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Commit 81caba23 authored by tomrink's avatar tomrink
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modules/icing/util.py
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...@@ -3,15 +3,16 @@ import deeplearning.icing_fcn as icing_fcn ...@@ -3,15 +3,16 @@ import deeplearning.icing_fcn as icing_fcn
import deeplearning.icing_cnn as icing_cnn import deeplearning.icing_cnn as icing_cnn
from icing.pirep_goes import setup, time_filter_3 from icing.pirep_goes import setup, time_filter_3
from icing.moon_phase import moon_phase from icing.moon_phase import moon_phase
from util.util import get_time_tuple_utc, is_day, check_oblique, get_median, homedir, write_icing_file_nc4,\ from util.util import get_time_tuple_utc, is_day, check_oblique, get_median, homedir, \
write_icing_file_nc4_viirs, get_training_parameters,\ get_cartopy_crs, \
make_for_full_domain_predict, make_for_full_domain_predict_viirs_clavrx, prepare_evaluate get_fill_attrs, get_grid_values, GenericException, get_timestamp, get_cf_nav_parameters
from util.plot import make_icing_image from util.plot import make_icing_image
from util.geos_nav import get_navigation, get_lon_lat_2d_mesh from util.geos_nav import get_navigation, get_lon_lat_2d_mesh
from util.setup import model_path_day, model_path_night from util.setup import model_path_day, model_path_night
from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, CLAVRx_H08, CLAVRx_H09 from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, CLAVRx_H08, CLAVRx_H09
import h5py import h5py
import datetime import datetime
from netCDF4 import Dataset
import tensorflow as tf import tensorflow as tf
import os import os
# from scipy.signal import medfilt2d # from scipy.signal import medfilt2d
...@@ -24,6 +25,679 @@ flt_level_ranges[2] = [4000.0, 6000.0] ...@@ -24,6 +25,679 @@ flt_level_ranges[2] = [4000.0, 6000.0]
flt_level_ranges[3] = [6000.0, 8000.0] flt_level_ranges[3] = [6000.0, 8000.0]
flt_level_ranges[4] = [8000.0, 15000.0] flt_level_ranges[4] = [8000.0, 15000.0]
# Taiwan domain:
# lon, lat = 120.955098, 23.834310
# elem, line = (1789, 1505)
# # UR from Taiwan
# lon, lat = 135.0, 35.0
# elem_ur, line_ur = (2499, 995)
taiwan_i0 = 1079
taiwan_j0 = 995
taiwan_lenx = 1420
taiwan_leny = 1020
# geos.transform_point(135.0, 35.0, ccrs.PlateCarree(), False)
# geos.transform_point(106.61, 13.97, ccrs.PlateCarree(), False)
taiwain_extent = [-3342, -502, 1470, 3510] # GEOS coordinates, not line, elem
def get_training_parameters(day_night='DAY', l1b_andor_l2='both', satellite='GOES16', use_dnb=False):
if day_night == 'DAY':
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
if satellite == 'GOES16':
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
# 'refl_2_10um_nom']
elif satellite == 'H08':
train_params_l1b = ['temp_10_4um_nom', 'temp_12_0um_nom', 'temp_8_5um_nom', 'temp_3_75um_nom', 'refl_2_10um_nom',
'refl_1_60um_nom', 'refl_0_86um_nom', 'refl_0_47um_nom']
else:
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
if use_dnb is True:
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
if satellite == 'GOES16':
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
elif satellite == 'H08':
train_params_l1b = ['temp_10_4um_nom', 'temp_12_0um_nom', 'temp_8_5um_nom', 'temp_3_75um_nom']
if l1b_andor_l2 == 'both':
train_params = train_params_l1b + train_params_l2
elif l1b_andor_l2 == 'l1b':
train_params = train_params_l1b
elif l1b_andor_l2 == 'l2':
train_params = train_params_l2
return train_params, train_params_l1b, train_params_l2
def make_for_full_domain_predict(h5f, name_list=None, satellite='GOES16', domain='FD', res_fac=1,
data_extent=None):
w_x = 16
w_y = 16
i_0 = 0
j_0 = 0
s_x = int(w_x / res_fac)
s_y = int(w_y / res_fac)
geos, xlen, xmin, xmax, ylen, ymin, ymax = get_cartopy_crs(satellite, domain)
geos_nav = get_navigation(satellite, domain)
if satellite == 'GOES16':
if data_extent is not None:
ll_pt_a = data_extent[0]
ll_pt_b = data_extent[1]
if domain == 'CONUS':
y_a, x_a = geos_nav.earth_to_lc(ll_pt_a['lon'], ll_pt_a['lat'])
y_b, x_b = geos_nav.earth_to_lc(ll_pt_b['lon'], ll_pt_b['lat'])
if satellite == 'H08':
xlen = taiwan_lenx
ylen = taiwan_leny
i_0 = taiwan_i0
j_0 = taiwan_j0
elif satellite == 'H09':
xlen = taiwan_lenx
ylen = taiwan_leny
i_0 = taiwan_i0
j_0 = taiwan_j0
grd_dct = {name: None for name in name_list}
cnt_a = 0
for ds_name in name_list:
fill_value, fill_value_name = get_fill_attrs(ds_name)
gvals = get_grid_values(h5f, ds_name, j_0, i_0, None, num_j=ylen, num_i=xlen, fill_value_name=fill_value_name, fill_value=fill_value)
if gvals is not None:
grd_dct[ds_name] = gvals
cnt_a += 1
if cnt_a > 0 and cnt_a != len(name_list):
raise GenericException('weirdness')
grd_dct_n = {name: [] for name in name_list}
n_x = int(xlen/s_x) - 1
n_y = int(ylen/s_y) - 1
r_x = xlen - (n_x * s_x)
x_d = 0 if r_x >= w_x else int((w_x - r_x)/s_x)
n_x -= x_d
r_y = ylen - (n_y * s_y)
y_d = 0 if r_y >= w_y else int((w_y - r_y)/s_y)
n_y -= y_d
ll = [j_0 + j*s_y for j in range(n_y)]
cc = [i_0 + i*s_x for i in range(n_x)]
for ds_name in name_list:
for j in range(n_y):
j_ul = j * s_y
j_ul_b = j_ul + w_y
for i in range(n_x):
i_ul = i * s_x
i_ul_b = i_ul + w_x
grd_dct_n[ds_name].append(grd_dct[ds_name][j_ul:j_ul_b, i_ul:i_ul_b])
grd_dct = {name: None for name in name_list}
for ds_name in name_list:
grd_dct[ds_name] = np.stack(grd_dct_n[ds_name])
return grd_dct, ll, cc
def make_for_full_domain_predict_viirs_clavrx(h5f, name_list=None, res_fac=1, day_night='DAY', use_dnb=False):
w_x = 16
w_y = 16
i_0 = 0
j_0 = 0
s_x = int(w_x / res_fac)
s_y = int(w_y / res_fac)
ylen = h5f['scan_lines_along_track_direction'].shape[0]
xlen = h5f['pixel_elements_along_scan_direction'].shape[0]
use_nl_comp = False
if (day_night == 'NIGHT' or day_night == 'AUTO') and use_dnb:
use_nl_comp = True
grd_dct = {name: None for name in name_list}
cnt_a = 0
for ds_name in name_list:
name = ds_name
if use_nl_comp:
if ds_name == 'cld_reff_dcomp':
name = 'cld_reff_nlcomp'
