snapshot...

modules/util/abi_surfrad_opd.py

+import numpy as np
+import h5py
+from util.util import get_grid_values, is_day
+import glob
+
+# target_param = 'cloud_probability'
+# target_param = 'cld_opd_dcomp'
+target_param = 'cld_opd_dcomp_1'
+# target_param = 'cld_opd_dcomp_2'
+# target_param = 'cld_opd_dcomp_3'
+
+group_name_i = 'super/'
+group_name_m = 'orig/'
+
+solzen_name = group_name_m + 'solar_zenith'
+snow_class_name = group_name_m + 'snow_class'
+
+params_i = [group_name_i+'temp_ch38', group_name_i+'refl_ch01', group_name_i+'cloud_probability',
+            group_name_i+target_param]
+
+params_m = [group_name_m+'temp_ch38', group_name_m+'refl_ch01',
+            group_name_m+'refl_submin_ch01', group_name_m+'refl_submax_ch01', group_name_m+'refl_substddev_ch01',
+            group_name_m+'cloud_probability',
+            group_name_m+target_param]
+
+param_idx_m = params_m.index(group_name_m + target_param)
+param_idx_i = params_i.index(group_name_i + target_param)
+cp_idx_i = params_i.index(group_name_i + 'cloud_probability')
+
+DO_WRITE_OUTFILE = True
+
+
+def snow_covered(tile):
+    return np.any(tile > 1)
+
+
+def is_missing(p_idx, tile):
+    keep = np.invert(np.isnan(tile[p_idx, ]))
+    if np.sum(keep) / keep.size < 0.98:
+        return True
+
+
+def keep_tile(cp_idx, opd_idx, tile):
+    opd = tile[opd_idx, ].copy()
+    cp = tile[cp_idx, ].copy()
+
+    cp = process_cld_prob(cp)
+    if cp is None:
+        return None
+
+    opd = process_cld_opd(opd)
+    if opd is not None:
+        tile[opd_idx, ] = opd
+        return tile
+    else:
+        return None
+
+
+def process_cld_prob(cld_prob):
+    keep = np.invert(np.isnan(cld_prob))
+    num_keep = np.sum(keep)
+    keep = np.where(keep, np.logical_or(cld_prob < 0.35, cld_prob > 0.65), False)
+    frac_keep = np.sum(keep)/num_keep
+    if not (frac_keep > 0.70):
+        return None
+    grd_k = np.where(np.invert(keep), 0, cld_prob)  # Convert NaN to 0
+    return grd_k
+
+
+def process_cloud_frac(grd_k):
+    pass
+
+
+def get_cloud_frac_5cat(grd_k):
+    grd_k = np.where(np.isnan(grd_k), 0, grd_k)
+    grd_k = np.where(grd_k < 0.5, 0, 1)
+
+    s = grd_k[:, 0::4, 0::4] + grd_k[:, 1::4, 0::4] + grd_k[:, 2::4, 0::4] + grd_k[:, 3::4, 0::4] + \
+        grd_k[:, 0::4, 1::4] + grd_k[:, 1::4, 1::4] + grd_k[:, 2::4, 1::4] + grd_k[:, 3::4, 1::4] + \
+        grd_k[:, 0::4, 2::4] + grd_k[:, 1::4, 2::4] + grd_k[:, 2::4, 2::4] + grd_k[:, 3::4, 2::4] + \
+        grd_k[:, 0::4, 3::4] + grd_k[:, 1::4, 3::4] + grd_k[:, 2::4, 3::4] + grd_k[:, 3::4, 3::4]
+
+    cat_0 = np.logical_and(s >= 0, s < 2)
+    cat_1 = np.logical_and(s >= 2, s < 6)
+    cat_2 = np.logical_and(s >= 6, s < 11)
+    cat_3 = np.logical_and(s >= 11, s < 15)
+    cat_4 = np.logical_and(s >= 15, s <= 16)
+
+    s[cat_0] = 0
+    s[cat_1] = 1
+    s[cat_2] = 2
+    s[cat_3] = 3
+    s[cat_4] = 4
+
+    return s
+
+
+def process_cld_opd(grd_k):
+    keep = np.invert(np.isnan(grd_k))
+    num_keep = np.sum(keep)
+    keep_cld = np.where(keep, np.logical_and(2.0 < grd_k, grd_k < 158.0), False)
+    # keep_cld = np.where(keep, 2.0 < grd_k, False)
+    frac_cld = np.sum(keep_cld)/num_keep
+    if not (0.20 < frac_cld < 0.90):
+    # if not (0.20 < frac_cld):
+        return None
+    grd_k = np.where(np.invert(keep), 0, grd_k)  # Convert NaN to 0
+    return grd_k
+
+
+def run_all(directory, out_directory, day_night='ANY', pattern='clavrx_*.nc', start=10, is_snow_covered=None):
+    cnt = start
+    total_num_train_samples = 0
+    total_num_valid_samples = 0
+
+    # path = directory + '**' + '/' + pattern
+    path = directory + '*_v3/2020/' + '**' + '/' + pattern
+
+    all_files = glob.glob(path, recursive=True)
+    test_files = glob.glob(directory + '*_v3/2020/*/01/*/*.nc', recursive=True)
+    valid_files = glob.glob(directory + '*_v3/2020/*/0[2-6]/*/*.nc', recursive=True)
+    train_files = [f for f in all_files if f not in valid_files + test_files]
+
+    data_tiles_i = []
+    data_tiles_m = []
+    f_cnt = 0
+
+    num_files = len(valid_files)
+    print('Start, number of valid files: ', num_files)
+
+    total_num_not_missing = 0
+    num_skip = 3
+
+    param_train_hist = np.zeros([16], dtype=np.int64)
+    param_valid_hist = np.zeros([16], dtype=np.int64)
+
+    # cloud_prob to cloud fraction
+    # ----------------------------
+    tile_width = 32
