some plotting scripts

plotting/agn_corner.py

@@ -6,8 +6,57 @@ import matplotlib
 matplotlib.rcParams.update({'font.size': 16})
 
 
+def getAGNPost(nFreqBins):
 
-def fiveDCorner(data):
+    if nFreqBins==9: 
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==5:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==3:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+
+    #combine the ns 
+    post0And1 = np.concatenate((post0['logn'], post1['logn']))
+    logns = np.concatenate((post0And1, post2['logn']))
+
+    post0And1 = np.concatenate((post0['alpha'], post1['alpha']))
+    alphas = np.concatenate((post0And1, post2['alpha']))
+
+    post0And1 = np.concatenate((post0['delta'], post1['delta']))
+    deltas = np.concatenate((post0And1, post2['delta']))
+
+    post0And1 = np.concatenate((post0['beta'], post1['beta']))
+    betas = np.concatenate((post0And1, post2['beta']))
+
+    post0And1 = np.concatenate((post0['gamma'], post1['gamma']))
+    gammas = np.concatenate((post0And1, post2['gamma']))
+
+    posteriorCombined = {'logn': logns, 
+                         'alpha': alphas, 
+                         'delta': deltas,
+                         'beta': betas,
+                         'gamma': gammas}
+
+    return posteriorCombined
+
+
+
+def fiveDCorner(data,nFBins):
 
     dataMstar = [ np.log10(10.0**(delta+7.0)) for delta in data['delta'] ]
 
@@ -23,18 +72,17 @@ def fiveDCorner(data):
 
     figure = corner.corner(d2,labels=parameterLabels,
                            quantiles=[0.05, 0.5, 0.95])
-    plt.savefig('corner.png')
-   
-    plt.show()
+    plt.savefig('forPaper/agn_fullCorner_fBins{}.pdf'.format(nFBins))
+    plt.savefig('forPaper/agn_fullCorner_fBins{}.png'.format(nFBins))
     return 
 
 
-
-posterior = np.genfromtxt('posterior.dat', names=True)
-fiveDCorner(posterior)
-
+n=9
+posterior = getAGNPost(n)#np.genfromtxt('posterior.dat', names=True)
+fiveDCorner(posterior,n)
 
 
+exit()
 names = ['logn', 'beta', 'gamma', 'alpha', 'delta']
 for i in range(5):
 

plotting/agn_getMlines.py

@@ -4,6 +4,55 @@ import astropy.units as u
 from astropy.cosmology import Planck18 as cosmo
 
 
+def getAGNPost(nFreqBins):
+
+    if nFreqBins==9: 
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==5:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==3:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+
+    #combine the ns 
+    post0And1 = np.concatenate((post0['logn'], post1['logn']))
+    logns = np.concatenate((post0And1, post2['logn']))
+
+    post0And1 = np.concatenate((post0['alpha'], post1['alpha']))
+    alphas = np.concatenate((post0And1, post2['alpha']))
+
+    post0And1 = np.concatenate((post0['delta'], post1['delta']))
+    deltas = np.concatenate((post0And1, post2['delta']))
+
+    post0And1 = np.concatenate((post0['beta'], post1['beta']))
+    betas = np.concatenate((post0And1, post2['beta']))
+
+    post0And1 = np.concatenate((post0['gamma'], post1['gamma']))
+    gammas = np.concatenate((post0And1, post2['gamma']))
+
+    posteriorCombined = {'logn': logns, 
+                         'alpha': alphas, 
+                         'delta': deltas,
+                         'beta': betas,
+                         'gamma': gammas}
+
+    saveLinesHere = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{0}/f{0}_plotting_things/'.format(nFreqBins)
+    return posteriorCombined, saveLinesHere
+
 
 def zInt(beta,gamma,zlow,zhigh):
 
@@ -52,23 +101,25 @@ def dndlog10M(theta,log10Ms):
 
 def main():
 
-    pathToRuns = '../../runs/simpleModel/logNormLikeMstar6to10/'
-    pathToRuns = '../models/agnostic_model/posterior.dat'
-    nRuns=5
-    simpleModelData = np.genfromtxt(pathToRuns,names=True)#readPosterior.readPosterior(pathToRuns, nRuns=nRuns)
+    #pathToRuns = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/nFreq3/posterior.dat'
+    #pathToRuns = '../models/agnostic_model/posterior.dat'
+    
+    nFreqBins=9
+    simpleModelData,resultsDir = getAGNPost(nFreqBins)
+
 
