def _clone_processor_as_child(self,
processor: Processor,
parent_processor: Processor,
name: str = None,
other_attributes: Dict = {}) -> Processor:
# Clone inherits some attributes from parent
inherited_attributes = dict(
subsystem_type=parent_processor.subsystem_type,
processor_system=parent_processor.processor_system,
instance_or_archetype=parent_processor.instance_or_archetype)
processor_clone, processor_clone_children = processor.clone(
state=self._glb_idx,
name=name,
inherited_attributes={
**inherited_attributes,
**other_attributes
})
# Create PART-OF relation
relationship = ProcessorsRelationPartOfObservation.create_and_append(
parent=parent_processor, child=processor_clone)
self._glb_idx.put(relationship.key(), relationship)
# Add cloned processor hierarchical names to global index
Processor.register([processor_clone] + list(processor_clone_children),
self._glb_idx)
return processor_clone
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_032_new_processor_declaration_convention(self):
"""
Test new convention for creation of Processors, using BareProcessors command
See the descriptions in the file
:return:
"""
file_path = os.path.dirname(
os.path.abspath(__file__)
) + "/z_input_files/v2/19_naming_processors_new_convention.xlsx"
isess = execute_file(file_path, generator_type="spreadsheet")
# issues = prepare_and_solve_model(isess.state)
# for idx, issue in enumerate(issues):
# print(f"Issue {idx + 1}/{len(issues)} = {issue}")
# Check State of things
glb_idx, p_sets, hh, datasets, mappings = get_case_study_registry_objects(
isess.state)
ps = glb_idx.get(Processor.partial_key())
lst1 = []
for p in ps:
lst = p.full_hierarchy_names(glb_idx)
lst1.extend(lst)
p1 = glb_idx.get(Processor.partial_key("P2.C"))
p2 = glb_idx.get(Processor.partial_key("P3.C"))
self.assertEqual(p1[0], p2[0])
p = glb_idx.get(Processor.partial_key("P1.C.P2"))
self.assertEqual(len(p), 1)
# Close interactive session
isess.close_db_session()
def get_processors(glb_idx: PartialRetrievalDictionary) -> pd.DataFrame:
procs = set(glb_idx.get(Processor.partial_key())) # Unique processors
d = {}
for p in procs:
parent_relations = glb_idx.get(
ProcessorsRelationPartOfObservation.partial_key(child=p))
d[p.ident] = set([p.parent_processor.ident for p in parent_relations])
lst = [[
"ProcessorGroup", "Processor", "ParentProcessor", "SubsystemType",
"System", "FunctionalOrStructural", "Accounted", "Stock",
"Description", "GeolocationRef", "GeolocationCode",
"GeolocationLatLong", "Attributes"
]]
# Elaborate a DAG, then iterate over it
for ident in list(toposort.toposort_flatten(d)):
p = glb_idx.get(Processor.partial_key(ident=ident))[0]
gref = p.geolocation.reference if p.geolocation else ""
gcode = p.geolocation.code if p.geolocation else ""
pgroup = p.attributes.get("processor_group")
fname = p.full_hierarchy_names(glb_idx)[0]
t = [
pgroup if pgroup else "", p.name, "",
p.attributes.get("subsystem_type", ""),
p.attributes.get("processor_system", ""),
p.attributes.get("functional_or_structural", ""),
p.attributes.get("instance_or_archetype", ""),
p.attributes.get("stock"), "", gref, gcode, "", ""
]
parent_relations = glb_idx.get(
ProcessorsRelationPartOfObservation.partial_key(child=p))
if len(parent_relations) == 0:
lst.append(t)
else:
first = True
for rel in parent_relations:
t[2] = rel.parent_processor.full_hierarchy_names(glb_idx)[0]
lst.append(t.copy())
if first:
first = False
proper = None
for n in p.full_hierarchy_names(glb_idx):
if t[2] in n:
proper = n
break
t[1] = proper if proper else t[1]
return list_to_dataframe(lst)
def get_processor(attribute, value, prd: PartialRetrievalDictionary = None):
"""
Obtain the name of a Processor given the value of an attribute
(Obtain the registry of objects)
:param attribute:
:param value:
:param prd: A PartialRetrievalDictionary, passed in State "_glb_idx" to the AST evaluator by
:return:
"""
if not prd:
raise Exception(f"No Global-Index parameter passed to Processor function")
