def post_process(self, *args, **kwargs):
# make a sym link in the nfs dir
wrf_config = self.get_config(**kwargs)
wps_dir = utils.get_wps_dir(wrf_config.get('wrf_home'))
nfs_metgrid_dir = os.path.join(wrf_config.get('nfs_dir'), 'metgrid')
utils.create_dir_if_not_exists(nfs_metgrid_dir)
# utils.delete_files_with_prefix(nfs_metgrid_dir, 'met_em.d*')
# utils.create_symlink_with_prefix(wps_dir, 'met_em.d*', nfs_metgrid_dir)
utils.create_zip_with_prefix(wps_dir, 'met_em.d*',
os.path.join(wps_dir, 'metgrid.zip'))
utils.delete_files_with_prefix(nfs_metgrid_dir, 'met_em.d*')
utils.move_files_with_prefix(wps_dir, 'metgrid.zip', nfs_metgrid_dir)
def create_rf_plots_wrf(nc_f, plots_output_dir, plots_output_base_dir, lon_min=None, lat_min=None, lon_max=None,
lat_max=None, filter_threshold=0.05, run_prefix='WRF'):
if not all([lon_min, lat_min, lon_max, lat_max]):
lon_min, lat_min, lon_max, lat_max = constants.SRI_LANKA_EXTENT
variables = ext_utils.extract_variables(nc_f, 'RAINC, RAINNC', lat_min, lat_max, lon_min, lon_max)
lats = variables['XLAT']
lons = variables['XLONG']
# cell size is calc based on the mean between the lat and lon points
cz = np.round(np.mean(np.append(lons[1:len(lons)] - lons[0: len(lons) - 1], lats[1:len(lats)]
- lats[0: len(lats) - 1])), 3)
clevs = [0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40, 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 750]
cmap = cm.s3pcpn
basemap = Basemap(projection='merc', llcrnrlon=lon_min, llcrnrlat=lat_min, urcrnrlon=lon_max,
urcrnrlat=lat_max, resolution='h')
data = variables['RAINC'] + variables['RAINNC']
logging.info('Filtering with the threshold %f' % filter_threshold)
data[data < filter_threshold] = 0.0
variables['PRECIP'] = data
prefix = 'wrf_plots'
with TemporaryDirectory(prefix=prefix) as temp_dir:
t0 = dt.datetime.strptime(variables['Times'][0], '%Y-%m-%d_%H:%M:%S')
t1 = dt.datetime.strptime(variables['Times'][1], '%Y-%m-%d_%H:%M:%S')
step = (t1 - t0).total_seconds() / 3600.0
inst_precip = ext_utils.get_two_element_average(variables['PRECIP'])
cum_precip = ext_utils.get_two_element_average(variables['PRECIP'], return_diff=False)
for i in range(1, len(variables['Times'])):
time = variables['Times'][i]
ts = dt.datetime.strptime(time, '%Y-%m-%d_%H:%M:%S')
lk_ts = utils.datetime_utc_to_lk(ts, shift_mins=30)
logging.info('processing %s', time)
# instantaneous precipitation (hourly)
inst_file = os.path.join(temp_dir, 'wrf_inst_' + lk_ts.strftime('%Y-%m-%d_%H:%M:%S'))
ext_utils.create_asc_file(np.flip(inst_precip[i - 1], 0), lats, lons, inst_file + '.asc', cell_size=cz)
title = {
'label': 'Hourly rf for %s LK' % lk_ts.strftime('%Y-%m-%d_%H:%M:%S'),
'fontsize': 30
}
ext_utils.create_contour_plot(inst_precip[i - 1], inst_file + '.png', lat_min, lon_min, lat_max, lon_max,
title, clevs=clevs, cmap=cmap, basemap=basemap)
if (i * step) % 24 == 0:
t = 'Daily rf from %s LK to %s LK' % (
(lk_ts - dt.timedelta(hours=24)).strftime('%Y-%m-%d_%H:%M:%S'), lk_ts.strftime('%Y-%m-%d_%H:%M:%S'))
d = int(i * step / 24) - 1
logging.info('Creating images for D%d' % d)
cum_file = os.path.join(temp_dir, 'wrf_cum_%dd' % d)
if i * step / 24 > 1:
cum_precip_24h = cum_precip[i - 1] - cum_precip[i - 1 - int(24 / step)]
else:
cum_precip_24h = cum_precip[i - 1]