     # set up M range and write to file
     log10MRange = np.linspace(6.0, 11.0, 40)
-    writeMs = open('tmp2_ms.dat'.format(pathToRuns),'w')
+    writeMs = open('{}/ms.dat'.format(resultsDir),'w')
     for logM in log10MRange:
         writeMs.write('{}\n'.format(10.0**logM))
     writeMs.close()
 
     # place to write results 
-    writeLines = open('tmp2_mLines.dat'.format(pathToRuns),'w')    
+    writeLines = open('{}/mLines.dat'.format(resultsDir),'w')    
 
     # compute lines
-    for i in range(len(simpleModelData)):
+    for i in range(len(simpleModelData['logn'])):
         thetas = simpleModelData['logn'][i],  \
                  simpleModelData['beta'][i],  \
                  simpleModelData['gamma'][i], \

plotting/agn_gethLines.py

+import numpy as np
+from scipy.integrate import quad
+import astropy.units as u
+from astropy.cosmology import Planck18 as cosmo
+
+
+import sys
+
+sys.path.insert(1, '../models/agnostic_model/')
+import hmodel
+
+def getAGNPost(nFreqBins):
+
+    if nFreqBins==9: 
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==5:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==3:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+
+    #combine the ns 
+    post0And1 = np.concatenate((post0['logn'], post1['logn']))
+    logns = np.concatenate((post0And1, post2['logn']))
+
+    post0And1 = np.concatenate((post0['alpha'], post1['alpha']))
+    alphas = np.concatenate((post0And1, post2['alpha']))
+
+    post0And1 = np.concatenate((post0['delta'], post1['delta']))
+    deltas = np.concatenate((post0And1, post2['delta']))
+
+    post0And1 = np.concatenate((post0['beta'], post1['beta']))
+    betas = np.concatenate((post0And1, post2['beta']))
+
+    post0And1 = np.concatenate((post0['gamma'], post1['gamma']))
+    gammas = np.concatenate((post0And1, post2['gamma']))
+
+    posteriorCombined = {'logn': logns, 
+                         'alpha': alphas, 
+                         'delta': deltas,
+                         'beta': betas,
+                         'gamma': gammas}
+
+    saveLinesHere = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{0}/f{0}_plotting_things/'.format(nFreqBins)
+    return posteriorCombined, saveLinesHere
+    
+    
+def converthc(Ayr,freq):
+    '''
+    convert from hc at 1/1yr to whatever frequency we want
+    '''
+    # 1/1yr in Hz
+    freq1yr = 1./(1.*u.year.to(u.s))
+    
+    # convert 
+    freqRatio = (freq / freq1yr)
+    hc = Ayr*(freqRatio**(-2./3.))
+
+    log10hc = np.log10(float(hc))
+    return log10hc
+    
+    
+    
+
+
+posteriors, directory = getAGNPost(9)
+nSamples = np.shape(posteriors['delta'])[0]
+
+
+writeLines = open('./agn_h_nF{}.dat'.format(9),'w')
+
+for i in range(nSamples):
+    theta = posteriors['logn'][i],\
+            posteriors['alpha'][i],\
+            posteriors['delta'][i],\
+            posteriors['beta'][i],\
+            posteriors['gamma'][i]
+    # integration limits
+    MlowIntLimit  = 10.0**6.0	
+    MhighIntLimit = 10.0**11.0
+    zlowIntLimit  = 0.0 
+    zhighIntLimit = 5.0 
+    h1yr = hmodel.hmodel(theta,MlowIntLimit,MhighIntLimit,zlowIntLimit,zhighIntLimit)
+    
+    logfs = np.linspace(-9.,-6.,15)
+    
+    hLine = [ converthc(h1yr,10.**logf) for logf in logfs]
+
+    for yi in hLine:        
+        writeLines.write('{}\t'.format(yi))
+    writeLines.write('\n')
+    

