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]))
def extract_points_array_rf_series(nc_f,
points_array,
boundaries=None,
rf_var_list=None,
lat_var='XLAT',
lon_var='XLONG',
time_var='Times'):
"""
:param boundaries: list [lat_min, lat_max, lon_min, lon_max]
:param nc_f:
:param points_array: multi dim array (np structured array) with a row [name, lon, lat]
:param rf_var_list:
:param lat_var:
:param lon_var:
:param time_var:
:return: np structured array with [(time, name1, name2, .... )]
"""
if rf_var_list is None:
rf_var_list = ['RAINC', 'RAINNC']
if boundaries is None:
lat_min = np.min(points_array[points_array.dtype.names[2]])
lat_max = np.max(points_array[points_array.dtype.names[2]])
lon_min = np.min(points_array[points_array.dtype.names[1]])
lon_max = np.max(points_array[points_array.dtype.names[1]])
else:
lat_min, lat_max, lon_min, lon_max = boundaries
variables = extract_variables(nc_f, rf_var_list, lat_min, lat_max, lon_min,
lon_max, lat_var, lon_var, time_var)
prcp = variables[rf_var_list[0]]
for i in range(1, len(rf_var_list)):
prcp = prcp + variables[rf_var_list[i]]
diff = get_two_element_average(prcp, return_diff=True)
result = np.array([
utils.datetime_utc_to_lk(
dt.datetime.strptime(t, '%Y-%m-%d_%H:%M:%S'),
shift_mins=30).strftime('%Y-%m-%d %H:%M:%S').encode('utf-8')
for t in variables[time_var][:-1]
],
dtype=np.dtype([(time_var, 'U19')]))
for p in points_array:
lat_start_idx = np.argmin(abs(variables['XLAT'] - p[2]))
lon_start_idx = np.argmin(abs(variables['XLONG'] - p[1]))
rf = np.round(diff[:, lat_start_idx, lon_start_idx], 6)
# use this for 4 point average
# rf = np.round(np.mean(diff[:, lat_start_idx:lat_start_idx + 2, lon_start_idx:lon_start_idx + 2], axis=(1, 2)),
# 6)
result = append_fields(result, p[0].decode(), rf, usemask=False)
return result
def process_jaxa_zip_file(zip_file_path,
out_file_path,
lat_min,
lon_min,
lat_max,
lon_max,
output_prefix='jaxa_sat'):
sat_zip = zipfile.ZipFile(zip_file_path)
sat = np.genfromtxt(sat_zip.open(
os.path.basename(zip_file_path).replace('.zip', '')),
delimiter=',',
names=True)
print(':', [])
sat_filt = np.sort(sat[(sat['Lat'] <= lat_max) & (sat['Lat'] >= lat_min) &
(sat['Lon'] <= lon_max) & (sat['Lon'] >= lon_min)],
order=['Lat', 'Lon'])
lats = np.sort(np.unique(sat_filt['Lat']))
lons = np.sort(np.unique(sat_filt['Lon']))
data = sat_filt['RainRate'].reshape(len(lats), len(lons))
ext_utils.create_asc_file(np.flip(data, 0), lats, lons, out_file_path)
# clevs = np.concatenate(([-1, 0], np.array([pow(2, i) for i in range(0, 9)])))
# clevs = 10 * np.array([0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30])
# norm = colors.BoundaryNorm(boundaries=clevs, ncolors=256)
# cmap = plt.get_cmap('jet')
clevs = [
0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40, 50, 75, 100, 150, 200, 250, 300
]
# clevs = [0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 75, 100]
norm = None
cmap = cm.s3pcpn
ts = dt.datetime.strptime(
os.path.basename(out_file_path).replace(output_prefix + '_',
'').replace('.asc', ''),
'%Y-%m-%d_%H:%M')
lk_ts = utils.datetime_utc_to_lk(ts)
title_opts = {
'label':
output_prefix + ' ' + lk_ts.strftime('%Y-%m-%d %H:%M') + ' LK\n' +
ts.strftime('%Y-%m-%d %H:%M') + ' UTC',
'fontsize':
30
}
create_contour_plot(data,
out_file_path + '.png',
np.min(lats),
np.min(lons),
np.max(lats),
