Update to the MC runs

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),

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')
 

Paper/AllSkyMC/submitfile

@@ -9,4 +9,4 @@ Log=CollectedOutput/log.$(Process)
 request_cpus = 1
 request_memory = 16 GB
 
-Queue 90
+Queue 100

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')
 
@@ -25,11 +28,14 @@ def binomialConfidenceInterval(N, K, confidence=0.95):
     [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_list = []
+for fn in filenames:
     df = pd.read_csv(
-    results_file_name, sep=' ', names=['depth', 'h0', 'dF0', 'dF1',
-                                       'twoF_predicted', 'twoF', 'runTime'])
+        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')