remove dead code

modules/deeplearning/icing_fcn.py

@@ -1063,45 +1063,6 @@ def run_evaluate_static_avg(data_dct, ll, cc, ckpt_dir_s_path, day_night='DAY',
     return ice_lons, ice_lats, preds_2d
 
 
-# def run_evaluate_static_fcn(data_dct, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both', prob_thresh=0.5,
-#                             flight_levels=[0, 1, 2, 3, 4], use_flight_altitude=False):
-#
-#     ckpt_dir_s = os.listdir(ckpt_dir_s_path)
-#     ckpt_dir = ckpt_dir_s_path + ckpt_dir_s[0]
-#
-#     if not use_flight_altitude:
-#         flight_levels = [0]
-#
-#     probs_dct = {flvl: None for flvl in flight_levels}
-#     preds_dct = {flvl: None for flvl in flight_levels}
-#
-#     nn = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_or_l2, use_flight_altitude=use_flight_altitude)
-#     nn.num_data_samples = 1
-#     nn.build_model()
-#     nn.build_training()
-#     nn.build_evaluation()
-#
-#     ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=nn.model)
-#     ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
-#     ckpt.restore(ckpt_manager.latest_checkpoint)
-#
-#     for flvl in flight_levels:
-#         nn.flight_level = flvl
-#         nn.setup_eval_pipeline(data_dct)
-#         nn.do_evaluate(ckpt_dir)
-#
-#         probs = nn.test_probs
-#         if NumClasses == 2:
-#             preds = np.where(probs > prob_thresh, 1, 0)
-#         else:
-#             preds = np.argmax(probs, axis=1)
-#
-#         probs_dct[flvl] = probs
-#         preds_dct[flvl] = preds
-#
-#     return preds_dct, probs_dct
-
-
 def run_evaluate_static_fcn(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both', prob_thresh=0.5,
                             flight_levels=[0, 1, 2, 3, 4], use_flight_altitude=False):