def test_netcdf_time_to_timestamp(self):
s = "2015-01-01"
dt = eva.netcdf_time_to_timestamp(s)
self.assertEqual(dt.year, 2015)
self.assertEqual(dt.month, 1)
self.assertEqual(dt.day, 1)
self.assertEqual(dt.hour, 0)
self.assertEqual(dt.minute, 0)
self.assertEqual(dt.second, 0)
self.assertEqual(dt.tzinfo.tzname(None), 'UTC')
s = "2016-06-13 06"
dt = eva.netcdf_time_to_timestamp(s)
self.assertEqual(dt.year, 2016)
self.assertEqual(dt.month, 6)
self.assertEqual(dt.day, 13)
self.assertEqual(dt.hour, 6)
self.assertEqual(dt.minute, 0)
self.assertEqual(dt.second, 0)
self.assertEqual(dt.tzinfo.tzname(None), 'UTC')
def parse_file_recognition_output(self, lines):
"""!
@brief Parse standard output containing time dimensions from NetCDF
files into a structured format.
@returns A list of dictionaries with file and time dimension information.
"""
result = []
for line in lines:
# Each output line looks like this:
# /tmp/meteo20160606_00.nc time = "2016-06-06 12", "2016-06-06 15", "2016-06-06 18", "2016-06-06 21", "2016-06-07" ;
if len(line) < 5:
continue
data = {}
tokens = line.split()
data['path'] = tokens[0]
data['extension'] = os.path.splitext(data['path'])[1]
if self.is_netcdf_data_output(data):
time_str = ' '.join(tokens[3:-1])
times = sorted([eva.netcdf_time_to_timestamp(x.strip(' "')) for x in time_str.split(',')])
data['time_steps'] = times
result += [data]
return result