+    kernel_size = 5
+    factor = 4
+
+    # tile_width = 64
+    # kernel_size = 7
+    # factor = 4
+
+    # hist_range = [0.0, 1.0]
+    hist_range = [0.0, 160.0]
+
+    for idx, data_f in enumerate(valid_files):
+        if idx % num_skip == 0:  # if we want to skip some files
+            try:
+                h5f = h5py.File(data_f, 'r')
+            except:
+                print('cant open file: ', data_f)
+                continue
+
+            try:
+                num_not_missing, num_snow_covered = \
+                    run(h5f, params_m, data_tiles_m, params_i, data_tiles_i,
+                        tile_width=tile_width, kernel_size=kernel_size, factor=factor,
+                        day_night=day_night, is_snow_covered=is_snow_covered)
+            except Exception as e:
+                print(e)
+                h5f.close()
+                continue
+            print(data_f)
+            f_cnt += 1
+            h5f.close()
+
+            total_num_not_missing += num_not_missing
+
+            if len(data_tiles_m) == 0:
+                continue
+
+            if (f_cnt % 100) == 0:
+                num_valid_samples = 0
+                if len(data_tiles_m) > 0:
+                    valid_i = np.stack(data_tiles_i)
+                    valid_m = np.stack(data_tiles_m)
+                    if DO_WRITE_OUTFILE:
+                        np.save(out_directory + 'valid_mres_' + f'{cnt:04d}', valid_m)
+                        np.save(out_directory + 'valid_ires_' + f'{cnt:04d}', valid_i)
+                    num_valid_samples = valid_m.shape[0]
+
+                    param_valid_hist += np.histogram(valid_m[:, param_idx_m, :, :], bins=16, range=hist_range)[0]
+
+                data_tiles_i = []
+                data_tiles_m = []
+
+                print('  num_valid_samples, progress % : ', num_valid_samples, int((f_cnt/(num_files/num_skip))*100))
+                total_num_valid_samples += num_valid_samples
+                print('total_num_valid_samples, total_num_not_missing: ', total_num_valid_samples, total_num_not_missing)
+                print('--------------------------------------------------')
+
+                cnt += 1
+
+    # Write out leftover, if any. Maybe make this better someday
+    num_valid_samples = 0
+    if len(data_tiles_m) > 0:
+        valid_i = np.stack(data_tiles_i)
+        valid_m = np.stack(data_tiles_m)
+        if DO_WRITE_OUTFILE:
+            np.save(out_directory + 'valid_mres_' + f'{cnt:04d}', valid_m)
+            np.save(out_directory + 'valid_ires_' + f'{cnt:04d}', valid_i)
+        num_valid_samples = valid_m.shape[0]
+        param_valid_hist += np.histogram(valid_m[:, param_idx_m, :, :], bins=16, range=hist_range)[0]
+    total_num_valid_samples += num_valid_samples
+    print('total_num_valid_samples, total_num_not_missing: ', total_num_valid_samples, total_num_not_missing)
+    print(param_valid_hist)
+    print('--------------------------------------------------')
+    print('----------------------------------------------------------------')
+
+    data_tiles_i = []
+    data_tiles_m = []
+    f_cnt = 0
+    cnt = start
+    total_num_not_missing = 0
+    num_files = len(train_files)
+    print('Start, number of train files: ', num_files)
+
+    for idx, data_f in enumerate(train_files):
+        if idx % num_skip == 0:  # if we want to skip some files
+            try:
+                h5f = h5py.File(data_f, 'r')
+            except:
+                print('cant open file: ', data_f)
+                continue
+
+            try:
+                num_not_missing, num_snow_covered = \
+                    run(h5f, params_m, data_tiles_m, params_i, data_tiles_i,
+                        tile_width=tile_width, kernel_size=kernel_size, factor=factor,
+                        day_night=day_night, is_snow_covered=is_snow_covered)
+            except Exception as e:
+                print(e)
+                h5f.close()
+                continue
+            print(data_f)
+            f_cnt += 1
+            h5f.close()
+
+            total_num_not_missing += num_not_missing
+
+            if len(data_tiles_m) == 0:
+                continue
+
+            if (f_cnt % 100) == 0:
+                num_train_samples = 0
+                if len(data_tiles_m) > 0:
+                    train_i = np.stack(data_tiles_i)
+                    train_m = np.stack(data_tiles_m)
