Anselmoo · Anselmoo · Dec 13, 2022 · Dec 13, 2022 · Dec 13, 2022
@@ -1,7 +1,7 @@
 [tool.poetry]
 name = "SpectraFit"
-version = "0.13.1"
-description = "Fast fitting of 2D-Spectra with established routines"
+version = "0.13.2"
+description = "Fast fitting of 2D- and 3D-Spectra with established routines"
 readme = "README.md"
 authors = ["Anselm Hahn <[email protected]>"]
 maintainers = ["Anselm Hahn <[email protected]>"]

@@ -1,2 +1,2 @@
 """SpectraFit, fast command line tool for fitting data."""
-__version__ = "0.13.1"
+__version__ = "0.13.2"
@@ -703,6 +703,7 @@ class SpectraFitNotebook(
     df_fit: pd.DataFrame
     df_pre: pd.DataFrame = pd.DataFrame()
     df_metric: pd.DataFrame = pd.DataFrame()
+    df_peaks: pd.DataFrame = pd.DataFrame()
     initial_model: List[Dict[str, Dict[str, Dict[str, Any]]]]
 
     def __init__(
@@ -993,6 +994,7 @@ def solver_model(
         show_plot: bool = True,
         show_metric: bool = True,
         show_df: bool = False,
+        show_peaks: bool = False,
         conf_interval: Union[bool, Dict[str, Any]] = False,
         bar_criteria: Optional[Union[str, List[str]]] = None,
         line_criteria: Optional[Union[str, List[str]]] = None,
@@ -1008,6 +1010,7 @@ def solver_model(
             show_metric (bool, optional): Show the metric of the fit. Defaults to True.
             show_df (bool, optional): Show current fit results as dataframe. Defaults
                  to False.
+            show_peaks (bool, optional): Show the peaks of fit. Defaults to False.
             conf_interval (Union[bool,Dict[str, Any]], optional): Bool or dictionary for
                  the parameter with the parameter for calculating the confidence
                  interval. Using `conf_interval=False` turns of the calculation of
@@ -1052,6 +1055,7 @@ def solver_model(
             )(),
         )()
         self.update_metric
+        self.update_peaks
         if show_plot:
             self.plot_fit_df
 
@@ -1063,6 +1067,37 @@ def solver_model(
         if show_df:
             self.interactive_display(df=self.df_fit)
 
+        if show_peaks:
+            self.interactive_display(df=self.df_peaks)
+
+    @property
+    def update_peaks(self) -> None:
+        """Update the peaks dataframe as multi-column dataframe.
+
+        The multi-column dataframe is used for the interactive display of the
+        peaks with initial, current (model), and best fit values.
+        """
+        tuples = []
+        _list = []
+        for key_1, _dict in self.args["fit_insights"]["variables"].items():
+            tuples.extend([(key_1, key_2) for key_2, val in _dict.items()])
+            _list.extend([val for _, val in _dict.items()])
+
+        self.df_peaks = pd.concat(
+            [
+                self.df_peaks,
+                pd.DataFrame(
+                    pd.Series(
+                        _list,
+                        index=pd.MultiIndex.from_tuples(
+                            tuples, names=["component", "parameter"]
+                        ),
+                    )
+                ).T,
+            ],
+            ignore_index=True,
+        )
+
     @property
     def update_metric(self) -> None:
         """Update the metric dataframe."""

@@ -494,6 +494,7 @@ def test_conv_1(
                 show_plot=False,
                 show_df=True,
                 show_metric=True,
+                show_peaks=True,
                 conf_interval=True,
             )
             mock_show.assert_called_once()
@@ -512,6 +513,7 @@ def test_conv_2(
                 show_plot=False,
                 show_df=True,
                 show_metric=True,
+                show_peaks=True,
                 conf_interval={},
             )
 

@@ -151,7 +151,7 @@ def __call__(self) -> Dict[Hashable, Any]:
                 try:
                     metric_dict[fnc.__name__].append(fnc(y_true, y_pred))
                 except ValueError as err:
-                    print(f"ValueError: {err} for {fnc.__name__}!")
+                    print(f"## Warning: {err} for {fnc.__name__}!")
                     metric_dict[fnc.__name__].append(np.nan)
         return pd.DataFrame(metric_dict).T.to_dict(orient="split")