else:
# Obtain ALL Processors, then ONE having attribute "attribute" with value <value>
procs = prd.get(Processor.partial_key())
ret = None
for proc in procs:
v = vars(proc).get(attribute)
if not v:
v = proc.attributes.get(attribute)
if v and (strcmp(v, str(value)) or (is_float(value) and float(v) == float(value))):
ret = proc.name
break
if ret:
return ret
else:
raise Exception(f"No Processor found having attribute '{attribute}' with value '{value}'")
def test_029_dataset_expansion2(self):
"""
Test dataset expansion using advanced expansion expression
(function calls returning either InterfaceTypes or Processors)
:return:
"""
file_path = os.path.dirname(os.path.abspath(
__file__)) + "/z_input_files/v2/18_dataset_expansion_2.xlsx"
isess = execute_file(file_path, generator_type="spreadsheet")
# issues = prepare_and_solve_model(isess.state)
# for idx, issue in enumerate(issues):
# print(f"Issue {idx + 1}/{len(issues)} = {issue}")
# Check State of things
glb_idx, p_sets, hh, datasets, mappings = get_case_study_registry_objects(
isess.state)
p = glb_idx.get(Processor.partial_key())
self.assertEqual(len(p), 2)
p = p[0]
# Close interactive session
isess.close_db_session()
def xml_processor(p: Processor, registry: PartialRetrievalDictionary,
p_map: Dict[str, Processor]):
"""
Return the XML of a processor
Recursive into children
:param p:
:return:
"""
def xml_interface(iface: Factor):
"""
:param iface:
:return:
"""
s = f'<{iface.name} type="{iface.taxon.name}" sphere="{iface.sphere}" ' \
f'roegen_type="{iface.roegen_type}" orientation="{iface.orientation}" ' \
f'opposite_processor_type="{iface.opposite_processor_type}" />'
if case_sensitive:
return s
else:
return s.lower()
children = p.children(registry)
full_name = p.full_hierarchy_names(registry)[0]
if case_sensitive:
p_map[full_name] = p
else:
p_map[full_name.lower()] = p
s = f"""
<{p.name} fullname="{full_name}" level="{p.level}" system="{p.processor_system}" subsystem="{p.subsystem_type}" functional="{"true" if strcmp(p.functional_or_structural, "Functional") else "false"}" >
<interfaces>
{chr(10).join([xml_interface(f) for f in p.factors])}
</interfaces>
{chr(10).join([xml_processor(c, registry, p_map) for c in children])}
</{p.name}>"""
if case_sensitive:
return s
else:
return s.lower()
def _get_interface_from_field(self, field_name: str,
processor: Processor) -> Factor:
interface_name = self._fields[field_name]
if interface_name is None:
raise CommandExecutionError(
f"No interface has been defined for field '{field_name}'.")
interface = processor.factors_find(interface_name)
if not interface:
raise CommandExecutionError(
f"The interface '{interface_name}' has not been found in "
f"processor '{processor.name}'.")
return interface
def generate_geojson(s):
"""
:param s:
:return:
"""
features = []
# Obtain principal structures
glb_idx, p_sets, hierarchies, datasets, mappings = get_case_study_registry_objects(
s)
ps = glb_idx.get(Processor.partial_key())
for p in ps:
# TODO If there is Geolocation information, obtain the region
# Collect also the attributes of the processor and of all the interfaces, from "flow graph solution"
# url = "https://raw.githubusercontent.com/eurostat/Nuts2json/master/2016/4326/20M/nutsrg_1.json"
if not p.geolocation:
continue
gr = glb_idx.get(
GeographicReference.partial_key(name=p.geolocation.reference))
url = gr[0].attributes["data_location"]
geo_id = p.geolocation.code
j, ids = read_geojson(url)
# Obtain element of interest (GeoJSON)
tmp = j["features"][ids[geo_id]]
feature = tmp.copy()
# Add processor properties
feature["properties"]["processor"] = dict(
name=p.name,
h_name=p.full_hierarchy_names(glb_idx)[0],
subsystem_type=p.subsystem_type,
system=p.processor_system,
level=p.level,
functional_or_structural=p.functional_or_structural,
instance_or_archetype=p.instance_or_archetype,
stock=p.stock)
# Add interface properties
interfaces = []
for i in p.factors:
idict = dict()
interfaces.append(idict)
