user specified flight level...

modules/deeplearning/icing_cnn.py

@@ -209,6 +209,8 @@ class IcingIntensityNN:
         self.inputs.append(self.X_img)
         self.inputs.append(tf.keras.Input(5))
 
+        self.flight_level = 0
+
         self.DISK_CACHE = False
 
         if datapath is not None:
@@ -367,9 +369,13 @@ class IcingIntensityNN:
         data = np.stack(data)
         data = data.astype(np.float32)
         data = np.transpose(data, axes=(1, 2, 3, 0))
+
         # TODO: altitude data will be specified by user at run-time
+        nda = np.zeros([nd_idxs.size])
+        nda = self.flight_level
+        nda = tf.one_hot(nda, 5).numpy()
 
-        return data
+        return data, nda
 
     @tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
     def data_function(self, indexes):
@@ -384,7 +390,7 @@ class IcingIntensityNN:
     @tf.function(input_signature=[tf.TensorSpec(None, tf.int32)])
     def data_function_evaluate(self, indexes):
         # TODO: modify for user specified altitude
-        out = tf.numpy_function(self.get_in_mem_data_batch_eval, [indexes], tf.float32)
+        out = tf.numpy_function(self.get_in_mem_data_batch_eval, [indexes], [tf.float32, tf.float32])
         return out
 
     def get_train_dataset(self, indexes):
@@ -1011,7 +1017,7 @@ def run_restore_static(filename_l1b, filename_l2, ckpt_dir_s_path):
     return labels, prob_avg, cm_avg
 
 
-def run_evaluate_static(h5f, ckpt_dir_s_path, prob_thresh=0.5, satellite='GOES16', domain='FD'):
+def run_evaluate_static(h5f, ckpt_dir_s_path, flight_level=4, prob_thresh=0.5, satellite='GOES16', domain='FD'):
     data_dct, ll, cc = make_for_full_domain_predict(h5f, name_list=train_params, domain=domain)
     num_elems = len(cc)
     num_lines = len(ll)
@@ -1026,6 +1032,7 @@ def run_evaluate_static(h5f, ckpt_dir_s_path, prob_thresh=0.5, satellite='GOES16
         if not os.path.isdir(ckpt_dir):
             continue
         nn = IcingIntensityNN()
+        nn.flight_level = flight_level
         nn.setup_eval_pipeline(data_dct, num_lines * num_elems)
         nn.build_model()
         nn.build_training()