def test_run_dir(run_dir):
print 'testing metrics', run_dir
recent_dir = join(run_dir, 'brain', 'Recent')
for root, dirs, files in os.walk(recent_dir):
break
for dir in dirs:
time_dir = join(recent_dir, dir)
base_metric_file = join(time_dir, BASE_METRIC_FILE)
base_digest = datalib.parse_digest(base_metric_file)
base_tables = base_digest['tables']
base_nrows = base_tables[base_tables.keys()[0]]['nrows']
files_to_test = []
for expression in METRIC_FILES_TO_TEST:
files_to_test += glob.glob(join(time_dir, expression))
for metric_file in files_to_test:
if metric_file != base_metric_file:
if os.access(metric_file, os.F_OK):
metric_digest = datalib.parse_digest(metric_file)
metric_tables = metric_digest['tables']
metric_nrows = metric_tables[metric_tables.keys()
[0]]['nrows']
if metric_nrows != base_nrows:
print 'bad: %s (%d != %d)' % (metric_file,
metric_nrows, base_nrows)
def test_run_dir(run_dir): print 'testing metrics', run_dir recent_dir = join(run_dir, 'brain', 'Recent') for root, dirs, files in os.walk(recent_dir): break for dir in dirs: time_dir = join(recent_dir, dir) base_metric_file = join(time_dir, BASE_METRIC_FILE) base_digest = datalib.parse_digest(base_metric_file) base_tables = base_digest['tables'] base_nrows = base_tables[base_tables.keys()[0]]['nrows'] files_to_test = [] for expression in METRIC_FILES_TO_TEST: files_to_test += glob.glob(join(time_dir, expression)) for metric_file in files_to_test: if metric_file != base_metric_file: if os.access(metric_file, os.F_OK): metric_digest = datalib.parse_digest(metric_file) metric_tables = metric_digest['tables'] metric_nrows = metric_tables[metric_tables.keys()[0]]['nrows'] if metric_nrows != base_nrows: print 'bad: %s (%d != %d)' % (metric_file, metric_nrows, base_nrows)
def get_random_classifications(path_run, recent_type = None):
if recent_type == None:
classifications = []
for recent_type in common_functions.RECENT_TYPES:
classifications += get_random_classifications(path_run, recent_type)
return classifications
path = path_avr( path_run, None, recent_type )
if os.path.exists( path ):
digest = datalib.parse_digest( path )
classifications = set()
for name in digest['tables'].keys():
index = name.find(RANDOM_PIECE)
if index > -1:
classifications.add( name[index + len(RANDOM_PIECE):] )
return list( classifications )
else:
return []
def get_random_classifications(path_run, recent_type = None):
if recent_type == None:
classifications = []
for recent_type in common_functions.RECENT_TYPES:
classifications += get_random_classifications(path_run, recent_type)
return classifications
path = path_avr( path_run, recent_type )
if os.path.exists( path ):
digest = datalib.parse_digest( path )
classifications = set()
for name in digest['tables'].keys():
index = name.find(RANDOM_PIECE)
if index > -1:
classifications.add( name[index + len(RANDOM_PIECE):] )
return list( classifications )
else:
return []
def getAgentCount(run):
return datalib.parse_digest(os.path.join(run, "lifespans.txt"))["tables"]["LifeSpans"]["nrows"]