plotting/agn_plotChain.py

+import numpy as np
+import matplotlib.pyplot as plt
+
+pathToResults = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/single/'
+run='singleChain2'
+name = '_s2'
+
+
+chains = np.genfromtxt(pathToResults+run+'/posterior.dat', names=True)
+
+nparameters = 5
+fig, axes = plt.subplots(nparameters, figsize=(10, 10), sharex=True)
+
+labels = (['logn', 'beta', 'gamma', 'alpha', 'delta'])
+
+print(labels)
+#labels = ["m", "b", "log(f)"]
+for i in range(nparameters):
+    ax = axes[i]
+    ax.plot(chains[labels[i]], "k", alpha=0.7)
+    ax.set_xlim(0, len(chains))
+    ax.set_ylabel(labels[i],rotation=0)
+    ax.yaxis.set_label_coords(-0.1, 0.5)
+
+
+
+axes[-1].set_xlabel("step number");
+
+
+plt.savefig('{0}{1}/chains{2}.png'.format(pathToResults,run,name))
+plt.show()

plotting/gal_getMlines.py

+import numpy as np
+
+import GAL_PLOT
+
+
+def getchains(nFreqBins):
+
+    
+    if nFreqBins==9:
+        path = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f{0}/ptmcmc_f{0}_1/output/'.format(nFreqBins)
+        saveResultsHere = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f{0}/f{0}_plotting_things_prior'.format(nFreqBins)
+    elif nFreqBins==5: 
+        path = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f5/ptmcmc_2/output/'.format(nFreqBins)
+    elif nFreqBins==3:
+        path = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f3/ptmcmc_f{}_1/output/'.format(nFreqBins)
+
+    readChains = np.genfromtxt('{}/chain_1.txt'.format(path))
+
+    burnin = int(0.25*len(readChains))
+
+    chains = readChains[burnin:,:]
+
+    priorChains = np.genfromtxt('/home/ADF/middlehr/repositories/PTAInference/runs/galaxyModel_ext/prior.dat')
+
+    
+    
+    return chains, priorChains, saveResultsHere
+
+
+chain, prior, directory = getchains(9)
+print(chain)
+print(prior)
+
+
+
+GAL_PLOT.conf_hmz(chain,directory,priorChains=prior)

plotting/gal_hlines.py

+import numpy as np
+
+
+import matplotlib.pyplot as plt
+
+
+
+import matplotlib
+matplotlib.rcParams.update({'font.size': 16})
+
+
+plt.rcParams.update({
+    "text.usetex": True,
+    "font.family": "Helvetica"
+})
+
+
+
+
+
+def getPercentileLines(lineData,xs):
+    print(np.shape(lineData))
+
+    p05,p25,p50,p75,p95 = np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs))
+    
+    for i in range(len(xs)):
+        a = lineData[:,i]
+        p05[i] = np.percentile(a,  5)
+        p25[i] = np.percentile(a, 25)
+        p50[i] = np.percentile(a, 50)
+        p75[i] = np.percentile(a, 75)
+        p95[i] = np.percentile(a, 95)
+
+    return p05, p25, p50, p75, p95
+
+
+orange = '#E69F00'
+blue = '#0072B2'
+green = '#009E73'
+red = '#D55E00'
+
+
+green2 = '#004D40'
+blue2 = '#1E88E5'
+orange2 = '#FFC107'
+red2 = '#D81B60'
+
+colSimp=orange
+colGalExt=green
+colGal = 'k'
+
+agnHs = np.genfromtxt('agn_h_nF9.dat')
+logfs = np.linspace(-9.,-6.,15)
+p05,p25,p50,p75,p95 = getPercentileLines(agnHs,logfs)
+
+
+p05,p25,p50,p75,p95 = getPercentileLines(agnHs,logfs)
+
+print(np.shape(agnHs))
+
+
+fs = [10.**f for f in logfs]
+fs = np.atleast_1d(fs)
+
+plt.fill_between(logfs,p05,p95,alpha=0.3,color=colSimp)
+plt.fill_between(logfs,p25,p75,alpha=0.3,color=colSimp)
+plt.plot(logfs,p50,color=colSimp,ls='--')
+
+
+
+nFreqs=9
+galLinesDir = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f{0}/f{0}_plotting_things/'.format(nFreqs)
+hdata = np.genfromtxt('{}/h_lines.dat'.format(galLinesDir))
+
+print(np.shape(hdata))
+
+p05,p25,p50,p75,p95 = getPercentileLines(hdata,logfs)
+
+fs = [10.**f for f in logfs]
+fs = np.atleast_1d(fs)
+
+plt.fill_between(logfs,p05,p95,alpha=0.3,color=colGalExt)
+plt.fill_between(logfs,p25,p75,alpha=0.3,color=colGalExt)
+plt.plot(logfs,p50,color=colGalExt,ls='--')
+
+
+
+
+plt.ylabel(r'$h_{\rm c}$')
+plt.xlabel(r'$\log_{10} f\,({\rm Hz})$')
+plt.tight_layout()
+plt.savefig('hlines.png')
+plt.show()
+

