def non_hep_filter_simple(sql_results, globals=globals(), **kw):
"""
Removes results for HEP VOs for a pivot-type computation.
"""
results, md = simple_results_parser(sql_results, globals=globals, **kw)
hep_vos = HEP_classifier(results.keys(), globals=globals)
filtered_results = {}
for pivot, group in results.items():
if pivot not in hep_vos:
filtered_results[pivot] = group
return filtered_results, md
def non_hep_filter_simple(sql_results, globals=globals(), **kw):
"""
Removes results for HEP VOs for a pivot-type computation.
"""
results, md = simple_results_parser(sql_results, globals=globals, **kw)
hep_vos = HEP_classifier(results.keys(), globals=globals)
filtered_results = {}
for pivot, group in results.items():
if pivot not in hep_vos:
filtered_results[pivot] = group
return filtered_results, md
def usage_statistics(sql_results, globals=globals(), **kw):
"""
This data mining function is meant to calculate the current usage of each
site, the maximum historical usage, and the mean historical usage. It is
meant to be the results parser class of the facility_usage_statistics graph.
"""
# Query the last 4 months or so of data
starttime = convert_to_datetime(kw['starttime'])
endtime = convert_to_datetime(kw['endtime'])
diff = endtime - starttime
span = 86400 * diff.days + diff.seconds
kw2 = dict(kw)
del kw2['results']
del kw2['query']
del kw2['fixed-height']
del kw2['pivot_transform']
kw2['span'] = span
# cover 4 months (13 weeks)
kw2['starttime'] = endtime - datetime.timedelta(7 * 13, 0)
historical_info, _ = globals['GratiaBarQueries'].facility_hours_bar_smry( \
globals=globals, **kw2)
results, metadata = simple_results_parser(sql_results,
globals=globals,
**kw)
filtered_results = {}
for pivot, val in results.items():
historical_values = historical_info.get(pivot, {0: val}).values()
if historical_values:
historical_max = max(historical_values)
historical_avg = numpy.average(historical_values)
else:
historical_max, historical_avg = 0, 0
left_error = val - historical_avg
right_error = historical_max - val
filtered_results[pivot] = (val, left_error, right_error)
return filtered_results, metadata
def usage_statistics(sql_results, globals=globals(), **kw):
"""
This data mining function is meant to calculate the current usage of each
site, the maximum historical usage, and the mean historical usage. It is
meant to be the results parser class of the facility_usage_statistics graph.
"""
# Query the last 4 months or so of data
starttime = convert_to_datetime(kw['starttime'])
endtime = convert_to_datetime(kw['endtime'])
diff = endtime - starttime
span = 86400*diff.days + diff.seconds
kw2 = dict(kw)
del kw2['results']
del kw2['query']
del kw2['fixed-height']
del kw2['pivot_transform']
kw2['span'] = span
# cover 4 months (13 weeks)
kw2['starttime'] = endtime - datetime.timedelta(7*13, 0)
historical_info, _ = globals['GratiaBarQueries'].facility_hours_bar_smry( \
globals=globals, **kw2)
results, metadata = simple_results_parser(sql_results, globals=globals,
**kw)
filtered_results = {}
for pivot, val in results.items():
historical_values = historical_info.get(pivot, {0: val}).values()
if historical_values:
historical_max = max(historical_values)
historical_avg = numpy.average(historical_values)
else:
historical_max, historical_avg = 0, 0
left_error = val - historical_avg
right_error = historical_max - val
filtered_results[pivot] = (val, left_error, right_error)
return filtered_results, metadata