np.max(lons),
title_opts,
clevs=clevs,
cmap=cmap,
norm=norm)
def extract_mean_rainfall_from_shp_file(nc_f, wrf_output, output_prefix, output_name, basin_shp_file, basin_extent,
curw_db_adapter=None, curw_db_upsert=False, run_prefix='WRF',
run_name='Cloud-1'):
lon_min, lat_min, lon_max, lat_max = basin_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)
polys = shapefile.Reader(basin_shp_file)
output_dir = utils.create_dir_if_not_exists(os.path.join(wrf_output, output_prefix))
with TemporaryDirectory(prefix=output_prefix) as temp_dir:
output_file_path = os.path.join(temp_dir, output_prefix + '.txt')
kub_rf = {}
with open(output_file_path, 'w') as output_file:
kub_rf[output_name] = []
for t in range(0, len(times) - 1):
cnt = 0
rf_sum = 0.0
for y in range(0, len(lats)):
for x in range(0, len(lons)):
if utils.is_inside_polygon(polys, lats[y], lons[x]):
cnt = cnt + 1
rf_sum = rf_sum + diff[t, y, x]
mean_rf = rf_sum / cnt
t_str = (
utils.datetime_utc_to_lk(dt.datetime.strptime(times[t], '%Y-%m-%d_%H:%M:%S'),
shift_mins=30)).strftime('%Y-%m-%d %H:%M:%S')
output_file.write('%s\t%.4f\n' % (t_str, mean_rf))
kub_rf[output_name].append([t_str, mean_rf])
utils.move_files_with_prefix(temp_dir, '*.txt', output_dir)
if curw_db_adapter is not None:
station = [Station.CUrW, output_name, output_name, -999, -999, 0, 'Kelani upper basin mean rainfall']
if ext_utils.create_station_if_not_exists(curw_db_adapter, station):
logging.info('%s station created' % output_name)
logging.info('Pushing data to the db...')
ext_utils.push_rainfall_to_db(curw_db_adapter, kub_rf, upsert=curw_db_upsert, name=run_name,
source=run_prefix)
else:
logging.info('curw_db_adapter not available. Unable to push data!')
def extract_weather_stations(nc_f, wrf_output, weather_stations=None, curw_db_adapter=None, curw_db_upsert=False,
run_prefix='WRF', run_name='Cloud-1'):
if weather_stations is None:
weather_stations = res_mgr.get_resource_path('extraction/local/kelani_basin_stations.txt')
nc_fid = Dataset(nc_f, 'r')
times_len, times = ext_utils.extract_time_data(nc_f)
prefix = 'stations_rf'
stations_dir = utils.create_dir_if_not_exists(os.path.join(wrf_output, prefix))
stations_rf = {}
with TemporaryDirectory(prefix=prefix) as temp_dir:
with open(weather_stations, 'r') as csvfile:
stations = csv.reader(csvfile, delimiter=' ')
for row in stations:
logging.info(' '.join(row))
lon = row[1]
lat = row[2]
station_prcp = nc_fid.variables['RAINC'][:, lat, lon] + nc_fid.variables['RAINNC'][:, lat, lon]
station_diff = ext_utils.get_two_element_average(station_prcp)
stations_rf[row[0]] = []
station_file_path = os.path.join(temp_dir, row[0] + '_%s.txt' % prefix)
with open(station_file_path, 'w') as station_file:
for t in range(0, len(times) - 1):
t_str = (
utils.datetime_utc_to_lk(dt.datetime.strptime(times[t], '%Y-%m-%d_%H:%M:%S'),
shift_mins=30)).strftime('%Y-%m-%d %H:%M:%S')
station_file.write('%s\t%.4f\n' % (t_str, station_diff[t]))
stations_rf[row[0]].append([t_str, station_diff[t]])
utils.move_files_with_prefix(temp_dir, '*.txt', stations_dir)
if curw_db_adapter is not None:
logging.info('Pushing data to the db...')
ext_utils.push_rainfall_to_db(curw_db_adapter, stations_rf, upsert=curw_db_upsert, name=run_name,
source=run_prefix)
else:
logging.info('curw_db_adapter not available. Unable to push data!')
