refactor(bud_to_bud_plot): changes for clarity

src/wela/plotting.py

@@ -609,7 +609,7 @@ def bud_to_bud_plot(
     dl,
     colour="b",
     group=None,
-    nbins=None,
+    no_t_values=None,
     no_future_buddings=1,
     return_signal=False,
     df=None,
@@ -637,9 +637,9 @@ def bud_to_bud_plot(
         Colour of lines.
     group: str, optional
         The name of the group to plot.
-    nbins: int, optional
-        The number of time bins to partition the interval between the
-        first and the second budding event.
+    no_t_values: int, optional
+        The number of time points into which to partition the interval between
+        budding events.
     no_future_buddings: int, optional
         The number of future budding events to include. Default is 1.
     return_signal: boolean, optional
@@ -664,10 +664,10 @@ def bud_to_bud_plot(
     >>> bud_to_bud_plot(4, "flavin", dl, group="fy4", filter_func=butterworth_filter)
     """
     if df is None:
-        t, signal_data = dl.get_time_series(signal, group=group)
+        _, signal_data = dl.get_time_series(signal, group=group)
         t, buddings = dl.get_time_series("buddings", group=group)
     else:
-        t, signal_data = dl.get_time_series(signal, group=group, df=df)
+        _, signal_data = dl.get_time_series(signal, group=group, df=df)
         t, buddings = dl.get_time_series("buddings", group=group, df=df)
     if filter_func is not None:
         signal_data = filter_func(signal_data)
@@ -684,11 +684,14 @@ def bud_to_bud_plot(
         no_future_buddings_index=no_future_buddings - 1,
     )
     if local_times:
-        # find bins for normalised time, between 0 and no_future_buddings
-        nbins = int(np.median([len(local_time) for local_time in local_times]))
-        ntbins = np.linspace(0, no_future_buddings, nbins)
+        # find values for normalised time between 0 and no_future_buddings
+        if no_t_values is None:
+            no_t_values = int(
+                np.median([len(local_time) for local_time in local_times])
+            )
+        t_values = np.linspace(0, no_future_buddings, no_t_values)
         # interpolate each local signal to make a new signal
-        new_signal = np.nan * np.ones((len(local_signals), nbins))
+        new_signal = np.nan * np.ones((len(local_signals), no_t_values))
         for i in range(len(local_signals)):
             s = local_signals[i]
             # normalise time between 0 and no_future_buddings
@@ -696,7 +699,7 @@ def bud_to_bud_plot(
             nt = nt / nt[-1] * no_future_buddings
             # interpolate into the bins
             new_signal[i, :] = np.interp(
-                ntbins,
+                t_values,
                 nt[~np.isnan(s)],
                 s[~np.isnan(s)],
                 left=np.nan,
@@ -705,10 +708,10 @@ def bud_to_bud_plot(
         # plot median and percentiles
         if show_figure:
             plt.figure()
-        plt.plot(ntbins, np.nanmedian(new_signal, axis=0), f"{colour}.-")
+        plt.plot(t_values, np.nanmedian(new_signal, axis=0), f"{colour}.-")
         for lower, upper in zip([45, 40, 35], [55, 60, 65]):
             plt.fill_between(
-                ntbins,
+                t_values,
                 np.nanpercentile(new_signal, lower, axis=0),
                 np.nanpercentile(new_signal, upper, axis=0),
                 alpha=0.06,