def treemap_data(session, request):
'''
Dumps data that represents the system state at some point in time. Currently it dumps the latest snapshot of the
system for continuous metrics and data over the past x months for discontinous metrics.
QueryDict params:
cmp_type (str): The type of component for which child data is gathered. E.g if a subsystem is provided,
the returned data is all data for the files in that subsystem.
cmp_id (int): The component id.
metric (str): A metric that must exist in the global METRICS dict.
response_type (str): "application/json"|"text/csv"
'''
metric = request.GET['metric']
cmp_type = request.GET['type']
cmp_id = request.GET.get('id')
response_type = request.GET.get("response_type", "application/json")
if metric not in METRICS.keys():
return HttpResponseNotFound("<h3>Metric not found</h3>")
query = MetricsDb_TreemapData.get_query(session, metric, cmp_type, cmp_id)
data = [views_utils.get_query_columns(query)] + query.all()
return views_utils.dump_data(data,
response_type,
filename="treemap_data_" + metric)
def metric_descriptions(_):
'''
Todo: Make the output csv compatible
'''
# The frontend shifts the first row because it should be agnostic towards both csv and json output
metrics = ["empty entry for csv"]
for metric_name, data in METRICS.iteritems():
entry = {'metric': metric_name}
entry.update(data)
metrics.append(entry)
return views_utils.dump_data(metrics, "application/json")
def metric_descriptions(_):
'''
Todo: Make the output csv compatible
'''
# The frontend shifts the first row because it should be agnostic towards both csv and json output
metrics = ["empty entry for csv"]
for metric_name, data in METRICS.iteritems():
entry = {'metric': metric_name}
entry.update(data)
metrics.append(entry)
return views_utils.dump_data(metrics, "application/json")
def lineview_data(session, request):
'''
Dumps data that has accumulated over time. This data is best suited for chart like visualizations.
QueryDict params:
cmp_type (str): The type of component. "subsystem"|"file"|"function"
cmp_id (int): The component id found in the database
metric (str): A metric that must exist in the global METRICS dict.
response_type (str): "application/json"|"text/csv"
Args:
request (django.http.HttpRequest)
Returns:
django.http.HttpResponse
'''
cmp_type = request.GET['type']
cmp_id = request.GET['id']
metric = request.GET['metric']
response_type = request.GET.get("response_type", "application/json")
if metric not in METRICS.keys():
return HttpResponseNotFound("<h3>Metric not found</h3>")
db = settings.METRICSDB
cmp_name = ""
if cmp_type == "subsystem":
cmp_name = session.query(
Subsystem.subsystem).filter(Subsystem.id == cmp_id).scalar()
data = db.get_changemetric_for_subsystem(session, cmp_id, metric)
elif cmp_type == "file":
cmp_name = session.query(File.file).filter(File.id == cmp_id).scalar()
data = db.get_changemetric_for_file(session, cmp_id, metric)
elif cmp_type == "function":
cmp_name = session.query(
Function.function).filter(Function.id == cmp_id).scalar()
data = db.get_changemetric_for_function(session, cmp_id, metric)
columns = [["date", "value"]]
data = columns + views_utils.adapt_data(data)
return views_utils.dump_data(data,
response_type,
filename=cmp_name + "_" + metric)
def lineview_data(session, request):
'''
Dumps data that has accumulated over time. This data is best suited for chart like visualizations.
QueryDict params:
cmp_type (str): The type of component. "subsystem"|"file"|"function"
cmp_id (int): The component id found in the database
metric (str): A metric that must exist in the global METRICS dict.
response_type (str): "application/json"|"text/csv"
Args:
request (django.http.HttpRequest)
Returns:
django.http.HttpResponse
'''
cmp_type = request.GET['type']
cmp_id = request.GET['id']
metric = request.GET['metric']
response_type = request.GET.get("response_type", "application/json")
if metric not in METRICS.keys():
return HttpResponseNotFound("<h3>Metric not found</h3>")
db = settings.METRICSDB
cmp_name = ""
if cmp_type == "subsystem":
cmp_name = session.query(Subsystem.subsystem).filter(Subsystem.id == cmp_id).scalar()
data = db.get_changemetric_for_subsystem(session, cmp_id, metric)
elif cmp_type == "file":
cmp_name = session.query(File.file).filter(File.id == cmp_id).scalar()
data = db.get_changemetric_for_file(session, cmp_id, metric)
elif cmp_type == "function":
cmp_name = session.query(Function.function).filter(Function.id == cmp_id).scalar()
data = db.get_changemetric_for_function(session, cmp_id, metric)
columns = [["date", "value"]]
data = columns + views_utils.adapt_data(data)
return views_utils.dump_data(data, response_type, filename=cmp_name + "_" + metric)
def treemap_data(session, request):
'''
Dumps data that represents the system state at some point in time. Currently it dumps the latest snapshot of the
system for continuous metrics and data over the past x months for discontinous metrics.
QueryDict params:
cmp_type (str): The type of component for which child data is gathered. E.g if a subsystem is provided,
the returned data is all data for the files in that subsystem.
cmp_id (int): The component id.
metric (str): A metric that must exist in the global METRICS dict.
response_type (str): "application/json"|"text/csv"
'''
metric = request.GET['metric']
cmp_type = request.GET['type']
cmp_id = request.GET.get('id')
response_type = request.GET.get("response_type", "application/json")
if metric not in METRICS.keys():
return HttpResponseNotFound("<h3>Metric not found</h3>")
query = MetricsDb_TreemapData.get_query(session, metric, cmp_type, cmp_id)
data = [views_utils.get_query_columns(query)] + query.all()
return views_utils.dump_data(data, response_type, filename="treemap_data_" + metric)
