Adjusted memory request, need to change range

code/RDGW150914_ptemcee1.py

@@ -41,11 +41,11 @@ tshift=3.6
 vary_fund = True
 
 #sampler parameters
-npoints=1002 
+npoints=13700 
 nwalkers = 840
 ntemps=12
 ndim = int(4*(nmax+1))
-burnin = 500  #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 4200  #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
             #This is trivial but often forgotten: this cannot be more than npoints! Usually 1/5~1/4 npoints is what I observe.
 numbins = 42 #corner plot parameter - how many bins you want
 datacolor = '#105670' #'#4fa3a7'
@@ -176,8 +176,8 @@ def log_probability(theta):
 
 #Fit with ptemcee
 #Set the number of cores of your processors
-pool = choose_pool(64)
-pool.size = 64
+pool = choose_pool(16)
+pool.size = 16
 vary_param = float(vary_fund)
 np.random.seed(42)
 pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])

code/RDGW150914_ptemcee2.py

@@ -35,17 +35,17 @@ from scipy.optimize import minimize
 #tshift: time shift after the strain peak
 #vary_fund: whether you vary the fundamental frequency. Works in the model_dv function.
 
-rootpath="/Users/RayneLiu"#"/work/rayne.liu" 
+rootpath="/work/rayne.liu"#"/Users/RayneLiu"
 nmax=1
 tshift=19
 vary_fund = True
 
 #sampler parameters
-npoints=102 
-nwalkers = 42
+npoints=13700 
+nwalkers = 840
 ntemps=12
 ndim = int(4*(nmax+1))
-burnin = 20 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 4200 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
             #This is trivial but often forgotten: this cannot be more than npoints! Usually 1/5~1/4 npoints is what I observe.
 numbins = 42 #corner plot parameter - how many bins you want
 datacolor = '#105670' #'#4fa3a7'
@@ -177,8 +177,8 @@ def log_probability(theta):
 
 #Fit with ptemcee
 #Set the number of cores of your processors
-pool = choose_pool(64)
-pool.size = 64
+pool = choose_pool(16)
+pool.size = 16
 vary_param = float(vary_fund)
 np.random.seed(42)
 pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])

code/RDGW150914_ptemcee3.py

@@ -42,11 +42,11 @@ tshift=0
 vary_fund = False
 
 #sampler parameters
-npoints=100 
-nwalkers = 42
+npoints=13700 
+nwalkers = 840
 ntemps=12
 ndim = int(4*(nmax+1))
-burnin = 20 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 4200 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
             #This is trivial but often forgotten: this cannot be more than npoints! Usually 1/5~1/4 npoints is what I observe.
 numbins = 42 #corner plot parameter - how many bins you want
 datacolor = '#105670' #'#4fa3a7'
@@ -178,8 +178,8 @@ def log_probability(theta):
 
 #Fit with ptemcee
 #Set the number of cores of your processors
-pool = choose_pool(64)
-pool.size = 64
+pool = choose_pool(16)
+pool.size = 16
 vary_param = float(vary_fund)
 np.random.seed(42)
 pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])

code/RDGW150914_ptemcee4.py

@@ -42,11 +42,11 @@ tshift=19
 vary_fund = False
 
 #sampler parameters
-npoints=102 
-nwalkers = 42
+npoints=13700 
+nwalkers = 840
 ntemps=12
 ndim = int(4*(nmax+1))
-burnin = 20 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
+burnin = 4200 #How many points do you burn before doing the corner plot. You need to watch the convergence of the chain plot a bit.
             #This is trivial but often forgotten: this cannot be more than npoints! Usually 1/5~1/4 npoints is what I observe.
 numbins = 42 #corner plot parameter - how many bins you want
 datacolor = '#105670' #'#4fa3a7'
@@ -178,8 +178,8 @@ def log_probability(theta):
 
 #Fit with ptemcee
 #Set the number of cores of your processors
-pool = choose_pool(64)
-pool.size = 64
+pool = choose_pool(16)
+pool.size = 16
 vary_param = float(vary_fund)
 np.random.seed(42)
 pos = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), 4.28313743e-01, random.uniform(2.5, 2.6) + (1-vary_param) * np.pi]])

code/condor_submit_RdownPtemcee1.sub

@@ -12,8 +12,8 @@ initialdir = .
 notify_user = rl746@cornell.edu
 notification = Complete
 arguments  = "-processid $(Process)" 
-request_memory = 512GB
-request_cpus = 64
+request_memory = 232GB
+request_cpus = 16
 on_exit_remove = (ExitBySignal == False) || ((ExitBySignal == True) && (ExitSignal != 11))
 accounting_group = aei.dev.test_dynesty
 queue 1

code/condor_submit_RdownPtemcee2.sub

@@ -12,8 +12,8 @@ initialdir = .
 notify_user = rl746@cornell.edu
 notification = Complete
 arguments  = "-processid $(Process)" 
-request_memory = 512GB
-request_cpus = 64
+request_memory = 232GB
+request_cpus = 16
 on_exit_remove = (ExitBySignal == False) || ((ExitBySignal == True) && (ExitSignal != 11))
 accounting_group = aei.dev.test_dynesty
 queue 1

code/condor_submit_RdownPtemcee3.sub

@@ -12,8 +12,8 @@ initialdir = .
 notify_user = rl746@cornell.edu
 notification = Complete
 arguments  = "-processid $(Process)" 
-request_memory = 512GB
-request_cpus = 64
+request_memory = 232GB
+request_cpus = 16
 on_exit_remove = (ExitBySignal == False) || ((ExitBySignal == True) && (ExitSignal != 11))
 accounting_group = aei.dev.test_dynesty
 queue 1

code/condor_submit_RdownPtemcee4.sub

@@ -12,8 +12,8 @@ initialdir = .
 notify_user = rl746@cornell.edu
 notification = Complete
 arguments  = "-processid $(Process)" 
-request_memory = 512GB
-request_cpus = 64
+request_memory = 232GB
+request_cpus = 16
 on_exit_remove = (ExitBySignal == False) || ((ExitBySignal == True) && (ExitSignal != 11))
 accounting_group = aei.dev.test_dynesty
 queue 1