feature["properties"]["interfaces"] = interfaces
# TODO Add relationships?
features.append(feature)
return dict(type="FeatureCollection", features=features)
def get_processor_names_to_processors_dictionary(
state: PartialRetrievalDictionary):
"""
Obtain a dictionary with all processor names (a processor may have multiple names) and
the corresponding Processor object
:param state:
:return:
"""
ps = state.get(Processor.partial_key())
ps = set(ps) # Avoid repeating Processor objects
d = create_dictionary()
for p in ps:
for n in p.full_hierarchy_names(state):
d[n] = p
return d
def generate_rdf_from_object_model(state: State):
"""
Using the base ontology "musiasem.owl", generate a file
:param state:
:return:
"""
onto = get_ontology(
"file:////home/rnebot/Dropbox/nis-backend/nexinfosys/ie_exports/musiasem.owl"
).load()
glb_idx, p_sets, hh, datasets, mappings = get_case_study_registry_objects(
state)
# onto = get_ontology("file:///musiasem.owl").load()
# Individuals / instances
# InterfaceTypes
fts = glb_idx.get(FactorType.partial_key())
# Processors
procs = glb_idx.get(Processor.partial_key())
# Interfaces and Quantities
fs = glb_idx.get(Factor.partial_key())
def get_interfaces(glb_idx: PartialRetrievalDictionary) -> pd.DataFrame:
# Used to examine "value" as expression, and find variables that are interface names vs parameter names
params = create_dictionary(
data={p.name: None
for p in glb_idx.get(Parameter.partial_key())})
s = State()
procs = glb_idx.get(Processor.partial_key())
d = {}
for p in procs:
parent_relations = glb_idx.get(
ProcessorsRelationPartOfObservation.partial_key(child=p))
d[p.ident] = set([p.parent_processor.ident for p in parent_relations])
lst = [[
"Processor", "InterfaceType", "Interface", "Sphere", "RoegenType",
"Orientation", "OppositeSubsystemType", "GeolocationRef",
"GeolocationCode", "InterfaceAttributes", "Value", "Unit",
"RelativeTo", "Uncertainty", "Assessment", "PedigreeMatrix",
"Pedigree", "Time", "Source", "NumberAttributes", "Comments"
]]
# Elaborate a DAG, then iterate over it
for ident in list(toposort.toposort_flatten(d)):
p = glb_idx.get(Processor.partial_key(ident=ident))[0]
ifaces = glb_idx.get((Factor.partial_key(processor=p)))
iface_names = create_dictionary(
data={iface.name: iface
for iface in ifaces})
# Elaborate DAG of Interfaces because of Observations
d = {}
for iface in ifaces:
if iface.ident not in d:
d[iface.ident] = set()
for obs in iface.quantitative_observations:
if obs.relative_factor:
d[iface.ident].add(obs.relative_factor.ident)
# Consider obs.value and non linear dependencies
if isinstance(obs.value, str):
ast = string_to_ast(expression_with_parameters, obs.value)
evaluation_issues = []
value, unresolved_vars = ast_evaluator(
exp=ast,
state=s,
obj=None,
issue_lst=evaluation_issues)
for unresolved in unresolved_vars:
if unresolved not in params:
d[iface.ident].add(iface_names[unresolved].ident)
for ident2 in list(toposort.toposort_flatten(d)):
iface = glb_idx.get(Factor.partial_key(ident=ident2))[0]
lst1 = [
iface.processor.name, iface.taxon.name, iface.name,
iface.sphere, iface.roegen_type.name, iface.orientation,
iface.opposite_processor_type, "", "", ""
]
observations = iface.quantitative_observations
if len(observations) > 0:
for obs in observations:
lst2 = [
obs.value,
obs.attributes.get("unit",
""), obs.relative_factor.name
if obs.relative_factor else "",
obs.attributes.get("spread", ""),
obs.attributes.get("assessment", ""),
obs.attributes.get("pedigree_template", ""),
obs.attributes.get("pedigree", ""),
obs.attributes.get("time", ""),
obs.observer.name if obs.observer else "", "",
obs.attributes.get("comments", "")
]
lst.append(lst1 + lst2)
else:
lst.append(lst1 + ["", "", "", "", "", "", "", "", "", ""])
return list_to_dataframe(lst)
def export_model_to_xml(
registry: PartialRetrievalDictionary
) -> Tuple[str, Dict[str, Processor]]:
"""
Elaborate an XML string containing the nested processors and their attributes.
Also the interfaces inside processors
<processors>
<root_p1 fullname="" level="" system="" subsystem="" functional="true|false">
<interfaces>
<i1 type="" sphere="" roegen_type="" orientation="" opposite_processor_type="" />
...
