diff --git a/Paper/AllSkyMC/generate_data.py b/Paper/AllSkyMC/generate_data.py
index 218f96eb203c677fcc795caa106e44d7ce559a5b..abcb5ddf995d9218c1d28a1a069eb006fa363844 100644
--- a/Paper/AllSkyMC/generate_data.py
+++ b/Paper/AllSkyMC/generate_data.py
@@ -28,7 +28,8 @@ VF0 = VF1 = 100
 DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
 DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
 
-depths = np.linspace(100, 400, 7)
+#depths = np.linspace(100, 400, 7)
+depths = [50, 75]
 
 nsteps = 50
 run_setup = [((nsteps, 0), 20, False),
diff --git a/Paper/AllSkyMC/plot_data.py b/Paper/AllSkyMC/plot_data.py
index 9f12e155ba7a34766526fbb8457fee2141a1e633..37811a586c174cfdd4ab586678a2b7f0055b985c 100644
--- a/Paper/AllSkyMC/plot_data.py
+++ b/Paper/AllSkyMC/plot_data.py
@@ -79,8 +79,10 @@ fig.tight_layout()
 fig.savefig('allsky_recovery.png')
 
 
+total_number_steps = 6 * 50.
 fig, ax = plt.subplots()
-ax.hist(df.runTime, bins=20)
-ax.set_xlabel('runTime per follow-up [s]')
+ax.hist(df.runTime/total_number_steps, bins=50)
+ax.set_xlabel('run-time per step [s]')
+fig.tight_layout()
 fig.savefig('runTimeHist.png')
 
diff --git a/Paper/AllSkyMC/runTimeHist.png b/Paper/AllSkyMC/runTimeHist.png
new file mode 100644
index 0000000000000000000000000000000000000000..ee6df4216d4f111e51301ca2dc30b61a8fb99e33
Binary files /dev/null and b/Paper/AllSkyMC/runTimeHist.png differ
diff --git a/Paper/AllSkyMC/submitfile b/Paper/AllSkyMC/submitfile
index 1c7d5e59431ce5a4f5b13cc2b83ac35dfe57cf63..191a502764a6e221a87d0ccc89d49be609c8587c 100644
--- a/Paper/AllSkyMC/submitfile
+++ b/Paper/AllSkyMC/submitfile
@@ -9,4 +9,4 @@ Log=CollectedOutput/log.$(Process)
 request_cpus = 1
 request_memory = 16 GB
 
-Queue 90
+Queue 100
diff --git a/Paper/DirectedMC/plot_data.py b/Paper/DirectedMC/plot_data.py
index 8dc41ce85d1a4621610c25868c0364bc1bbfc42b..fe9a2e39efe3dccfd963ba6a5a0841565ecd8a4d 100644
--- a/Paper/DirectedMC/plot_data.py
+++ b/Paper/DirectedMC/plot_data.py
@@ -4,6 +4,9 @@ import numpy as np
 import os
 from tqdm import tqdm
 from oct2py import octave
+import glob
+
+filenames = glob.glob("CollectedOutput/*.txt")
 
 plt.style.use('paper')
 
@@ -22,14 +25,17 @@ def Recovery(Tspan, Depth, twoFstar=60, detectors='H1,L1'):
 def binomialConfidenceInterval(N, K, confidence=0.95):
     cmd = '[fLow, fUpper] = binomialConfidenceInterval({}, {}, {})'.format(
         N, K, confidence)
-    [l, u] =  octave.eval(cmd, verbose=False, return_both=True)[0].split('\n')
+    [l, u] = octave.eval(cmd, verbose=False, return_both=True)[0].split('\n')
     return float(l.split('=')[1]), float(u.split('=')[1])
 
-results_file_name = 'MCResults.txt'
-
-df = pd.read_csv(
-    results_file_name, sep=' ', names=['depth', 'h0', 'dF0', 'dF1',
-                                       'twoF_predicted', 'twoF', 'runTime'])
+df_list = []
+for fn in filenames:
+    df = pd.read_csv(
+        fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF_predicted',
+                            'twoF', 'runTime'])
+    df['CLUSTER_ID'] = fn.split('_')[1]
+    df_list.append(df)
+df = pd.concat(df_list)
 
 twoFstar = 60
 depths = np.unique(df.depth.values)
@@ -73,8 +79,11 @@ fig.tight_layout()
 fig.savefig('directed_recovery.png')
 
 
+total_number_steps = 5*20.
+df_clean = df[df.CLUSTER_ID == '969049']  # Hack due to a change in the code
 fig, ax = plt.subplots()
-ax.hist(df.runTime, bins=50)
-ax.set_xlabel('runTime per follow-up [s]')
+ax.hist(df_clean.runTime/total_number_steps, bins=50)
+ax.set_xlabel('run-time per step [s]')
+fig.tight_layout()
 fig.savefig('runTimeHist.png')
 
diff --git a/Paper/DirectedMC/runTimeHist.png b/Paper/DirectedMC/runTimeHist.png
new file mode 100644
index 0000000000000000000000000000000000000000..13cb78f5b50209ae275f7dda07dfe150d0f42403
Binary files /dev/null and b/Paper/DirectedMC/runTimeHist.png differ
diff --git a/Paper/allsky_recovery.png b/Paper/allsky_recovery.png
index c015222c5cb2e53607ac0a37e43b99e74e3cecdb..aef4bf549a6be72b4719f42ca2b2db77048eea65 100644
Binary files a/Paper/allsky_recovery.png and b/Paper/allsky_recovery.png differ