def generate_metrics(**args):
# First we gather the necessary 'attributes' for all publications involved
# (see above methods for more details)
attr_list,num_cit,num_cit_ref = get_attributes(args)
# What types of metrics are we gather (everything by default)
stats_models = []
# Determine the output format (really only used to get the 'legacy format')
format = args.get('fmt','')
model_types = args.get('types',config.METRICS_DEFAULT_MODELS)
# Instantiate the metrics classes, defined in the 'models' module
for model_class in metricsmodels.data_models(models=model_types.split(',')):
model_class.attributes = attr_list
model_class.num_citing = num_cit
model_class.num_citing_ref = num_cit_ref
model_class.results = {}
stats_models.append(model_class)
# The metrics calculations are sent off in parallel
rez=Pool(config.METRICS_THREADS).map(generate_data, stats_models)
# Now shape the results in the final format
results = format_results(model_results)
# Send the result back to our caller
if format == 'legacy':
return legacy_format(results)
else:
return results
def generate_metrics(**args):
timer = statsd.timer("bibutils.generate_metrics.generate_time")
timer.start()
# First we gather the necessary 'attributes' for all publications involved
# (see above methods for more details)
attr_list,num_cit,num_cit_ref = get_attributes(args)
# What types of metrics are we gather (everything by default)
stats_models = []
# Determine the output format (really only used to get the 'legacy format')
format = args.get('fmt','')
model_types = args.get('types',config.METRICS_DEFAULT_MODELS)
# Instantiate the metrics classes, defined in the 'models' module
for model_class in metricsmodels.data_models(models=model_types.split(',')):
model_class.attributes = attr_list
model_class.num_citing = num_cit
model_class.num_citing_ref = num_cit_ref
model_class.results = {}
stats_models.append(model_class)
# The metrics calculations are sent off in parallel
# rez=Pool(config.METRICS_THREADS).map(generate_data, stats_models)
po = Pool()
rez = po.map_async(generate_data, stats_models)
model_results = rez.get()
# Now shape the results in the final format
results = format_results(model_results)
timer.stop()
# Send the result back to our caller
if format == 'legacy':
return legacy_format(results)
elif format == 'API':
for key in results.keys():
newkey = key.replace(' ','_')
results[newkey] = results.pop(key)
if not 'histogram' in key or not 'series' in key:
for kee in results[newkey].keys():
results[newkey][kee.replace(' ','_')] = results[newkey].pop(kee)
return results
else:
return results
