changed the plotting (especially for abs values)

rdstar/studies/low_level_cut_study/plotting.py

@@ -49,29 +49,22 @@ class LowLevelCutPlotter:
                 if len(df) == 0:
                     continue
 
-                var_range = (df[cut_var].min(),df[cut_var].max())
-
                 fig, ax = plt.subplots()
                 cuts = list(new_cut_info.cut_limits) if isinstance(new_cut_info.cut_limits, Tuple) else [new_cut_info.cut_limits]
                 for cut in cuts:
                     ax.axvline(cut, color=KITColors.kit_black)
                 df_bkg = df.query("isSignal == 0")
-                df_bkg[cut_var].hist(
+                bkg_data = df_bkg[cut_var].abs() if new_cut_info.cut_abs else df_bkg[cut_var]
+                df_sig = df.query("isSignal == 1")
+                sig_data = df_sig[cut_var].abs() if new_cut_info.cut_abs else df_sig[cut_var]
+                ax.hist(
+                    [bkg_data, sig_data],
                     bins=50,
-                    range=var_range,
                     histtype="stepfilled",
+                    stacked=True,
                     lw=1.5,
-                    color=KITColors.kit_green,
-                    label="Background"
-                )
-                df = df.query("isSignal == 1")
-                df[cut_var].hist(
-                    bins=50,
-                    range=var_range,
-                    histtype="step",
-                    lw=2,
-                    color=KITColors.kit_red,
-                    label="Signal"
+                    color=[KITColors.kit_green, KITColors.kit_red],
+                    label=["Background", "Signal"]
                 )
 
                 plt.grid(False)
@@ -88,6 +81,6 @@ class LowLevelCutPlotter:
 if __name__ == '__main__':
     #df = pd.read_hdf("/ceph/pecker/rdstar/low_level_cut_study/CombinedNTuples/e_ntuple_total.h5")
     df = pd.read_hdf("/ceph/pecker/rdstar/low_level_cut_study/CombinedNTuples/dcharged_ntuple_total.h5")
-    llcp = LowLevelCutPlotter(n_tuple="dcharged_ntuple", df=df, cut_infos=D_cut_dict)
+    llcp = LowLevelCutPlotter(n_tuple="dcharged_ntuple", df=df)
     #llcp.apply_cuts(pivot=fsp_cut_dict["e_ntuple"]["cuts"][0], cut_infos=fsp_cut_dict["e_ntuple"]["cuts"])
     llcp.plot_on(target_path="")
\ No newline at end of file