refine

Author: zhiminzhang0830

examples/yinglong/README.md

@@ -55,7 +55,7 @@ tar -xvf inference.tar
 
 ### 2. Run the code
 
-The following code runs the YingLong model, and the model output will be saved in 'outputs_yinglong_eastern(western)/result.npy'.
+The following code runs the YingLong model, and the model output will be saved in `outputs_yinglong_eastern(western)/result.npy`.
 
 ``` shell
 # yinglong_eastern

examples/yinglong/predict.py

@@ -65,16 +65,16 @@ def inference(cfg: DictConfig):
     # The variable name is 'z50', 'z500', 'z850', 'z1000', 't50', 't500', 't850', 'z1000',
     # 's50', 's500', 's850', 's1000', 'u50', 'u500', 'u850', 'u1000', 'v50', 'v500',
     # 'v850', 'v1000', 'mslp', 'u10', 'v10', 't2m'.
-    input_file = read_h5py(cfg.INFER.input_file)
-    nwp_file = read_h5py(cfg.INFER.nwp_file)
-    geo_file = read_h5py(cfg.INFER.geo_file)
+    input_data = read_h5py(cfg.INFER.input_file)
+    nwp_data = read_h5py(cfg.INFER.nwp_file)
+    geo_data = read_h5py(cfg.INFER.geo_file)
 
     # input_data.shape: (1, 24, 440, 408)
-    input_data = input_file[0:1]
+    input_data_0 = input_data[0:1]
     # nwp_data.shape: # (num_timestamps, 24, 440, 408)
-    nwp_data = nwp_file[0:num_timestamps]
+    nwp_data = nwp_data[0:num_timestamps]
     # ground_truth.shape: (num_timestamps, 24, 440, 408)
-    ground_truth = input_file[1 : num_timestamps + 1]
+    ground_truth = input_data[1 : num_timestamps + 1]
 
     # create time stamps
     cur_time = pd.to_datetime(cfg.INFER.init_time, format="%Y/%m/%d/%H")
@@ -84,7 +84,7 @@ def inference(cfg: DictConfig):
         time_stamps.append([cur_time])
 
     # run predictor
-    pred_data = predictor.predict(input_data, time_stamps, nwp_data, geo_file)
+    pred_data = predictor.predict(input_data_0, time_stamps, nwp_data, geo_data)
     pred_data = pred_data.squeeze(axis=1)  # (num_timestamps, 24, 440, 408)
 
     # save predict data

examples/yinglong/predictor.py

@@ -105,7 +105,7 @@ def predict(
             input_data (np.ndarray): Input data of shape (N, T, H, W).
             time_stamps (List[List[pd.Timestamp]]): Timestamps data.
             nwp_data (np.ndarray): NWP data.
-            geo_data (np.ndarray): Geo data.
+            geo_data (np.ndarray): Geographic data.
             batch_size (int, optional): Batch size, now only support 1. Defaults to 1.
 
         Returns: