def netcdf_to_ascii_PNNL2018(homedir, subdir, netcdfs, mappingfile,
catalog_label, meta_file):
# initialize list of dataframe outputs
outfiledict = {}
# generate destination folder
filedir = os.path.join(homedir, subdir)
ogh.ensure_dir(filedir)
# connect with collection of netcdfs
ds_mf = xray.open_mfdataset(netcdfs, engine='netcdf4')
# convert netcdfs to pandas.Panel API
ds_pan = ds_mf.to_dataframe().reset_index('TIME')
# generate list of variables
ds_vars = [
ds_var for ds_var in ds_pan.columns
if ds_var not in ['YEAR', 'MONTH', 'DAY', 'TIME', 'LAT', 'LON']
]
# read in gridded cells of interest
maptable, nstation = ogh.mappingfileToDF(mappingfile, colvar=None)
# maptable = maptable[0:3]
# at each latlong of interest
for ind, eachrow in maptable.iterrows():
# generate ASCII time-series
ds_df = ds_pan.loc[eachrow['SN'], eachrow['WE'], :].reset_index(
drop=True).loc[:, ds_vars]
# create file name
# outfilename = os.path.join(filedir, catalog_label + '_' + time_nm + '_{0}_{1}'.format(eachrow['LAT'],eachrow['LONG_']))
outfilename = os.path.join(
filedir, catalog_label +
'_{0}_{1}'.format(eachrow['LAT'], eachrow['LONG_']))
# save ds_df
outfiledict[outfilename] = da.delayed(ds_df.to_csv)(
path_or_buf=outfilename, sep='\t', header=False, index=False)
# compute ASCII time-series files
ProgressBar().register()
outfiledict = da.compute(outfiledict)[0]
# update metadata file
meta_file[catalog_label]['variable_info'].update(dict(ds_mf.attrs))
meta_file[catalog_label]['variable_info'].update(dict(ds_mf.variables))
meta_file[catalog_label]['variable_list'] = np.array(ds_vars)
# catalog the output files
ogh.addCatalogToMap(outfilepath=mappingfile,
maptable=maptable,
folderpath=filedir,
catalog_label=catalog_label)
os.chdir(homedir)
return (list(outfiledict.keys()))
def get_x_hourlywrf_PNNL2018(
homedir,
spatialbounds,
subdir='PNNL2018/Hourly_WRF_1981_2015/SaukSpatialBounds',
nworkers=4,
start_date='2005-01-01',
end_date='2007-12-31',
time_resolution='H',
time_steps=24,
file_prefix='sp_',
rename_timelatlong_names={
'south_north': 'SN',
'west_east': 'WE',
'time': 'TIME'
},
replace_file=True):
"""
get hourly WRF data from a 2018 PNNL WRF run using xarray on netcdf files
"""
# check and generate data directory
filedir = os.path.join(homedir, subdir)
ogh.ensure_dir(filedir)
# modify each month between start_date and end_date to year-month
dates = [
x.strftime('%Y%m%d')
for x in pd.date_range(start=start_date, end=end_date, freq='D')
]
# initialize parallel workers
da.set_options(pool=ThreadPool(nworkers))
ProgressBar().register()
# generate the list of files to download
filelist = compile_x_wrfpnnl2018_raw_locations(dates)
# download files of interest
NetCDFs = []
for url, date in zip(filelist, dates):
NetCDFs.append(
da.delayed(wget_x_download_spSubset_PNNL)(
fileurl=url,
filedate=date,
time_resolution=time_resolution,
time_steps=time_steps,
spatialbounds=spatialbounds,
file_prefix=file_prefix,
rename_timelatlong_names=rename_timelatlong_names,
replace_file=replace_file))
# run operations
outputfiles = da.compute(NetCDFs)[0]
# reset working directory
os.chdir(homedir)
return (outputfiles)
def get_x_dailymet_Livneh2013_raw(
homedir,
spatialbounds,
subdir='livneh2013/Daily_MET_1915_2011/raw_netcdf',
nworkers=4,
start_date='1915-01-01',
end_date='2011-12-31',
rename_timelatlong_names={
'lat': 'LAT',
'lon': 'LON',
'time': 'TIME'
},
file_prefix='sp_',
