removing and purging

code/RDGW150914_ptemceeX.py

-#!/usr/bin/env python
-# coding: utf-8
-'''
-This script calculates the RDGW150914 constraints with ptemcee - specifically, the n=1 case both varying or not varying the fundamental frequencies. It produces the chain plot, corner plot, parameter constraints, and data plotted with the 1-sigma band. Since we are working specifically for the n=1 case, we also add in the corner plot the combined chain of alpha_0 and alpha_1, in order to demonstrate that the two are indistinguishable with each other.
-
---- R^a_{yne} L^i_u, 08/09/2020
-'''
-
-import numpy as np
-import corner
-import matplotlib.pyplot as plt
-from matplotlib.ticker import MaxNLocator
-from matplotlib import rc
-#plt.rcParams['font.family'] = 'DejaVu Sans'
-#rc('text', usetex=True)
-plt.rcParams.update({'font.size': 16.5})
-
-import ptemcee
-from pycbc.pool import choose_pool
-#from multiprocessing import Pool
-import math
-import h5py
-import inspect
-import pandas as pd
-import json
-import qnm
-import random
-from scipy.optimize import minimize
-
-#Remember to change the following global variables
-#rootpath: root path to nr data
-#npoints: number of points you re using for your sampling
-#nmax: tone index --> nmax = 0 if fitting the fundamental tone
-#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="/Users/xisco"
-nmax=1
-tshift=19
-vary_fund = True
-
-#sampler parameters
-npoints=101 
-nwalkers = 42
-ntemps=12
-ndim = int(4*(nmax+1))
-burnin = 10 #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 = 21 #corner plot parameter - how many bins you want
-datacolor = '#105670' #'#4fa3a7'
-pkcolor = '#f2c977' #'#ffb45f'
-mediancolor = '#f7695c' #'#9b2814'
-
-#Import data and necessary functions
-
-#TimeOfMaximum
-def FindTmaximum(y):
-    #Determines the maximum absolute value of the complex waveform
-    absval = y[:,1]*y[:,1]+y[:,2]*y[:,2]
-    vmax=np.max(absval)
-    index = np.argmax(absval == vmax)
-    timemax=gw_sxs_bbh_0305[index,0]
-    return timemax
-
-
-
-
-#This loads the 22 mode data
-gw = {}
-gw["SXS:BBH:0305"] = h5py.File(rootpath+"/SXS/BBH_SKS_d14.3_q1.22_sA_0_0_0.330_sB_0_0_-0.440/Lev6/rhOverM_Asymptotic_GeometricUnits_CoM.h5", 'r')
-gw_sxs_bbh_0305 = gw["SXS:BBH:0305"]["Extrapolated_N2.dir"]["Y_l2_m2.dat"]
-
-# Remember to download metadata.json from the simulation with number: 0305. Download Lev6/metadata.json
-# This postprocesses the metadata file to find the final mass and final spin
-metadata = {}
-with open(rootpath+"/git/rdstackingproject/SXS/BBH_SKS_d14.3_q1.22_sA_0_0_0.330_sB_0_0_-0.440/Lev6/metadata.json") as file:
-    metadata["SXS:BBH:0305"] = json.load(file)
-
-af = metadata["SXS:BBH:0305"]['remnant_dimensionless_spin'][-1]
-mf = metadata["SXS:BBH:0305"]['remnant_mass']
-
-
-
-#times --> x axis of your data
-times = gw_sxs_bbh_0305[:,0]
-tmax=FindTmaximum(gw_sxs_bbh_0305)
-t0=tmax +tshift
-
-#Select the data from t0 onwards
-position = np.argmax(times >= (t0))
-gw_sxs_bbh_0305rd=gw_sxs_bbh_0305[position:-1]
-timesrd=gw_sxs_bbh_0305[position:-1][:,0]
-
-# Depending on nmax, you load nmax number of freqs. and damping times from the qnm package
-omegas = [qnm.modes_cache(s=-2,l=2,m=2,n=i)(a=af)[0] for i in range (0,nmax+1)]
-
-
-
-
-#Fitting
-#RD model for nmax tones. Amplitudes are in (xn*Exp[i yn]) version. Used here.
