From 6f04c860ca4c7a053cf3dcc776479cf7eaa7be51 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 31 Jan 2023 13:09:03 -0600
Subject: [PATCH] initial commit...
---
modules/util/viirs_surfrad.py | 365 ++++++++++++++++++++++++++++++++++
1 file changed, 365 insertions(+)
create mode 100644 modules/util/viirs_surfrad.py
diff --git a/modules/util/viirs_surfrad.py b/modules/util/viirs_surfrad.py
new file mode 100644
index 00000000..bdc2ff8b
--- /dev/null
+++ b/modules/util/viirs_surfrad.py
@@ -0,0 +1,365 @@
+import numpy as np
+import h5py
+from util.util import get_grid_values, get_grid_values_all, is_night, is_day, compute_lwc_iwc, get_fill_attrs
+import glob
+import os
+from aeolus.datasource import CLAVRx_VIIRS
+from icing.moon_phase import *
+from pathlib import Path
+
+# --- CLAVRx Radiometric parameters and metadata ------------------------------------------------
+l1b_ds_list = ['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']
+
+l1b_ds_types = {ds: 'f4' for ds in l1b_ds_list}
+l1b_ds_fill = {l1b_ds_list[i]: -32767 for i in range(10)}
+l1b_ds_fill.update({l1b_ds_list[i+10]: -32768 for i in range(5)})
+l1b_ds_range = {ds: 'actual_range' for ds in l1b_ds_list}
+
+# --- CLAVRx L2 parameters and metadata
+ds_list = ['cld_height_acha', 'cld_geo_thick', 'cld_press_acha', 'sensor_zenith_angle', 'supercooled_prob_acha',
+ 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_opd_acha', 'solar_zenith_angle',
+ 'cld_reff_acha', 'cld_reff_dcomp', 'cld_reff_dcomp_1', 'cld_reff_dcomp_2', 'cld_reff_dcomp_3',
+ 'cld_opd_dcomp', 'cld_opd_dcomp_1', 'cld_opd_dcomp_2', 'cld_opd_dcomp_3', 'cld_cwp_dcomp', 'iwc_dcomp',
+ 'lwc_dcomp', 'cld_emiss_acha', 'conv_cloud_fraction', 'cloud_type', 'cloud_phase', 'cloud_mask']
+
+ds_types = {ds_list[i]: 'f4' for i in range(23)}
+ds_types.update({ds_list[i+23]: 'i1' for i in range(3)})
+ds_fill = {ds_list[i]: -32768 for i in range(23)}
+ds_fill.update({ds_list[i+23]: -128 for i in range(3)})
+ds_range = {ds_list[i]: 'actual_range' for i in range(23)}
+ds_range.update({ds_list[i]: None for i in range(3)})
+
+ds_types.update(l1b_ds_types)
+ds_fill.update(l1b_ds_fill)
+ds_range.update(l1b_ds_range)
+
+ds_types.update({'temp_3_9um_nom': 'f4'})
+ds_types.update({'cloud_fraction': 'f4'})
+ds_fill.update({'temp_3_9um_nom': -32767})
+ds_fill.update({'cloud_fraction': -32768})
+ds_range.update({'temp_3_9um_nom': 'actual_range'})
+ds_range.update({'cloud_fraction': 'actual_range'})
+
+
+emis_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_9um_nom',
+ 'temp_6_7um_nom']
+# refl_params = ['refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
+# data_params = refl_params + emis_params
+# data_params = emis_params
+
+# target_param = 'cloud_probability'
+target_param = 'cld_opd_dcomp'
+
+group_name_i = 'super/'
+group_name_m = 'orig/'
+
+solzen_name = group_name_m + 'solar_zenith'
+
+label_params = group_name_i+target_param
+data_params = [group_name_m+'temp_11_0um', group_name_m+'temp_12_0um', group_name_m+'refl_0_65um', group_name_m+target_param]
+
+
+def keep_tile(param_s, tile):
+ k = param_s.index(group_name_m + target_param)
+ grd_k = tile[k, ].copy()
+
+ if target_param == 'cloud_probability':
+ grd_k = process_cld_prob_(grd_k)
+ elif target_param == 'cld_opd_dcomp':
+ grd_k = process_cld_opd_(grd_k)
+
+ if grd_k is not None:
+ tile[k, ] = grd_k
+ return tile
+ else:
+ return None
+
+
+def process_cld_prob_(grd_k):
+ keep = np.invert(np.isnan(grd_k))
+ num_keep = np.sum(keep)
+ if num_keep / grd_k.size < 0.98:
+ return None
+ keep = np.where(keep, np.logical_and(0.05 < grd_k, grd_k < 0.95), False)
+ if np.sum(keep)/num_keep < 0.50:
+ return None
+ grd_k = np.where(np.invert(keep), 0, grd_k)
+ return grd_k
+
+
+def process_cld_opd_(grd_k):
+ keep = np.invert(np.isnan(grd_k))
+ num_keep = np.sum(keep)