</interfaces>
<child_p2>
...
</child_p2>
</root_p1>
...
</processors>
Example (abstract):
'/processors//[level="n"]'
:param registry:
:return:
"""
def xml_processor(p: Processor, registry: PartialRetrievalDictionary,
p_map: Dict[str, Processor]):
"""
Return the XML of a processor
Recursive into children
:param p:
:return:
"""
def xml_interface(iface: Factor):
"""
:param iface:
:return:
"""
s = f'<{iface.name} type="{iface.taxon.name}" sphere="{iface.sphere}" ' \
f'roegen_type="{iface.roegen_type}" orientation="{iface.orientation}" ' \
f'opposite_processor_type="{iface.opposite_processor_type}" />'
if case_sensitive:
return s
else:
return s.lower()
children = p.children(registry)
full_name = p.full_hierarchy_names(registry)[0]
if case_sensitive:
p_map[full_name] = p
else:
p_map[full_name.lower()] = p
s = f"""
<{p.name} fullname="{full_name}" level="{p.level}" system="{p.processor_system}" subsystem="{p.subsystem_type}" functional="{"true" if strcmp(p.functional_or_structural, "Functional") else "false"}" >
<interfaces>
{chr(10).join([xml_interface(f) for f in p.factors])}
</interfaces>
{chr(10).join([xml_processor(c, registry, p_map) for c in children])}
</{p.name}>"""
if case_sensitive:
return s
else:
return s.lower()
# Part of relationships
por = registry.get(ProcessorsRelationPartOfObservation.partial_key())
# Set of all instance processors NOT touched by part-of relationships
unaffected_procs = set([
p for p in registry.get(Processor.partial_key())
if strcmp(p.instance_or_archetype, "Instance")
])
for po in por:
try:
unaffected_procs.remove(po.parent_processor)
except KeyError:
pass
try:
unaffected_procs.remove(po.child_processor)
except KeyError:
pass
# Keep those affecting Instance processors
por = [
po for po in por
if strcmp(po.parent_processor.instance_or_archetype, "Instance")
]
# Get root processors (set of processors not appearing as child_processor)
parents = set([po.parent_processor for po in por])
children = set([po.child_processor for po in por])
roots = parents.difference(children).union(unaffected_procs)
# leaves = children.difference(parents)
result = '<processors>' # <?xml version="1.0" encoding="utf-8"?>\n
p_map = {}
for p in roots:
result += xml_processor(p, registry, p_map)
result += "\n</processors>"
return result, p_map
def test_005_execute_file_five(self):
"""
Just Structure. From Soslaires.
:return:
"""
file_path = os.path.dirname(
os.path.abspath(__file__)) + "/z_input_files/Soslaires.xlsx"
isess = execute_file(file_path, generator_type="spreadsheet")
# # Save state
s = serialize_state(isess.state)
# Changed "wt" to "wb": the output of "serialize_state" is a byte array (it is compressed now)
with open("/home/rnebot/GoogleDrive/AA_MAGIC/Soslaires.serialized",
"wb") as f:
f.write(s)
local_state = deserialize_state(s)
# Check State of things
glb_idx, p_sets, hh, datasets, mappings = get_case_study_registry_objects(
local_state)
# Four processor sets
self.assertEqual(len(p_sets), 4)
# Obtain all Observers
print("---- Observer ----")
oers = glb_idx.get(Observer.partial_key())
for i in oers:
print(i.name)
# Obtain all processors
print("---- Processor ----")
procs = glb_idx.get(Processor.partial_key())
for i in procs:
print(i.name)
# Obtain all FactorTypes
print("---- FactorType ----")
fts = glb_idx.get(FactorType.partial_key())
for i in fts:
print(i.name)
# Obtain all Factors
print("---- Factor ----")
fs = glb_idx.get(Factor.partial_key())
for i in fs:
print(i.processor.name + ":" + i.taxon.name)
# Obtain all Quantitative Observations
print("---- Quantities ----")
qqs = glb_idx.get(FactorQuantitativeObservation.partial_key())
for i in qqs:
print(i.factor.processor.name + ":" + i.factor.taxon.name + "= " +
str(i.value.expression if i.value else ""))
# Obtain all part-of Relation Observations
print("---- Part-of relations (P-P) ----")
po_rels = glb_idx.get(
ProcessorsRelationPartOfObservation.partial_key())
for i in po_rels:
print(i.parent_processor.name + " \/ " + i.child_processor.name)
# Obtain all undirected flow Relation Observations
print("---- Undirected flow relations (P-P) ----")
uf_rels = glb_idx.get(
ProcessorsRelationUndirectedFlowObservation.partial_key())
for i in uf_rels:
print(i.source_processor.name + " <> " + i.target_processor.name)
# Obtain all upscale Relation Observations
print("---- Upscale relations (P-P) ----")
up_rels = glb_idx.get(