nc_fid.close()
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 push_wrf_rainfall_to_db(nc_f, curw_db_adapter=None, lon_min=None, lat_min=None, lon_max=None,
lat_max=None, run_prefix='WRF', upsert=False, run_name='Cloud-1', station_prefix='wrf'):
"""
:param run_name:
:param nc_f:
:param curw_db_adapter: If not none, data will be pushed to the db
:param run_prefix:
:param lon_min:
:param lat_min:
:param lon_max:
:param lat_max:
:param upsert:
:return:
"""
if curw_db_adapter is None:
logging.info('curw_db_adapter not available. Unable to push data!')
return
if not all([lon_min, lat_min, lon_max, lat_max]):
lon_min, lat_min, lon_max, lat_max = constants.SRI_LANKA_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)
def random_check_stations_exist():
for _ in range(10):
_x = lons[int(random() * width)]
_y = lats[int(random() * height)]
_name = '%s_%.6f_%.6f' % (station_prefix, _x, _y)
_query = {'name': _name}
if curw_db_adapter.get_station(_query) is None:
logging.debug('Random stations check fail')
return False
logging.debug('Random stations check success')
return True
stations_exists = random_check_stations_exist()
rf_ts = {}
for y in range(height):
for x in range(width):
lat = lats[y]
lon = lons[x]
station_id = '%s_%.6f_%.6f' % (station_prefix, lon, lat)
name = station_id
if not stations_exists:
logging.info('Creating station %s ...' % name)
station = [Station.WRF, station_id, name, str(lon), str(lat), str(0), "WRF point"]
curw_db_adapter.create_station(station)
# add rf series to the dict
ts = []
for i in range(len(diff)):
t = utils.datetime_utc_to_lk(dt.datetime.strptime(times[i], '%Y-%m-%d_%H:%M:%S'), shift_mins=30)
ts.append([t.strftime('%Y-%m-%d %H:%M:%S'), diff[i, y, x]])
rf_ts[name] = ts
ext_utils.push_rainfall_to_db(curw_db_adapter, rf_ts, source=run_prefix, upsert=upsert, name=run_name)
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 process_jaxa_zip_file(zip_file_path,
out_file_path,
lat_min,
lon_min,
lat_max,
lon_max,
archive_data=False,
output_prefix='jaxa_sat',
db_adapter_config=None):
sat_zip = zipfile.ZipFile(zip_file_path)
sat = np.genfromtxt(sat_zip.open(
os.path.basename(zip_file_path).replace('.zip', '')),
delimiter=',',
names=True)
sat_filt = np.sort(sat[(sat['Lat'] <= lat_max) & (sat['Lat'] >= lat_min) &
(sat['Lon'] <= lon_max) & (sat['Lon'] >= lon_min)],
order=['Lat', 'Lon'])
lats = np.sort(np.unique(sat_filt['Lat']))
lons = np.sort(np.unique(sat_filt['Lon']))
data = sat_filt['RainRate'].reshape(len(lats), len(lons))
ext_utils.create_asc_file(np.flip(data, 0), lats, lons, out_file_path)
# clevs = np.concatenate(([-1, 0], np.array([pow(2, i) for i in range(0, 9)])))
# clevs = 10 * np.array([0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30])
# norm = colors.BoundaryNorm(boundaries=clevs, ncolors=256)
# cmap = plt.get_cmap('jet')
clevs = [
0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40, 50, 75, 100, 150, 200, 250, 300
]
# clevs = [0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 75, 100]
norm = None
cmap = cm.s3pcpn
ts = dt.datetime.strptime(
os.path.basename(out_file_path).replace(output_prefix + '_',
'').replace('.asc', ''),
'%Y-%m-%d_%H:%M')
lk_ts = utils.datetime_utc_to_lk(ts)
title_opts = {
'label':
output_prefix + ' ' + lk_ts.strftime('%Y-%m-%d %H:%M') + ' LK\n' +
ts.strftime('%Y-%m-%d %H:%M') + ' UTC',
'fontsize':
30
}
ext_utils.create_contour_plot(data,
out_file_path + '.png',
np.min(lats),
np.min(lons),
np.max(lats),
np.max(lons),
title_opts,
clevs=clevs,
cmap=cmap,
norm=norm)
if archive_data and not utils.file_exists_nonempty(out_file_path +
'.archive'):
np.savetxt(out_file_path + '.archive', data, fmt='%g')
else:
logging.info('%s already exits' % (out_file_path + '.archive'))
if not db_adapter_config:
logging.info('db_adapter not available. Unable to push data!')
