reserve new parameter

Author: elDan101

datafold/appfold/edmd.py

@@ -920,6 +920,7 @@ def fit(
         self,
         X: TimePredictType,
         U: Optional[TimePredictType] = None,
+        P: Optional[pd.DataFrame] = None,
         y: Optional[TSCDataFrame] = None,
         **fit_params,
     ) -> "EDMD":
@@ -941,6 +942,9 @@ def fit(
             Time series with control states acting on the system. The states are passed to the
             DMD model, at which point the time indices must be identical to the states in `X`.
 
+        P
+            ignored -- reservered for parameters
+
         y
             A different set of target values than the original states to map to with
             Koopman modes. **This is an experimental feature.**
@@ -1081,6 +1085,7 @@ def predict(
         X: InitialConditionType,
         *,
         U: Optional[InitialConditionType] = None,
+        P: Optional[pd.DataFrame] = None,
         time_values: Optional[np.ndarray] = None,
         qois: Optional[Union[pd.Index, list[str]]] = None,
         **predict_params,
@@ -1111,6 +1116,9 @@ def predict(
             the control states over the prediction horizon in `U`. Each time series in `U`
             must have the same time values. The time horizon is taken from `U` (i.e.
             ``time_values`` has to be either identical or ``None``).
+        
+        P 
+            ignored -- reserved for parameters
 
         time_values
             The time values to evaluate the model at for each initial condition. The values
@@ -1204,7 +1212,9 @@ def predict(
     def fit_predict(
         self,
         X: TSCDataFrame,
+        *,
         U: Optional[TSCDataFrame] = None,
+        P: Optional[pd.DataFrame] = None,
         y=None,
         qois: Optional[Union[pd.Index, list[str]]] = None,
         **fit_params,
@@ -1220,9 +1230,11 @@ def fit_predict(
         U
             Control time series passed to the DMD model. At this point the index of `U` must
             be identical to `X_dict`.
+        P
+            ignored -- reserved for parameters
 
-        y: None
-            ignored
+        y
+            TODO: update docs
 
         qois
             A list of feature names of interest to be included in the returned
@@ -1271,7 +1283,15 @@ def __getitem__(self, ind):
         else:
             return super().__getitem__(ind)
 
-    def partial_fit(self, X: TimePredictType, U=None, y=None, **fit_params) -> "EDMD":
+    def partial_fit(
+        self,
+        X: TimePredictType,
+        *,
+        U: Optional[Union[np.ndarray, TSCDataFrame]] = None,
+        P: Optional[pd.DataFrame],
+        y=None,
+        **fit_params,
+    ) -> "EDMD":
         """Incremental fit of the model.
 
         The partial fit call is forwarded to all transformers in the dictionary and the set
@@ -1286,6 +1306,9 @@ def partial_fit(self, X: TimePredictType, U=None, y=None, **fit_params) -> "EDMD
             Currently, there is no implementation that supports both an online/streaming
             setting with control.
 
+        P 
+            ignored -- reserved for parameters
+
         y
             ignored
 

datafold/dynfold/dmd.py

@@ -156,7 +156,7 @@ def _read_predict_params(self, predict_params):
 
     @abc.abstractmethod
     def fit(
-        self, X: TimePredictType, *, U: Optional[TSCDataFrame], y=None, **fit_params
+        self, X: TimePredictType, *, U: Optional[TSCDataFrame], P=None, y=None, **fit_params
     ) -> "DMDBase":
         """Abstract method to train DMD model.
 
@@ -169,6 +169,10 @@ def fit(
             Control input (set to ``None`` by default if the subclass does not support
             control input).
 
+        P  
+            ignored 
+                reserved for parameter input
+        
         y
             ignored
               TODO for future devevelopment: This parameter is reserved to specify an