-def model_dv(theta):
-    #x0, y0= theta
-    #Your nmax might not align with the dim of theta. Better check it here.
-    assert int(len(theta)/4) == nmax + 1, 'Please recheck your n and parameters'
-    w = (np.real(omegas))/mf
-    tau=-1/(np.imag(omegas))*mf
-    dim =int(len(theta)/4)        
-    
-    avars = theta[ : (nmax+1)]
-    bvars = theta[(nmax+1) : 2*(nmax+1)]
-    xvars = theta[2*(nmax+1) : 3*(nmax+1)]
-    yvars = theta[3*(nmax+1) : ]
-    
-    if vary_fund == False:
-        avars[0]=0
-        bvars[0]=0
-        
-    ansatz = 0
-    for i in range (0,dim):
-        #bvars[1]=0
-        #avars[1]=0
-        ansatz += (xvars[i]*np.exp(1j*yvars[i]))*np.exp(-(timesrd-timesrd[0])/(tau[i]*(1+bvars[i]))) * (np.cos((1+avars[i])*w[i]*timesrd)-1j*np.sin((1+avars[i])*w[i]*timesrd))
-    # -1j to agree with SXS convention
-    return ansatz
-
-
-# Logprior distribution. It defines the allowed range my variables can vary over. 
-#It works for the (xn*Exp[iyn]) version. 
-def log_prior(theta): 
-    #Warning: we are specifically working with nmax=1 so here individual prior to the parameters are manually adjusted. This does not apply to all other nmax's.
-    a_0 = theta[0]
-    a_1 = theta[1]
-    b_0 = theta[2]
-    b_1 = theta[3]
-    x_0 = theta[4]
-    x_1 = theta[5]
-    y_0 = theta[6]
-    y_1 = theta[7]
-
-    if all([nmax == 1, 0 <= tshift <= 5, vary_fund == True, 0 <= x_0 <= 2.0, 0 <= y_0 <= 2*np.pi, -0.4 <= a_0 <= 0.4, -1.0 <= b_0 <= 1.6, 0 <= x_1 <= 1.8, 0 <= y_1 <= 2*np.pi, -1.0 <= a_1 <= 1.6, -1.0 <= b_1 <= 2.0]):        
-        return 0.0
-    elif all([nmax == 1, tshift == 19, vary_fund == True, 0 <= x_0 <= 1.5, 0 <= y_0 <= 2*np.pi, -1.0 <= a_0 <= 3.0, -1.0 <= b_0 <= 2.0, 0 <= x_1 <= 1.8, 0 <= y_1 <= 2*np.pi, -1.0 <= a_1 <= 3.0, -1.0 <= b_1 <= 2.0]):
-        return 0.0
-    
-#PAY EXTRA ATTENTION TO THESE TWO CASES, SINCE WE SHIFTED y_0. THE CORNER PLOTS LABELS NEED TO BE TREATED WITH CARE.
-    elif all([nmax == 1, 0 <= tshift <= 5, vary_fund == False, 0 <= x_0 <= 2.0, 0 <= y_0-np.pi <= 2*np.pi, -1.0 <= a_0 <= 1.0, -1.0 <= b_0 <= 1.0, 0 <= x_1 <= 1.6, 0 <= y_1 <= 2*np.pi, -1.0 <= a_1 <= 1.2, -1.0 <= b_1 <= 2.8]):
-        return 0.0
-    elif all([nmax == 1, tshift == 19, vary_fund == False, 0 <= x_0 <= 0.6, 0 <= y_0-np.pi <= 2*np.pi, -1.0 <= a_0 <= 1.0, -1.0 <= b_0 <= 1.0, 0 <= x_1 <= 1.2, 0 <= y_1 <= 2*np.pi, -1.0 <= a_1 <= 3.0, -1.0 <= b_1 <= 2.0]):
-        return 0.0
-
-    return -np.inf
-
-
-# LogLikelihood function. It is just a Gaussian loglikelihood based on computing the residuals^2
-def log_likelihood(theta):
-    modelev = model_dv(theta)
-    return  -np.sum((gw_sxs_bbh_0305rd[:,1] - (modelev.real))**2+(gw_sxs_bbh_0305rd[:,2] - (modelev.imag))**2)
-
-
-# Logposterior distribution for the residuals case.
