for var in odvars:
print(var)
data = reader.read_var(var, start=start, ts_type=freq)
if not data.ts_type == 'monthly':
data = data.resample_time('monthly')
loaded.append(data)
print('Regrid median SS to Noresm res.')
first = loaded[0]
medss = medss.regrid(first)
medss.time.units = first.time.units
medss.time.points = first.time.points
medss.time.attributes = first.time.attributes
medss.time.standard_name = first.time.standard_name
for data in loaded:
print('Adding', data.var_name)
added = pya.io.aux_read_cubes.add_cubes(medss.cube, data.cube)
medss = pya.GriddedData(added)
medss.var_name = 'od550aer'
medss.cube.attributes = {}
medss.cube.attributes['data_id'] = OUT_ID
medss.cube.attributes['ts_type'] = 'monthly'
print(medss.time_stamps())
medss.to_netcdf(outdir, vert_code='Column')
"data settings. These will be unified using the "
"metadata dictionary of the first cube "
"(otherwise the method concatenate of the iris "
"package won't work)".format(var_name))
for cube in cubes:
cube.metadata = meta_init
#now put the CubeList together and form one cube
#1st equalise the cubes (remove non common attributes)
equalise_attributes(cubes)
#unify time units
iris.util.unify_time_units(cubes)
#now concatenate the cube list to one cube
cubes_concat = iris._concatenate.concatenate(cubes, error_on_mismatch)
return cubes_concat[0]
if __name__== "__main__":
import pyaerocom as pya
ddir = '/lustre/storeA/project/aerocom/aerocom-users-database/SATELLITE-DATA/CALIOP3/renamed/'
f1 = 'aerocom.CALIOP3.monthly.ec5323Ddust.2006.nc'
fconv = pya.io.FileConventionRead(from_file=f1)
cube = load_cube_custom(ddir + f1)
print(cube)
data = pya.GriddedData(cube)
print(data.start)
print(data.stop)
print(data)
'standard_name'] = 'atmosphere_hybrid_sigma_pressure_coordinate'
if ds.lev.data[0] == ds.lev.data[1]:
OK = False
issues.append('Level values not strictly monotonic')
lev_vals = np.arange(len(ds.lev.data))
ds.lev.data = lev_vals
if not OK:
file = file.split('.nc')[0] + '_CORR.nc'
ds.to_netcdf(file)
return (file, OK, issues)
for file in glob.glob('*CORR.nc'):
os.remove(file)
file = glob.glob('aerocom3_Oslo*monthly.nc')[0]
print(file)
(file, OK, issues) = check_file_okay(file)
d = pya.GriddedData(file, var_name='ec550aer')
print(d)
if len(issues) > 0:
print('------------------------------------------')
print('Detected following issues:')
for issue in issues:
print(issue)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 7 13:35:07 2018
@author: jonasg
"""
import pyaerocom
from pandas import Timestamp
import numpy as np
if __name__ == "__main__":
data = pyaerocom.GriddedData()
data._init_testdata_default()
start = Timestamp("2018-1-22")
stop = Timestamp("2018-2-5")
lons = np.linspace(-10, 30, 20)
lats = np.linspace(40, 80, 20)
data = data.crop(lon_range=(lons[0], lons[-1]),
lat_range=(lats[0], lats[-1]),
time_range=(start, stop))
fig = data.quickplot_map()
fig.suptitle("CROPPED")
sample_points = [("longitude", lons), ("latitude", lats)]
coords_avail = data.coords_order
d21 = iris.load(d2 + f21)
d22 = iris.load(d2 + f22)
d23 = iris.load(d2 + f23)
data = [d11, d12, d21, d22, d23]
#reader = pya.io.ReadGridded('TM5_AP3-INSITU')
#dat = reader.read_var('vmrch4')
for i, cubes in enumerate(data):
print('At iter {}'.format(i))
for d in cubes:
print('\n')
print(d.var_name)
print(d.shape)
print(d.ndim)
print([c.standard_name for c in d.dim_coords])
print([c.var_name for c in d.dim_coords])
print('\n\n')
print(d11)
ds11 = xarray.open_dataset(d1 + f11)
ds21 = xarray.open_dataset(d2 + f21)
data11 = pya.GriddedData(d1 + f11, var_name='abs5503Daer')
data21 = pya.GriddedData(d2 + f21, var_name='vmrch4')
print(ds21)
for var_name, vc in zip(varlist, vclist):
outname = pya.io.helpers.aerocom_savename(data_id,
var_name,
vc, year,
TS_TYPE)
models = USE_MODELS
if var_name in match_models:
if year in match_models[var_name]:
_year = match_models[var_name][year]
outname_other = outname.replace(str(year), str(_year))
fp = os.path.join(OUT_DIR, outname_other)
if not os.path.exists(fp):
raise ValueError(
f'Please process {_year} before {year}')
data = pya.GriddedData(fp)
models = data.metadata['from_models']
pya.const.print_log.info(f'\nPROCESSING {outname}\n')
logfile.write('\n\n{}'.format(var_name))
if os.path.exists(os.path.join(OUT_DIR, outname)) and reanalyse_existing==False:
pya.const.print_log.info('exists... -> skipping.')
logfile.write(': SKIPPED')
continue
surf = vc=='Surface'
comment = (f'AeroCom {avg_how} model data for variable '
f'{var_name}')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 20 08:49:37 2018
@author: jonasg
"""
from matplotlib.colors import LogNorm
import pyaerocom
if __name__ == "__main__":
data = pyaerocom.GriddedData()._init_testdata_default()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 11 09:55:03 2020
@author: jonasg
"""
import pyaerocom as pya
import matplotlib.pyplot as plt
plt.close('all')
data_dir = '/lustre/storeA/project/fou/kl/CAMS61/CAMS_2018/EMEP/'
data_file = 'EMEP_20180101_O3_0H24H.nc'
data = pya.GriddedData(data_dir + data_file)
data = data.extract_surface_level()
data.metadata['ts_type'] = None
# THIS WORKS NOW
data.quickplot_map()
# NOW CHECK PALEO DATE
data.change_base_year(901)
data.quickplot_map()
print("ALL GOOD")
if __name__ == '__main__':
pya.change_verbosity('critical')
obs_reader = pya.io.ReadUngridded()
obs = obs_reader.read(OBS_ID, OBS_VARS)
model_reader = pya.io.ReadGridded(MODEL_ID_3D)
model = model_reader.read_var(MODEL_VAR, start=YEAR)
print(obs)
print(model)
model_at_stats = model.compute_at_stations_file(out_dir='out',
obs_data=obs)
model_reload = pya.GriddedData(model_at_stats)
coll1 = pya.colocation.colocate_gridded_ungridded_2D(model,
obs,
'monthly',
var_ref='scatc550aer',
vert_scheme='surface')
coll2 = pya.colocation.colocate_gridded_ungridded_2D(model_reload,
obs,
'monthly',
var_ref='scatc550aer',
vert_scheme='surface')
coll1.plot_scatter()