def case_filter_plt(request):
event_logs_path = os.path.join(settings.MEDIA_ROOT, "event_logs")
log_information = None
# TODO Load the Log Information, else throw/redirect to Log Selection
if "current_log" in request.session and request.session["current_log"] is not None:
log_information = request.session["current_log"]
if log_information is not None:
event_log = os.path.join(event_logs_path, log_information["log_name"])
log_format = log_import.get_log_format(log_information["log_name"])
# Import the Log considering the given Format
log, activities = log_import.log_import(event_log, log_format, log_information)
if request.method == "POST":
selected_case = request.POST["selected_case"]
df = plotting.create_df_case(log, log_format, [selected_case], log_information)
plot_div = plotting.timeframe_plot(df)
html = loader.render_to_string("view_plot.html", {"plot_div": plot_div})
return HttpResponse(html)
else:
print("DEBUG: POST URL REQUESTED!")
def case_filter_dfg(request):
event_logs_path = os.path.join(settings.MEDIA_ROOT, "event_logs")
log_information = None
# TODO Load the Log Information, else throw/redirect to Log Selection
if "current_log" in request.session and request.session["current_log"] is not None:
log_information = request.session["current_log"]
print(log_information)
if log_information is not None:
event_log = os.path.join(event_logs_path, log_information["log_name"])
log_format = log_import.get_log_format(log_information["log_name"])
# Import the Log considering the given Format
log, activities = log_import.log_import(event_log, log_format, log_information)
if request.method == "POST":
selected_case = request.POST["selected_case"]
if log_format == "xes":
filtered_log = pm4py.filter_trace_attribute_values(
log, log_information["case_id"], [selected_case], retain=True)
else:
filtered_log = log[log["case:concept:name"].isin([selected_case])]
dfg = dfg_discovery.apply(filtered_log)
this_data, temp_file = plotting.dfg_to_g6(dfg)
re.escape(temp_file)
message = {
"success": True,
"data": json.dumps(this_data),
"responseText": "Inactivated successfully!",
}
return JsonResponse(message)
def group_analysis(request):
event_logs_path = os.path.join(settings.MEDIA_ROOT, "event_logs")
log_information = None
active_group_details = None
# TODO Running Example, on how to display Plot
# Use this to include it in the UI
# TODO Load the Log Information, else throw/redirect to Log Selection
if "current_log" in request.session and request.session[
"current_log"] is not None:
log_information = request.session["current_log"]
print("Log Information: ", log_information)
# TODO Get the Groups, from the Post
if log_information is not None:
event_log = os.path.join(event_logs_path, log_information["log_name"])
log_format = log_import.get_log_format(log_information["log_name"])
# Import the Log considering the given Format
_, activities = log_import.log_import(event_log, log_format,
log_information)
# Set the activities to the activities of the loaded log.
request.session["activities"] = list(activities)
active_group_details = get_active_groups(request)
return render(
request,
"group_analysis.html",
{
"log_name": settings.EVENT_LOG_NAME,
"active_group_details": active_group_details,
},
)
else:
if check_group_managment(request):
active_group_details = get_active_groups(request)
return render(
request,
"group_analysis.html",
{"active_group_details": active_group_details},
)
def perspective(request):
event_logs_path = os.path.join(settings.MEDIA_ROOT, "event_logs")
load_log_succes = False
log_information = None
# TODO Load the Log Information, else throw/redirect to Log Selection
if "current_log" in request.session and request.session["current_log"] is not None:
log_information = request.session["current_log"]
print(log_information)
if log_information is not None:
event_log = os.path.join(event_logs_path, log_information["log_name"])
log_format = log_import.get_log_format(log_information["log_name"])
# Import the Log considering the given Format
log, activities = log_import.log_import(event_log, log_format, log_information)
load_log_succes = True
if request.method == "POST":
# TODO Throw some error
print("Not yet implemented")
else:
if load_log_succes:
result = stats.get_log_statistics(log, log_format, log_information)
