diff --git a/IEEEProc2025_plots/plotting_diagonal_4methods.py b/IEEEProc2025_plots/plotting_diagonal_4methods.py
index 488e700..c0991fb 100644
--- a/IEEEProc2025_plots/plotting_diagonal_4methods.py
+++ b/IEEEProc2025_plots/plotting_diagonal_4methods.py
@@ -6,7 +6,7 @@ import quapy as qp
 from data import LabelledCollection
 from method.non_aggregative import DMx
 from protocol import APP
-from quapy.method.aggregative import CC, DMy, ACC
+from quapy.method.aggregative import CC, DMy, ACC, EMQ
 from sklearn.svm import LinearSVC
 import numpy as np
 from tqdm import tqdm
@@ -15,12 +15,15 @@ qp.environ['SAMPLE_SIZE'] = 500
 
 def cls():
     return LogisticRegressionCV(n_jobs=-1,Cs=10)
+    # return LogisticRegression(C=.1)
 
 def gen_methods():
-    yield CC(cls()), 'CC$_{10' + '\%}$'
+    yield CC(cls()), r'CC$_{10' + r'\%}$'
     yield ACC(cls()), 'ACC'
     yield DMy(cls(), val_split=10, nbins=10, n_jobs=-1), 'HDy'
     yield DMx(nbins=10, n_jobs=-1), 'HDx'
+    yield EMQ(cls()), 'SLD'
+    # yield EMQ(cls(), calib='vs'), 'SLD-VS'
 
 def gen_data():
 
@@ -31,6 +34,7 @@ def gen_data():
     training_size = 5000
     # since the problem is binary, it suffices to specify the negative prevalence, since the positive is constrained
     train_sample = train.sampling(training_size, 1-training_prevalence, random_state=0)
+    # train_sample = train
 
     for model, method_name in tqdm(gen_methods(), total=4):
         with qp.util.temp_seed(1):
@@ -43,10 +47,10 @@ def gen_data():
                 X, y = test.Xy
                 test_dense = LabelledCollection(svd.transform(X), y)
 
-                model.fit(train_sample_dense)
+                model.fit(*train_sample_dense.Xy)
                 true_prev, estim_prev = qp.evaluation.prediction(model, APP(test_dense, repeats=100, random_state=0))
             else:
-                model.fit(train_sample)
+                model.fit(*train_sample.Xy)
                 true_prev, estim_prev = qp.evaluation.prediction(model, APP(test, repeats=100, random_state=0))
         method_data.append((method_name, true_prev, estim_prev, train_sample.prevalence()))
 
@@ -55,5 +59,5 @@ def gen_data():
 
 method_names, true_prevs, estim_prevs, tr_prevs = gen_data()
 
-qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, savepath='./plots_cacm/bin_diag_4methods.pdf')
-qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=10, savepath='./plots_cacm/err_drift_4methods.pdf', title='', show_density=False, show_std=True)
+# qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, savepath='./plots_ieee/bin_diag_4methods.pdf')
+qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=10, savepath='./plots_ieee/err_drift_4methods.pdf', title='', show_density=False, show_std=True)
diff --git a/quapy/plot.py b/quapy/plot.py
index 1f062e9..7659110 100644
--- a/quapy/plot.py
+++ b/quapy/plot.py
@@ -314,7 +314,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
         for p,ind in enumerate(range(len(bins))):
             selected = inds==ind
             if selected.sum() > 0:
-                xs.append(ind*binwidth-binwidth/2)
+                xs.append(ind*binwidth)
                 ys.append(np.mean(method_drifts[selected]))
                 ystds.append(np.std(method_drifts[selected]))
                 npoints[p] += len(method_drifts[selected])