# Path: django_ai/bayesian_networks/utils.py
# def parse_node_args(args_string, flat=False):
#     """
#     Parses the string intended for Node Initialization
#     Based on https://groups.google.com/forum/#!msg/comp.lang.python/vgOWCZ7Z8Yw/ZQgDJfXtCj0J
#     """
#     LPAR, RPAR, LBRACK, RBRACK, EQ, COMMA = \
#         map(pp.Suppress, "()[]=,")
# 
#     # Booleans and Keywords
#     noneLiteral = pp.Literal("None")
#     boolLiteral = pp.oneOf("True False")
#     # Scalars
#     integer = pp.Combine(pp.Optional(pp.oneOf("+ -")) +
#                          pp.Word(pp.nums)).setName("integer")
#     real = pp.Combine(pp.Optional(pp.oneOf("+ -")) +
#                       pp.Word(pp.nums) + pp.oneOf(". e") +
#                       pp.Optional(pp.oneOf("+ -")) +
#                       pp.Optional(pp.Word(pp.nums))).setName("real")
#     # Identifiers
#     identifier = pp.Word(pp.alphas + "_:", pp.alphanums + "_:")
#     funStr = pp.Forward().setResultsName("fun")
#     # Structures
#     listStr = pp.Forward()
#     tupleStr = pp.Forward()
# 
#     listItem = (real | integer |
#                 noneLiteral | boolLiteral |
#                 pp.quotedString.setParseAction(pp.removeQuotes) |
#                 pp.Group(listStr()) | tupleStr() | identifier)
# 
#     funStr << (pp.Optional("@") +
#                pp.delimitedList(identifier, delim=".", combine=True) +
#                (LPAR + pp.Optional(pp.delimitedList(listItem)) + RPAR))
#     listStr << (LBRACK + pp.Optional(pp.delimitedList(listItem)) +
#                 pp.Optional(COMMA) + RBRACK)
#     tupleStr << (LPAR + pp.Optional(pp.delimitedList(listItem)) +
#                  pp.Optional(COMMA) + RPAR)
#     kwarg = (pp.Group(identifier("kwarg") + EQ +
#                       (pp.Group(funStr("fun")) | listItem()
#                        ).setResultsName("kwvalue")))
# 
#     arg = pp.Group(funStr("fun")) | kwarg("kwarg") | listItem
#     args = pp.delimitedList(arg)
# 
#     # parse actions perform parse-time conversions
#     noneLiteral.setParseAction(lambda: None)
#     boolLiteral.setParseAction(lambda toks: toks[0] == "True")
#     integer    .setParseAction(lambda toks: int(toks[0]))
#     real       .setParseAction(lambda toks: float(toks[0]))
#     listStr    .setParseAction(lambda toks: toks.asList())
#     tupleStr   .setParseAction(lambda toks: tuple(toks.asList()))
#     funStr     .setParseAction(lambda toks: toks.asList())
# 
#     parsedArgs = args.parseString(args_string)
# 
#     f_args = []
#     f_kwargs = {}
# 
#     for item in parsedArgs:
#         if isinstance(item, pp.ParseResults):
#             if 'kwarg' in item:
#                 if 'fun' in item.kwvalue:
#                     kw_value = eval_function(item.kwvalue)
#                     f_kwargs[item.kwarg] = kw_value
#                 else:
#                     kw = item.asList()[0]
#                     kw_val = item.asList()[1]
#                     f_kwargs[kw] = kw_val
#             else:
#                 if 'fun' in item:
#                     f_args.append(eval_function(item))
#                 else:
#                     f_args.append(item.asList())
#         else:
#             # It is one of the "basic objects" and passed as an arg
#             f_args.append(item)
# 
#     if flat:
#         return f_args + list(f_kwargs.values())
#     else:
#         return({"args": f_args, "kwargs": f_kwargs})


import os
import matplotlib
import numpy as np
import bayespy as bp
from importlib import import_module
from collections import Counter
from pickle import HIGHEST_PROTOCOL as pickle_HIGHEST_PROTOCOL
from graphviz import Digraph
from django.db import models
from django.core.files.base import ContentFile
from django.contrib.contenttypes.models import ContentType
from django.utils import timezone
from django.db.models.signals import post_save
from django.conf import settings
from django.utils.translation import ugettext_lazy as _
from django.core.exceptions import ValidationError
from picklefield.fields import PickledObjectField
from jsonfield import JSONField
from .utils import (parse_node_args, )
    from django_ai.base.models import StatisticalModel
    from django_ai.bayesian_networks import bayespy_constants as bp_consts
    from base.models import StatisticalModel
    from bayesian_networks import bayespy_constants as bp_consts
        if error_dict:
            raise ValidationError(error_dict)
        # SECOND STEP: Check validity on Stochastic Nodes
        if self.node_type == self.NODE_TYPE_STOCHASTIC:
            # Validations on the Distribution of Stochastic Nodes
            if self.distribution is None:
                error_dict['distribution'] = _(
                    'A Stochastic Node must have a distribution')
            if self.distribution_params is None:
                error_dict['distribution_params'] = _(
                    'A Stochastic Node must have a distribution'
                    'parameters')
            # Raise if any
            if error_dict:
                raise ValidationError(error_dict)
        # THIRD STEP: Check Validity on Deterministic Nodes
        else:
            if self.deterministic is None:
                error_dict['deterministic'] = _(
                    'A Deterministic Node must have a function')
            if self.deterministic_params is None:
                error_dict['deterministic_params'] = _(
                    'A Deterministic Node must have deterministic'
                    'parameters')
            # Raise if any
            if error_dict:
                raise ValidationError(error_dict)

