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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]
# -------------------------------------------------------------------------------------------
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 = 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
elif satellite == 'H09':
xlen = taiwan_lenx
ylen = taiwan_leny
i_0 = taiwan_i0
j_0 = taiwan_j0
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 = [(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)
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-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]
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, 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 get_cf_nav_parameters(satellite='GOES16', domain='FD'):
param_dct = None
if satellite == 'H08': # We presently only have FD
param_dct = {'semi_major_axis': 6378.137,
'semi_minor_axis': 6356.7523,
'perspective_point_height': 35785.863,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': 140.7,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'y',
'x_scale_factor': 5.58879902955962e-05,
'x_add_offset': -0.153719917308037,
'y_scale_factor': -5.58879902955962e-05,
'y_add_offset': 0.153719917308037}
elif satellite == 'H09':
param_dct = {'semi_major_axis': 6378.137,
'semi_minor_axis': 6356.7523,
'perspective_point_height': 35785.863,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': 140.7,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'y',
'x_scale_factor': 5.58879902955962e-05,
'x_add_offset': -0.153719917308037,
'y_scale_factor': -5.58879902955962e-05,
'y_add_offset': 0.153719917308037}
elif satellite == 'GOES16':
if domain == 'CONUS':
param_dct = {'semi_major_axis': 6378137.0,
'semi_minor_axis': 6356752.31414,
'perspective_point_height': 35786023.0,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': -75,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'x',
'x_scale_factor': 5.6E-05,
'x_add_offset': -0.101332,
'y_scale_factor': -5.6E-05,
'y_add_offset': 0.128212}
elif domain == 'FD':
param_dct = {'semi_major_axis': 6378137.0,
'semi_minor_axis': 6356752.31414,
'perspective_point_height': 35786023.0,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': -75,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'x',
'x_scale_factor': 5.6E-05,
'x_add_offset': -0.151844,
'y_scale_factor': -5.6E-05,
'y_add_offset': 0.151844}
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'
dim_0_name = 'x'
dim_1_name = 'y'
flt_lvls = list(preds_dct.keys())
for flvl in flt_lvls:
preds = preds_dct[flvl]
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
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
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('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('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.dims[0].label = dim_1_name
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
def write_icing_file_nc4(clvrx_str_time, output_dir, preds_dct, probs_dct,
x, y, lons, lats, elems, lines, satellite='GOES16', domain='CONUS',
outfile_name = output_dir + 'icing_prediction_'+clvrx_str_time+'.nc'
rootgrp = Dataset(outfile_name, 'w', format='NETCDF4')
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('grid_mapping', 'Projection')
icing_pred_ds.setncattr('missing', -1)
icing_pred_ds[:,] = preds
for flvl in flt_lvls:
probs = probs_dct[flvl]
icing_prob_ds = rootgrp.createVariable('icing_probability_level_'+flt_level_ranges_str[flvl], 'f4', var_dim_list)
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 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('grid_mapping', 'Projection')
if not use_nan:
icing_prob_ds.setncattr('missing', -1.0)
else:
icing_prob_ds.setncattr('missing', np.nan)
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)
icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
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('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('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('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
def write_icing_file_nc4_viirs(clvrx_str_time, output_dir, preds_dct, probs_dct, lons, lats,
