def thumbjson():
# TODO: Add support for 'calc of calcs' instead of max
all_json = get_graph_data_from_request(request, THUMBNAIL_DAYS)
all_results = {}
for benchname, entries in all_json['results'].items():
dtToEntries = {}
for entry in entries:
roundedDt = round_date(entry['key'])
dtToEntries.setdefault(roundedDt, []).append(entry['value'])
dtToSingleEntry = {dt: dbmath.mean(items) for dt, items in dtToEntries.iteritems()}
newresult = [{'key': k, 'value': v} for k, v in sorted(dtToSingleEntry.iteritems())]
all_results[benchname] = newresult
# Just mutate the original results since we're throwing them away.
result = all_json
result['results'] = all_results
return jsonify(result)
def append_aggregated_data(resultdata):
"""
Appends an aggregated metrics key onto a result set.
:param resultdata: The resultdata should be a dictionary consistent with
the dbmodel
:return: The result set with aggregated data appended.
"""
resultdata['AggregateData'] = dict(resultdata['metrics'])
for key, entryTimesAndValues in resultdata['AggregateData'].items():
metricvals = [item[1] for item in entryTimesAndValues]
resultdata['AggregateData'][key] = {
'mean': dbmath.mean(metricvals),
'median': dbmath.median(metricvals),
'stddev': dbmath.standard_deviation(metricvals),
'min': min(metricvals),
'max': max(metricvals)
}
return resultdata