        # FOURTH STEP: Check args parsing
        try:

# Path: django_ai/bayesian_networks/utils.py
# def parse_node_args(args_string, flat=False):
#     """
#     Parses the string intended for Node Initialization
#     Based on https://groups.google.com/forum/#!msg/comp.lang.python/vgOWCZ7Z8Yw/ZQgDJfXtCj0J
#     """
#     LPAR, RPAR, LBRACK, RBRACK, EQ, COMMA = \
#         map(pp.Suppress, "()[]=,")
# 
#     # Booleans and Keywords
#     noneLiteral = pp.Literal("None")
#     boolLiteral = pp.oneOf("True False")
#     # Scalars
#     integer = pp.Combine(pp.Optional(pp.oneOf("+ -")) +
#                          pp.Word(pp.nums)).setName("integer")
#     real = pp.Combine(pp.Optional(pp.oneOf("+ -")) +
#                       pp.Word(pp.nums) + pp.oneOf(". e") +
#                       pp.Optional(pp.oneOf("+ -")) +
#                       pp.Optional(pp.Word(pp.nums))).setName("real")
#     # Identifiers
#     identifier = pp.Word(pp.alphas + "_:", pp.alphanums + "_:")
#     funStr = pp.Forward().setResultsName("fun")
#     # Structures
#     listStr = pp.Forward()
#     tupleStr = pp.Forward()
# 
#     listItem = (real | integer |
#                 noneLiteral | boolLiteral |
#                 pp.quotedString.setParseAction(pp.removeQuotes) |
#                 pp.Group(listStr()) | tupleStr() | identifier)
# 
#     funStr << (pp.Optional("@") +
#                pp.delimitedList(identifier, delim=".", combine=True) +
#                (LPAR + pp.Optional(pp.delimitedList(listItem)) + RPAR))
#     listStr << (LBRACK + pp.Optional(pp.delimitedList(listItem)) +
#                 pp.Optional(COMMA) + RBRACK)
#     tupleStr << (LPAR + pp.Optional(pp.delimitedList(listItem)) +
#                  pp.Optional(COMMA) + RPAR)
#     kwarg = (pp.Group(identifier("kwarg") + EQ +
#                       (pp.Group(funStr("fun")) | listItem()
#                        ).setResultsName("kwvalue")))
# 
#     arg = pp.Group(funStr("fun")) | kwarg("kwarg") | listItem
#     args = pp.delimitedList(arg)
# 
#     # parse actions perform parse-time conversions
#     noneLiteral.setParseAction(lambda: None)
#     boolLiteral.setParseAction(lambda toks: toks[0] == "True")
#     integer    .setParseAction(lambda toks: int(toks[0]))
#     real       .setParseAction(lambda toks: float(toks[0]))
#     listStr    .setParseAction(lambda toks: toks.asList())
#     tupleStr   .setParseAction(lambda toks: tuple(toks.asList()))
#     funStr     .setParseAction(lambda toks: toks.asList())
# 
#     parsedArgs = args.parseString(args_string)
# 
#     f_args = []
#     f_kwargs = {}
# 
#     for item in parsedArgs:
#         if isinstance(item, pp.ParseResults):
#             if 'kwarg' in item:
#                 if 'fun' in item.kwvalue:
#                     kw_value = eval_function(item.kwvalue)
#                     f_kwargs[item.kwarg] = kw_value
#                 else:
#                     kw = item.asList()[0]
#                     kw_val = item.asList()[1]
#                     f_kwargs[kw] = kw_val
#             else:
#                 if 'fun' in item:
#                     f_args.append(eval_function(item))
#                 else:
#                     f_args.append(item.asList())
#         else:
#             # It is one of the "basic objects" and passed as an arg
#             f_args.append(item)
# 
#     if flat:
#         return f_args + list(f_kwargs.values())
#     else:
#         return({"args": f_args, "kwargs": f_kwargs})


# Path: django_ai/bayesian_networks/models.py
import os
import matplotlib
import numpy as np
import bayespy as bp
from importlib import import_module
from collections import Counter
from pickle import HIGHEST_PROTOCOL as pickle_HIGHEST_PROTOCOL
from graphviz import Digraph
from django.db import models
from django.core.files.base import ContentFile
from django.contrib.contenttypes.models import ContentType
from django.utils import timezone
from django.db.models.signals import post_save
from django.conf import settings
from django.utils.translation import ugettext_lazy as _
from django.core.exceptions import ValidationError
from picklefield.fields import PickledObjectField
from jsonfield import JSONField
from .utils import (parse_node_args, )
    from django_ai.base.models import StatisticalModel
    from django_ai.bayesian_networks import bayespy_constants as bp_consts
    from base.models import StatisticalModel
    from bayesian_networks import bayespy_constants as bp_consts

        if error_dict:
            raise ValidationError(error_dict)
        # SECOND STEP: Check validity on Stochastic Nodes
        if self.node_type == self.NODE_TYPE_STOCHASTIC:
            # Validations on the Distribution of Stochastic Nodes
            if self.distribution is None:
                error_dict['distribution'] = _(
                    'A Stochastic Node must have a distribution')
            if self.distribution_params is None:
                error_dict['distribution_params'] = _(
                    'A Stochastic Node must have a distribution'
                    'parameters')
            # Raise if any
            if error_dict:
                raise ValidationError(error_dict)
        # THIRD STEP: Check Validity on Deterministic Nodes
        else:
            if self.deterministic is None:
                error_dict['deterministic'] = _(
                    'A Deterministic Node must have a function')
            if self.deterministic_params is None:
                error_dict['deterministic_params'] = _(
                    'A Deterministic Node must have deterministic'
                    'parameters')
            # Raise if any
            if error_dict:
                raise ValidationError(error_dict)

        # FOURTH STEP: Check args parsing
        try:

            params = parse_node_args(self.get_params(), flat=True)