Commit 0e89feb6 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Update Directed MC to new format of run_setup

parent 78ab5b6d
...@@ -2,6 +2,8 @@ import pyfstat ...@@ -2,6 +2,8 @@ import pyfstat
import numpy as np import numpy as np
import os import os
import sys import sys
import time
ID = sys.argv[1] ID = sys.argv[1]
outdir = sys.argv[2] outdir = sys.argv[2]
...@@ -32,15 +34,19 @@ DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2) ...@@ -32,15 +34,19 @@ DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
depths = np.linspace(100, 400, 7) depths = np.linspace(100, 400, 7)
depths = [125, 175] depths = [125, 175]
run_setup = [((10, 0), 16, False), nsteps = 20
((10, 0), 5, False), run_setup = [((nsteps, 0), 20, False),
((10, 10), 1, False)] ((nsteps, 0), 7, False),
((nsteps, 0), 2, False),
((nsteps, nsteps), 1, False)]
for depth in depths: for depth in depths:
startTime = time.time()
h0 = sqrtSX / float(depth) h0 = sqrtSX / float(depth)
r = np.random.uniform(0, 1) r = np.random.uniform(0, 1)
theta = np.random.uniform(0, 2*np.pi) theta = np.random.uniform(0, 2*np.pi)
F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2) F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4) F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
psi = np.random.uniform(-np.pi/4, np.pi/4) psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi) phi = np.random.uniform(0, 2*np.pi)
...@@ -81,7 +87,8 @@ for depth in depths: ...@@ -81,7 +87,8 @@ for depth in depths:
d, maxtwoF = mcmc.get_max_twoF() d, maxtwoF = mcmc.get_max_twoF()
dF0 = F0 - d['F0'] dF0 = F0 - d['F0']
dF1 = F1 - d['F1'] dF1 = F1 - d['F1']
runTime = time.time() - startTime
with open(results_file_name, 'a') as f: with open(results_file_name, 'a') as f:
f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e}\n' f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
.format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF)) .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label)) os.system('rm {}/*{}*'.format(outdir, label))
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)
...@@ -29,7 +29,7 @@ results_file_name = 'MCResults.txt' ...@@ -29,7 +29,7 @@ results_file_name = 'MCResults.txt'
df = pd.read_csv( df = pd.read_csv(
results_file_name, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', results_file_name, sep=' ', names=['depth', 'h0', 'dF0', 'dF1',
'twoF_predicted', 'twoF']) 'twoF_predicted', 'twoF', 'runTime'])
twoFstar = 60 twoFstar = 60
depths = np.unique(df.depth.values) depths = np.unique(df.depth.values)
......
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