(by `json.loads`, for instance).
binary : bool
Whether `json_obj` is a binary format or not. If so, then the decoding
expects all strings to be binary strings i.e. `b'name'` instead of just
`'name'`. The value of this argument should match that used in the
original call to :func:`encode_obj`.
Returns
-------
object
A Python object.
"""
B = _tobin if binary else _ident
if isinstance(json_obj, dict):
if B('__pygsticlass__') in json_obj:
modname, clsname = json_obj[B('__pygsticlass__')]
module = _importlib.import_module(_tostr(modname))
class_ = getattr(module, _tostr(clsname))
return class_
elif B('__pygstiobj__') in json_obj:
#DEBUG
#print("DB: creating %s" % str(json_obj['__pygstiobj__']))
#print("DB: json_obj is type %s with keyvals:" % type(json_obj))
#for k,v in json_obj.items():
# print("%s (%s): %s (%s)" % (k,type(k),v,type(v)))
modname, clsname = json_obj[B('__pygstiobj__')]
module = _importlib.import_module(_tostr(modname))
class_ = getattr(module, _tostr(clsname))
if B('__init_fn__') in json_obj: # construct via this function instead of class_.__init__
ifn_modname, ifn_fnname = decode_obj(json_obj[B('__init_fn__')], binary)
if ifn_modname is None and ifn_fnname == "__new__": # special behavior
initfn = class_.__new__
else:
initfn = getattr(_importlib.import_module(_tostr(ifn_modname)), _tostr(ifn_fnname))
else:
initfn = class_ # just use the class a the callable initialization function
if B('__init_args__') in json_obj: # construct via __init__
args = decode_obj(json_obj[B('__init_args__')], binary)
instance = initfn(*args)
else: # init via __new__ and set state
try:
instance = class_.__new__(class_)
except Exception as e:
raise ValueError("Could not create class " + str(class_) + ": " + str(e))
#Create state dict
state_dict = {}
for k, v in json_obj.items():
if k in (B('__pygstiobj__'), B('__init_args__'), B('__std_base__')): continue
state_dict[_tostr(k)] = decode_obj(v, binary)
state_obj = state_dict.get('__state_obj__', state_dict)
#Set state
if _class_hasattr(instance, '__pygsti_setstate__'):
instance.__pygsti_setstate__(state_obj)
elif _class_hasattr(instance, '__setstate__'):
instance.__setstate__(state_obj)
elif hasattr(instance, '__dict__'): # just update __dict__
instance.__dict__.update(state_dict)
elif len(state_dict) > 0:
raise ValueError("Cannot set nontrivial state of %s object" % type(instance))
#update instance with std-object info if needed (only if __init__ not called)
if B('__std_base__') in json_obj:
_decode_std_base(json_obj[B('__std_base__')], instance, binary)
return instance
elif B('__plotlyfig__') in json_obj:
import plotly.graph_objs as go
return go.Figure(decode_obj(json_obj[B('__plotlyfig__')], binary))
else:
return _decode_std_obj(json_obj, binary)
else:
return json_obj
def _decode_std_base(json_obj, start, binary):
"""
Helper to :func:`decode_obj` for decoding pyGSTi objects that are derived from a standard type.
Parameters
----------
json_obj : dict
json-loaded dict to decode from
start : various
Starting object that serves as a container for elements of the
standard-Python base class (e.g. a list).
binary : bool
Whether or not to use binary-mode strings as dict keys.
Returns
-------
object
"""
B = _tobin if binary else _ident
if B('__tuple__') in json_obj:
#OK if __init_args since this means we knew how to construct it (e.g. namedtuples)
assert(B('__init_args') in json_obj), "No support for sub-classing tuple"
elif B('__list__') in json_obj:
for v in json_obj[B('__list__')]:
start.append(decode_obj(v, binary))
elif B('__set__') in json_obj:
for v in json_obj[B('__set__')]:
start.add(decode_obj(v, binary))
elif B('__ndict__') in json_obj:
for k, v in json_obj[B('__ndict__')]:
start[decode_obj(k, binary)] = decode_obj(v, binary)
elif B('__odict__') in json_obj:
for k, v in json_obj[B('__odict__')]:
start[decode_obj(k, binary)] = decode_obj(v, binary)
elif B('__uuid__') in json_obj:
assert(False), "No support for sub-classing UUID"
elif B('__ndarray__') in json_obj:
assert(False), "No support for sub-classing ndarray"
elif B('__npgeneric__') in json_obj:
assert(False), "No support for sub-classing numpy generics"
elif B('__complex__') in json_obj:
assert(False), "No support for sub-classing complex"
elif B('__counter__') in json_obj:
assert(False), "No support for sub-classing Counter"
elif B('__slice__') in json_obj:
assert(False), "No support for sub-classing slice"
def _decode_std_obj(json_obj, binary):
"""
Helper to :func:`decode_obj` that decodes standard (non-pyGSTi) types.