plotting/gal_plotChains.py

+import numpy as np
+import matplotlib.pyplot as plt
+
+pathToResults = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f9/'
+run='ptmcmc_f9_2'
+name = '_f9_2'
+
+
+chains = np.genfromtxt(pathToResults+run+'/output/chain_1.txt')
+
+nparameters = 18
+fig, axes = plt.subplots(nparameters, figsize=(10, 10), sharex=True)
+
+labels = np.genfromtxt('pars.txt',dtype=str)
+print(labels)
+#labels = ["m", "b", "log(f)"]
+for i in range(nparameters):
+    ax = axes[i]
+    ax.plot(chains[:,i], "k", alpha=0.7)
+    ax.set_xlim(0, len(chains))
+    ax.set_ylabel(labels[i],rotation=0)
+    ax.yaxis.set_label_coords(-0.1, 0.5)
+
+
+
+axes[-1].set_xlabel("step number");
+
+
+plt.savefig('{0}{1}/chains{2}.png'.format(pathToResults,run,name))
+plt.show()

plotting/nhistogram.py

+
+from matplotlib import pylab
+import numpy as np
+from math import ceil
+import matplotlib.pyplot as plt
+
+import matplotlib
+matplotlib.rcParams.update({'font.size': 16})
+
+
+plt.rcParams.update({
+    "text.usetex": True,
+    "font.family": "Helvetica"
+})
+
+
+
+def fpairtest(delta):
+    return 1./(delta+1.)*(1.-0.25**(delta+1.))
+
+def getneffGAL(nFreqBins):
+
+    
+    if nFreqBins==9:
+        path = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f9/ptmcmc_f{}_1/output/'.format(nFreqBins)
+    elif nFreqBins==5: 
+        path = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f5/ptmcmc_2/output/'.format(nFreqBins)
+    elif nFreqBins==3:
+        path = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f3/ptmcmc_f{}_1/output/'.format(nFreqBins)
+
+    chains = np.genfromtxt('{}/chain_1.txt'.format(path))
+
+    burnin = int(0.25*len(chains))
+
+
+    n1 = chains[burnin:,0]-chains[burnin:,2]+np.log10(chains[burnin:,5]/chains[burnin:,9]/fpairtest(chains[burnin:,8]))+chains[burnin:,10]*np.log10(0.4/0.7)+(chains[burnin:,10]-chains[burnin:,6])*(11.-chains[burnin:,2])+11.
+
+
+    priorChains = np.genfromtxt('/home/ADF/middlehr/repositories/PTAInference/runs/galaxyModel_ext/prior.dat')
+
+    priorn1 = priorChains[:,0]-priorChains[:,2]+np.log10(priorChains[:,5]/priorChains[:,9]/fpairtest(priorChains[:,8]))+priorChains[:,10]*np.log10(0.4/0.7)+(priorChains[:,10]-priorChains[:,6])*(11.-priorChains[:,2])+11.
+    
+    return n1, priorn1
+    
+    
+def getnAGN(nFreqBins):
+
+    if nFreqBins==9: 
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq9_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==5:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq5_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+    elif nFreqBins==3:
+        path0 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3/posterior.dat'.format(nFreqBins)
+        path1 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_1/posterior.dat'.format(nFreqBins)
+        path2 = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{}/nFreq3_2/posterior.dat'.format(nFreqBins)
+        post0 = np.genfromtxt(path0,names=True)
+        post1 = np.genfromtxt(path1,names=True)
+        post2 = np.genfromtxt(path2,names=True)
+
+    #combine the ns 
+    post0And1 = np.concatenate((post0['logn'], post1['logn']))
+    logns = np.concatenate((post0And1, post2['logn']))
+
+    return logns
+
+
+def histOutline(dataIn, *args, **kwargs):
+    """
