fix alpha specification for segment plots

neurokit2/ecg/ecg_segment.py

@@ -120,10 +120,10 @@ def _ecg_segment_plot(heartbeats, heartrate=0, ytitle="ECG", color="#F44336", ax
     )
 
     # Alpha of individual beats decreases with more heartbeats
-    alpha = 1 / np.log1p(np.log2(1 + df_pivoted.shape[1]))
+    alpha = 1 / np.log2(np.log2(1 + df_pivoted.shape[1]))
 
     # Plot all heartbeats
-    ax.plot(df_pivoted, color="grey", linewidth=alpha, zorder=2)
+    ax.plot(df_pivoted, color="grey", linewidth=alpha, alpha=alpha, zorder=2)
 
     # Plot individual waves
     for wave in [

neurokit2/stats/rescale.py

@@ -12,7 +12,9 @@ def rescale(data, to=[0, 1], scale=None):
     data : Union[list, np.array, pd.Series]
         Raw data.
     to : list
-        New range of values of the data after rescaling.
+        New range of values of the data after rescaling. Must be a list or tuple of two values. If
+        more values, the function will assume it is another signal and will derive the min and max
+        from it.
     scale : list
         A list or tuple of two values specifying the actual range
         of the data. If ``None``, the minimum and the maximum of the
@@ -30,9 +32,22 @@ def rescale(data, to=[0, 1], scale=None):
 
       import neurokit2 as nk
 
+      # Normalize to 0-1
       nk.rescale([3, 1, 2, 4, 6], to=[0, 1])
 
+      # Rescale to 0-1, but specify that 0 corresponds to another value that the
+      # minimum of the data.
+      nk.rescale([3, 1, 2, 4, 6], to=[0, 1], scale=[0, 6])
+
+      # Rescale to 0-4 (the min-max of another signal)
+      nk.rescale([3, 1, 2, 4, 6], to=[0, 1, 3, 4])
+
     """
+    # Sanity checks
+    if len(to) < 2:
+        raise ValueError("'to' must have at least 2 values.")
+    if len(to) > 2:  # We assume it is a signal
+        to = [np.nanmin(to), np.nanmax(to)]
 
     # Return appropriate type
     if isinstance(data, list):