def get_scaled(scenarios, scenario_params, relations_scale,
observations_by_time):
# Obtain a i2i Scales Graph
graph = create_scales_graph(relations_scale)
# Compute the scales for the different scenarios and time periods, and store the results in
# another partial retrieval dictionary
scale_beginning_interfaces = get_scale_beginning_interfaces(graph)
scales_prd = PartialRetrievalDictionary()
for scenario_idx, scenario_name in enumerate(scenarios):
for time_period, observations in observations_by_time.items():
# Filter, and prepare dictionary for the update of the scaling
beginning_values: Dict[Factor, Tuple[Any, FactorQuantitativeObservation]] = \
{obs.factor: (value, obs) for value, obs in observations if obs.factor in scale_beginning_interfaces}
# Evaluate expressions
set_update_scales_graph(graph, scenario_params[scenario_name],
beginning_values)
# Write data to the PartialRetrieveDictionary
for interface, data in graph.nodes(
data=True): # type: Factor, Dict
key = dict(__i=interface, __t=time_period, __s=scenario_idx)
scales_prd.put(
key, (interface,
data["value"])) # ERROR: Observation not hashable
return scales_prd
def get_observations_OLD(prd: PartialRetrievalDictionary) \
-> Tuple[PartialRetrievalDictionary, PartialRetrievalDictionary, Dict[str, int]]:
"""
Process All QQ observations (intensive or extensive):
* Store in a compact way (then clear), by Time-period, by Interface, by Observer.
* Convert to float or prepare AST
* Store as value the result plus the QQ observation (in a tuple)
:param prd:
:param relative: True->a QQ observation relative to the value of another interface
:return: another PartialRetrievalDictionary, the Observers and the Time Periods (indexed)
"""
observations_prd = PartialRetrievalDictionary()
relative_observations_prd = PartialRetrievalDictionary()
time_periods: Dict[str, int] = create_dictionary(
) # Dictionary of time periods and the associated IDX
state = State()
next_time_period_idx = 0
for observation in find_quantitative_observations(
prd, processor_instances_only=True):
# Obtain time period index
time = observation.attributes["time"]
if time not in time_periods:
time_periods[time] = next_time_period_idx
next_time_period_idx += 1
# Elaborate Key: Interface, Time, Observer
key = dict(__i=observation.factor,
__t=time_periods[time],
__o=observation.observer)
value, ast, _, issues = evaluate_numeric_expression_with_parameters(
observation.value, state)
if not value:
value = ast
# Store Key: (Value, FactorQuantitativeObservation)
if observation.is_relative:
relative_observations_prd.put(key, (value, observation))
else:
observations_prd.put(key, (value, observation))
return observations_prd, relative_observations_prd, time_periods
def deserialize_state(st: str, state_version: int = MODEL_VERSION):
"""
Deserializes an object previously serialized using "serialize_state"
It can receive also a "State" modified for the serialization to restore it
:param state_version: version number of the internal models
:param st:
:return:
"""
def deserialize_dataframe(t):
df = pd.read_json(t, orient="split") # pd.DataFrame(t[1])
# df.index.names = t[0]
return df
print(" deserialize_state")
if isinstance(st, str):
# TODO: use state_version to convert a previous version to the latest one
# This means transforming the old json to the latest json
if state_version == MODEL_VERSION:
state = deserialize_to_object(st)
else:
raise Exception(
f"The model version {state_version} is not supported. Current version is {MODEL_VERSION}."
)
else:
raise Exception(
f"Serialized state must be a string: currently is of type {type(st)}"
)
# Iterate all namespaces
for ns in state.list_namespaces():
glb_idx = state.get("_glb_idx", ns)
if isinstance(glb_idx, dict):
glb_idx = PartialRetrievalDictionary().from_pickable(glb_idx)
state.set("_glb_idx", glb_idx)
glb_idx, p_sets, hh, datasets, mappings = get_case_study_registry_objects(
state, ns)
if isinstance(glb_idx, dict):
print("glb_idx is DICT, after deserialization!!!")
# TODO Deserialize DataFrames
# In datasets
for ds_name in datasets:
lst = datasets[ds_name]
ds = deserialize(lst[:-1])[0]
ds.data = deserialize_dataframe(lst[-1])
datasets[ds_name] = ds
return state
def prepare_partial_key_dictionary():
prd = PartialRetrievalDictionary()
oer = Observer("tester")
p0 = Processor("A1")
p1 = Processor("A2")
p2 = Processor("B")
p3 = Processor("C")
prd.put({"_type": "Processor", "_name": "A1"}, p0)
prd.put({"_type": "Processor", "_name": "A2"}, p1)
prd.put({"_type": "Processor", "_name": "B"}, p2)
prd.put({"_type": "Processor", "_name": "C"}, p3)
prd.put({"_type": "PartOf", "_parent": "A1", "_child": "B"}, ProcessorsRelationPartOfObservation(p0, p2, oer))
prd.put({"_type": "PartOf", "_parent": "A2", "_child": "B"}, ProcessorsRelationPartOfObservation(p1, p2, oer))
prd.put({"_type": "PartOf", "_parent": "B", "_child": "C"}, ProcessorsRelationPartOfObservation(p2, p3, oer))
return prd
def test_002_serialization_deserialization(self):
prd = prepare_partial_key_dictionary()
prd2 = prd.to_pickable()
s = serialize_from_object(prd2)
prd2 = deserialize_to_object(s)
prd = PartialRetrievalDictionary().from_pickable(prd2)
# Tests
res = prd.get({"_type": "PartOf"})
self.assertEqual(len(res), 3)
res = prd.get({"_type": "PartOf", "_child": "B"})
self.assertEqual(len(res), 2)
res = prd.get({"_type": "PartOf", "_parent": "A1"})
self.assertEqual(len(res), 1)
res = prd.get({"_type": "PartOf", "_parent": "C"})
self.assertEqual(len(res), 0)
res = prd.get({"_typer": "PartOf", "_parent": "C"})
self.assertEqual(len(res), 0)
def get_case_study_registry_objects(state, namespace=None):
"""
Obtain the main entries of the state
:param state: Input state (modified also)
:param namespace: State supports several namespaces. This one serves to specify which one. Default=None
:return: Tuple: (global index, processor sets, hierarchies, datasets, mappings)
"""
# Index of ALL objects
glb_idx = state.get("_glb_idx", namespace)
if not glb_idx:
glb_idx = PartialRetrievalDictionary()
state.set("_glb_idx", glb_idx, namespace)
# ProcessorSet dict (dict of sets)
p_sets = state.get("_processor_sets", namespace)
if not p_sets:
p_sets = create_dictionary()
state.set("_processor_sets", p_sets, namespace)
# Hierarchies Dict
hh = state.get("_hierarchies", namespace)
if not hh:
hh = create_dictionary()
state.set("_hierarchies", hh, namespace)
# Datasets Dict
datasets = state.get("_datasets", namespace)
if not datasets:
datasets = create_dictionary()
state.set("_datasets", datasets, namespace)
# Mappings Dict
mappings = state.get("_mappings", namespace)
if not mappings:
mappings = create_dictionary()
state.set("_mappings", mappings, namespace)
return glb_idx, p_sets, hh, datasets, mappings