def test_download_histalp(self):
tmp = cfg.PATHS['cru_dir']
cfg.PATHS['cru_dir'] = os.path.join(self.dldir, 'cru_extract')
of = utils.get_histalp_file('tmp')
self.assertTrue(os.path.exists(of))
of = utils.get_histalp_file('pre')
self.assertTrue(os.path.exists(of))
cfg.PATHS['cru_dir'] = tmp
def test_histalp(self):
# Create fake histalp file
cf = os.path.join(self.dldir, 'HISTALP_temperature_1780-2014.nc.bz2')
with bz2.open(cf, 'wb') as gz:
gz.write(b'dummy')
def down_check(url, cache_name=None, reset=False):
expected = ('http://www.zamg.ac.at/histalp/download/grid5m/'
'HISTALP_temperature_1780-2014.nc.bz2')
self.assertEqual(url, expected)
return cf
with FakeDownloadManager('_progress_urlretrieve', down_check):
tf = utils.get_histalp_file('tmp')
assert os.path.exists(tf)
def process_histalp_data(gdir):
"""Processes and writes the climate data for this glacier.
Extracts the nearest timeseries and writes everything to a NetCDF file.
"""
if cfg.PARAMS['baseline_climate'] != 'HISTALP':
raise ValueError("cfg.PARAMS['baseline_climate'] should be set to "
"HISTALP.")
# read the time out of the pure netcdf file
ft = utils.get_histalp_file('tmp')
fp = utils.get_histalp_file('pre')
with utils.ncDataset(ft) as nc:
vt = nc.variables['time']
assert vt[0] == 0
assert vt[-1] == vt.shape[0] - 1
t0 = vt.units.split(' since ')[1][:7]
time_t = pd.date_range(start=t0, periods=vt.shape[0], freq='MS')
with utils.ncDataset(fp) as nc:
vt = nc.variables['time']
assert vt[0] == 0.5
assert vt[-1] == vt.shape[0] - .5
t0 = vt.units.split(' since ')[1][:7]
time_p = pd.date_range(start=t0, periods=vt.shape[0], freq='MS')
# Now open with salem
nc_ts_tmp = salem.GeoNetcdf(ft, time=time_t)
nc_ts_pre = salem.GeoNetcdf(fp, time=time_p)
# set temporal subset for the ts data (hydro years)
# the reference time is given by precip, which is shorter
sm = cfg.PARAMS['hydro_month_nh']
em = sm - 1 if (sm > 1) else 12
yrs = nc_ts_pre.time.year
y0, y1 = yrs[0], yrs[-1]
if cfg.PARAMS['baseline_y0'] != 0:
y0 = cfg.PARAMS['baseline_y0']
if cfg.PARAMS['baseline_y1'] != 0:
y1 = cfg.PARAMS['baseline_y1']
nc_ts_tmp.set_period(t0='{}-{:02d}-01'.format(y0, sm),
t1='{}-{:02d}-01'.format(y1, em))
nc_ts_pre.set_period(t0='{}-{:02d}-01'.format(y0, sm),
t1='{}-{:02d}-01'.format(y1, em))
time = nc_ts_pre.time
ny, r = divmod(len(time), 12)
assert r == 0
# Units
assert nc_ts_tmp._nc.variables['HSURF'].units.lower() in ['m', 'meters',
'meter',
'metres',
'metre']
assert nc_ts_tmp._nc.variables['T_2M'].units.lower() in ['degc', 'degrees',
'degrees celcius',
'degree', 'c']
assert nc_ts_pre._nc.variables['TOT_PREC'].units.lower() in ['kg m-2',
'l m-2', 'mm',
'millimeters',
'millimeter']
# geoloc
lon = gdir.cenlon
lat = gdir.cenlat
nc_ts_tmp.set_subset(corners=((lon, lat), (lon, lat)), margin=1)
nc_ts_pre.set_subset(corners=((lon, lat), (lon, lat)), margin=1)
# read the data
temp = nc_ts_tmp.get_vardata('T_2M')
prcp = nc_ts_pre.get_vardata('TOT_PREC')
hgt = nc_ts_tmp.get_vardata('HSURF')
ref_lon = nc_ts_tmp.get_vardata('lon')
ref_lat = nc_ts_tmp.get_vardata('lat')
source = nc_ts_tmp._nc.title[:7]
nc_ts_tmp._nc.close()
nc_ts_pre._nc.close()
# Should we compute the gradient?
use_grad = cfg.PARAMS['temp_use_local_gradient']
igrad = None
if use_grad:
igrad = np.zeros(len(time)) * np.NaN
for t, loct in enumerate(temp):
slope, _, _, p_val, _ = stats.linregress(hgt.flatten(),
loct.flatten())
igrad[t] = slope if (p_val < 0.01) else np.NaN
gdir.write_monthly_climate_file(time, prcp[:, 1, 1], temp[:, 1, 1],
hgt[1, 1], ref_lon[1], ref_lat[1],
gradient=igrad)
# metadata
out = {'baseline_climate_source': source,
'baseline_hydro_yr_0': y0 + 1,
'baseline_hydro_yr_1': y1}
gdir.write_pickle(out, 'climate_info')