def main():
parser = argparse.ArgumentParser(
description='NPF cross-repository comparator')
npf.add_verbosity_options(parser)
parser.add_argument('repos',
metavar='repo',
type=str,
nargs='+',
help='names of the repositories to watch')
parser.add_argument('--graph-title',
type=str,
nargs='?',
help='Graph title')
b = npf.add_building_options(parser)
t = npf.add_testing_options(parser)
g = npf.add_graph_options(parser)
args = parser.parse_args()
npf.parse_nodes(args)
# Parsing repo list and getting last_build
repo_list = []
for repo_name in args.repos:
repo = Repository.get_instance(repo_name, args)
repo.last_build = None
repo_list.append(repo)
comparator = Comparator(repo_list)
filename = npf.build_output_filename(args, repo_list)
savedir = Path(os.path.dirname(filename))
if not savedir.exists():
os.makedirs(savedir.as_posix())
if not os.path.isabs(filename):
filename = os.getcwd() + os.sep + filename
series, time_series = comparator.run(
testie_name=args.test_files,
tags=args.tags,
options=args,
on_finish=lambda series, time_series: do_graph(
filename, args, series, time_series, options=args)
if args.iterative else None)
do_graph(filename, args, series, time_series, options=args)
def do_graph(filename,args,series,kind_series,options):
if series is None:
return
#Group repo if asked to do so
if options.group_repo:
repo_series=OrderedDict()
for i, (testie, build, dataset) in enumerate(series):
repo_series.setdefault(build.repo.reponame,(testie,build,OrderedDict()))
for run, run_results in dataset.items():
run.variables['SERIE'] = build.pretty_name()
repo_series[build.repo.reponame][2][run] = run_results
series = []
for reponame, (testie, build, dataset) in repo_series.items():
build._pretty_name = reponame
build.version = reponame
series.append((testie, build, dataset))
# Merge series with common name
merged_series = OrderedDict()
for testie, build, dataset in series:
#Group series by serie name
merged_series.setdefault(build.pretty_name(), []).append((testie, build, dataset))
series = []
for sname,slist in merged_series.items():
if len(slist) == 1:
series.append(slist[0])
else:
all_r = {}
for results in [l[2] for l in slist]:
all_r.update(results)
series.append((slist[0][0], slist[0][1], all_r))
# We must find the common variables to all series, and change dataset to reflect only those
all_variables = []
for testie, build, dataset in series:
v_list = set()
for name, variable in testie.variables.vlist.items():
v_list.add(name)
all_variables.append(v_list)
if args.statistics:
Statistics.run(build,dataset, testie, max_depth=args.statistics_maxdepth, filename=args.statistics_filename if args.statistics_filename else npf.build_output_filename(args, [build.repo for t,build,d in series]))
common_variables = set.intersection(*map(set, all_variables))
#Remove variables that are totally defined by the series, that is
# variables that only have one value inside each serie
# but have different values accross series
useful_variables=[]
for variable in common_variables:
all_values = set()
all_alone=True
for i, (testie, build, dataset) in enumerate(series):
serie_values = set()
for run, result_types in dataset.items():
if variable in run.variables:
val = run.variables[variable]
serie_values.add(val)
if len(serie_values) > 1:
all_alone = False
break
if all_alone:
pass
else:
useful_variables.append(variable)
if options.group_repo:
useful_variables.append('SERIE')
for v in series[0][0].config.get_list("graph_hide_variables"):
if v in useful_variables:
useful_variables.remove(v)
#Keep only the variables in Run that are usefull as defined above
for i, (testie, build, dataset) in enumerate(series):
ndataset = OrderedDict()
for run, results in dataset.items():
ndataset[run.intersect(useful_variables)] = results
series[i] = (testie, build, ndataset)
#Keep only the variables in Time Run that are usefull as defined above
if options.do_time:
n_kind_series=OrderedDict()
for i, (testie, build, kind_dataset) in enumerate(kind_series):
for kind, dataset in kind_dataset.items():
ndataset = OrderedDict()
n_kind_series.setdefault(kind,[])
for run, results in dataset.items():
ndataset[run.intersect(useful_variables + [kind])] = results
if ndataset:
n_kind_series[kind].append((testie, build, ndataset))
grapher = Grapher()
print("Generating graphs...")
g = grapher.graph(series=series,
filename=filename,
options=args,
title=args.graph_title)
if options.do_time:
for kind,series in n_kind_series.items():
print("Generating graph %s..." % kind)
g = grapher.graph(series=series,
filename=filename,
fileprefix=kind,
options=args,
title=args.graph_title)