add new parameter quantities_names to the TimModel class

hydrolib/core/dflowfm/tim/models.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Callable, Dict, List
+from typing import Any, Callable, Dict, List, Optional
 
 from pandas import DataFrame
 from pydantic.v1 import Field
@@ -31,6 +31,8 @@ class TimModel(ParsableFileModel):
         Header comments from the .tim file.
     timeseries : List[TimRecord]
         A list of TimRecord objects, each containing a time value and associated data.
+    quantities_names : Optional[List[str]]
+        List of names for the quantities in the timeseries.
 
     Methods:
     --------
@@ -44,6 +46,10 @@ class TimModel(ParsableFileModel):
         Returns the parser callable for .tim files.
     _validate_timeseries_values(cls, v: List[TimRecord]) -> List[TimRecord]
         Validates the timeseries data.
+    as_dataframe(columns: List[Any] = None) -> DataFrame
+        Returns the timeseries as a pandas DataFrame.
+    _validate_quantities_names(cls, v, values) -> List[str]
+        Validates the quantities_names equals to the values or each record.
 
     Args:
     -----
@@ -106,6 +112,8 @@ class TimModel(ParsableFileModel):
     timeseries: List[TimRecord] = Field(default_factory=list)
     """List[TimRecord]: A list containing the timeseries."""
 
+    quantities_names: Optional[List[str]] = Field(default=None)
+
     @classmethod
     def _ext(cls) -> str:
         return ".tim"
@@ -171,9 +179,28 @@ def _raise_error_if_duplicate_time(timeseries: List[TimRecord]) -> None:
                 )
             seen_times.add(timrecord.time)
 
-    def as_dataframe(self) -> DataFrame:
+    @validator("quantities_names")
+    def _validate_quantities_names(cls, v, values):
+        """Validate if the amount of quantities_names match the amount of columns in the timeseries.
+
+        The validator compared the amount of quantities_names with the amount of columns in the first record of
+        the timeseries.
+        """
+        if v is not None:
+            first_records_data = values["timeseries"][0].data
+            if len(v) != len(first_records_data):
+                raise ValueError(
+                    f"The number of quantities_names ({len(v)}) must match the number of columns in the Tim file ({len(first_records_data)})."
+                )
+        return v
+
+    def as_dataframe(self, columns: List[Any] = None) -> DataFrame:
         """Return the timeseries as a pandas DataFrame.
 
+        Args:
+            columns (List[Any, str], optional, Defaults to None):
+                The column names for the DataFrame.
+
         Returns:
             DataFrame: The timeseries as a pandas DataFrame.
 
@@ -187,7 +214,15 @@ def as_dataframe(self) -> DataFrame:
         10.0  1.232  2.343  3.454
         20.0  4.565  5.676  6.787
         30.0  1.500  2.600  3.700
+
+        To add column names to the DataFrame:
+        >>> df = tim_model.as_dataframe(columns=["Column1", "Column2", "Column3"])
+        >>> print(df)
+              Column1  Column2  Column3
+        10.0    1.232    2.343    3.454
+        20.0    4.565    5.676    6.787
+        30.0    1.500    2.600    3.700
         """
         time_series = [record.data for record in self.timeseries]
         index = [record.time for record in self.timeseries]
-        return DataFrame(time_series, index=index)
+        return DataFrame(time_series, index=index, columns=columns)

tests/dflowfm/test_tim.py

@@ -127,6 +127,20 @@ def test_as_dataframe(self):
         ]
         assert df.loc[:, 0].to_list() == vals
 
+        df = model.as_dataframe(columns=["data"])
+        assert df.columns.to_list() == ["data"]
+
+    def test_with_quantities_names(self):
+        model = TimModel(
+            timeseries=self.single_data_for_timeseries_floats, quantities_names=["a"]
+        )
+        assert model.quantities_names == ["a"]
+        with pytest.raises(ValueError):
+            TimModel(
+                timeseries=self.single_data_for_timeseries_floats,
+                quantities_names=["a", "b"],
+            )
+
     @pytest.mark.parametrize(
         "input_data, reference_path",
         [