+    code copied from http://www.scipy.org/Cookbook/Matplotlib/UnfilledHistograms?action=AttachFile&do=get&target=histNofill.py
+
+    Make a histogram that can be plotted with plot() so that
+    the histogram just has the outline rather than bars as it
+    usually does.
+
+    Example Usage:
+    binsIn = numpy.arange(0, 1, 0.1)
+    angle = pylab.rand(50)
+
+    (bins, data) = histOutline(binsIn, angle)
+    plot(bins, data, 'k-', linewidth=2)
+
+    """
+
+    (histIn, binsIn) = np.histogram(dataIn, *args, **kwargs)
+
+    stepSize = binsIn[1] - binsIn[0]
+
+    bins = np.zeros(len(binsIn)*2 + 2, dtype=float)
+    data = np.zeros(len(binsIn)*2 + 2, dtype=float)    
+    for bb in range(len(binsIn)):
+        bins[2*bb + 1] = binsIn[bb]
+        bins[2*bb + 2] = binsIn[bb] + stepSize
+        if bb < len(histIn):
+            data[2*bb + 1] = histIn[bb]
+            data[2*bb + 2] = histIn[bb]
+
+    bins[0] = bins[1]
+    bins[-1] = bins[-2]
+    data[0] = 0
+    data[-1] = 0
+    
+    return (bins, data)
+
+def plotOneDPosterior(data,paramLabel,setBins,colour='k-',label='',prior=False):
+
+    #pathToRuns = "../../runs/simpleModelPosteriors/"
+    #setBins = np.arange(int(min(data[paramName])), ceil(max(data[paramName])),0.5)
+    #setBins = np.linspace(min(data[paramName]), max(data[paramName]),50)
+    #setBins = np.linspace(-14.5,2.5,50)
+
+    p05 = np.percentile(data,5)
+    p50 = np.percentile(data,50)
+    p95 = np.percentile(data,95)
+    
+
+    if prior==True: 
+        plt.hist(data, bins=setBins, histtype='step', facecolor=colour, alpha=0.3, edgecolor=None,density=1,fill=True,label=label)
+
+    else: 
+        (bins,dat) = histOutline(data,setBins,density=1)
+        pylab.plot(bins,dat,colour,linewidth=2,alpha=1.0,label=label)
+        plt.axvline(p05,ls=':',color=colour)
+        plt.axvline(p95,ls=':',color=colour)
+    plt.xlabel(paramLabel)
+    plt.ylabel(r'${\rm Normalised~counts}$')
+    plt.tight_layout()
+    #plt.savefig('{}lognoHist.pdf'.format(runLoc))
+    #plt.savefig('logno.png')
+    #pylab.show()
+
+
+    #print("""{}
+    #05%: {:.2f} ({})
+    #50%: {:.2f} ({})
+    #95%: {:.2f} ({})
+    #""".format(label,p05,10**p05,p50,10.**p50,p95,10.**p95))
+    #
+    print("""{}
+    {:.2f}^{{+{:.2f}}}_{{-{:.2f}}}""".format(label, p50, p95-p50, p50-p05))
+
+    return 0
+
+
+
+n = 9
+
+print('Number of frequency bins = {}'.format(n))
+
+
+neffGAL, neffPriorGAL = getneffGAL(n)
+nAGN = getnAGN(n)
+
+# bins
+low  = int(min(nAGN))
+high = ceil(max(nAGN))
+bins = np.linspace(low,high,80)
+
+orange = '#E69F00'
+blue = '#0072B2'
+green = '#009E73'
+red = '#D55E00'
+purple = '#CC79A7'
+
+colSimp=orange
+colGalExt=green
+#colGal = 'k'
+#colSimpNegAlpha = red
+
+lognoLabel = r'$\log_{10}\frac{{\dot n}_0}{{\rm Mpc}^{3}{\rm Gyr}}$'
+
+plotOneDPosterior(nAGN,lognoLabel,bins,colour=colSimp,label=r'$\textrm{Agnostic posterior}$')
+plotOneDPosterior(neffGAL,lognoLabel,bins,colour=colGalExt,label=r'$\textrm{Astro-informed~posterior}$')
+plotOneDPosterior(neffPriorGAL,lognoLabel,bins,colour=colGalExt,label=r'$\textrm{Astro-informed prior}$',prior=True)
+plt.legend(fontsize=12)
+plt.xlim(min(bins),max(bins))
+plt.savefig('forPaper/nhistogram_nFreq{}.png'.format(n))
+plt.savefig('forPaper/nhistogram_nFreq{}.pdf'.format(n))
+plt.show()
+
+
+

