Fix cut acceptance plotting (#135)

* Add YBe Generator

* Fix Cut Acceptance Plotting

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* clean mess

* clean more mess

* cut ac

* debug

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* revert (#136)

---------

Co-authored-by: Husheng Guan <ghusheng@midway2-login2.rcc.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lanqing Yuan <yuanlq@uchicago.edu>

saltax/match/utils.py

@@ -671,13 +671,6 @@ def get_cut_eff(
     else:
         raise NotImplementedError
 
-    if indv_cut_type == "n_minus_1":
-        cut_func = apply_n_minus_1_cuts
-    elif indv_cut_type == "single":
-        cut_func = apply_single_cut
-    else:
-        raise NotImplementedError
-
     result_dict = {}
     for cut in all_cut_list:
         result_dict[cut] = np.zeros(n_bins - 1)
@@ -698,13 +691,30 @@ def get_cut_eff(
         result_dict["all_cuts"][i] = len(selected_events_all_cut) / len(selected_events)
         result_dict["all_cuts_upper"][i] = interval.high
         result_dict["all_cuts_lower"][i] = interval.low
+
         for cut_oi in all_cut_list:
-            selected_events_cut_oi = selected_events[apply_single_cut(selected_events, cut_oi)]
-            # Efficiency curves with Clopper-Pearson uncertainty estimation
-            interval = binomtest(len(selected_events_cut_oi), len(selected_events)).proportion_ci()
-            result_dict[cut_oi][i] = len(selected_events_cut_oi) / len(selected_events)
-            result_dict[cut_oi + "_upper"][i] = interval.high
-            result_dict[cut_oi + "_lower"][i] = interval.low
+            if indv_cut_type == "n_minus_1":
+                selected_events_cut_oi = selected_events[
+                    apply_n_minus_1_cuts(selected_events, cut_oi, all_cut_list)
+                ]
+                # Efficiency curves with Clopper-Pearson uncertainty estimation
+                interval = binomtest(
+                    len(selected_events_all_cut), len(selected_events_cut_oi)
+                ).proportion_ci()
+                result_dict[cut_oi][i] = len(selected_events_all_cut) / len(selected_events_cut_oi)
+                result_dict[cut_oi + "_upper"][i] = interval.high
+                result_dict[cut_oi + "_lower"][i] = interval.low
+            elif indv_cut_type == "single":
+                selected_events_cut_oi = selected_events[apply_single_cut(selected_events, cut_oi)]
+                # Efficiency curves with Clopper-Pearson uncertainty estimation
+                interval = binomtest(
+                    len(selected_events_cut_oi), len(selected_events)
+                ).proportion_ci()
+                result_dict[cut_oi][i] = len(selected_events_cut_oi) / len(selected_events)
+                result_dict[cut_oi + "_upper"][i] = interval.high
+                result_dict[cut_oi + "_lower"][i] = interval.low
+            else:
+                raise NotImplementedError
 
     if plot:
         colors = plt.cm.rainbow(