def api_dataset_view(request, DataSet_UKCL, root_id, response_format):
'''
it returns the data of the istance with pk=root_id in the dataset (which is a view)
if we are browsing a view there is not just one single root that we can explore
but a list of instances that match the criteria; root_id tells us which one to browse
CAN BE CACHED
'''
response_format = response_format.upper()
DataSet_UKCL_decoded = urllib.parse.unquote(DataSet_UKCL).replace("%2F","/")
dataset = DataSet.retrieve_locally(DataSet_UKCL_decoded)
actual_instance = ""
actual_instance_json = ""
# this dataset is a view; I shall use root_id to retrieve the actual instance
module_name = dataset.dataset_structure.root_node.model_metadata.module
dataset_uri = KsUrl(DataSet_UKCL_decoded)
actual_instance_class = OrmWrapper.load_class(dataset_uri.netloc, module_name, dataset.dataset_structure.root_node.model_metadata.name)
actual_instance = actual_instance_class.objects.get(pk=root_id)
if response_format == 'HTML' or response_format == 'BROWSE':
actual_instance_json = '{' + actual_instance.serialize(dataset.dataset_structure.root_node, export_format='json', exported_instances = []) + '}'
if response_format == 'JSON':
ar = ApiResponse()
ar.content = { "DataSet": dataset.export(export_format = 'DICT') }
ar.status = ApiResponse.success
return render(request, 'knowledge_server/export.json', {'json': ar.json()}, content_type="application/json")
if response_format == 'XML':
ar = ApiResponse()
ar.status = ApiResponse.success
ar.content = dataset.export(export_format = response_format)
return render(request, 'knowledge_server/export.xml', {'xml': ar.xml()}, content_type="application/xhtml+xml")
if response_format == 'HTML' or response_format == 'BROWSE':
this_ks = KnowledgeServer.this_knowledge_server()
cont = RequestContext(request, {'dataset': dataset, 'actual_instance': actual_instance, 'actual_instance_json': actual_instance_json, 'sn': dataset.dataset_structure.root_node, 'DataSet_UKCL': DataSet_UKCL, 'this_ks':this_ks, 'this_ks_encoded_url':this_ks.url(True)})
return render_to_response('knowledge_server/browse_dataset.html', context_instance=cont)
def forwards_func(apps, schema_editor):
this_ks_d = KnowledgeServer.this_knowledge_server('default')
dss_workflow = DataSetStructure(name='Workflow')
dss_workflow.description = 'Workflow-Method-Attribute-PermissionStatement'
dss_workflow.SetNotNullFields()
dss_workflow.save()
mm_workflow = Workflow().get_model_metadata(db_alias='default')
mm_workflow_method = dss_workflow.create_model_metadata(name="WorkflowMethod",module="ap",name_field="name",description_field="description")
mm_attribute = dss_workflow.create_model_metadata(name="Attribute",module="ap",name_field="",description_field="")
mm_widget = dss_workflow.create_model_metadata(name="Widget",module="ap",name_field="",description_field="")
mm_attribute_in_a_method = dss_workflow.create_model_metadata(name="AttributeInAMethod",module="ap",name_field="",description_field="")
mm_permission_statement = dss_workflow.create_model_metadata(name="PermissionStatement",module="ap",name_field="",description_field="")
mm_permission_holder = dss_workflow.create_model_metadata(name="PermissionHolder",module="ap",name_field="",description_field="")
