def get_datalist_radar(inargs, date):
"""
Get data time series for radar observation.
Parameters
----------
inargs : : argparse object
Argparse object with all input arguments
date : str
Date in format yyyymmddhh. If 'all', radar data for all days are returned
radar_mask : 2D or 3D numpy array
Radar mask to create masked arrays.
Returns
-------
datalist : list
List of 2D masked arrays
"""
# Get file name
radarpref = (get_config(inargs, 'paths', 'radar_data') +
get_config(inargs, 'paths', 'radar_prefx'))
radarsufx = get_config(inargs, 'paths', 'radar_sufix')
dtradar = timedelta(minutes=10)
if date is 'all':
date_start = (yyyymmddhh_strtotime(inargs.date_start) +
timedelta(hours=inargs.time_start) - dtradar)
date_end = (yyyymmddhh_strtotime(inargs.date_end) +
timedelta(hours=inargs.time_end) - dtradar)
else:
dateobj = yyyymmddhh_strtotime(date)
date_start = dateobj + timedelta(hours=inargs.time_start) - dtradar
date_end = dateobj + timedelta(hours=inargs.time_end) - dtradar
datalist = getfobj_ncdf_timeseries(radarpref,
date_start,
date_end,
timedelta(hours=inargs.time_inc),
reftime=date_start,
ncdffn_sufx=radarsufx,
fieldn='pr',
abs_datestr='yymmddhhmm',
dwdradar=True,
return_arrays=True)
# Crop data
l11, l12, l21, l22, l11_rad, l12_rad, l21_rad, l22_rad = \
get_domain_limits(inargs)
for i, data in enumerate(datalist):
datalist[i] = data[l11_rad:l12_rad, l21_rad:l22_rad]
return datalist
#meanQtot = rootgrp.createVariable('meanQtot', 'f8', ('time','n','x','y'))
if args.ana == 'vert':
levs = rootgrp.variables['levs']
height = rootgrp.variables['height']
Mtot = rootgrp.variables['Mtot']
Msouth = rootgrp.variables['Msouth']
Mnorth = rootgrp.variables['Mnorth']
# End allocation
################################################################################
# Preliminaries before time loop
if args.ana == 'prec':
# Load radar data for all times
dateobj = yyyymmddhh_strtotime(args.date)
dtradar = timedelta(minutes=10)
radarts = getfobj_ncdf_timeseries(radarpref,
dateobj + tstart - dtradar,
dateobj + tend - dtradar,
tinc,
reftime=dateobj,
ncdffn_sufx=radarsufx,
fieldn='pr',
abs_datestr='yymmddhhmm',
dwdradar=True)
# Get mask
radarmask = get_totmask(radarts)
# Crop data
radarmask = radarmask[lx1 + 62:lx2 - 42, ly1 + 22:ly2 - 42]
def plot_prec_stamps(inargs):
"""
Plots precipitation stamps of obs, det and ensemble every hour for each
date and time specified
Parameters
----------
inargs : argparse object
Argparse object with all input arguments
"""
# Loop over dates
for idate, date in enumerate(make_datelist(inargs)):
print('Date ' + date)
# Loop over times
for t in make_timelist(timedelta(hours=inargs.time_start),
timedelta(hours=inargs.time_end),
timedelta(hours=1)):
print('Time ' + str(t))
# Load all CU objects
fobjlist = []
titlelist = []
# 1st: Radar
radarpref = (get_config(inargs, 'paths', 'radar_data') +
get_config(inargs, 'paths', 'radar_prefx'))
radarsufx = get_config(inargs, 'paths', 'radar_sufix')
dtradar = timedelta(minutes=10)
radartime = yymmddhhmm(yyyymmddhh_strtotime(date) + t - dtradar)
radarfn = radarpref + radartime + radarsufx
radar_fobj = getfobj_ncdf(radarfn, fieldn='pr', dwdradar=True)
# Crop data
l11, l12, l21, l22, l11_rad, l12_rad, l21_rad, l22_rad = \
get_domain_limits(inargs)
l11_diff = l11_rad - l11
l12_diff = l12_rad - l12
l21_diff = l21_rad - l21
l22_diff = l22_rad - l22
radar_fobj.data = radar_fobj.data[l11_diff:l12_diff,
l21_diff:l22_diff]
radar_fobj.lats = radar_fobj.lats[l11_diff:l12_diff,
l21_diff:l22_diff]
radar_fobj.lons = radar_fobj.lons[l11_diff:l12_diff,
l21_diff:l22_diff]
fobjlist.append(radar_fobj)
titlelist.append('Radar')
