def test_statistics_without_resolution_unresolved(
statistics, correct_result_statistics
):
result = calculate_statistics(
statistics, ["Comments", "Attachments", "Time to Resolve"]
)
assert result == correct_result_statistics
def test_statistics_with_resolution_unresolved_piece(
statistics_2, correct_result_statistics
):
result = calculate_statistics(
statistics_2, ["Comments", "Attachments", "Time to Resolve"]
)
corr_result = correct_result_statistics.copy()
corr_result["Time to Resolve"]["minimum"] = "1"
assert result == corr_result
def test_statistics_with_resolution_unresolved_all(
statistics_3, correct_result_statistics
):
result = calculate_statistics(
statistics_3, ["Comments", "Attachments", "Time to Resolve"]
)
corr_result = correct_result_statistics.copy()
corr_result["Time to Resolve"] = {
"minimum": "0",
"maximum": "0",
"mean": "0",
"std": "0",
}
assert result == corr_result
def test_statistics_error(statistics, correct_result_statistics):
statistics.iloc[0] = "None"
result = calculate_statistics(
statistics, ["Comments", "Attachments", "Time to Resolve"]
)
corr_result = correct_result_statistics.copy()
for key in corr_result:
corr_result[key] = {
"minimum": "0",
"maximum": "0",
"mean": "0",
"std": "0",
}
assert result == corr_result
def get(self, request):
cache = redis_conn.get(
f"user:{request.user.id}:analysis_and_training:filters"
)
filters = loads(cache) if cache else None
fields = get_issues_fields(request.user.id)
issues = get_issues_dataframe(fields=fields, filters=filters)
if issues.empty:
return Response({})
statistics = calculate_statistics(
issues,
["Comments", "Attachments", "Time to Resolve"],
)
context = {"statistics": statistics}
return Response(context)
def get(self, request):
cache = redis_conn.get(f"analysis_and_training:{request.user.id}")
if cache:
return Response(loads(cache))
fields = get_issues_fields(request.user)
issues = get_issues(fields=fields)
if not issues:
# TODO: FE shows progress bar when data is empty
return Response({})
issues = pd.DataFrame.from_records(issues)
freq_terms = calculate_frequently_terms(issues)
statistics = calculate_statistics(
df=issues, series=["Comments", "Attachments", "Time to Resolve"])
defect_submission = calculate_defect_submission(df=issues,
period="Month")
significant_terms = get_significant_terms(issues)
filters = get_filters(request.user, issues=issues)
context = {
"records_count": {
"total": len(issues),
"filtered": len(issues)
},
"frequently_terms": freq_terms,
"statistics": statistics,
"submission_chart": defect_submission,
"significant_terms": significant_terms,
"filters": filters,
}
redis_conn.set(
name=f"analysis_and_training:{request.user.id}",
value=dumps(context),
ex=60 * 30,
)
return Response(context)
def post(self, request):
fields = get_issues_fields(request.user)
filters = get_filters(
request.user,
issues=pd.DataFrame.from_records(get_issues(fields=fields)),
)
if request.data.get("action") == "apply":
new_filters = request.data.get("filters")
if new_filters:
for new_filter in new_filters:
for filter_ in filters:
if new_filter["name"] == filter_["name"]:
filter_.update({
"current_value":
new_filter["current_value"],
"filtration_type":
new_filter["filtration_type"],
"exact_match":
new_filter["exact_match"],
})
issues = get_issues(filters=filters, fields=fields)
else:
issues = get_issues(fields=fields)
else:
issues = get_issues(fields=fields)
if len(issues) == 0:
context = {
"records_count": {
"total": get_issue_count(),
"filtered": 0
},
"frequently_terms": [],
"statistics": {},
"submission_chart": {},
"significant_terms": {},
"filters": filters,
}
redis_conn.set(f"analysis_and_training:{request.user.id}",
dumps(context))
return Response({})
issues = pd.DataFrame.from_records(issues)
freq_terms = calculate_frequently_terms(issues)
statistics = calculate_statistics(
df=issues, series=["Comments", "Attachments", "Time to Resolve"])
submission_chart = calculate_defect_submission(df=issues,
period="Month")
significant_terms = get_significant_terms(
issues, get_training_settings(request.user))
context = {
"records_count": {
"total": get_issue_count(),
"filtered": len(issues),
},
"frequently_terms": freq_terms,
"statistics": statistics,
"submission_chart": submission_chart,
"significant_terms": significant_terms,
"filters": filters,
}
redis_conn.set(f"analysis_and_training:{request.user.id}",
dumps(context))
return Response(context)
def test_statistics_result(statistics):
result = calculate_statistics(statistics, ["Comments", "Attachments"])
assert result == CORRECT_RESULT