Parameters
----------
json_obj : dict
json-loaded dictionary encoding an object
binary : bool
Whether or not to use binary-mode strings as dict keys.
Returns
-------
object
"""
B = _tobin if binary else _ident
if B('__tuple__') in json_obj:
return tuple([decode_obj(v, binary) for v in json_obj[B('__tuple__')]])
elif B('__list__') in json_obj:
return list([decode_obj(v, binary) for v in json_obj[B('__list__')]])
elif B('__set__') in json_obj:
return set([decode_obj(v, binary) for v in json_obj[B('__set__')]])
elif B('__slice__') in json_obj:
v = json_obj[B('__slice__')]
return slice(decode_obj(v[0], binary), decode_obj(v[1], binary),
decode_obj(v[2], binary))
elif B('__range__') in json_obj:
start, stop, step = json_obj[B('__range__')]
return range(start, stop, step)
elif B('__ndict__') in json_obj:
return dict([(decode_obj(k, binary), decode_obj(v, binary))
for k, v in json_obj[B('__ndict__')]])
elif B('__odict__') in json_obj:
return _collections.OrderedDict(
[(decode_obj(k, binary), decode_obj(v, binary)) for k, v in json_obj[B('__odict__')]])
elif B('__counter__') in json_obj:
return _collections.Counter(
{decode_obj(k, binary): decode_obj(v, binary) for k, v in json_obj[B('__counter__')]})
elif B('__uuid__') in json_obj:
return _uuid.UUID(hex=_tostr(json_obj[B('__uuid__')]))
elif B('__bytes__') in json_obj:
return json_obj[B('__bytes__')] if binary else \
_base64.b64decode(json_obj[B('__bytes__')])
elif B('__string__') in json_obj:
return _tostr(json_obj[B('__string__')]) if binary else \
json_obj[B('__string__')]
# check for numpy
elif B('__ndarray__') in json_obj:
# Check if 'kind' is in json_obj to enable decoding of data
# serialized with older versions:
if json_obj[B('kind')] == B('V'):
descr = [tuple(_tostr(t) if isinstance(t, bytes) else t for t in d)
for d in json_obj[B('dtype')]]
else:
descr = json_obj[B('dtype')]
if json_obj[B('kind')] == B('O'): # special decoding for object-type arrays
data = [decode_obj(el, binary) for el in json_obj[B('__ndarray__')]]
flat_ar = _np.empty(len(data), dtype=_np.dtype(descr))
for i, el in enumerate(data):
flat_ar[i] = el # can't just make a np.array(data) because data may be, e.g., tuples
return flat_ar.reshape(json_obj[B('shape')])
else:
data = json_obj[B('__ndarray__')] if binary else \
_base64.b64decode(json_obj[B('__ndarray__')])
return _np.fromstring(data, dtype=_np.dtype(descr)).reshape(json_obj[B('shape')])
elif B('__scipy_csrmatrix__') in json_obj:
if json_obj[B('kind')] == 'V':
descr = [tuple(_tostr(t) if isinstance(t, bytes) else t for t in d)
for d in json_obj[B('dtype')]]
else:
descr = json_obj[B('dtype')]
data = decode_obj(json_obj[B('__scipy_csrmatrix__')], binary)
indices = decode_obj(json_obj[B('indices')], binary)
indptr = decode_obj(json_obj[B('indptr')], binary)
return _sps.csr_matrix((data, indices, indptr), dtype=_np.dtype(descr))
elif B('__npgeneric__') in json_obj:
data = json_obj[B('__npgeneric__')] if binary else \
_base64.b64decode(json_obj[B('__npgeneric__')])
return _np.fromstring(
data, dtype=_np.dtype(json_obj[B('dtype')])
)[0]
elif B('__complex__') in json_obj:
return complex(_tostr(json_obj[B('__complex__')]))
elif B('__function__') in json_obj:
modname, fnname = json_obj[B('__function__')]
module = _importlib.import_module(_tostr(modname))
return getattr(module, _tostr(fnname))
def _tostr(x):
"""
Convert a value to the native string format.
Parameters
----------
x : str or bytes
value to convert to a native string.
Returns
-------
str
"""
if isinstance(x, bytes):
return x.decode()
else:
return str(x)
def _tobin(x):
"""
Serialize strings to UTF8
Parameters
----------
x : str or bytes
value to convert to a UTF8 binary string.
Returns
-------
bytes
"""
if isinstance(x, str):
return bytes(x, 'utf-8')
else:
return x
def _ident(x):
return x