Update Directed MC to new format of run_setup

Paper/DirectedMC/generate_data.py

@@ -2,6 +2,8 @@ import pyfstat
 import numpy as np
 import os
 import sys
+import time
+
 
 ID = sys.argv[1]
 outdir = sys.argv[2]
@@ -32,15 +34,19 @@ DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
 depths = np.linspace(100, 400, 7)
 depths = [125, 175]
 
-run_setup = [((10, 0), 16, False),
-             ((10, 0), 5, False),
-             ((10, 10), 1, False)]
+nsteps = 20
+run_setup = [((nsteps, 0), 20, False),
+             ((nsteps, 0), 7, False),
+             ((nsteps, 0), 2, False),
+             ((nsteps, nsteps), 1, False)]
+
 for depth in depths:
+    startTime = time.time()
     h0 = sqrtSX / float(depth)
     r = np.random.uniform(0, 1)
     theta = np.random.uniform(0, 2*np.pi)
-    F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
-    F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
+    F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
+    F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
 
     psi = np.random.uniform(-np.pi/4, np.pi/4)
     phi = np.random.uniform(0, 2*np.pi)
@@ -81,7 +87,8 @@ for depth in depths:
     d, maxtwoF = mcmc.get_max_twoF()
     dF0 = F0 - d['F0']
     dF1 = F1 - d['F1']
+    runTime = time.time() - startTime
     with open(results_file_name, 'a') as f:
-        f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e}\n'
-                .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF))
+        f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
+                .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF, runTime))
     os.system('rm {}/*{}*'.format(outdir, label))

Paper/DirectedMC/generate_table.py

+import pyfstat
+import numpy as np
+
+outdir = 'data'
+
+label = 'directed_setup'
+data_label = '{}_data'.format(label)
+
+# Properties of the GW data
+sqrtSX = 2e-23
+tstart = 1000000000
+Tspan = 100*86400
+tend = tstart + Tspan
+
+# Fixed properties of the signal
+F0_center = 30
+F1_center = 1e-10
+F2 = 0
+Alpha = 5e-3
+Delta = 6e-2
+tref = .5*(tstart+tend)
+
+
+VF0 = VF1 = 100
+DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
+DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
+
+depth = 100
+
+nsteps = 50
+run_setup = [((nsteps, 0), 20, False),
+             ((nsteps, 0), 7, False),
+             ((nsteps, 0), 2, False),
+             ((nsteps, nsteps), 1, False)]
+
+h0 = sqrtSX / float(depth)
+r = np.random.uniform(0, 1)
+theta = np.random.uniform(0, 2*np.pi)
+F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
+F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
+
+psi = np.random.uniform(-np.pi/4, np.pi/4)
+phi = np.random.uniform(0, 2*np.pi)
+cosi = np.random.uniform(-1, 1)
+
+data = pyfstat.Writer(
+    label=data_label, outdir=outdir, tref=tref,
+    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
+    Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
+    detector='H1,L1')
+data.make_data()
+predicted_twoF = data.predict_fstat()
+
+theta_prior = {'F0': {'type': 'unif',
+                      'lower': F0-DeltaF0/2.,
+                      'upper': F0+DeltaF0/2.},
+               'F1': {'type': 'unif',
+                      'lower': F1-DeltaF1/2.,
+                      'upper': F1+DeltaF1/2.},
+               'F2': F2,
+               'Alpha': Alpha,
+               'Delta': Delta
+               }
+
+ntemps = 1
+log10temperature_min = -1
+nwalkers = 100
+
+mcmc = pyfstat.MCMCFollowUpSearch(
+    label=label, outdir=outdir,
+    sftfilepath='{}/*{}*sft'.format(outdir, data_label),
+    theta_prior=theta_prior,
+    tref=tref, minStartTime=tstart, maxStartTime=tend,
+    nwalkers=nwalkers, ntemps=ntemps,
+    log10temperature_min=log10temperature_min)
+mcmc.run(run_setup)

Paper/DirectedMC/plot_data.py

@@ -29,7 +29,7 @@ results_file_name = 'MCResults.txt'
 
 df = pd.read_csv(
     results_file_name, sep=' ', names=['depth', 'h0', 'dF0', 'dF1',
-                                       'twoF_predicted', 'twoF'])
+                                       'twoF_predicted', 'twoF', 'runTime'])
 
 twoFstar = 60
 depths = np.unique(df.depth.values)