change(plot_binned): variables renamed for clarity

src/wela/plotting.py

@@ -767,7 +767,7 @@ def plot_binned(
     df,
     x_signal,
     y_signal,
-    statistic,
+    y_statistic,
     bins=10,
     equal_bins=False,
     groups=None,
@@ -798,7 +798,7 @@ def plot_binned(
         Name of the signal to bin and plot on the x-axis.
     y_signal: str
         Name of the signal from which the statistic will be calculated.
-    statistic: function
+    y_statistic: function
         Statistic to calculate for y_signal for cells in bins of x_signal.
     bins: int, List[float]
         Number of bins.
@@ -841,11 +841,11 @@ def plot_binned(
         if x_signal == y_signal:
             sdf = df[df.group == group][[x_signal]].dropna()
             x = sdf[x_signal].values
-            values = x
+            y_values = x
         else:
             sdf = df[df.group == group][[x_signal, y_signal]].dropna()
             x = sdf[x_signal].values
-            values = sdf[y_signal].values
+            y_values = sdf[y_signal].values
         if isinstance(bins, int) and equal_bins:
             _, bin_edges = pd.qcut(x, bins, retbins=True, duplicates="drop")
             bs_bins = bin_edges
@@ -853,8 +853,8 @@ def plot_binned(
             bs_bins = bins
         res[f"stat_{group}"], bin_edges, bin_nos = binned_statistic(
             x,
-            values,
-            statistic=statistic,
+            y_values,
+            statistic=y_statistic,
             bins=bs_bins,
         )
         res[f"bin_midpoints_{group}"] = np.array(
@@ -865,15 +865,16 @@ def plot_binned(
         )
         res[f"bin_size_{group}"] = np.array(
             [
-                values[bin_nos == bin_no].size
+                y_values[bin_nos == bin_no].size
                 for bin_no in np.arange(1, bin_edges.size)
             ]
         )
+        res[f"bin_edges_{group}"] = bin_edges
         # estimate errors
         print(group)
         err = []
         for bin_no in np.arange(1, bin_edges.size):
-            data = values[bin_nos == bin_no]
+            data = y_values[bin_nos == bin_no]
             if data.size > 1:
                 print(
                     f" Bin {bin_no}: bootstrapping with {data.size} data points."
@@ -881,7 +882,7 @@ def plot_binned(
                 data = (data,)
                 bootstrap_ci = bootstrap(
                     data,
-                    statistic,
+                    y_statistic,
                     confidence_level=confidence_level,
                     random_state=rng,
                     n_resamples=no_bootstrap_samples,