-# The evidence is just a normalization factor
-def log_probability(theta):
-    lp = log_prior(theta)
-    #print('lp:')
-    #print(lp)
-    if not np.isfinite(lp):
-        return -np.inf
-    return lp + log_likelihood(theta)
-
-
-
-
-#Fit with ptemcee
-#Set the number of cores of your processors
-pool = choose_pool(4)
-pool.size = 4
-vary_param = float(vary_fund)
-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]])
-for i in range (1,nmax+1):
-    pos_aux = np.array([[random.uniform(-0.1,0.1), random.uniform(-0.1,0.1), random.uniform(0.3,0.4), random.uniform(2.01, 2.02) + (1-vary_param) * np.pi]])
-    pos = np.concatenate((pos, pos_aux), axis = 0)
-    
-pos = pos.T.flatten()
-pos = list(pos)
-pos += 1e-5 * np.random.randn(ntemps, nwalkers, ndim)
-#print(pos)
-
-sampler = ptemcee.Sampler(nwalkers, ndim, log_likelihood, log_prior, ntemps=ntemps, pool=pool)
-sampler.run_mcmc(pos,npoints)
-
-
-
-
-#Define labels and start plotting
-paramlabels_a = [r'$\alpha_'+str(i)+'$' for i in range (nmax+1)]
-paramlabels_b = [r'$\beta_'+str(i)+'$' for i in range (nmax+1)]
-paramlabels_x = [r'$x_'+str(i)+'$' for i in range (nmax+1)]
-paramlabels_y = [r'$y_'+str(i)+'$' for i in range (nmax+1)] if vary_fund == True else ['$y_'+str(i)+'-\pi$' for i in range (nmax+1)]
-paramlabels = paramlabels_a + paramlabels_b + paramlabels_x + paramlabels_y
-#Need to delete alpha_0 and alpha_1 for the corner plot
-paramlabels_corner = paramlabels_a + paramlabels_b + paramlabels_x + paramlabels_y
-if vary_fund == False:
-    del paramlabels_corner[0]
-    del paramlabels_corner[1]
-
-
-
-#Chain plot
-fig, axes = plt.subplots(4*(nmax+1), 1, sharex=True, figsize=(12, 9*(nmax+1)))
-for i in range(4*(nmax+1)):
-    axes[i].plot(sampler.chain[0,:, :, i].T, color="k", alpha=0.4)
-    axes[i].yaxis.set_major_locator(MaxNLocator(5))
-    axes[i].set_ylabel(paramlabels[i])
-axes[-1].set_xlabel('Iterations')
-#plt.show()
-fig.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_chain.pdf', format = 'pdf')
-
-
-
-#Burn samples, calculate peak likelihood value (not necessarily so in atlas) and make corner plot
-samples = sampler.chain[0,:, burnin:, :].reshape((-1, ndim))
-#samples for corner plot
-samples_corn = samples if vary_fund == True else np.delete(samples, np.s_[0,2], 1)
-#print('Values with peak likelihood:')
-lglk = np.array([log_likelihood(samples[i]) for i in range(len(samples))])
-pk = samples[np.argmax(lglk)]
-#print('pk:')
-#print(pk)
-pk_corn = pk if vary_fund == True else np.delete(pk, [0,2])
-#print('pkFalse:')
-#print(pk)
-    
-#print(pk) 
-#Now calculate median (50-percentile) value
-median = np.median(samples_corn, axis=0)
-#print(samples)
-#print(samples_corn)
-
-figcorn = corner.corner(samples_corn, bins = numbins, hist_bin_factor = 5, color = datacolor, truths=pk_corn, truth_color = pkcolor, plot_contours = True, labels = paramlabels_corner, quantiles=(0.05, 0.16, 0.5, 0.84, 0.95), levels=[1-np.exp(-0.5), 1-np.exp(-1.64 ** 2/2)], show_titles=True)
-
-#Extract the axes in order to add more important line plots
-naxes = len(pk_corn)
-axes = np.array(figcorn.axes).reshape((naxes, naxes))
-
-# Loop over the diagonal
-for i in range(naxes):
-    ax = axes[i, i]