ProcessorsRelationUpscaleObservation.partial_key())
for i in up_rels:
print(i.parent_processor.name + " \/ " + i.child_processor.name +
"(" + i.factor_name + ": " + str(i.quantity) + ")")
# Obtain all directed flow Relation Observations
print("---- Directed flow relations (F-F) ----")
df_rels = glb_idx.get(
FactorsRelationDirectedFlowObservation.partial_key())
for i in df_rels:
print(i.source_factor.processor.name + ":" +
i.source_factor.taxon.name + " -> " +
i.target_factor.processor.name + ":" +
i.target_factor.taxon.name +
(" (" + str(i.weight) + ")" if i.weight else ""))
# Obtain all hierarchies
print("---- FactorType Hierarchies ----")
hies = glb_idx.get(Hierarchy.partial_key())
for i in hies:
print(i.name)
# Close interactive session
isess.close_db_session()
def prepare_model(state) -> NoReturn:
"""
Modify the state so that:
* Implicit references of Interfaces to subcontexts are materialized
* Creating processors
* Creating interfaces in these processors
* Creating relationships in these processors
:param state:
"""
# TODO: currently when an interface is defined as a Scale from two or more interfaces, the computed values are
# added while the intuition tells us that only one scale should be defined. We have to give a warning message
# if this situation happens.
# Registry and the other objects also
glb_idx, _, _, _, _ = get_case_study_registry_objects(state)
# Prepare a Query to obtain ALL interfaces
query = BasicQuery(state)
filt = {}
objs = query.execute([Factor], filt)
for iface in objs[Factor]: # type: Factor
if strcmp(
iface.processor.instance_or_archetype, 'Archetype') or strcmp(
iface.processor.instance_or_archetype, 'No'):
continue
# If the Interface is connected to a "Subcontext" different than the owning Processor
if iface.opposite_processor_type:
if iface.opposite_processor_type.lower(
) != iface.processor.subsystem_type.lower():
# Check if the interface has flow relationships
# TODO An alternative is to search "observations" of type FactorsRelationDirectedFlowObservation
# in the same "iface"
if iface.orientation.lower() == "input":
parameter = {"target": iface}
else:
parameter = {"source": iface}
relations = glb_idx.get(
FactorsRelationDirectedFlowObservation.partial_key(
**parameter))
# If it does not have flow relationships:
# * define default Processor name and retrieve it (or if it does not exist, create it)
# * create an Interface into that Processor and a Flow Relationship
if len(relations) == 0:
# Define the name of a Processor in the same context but in different subcontext
p_name = iface.processor.processor_system + "_" + iface.opposite_processor_type
p = glb_idx.get(Processor.partial_key(p_name))
if len(p) == 0:
attributes = {
'subsystem_type': iface.opposite_processor_type,
'processor_system':
iface.processor.processor_system,
'functional_or_structural': 'Functional',
'instance_or_archetype': 'Instance'
# 'stock': None
}
p = Processor(p_name, attributes=attributes)
glb_idx.put(p.key(), p)
else:
p = p[0]
attributes = {
'sphere':
'Technosphere'
if iface.opposite_processor_type.lower()
in ["local", "external"] else 'Biosphere',
'roegen_type':
iface.roegen_type,
'orientation':
"Input"
if iface.orientation.lower() == "output" else "Output",
'opposite_processor_type':
iface.processor.subsystem_type
}
# Create Interface (if it does not exist)
if not p.factors_find(iface.taxon.name):
f = Factor.create_and_append(
name=iface.taxon.name,
processor=p,
in_processor_type=FactorInProcessorType(
external=False,
incoming=iface.orientation.lower() ==
"output"),
attributes=attributes,
taxon=iface.taxon)
glb_idx.put(f.key(), f)
# Create Flow Relationship
if iface.orientation.lower() == "output":
source = iface
target = f
else:
source = f
target = iface
fr = FactorsRelationDirectedFlowObservation.create_and_append(
source=source, target=target, observer=None)
glb_idx.put(fr.key(), fr)
def _process_row(self, field_values: Dict[str, Any], subrow=None) -> None:
# Transform text of "attributes" into a dictionary
if field_values.get("attributes"):
try:
field_values["attributes"] = dictionary_from_key_value_list(
field_values["attributes"], self._glb_idx)
except Exception as e:
self._add_issue(IType.ERROR,
str(e) + subrow_issue_message(subrow))
return
else:
field_values["attributes"] = {}
# Process specific fields
# Obtain the parent: it must exist. It could be created dynamically but it's important to specify attributes
if field_values.get("parent_processor"):
