diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py index 29761920c6ec4c48fd71fede21b0c1c722bd76a9..730207e37fc3aff40a41ffd5fea2ad010e65c006 100644 --- a/modules/deeplearning/cloud_opd_srcnn_abi.py +++ b/modules/deeplearning/cloud_opd_srcnn_abi.py @@ -207,7 +207,7 @@ class SRCNN: self.test_label_files = None # self.n_chans = len(data_params_half) + len(data_params_full) + 1 - self.n_chans = 5 + self.n_chans = 3 self.X_img = tf.keras.Input(shape=(None, None, self.n_chans)) @@ -266,18 +266,18 @@ class SRCNN: tmp = normalize(tmp, param, mean_std_dct) data_norm.append(tmp) - for param in sub_fields: - idx = params.index(param) - tmp = input_data[:, idx, :, :] - tmp = np.where(np.isnan(tmp), 0.0, tmp) - tmp = tmp[:, self.slc_y_m, self.slc_x_m] - tmp = self.upsample(tmp) - # if param != 'refl_substddev_ch01': - if False: - tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct) - else: - tmp = np.where(np.isnan(tmp), 0.0, tmp) - data_norm.append(tmp) + # for param in sub_fields: + # idx = params.index(param) + # tmp = input_data[:, idx, :, :] + # tmp = np.where(np.isnan(tmp), 0.0, tmp) + # tmp = tmp[:, self.slc_y_m, self.slc_x_m] + # tmp = self.upsample(tmp) + # # if param != 'refl_substddev_ch01': + # if False: + # tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct) + # else: + # tmp = np.where(np.isnan(tmp), 0.0, tmp) + # data_norm.append(tmp) # for param in sub_fields: # idx = params.index(param)