+ if num_keep / grd_k.size < 0.98:
+ return None
+ grd_k = np.where(np.invert(keep), 0, grd_k)
+ keep = np.where(keep, np.logical_and(0.1 < grd_k, grd_k < 158.0), False)
+ if np.sum(keep)/num_keep < 0.50:
+ return None
+ return grd_k
+
+
+def run_all(directory, out_directory, day_night='ANY', start=10):
+ cnt = start
+ total_num_train_samples = 0
+ total_num_valid_samples = 0
+ num_keep_x_tiles = 8
+
+ pattern = 'clavrx_VNP02MOD*.highres.nc.level2.nc'
+ pattern = 'clavrx_*.nc'
+ path = directory + '**' + '/' + pattern
+
+ data_files = glob.glob(path, recursive=True)
+
+ label_valid_tiles = []
+ label_train_tiles = []
+ data_valid_tiles = []
+ data_train_tiles = []
+ f_cnt = 0
+
+ num_files = len(data_files)
+
+ print('Start, number of files: ', num_files)
+
+ for idx, data_f in enumerate(data_files):
+ # if idx % 4 == 0: # if we want to skip some files
+ if True:
+ try:
+ h5f = h5py.File(data_f, 'r')
+ except:
+ print('cant open file: ', data_f)
+ continue
+
+ try:
+ run(h5f, data_params, data_train_tiles, data_valid_tiles, label_params, label_train_tiles, label_valid_tiles,
+ num_keep_x_tiles=num_keep_x_tiles, tile_width=64, kernel_size=3, day_night=day_night)
+ except Exception as e:
+ print(e)
+ h5f.close()
+ # label_h5f.close()
+ continue
+ print(data_f)
+ f_cnt += 1
+
+ h5f.close()
+ # label_h5f.close()
+
+ if len(data_train_tiles) == 0:
+ continue
+
+ if (f_cnt % 5) == 0:
+ num_valid_samples = 0
+ if len(data_valid_tiles) > 0:
+ # label_valid = np.stack(label_valid_tiles)
+ data_valid = np.stack(data_valid_tiles)
+ np.save(out_directory + 'data_valid_' + str(cnt), data_valid)
+ # np.save(out_directory+'label_valid_' + str(cnt), label_valid)
+ num_valid_samples = data_valid.shape[0]
+
+ # label_train = np.stack(label_train_tiles)
+ # np.save(out_directory+'label_train_' + str(cnt), label_train)
+ data_train = np.stack(data_train_tiles)
+ np.save(out_directory+'data_train_' + str(cnt), data_train)
+ num_train_samples = data_train.shape[0]
+
+ label_valid_tiles = []
+ label_train_tiles = []
+ data_valid_tiles = []
+ data_train_tiles = []
+
+ print(' num_train_samples, num_valid_samples, progress % : ', num_train_samples, num_valid_samples, int((f_cnt/num_files)*100))
+ total_num_train_samples += num_train_samples
+ total_num_valid_samples += num_valid_samples
+ print('total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)
+
+ cnt += 1
+
+ print('** total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)
+
+
+# tile_width: Must be even!
+# kernel_size: Must be odd!
+def run(h5f, param_s, train_tiles, valid_tiles, lbl_param_s, lbl_train_tiles, lbl_valid_tiles,
+ num_keep_x_tiles=8, tile_width=64, kernel_size=3, day_night='DAY'):
+
+ border = int((kernel_size - 1)/2) + 1
+
+ param_name = param_s[0]
+
+ num_lines = h5f[param_name].shape[0]
+ num_pixels = h5f[param_name].shape[1] # Must be even
+
+ if day_night != 'BOTH':
+ solzen = get_grid_values(h5f, solzen_name, 0, 0, None, num_lines, num_pixels)
+
+ grd_s = []
+ for param in param_s:
+ fill_value, fill_value_name = get_fill_attrs(param)
+ try:
+ grd = get_grid_values(h5f, param, 0, 0, None, num_lines, num_pixels, fill_value_name=fill_value_name, fill_value=fill_value)
+ grd_s.append(grd)
+ except Exception as e:
+ print(e)
+ return
+ data = np.stack(grd_s)
+
+ grd_s = []
+ for param in lbl_param_s:
+ fill_value, fill_value_name = get_fill_attrs(param)
+ try:
+ grd = get_grid_values(h5f, param, 0, 0, None, num_lines*2, num_pixels*2, fill_value_name=fill_value_name, fill_value=fill_value)
+ grd_s.append(grd)
+ except Exception as e:
+ print(e)
+ return
+ label = np.stack(grd_s)
+
+ tile_width += 2 * border
+
+ i_skip = tile_width
+ j_skip = tile_width
+ i_start = int(num_pixels / 2) - int((num_keep_x_tiles * tile_width) / 2)
+ j_start = 0
+
+ num_keep_y_tiles = int(num_lines / tile_width) - 3