# It creates a DataSet for each of them; having the DataSetStructure makes it
# possible to release and materialize the datasets with dangling references that will be
# resolved once the dss is released and materialized.
KnowledgeServer.register_models([mm_workflow_method, mm_attribute_in_a_method, mm_permission_statement, mm_attribute, mm_widget, mm_permission_holder])
# it creates the root node from the ModelMetadata provided
dss_workflow.root_model_metadata(mm_workflow)
# child nodes for two attributes/fields
dss_workflow.root_node.children_nodes_for(["methods"], this_ks_d.netloc)
dss_workflow.root_node.children_external_references_nodes_for( [ "type" ], this_ks_d.netloc )
method_node = dss_workflow.root_node.child_nodes.all()[0]
method_node.children_external_references_nodes_for(["initial_statuses", "final_status"], this_ks_d.netloc)
method_node.children_nodes_for(["attributeinamethod_set", "permission"], this_ks_d.netloc)
attribute_in_a_method_node = [cn for cn in method_node.child_nodes.all() if cn.attribute == "attributeinamethod_set"][0]
attribute_in_a_method_node.children_external_references_nodes_for( [ "attribute", "custom_widget" ], this_ks_d.netloc )
permission_node = [cn for cn in method_node.child_nodes.all() if cn.attribute == "permission"][0]
permission_node.children_external_references_nodes_for( [ "permission_holder" ], this_ks_d.netloc )
dss_workflow.save()
dss_dss = DataSetStructure.get_from_name(DataSetStructure.dataset_structure_DSN)
ds = DataSet(description='DataSet for data set structure "Workflow-Method-Attribute-PermissionStatement"', knowledge_server=this_ks_d, dataset_structure=dss_dss, root=dss_workflow, version_major=0, version_minor=1, version_patch=0, version_description="")
ds.save()
ds.set_released()
# Application now is on its own dataset structure so we don't create it and just use the shallow one
mm_application = dss_workflow.create_model_metadata(name='Application',module='ap',name_field="name",description_field="description")
KnowledgeServer.register_models([mm_application])
def api_dataset_view(request):
'''
it returns the data of the istance with pk=root_id in the dataset (which is a view)
if we are browsing a view there is not just one single root that we can explore
but a list of instances that match the criteria; root_id tells us which one to browse
CAN BE CACHED
'''
DataSet_UKCL = request.GET['UKCL']
root_id = request.GET['id']
ar = ApiResponse(request=request)
dataset = DataSet.retrieve_locally(DataSet_UKCL)
actual_instance = ""
actual_instance_json = ""
# this dataset is a view; I shall use root_id to retrieve the actual instance
module_name = dataset.dataset_structure.root_node.model_metadata.module
dataset_uri = KsUrl(DataSet_UKCL)
actual_instance_class = OrmWrapper.load_class(
dataset_uri.netloc, module_name,
dataset.dataset_structure.root_node.model_metadata.name)
actual_instance = actual_instance_class.objects.get(pk=root_id)
if ar.response_format == 'HTML' or ar.response_format == 'BROWSE':
actual_instance_json = '{' + actual_instance.serialize(
dataset.dataset_structure.root_node,
export_format='json',
exported_instances=[]) + '}'
if ar.response_format == 'JSON':
ar.content = {"DataSet": dataset.export(export_format='DICT')}
ar.status = ApiResponse.success
return render(request,
'knowledge_server/export.json', {'json': ar.json()},
content_type="application/json")
if ar.response_format == 'XML':
ar.status = ApiResponse.success
ar.content = dataset.export(export_format=ar.response_format)
return render(request,
'knowledge_server/export.xml', {'xml': ar.xml()},
content_type="application/xhtml+xml")
if ar.response_format == 'HTML' or ar.response_format == 'BROWSE':
this_ks = KnowledgeServer.this_knowledge_server()
cont = RequestContext(
request, {
'dataset': dataset,
'actual_instance': actual_instance,
'actual_instance_json': actual_instance_json,
'sn': dataset.dataset_structure.root_node,
'DataSet_UKCL': DataSet_UKCL,
'this_ks': this_ks,
'this_ks_encoded_url': this_ks.url(True)
})
return render_to_response('knowledge_server/browse_dataset.html',
context_instance=cont)
def forwards_func(apps, schema_editor):
org_ks = {
"Organization": {
"name": "A test Organization",
"website": "http://new_org.example.com",
"description": ""
},
"KnowledgeServer": {
"name": "A test OKS.",
"netloc": "test.thekoa.org",
"description": "It has some datasets and structures",
"html_home": "",
"html_disclaimer": ""
}
}
KnowledgeServer.create_this_ks(org_ks)
this_ks_d = KnowledgeServer.this_knowledge_server('default')
dssContinentState = DataSetStructure()
dssContinentState.name = "Test Continent-SubContinent-State"
dssContinentState.SetNotNullFields()
dssContinentState.save()
mmContinent = dssContinentState.create_model_metadata(
"Continent", "test1", "name")
mmSubContinent = dssContinentState.create_model_metadata(
"SubContinent", "test1", "name")
mmState = dssContinentState.create_model_metadata("State", "test1", "name")