+                    if DO_WRITE_OUTFILE:
+                        np.save(out_directory + 'train_ires_' + f'{cnt:04d}', train_i)
+                        np.save(out_directory + 'train_mres_' + f'{cnt:04d}', train_m)
+                    num_train_samples = train_m.shape[0]
+
+                    param_train_hist += np.histogram(train_m[:, param_idx_m, :, :], bins=16, range=hist_range)[0]
+
+                data_tiles_i = []
+                data_tiles_m = []
+
+                print('  num_train_samples, progress % : ', num_train_samples, int((f_cnt/(num_files/num_skip))*100))
+                total_num_train_samples += num_train_samples
+                print('total_num_train_samples, total_num_not_missing: ', total_num_train_samples, total_num_not_missing)
+                print('--------------------------------------------------')
+
+                cnt += 1
+
+    # Write out leftover, if any. Maybe make this better someday
+    num_train_samples = 0
+    if len(data_tiles_m) > 0:
+        train_i = np.stack(data_tiles_i)
+        train_m = np.stack(data_tiles_m)
+        if DO_WRITE_OUTFILE:
+            np.save(out_directory + 'train_ires_' + f'{cnt:04d}', train_i)
+            np.save(out_directory + 'train_mres_' + f'{cnt:04d}', train_m)
+        num_train_samples = train_m.shape[0]
+        param_train_hist += np.histogram(train_m[:, param_idx_m, :, :], bins=16, range=hist_range)[0]
+    total_num_train_samples += num_train_samples
+    print('total_num_train_samples,  total_num_not_missing: ', total_num_train_samples, total_num_not_missing)
+    print(param_train_hist)
+    print('--------------------------------------------------')
+
+    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, params_m, data_tiles_m, params_i, data_tiles_i, tile_width=64, kernel_size=3, factor=2,
+        day_night='ANY', is_snow_covered=None):
+
+    border = int((kernel_size - 1)/2) + 1  # Need to add for interpolation with no edge effects
+
+    param_name = params_m[0]
+
+    num_lines = h5f[param_name].shape[0]
+    num_pixels = h5f[param_name].shape[1]  # Must be even
+
+    if day_night != 'ANY':
+        solzen = get_grid_values(h5f, solzen_name, 0, 0, None, num_lines, num_pixels)
+
+    if is_snow_covered is not None:
+        snow = get_grid_values(h5f, snow_class_name, 0, 0, None, num_lines, num_pixels)
+
+    grd_s = []
+    for param in params_m:
+        try:
+            grd = get_grid_values(h5f, param, 0, 0, None, num_lines, num_pixels)
+            grd_s.append(grd)
+        except Exception as e:
+            print(e)
+            return
+    data_m = np.stack(grd_s)
+
+    grd_s = []
+    for param in params_i:
+        try:
+            grd = get_grid_values(h5f, param, 0, 0, None, num_lines*factor, num_pixels*factor)
+            grd_s.append(grd)
+        except Exception as e:
+            print(e)
+            return
+    data_i = np.stack(grd_s)
+
+    tile_width += 2 * border
+
+    i_skip = tile_width
+    j_skip = tile_width
+    i_start = border - 1  # zero-based
+    j_start = border - 1  # zero-based
+
+    num_y_tiles = int(num_lines / tile_width)
+    num_x_tiles = int(num_pixels / tile_width)
+
+    num_not_missing = 0
+    num_snow_covered = 0
+
+    for j in range(num_y_tiles):
+        j_a = j_start + j * j_skip
+        j_b = j_a + tile_width
+
+        for i in range(num_x_tiles):
+            i_a = i_start + i * i_skip
+            i_b = i_a + tile_width
+
+            if is_snow_covered is not None:
+                if is_snow_covered:
+                    if not snow_covered(snow[j_a:j_b, i_a:i_b]):
+                        continue
+                    num_snow_covered += 1
+                else:
+                    if snow_covered(snow[j_a:j_b, i_a:i_b]):
+                        num_snow_covered += 1
+                        continue
+
+            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_m = data_m[:, j_a:j_b, i_a:i_b]
+            nda_i = data_i[:, j_a*factor:j_b*factor, i_a*factor:i_b*factor]
+            if is_missing(param_idx_i, nda_i):
+                continue
+            num_not_missing += 1
+
+            nda_i = keep_tile(cp_idx_i, param_idx_i, nda_i)
+            if nda_i is not None:
+                data_tiles_m.append(nda_m)
+                data_tiles_i.append(nda_i)
+
+    return num_not_missing, num_snow_covered