ext_utils.create_asc_file(np.flip(cum_precip_24h, 0), lats, lons, cum_file + '.asc', cell_size=cz)
ext_utils.create_contour_plot(cum_precip_24h, cum_file + '.png', lat_min, lon_min, lat_max, lon_max, t,
clevs=clevs, cmap=cmap, basemap=basemap)
gif_file = os.path.join(temp_dir, 'wrf_inst_%dd' % d)
images = [os.path.join(temp_dir, 'wrf_inst_' + j.strftime('%Y-%m-%d_%H:%M:%S') + '.png') for j in
np.arange(lk_ts - dt.timedelta(hours=24 - step), lk_ts + dt.timedelta(hours=step),
dt.timedelta(hours=step)).astype(dt.datetime)]
ext_utils.create_gif(images, gif_file + '.gif')
logging.info('Creating the zips')
utils.create_zip_with_prefix(temp_dir, '*.png', os.path.join(temp_dir, 'pngs.zip'))
utils.create_zip_with_prefix(temp_dir, '*.asc', os.path.join(temp_dir, 'ascs.zip'))
logging.info('Cleaning up instantaneous pngs and ascs - wrf_inst_*')
utils.delete_files_with_prefix(temp_dir, 'wrf_inst_*.png')
utils.delete_files_with_prefix(temp_dir, 'wrf_inst_*.asc')
logging.info('Copying pngs to ' + plots_output_dir)
utils.move_files_with_prefix(temp_dir, '*.png', plots_output_dir)
logging.info('Copying ascs to ' + plots_output_dir)
utils.move_files_with_prefix(temp_dir, '*.asc', plots_output_dir)
logging.info('Copying gifs to ' + plots_output_dir)
utils.copy_files_with_prefix(temp_dir, '*.gif', plots_output_dir)
logging.info('Copying zips to ' + plots_output_dir)
utils.copy_files_with_prefix(temp_dir, '*.zip', plots_output_dir)
plots_latest_dir = os.path.join(plots_output_base_dir, 'latest', run_prefix, os.path.basename(plots_output_dir))
# <nfs>/latest/wrf0 .. 3
utils.create_dir_if_not_exists(plots_latest_dir)
# todo: this needs to be adjusted to handle the multiple runs
logging.info('Copying gifs to ' + plots_latest_dir)
utils.copy_files_with_prefix(temp_dir, '*.gif', plots_latest_dir)
def extract_metro_colombo(nc_f, wrf_output, wrf_output_base, curw_db_adapter=None, curw_db_upsert=False,
run_prefix='WRF', run_name='Cloud-1'):
"""
extract Metro-Colombo rf and divide area into to 4 quadrants
:param wrf_output_base:
:param run_name:
:param nc_f:
:param wrf_output:
:param curw_db_adapter: If not none, data will be pushed to the db
:param run_prefix:
:param curw_db_upsert:
:return:
"""
prefix = 'met_col'
lon_min, lat_min, lon_max, lat_max = constants.COLOMBO_EXTENT
nc_vars = ext_utils.extract_variables(nc_f, ['RAINC', 'RAINNC'], lat_min, lat_max, lon_min, lon_max)
lats = nc_vars['XLAT']
lons = nc_vars['XLONG']
prcp = nc_vars['RAINC'] + nc_vars['RAINNC']
times = nc_vars['Times']
diff = ext_utils.get_two_element_average(prcp)
width = len(lons)
height = len(lats)
output_dir = utils.create_dir_if_not_exists(os.path.join(wrf_output, prefix))
basin_rf = np.mean(diff[0:(len(times) - 1 if len(times) < 24 else 24), :, :])
alpha_file_path = os.path.join(wrf_output_base, prefix + '_alphas.txt')
utils.create_dir_if_not_exists(os.path.dirname(alpha_file_path))
with open(alpha_file_path, 'a+') as alpha_file:
t = utils.datetime_utc_to_lk(dt.datetime.strptime(times[0], '%Y-%m-%d_%H:%M:%S'), shift_mins=30)
alpha_file.write('%s\t%f\n' % (t.strftime('%Y-%m-%d_%H:%M:%S'), basin_rf))
cz = ext_utils.get_mean_cell_size(lats, lons)
no_data = -99
divs = (2, 2)
div_rf = {}
for i in range(divs[0] * divs[1]):