# It creates ModelMetadata and a DataSet for each of them; having the DataSetStructure makes it
# possible to release and materialize the datasets with dangling references that will be
# resolved once the dss is released and materialized.
KnowledgeServer.register_models([mmContinent, mmSubContinent, mmState])
# it creates the root node of the dataset structure
dssContinentState.root_model_metadata(mmContinent)
# and child nodes for two attributes/fields
dssContinentState.root_node.children_nodes_for(
["subcontinent_set", "state_set"], this_ks_d.netloc)
dssContinentState.save()
dss_dss = DataSetStructure.get_from_name(
DataSetStructure.dataset_structure_DSN)
ds = DataSet(
description=
'DataSet for data set structure "Continent-SubContinent-State"',
knowledge_server=this_ks_d,
dataset_structure=dss_dss,
root=dssContinentState,
version_major=0,
version_minor=1,
version_patch=0)
ds.save()
ds.set_released()
europe = Continent()
europe.name = "Europe"
europe.save()
asia = Continent()
asia.name = "Asia"
asia.save()
south_europe = SubContinent()
south_europe.name = "South Europe"
south_europe.continent = europe
south_europe.save()
central_europe = SubContinent()
central_europe.name = "Central Europe"
central_europe.continent = europe
central_europe.save()
italy = State()
italy.name = "Italy"
italy.sub_continent = south_europe
italy.continent = europe
italy.save()
spain = State()
spain.name = "Spain"
spain.sub_continent = south_europe
spain.continent = europe
spain.save()
germany = State()
germany.name = "Germany"
germany.sub_continent = central_europe
germany.continent = europe
germany.save()
ds = DataSet(knowledge_server=this_ks_d,
dataset_structure=dssContinentState,
root=europe,
description="Europe",
version_major=0,
version_minor=1,
version_patch=0,
version_description="")
ds.save()
ds.set_released()
ds = DataSet(knowledge_server=this_ks_d,
dataset_structure=dssContinentState,
root=asia,
description="Asia",
version_major=0,
version_minor=1,
version_patch=0,
version_description="")
ds.save()
ds.set_released()
def forwards_func(apps, schema_editor):
this_ks_d = KnowledgeServer.this_knowledge_server('default')
dss_workflow = DataSetStructure(name='Workflow')
dss_workflow.description = 'Workflow-Method-Attribute-PermissionStatement'
dss_workflow.SetNotNullFields()
dss_workflow.save()
mm_workflow = Workflow().get_model_metadata(db_alias='default')
models_for_dss_workflow = [
{
"name": "Attribute",
"module": "ap",
"name_field": "",
"description_field": ""
},
{
"name": "Widget",
"module": "ap",
"name_field": "",
"description_field": ""
},
{
"name": "AttributeInAMethod",
"module": "ap",
"name_field": "",
"description_field": ""
},
{
"name": "PermissionStatement",
"module": "ap",
"name_field": "",
"description_field": ""
},
]
mm_attribute, mm_widget, mm_attribute_in_a_method, mm_permission_statement, mm_permission_holder = dss_workflow.create_many_model_metadata(
models_for_dss_workflow)
mm_workflow_method = dss_workflow.create_model_metadata(
name="WorkflowMethod", module="ap")
mm_attribute = dss_workflow.create_model_metadata(name="Attribute",
module="ap",
name_field="",
description_field="")
mm_widget = dss_workflow.create_model_metadata(name="Widget",
module="ap",
name_field="",
description_field="")
mm_attribute_in_a_method = dss_workflow.create_model_metadata(
name="AttributeInAMethod",
module="ap",
name_field="",
description_field="")
mm_permission_statement = dss_workflow.create_model_metadata(
name="PermissionStatement",
module="ap",
name_field="",
description_field="")
# check quale DSS
mm_attribute_type = dss_workflow.create_model_metadata(
name="AttributeType", module="ap")
mm_attribute_group = dss_workflow.create_model_metadata(
name="AttributeGroup", module="ap")