elif ds_name == 'cld_opd_dcomp':
name = 'cld_opd_nlcomp'
fill_value, fill_value_name = get_fill_attrs(name)
gvals = get_grid_values(h5f, name, j_0, i_0, None, num_j=ylen, num_i=xlen, fill_value_name=fill_value_name, fill_value=fill_value)
if gvals is not None:
grd_dct[ds_name] = gvals
cnt_a += 1
if cnt_a > 0 and cnt_a != len(name_list):
raise GenericException('weirdness')
# TODO: need to investigate discrepencies with compute_lwc_iwc
# if use_nl_comp:
# cld_phase = get_grid_values(h5f, 'cloud_phase', j_0, i_0, None, num_j=ylen, num_i=xlen)
# cld_dz = get_grid_values(h5f, 'cld_geo_thick', j_0, i_0, None, num_j=ylen, num_i=xlen)
# reff = grd_dct['cld_reff_dcomp']
# opd = grd_dct['cld_opd_dcomp']
#
# lwc_nlcomp, iwc_nlcomp = compute_lwc_iwc(cld_phase, reff, opd, cld_dz)
# grd_dct['iwc_dcomp'] = iwc_nlcomp
# grd_dct['lwc_dcomp'] = lwc_nlcomp
grd_dct_n = {name: [] for name in name_list}
n_x = int(xlen/s_x) - 1
n_y = int(ylen/s_y) - 1
r_x = xlen - (n_x * s_x)
x_d = 0 if r_x >= w_x else int((w_x - r_x)/s_x)
n_x -= x_d
r_y = ylen - (n_y * s_y)
y_d = 0 if r_y >= w_y else int((w_y - r_y)/s_y)
n_y -= y_d
ll = [j_0 + j*s_y for j in range(n_y)]
cc = [i_0 + i*s_x for i in range(n_x)]
for ds_name in name_list:
for j in range(n_y):
j_ul = j * s_y
j_ul_b = j_ul + w_y
for i in range(n_x):
i_ul = i * s_x
i_ul_b = i_ul + w_x
grd_dct_n[ds_name].append(grd_dct[ds_name][j_ul:j_ul_b, i_ul:i_ul_b])
grd_dct = {name: None for name in name_list}
for ds_name in name_list:
grd_dct[ds_name] = np.stack(grd_dct_n[ds_name])
lats = get_grid_values(h5f, 'latitude', j_0, i_0, None, num_j=ylen, num_i=xlen)
lons = get_grid_values(h5f, 'longitude', j_0, i_0, None, num_j=ylen, num_i=xlen)
ll_2d, cc_2d = np.meshgrid(ll, cc, indexing='ij')
lats = lats[ll_2d, cc_2d]
lons = lons[ll_2d, cc_2d]
return grd_dct, ll, cc, lats, lons
def make_for_full_domain_predict2(h5f, satellite='GOES16', domain='FD', res_fac=1):
w_x = 16
w_y = 16
i_0 = 0
j_0 = 0
s_x = int(w_x / res_fac)
s_y = int(w_y / res_fac)
geos, xlen, xmin, xmax, ylen, ymin, ymax = get_cartopy_crs(satellite, domain)
if satellite == 'H08':
xlen = taiwan_lenx
ylen = taiwan_leny
i_0 = taiwan_i0
j_0 = taiwan_j0
n_x = int(xlen/s_x)
n_y = int(ylen/s_y)
solzen = get_grid_values(h5f, 'solar_zenith_angle', j_0, i_0, None, num_j=ylen, num_i=xlen)
satzen = get_grid_values(h5f, 'sensor_zenith_angle', j_0, i_0, None, num_j=ylen, num_i=xlen)
solzen = solzen[0:(n_y-1)*s_y:s_y, 0:(n_x-1)*s_x:s_x]
satzen = satzen[0:(n_y-1)*s_y:s_y, 0:(n_x-1)*s_x:s_x]
return solzen, satzen
# -------------------------------------------------------------------------------------------
def prepare_evaluate(h5f, name_list, satellite='GOES16', domain='FD', res_fac=1, offset=0):
w_x = 16
w_y = 16
i_0 = 0
j_0 = 0
s_x = w_x // res_fac
s_y = w_y // res_fac
geos, xlen, xmin, xmax, ylen, ymin, ymax = get_cartopy_crs(satellite, domain)
if satellite == 'H08':
xlen = taiwan_lenx
ylen = taiwan_leny
i_0 = taiwan_i0
j_0 = taiwan_j0
elif satellite == 'H09':
xlen = taiwan_lenx
ylen = taiwan_leny
i_0 = taiwan_i0