div_rf[prefix + str(i)] = []
with TemporaryDirectory(prefix=prefix) as temp_dir:
subsection_file_path = os.path.join(temp_dir, 'sub_means.txt')
with open(subsection_file_path, 'w') as subsection_file:
for tm in range(0, len(times) - 1):
t_str = (
utils.datetime_utc_to_lk(dt.datetime.strptime(times[tm], '%Y-%m-%d_%H:%M:%S'),
shift_mins=30)).strftime('%Y-%m-%d %H:%M:%S')
output_file_path = os.path.join(temp_dir, 'rf_' + t_str.replace(' ', '_') + '.asc')
ext_utils.create_asc_file(np.flip(diff[tm, :, :], 0), lats, lons, output_file_path, cell_size=cz,
no_data_val=no_data)
# writing subsection file
x_idx = [round(i * width / divs[0]) for i in range(0, divs[0] + 1)]
y_idx = [round(i * height / divs[1]) for i in range(0, divs[1] + 1)]
subsection_file.write(t_str)
for j in range(len(y_idx) - 1):
for i in range(len(x_idx) - 1):
quad = j * divs[1] + i
sub_sec_mean = np.mean(diff[tm, y_idx[j]:y_idx[j + 1], x_idx[i]: x_idx[i + 1]])
subsection_file.write('\t%.4f' % sub_sec_mean)
div_rf[prefix + str(quad)].append([t_str, sub_sec_mean])
subsection_file.write('\n')
utils.create_zip_with_prefix(temp_dir, 'rf_*.asc', os.path.join(temp_dir, 'ascs.zip'), clean_up=True)
utils.move_files_with_prefix(temp_dir, '*', output_dir)
# writing to the database
if curw_db_adapter is not None:
for i in range(divs[0] * divs[1]):
name = prefix + str(i)
station = [Station.CUrW, name, name, -999, -999, 0, "met col quadrant %d" % i]
if ext_utils.create_station_if_not_exists(curw_db_adapter, station):
logging.info('%s station created' % name)
logging.info('Pushing data to the db...')
ext_utils.push_rainfall_to_db(curw_db_adapter, div_rf, upsert=curw_db_upsert, source=run_prefix, name=run_name)
else:
logging.info('curw_db_adapter not available. Unable to push data!')
return basin_rf
def extract_kelani_basin_rainfall_flo2d(nc_f, nc_f_prev_days, output_dir, avg_basin_rf=1.0, kelani_basin_file=None,
target_rfs=None, output_prefix='RAINCELL'):
"""
:param output_prefix:
:param nc_f:
:param nc_f_prev_days:
:param output_dir:
:param avg_basin_rf:
:param kelani_basin_file:
:param target_rfs:
:return:
"""
if target_rfs is None:
target_rfs = [100, 150, 200, 250, 300]
if kelani_basin_file is None:
kelani_basin_file = res_mgr.get_resource_path('extraction/local/kelani_basin_points_250m.txt')
points = np.genfromtxt(kelani_basin_file, delimiter=',')
kel_lon_min = np.min(points, 0)[1]
kel_lat_min = np.min(points, 0)[2]
kel_lon_max = np.max(points, 0)[1]
kel_lat_max = np.max(points, 0)[2]
diff, kel_lats, kel_lons, times = ext_utils.extract_area_rf_series(nc_f, kel_lat_min, kel_lat_max, kel_lon_min,
kel_lon_max)
def get_bins(arr):
sz = len(arr)
return (arr[1:sz - 1] + arr[0:sz - 2]) / 2
lat_bins = get_bins(kel_lats)
lon_bins = get_bins(kel_lons)
t0 = dt.datetime.strptime(times[0], '%Y-%m-%d_%H:%M:%S')
t1 = dt.datetime.strptime(times[1], '%Y-%m-%d_%H:%M:%S')
t_end = dt.datetime.strptime(times[-1], '%Y-%m-%d_%H:%M:%S')
utils.create_dir_if_not_exists(output_dir)
prev_diff = []
prev_days = len(nc_f_prev_days)
for i in range(prev_days):
if nc_f_prev_days[i]:
p_diff, _, _, _ = ext_utils.extract_area_rf_series(nc_f_prev_days[i], kel_lat_min, kel_lat_max, kel_lon_min,
kel_lon_max)
prev_diff.append(p_diff)
else:
prev_diff.append(None)