dfg = dfg_discovery.apply(log)
this_data, temp_file = plotting.dfg_to_g6(dfg)
re.escape(temp_file)
result["Nunique_Activities"] = len(activities)
return render(
request,
"perspective_view.html",
{
"log_name": settings.EVENT_LOG_NAME,
"json_file": temp_file,
"data": json.dumps(this_data),
"activities": activities,
"result": result,
},
)
else:
return render(request, "perspective_view.html")
def cohort_analysis_data(request):
if request.method == "POST":
event_logs_path = os.path.join(settings.MEDIA_ROOT, "event_logs")
post_data = dict(request.POST.lists())
log_information = request.session["current_log"]
event_log = os.path.join(event_logs_path, log_information["log_name"])
log_format = log_import.get_log_format(log_information["log_name"])
# Loading Group details
group_details = request.session["group_details"]
# Loading the Log
log, activities = log_import.log_import(event_log, log_format, log_information)
# Creating the Plotting Data
df = plotting_data.create_plotting_data(log, log_format, log_information)
# Consider Pickeling the Data for a quick performance boost after the first load
if request.POST["operation_type"] == "timeframe":
# TODO Replace this with the Interval picker values covered by the UI
start_time, end_time = tuple(request.POST["start_end_time"].split(" - "))
group = Group(
group_details[request.POST["selected_group_name"]]["group_name"],
group_details[request.POST["selected_group_name"]][
"selected_activities"
].split(", "),
)
df = plotting_data.create_timeframe_dataframe(
df, group, start_time, end_time
)
plot_div = plotting.timeframe_plot_factory(df)
else:
Groups = [
Group(
group_details[name]["group_name"],
group_details[name]["selected_activities"].split(", "),
)
for name in group_details.keys()
if name in post_data["selected_group_names[]"]
]
freq = request.POST["selected_time"]
date_frame = plotting_data.create_concurrency_frame(df, Groups)
if request.POST["plot_type"] == "standard":
plot_div = plotting.concurrency_plot_factory(
date_frame,
Groups,
freq=freq,
aggregate=settings.AGGREGATE_FUNCTIONS[
request.POST["selected_aggregation"]
],
)
else:
uniform = (
True if request.POST["amplitude_plot_type"] == "uniform" else False
)
plot_div = plotting.amplitude_plot_factory(date_frame, Groups, uniform)
post_data["plot_div"] = plot_div
html = loader.render_to_string("cohort_analysis_plot.html", post_data)
print("Finished Plot Creation")
return HttpResponse(html)
def activity_filter(request):
event_logs_path = os.path.join(settings.MEDIA_ROOT, "event_logs")
load_log_succes = False
log_information = None
filteredresult = None
# TODO Load the Log Information, else throw/redirect to Log Selection
if "current_log" in request.session and request.session["current_log"] is not None:
log_information = request.session["current_log"]
print(log_information)
if log_information is not None:
event_log = os.path.join(event_logs_path, log_information["log_name"])
log_format = log_import.get_log_format(log_information["log_name"])
# Import the Log considering the given Format
log, activities = log_import.log_import(event_log, log_format, log_information)
load_log_succes = True
if request.method == "POST":
selected_activity = request.POST["selected_activity"]
result = stats.get_log_statistics(log, log_format, log_information)
case_ids = stats.get_case_ids_by_activity(
log, selected_activity, log_format, log_information
)
if log_format == "xes":
filtered_log = pm4py.filter_trace_attribute_values(
log, log_information["case_id"], case_ids, retain=True)
else:
filtered_log = log[log["case:concept:name"].isin(case_ids)]
filteredresult = stats.get_log_statistics(
filtered_log, log_format, log_information
)
dfg = dfg_discovery.apply(filtered_log)
this_data, temp_file = plotting.dfg_to_g6(dfg)
re.escape(temp_file)
network = {}
if filteredresult is None:
filteredresult = result
keys_to_extract = [
"Nvariant",
"Nunique_Activities",
"Nactivities",
"Ncase",
"StartTime",
"EndTime",
"TotalDuration",
"MedianCaseDuration",
"MeanCaseDuration",
"MinCaseDuration",
"MaxCaseDuration",
]
subsetfilteredresult = {
key: str(filteredresult[key]) for key in keys_to_extract
}
message = {
"success": True,
"filtered_result": subsetfilteredresult,
"data": json.dumps(this_data),
"responseText": "Inactivated successfully!",
}
return JsonResponse(message)