return
db_adapter = ext_utils.get_curw_adapter(mysql_config=db_adapter_config)
width = len(lons)
height = len(lats)
station_prefix = 'sat'
run_name = 'Cloud-1'
upsert = True
def random_check_stations_exist():
for _ in range(10):
_x = lons[int(random() * width)]
_y = lats[int(random() * height)]
_name = '%s_%.6f_%.6f' % (station_prefix, _x, _y)
_query = {'name': _name}
if db_adapter.get_station(_query) is None:
logging.debug('Random stations check fail')
return False
logging.debug('Random stations check success')
return True
stations_exists = random_check_stations_exist()
rf_ts = {}
for y in range(height):
for x in range(width):
lat = lats[y]
lon = lons[x]
station_id = '%s_%.6f_%.6f' % (station_prefix, lon, lat)
name = station_id
if not stations_exists:
logging.info('Creating station %s ...' % name)
station = [
Station.Sat, station_id, name,
str(lon),
str(lat),
str(0), "WRF point"
]
db_adapter.create_station(station)
# add rf series to the dict
rf_ts[name] = [[lk_ts.strftime('%Y-%m-%d %H:%M:%S'), data[y, x]]]
ext_utils.push_rainfall_to_db(db_adapter,
rf_ts,
source=station_prefix,
name=run_name,
types=['Observed'],
upsert=upsert)
# clevs = np.concatenate(([-1, 0], np.array([pow(2, i) for i in range(0, 9)])))
# clevs = 10 * np.array([0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30])
# norm = colors.BoundaryNorm(boundaries=clevs, ncolors=256)
# cmap = plt.get_cmap('jet')
clevs = [
0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40, 50, 75, 100, 150, 200, 250, 300
]
# clevs = [0.1, 0.5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 75, 100]
norm = None
cmap = cm.s3pcpn
ts = dt.datetime.strptime(
os.path.basename(out_file_path).replace(output_prefix + '_',
'').replace('.asc', ''),
'%Y-%m-%d_%H:%M')
lk_ts = utils.datetime_utc_to_lk(ts)
title_opts = {
'label':
output_prefix + ' ' + lk_ts.strftime('%Y-%m-%d %H:%M') + ' LK\n' +
ts.strftime('%Y-%m-%d %H:%M') + ' UTC',
'fontsize':
30
}
ext_utils.create_contour_plot(data,
out_file_path + '.png',
np.min(lats),
np.min(lons),
np.max(lats),
np.max(lons),
title_opts,
clevs=clevs,
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_d01_' + 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_d01'))
shutil.copy2(d03_sl, temp_dir)
d03_sl = os.path.join(temp_dir, os.path.basename(d03_sl))
lat_min = -3.06107
lon_min = 71.2166
lat_max = 18.1895
lon_max = 90.3315
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
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
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':
'3Hourly rf for %s LK\n%s UTC' %
(lk_ts.strftime('%Y-%m-%d_%H:%M:%S'), time),
'fontsize':
30
}
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)
if i % 8 == 0:
d = int(i / 8) - 1
logging.info('Creating gif for D%d' % d)
gif_file = os.path.join(temp_dir, 'wrf_inst_D01_%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=24 - 3), ts +
dt.timedelta(
hours=3), dt.timedelta(
hours=3)).astype(dt.datetime)
]
ext_utils.create_gif(images, gif_file + '.gif')
# move all the data in the tmp dir to the nfs
logging.info('Copying gifs to ' + d03_dir)
utils.copy_files_with_prefix(temp_dir, '*.gif', 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 the dir ' + temp_dir)
shutil.rmtree(temp_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)