def write_forecast_to_raincell_file(output_file_path, alpha):
with open(output_file_path, 'w') as output_file:
res_mins = int((t1 - t0).total_seconds() / 60)
data_hours = int(len(times) + prev_days * 24 * 60 / res_mins)
start_ts = utils.datetime_utc_to_lk(t0 - dt.timedelta(days=prev_days), shift_mins=30).strftime(
'%Y-%m-%d %H:%M:%S')
end_ts = utils.datetime_utc_to_lk(t_end, shift_mins=30).strftime('%Y-%m-%d %H:%M:%S')
output_file.write("%d %d %s %s\n" % (res_mins, data_hours, start_ts, end_ts))
for d in range(prev_days):
for t in range(int(24 * 60 / res_mins)):
for point in points:
rf_x = np.digitize(point[1], lon_bins)
rf_y = np.digitize(point[2], lat_bins)
if prev_diff[prev_days - 1 - d] is not None:
output_file.write('%d %.1f\n' % (point[0], prev_diff[prev_days - 1 - d][t, rf_y, rf_x]))
else:
output_file.write('%d %.1f\n' % (point[0], 0))
for t in range(len(times)):
for point in points:
rf_x = np.digitize(point[1], lon_bins)
rf_y = np.digitize(point[2], lat_bins)
if t < int(24 * 60 / res_mins):
output_file.write('%d %.1f\n' % (point[0], diff[t, rf_y, rf_x] * alpha))
else:
output_file.write('%d %.1f\n' % (point[0], diff[t, rf_y, rf_x]))
with TemporaryDirectory(prefix='curw_raincell') as temp_dir:
raincell_temp = os.path.join(temp_dir, output_prefix + '.DAT')
write_forecast_to_raincell_file(raincell_temp, 1)
for target_rf in target_rfs:
write_forecast_to_raincell_file('%s.%d' % (raincell_temp, target_rf), target_rf / avg_basin_rf)
utils.create_zip_with_prefix(temp_dir, output_prefix + '.DAT*', os.path.join(temp_dir, output_prefix + '.zip'),
clean_up=True)
utils.move_files_with_prefix(temp_dir, output_prefix + '.zip', utils.create_dir_if_not_exists(output_dir))
def run_em_real(wrf_config):
logging.info('Running em_real...')
wrf_home = wrf_config.get('wrf_home')
em_real_dir = utils.get_em_real_dir(wrf_home)
procs = wrf_config.get('procs')
run_id = wrf_config.get('run_id')
output_dir = utils.create_dir_if_not_exists(
os.path.join(wrf_config.get('nfs_dir'), 'results', run_id, 'wrf'))
archive_dir = utils.create_dir_if_not_exists(
os.path.join(wrf_config.get('archive_dir'), 'results', run_id, 'wrf'))
logging.info('Backup the output dir')
utils.backup_dir(output_dir)
logs_dir = utils.create_dir_if_not_exists(os.path.join(output_dir, 'logs'))
logging.info('Copying metgrid.zip')
metgrid_dir = os.path.join(wrf_config.get('nfs_dir'), 'metgrid')
if wrf_config.is_set('wps_run_id'):
logging.info('wps_run_id is set. Copying metgrid from ' +
wrf_config.get('wps_run_id'))
utils.copy_files_with_prefix(
metgrid_dir,
wrf_config.get('wps_run_id') + '_metgrid.zip', em_real_dir)
metgrid_zip = os.path.join(
em_real_dir,
wrf_config.get('wps_run_id') + '_metgrid.zip')
else:
utils.copy_files_with_prefix(metgrid_dir,
wrf_config.get('run_id') + '_metgrid.zip',
em_real_dir)
metgrid_zip = os.path.join(em_real_dir,
wrf_config.get('run_id') + '_metgrid.zip')
logging.info('Extracting metgrid.zip')
ZipFile(metgrid_zip, 'r',
compression=ZIP_DEFLATED).extractall(path=em_real_dir)
# logs destination: nfs/logs/xxxx/rsl*
try:
try:
logging.info('Starting real.exe')
utils.run_subprocess(
'mpirun --allow-run-as-root -np %d ./real.exe' % procs,
cwd=em_real_dir)
finally:
logging.info('Moving Real log files...')