try:
parent_processor = self._get_processor_from_field(
"parent_processor")
# parents = find_processors_matching_name(parent_processor)
# if len(parents) > 1:
# self._add_issue(IType.WARNING,
# f"Parent processor '{parent_processor}' not unique. Matches: {', '.join(p.hierarchical_names[0] for p in parents)}. Skipped." + subrow_issue_message(subrow))
# return
except CommandExecutionError:
self._add_issue(
IType.ERROR,
f"Specified parent processor, '{field_values.get('parent_processor')}', does not exist"
+ subrow_issue_message(subrow))
return
else:
parent_processor = None
behave_as_processor: Optional[Processor] = None
if field_values.get("behave_as_processor"):
try:
behave_as_processor = self._get_processor_from_field(
"behave_as_processor")
except CommandExecutionError:
self._add_issue(
IType.WARNING,
f"Specified 'behave as' processor, '{field_values.get('behave_as_processor')}', does not exist, value ignored"
+ subrow_issue_message(subrow))
# Find or create processor and REGISTER it in "glb_idx"
# TODO Now, only Simple name allowed
# TODO Improve allowing hierarchical names, and hierarchical names with wildcards
pgroup = field_values.get("processor_group")
# Get internal and user-defined attributes in one dictionary
attributes = {
c.name: field_values[c.name]
for c in self._command_fields if c.attribute_of == Processor
}
attributes.update(field_values["attributes"])
attributes["processor_group"] = pgroup
# Needed to support the new name of the field, "Accounted" (previously it was "InstanceOrArchetype")
# (internally the values have the same meaning, "Instance" for a processor which has to be accounted,
# "Archetype" for a processor which hasn't)
v = attributes.get("instance_or_archetype", None)
if strcmp(v, "Yes"):
v = "Instance"
elif strcmp(v, "No"):
v = "Archetype"
if v:
attributes["instance_or_archetype"] = v
name = field_values["processor"]
p_names, _ = obtain_name_parts(name)
geolocation = Geolocation.create(field_values["geolocation_ref"],
field_values["geolocation_code"])
ps = find_processors_matching_name(name, self._glb_idx)
more_than_one = len(ps) > 1
simple = len(p_names) == 1
exists = True if len(ps) == 1 else False
# SIMPLE? EXISTS? PARENT? ACTION:
# Yes Yes Yes NEW; HANG FROM PARENT
# Yes Yes No Warning: repeated
# Yes No Yes NEW; HANG FROM PARENT
# Yes No No NEW
# No Yes Yes Warning: cannot hang from parent
# No Yes No Warning: repeated AND not simple not allowed
# No No Yes Warning: cannot create more than one processor AND not simple not allowed
# No No No Warning: cannot create more than one processor AND not simple not allowed
create_new = False
if not simple:
if not parent_processor:
self._add_issue(
IType.WARNING,
f"When a processor does not have parent, the name must be simple. Skipped."
+ subrow_issue_message(subrow))
return
else:
if exists and not parent_processor:
self._add_issue(
IType.WARNING,
f"Repeated declaration of {name}. Skipped." +
subrow_issue_message(subrow))
return
create_new = True
if create_new:
p = find_or_create_processor(state=self._glb_idx,
name=name,
proc_attributes=attributes,
proc_location=geolocation)
else:
if exists:
p = ps[0]
# Add to ProcessorsGroup, if specified
if pgroup:
p_set = self._p_sets.get(pgroup, ProcessorsSet(pgroup))
self._p_sets[pgroup] = p_set
if p_set.append(
p, self._glb_idx
): # Appends codes to the pset if the processor was not member of the pset
p_set.append_attributes_codes(field_values["attributes"])
# If geolocation specified, check if it exists
# Inside it, check it the code exists
if p.geolocation and p.geolocation.reference:
# Geographical reference
gr = self._glb_idx.get(
GeographicReference.partial_key(name=p.geolocation.reference))
if len(gr) == 0:
self._add_issue(
IType.ERROR,
f"Geographical reference {p.geolocation.reference} not found "
+ subrow_issue_message(subrow))
return
if p.geolocation.reference and not p.geolocation.code:
self._add_issue(
IType.ERROR,
f"Geographical reference was specified but not the code in it "
+ subrow_issue_message(subrow))
return
geo_id = p.geolocation.code
try:
url = gr[0].attributes["data_location"]
except:
self._add_issue(
IType.ERROR,
f"URL not found in geographical reference {p.geolocation.reference} "
+ subrow_issue_message(subrow))
return
try:
j, ids = read_geojson(
url
) # READ the file!! (or get it from cache). Could take some time...