+
+ num_y_valid = int(num_keep_y_tiles * 0.1) + 1
+ num_y_train = num_keep_y_tiles - num_y_valid - 1
+
+ for j in range(num_y_train):
+ j_a = j_start + j * j_skip
+ j_b = j_a + tile_width
+
+ for i in range(num_keep_x_tiles):
+ i_a = i_start + i * i_skip
+ i_b = i_a + tile_width
+
+ if day_night == 'DAY' and not is_day(solzen[j_a:j_b, i_a:i_b]):
+ continue
+ elif day_night == 'NIGHT' and is_day(solzen[j_a:j_b, i_a:i_b]):
+ continue
+
+ nda = data[:, j_a:j_b, i_a:i_b]
+ nda_lbl = label[:, j_a*2:j_b*2, i_a*2:i_b*2]
+ nda = keep_tile(param_s, nda)
+ if nda is not None:
+ train_tiles.append(nda)
+ lbl_train_tiles.append(nda_lbl)
+
+ j_start = num_y_train * tile_width + 2*tile_width
+ for j in range(num_y_valid):
+ j_a = j_start + j * j_skip
+ j_b = j_a + tile_width
+
+ for i in range(num_keep_x_tiles):
+ i_a = i_start + i * i_skip
+ i_b = i_a + tile_width
+
+ if day_night == 'DAY' and not is_day(solzen[j_a:j_b, i_a:i_b]):
+ continue
+ elif day_night == 'NIGHT' and is_day(solzen[j_a:j_b, i_a:i_b]):
+ continue
+
+ nda = data[:, j_a:j_b, i_a:i_b]
+ nda_lbl = label[:, j_a * 2:j_b * 2, i_a * 2:i_b * 2]
+ nda = keep_tile(param_s, nda)
+ if nda is not None:
+ valid_tiles.append(nda)
+ lbl_valid_tiles.append(nda_lbl)
+
+
+def scan(directory):
+
+ data_src = CLAVRx_VIIRS(directory)
+ files = data_src.flist
+
+ for idx, file in enumerate(files):
+ h5f = h5py.File(file, 'r')
+ ts = data_src.ftimes[idx][0]
+ try:
+ solzen = get_grid_values_all(h5f, 'solar_zenith_angle')
+ except Exception as e:
+ # print(e)
+ h5f.close()
+ continue
+
+ # if is_day(solzen) and moon_phase(ts):
+ if is_night(solzen) and moon_phase(ts):
+ print(file)
+ h5f.close()
+
+
+def scan_for_location(txt_file, lon_range=[111.0, 130.0], lat_range=[14.0, 32.0]):
+ with open(txt_file) as file:
+ for idx, fpath in enumerate(file):
+ fpath = fpath.strip()
+ h5f = h5py.File(fpath, 'r')
+ try:
+ lon_s = get_grid_values_all(h5f, 'longitude', stride=4)
+ lat_s = get_grid_values_all(h5f, 'latitude', stride=4)
+ c_lon, c_lat = lon_s[406, 400], lat_s[406, 400]
+ if (lon_range[0] < c_lon < lon_range[1]) and (lat_range[0] < c_lat < lat_range[1]):
+ print(fpath)
+
+ except Exception as e:
+ # print(e)
+ h5f.close()
+ continue
+
+
+def test_nlcomp(file):
+ h5f = h5py.File(file, 'r')
+
+ cld_phs = get_grid_values_all(h5f, 'cloud_phase', scale_factor_name=None, range_name=None)
+ keep_0 = np.invert(np.isnan(cld_phs))
+
+ reff = get_grid_values_all(h5f, 'cld_reff_nlcomp')
+ keep_1 = np.invert(np.isnan(reff))
+
+ opd = get_grid_values_all(h5f, 'cld_opd_nlcomp')
+ keep_2 = np.invert(np.isnan(opd))
+
+ cld_dz = get_grid_values_all(h5f, 'cld_geo_thick')
+ keep_3 = np.logical_and(np.invert(np.isnan(cld_dz)), cld_dz > 5.0)
+
+ keep = keep_0 & keep_1 & keep_2 & keep_3
+
+ cld_phs = cld_phs[keep]
+ reff = reff[keep]
+ opd = opd[keep]
+ cld_dz = cld_dz[keep]
+
+ lwc_c, iwc_c = compute_lwc_iwc(cld_phs, reff, opd, cld_dz)
+
+ return lwc_c, iwc_c
+
+
+# def run_mean_std(directory):
+#
+# data_dct = {name: [] for name in mod_res_params}
+# mean_dct = {name: 0 for name in mod_res_params}
+# std_dct = {name: 0 for name in mod_res_params}
+#
+# for p in os.scandir(directory):
+# if not p.is_dir():
+# continue
+# mod_files = glob.glob(directory+p.name+'/'+'VNP02MOD*.uwssec.nc')
+#
+# for idx, mfile in enumerate(mod_files):
+# if idx % 8 == 0:
+# h5f = h5py.File(mfile, 'r')
+# for param in mod_res_params:
+# name = 'observation_data/'+param
+# gvals = get_grid_values_all(h5f, name, range_name=None, stride=10)
+# data_dct[param].append(gvals.flatten())
+# print(mfile)
+# h5f.close()
+#
+# for param in mod_res_params:
+# data = data_dct[param]
+# data = np.concatenate(data)
+#
+# mean_dct[param] = np.nanmean(data)
+# std_dct[param] = np.nanstd(data)
--
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