# WorkflowsMethods è un many to many through, va fatto ?
WorkflowTransition
Application
ModelMetadataSearch
ApplicationStructureNodeSearch
AttributeInASearch
KSUser
KSRole
KSGroup
# check quale DSS; sul diagramma sul foglio?
# It creates a DataSet for each of them; having the DataSetStructure makes it
# possible to release and materialize the datasets with dangling references that will be
# resolved once the dss is released and materialized.
KnowledgeServer.register_models([
mm_workflow_method, mm_attribute_in_a_method, mm_permission_statement,
mm_attribute, mm_attribute_type, mm_attribute_group, mm_widget,
mm_permission_holder
])
# it creates the root node from the ModelMetadata provided
dss_workflow.root_model_metadata(mm_workflow)
# child nodes for two attributes/fields
dss_workflow.root_node.children_nodes_for(["methods"], this_ks_d.netloc)
dss_workflow.root_node.children_external_references_nodes_for(
["type"], this_ks_d.netloc)
method_node = dss_workflow.root_node.child_nodes.all()[0]
method_node.children_external_references_nodes_for(
["initial_statuses", "final_status"], this_ks_d.netloc)
method_node.children_nodes_for(["attributeinamethod_set", "permission"],
this_ks_d.netloc)
attribute_in_a_method_node = [
cn for cn in method_node.child_nodes.all()
if cn.attribute == "attributeinamethod_set"
][0]
attribute_in_a_method_node.children_external_references_nodes_for(
["attribute", "custom_widget"], this_ks_d.netloc)
permission_node = [
cn for cn in method_node.child_nodes.all()
if cn.attribute == "permission"
][0]
permission_node.children_external_references_nodes_for(
["permission_holder"], this_ks_d.netloc)
dss_workflow.save()
dss_dss = DataSetStructure.get_from_name(
DataSetStructure.dataset_structure_DSN)
ds = DataSet(
description=
'DataSet for data set structure "Workflow-Method-Attribute-PermissionStatement"',
knowledge_server=this_ks_d,
dataset_structure=dss_dss,
root=dss_workflow,
version_major=0,
version_minor=1,
version_patch=0,
version_description="")
ds.save()
ds.set_released()
# Application now is on its own dataset structure so we don't create it and just use the shallow one
mm_application = dss_workflow.create_model_metadata(
name='Application',
module='ap',
name_field="name",
description_field="description")
KnowledgeServer.register_models([mm_application])
def api_dataset_info(request):
'''
#52
Parameters:
* response_format { 'XML' | 'JSON' | 'HTML' = 'BROWSE' }
* DataSet_UKCL: UKCL of the DataSet
Implementation:
it fetches the DataSet, then the list of all that share the same root
it returns DataSet.export(response_format) and for each on the above list:
the UKCL of the DataSet
the version status {working | released | obsolete}
the version number (e.g. 0.1.0)
the version date
the version description
other version metadata
CAN BE CACHED
'''
DataSet_UKCL = request.GET['UKCL']
ar = ApiResponse(request=request)
DataSet_UKCL_unquoted = urllib.parse.unquote(DataSet_UKCL).replace(
"%2F", "/")
dataset = DataSet.retrieve_locally(DataSet_UKCL_unquoted)
all_versions = DataSet.objects.filter(first_version=dataset.first_version)
all_versions_serialized = ""
all_versions_list = []
