diff --git a/Experiments/plot_coverage_vs_q.py b/Experiments/plot_coverage_vs_q.py
index b464c28953ca3471f399dd1e8844f7a2f3bbe5d1..6d578e8e214d4b31229547eb5374c89f1a58601c 100644
--- a/Experiments/plot_coverage_vs_q.py
+++ b/Experiments/plot_coverage_vs_q.py
@@ -12,6 +12,7 @@ import torch
 import torch.backends.cudnn
 from torch.utils.data import DataLoader
 from matplotlib.pyplot import cm
+import matplotlib
 import matplotlib.pyplot as plt
 from tqdm import tqdm
 
@@ -20,6 +21,11 @@ from EIVTrainingRoutines import train_and_store
 from EIVGeneral.coverage_collect import get_coverage_distribution
 from EIVGeneral.manipulate_datasets import VerticalCut
 
+font = {'family' : 'DejaVu Sans',
+        'weight' : 'normal',
+        'size'   : 16}
+
+matplotlib.rc('font', **font)
 # coverages to consider
 q_range = np.linspace(0.1, 0.95)
 
@@ -89,9 +95,8 @@ def coverage_diagonal_plot(eiv_coverages, noneiv_coverages, color,
 # create figures, together with title and axis labels
 plt.figure(1)
 plt.clf()
-plt.title('Coverage for datasets with ground truth')
 plt.xlabel('q')
-plt.ylabel('coverage')
+plt.ylabel('coverage ground truth')
 # datasets to plot and their coloring
 datasets = ['linear', 'quadratic','cubic','sine']
 
@@ -251,5 +256,6 @@ if __name__ == '__main__':
     plt.legend()
 
     # save and show
+    plt.tight_layout()
     plt.savefig('results/figures/true_coverage_vs_q.pdf')
     plt.show()