def session(request,department_Name,session_Start):
dep = get_object_or_404(Department, Name=department_Name)
session = get_object_or_404(Session, Start=session_Start, Department= dep)
rdfextras.registerplugins()
g=rdflib.Graph()
g.parse(session.Student_file)
g.parse(dep.Professor_file)
res1 = g.query("""
PREFIX nit: <http://nitdgp.ac.in/>
SELECT ?fname ?lname ?email ?roll ?dept ?regno ?address ?prof_fname ?prof_lname ?bgroup ?height ?weight
WHERE {
?x nit:fname ?fname.
?x nit:lname ?lname.
?x nit:email ?email.
?x nit:roll ?roll.
?x nit:dept ?dept.
?x nit:regno ?regno.
?x nit:address ?address.
?x nit:email ?email.
?x nit:researchUnder ?researchUnder.
?x nit:bgroup ?bgroup.
?x nit:height ?height.
?x nit:weight ?weight.
?researchUnder nit:fname ?prof_fname.
?researchUnder nit:lname ?prof_lname
}
""")
context = { 'dep' : dep,
'res1' : res1,
'session' : session,
}
return render(request, 'department/session.html', context)
def get_asset_ids(assets_data, content_type):
"""
This function extracts source_id, source_id_type and entity_uri of each asset in the data.
It does this by creating an in memory graph via RDF Lib.
* Caution this is potentially very cpu expensive and could use a lot of memory
:param assets_data: A blob of triples
:param content_type: The type of the blob
:return: an array of dicts that show the ids of assets
"""
graph = rdflib.graph.Graph()
graph.parse(data=assets_data, format=TYPE_MAPPING[content_type])
result = graph.query(SPARQL_PREFIXES + ASSET_QUERY_ALL_ENTITY_IDS)
result = [{u'entity_id': x[u'entity_id'].split('/')[-1]} for x in result]
return result
def display(request):
dept_P = request.GET['dept']
session_P = request.GET['session']
regno_P = request.GET['regno']
sem_P = request.GET['sem']
dep = get_object_or_404(Department, Name=dept_P)
session = get_object_or_404(Session, Start=session_P, Department= dep)
result = get_object_or_404(Result, Session=session, Department=dep, Semester=sem_P)
rdfextras.registerplugins()
g=rdflib.Graph()
g.parse(result.Result_file)
S = """
PREFIX nit: <http://nitdgp.ac.in/>
SELECT ?fname ?lname ?roll ?regno ?cgpa ?sgpa ?BT501LG ?BT502LG ?BT503LG ?OELG ?BT551LG ?BT552LG ?CH581LG
WHERE {
?x nit:fname ?fname.
?x nit:lname ?lname.
?x nit:regno """+"'"+regno_P+"'"+""".
?x nit:roll ?roll.
?x nit:regno ?regno.
?x nit:cgpa ?cgpa.
?x nit:sgpa ?sgpa.
?x nit:BT501LG ?BT501LG.
?x nit:BT502LG ?BT502LG.
?x nit:BT503LG ?BT503LG.
?x nit:OELG ?OELG.
?x nit:BT551LG ?BT551LG.
?x nit:BT552LG ?BT552LG.
?x nit:CH581LG ?CH581LG
}
"""
res1 = g.query(S)
context = { 'dep' : dep,
'res1' : res1,
'session' : session,
'sem' : sem_P,
}
return render(request, 'department/display.html',context)
def get_asset_source_ids(assets_data, content_type, entity_id):
"""
This function extracts source_id and source_id_type of each asset in the data.
It does this by creating an in memory graph via RDF Lib.
* Caution this is potentially very cpu expensive and could use a lot of memory
:param assets_data: A blob of triples
:param content_type: The type of the blob
:return: an array of dicts that show the ids of assets
"""
graph = rdflib.graph.Graph()
graph.parse(data=assets_data, format=TYPE_MAPPING[content_type])
result = graph.query(SPARQL_PREFIXES + (ASSET_GET_ALSO_IDENTIFIED.format(
entity_id=Asset.normalise_id(entity_id))))
result = [{
'source_id_type': x[0].split('/')[-1],
'source_id': str(x[1])
} for x in result]
return result
def detail(request, department_Name):
dep = get_object_or_404(Department, Name=department_Name)
sessions = dep.session_set.all()
rdfextras.registerplugins()
g=rdflib.Graph()
g.parse(dep.Professor_file)
res1 = g.query("""
PREFIX nit: <http://nitdgp.ac.in/>
SELECT ?fname ?lname ?email ?research ?designation
WHERE {
?x nit:fname ?fname.
?x nit:lname ?lname.
?x nit:email ?email.
?x nit:designation ?designation.
?x nit:research ?research
}
""")
context = { 'dep' : dep,
'res1' : res1,
'sessions' : sessions,
}
return render(request, 'department/detail.html', context)