if ar.response_format != 'HTML' and ar.response_format != 'BROWSE':
for v in all_versions:
if ar.response_format == 'JSON':
# note I am using DICT as a response_format so that I can merge the dict (.update) and then convert it to json
all_versions_list.append(
v.export(export_format='DICT',
force_external_reference=True))
else:
all_versions_serialized += v.export(
export_format=ar.response_format,
force_external_reference=True)
if ar.response_format == 'XML':
ar.status = ApiResponse.success
ar.content = "<DataSet>" + dataset.export(
export_format=ar.response_format, force_external_reference=True
) + "</DataSet><Versions>" + all_versions_serialized + "</Versions>"
return render(request,
'knowledge_server/export.xml', {'xml': ar.xml()},
content_type="application/xhtml+xml")
if ar.response_format == 'JSON':
ar.content = {
"DataSet":
dataset.export(export_format="DICT",
force_external_reference=True),
"Versions":
all_versions_list
}
ar.status = ApiResponse.success
return HttpResponse(ar.json(), content_type="application/json")
# return render(request, 'knowledge_server/export.json', {'json': ar.json()}, content_type="application/json")
if ar.response_format == 'HTML' or ar.response_format == 'BROWSE':
if dataset.dataset_structure.is_a_view:
instances = dataset.get_instances()
else:
instances = []
instances.append(dataset.root)
all_versions_with_instances = []
for v in all_versions:
if v.UKCL != dataset.UKCL:
version_with_instance = {}
version_with_instance['dataset'] = v
version_with_instance['root'] = []
# views have no version by themselves; only their components have and they can be different
# so if we are here we are not in a view hence there is just one instance:
# I get root and not .get_instances()
version_with_instance['root'].append(v.root)
all_versions_with_instances.append(version_with_instance)
this_ks = KnowledgeServer.this_knowledge_server()
cont = RequestContext(
request, {
'DataSet_UKCL': DataSet_UKCL,
'dataset': dataset,
'all_versions_with_instances': all_versions_with_instances,
'ks': dataset.knowledge_server,
'instances': instances,
'this_ks': this_ks,
'this_ks_encoded_url': this_ks.url(True)
})
return render_to_response('knowledge_server/api_dataset_info.html',
context_instance=cont)
def forwards_func(apps, schema_editor):
org_ks={
"Organization": {"name": "A test Organization", "website": "http://new_org.example.com", "description": ""},
"KnowledgeServer": {"name": "A test OKS.", "netloc": "test.thekoa.org", "description": "It has some datasets and structures", "html_home": "", "html_disclaimer": ""}
}
KnowledgeServer.create_this_ks(org_ks)
this_ks_d = KnowledgeServer.this_knowledge_server('default')
dssContinentState=DataSetStructure()
dssContinentState.name="Test Continent-SubContinent-State";
dssContinentState.SetNotNullFields()
dssContinentState.save()
mmContinent=dssContinentState.create_model_metadata(name="Continent",module="test1",name_field="name",description_field="")
mmSubContinent=dssContinentState.create_model_metadata(name="SubContinent",module="test1",name_field="name",description_field="")
mmState=dssContinentState.create_model_metadata(name="State",module="test1",name_field="name",description_field="")