-    ax.axvline(median[i], color=mediancolor)
-
-# Loop over the histograms
-for yi in range(naxes):
-    for xi in range(yi):
-        ax = axes[yi, xi]
-        ax.axvline(median[xi], color=mediancolor)
-        ax.axhline(median[yi], color=mediancolor)
-        ax.plot(median[xi], median[yi], color = mediancolor, marker = 's')
-figcorn.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_corner.pdf', format = 'pdf')
-
-
-
-#Now plot alpha_0 on top of alpha_1 - only on top, not stacked, and only valid for vary_fund == True
-if vary_fund == True:
-    samplea0 = samples.T[0]
-    samplea1 = samples.T[1]
-    fighist1 = plt.figure(figsize = (8, 6))
-    n0, bins0, patches0 = plt.hist(samplea0, bins = numbins * 6, alpha = 0.5, label = r'$\alpha_0$')
-    n1, bins1, patches1 = plt.hist(samplea1, bins = numbins * 10, alpha = 0.5, label = r'$\alpha_1$')
-    #n01, bins01, patches01 = plt.hist([samplea0, samplea1], numbins * 10, rwidth = 3, alpha = 0.5, label = [r'$\alpha_0$', r'$\alpha_1$'])
-    plt.legend()
-    fighist1.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_histtop.pdf', format = 'pdf')
-
-    #This plot is stacked
-    fighist1 = plt.figure(figsize = (8, 6))
-    sn01, sbins01, spatches01 = plt.hist([samplea0, samplea1], numbins * 10, rwidth = 1, stacked = True, alpha = 0.5, label = [r'$\alpha_0$', r'$\alpha_1$'])
-    plt.legend()
-    fighist1.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_histstack.pdf', format = 'pdf')
-
-
-    
-#Now plot the NR data against the mcmc fit data, together with the 1-sigma varying error data
-onesig_bounds = np.array([np.percentile(samples[:, i], [16, 84]) for i in range(len(samples[0]))]).T
-modelfitpk = model_dv(pk)
-figband = plt.figure(figsize = (12, 9))
-#Plot the 1-sigma_percentile
-for j in range(len(samples)):
-    sample = samples[j]
-    if np.all(onesig_bounds[0] <= sample) and np.all(sample <= onesig_bounds[1]):
-        plt.plot(timesrd, model_dv(sample).real, "#79CAF2", alpha=0.3)
-    
-plt.plot(timesrd, gw_sxs_bbh_0305rd[:,1], "k", alpha=0.7, lw=2, label=r'NR_re')
-plt.plot(timesrd, modelfitpk.real, "r", alpha=0.7, lw=2, label=r'FitMCmax_re')
-plt.title(r'Comparison of the MC fit data and the $1-\sigma$ error band')
-plt.legend()
-plt.xlim(timesrd[0], timesrd[0]+80)
-plt.xlabel("t")
-plt.ylabel("h")
-
-figband.savefig(rootpath+'/git/rdstackingproject/plotsmc/vary'+str(vary_fund)+'nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'pt_band.pdf', format = 'pdf')

code/condor_submit_RdownPtemcee4X.sub

+universe   = vanilla
+getenv     = true
+# run script -- make sure that condor has execute permission for this file (chmod a+x script.py)
+executable = RDGW150914_ptemceeX.py
+# file to dump stdout (this directory should exist)
+output     = RDGW150914_ptemceeX.out
+# file to dump stderr
+error     = RDGW150914_ptemceeX.err
+# condor logs
+log     = RDGW150914_ptemceeX.log
+initialdir = .
+notify_user = rl746@cornell.edu
+notification = Complete
+arguments  = "-processid $(Process)" 
+request_memory = 16GB
+request_cpus = 1
+on_exit_remove = (ExitBySignal == False) || ((ExitBySignal == True) && (ExitSignal != 11))
+accounting_group = aei.dev.test_dynesty
+queue 1
+