added aperiodic model to output of model fit

schmidtfa · Aug 22, 2024 · 7e67975 · 7e67975
1 parent 6a61887
commit 7e67975
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Showing 6 changed files with 112 additions and 70 deletions.
diff --git a/examples/basic_functionality.ipynb b/examples/basic_functionality.ipynb
diff --git a/pyrasa/irasa_mne/mne_objs.py b/pyrasa/irasa_mne/mne_objs.py
@@ -242,7 +242,7 @@ def fit_aperiodic_model(
                     A DataFrame containing the fitted aperiodic parameters for each channel.
                 - gof : pd.DataFrame
                     A DataFrame containing the goodness of fit metrics for each channel.
-                - pred : pd.DataFrame
+                - model : pd.DataFrame
                     A DataFrame containing the predicted aperiodic model for each channel and each time point.
 
 
@@ -533,7 +533,7 @@ def fit_aperiodic_model(
                     A DataFrame containing the fitted aperiodic parameters for each channel.
                 - gof : pd.DataFrame
                     A DataFrame containing the goodness of fit metrics for each channel.
-                - pred : pd.DataFrame
+                - model : pd.DataFrame
                     A DataFrame containing the predicted aperiodic model for each channel and each time point.
 
 
@@ -572,9 +572,9 @@ def fit_aperiodic_model(
             slope_fit.gof['event_id'] = event_dict[events[ix]]
             aps_list.append(slope_fit.aperiodic_params.copy())
             gof_list.append(slope_fit.gof.copy())
-            pred_list.append(slope_fit.pred)
+            pred_list.append(slope_fit.model)
 
-        return AperiodicFit(aperiodic_params=pd.concat(aps_list), gof=pd.concat(gof_list), pred=pd.concat(pred_list))
+        return AperiodicFit(aperiodic_params=pd.concat(aps_list), gof=pd.concat(gof_list), model=pd.concat(pred_list))
 
 
 @define

diff --git a/pyrasa/utils/aperiodic_utils.py b/pyrasa/utils/aperiodic_utils.py
@@ -77,7 +77,7 @@ def compute_aperiodic_model(
                 A DataFrame containing the fitted aperiodic parameters for each channel.
             - gof : pd.DataFrame
                 A DataFrame containing the goodness of fit metrics for each channel.
-            - pred : pd.DataFrame
+            - model : pd.DataFrame
                 A DataFrame containing the predicted aperiodic model for each channel.
 
     Notes
@@ -156,7 +156,7 @@ def num_zeros(decimal: int) -> float:
         pred_list.append(pred)
 
     # combine & return
-    return AperiodicFit(aperiodic_params=pd.concat(ap_list), gof=pd.concat(gof_list), pred=pd.concat(pred_list))
+    return AperiodicFit(aperiodic_params=pd.concat(ap_list), gof=pd.concat(gof_list), model=pd.concat(pred_list))
 
 
 def compute_aperiodic_model_sprint(
@@ -208,7 +208,7 @@ def compute_aperiodic_model_sprint(
                 for each channel and each time point.
             - gof : pd.DataFrame
                 A DataFrame containing the goodness of fit metrics for each channel and each time point.
-            - pred : pd.DataFrame
+            - model : pd.DataFrame
                 A DataFrame containing the predicted aperiodic model for each channel and each time point.
 
     Notes
@@ -238,10 +238,10 @@ def compute_aperiodic_model_sprint(
         )
         aperiodic_fit.aperiodic_params['time'] = t
         aperiodic_fit.gof['time'] = t
-        aperiodic_fit.pred['time'] = t
+        aperiodic_fit.model['time'] = t
 
         ap_t_list.append(aperiodic_fit.aperiodic_params)
         gof_t_list.append(aperiodic_fit.gof)
-        pred_t_list.append(aperiodic_fit.pred)
+        pred_t_list.append(aperiodic_fit.model)
 
-    return AperiodicFit(aperiodic_params=pd.concat(ap_t_list), gof=pd.concat(gof_t_list), pred=pd.concat(pred_t_list))
+    return AperiodicFit(aperiodic_params=pd.concat(ap_t_list), gof=pd.concat(gof_t_list), model=pd.concat(pred_t_list))
diff --git a/pyrasa/utils/fit_funcs.py b/pyrasa/utils/fit_funcs.py
@@ -207,6 +207,7 @@ def fit_func(self) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         if self.log10_freq:
             freq = 10**freq
         df_pred = pd.DataFrame({'Frequency (Hz)': freq, 'aperiodic_model': pred})
+        df_pred['fit_type'] = self.label
 
         return df_params, df_gof, df_pred
 

diff --git a/pyrasa/utils/types.py b/pyrasa/utils/types.py
@@ -64,4 +64,4 @@ class AperiodicFit:
 
     aperiodic_params: pd.DataFrame
     gof: pd.DataFrame
-    pred: pd.DataFrame
+    model: pd.DataFrame
diff --git a/simulations/example_knee.png b/simulations/example_knee.png