harmonise augur

pertpy/tools/_augur.py

@@ -968,7 +968,11 @@ def predict_differential_prioritization(
         return delta
 
     def plot_dp_scatter(
-        self, results: pd.DataFrame, top_n=None, ax: Axes = None, return_figure: bool = False
+        self, 
+        results: pd.DataFrame, 
+        top_n: int = None, 
+        ax: Axes = None, 
+        return_figure: bool = False
     ) -> Figure | Axes:
         """Plot scatterplot of differential prioritization.
 
@@ -997,6 +1001,9 @@ def plot_dp_scatter(
             >>> pvals = ag_rfc.predict_differential_prioritization(augur_results1=results_15, augur_results2=results_48, \
                 permuted_results1=results_15_permute, permuted_results2=results_48_permute)
             >>> ag_rfc.plot_dp_scatter(pvals)
+        
+        Preview: 
+        # TODO: add preview
         """
         x = results["mean_augur_score1"]
         y = results["mean_augur_score2"]
@@ -1026,7 +1033,12 @@ def plot_dp_scatter(
         return fig if return_figure else ax
 
     def plot_important_features(
-        self, data: dict[str, Any], key: str = "augurpy_results", top_n=10, ax: Axes = None, return_figure: bool = False
+        self, 
+        data: dict[str, Any], 
+        key: str = "augurpy_results", 
+        top_n: int = 10, 
+        ax: Axes = None, 
+        return_figure: bool = False
     ) -> Figure | Axes:
         """Plot a lollipop plot of the n features with largest feature importances.
 
@@ -1047,6 +1059,9 @@ def plot_important_features(
             >>> loaded_data = ag_rfc.load(adata)
             >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
             >>> ag_rfc.plot_important_features(v_results)
+        
+        Preview:
+        # TODO: add preview
         """
         if isinstance(data, AnnData):
             results = data.uns[key]
@@ -1077,7 +1092,11 @@ def plot_important_features(
         return fig if return_figure else ax
 
     def plot_lollipop(
-        self, data: dict[str, Any], key: str = "augurpy_results", ax: Axes = None, return_figure: bool = False
+        self, 
+        data: dict[str, Any], 
+        key: str = "augurpy_results", 
+        ax: Axes = None, 
+        return_figure: bool = False
     ) -> Figure | Axes:
         """Plot a lollipop plot of the mean augur values.
 
@@ -1097,6 +1116,9 @@ def plot_lollipop(
             >>> loaded_data = ag_rfc.load(adata)
             >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
             >>> ag_rfc.plot_lollipop(v_results)
+        
+        Preview:
+        # TODO: add preview
         """
         if isinstance(data, AnnData):
             results = data.uns[key]
@@ -1124,7 +1146,11 @@ def plot_lollipop(
         return fig if return_figure else ax
 
     def plot_scatterplot(
-        self, results1: dict[str, Any], results2: dict[str, Any], top_n=None, return_figure: bool = False
+        self, 
+        results1: dict[str, Any], 
+        results2: dict[str, Any], 
+        top_n: int = None, 
+        return_figure: bool = False
     ) -> Figure | Axes:
         """Create scatterplot with two augur results.
 
@@ -1145,6 +1171,9 @@ def plot_scatterplot(
             >>> h_adata, h_results = ag_rfc.predict(loaded_data, subsample_size=20, n_threads=4)
             >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
             >>> ag_rfc.plot_scatterplot(v_results, h_results)
+        
+        Preview:
+        # TODO: add preview
         """
         cell_types = results1["summary_metrics"].columns