j_0 = taiwan_j0
n_x = (xlen // s_x)
n_y = (ylen // s_y)
# r_x = xlen - (n_x * s_x)
# x_d = 0 if r_x >= w_x else int((w_x - r_x)/s_x)
# n_x -= x_d
#
# r_y = ylen - (n_y * s_y)
# y_d = 0 if r_y >= w_y else int((w_y - r_y)/s_y)
# n_y -= y_d
ll = [(offset+j_0) + j*s_y for j in range(n_y)]
cc = [(offset+i_0) + i*s_x for i in range(n_x)]
grd_dct_n = {name: [] for name in name_list}
cnt_a = 0
for ds_name in name_list:
fill_value, fill_value_name = get_fill_attrs(ds_name)
gvals = get_grid_values(h5f, ds_name, j_0, i_0, None, num_j=ylen, num_i=xlen, fill_value_name=fill_value_name, fill_value=fill_value)
gvals = np.expand_dims(gvals, axis=0)
if gvals is not None:
grd_dct_n[ds_name] = gvals
cnt_a += 1
if cnt_a > 0 and cnt_a != len(name_list):
raise GenericException('weirdness')
solzen = get_grid_values(h5f, 'solar_zenith_angle', j_0, i_0, None, num_j=ylen, num_i=xlen)
satzen = get_grid_values(h5f, 'sensor_zenith_angle', j_0, i_0, None, num_j=ylen, num_i=xlen)
solzen = solzen[0:n_y*s_y:s_y, 0:n_x*s_x:s_x]
satzen = satzen[0:n_y*s_y:s_y, 0:n_x*s_x:s_x]
return grd_dct_n, solzen, satzen, ll, cc
flt_level_ranges_str = {k: None for k in range(5)}
flt_level_ranges_str[0] = '0_2000'
flt_level_ranges_str[1] = '2000_4000'
flt_level_ranges_str[2] = '4000_6000'
flt_level_ranges_str[3] = '6000_8000'
flt_level_ranges_str[4] = '8000_15000'
# flt_level_ranges_str = {k: None for k in range(1)}
# flt_level_ranges_str[0] = 'column'
def write_icing_file(clvrx_str_time, output_dir, preds_dct, probs_dct, x, y, lons, lats, elems, lines):
outfile_name = output_dir + 'icing_prediction_'+clvrx_str_time+'.h5'
h5f_out = h5py.File(outfile_name, 'w')
dim_0_name = 'x'
dim_1_name = 'y'
prob_s = []
pred_s = []
flt_lvls = list(preds_dct.keys())
for flvl in flt_lvls:
preds = preds_dct[flvl]
pred_s.append(preds)
icing_pred_ds = h5f_out.create_dataset('icing_prediction_level_'+flt_level_ranges_str[flvl], data=preds, dtype='i2')
icing_pred_ds.attrs.create('coordinates', data='y x')
icing_pred_ds.attrs.create('grid_mapping', data='Projection')
icing_pred_ds.attrs.create('missing', data=-1)
icing_pred_ds.dims[0].label = dim_0_name
icing_pred_ds.dims[1].label = dim_1_name
for flvl in flt_lvls:
probs = probs_dct[flvl]
prob_s.append(probs)
icing_prob_ds = h5f_out.create_dataset('icing_probability_level_'+flt_level_ranges_str[flvl], data=probs, dtype='f4')
icing_prob_ds.attrs.create('coordinates', data='y x')
icing_prob_ds.attrs.create('grid_mapping', data='Projection')
icing_prob_ds.attrs.create('missing', data=-1.0)
icing_prob_ds.dims[0].label = dim_0_name
icing_prob_ds.dims[1].label = dim_1_name
prob_s = np.stack(prob_s, axis=-1)
max_prob = np.max(prob_s, axis=2)
icing_prob_ds = h5f_out.create_dataset('max_icing_probability_column', data=max_prob, dtype='f4')
icing_prob_ds.attrs.create('coordinates', data='y x')
icing_prob_ds.attrs.create('grid_mapping', data='Projection')
icing_prob_ds.attrs.create('missing', data=-1.0)
icing_prob_ds.dims[0].label = dim_0_name
icing_prob_ds.dims[1].label = dim_1_name