except:
self._add_issue(
IType.ERROR,
f"URL {url} in reference {p.geolocation.reference} could not be read "
+ subrow_issue_message(subrow))
return
if geo_id not in ids:
self._add_issue(
IType.WARNING,
f"Could not find code {geo_id} in file {url}, geographical reference {p.geolocation.reference} "
+ subrow_issue_message(subrow))
# Add Relationship "part-of" if parent was specified
# The processor may have previously other parent processors that will keep its parentship
if parent_processor:
# Create "part-of" relationship
if len(
self._glb_idx.get(
ProcessorsRelationPartOfObservation.partial_key(
parent_processor, p))) > 0:
self._add_issue(
IType.WARNING,
f"{p.name} is already part-of {parent_processor.name}. Skipped."
+ subrow_issue_message(subrow))
return
o1 = ProcessorsRelationPartOfObservation.create_and_append(
parent_processor,
p,
None,
behave_as=behave_as_processor,
weight=field_values.get("parent_processor_weight")) # Part-of
self._glb_idx.put(o1.key(), o1)
for hname in parent_processor.full_hierarchy_names(self._glb_idx):
p_key = Processor.partial_key(f"{hname}.{p.name}", p.ident)
if attributes:
p_key.update({
k: ("" if v is None else v)
for k, v in attributes.items()
})
self._glb_idx.put(p_key, p)
def execute(self, state: "State"):
"""
For each parent processor clone all the child processors.
The cloning process may pass some factor observation, that may result in
"""
some_error = False
issues = []
parent_processor_type = self._content["parent_processor_type"]
child_processor_type = self._content["child_processor_type"]
scaled_factor = self._content["scaled_factor"]
source = self._content["source"]
# column_headers = self._content["column_headers"]
# row_headers = self._content["row_headers"]
scales = self._content["scales"]
# Find processor sets, for parent and child
glb_idx, p_sets, hh, datasets, mappings = get_case_study_registry_objects(
state)
if parent_processor_type not in p_sets:
some_error = True
issues.append((
3, "The processor type '" + parent_processor_type +
"' (appointed for parent) has not been found in the commands execute so far"
))
if child_processor_type not in p_sets:
some_error = True
issues.append((
3, "The processor type '" + child_processor_type +
"' (should be child processor) has not been found in the commands execute so far"
))
if some_error:
return issues, None
# CREATE the Observer of the Upscaling
oer = glb_idx.get(Observer.partial_key(source))
if not oer:
oer = Observer(source)
glb_idx.put(oer.key(), oer)
else:
oer = oer[0]
# Processor Sets have associated attributes, and each of them has a code list
parent = p_sets[parent_processor_type] # type: ProcessorsSet
child = p_sets[child_processor_type] # type: ProcessorsSet
# Form code lists from the command specification
code_lists = None
for sc_dict in scales:
codes = sc_dict["codes"]
if not code_lists:
code_lists = [set() for _ in codes]
for i, c in enumerate(codes):
code_lists[i].add(c)