# 2nd: det
ncdffn_pref = (get_config(inargs, 'paths', 'raw_data') + date +
'/deout_ceu_pspens/' + 'det' + '/OUTPUT/lfff')
fobjlist.append(
getfobj_ncdf(ncdffn_pref + ddhhmmss(t) + '.nc_30m_surf',
'PREC_ACCUM'))
titlelist.append('Det')
# 3rd: ens
ncdffn = 'lfff' + ddhhmmss(t) + '.nc_30m_surf'
date_dir = (get_config(inargs, 'paths', 'raw_data') + date +
'/deout_ceu_pspens/')
fobjlist.extend(
getfobj_ncdf_ens(date_dir,
'sub',
inargs.nens,
ncdffn,
dir_suffix='/OUTPUT/',
fieldn='PREC_ACCUM',
nfill=1))
titlelist.extend(
['Member ' + str(i + 1) for i in range(inargs.nens)])
# Now plot
n_panels = len(fobjlist)
n_cols = 4
n_rows = int(np.ceil(float(n_panels) / n_cols))
pw = get_config(inargs, 'plotting', 'page_width')
fig, axmat = plt.subplots(n_rows,
n_cols,
figsize=(pw, 3.0 * n_rows))
axflat = np.ravel(axmat)
for i in range(len(fobjlist)):
plt.sca(axflat[i])
cf = plot_stamp(inargs, fobjlist[i], cmPrec, levelsPrec,
axflat[i], 'PREC_ACCUM')
axflat[i].set_title(titlelist[i])
cb = fig.colorbar(cf,
cax=fig.add_axes([0.4, 0.15, 0.2, 0.02]),
orientation='horizontal')
cb.set_label('Accumulation [mm/h]')
titlestr = ((yyyymmddhh_strtotime(date) +
t).strftime('%d %b - %H UTC'))
fig.suptitle(titlestr)
plt.subplots_adjust(wspace=0.02, left=0.02, right=0.98)
# Save figure and log
save_fig_and_log(fig,
None,
inargs,
'prec_stamps',
datestr=((yyyymmddhh_strtotime(date) +
t).strftime('%Y%m%d_%H')))
def plot_individual(inargs):
"""
Parameters
----------
inargs
Returns
-------
"""
# Get data
date_dir = (get_config(inargs, 'paths', 'raw_data') + inargs.date_start +
'/deout_ceu_pspens/')
t = timedelta(hours=inargs.time_start)
if inargs.ind_var == 'PREC_ACCUM':
ncdffn = 'lfff' + ddhhmmss(t) + '.nc_30m_surf'
fobj = getfobj_ncdf(date_dir + str(inargs.ind_ens) + '/OUTPUT/' +
ncdffn,
fieldn='PREC_ACCUM')
cmap = None
colors = cmPrec
levels = levelsPrec
elif inargs.ind_var == 'radar':
radarpref = (get_config(inargs, 'paths', 'radar_data') +
get_config(inargs, 'paths', 'radar_prefx'))
radarsufx = get_config(inargs, 'paths', 'radar_sufix')
dtradar = timedelta(minutes=10)
radartime = yymmddhhmm(
yyyymmddhh_strtotime(inargs.date_start) + t - dtradar)
radarfn = radarpref + radartime + radarsufx
radar_fobj = getfobj_ncdf(radarfn, fieldn='pr', dwdradar=True)
# Crop data
l11, l12, l21, l22, l11_rad, l12_rad, l21_rad, l22_rad = \
get_domain_limits(inargs)
l11_diff = l11_rad - l11
l12_diff = l12_rad - l12
l21_diff = l21_rad - l21
l22_diff = l22_rad - l22
radar_fobj.data = radar_fobj.data[l11_diff:l12_diff, l21_diff:l22_diff]
radar_fobj.lats = radar_fobj.lats[l11_diff:l12_diff, l21_diff:l22_diff]
radar_fobj.lons = radar_fobj.lons[l11_diff:l12_diff, l21_diff:l22_diff]
fobj = radar_fobj
elif inargs.ind_var in ['obj_m', 'obj_prec']:
if inargs.ind_var == 'obj_m':
lvl = 30
ncdffn = 'lfff' + ddhhmmss(t) + '.nc_30m'
fobj = getfobj_ncdf(date_dir + str(inargs.ind_ens) + '/OUTPUT/' +
ncdffn,
fieldn='W',
levs=lvl)
opt_field = (getfield_ncdf(
date_dir + str(inargs.ind_ens) + '/OUTPUT/' + ncdffn,
fieldn='QC',
levs=lvl) + getfield_ncdf(
date_dir + str(inargs.ind_ens) + '/OUTPUT/' + ncdffn,
fieldn='QS',
levs=lvl) + getfield_ncdf(
date_dir + str(inargs.ind_ens) + '/OUTPUT/' + ncdffn,
fieldn='QI',
levs=lvl))
else:
ncdffn = 'lfff' + ddhhmmss(t) + '.nc_30m_surf'
fobj = getfobj_ncdf(date_dir + str(inargs.ind_ens) + '/OUTPUT/' +
ncdffn,
fieldn='PREC_ACCUM')
opt_field = None
labels, size_list, sum_list = identify_clouds(fobj.data,
1.,
opt_field=opt_field,
water=True,
neighborhood=3,
opt_thresh=0)
fobj.data = labels
cmap = plt.cm.prism
colors = None
levels = None
else:
raise Exception('Wrong variable!')