max_lvl = np.argmax(prob_s, axis=2)
icing_pred_ds = h5f_out.create_dataset('max_icing_probability_level', data=max_lvl, dtype='i2')
icing_pred_ds.attrs.create('coordinates', data='y x')
icing_pred_ds.attrs.create('grid_mapping', data='Projection')
icing_pred_ds.attrs.create('missing', data=-1)
icing_pred_ds.dims[0].label = dim_0_name
icing_pred_ds.dims[1].label = dim_1_name
lon_ds = h5f_out.create_dataset('longitude', data=lons, dtype='f4')
lon_ds.attrs.create('units', data='degrees_east')
lon_ds.attrs.create('long_name', data='icing prediction longitude')
lon_ds.dims[0].label = dim_0_name
lon_ds.dims[1].label = dim_1_name
lat_ds = h5f_out.create_dataset('latitude', data=lats, dtype='f4')
lat_ds.attrs.create('units', data='degrees_north')
lat_ds.attrs.create('long_name', data='icing prediction latitude')
lat_ds.dims[0].label = dim_0_name
lat_ds.dims[1].label = dim_1_name
proj_ds = h5f_out.create_dataset('Projection', data=0, dtype='b')
proj_ds.attrs.create('long_name', data='Himawari Imagery Projection')
proj_ds.attrs.create('grid_mapping_name', data='geostationary')
proj_ds.attrs.create('sweep_angle_axis', data='y')
proj_ds.attrs.create('units', data='rad')
proj_ds.attrs.create('semi_major_axis', data=6378.137)
proj_ds.attrs.create('semi_minor_axis', data=6356.7523)
proj_ds.attrs.create('inverse_flattening', data=298.257)
proj_ds.attrs.create('perspective_point_height', data=35785.863)
proj_ds.attrs.create('latitude_of_projection_origin', data=0.0)
proj_ds.attrs.create('longitude_of_projection_origin', data=140.7)
proj_ds.attrs.create('CFAC', data=20466275)
proj_ds.attrs.create('LFAC', data=20466275)
proj_ds.attrs.create('COFF', data=2750.5)
proj_ds.attrs.create('LOFF', data=2750.5)
if x is not None:
x_ds = h5f_out.create_dataset('x', data=x, dtype='f8')
x_ds.dims[0].label = dim_0_name
x_ds.attrs.create('units', data='rad')
x_ds.attrs.create('standard_name', data='projection_x_coordinate')
x_ds.attrs.create('long_name', data='GOES PUG W-E fixed grid viewing angle')
x_ds.attrs.create('scale_factor', data=5.58879902955962e-05)
x_ds.attrs.create('add_offset', data=-0.153719917308037)
x_ds.attrs.create('CFAC', data=20466275)
x_ds.attrs.create('COFF', data=2750.5)
y_ds = h5f_out.create_dataset('y', data=y, dtype='f8')
y_ds.dims[0].label = dim_1_name
y_ds.attrs.create('units', data='rad')
y_ds.attrs.create('standard_name', data='projection_y_coordinate')
y_ds.attrs.create('long_name', data='GOES PUG S-N fixed grid viewing angle')
y_ds.attrs.create('scale_factor', data=-5.58879902955962e-05)
y_ds.attrs.create('add_offset', data=0.153719917308037)
y_ds.attrs.create('LFAC', data=20466275)
y_ds.attrs.create('LOFF', data=2750.5)
if elems is not None:
elem_ds = h5f_out.create_dataset('elems', data=elems, dtype='i2')
elem_ds.dims[0].label = dim_0_name
line_ds = h5f_out.create_dataset('lines', data=lines, dtype='i2')
line_ds.dims[0].label = dim_1_name
pass
h5f_out.close()
def write_icing_file_nc4(clvrx_str_time, output_dir, preds_dct, probs_dct,