replace_file=True):
"""
get Daily MET data from Livneh et al. (2013) using xarray on netcdf files
"""
# check and generate DailyMET livneh 2013 data directory
filedir = os.path.join(homedir, subdir)
ogh.ensure_dir(filedir)
# modify each month between start_date and end_date to year-month
dates = [
x.strftime('%Y%m')
for x in pd.date_range(start=start_date, end=end_date, freq='M')
]
# initialize parallel workers
da.set_options(pool=ThreadPool(nworkers))
ProgressBar().register()
# generate the list of files to download
filelist = compile_x_dailymet_Livneh2013_raw_locations(dates)
# download files of interest
NetCDFs = []
for url in filelist:
NetCDFs.append(
da.delayed(ftp_x_download_spSubset)(
fileurl=url,
spatialbounds=spatialbounds,
file_prefix=file_prefix,
rename_timelatlong_names=rename_timelatlong_names,
replace_file=replace_file))
# run operations
outputfiles = da.compute(NetCDFs)[0]
# reset working directory
os.chdir(homedir)
return (outputfiles)
def netcdf_to_ascii(homedir,
subdir,
source_directory,
mappingfile,
catalog_label,
meta_file,
temporal_resolution='D',
netcdfs=None,
variable_list=None):
# initialize list of dataframe outputs
outfiledict = {}
# generate destination folder
filedir = os.path.join(homedir, subdir)
ogh.ensure_dir(filedir)
# connect with collection of netcdfs
if isinstance(netcdfs, type(None)):
netcdfs = [
os.path.join(source_directory, file)
for file in os.listdir(source_directory) if file.endswith('.nc')
]
ds_mf = xray.open_mfdataset(netcdfs, engine='netcdf4').sortby('TIME')
# generate list of variables
if not isinstance(variable_list, type(None)):
ds_vars = variable_list.copy()
else:
ds_vars = [
ds_var for ds_var in dict(ds_mf.variables).keys()
if ds_var not in ['YEAR', 'MONTH', 'DAY', 'TIME', 'LAT', 'LON']
]
# convert netcdfs to pandas.Panel API
ds_pan = ds_mf.to_dataframe()[ds_vars]
# read in gridded cells of interest
maptable, nstation = ogh.mappingfileToDF(mappingfile,
colvar=None,
summary=False)
# at each latlong of interest
for ind, eachrow in maptable.iterrows():
# generate ASCII time-series
ds_df = ds_pan.loc[eachrow['LAT'],
eachrow['LONG_'], :].reset_index(drop=True,
level=[0, 1])
# create file name
outfilename = os.path.join(
filedir, 'data_{0}_{1}'.format(eachrow['LAT'], eachrow['LONG_']))
# save ds_df
outfiledict[outfilename] = da.delayed(ds_df.to_csv)(
path_or_buf=outfilename, sep='\t', header=False, index=False)
# compute ASCII time-series files
ProgressBar().register()
outfiledict = da.compute(outfiledict)[0]
# annotate metadata file
meta_file[catalog_label] = dict(ds_mf.attrs)
meta_file[catalog_label]['variable_list'] = list(np.array(ds_vars))
meta_file[catalog_label]['delimiter'] = '\t'
meta_file[catalog_label]['start_date'] = pd.Series(
ds_mf.TIME).sort_values().iloc[0].strftime('%Y-%m-%d %H:%M:%S')
meta_file[catalog_label]['end_date'] = pd.Series(
ds_mf.TIME).sort_values().iloc[-1].strftime('%Y-%m-%d %H:%M:%S')
meta_file[catalog_label]['temporal_resolution'] = temporal_resolution
meta_file[catalog_label]['variable_info'] = dict(ds_mf.variables)
# catalog the output files
ogh.addCatalogToMap(outfilepath=mappingfile,
maptable=maptable,
folderpath=filedir,
catalog_label=catalog_label)
os.chdir(homedir)
return (list(outfiledict.keys()))
def test_ensuredir(self):
path0 = os.getcwd()
path1 = os.path.join(data_path, 'test_files')
ogh.ensure_dir(path1)
ogh.ensure_dir(path0)
assert os.path.exists(path1)