utils.create_zip_with_prefix(em_real_dir,
'rsl*',
os.path.join(em_real_dir,
'real_rsl.zip'),
clean_up=True)
utils.move_files_with_prefix(em_real_dir, 'real_rsl.zip', logs_dir)
try:
logging.info('Starting wrf.exe')
utils.run_subprocess(
'mpirun --allow-run-as-root -np %d ./wrf.exe' % procs,
cwd=em_real_dir)
finally:
logging.info('Moving WRF log files...')
utils.create_zip_with_prefix(em_real_dir,
'rsl*',
os.path.join(em_real_dir,
'wrf_rsl.zip'),
clean_up=True)
utils.move_files_with_prefix(em_real_dir, 'wrf_rsl.zip', logs_dir)
finally:
logging.info('Moving namelist input file')
utils.move_files_with_prefix(em_real_dir, 'namelist.input', output_dir)
logging.info('WRF em_real: DONE! Moving data to the output dir')
logging.info('Extracting rf from domain3')
d03_nc = glob.glob(os.path.join(em_real_dir, 'wrfout_d03_*'))[0]
ncks_query = 'ncks -v %s %s %s' % ('RAINC,RAINNC,XLAT,XLONG,Times', d03_nc,
d03_nc + '_rf')
utils.run_subprocess(ncks_query)
logging.info('Extracting rf from domain1')
d01_nc = glob.glob(os.path.join(em_real_dir, 'wrfout_d01_*'))[0]
ncks_query = 'ncks -v %s %s %s' % ('RAINC,RAINNC,XLAT,XLONG,Times', d01_nc,
d01_nc + '_rf')
utils.run_subprocess(ncks_query)
logging.info('Moving data to the output dir')
utils.move_files_with_prefix(em_real_dir, 'wrfout_d03*_rf', output_dir)
utils.move_files_with_prefix(em_real_dir, 'wrfout_d01*_rf', output_dir)
logging.info('Moving data to the archive dir')
utils.move_files_with_prefix(em_real_dir, 'wrfout_*', archive_dir)
logging.info('Cleaning up files')
utils.delete_files_with_prefix(em_real_dir, 'met_em*')
utils.delete_files_with_prefix(em_real_dir, 'rsl*')
os.remove(metgrid_zip)
def run_wps(wrf_config):
logging.info('Running WPS: START')
wrf_home = wrf_config.get('wrf_home')
wps_dir = utils.get_wps_dir(wrf_home)
output_dir = utils.create_dir_if_not_exists(
os.path.join(wrf_config.get('nfs_dir'), 'results',
wrf_config.get('run_id'), 'wps'))
logging.info('Backup the output dir')
utils.backup_dir(output_dir)
logs_dir = utils.create_dir_if_not_exists(os.path.join(output_dir, 'logs'))
logging.info('Cleaning up files')
utils.delete_files_with_prefix(wps_dir, 'FILE:*')
utils.delete_files_with_prefix(wps_dir, 'PFILE:*')
utils.delete_files_with_prefix(wps_dir, 'met_em*')
# Linking VTable
if not os.path.exists(os.path.join(wps_dir, 'Vtable')):
logging.info('Creating Vtable symlink')
os.symlink(os.path.join(wps_dir, 'ungrib/Variable_Tables/Vtable.NAM'),
os.path.join(wps_dir, 'Vtable'))
# Running link_grib.csh
gfs_date, gfs_cycle, start = utils.get_appropriate_gfs_inventory(
wrf_config)
dest = utils.get_gfs_data_url_dest_tuple(wrf_config.get('gfs_url'),
wrf_config.get('gfs_inv'),
gfs_date, gfs_cycle, '',
wrf_config.get('gfs_res'),
'')[1].replace('.grb2', '')
utils.run_subprocess('csh link_grib.csh %s/%s' %
(wrf_config.get('gfs_dir'), dest),
cwd=wps_dir)
try:
# Starting ungrib.exe
try:
utils.run_subprocess('./ungrib.exe', cwd=wps_dir)