# It creates ModelMetadata and a DataSet for each of them; having the DataSetStructure makes it
# possible to release and materialize the datasets with dangling references that will be
# resolved once the dss is released and materialized.
KnowledgeServer.register_models([mmContinent, mmSubContinent, mmState])
# it creates the root node from the ModelMetadata provided
dssContinentState.root_model_metadata(mmContinent)
# child nodes for two attributes/fields
dssContinentState.root_node.children_for(["subcontinent_set", "state_set"], this_ks_d.netloc)
dssContinentState.save()
dss_dss = DataSetStructure.get_from_name(DataSetStructure.dataset_structure_DSN)
ds = DataSet(description='DataSet for data set structure "Continent-SubContinent-State"', knowledge_server=this_ks_d, dataset_structure=dss_dss, root=dssContinentState, version_major=0, version_minor=1, version_patch=0, version_description="")
ds.save();
ds.set_released()
europe = Continent();europe.name="Europe";europe.save()
asia = Continent();asia.name="Asia";asia.save()
south_europe=SubContinent();south_europe.name="South Europe";south_europe.continent=europe;south_europe.save()
central_europe=SubContinent();central_europe.name="Central Europe";central_europe.continent=europe;central_europe.save()
italy=State();italy.name="Italy";italy.sub_continent=south_europe;italy.continent=europe;italy.save()
spain=State();spain.name="Spain";spain.sub_continent=south_europe;spain.continent=europe;spain.save()
germany=State();germany.name="Germany";germany.sub_continent=central_europe;germany.continent=europe;germany.save()
ds = DataSet(knowledge_server=this_ks_d,dataset_structure=dssContinentState,root=europe,
description="Europe",version_major=0,version_minor=1,version_patch=0,version_description="")
ds.save();ds.set_released();
ds = DataSet(knowledge_server=this_ks_d,dataset_structure=dssContinentState,root=asia,
description="Asia",version_major=0,version_minor=1,version_patch=0,version_description="")
ds.save();ds.set_released();
def api_dataset_info(request, DataSet_UKCL, response_format):
'''
#52
Parameters:
* response_format { 'XML' | 'JSON' | 'HTML' = 'BROWSE' }
* DataSet_UKCL: UKCL of the DataSet
Implementation:
it fetches the DataSet, then the list of all that share the same root
it returns DataSet.export(response_format) and for each on the above list:
the UKCL of the DataSet
the version status {working | released | obsolete}
the version number (e.g. 0.1.0)
the version date
the version description
other version metadata
CAN BE CACHED
'''
response_format = response_format.upper()
DataSet_UKCL_unquoted = urllib.parse.unquote(DataSet_UKCL).replace("%2F","/")
dataset = DataSet.retrieve_locally(DataSet_UKCL_unquoted)
all_versions = DataSet.objects.filter(first_version = dataset.first_version)
all_versions_serialized = ""
all_versions_list = []
if response_format != 'HTML' and response_format != 'BROWSE':
for v in all_versions:
if response_format == 'JSON':
# note I am using DICT as a response_format so that I can merge the dict (.update) and then convert it to json
all_versions_list.append(v.export(export_format = 'DICT', force_external_reference=True))
else:
all_versions_serialized += v.export(export_format = response_format, force_external_reference=True)
if response_format == 'XML':
ar = ApiResponse()
ar.status = ApiResponse.success
ar.content = "<DataSet>" + dataset.export(export_format = response_format, force_external_reference=True) + "</DataSet><Versions>" + all_versions_serialized + "</Versions>"
return render(request, 'knowledge_server/export.xml', {'xml': ar.xml()}, content_type="application/xhtml+xml")
if response_format == 'JSON':
ar = ApiResponse()
ar.content = { "DataSet": dataset.export(export_format = "DICT", force_external_reference=True), "Versions": all_versions_list }
ar.status = ApiResponse.success
return HttpResponse(ar.json(), content_type = "application/json")
# return render(request, 'knowledge_server/export.json', {'json': ar.json()}, content_type="application/json")
if response_format == 'HTML' or response_format == 'BROWSE':
if dataset.dataset_structure.is_a_view:
instances = dataset.get_instances()
else:
instances = []
instances.append(dataset.root)
all_versions_with_instances = []
for v in all_versions:
if v.UKCL != dataset.UKCL:
version_with_instance = {}
version_with_instance['dataset'] = v
version_with_instance['root'] = []
# views have no version by themselves; only their components have and they can be different
# so if we are here we are not in a view hence there is just one instance:
# I get root and not .get_instances()
version_with_instance['root'].append(v.root)
all_versions_with_instances.append(version_with_instance)
this_ks = KnowledgeServer.this_knowledge_server()
cont = RequestContext(request, {'DataSet_UKCL': DataSet_UKCL, 'dataset': dataset, 'all_versions_with_instances': all_versions_with_instances, 'ks': dataset.knowledge_server, 'instances': instances, 'this_ks':this_ks, 'this_ks_encoded_url':this_ks.url(True) })
return render_to_response('knowledge_server/api_dataset_info.html', context_instance=cont)