x, y, lons, lats, elems, lines, satellite='GOES16', domain='CONUS',
has_time=False, use_nan=False, prob_thresh=0.5, bt_10_4=None):
outfile_name = output_dir + 'icing_prediction_'+clvrx_str_time+'.nc'
rootgrp = Dataset(outfile_name, 'w', format='NETCDF4')
rootgrp.setncattr('Conventions', 'CF-1.7')
dim_0_name = 'x'
dim_1_name = 'y'
time_dim_name = 'time'
geo_coords = 'time y x'
dim_0 = rootgrp.createDimension(dim_0_name, size=x.shape[0])
dim_1 = rootgrp.createDimension(dim_1_name, size=y.shape[0])
dim_time = rootgrp.createDimension(time_dim_name, size=1)
tvar = rootgrp.createVariable('time', 'f8', time_dim_name)
tvar[0] = get_timestamp(clvrx_str_time)
tvar.units = 'seconds since 1970-01-01 00:00:00'
if not has_time:
var_dim_list = [dim_1_name, dim_0_name]
else:
var_dim_list = [time_dim_name, dim_1_name, dim_0_name]
prob_s = []
flt_lvls = list(preds_dct.keys())
for flvl in flt_lvls:
preds = preds_dct[flvl]
icing_pred_ds = rootgrp.createVariable('icing_prediction_level_'+flt_level_ranges_str[flvl], 'i2', var_dim_list)
icing_pred_ds.setncattr('coordinates', geo_coords)
icing_pred_ds.setncattr('grid_mapping', 'Projection')
icing_pred_ds.setncattr('missing', -1)
if has_time:
preds = preds.reshape((1, y.shape[0], x.shape[0]))
icing_pred_ds[:,] = preds
for flvl in flt_lvls:
probs = probs_dct[flvl]
prob_s.append(probs)
icing_prob_ds = rootgrp.createVariable('icing_probability_level_'+flt_level_ranges_str[flvl], 'f4', var_dim_list)
icing_prob_ds.setncattr('coordinates', geo_coords)
icing_prob_ds.setncattr('grid_mapping', 'Projection')
if not use_nan:
icing_prob_ds.setncattr('missing', -1.0)
else:
icing_prob_ds.setncattr('missing', np.nan)
if has_time:
probs = probs.reshape((1, y.shape[0], x.shape[0]))
if use_nan:
probs = np.where(probs < prob_thresh, np.nan, probs)
icing_prob_ds[:,] = probs
prob_s = np.stack(prob_s, axis=-1)
max_prob = np.max(prob_s, axis=2)
if use_nan:
max_prob = np.where(max_prob < prob_thresh, np.nan, max_prob)
if has_time:
max_prob = max_prob.reshape(1, y.shape[0], x.shape[0])
icing_prob_ds = rootgrp.createVariable('max_icing_probability_column', 'f4', var_dim_list)
icing_prob_ds.setncattr('coordinates', geo_coords)
icing_prob_ds.setncattr('grid_mapping', 'Projection')
if not use_nan:
icing_prob_ds.setncattr('missing', -1.0)
else:
icing_prob_ds.setncattr('missing', np.nan)
icing_prob_ds[:,] = max_prob
prob_s = np.where(prob_s < prob_thresh, -1.0, prob_s)
max_lvl = np.where(np.all(prob_s == -1, axis=2), -1, np.argmax(prob_s, axis=2))
if use_nan:
max_lvl = np.where(max_lvl == -1, np.nan, max_lvl)
if has_time:
max_lvl = max_lvl.reshape((1, y.shape[0], x.shape[0]))
icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
icing_pred_ds.setncattr('coordinates', geo_coords)
icing_pred_ds.setncattr('grid_mapping', 'Projection')
icing_pred_ds.setncattr('missing', -1)
icing_pred_ds[:,] = max_lvl
if bt_10_4 is not None:
bt_ds = rootgrp.createVariable('bt_10_4', 'f4', var_dim_list)
bt_ds.setncattr('coordinates', geo_coords)
bt_ds.setncattr('grid_mapping', 'Projection')
