diff --git a/modules/util/viirs_l1b_l2.py b/modules/util/viirs_l1b_l2.py
new file mode 100644
index 0000000000000000000000000000000000000000..96640821cae61cba4fb77c0f3c3e9d898837c4cb
--- /dev/null
+++ b/modules/util/viirs_l1b_l2.py
@@ -0,0 +1,156 @@
+import numpy as np
+import h5py
+from util.util import get_grid_values, get_grid_values_all
+import glob
+import os
+from pathlib import Path
+
+emis_params = ['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']
+l2_params = ['cld_opd_acha', 'cloud_fraction']
+
+
+def run_all(directory):
+
+ cnt = 0
+ for p in os.scandir(directory):
+ if not p.is_dir():
+ continue
+ cnt += 1
+ l1b_files = glob.glob(directory+p.name+'/'+'clavrx_snpp_viirs*.uwssec.nc')
+
+ l1b_tiles = []
+ l2_tiles = []
+
+ for idx, l1b_f in enumerate(l1b_files):
+ if idx % 8 == 0:
+ w_o_ext, ext = os.path.splitext(l1b_f)
+ l2_f = w_o_ext+'.highres'+ext
+ if not os.path.exists(l2_f):
+ continue
+ print(l1b_f)
+
+ try:
+ l1b_h5f = h5py.File(l1b_f, 'r')
+ except:
+ print('cant open file: ', l1b_f)
+ continue
+
+ try:
+ l2_h5f = h5py.File(l2_f, 'r')
+ except:
+ print('cant open file: ', l2_f)
+ l1b_h5f.close()
+ continue
+
+ try:
+ run(l1b_h5f, l2_h5f, l1b_tiles, l2_tiles)
+ except:
+ l1b_h5f.close()
+ l2_h5f.close()
+ continue
+
+ l1b_h5f.close()
+ l2_h5f.close()
+
+ if len(l1b_tiles) or len(l2_tiles) == 0:
+ continue
+
+ l1b_nda = np.stack(l1b_tiles)
+ l2_nda = np.stack(l2_tiles)
+
+ np.save('/data/Personal/rink/viirs_clavrx/l1b_'+str(cnt), l1b_nda)
+ np.save('/data/Personal/rink/viirs_clavrx/l2_'+str(cnt), l2_nda)
+
+
+def run(l1b_h5f, l2_h5f, l1b_tiles, l2_tiles, factor=2):
+ if l2_h5f is None:
+ l2_h5f = l1b_h5f
+
+ l1b_param_name = emis_params[0]
+ l2_param_name = l2_params[0]
+
+ mod_tile_width = 64
+ img_tile_width = mod_tile_width * factor
+
+ mod_num_lines = l1b_h5f[l1b_param_name].shape[0]
+ mod_num_pixels = l1b_h5f[l1b_param_name].shape[1]
+
+ img_num_lines = l2_h5f[l2_param_name].shape[0]
+ img_num_pixels = l2_h5f[l2_param_name].shape[1]
+
+ mod_num_y_tiles = int(mod_num_lines / mod_tile_width)
+ mod_num_x_tiles = int(mod_num_pixels / mod_tile_width)
+
+ l1b_grd_s = []
+ l2_grd_s = []
+
+ for param in emis_params:
+ l1b_grd_s.append(get_grid_values(l1b_h5f, param, 0, 0, None, mod_num_lines, mod_num_pixels, range_name=None))
+
+ for param in l2_params:
+ l2_grd_s.append(get_grid_values(l2_h5f, param, 0, 0, None, img_num_lines, img_num_pixels, range_name=None))
+
+ mod_data = np.stack(l1b_grd_s)
+ img_data = np.stack(l2_grd_s)
+
+ num_cntr_tiles = 10
+ i_c = int(mod_num_pixels / num_cntr_tiles) # center
+ j_skip = int(mod_num_y_tiles / num_cntr_tiles) * mod_tile_width
+ for k in range(num_cntr_tiles):
+ j_c = k * j_skip
+ j_m = j_c
+ j_i = j_m * factor
+
+ i_m = i_c
+ i_i = i_m * factor
+
+ nda = mod_data[:, j_m:j_m + mod_tile_width, i_m:i_m + mod_tile_width]
+ l1b_tiles.append(nda)
+
+ nda = img_data[:, j_i:j_i + img_tile_width, i_i:i_i + img_tile_width]
+ l2_tiles.append(nda)
+
+ # for j in range(mod_num_y_tiles):
+ # j_m = j * mod_tile_width
+ # j_i = j_m * 2
+ # for i in range(mod_num_x_tiles):
+ # i_m = i * mod_tile_width
+ # i_i = i_m * 2
+ #
+ # nda = mod_data[j_m:j_m+mod_tile_width, i_m:i_m+mod_tile_width]
+ # mod_tiles.append(nda)
+ # nda = img_data[j_i:j_i+img_tile_width, i_i:i_i+img_tile_width]
+ # img_tiles.append(nda)
+
+
+# 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)
+#
+# print(mean_dct)
+# print(std_dct)
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