finally:
utils.move_files_with_prefix(wps_dir, 'ungrib.log', logs_dir)
# Starting geogrid.exe'
if not check_geogrid_output(wps_dir):
logging.info('Geogrid output not available')
try:
utils.run_subprocess('./geogrid.exe', cwd=wps_dir)
finally:
utils.move_files_with_prefix(wps_dir, 'geogrid.log', logs_dir)
# Starting metgrid.exe'
try:
utils.run_subprocess('./metgrid.exe', cwd=wps_dir)
finally:
utils.move_files_with_prefix(wps_dir, 'metgrid.log', logs_dir)
finally:
logging.info('Moving namelist wps file')
utils.move_files_with_prefix(wps_dir, 'namelist.wps', output_dir)
logging.info('Running WPS: DONE')
logging.info('Zipping metgrid data')
metgrid_zip = os.path.join(wps_dir,
wrf_config.get('run_id') + '_metgrid.zip')
utils.create_zip_with_prefix(wps_dir, 'met_em.d*', metgrid_zip)
logging.info('Moving metgrid data')
dest_dir = os.path.join(wrf_config.get('nfs_dir'), 'metgrid')
utils.move_files_with_prefix(wps_dir, metgrid_zip, dest_dir)
def process(self, *args, **kwargs):
config = self.get_config(**kwargs)
logging.info('wrf conifg: ' + config.to_json_string())
start_date = config.get('start_date')
d03_dir = config.get('wrf_output_dir')
d03_sl = os.path.join(d03_dir, 'wrfout_d03_' + start_date + ':00_SL')
# create a temp work dir & get a local copy of the d03.._SL
temp_dir = utils.create_dir_if_not_exists(
os.path.join(config.get('wrf_home'), 'temp'))
shutil.copy2(d03_sl, temp_dir)
d03_sl = os.path.join(temp_dir, os.path.basename(d03_sl))
lat_min = 5.722969
lon_min = 79.52146
lat_max = 10.06425
lon_max = 82.18992
variables = ext_utils.extract_variables(d03_sl, 'RAINC, RAINNC',
lat_min, lat_max, lon_min,
lon_max)
lats = variables['XLAT']
lons = variables['XLONG']
# cell size is calc based on the mean between the lat and lon points
cz = np.round(
np.mean(
np.append(lons[1:len(lons)] - lons[0:len(lons) - 1],
lats[1:len(lats)] - lats[0:len(lats) - 1])), 3)
# clevs = 10 * np.array([0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30])
# clevs_cum = 10 * np.array([0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 75, 100])
# norm = colors.BoundaryNorm(boundaries=clevs, ncolors=256)
# norm_cum = colors.BoundaryNorm(boundaries=clevs_cum, ncolors=256)
# cmap = plt.get_cmap('jet')
clevs = [
0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40, 50, 70, 100, 150, 200, 250,
300, 400, 500, 600, 750
]
clevs_cum = clevs
norm = None
norm_cum = None
cmap = cm.s3pcpn
basemap = Basemap(projection='merc',
llcrnrlon=lon_min,
llcrnrlat=lat_min,
urcrnrlon=lon_max,
urcrnrlat=lat_max,
resolution='h')
filter_threshold = 0.05
data = variables['RAINC'] + variables['RAINNC']
logging.info('Filtering with the threshold %f' % filter_threshold)
data[data < filter_threshold] = 0.0
variables['PRECIP'] = data
pngs = []
ascs = []
for i in range(1, len(variables['Times'])):
time = variables['Times'][i]
ts = dt.datetime.strptime(time, '%Y-%m-%d_%H:%M:%S')
lk_ts = utils.datetime_utc_to_lk(ts)