bt_ds[:,] = bt_10_4
lon_ds = rootgrp.createVariable('longitude', 'f4', [dim_1_name, dim_0_name])
lon_ds.units = 'degrees_east'
lon_ds[:,] = lons
lat_ds = rootgrp.createVariable('latitude', 'f4', [dim_1_name, dim_0_name])
lat_ds.units = 'degrees_north'
lat_ds[:,] = lats
cf_nav_dct = get_cf_nav_parameters(satellite, domain)
if satellite == 'H08':
long_name = 'Himawari Imagery Projection'
elif satellite == 'H09':
long_name = 'Himawari Imagery Projection'
elif satellite == 'GOES16':
long_name = 'GOES-16/17 Imagery Projection'
proj_ds = rootgrp.createVariable('Projection', 'b')
proj_ds.setncattr('long_name', long_name)
proj_ds.setncattr('grid_mapping_name', 'geostationary')
proj_ds.setncattr('sweep_angle_axis', cf_nav_dct['sweep_angle_axis'])
proj_ds.setncattr('semi_major_axis', cf_nav_dct['semi_major_axis'])
proj_ds.setncattr('semi_minor_axis', cf_nav_dct['semi_minor_axis'])
proj_ds.setncattr('inverse_flattening', cf_nav_dct['inverse_flattening'])
proj_ds.setncattr('perspective_point_height', cf_nav_dct['perspective_point_height'])
proj_ds.setncattr('latitude_of_projection_origin', cf_nav_dct['latitude_of_projection_origin'])
proj_ds.setncattr('longitude_of_projection_origin', cf_nav_dct['longitude_of_projection_origin'])
if x is not None:
x_ds = rootgrp.createVariable(dim_0_name, 'f8', [dim_0_name])
x_ds.units = 'rad'
x_ds.setncattr('axis', 'X')
x_ds.setncattr('standard_name', 'projection_x_coordinate')
x_ds.setncattr('long_name', 'fixed grid viewing angle')
x_ds.setncattr('scale_factor', cf_nav_dct['x_scale_factor'])
x_ds.setncattr('add_offset', cf_nav_dct['x_add_offset'])
x_ds[:] = x
y_ds = rootgrp.createVariable(dim_1_name, 'f8', [dim_1_name])
y_ds.units = 'rad'
y_ds.setncattr('axis', 'Y')
y_ds.setncattr('standard_name', 'projection_y_coordinate')
y_ds.setncattr('long_name', 'fixed grid viewing angle')
y_ds.setncattr('scale_factor', cf_nav_dct['y_scale_factor'])
y_ds.setncattr('add_offset', cf_nav_dct['y_add_offset'])
y_ds[:] = y
if elems is not None:
elem_ds = rootgrp.createVariable('elems', 'i2', [dim_0_name])
elem_ds[:] = elems
line_ds = rootgrp.createVariable('lines', 'i2', [dim_1_name])
line_ds[:] = lines
pass
rootgrp.close()
def write_icing_file_nc4_viirs(clvrx_str_time, output_dir, preds_dct, probs_dct, lons, lats,
has_time=False, use_nan=False, prob_thresh=0.5, bt_10_4=None):
outfile_name = output_dir + 'icing_prediction_'+clvrx_str_time+'.nc'
rootgrp = Dataset(outfile_name, 'w', format='NETCDF4')
rootgrp.setncattr('Conventions', 'CF-1.7')
dim_0_name = 'x'
dim_1_name = 'y'
time_dim_name = 'time'
geo_coords = 'longitude latitude'
dim_1_len, dim_0_len = lons.shape
dim_0 = rootgrp.createDimension(dim_0_name, size=dim_0_len)
dim_1 = rootgrp.createDimension(dim_1_name, size=dim_1_len)
dim_time = rootgrp.createDimension(time_dim_name, size=1)
tvar = rootgrp.createVariable('time', 'f8', time_dim_name)
tvar[0] = get_timestamp(clvrx_str_time)
tvar.units = 'seconds since 1970-01-01 00:00:00'
if not has_time:
var_dim_list = [dim_1_name, dim_0_name]
else:
var_dim_list = [time_dim_name, dim_1_name, dim_0_name]
prob_s = []
flt_lvls = list(preds_dct.keys())
for flvl in flt_lvls:
preds = preds_dct[flvl]
icing_pred_ds = rootgrp.createVariable('icing_prediction_level_'+flt_level_ranges_str[flvl], 'i2', var_dim_list)
icing_pred_ds.setncattr('coordinates', geo_coords)
icing_pred_ds.setncattr('grid_mapping', 'Projection')
icing_pred_ds.setncattr('missing', -1)
if has_time:
preds = preds.reshape((1, dim_1_len, dim_0_len))
icing_pred_ds[:,] = preds
for flvl in flt_lvls:
probs = probs_dct[flvl]
prob_s.append(probs)
icing_prob_ds = rootgrp.createVariable('icing_probability_level_'+flt_level_ranges_str[flvl], 'f4', var_dim_list)
icing_prob_ds.setncattr('coordinates', geo_coords)
icing_prob_ds.setncattr('grid_mapping', 'Projection')
if not use_nan:
icing_prob_ds.setncattr('missing', -1.0)
else:
icing_prob_ds.setncattr('missing', np.nan)
if has_time:
probs = probs.reshape((1, dim_1_len, dim_0_len))
if use_nan:
probs = np.where(probs < prob_thresh, np.nan, probs)
icing_prob_ds[:,] = probs
prob_s = np.stack(prob_s, axis=-1)
max_prob = np.max(prob_s, axis=2)
if use_nan:
max_prob = np.where(max_prob < prob_thresh, np.nan, max_prob)
if has_time:
max_prob = max_prob.reshape(1, dim_1_len, dim_0_len)
icing_prob_ds = rootgrp.createVariable('max_icing_probability_column', 'f4', var_dim_list)
icing_prob_ds.setncattr('coordinates', geo_coords)
icing_prob_ds.setncattr('grid_mapping', 'Projection')
if not use_nan:
icing_prob_ds.setncattr('missing', -1.0)
else:
icing_prob_ds.setncattr('missing', np.nan)
icing_prob_ds[:,] = max_prob
prob_s = np.where(prob_s < prob_thresh, -1.0, prob_s)
max_lvl = np.where(np.all(prob_s == -1, axis=2), -1, np.argmax(prob_s, axis=2))
if use_nan:
max_lvl = np.where(max_lvl == -1, np.nan, max_lvl)
if has_time:
max_lvl = max_lvl.reshape((1, dim_1_len, dim_0_len))
icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
icing_pred_ds.setncattr('coordinates', geo_coords)
icing_pred_ds.setncattr('grid_mapping', 'Projection')
icing_pred_ds.setncattr('missing', -1)
icing_pred_ds[:,] = max_lvl
if bt_10_4 is not None:
bt_ds = rootgrp.createVariable('bt_10_4', 'f4', var_dim_list)
bt_ds.setncattr('coordinates', geo_coords)
bt_ds.setncattr('grid_mapping', 'Projection')
bt_ds[:,] = bt_10_4
lon_ds = rootgrp.createVariable('longitude', 'f4', [dim_1_name, dim_0_name])
lon_ds.units = 'degrees_east'
lon_ds[:,] = lons
lat_ds = rootgrp.createVariable('latitude', 'f4', [dim_1_name, dim_0_name])
lat_ds.units = 'degrees_north'
lat_ds[:,] = lats
proj_ds = rootgrp.createVariable('Projection', 'b')
proj_ds.setncattr('grid_mapping_name', 'latitude_longitude')
rootgrp.close()
def run_icing_predict(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=homedir, def run_icing_predict(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=homedir,
day_model_path=model_path_day, night_model_path=model_path_night, day_model_path=model_path_day, night_model_path=model_path_night,
......
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