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'
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[:,] = 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)
if not use_nan:
icing_prob_ds.setncattr('missing', -1.0)
else:
icing_prob_ds.setncattr('missing', np.nan)
if has_time:
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:
icing_prob_ds = rootgrp.createVariable('max_icing_probability_column', 'f4', var_dim_list)
icing_prob_ds.setncattr('coordinates', geo_coords)
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:
icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
icing_pred_ds.setncattr('coordinates', geo_coords)
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[:,] = 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')
def write_cld_prods_file_nc4(clvrx_str_time, outfile_name, cloud_fraction, cloud_frac_opd_1, cloud_frac_opd_2,
x, y, elems, lines, satellite='GOES16', domain='CONUS',
has_time=False):
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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]
cld_frac_ds = rootgrp.createVariable('cloud_fraction', 'i1', var_dim_list)
cld_frac_ds.setncattr('coordinates', geo_coords)
cld_frac_ds.setncattr('grid_mapping', 'Projection')
cld_frac_ds.setncattr('missing', -1)
if has_time:
cloud_fraction = cloud_fraction.reshape((1, y.shape[0], x.shape[0]))
cld_frac_ds[:, ] = cloud_fraction
cld_frac_opd_ds_1 = rootgrp.createVariable('cldy_fraction_opd_1', 'f4', var_dim_list)
cld_frac_opd_ds_1.setncattr('long_name', 'FCNN inferred fractional OPD')
cld_frac_opd_ds_1.setncattr('coordinates', geo_coords)
cld_frac_opd_ds_1.setncattr('grid_mapping', 'Projection')
cld_frac_opd_ds_1.setncattr('missing', -1.0)
cloud_frac_opd_1 = cloud_frac_opd_1.reshape((1, y.shape[0], x.shape[0]))
cld_frac_opd_ds_1[:, ] = cloud_frac_opd_1
cld_frac_opd_ds_2 = rootgrp.createVariable('cldy_fraction_opd_2', 'f4', var_dim_list)
cld_frac_opd_ds_2.setncattr('long_name', '2km OPD / cloud_fraction')
cld_frac_opd_ds_2.setncattr('coordinates', geo_coords)
cld_frac_opd_ds_2.setncattr('grid_mapping', 'Projection')
cld_frac_opd_ds_2.setncattr('missing', -1.0)
if has_time:
cloud_frac_opd_2 = cloud_frac_opd_2.reshape((1, y.shape[0], x.shape[0]))
cld_frac_opd_ds_2[:, ] = cloud_frac_opd_2
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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 downscale_2x(original, smoothing=False, samples_axis_first=False):
# if smoothing:
# original = scipy.ndimage.gaussian_filter(original, sigma = 1/2)
if not samples_axis_first:
lr = np.nanmean(np.array([original[0::2,0::2],
original[1::2,0::2],
original[0::2,1::2],
original[1::2,1::2]]),axis=0).squeeze()
elif samples_axis_first:
lr = np.nanmean(np.array([original[:,0::2,0::2],
original[:,1::2,0::2],
original[:,0::2,1::2],
original[:,1::2,1::2]]),axis=0).squeeze()
return lr
z_intrp = []
for k in range(z.shape[0]):
z_k = z[k, :, :]
f = RectBivariateSpline(x, y, z_k)
z_intrp.append(f(x_new, y_new))
def resample_2d_linear(x, y, z, x_new, y_new):
z_intrp = []
for k in range(z.shape[0]):
z_k = z[k, :, :]
f = interp2d(x, y, z_k)
z_intrp.append(f(x_new, y_new))
return np.stack(z_intrp)
def resample_2d_linear_one(x, y, z, x_new, y_new):
f = interp2d(x, y, z)
# Gaussian filter suitable for model training Data Pipeline
# z: input array. Must have dimensions: [BATCH_SIZE, Y, X]
# sigma: Standard deviation for Gaussian kernel
# returns stacked 2d arrays of same input dimension
def smooth_2d(z, sigma=1.0):
z_smoothed = []
for j in range(z.shape[0]):
z_j = z[j, :, :]
z_smoothed.append(gaussian_filter(z_j, sigma=sigma))
return np.stack(z_smoothed)
# For [Y, X], see above
def smooth_2d_single(z, sigma=1.0):
return gaussian_filter(z, sigma=sigma)
def median_filter_2d(z, kernel_size=3):
z_filtered = []
for j in range(z.shape[0]):
z_j = z[j, :, :]
z_filtered.append(medfilt2d(z_j, kernel_size=kernel_size))
return np.stack(z_filtered)
def median_filter_2d_single(z, kernel_size=3):
return medfilt2d(z, kernel_size=kernel_size)
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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_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_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