# Match existing code lists (from Processor attributes) with the ones gathered in the specification of
# the two (parent and child) processors sets.
# Form lists of attributes of processors used in the code lists
parent_attrs = []
child_attrs = []
matched = []
for i, cl in enumerate(code_lists):
found = False
for attr, attr_values in parent.attributes.items():
if set(attr_values).issuperset(cl):
parent_attrs.append(
(attr, i)) # (Attribute, code list index)
found = True
break
for attr, attr_values in child.attributes.items():
if set(attr_values).issuperset(cl):
child_attrs.append(
(attr, i)) # (Attribute, code list index)
found = True
break
matched.append(found)
for i, found in enumerate(matched):
if not found:
cl = code_lists[i]
# TODO Try cl as a list of names of parent or child processors
if not found:
issues.append((
2, "The code list: " + ", ".join(cl) +
" is not contained in the attributes of the parent processors set '"
+ parent_processor_type +
"' nor in the attributes of the child processors set '"
+ child_processor_type + "'"))
# Execute the upscale for each
cached_processors = {}
for sc_dict in scales:
try:
non_zero_weight = math.fabs(float(sc_dict["weight"])) > 1e-6
except:
non_zero_weight = True
if not non_zero_weight:
continue
codes = sc_dict["codes"]
# Find parent processor
parent_dict = {attr: codes[i] for attr, i in parent_attrs}
d2s = str(parent_dict)
if d2s in cached_processors:
parent = cached_processors[d2s]
if not parent:
issues.append((
3, "Either the tuple (" + d2s +
") did not match any Processor or matched more than one."
))
else:
parent_dict.update(Processor.partial_key())
# Obtain Processor matching the attributes <<<<<<<<<<
# Query the PartialRetrievalDictionary by attributes
parents = glb_idx.get(parent_dict)
if len(parents) > 1:
issues.append(
(3, "The tuple (" + str(parent_dict) + ") matches " +
str(len(parents)) + " Processors: " +
(", ".join([p.name for p in parents]))))
parent = None
elif len(parents) == 0:
issues.append((3, "The tuple (" + str(parent_dict) +
") did not match any Processor"))
parent = None
else:
parent = parents[0]
cached_processors[d2s] = parent
# Find child processor
child_dict = {attr: codes[i] for attr, i in child_attrs}
d2s = str(child_dict)
if d2s in cached_processors:
child = cached_processors[d2s]
if not child:
issues.append((
3, "Either the tuple (" + d2s +
") did not match any Processor or matched more than one."
))
else:
child_dict.update(Processor.partial_key())
# Obtain Processors matching the attributes
# Query the PartialRetrievalDictionary by attributes
children = glb_idx.get(child_dict)
if len(children) > 1:
issues.append(
(3, "The tuple (" + str(child_dict) + ") matches " +
str(len(parents)) + " Processors: " +
(", ".join([p.name for p in children]))))
child = None
elif len(children) == 0:
issues.append((3, "The tuple (" + str(child_dict) +
") did not match any Processor"))
child = None
else:
child = children[0] # type: Processor
cached_processors[d2s] = child
# Clone child processor (and its descendants) and add an upscale relation between "parent" and the clone
if parent and child:
if non_zero_weight:
# Clone the child processor
# TODO
cloned_child, cloned_children = child.clone(state=glb_idx)
Processor.register([cloned_child] + list(cloned_children),
glb_idx)
# Create the new Relation Observations
# - Part-of Relation
o1 = ProcessorsRelationPartOfObservation.create_and_append(
parent, cloned_child, oer) # Part-of
glb_idx.put(o1.key(), o1)
# - Upscale Relation
quantity = str(sc_dict["weight"])
if True:
# Find Interface named "scaled_factor"
for f in parent.factors:
if strcmp(f.name, scaled_factor):
origin = f
break
else:
origin = None
for f in cloned_child.factors:
if strcmp(f.name, scaled_factor):
destination = f
break
else:
destination = None
if origin and destination:
o3 = FactorsRelationScaleObservation.create_and_append(
origin,
destination,
observer=None,
quantity=quantity)
glb_idx.put(o3.key(), o3)
else:
raise Exception(
"Could not find Interfaces to define a Scale relation. Processors: "
+ parent.name + ", " + cloned_child.name +
"; Interface name: " + scaled_factor)
else:
o3 = ProcessorsRelationUpscaleObservation.create_and_append(
parent,
cloned_child,
observer=None,
factor_name=scaled_factor,
quantity=quantity)
glb_idx.put(o3.key(), o3)
else:
# TODO
parent_dict = str({attr: codes[i] for attr, i in parent_attrs})
child_dict = str({attr: codes[i] for attr, i in child_attrs})
if not parent and child:
issues.append((
2,
"Could not find parent Processor matching attributes: "
+ parent_dict))
elif not child and parent:
issues.append(
(2,
"Could not find child Processor matching attributes: "
+ child_dict))
else:
issues.append((
2,
"Could not find parent Processor matching attributes: "
+ parent_dict +
", nor child Processor matching attributes: " +
child_dict))
return issues, None
def get_processor_unique_label(p: Processor, reg: PartialRetrievalDictionary):
return p.full_hierarchy_names(reg)[0]