# Set up figure
pw = get_config(inargs, 'plotting', 'page_width')
width_fraction = 2. / 9.
ratio = 1.
fig, ax = plt.subplots(1,
1,
figsize=(pw * width_fraction,
pw * width_fraction * ratio))
cf = plot_stamp(inargs, fobj, cmPrec, levelsPrec, ax, 'PREC_ACCUM')
if inargs.ind_colorbar:
cb = fig.colorbar(cf, orientation='horizontal', shrink=0.6)
cb.set_label('Accumulation [mm/h]')
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
save_fig_and_log(fig, None, inargs, 'prec_individual', tight=False)
from cosmo_utils.pyncdf import getfobj_ncdf_timeseries
radarpref = '/project/meteo/w2w/A6/radolan/netcdf_cosmo_de/raa01-rw_10000-'
radarsufx = '-dwd---bin.nc'
from datetime import timedelta
from cosmo_utils.helpers import yyyymmddhh_strtotime
tstart = '2016052801'
tend = '2016060900'
dtradar = timedelta(minutes=10)
radarts = getfobj_ncdf_timeseries(radarpref,
yyyymmddhh_strtotime(tstart) - dtradar,
yyyymmddhh_strtotime(tend) - dtradar,
timedelta(minutes=60),
reftime=yyyymmddhh_strtotime(tstart),
ncdffn_sufx=radarsufx,
fieldn='pr',
abs_datestr='yymmddhhmm',
dwdradar=True)
from cosmo_utils.diag import get_totmask
radarmask = get_totmask(radarts)
sxo, syo = (357, 357)
# Determine analysis domain
lx1 = (sxo - 256 - 1) / 2 # ATTENTION first dimension is actually y
lx2 = -(lx1 + 1) # Number of grid pts to exclude at border
ly1 = (syo - 256 - 1) / 2
ly2 = -(ly1 + 1)
# Crop data
radarmask = radarmask[lx1 + 62:lx2 - 42, ly1 + 22:ly2 - 42]
def plot_prec_stamps(inargs):
"""
Plots precipitation stamps of obs, det and ensemble every hour for each
date and time specified
Parameters
----------
inargs : argparse object
Argparse object with all input arguments
"""
# TODO: Update colors
cmPrec = ((1, 1, 1), (0, 0.627, 1), (0.137, 0.235, 0.98),
(0.392, 0, 0.627), (0.784, 0, 0.627))
# (0.1 , 0.1 , 0.784),
levelsPrec = [0, 1, 3, 10, 30, 100.]
# Loop over dates
for idate, date in enumerate(make_datelist(inargs)):
# Loop over times
for t in make_timelist(timedelta(hours=inargs.time_start),
timedelta(hours=inargs.time_end),
timedelta(hours=1)):
# Load all CU objects
fobjlist = []
titlelist = []
# 1st: Radar
fobjlist.append(get_and_crop_radar_fobj(inargs, date, t))
titlelist.append('Radar')
# 2nd: det
ncdffn_pref = (get_config(inargs, 'paths', 'raw_data') + date +
'/deout_ceu_pspens/' + 'det' + '/OUTPUT/lfff')
fobjlist.append(
getfobj_ncdf(ncdffn_pref + ddhhmmss(t) + '.nc_30m_surf',
'PREC_ACCUM'))
titlelist.append('Det')
# 3rd: ens
ncdffn = 'lfff' + ddhhmmss(t) + '.nc_30m_surf'
date_dir = (get_config(inargs, 'paths', 'raw_data') + date +
'/deout_ceu_pspens/')
fobjlist.extend(
getfobj_ncdf_ens(date_dir,
'sub',
inargs.nens,
ncdffn,
dir_suffix='/OUTPUT/',
fieldn='PREC_ACCUM',
nfill=1))
titlelist.extend(['Mem ' + str(i + 1) for i in range(inargs.nens)])
# Now plot
n_panels = len(fobjlist)
n_cols = 4
n_rows = int(np.ceil(float(n_panels) / n_cols))
fig, axmat = plt.subplots(n_rows,
n_cols,
figsize=(10, 3.5 * n_rows))
axflat = np.ravel(axmat)
for i in range(len(fobjlist)):
plt.sca(axflat[i])
cf, tmp = ax_contourf(axflat[i],
fobjlist[i],
colors=cmPrec,
pllevels=levelsPrec,
ji0=(50 + inargs.zoom_lat1,
50 + inargs.zoom_lon1),
ji1=(357 - 51 + inargs.zoom_lat2,
357 - 51 + inargs.zoom_lon2),
sp_title=titlelist[i],
Basemap_drawrivers=False,
npars=0,
nmers=0)
cb = fig.colorbar(cf,
cax=fig.add_axes([0.4, 0.1, 0.2, 0.02]),
orientation='horizontal')
cb.set_label('Accumulation [mm/h]')
titlestr = (yyyymmddhh_strtotime(date).strftime(
get_config(inargs, 'plotting', 'date_fmt')) + ' ' +
str(t.seconds / 3600).zfill(2) + 'UTC')
fig.suptitle(titlestr)
plt.tight_layout(rect=[0, 0.1, 1, 0.93])
# Save figure and log
save_fig_and_log(fig,
None,
inargs,
'prec_stamps',
date=date,
time=str(t.seconds / 3600).zfill(2))