logging.info('processing %s', time)
# instantaneous precipitation (hourly)
inst_precip = variables['PRECIP'][i] - variables['PRECIP'][i - 1]
inst_file = os.path.join(temp_dir, 'wrf_inst_' + time)
title = {
'label':
'Hourly rf for %s LK\n%s UTC' %
(lk_ts.strftime('%Y-%m-%d_%H:%M:%S'), time),
'fontsize':
30
}
ext_utils.create_asc_file(np.flip(inst_precip, 0),
lats,
lons,
inst_file + '.asc',
cell_size=cz)
ascs.append(inst_file + '.asc')
ext_utils.create_contour_plot(inst_precip,
inst_file + '.png',
lat_min,
lon_min,
lat_max,
lon_max,
title,
clevs=clevs,
cmap=cmap,
basemap=basemap,
norm=norm)
pngs.append(inst_file + '.png')
if i % 24 == 0:
t = 'Daily rf from %s LK to %s LK' % (
(lk_ts -
dt.timedelta(hours=24)).strftime('%Y-%m-%d_%H:%M:%S'),
lk_ts.strftime('%Y-%m-%d_%H:%M:%S'))
d = int(i / 24) - 1
logging.info('Creating images for D%d' % d)
cum_file = os.path.join(temp_dir, 'wrf_cum_%dd' % d)
ext_utils.create_asc_file(np.flip(variables['PRECIP'][i], 0),
lats,
lons,
cum_file + '.asc',
cell_size=cz)
ascs.append(cum_file + '.asc')
ext_utils.create_contour_plot(variables['PRECIP'][i] -
variables['PRECIP'][i - 24],
cum_file + '.png',
lat_min,
lon_min,
lat_max,
lon_max,
t,
clevs=clevs,
cmap=cmap,
basemap=basemap,
norm=norm_cum)
pngs.append(inst_file + '.png')
gif_file = os.path.join(temp_dir, 'wrf_inst_%dd' % d)
images = [
os.path.join(
temp_dir,
'wrf_inst_' + i.strftime('%Y-%m-%d_%H:%M:%S') + '.png')
for i in np.arange(ts - dt.timedelta(hours=23), ts +
dt.timedelta(
hours=1), dt.timedelta(
hours=1)).astype(dt.datetime)
]
ext_utils.create_gif(images, gif_file + '.gif')
logging.info('Creating the zips')
utils.create_zip_with_prefix(temp_dir, '*.png',
os.path.join(temp_dir, 'pngs.zip'))
utils.create_zip_with_prefix(temp_dir, '*.asc',
os.path.join(temp_dir, 'ascs.zip'))
# utils.create_zipfile(pngs, os.path.join(temp_dir, 'pngs.zip'))
# utils.create_zipfile(ascs, os.path.join(temp_dir, 'ascs.zip'))
logging.info('Cleaning up instantaneous pngs and ascs - wrf_inst_*')
utils.delete_files_with_prefix(temp_dir, 'wrf_inst_*.png')
utils.delete_files_with_prefix(temp_dir, 'wrf_inst_*.asc')
logging.info('Copying pngs to ' + d03_dir)
utils.move_files_with_prefix(temp_dir, '*.png', d03_dir)
logging.info('Copying ascs to ' + d03_dir)
utils.move_files_with_prefix(temp_dir, '*.asc', d03_dir)
logging.info('Copying gifs to ' + d03_dir)
utils.copy_files_with_prefix(temp_dir, '*.gif', d03_dir)
logging.info('Copying zips to ' + d03_dir)
utils.copy_files_with_prefix(temp_dir, '*.zip', d03_dir)
d03_latest_dir = os.path.join(config.get('nfs_dir'), 'latest',
os.path.basename(config.get('wrf_home')))
# <nfs>/latest/wrf0 .. 3
utils.create_dir_if_not_exists(d03_latest_dir)
# todo: this needs to be adjusted to handle the multiple runs
logging.info('Copying gifs to ' + d03_latest_dir)
utils.copy_files_with_prefix(temp_dir, '*.gif', d03_latest_dir)
logging.info('Cleaning up temp dir')
shutil.rmtree(temp_dir)