✨ support more template in dset. support multi line zdef. add valid_t…

…ime in load_field_from_file for grads.

reki/format/grads/field.py

@@ -17,6 +17,7 @@ def load_field_from_file(
         level: Union[int, float, list] = None,
         level_dim: Optional[str] = None,
         latitude_direction: Literal["degree_north", "degree_south"] = "degree_north",
+        valid_time: Union[str, pd.Timestamp] = None,
         forecast_time: Union[str, pd.Timedelta] = None,
         **kwargs
 ) -> Optional[xr.DataArray]:
@@ -37,6 +38,7 @@ def load_field_from_file(
     latitude_direction
         * degree_north
         * degree_south
+    valid_time
     forecast_time
     kwargs
 
@@ -81,6 +83,8 @@ def load_field_from_file(
         forecast_time = pd.to_timedelta(forecast_time)
     if isinstance(file_path, str):
         file_path = Path(file_path)
+    if isinstance(valid_time, str):
+        valid_time = pd.to_datetime(valid_time)
 
     ctl_parser = GradsCtlParser()
     ctl_parser.parse(file_path)
@@ -118,6 +122,7 @@ def load_field_from_file(
             parameter=parameter,
             level=level,
             level_type=grads_level_type,
+            valid_time=valid_time,
             forecast_time=forecast_time,
         ):
             continue

reki/format/grads/grads_ctl.py

@@ -198,6 +198,9 @@ def _parse_levels_dimension(self, dim_name, tokens):
                  700.0000
                  ...
 
+            zdef 22 levels
+            1000 925 850 700 600 500 400 300 250 200 150 100 70 50 30 20 10 7 5 3 2 1
+
         """
         levels = list()
         count = int(tokens[1])
@@ -207,8 +210,10 @@ def _parse_levels_dimension(self, dim_name, tokens):
         while i < count:
             self.cur_no += 1
             cur_line = self.ctl_file_lines[self.cur_no]
-            levels.append(float(cur_line))
-            i += 1
+            current_line_tokens = cur_line.split()
+            current_levels = [float(l) for l in current_line_tokens]
+            levels.extend(current_levels)
+            i += len(current_line_tokens)
 
         setattr(self.grads_ctl, dim_name, {
             'type': 'levels',
@@ -374,7 +379,16 @@ def get_data_file_path(cls, grads_ctl, record) -> Path:
 
         tokens = str(grads_ctl.dset).split("%")
         token_mapper = {
-            "n2": lambda x: f"{x['forecast_time'].seconds // 60 % 60:02d}",
+            "y2": lambda x: f"{x['valid_time'].year % 100:02d}",
+            "y4": lambda x: f"{x['valid_time'].year:04d}",
+            "m1": lambda x: f"{x['valid_time'].month}",
+            "m2": lambda x: f"{x['valid_time'].month:02d}",
+            "d1": lambda x: f"{x['valid_time'].day}",
+            "d2": lambda x: f"{x['valid_time'].day:02d}",
+            "h1": lambda x: f"{x['valid_time'].hour}",
+            "h2": lambda x: f"{x['valid_time'].hour:02d}",
+            "h3": lambda x: f"{x['valid_time'].hour:03d}",
+            "n2": lambda x: f"{x['forecast_time'].seconds // 60 % 60:02d}",  # TODO: check n2
             "f2": lambda x: f"{int(x['forecast_time'] / pd.Timedelta(hours=1)):02d}",
             "f3": lambda x: f"{int(x['forecast_time'] / pd.Timedelta(hours=1)):03d}",
             "fn2": lambda x: f"{int(x['forecast_time'] / pd.Timedelta(minutes=1)):02d}",