plotting/pars.txt

+Phi0
+PhiI
+M0
+alpha0
+alphaI
+f0
+betaf
+alphaf
+gammaf
+tau0
+alphatau
+betatau
+gammatau
+Mstar
+alphastar
+epsilon
+e0
+zeta0
+lnprob
+lnlike
+acc_rate
+temp

plotting/plotData.py

+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import gaussian_kde as kde
+
+
+def readChains(nFreqBins):
+
+    ''' 
+    read in the data and return the lowest nFreqBins
+    chains and frequencies
+    '''
+    frequencies = np.genfromtxt('../chains/dr2new_fs/freqs.txt')
+    samples = np.genfromtxt('../chains/dr2new_fs/chains_h.txt')
+
+    chainsToUse = samples[:,0:nFreqBins]
+    frequenciesToUse = frequencies[0:nFreqBins]
+    
+    return frequenciesToUse, chainsToUse
+
+
+
+def makeKDEs(samples,nFreqBins):
+    '''
+    make the KDEs to use in the likelihood
+    '''
+   
+    KDE = {i: kde(samples[:,i]) for i in range(nFreqBins)}
+
+    return KDE
+
+
+f,data = readChains(5)
+kdes = makeKDEs(data,len(f))
+
+hcsToPlot = np.linspace(-17,-13,20)
+
+
+for i in range(len(f)):
+
+    chain = data[:,i]
+    kdeToPlot = kdes[i].logpdf(hcsToPlot)
+    print(kdeToPlot)
+    
+    plt.hist(chain,density=True,histtype='step',bins=50)
+    plt.plot(hcsToPlot,kdeToPlot)
+    plt.show()

plotting/plotMLines.py

+""" 
+plot distribution in chirp mass
+used to make fugure 3 in paper
+"""
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+
+
+import matplotlib
+matplotlib.rcParams.update({'font.size': 16})
+
+
+plt.rcParams.update({
+    "text.usetex": True,
+    "font.family": "Helvetica"
+})
+
+
+def getPercentileLines(lineData,xs):
+    print(np.shape(lineData))
+
+    p01,p05,p25,p50,p75,p95,p99 = np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs)), \
+                          np.zeros(len(xs))
+    
+    for i in range(len(xs)):
+        a = lineData[:,i]
+        p01[i] = np.percentile(a,  0.5)        
+        p05[i] = np.percentile(a,  5)
+        p25[i] = np.percentile(a, 25)
+        p50[i] = np.percentile(a, 50)
+        p75[i] = np.percentile(a, 75)
+        p95[i] = np.percentile(a, 95)
+        p99[i] = np.percentile(a, 99.5)
+    return p01, p05, p25, p50, p75, p95, p99
+    
+    
+
+orange = '#E69F00'
+blue = '#0072B2'
+green = '#009E73'
+red = '#D55E00'
+
+
+green2 = '#004D40'
+blue2 = '#1E88E5'
+orange2 = '#FFC107'
+red2 = '#D81B60'
+
+colSimp=orange
+colGalExt=green
+colGal = 'k'
+
+nFreqs = 9
+agnLinesDir = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/agn_model/f{0}/f{0}_plotting_things/'.format(nFreqs)
+lines = np.genfromtxt('{}/mLines.dat'.format(agnLinesDir))
+
+simpModMs = np.genfromtxt('{}/ms.dat'.format(agnLinesDir))
+
+
+print(type(simpModMs),np.shape(simpModMs))
+_, p05, p25, p50, p75, p95, _ = getPercentileLines(lines,simpModMs)
+
+
+plt.fill_between(simpModMs,p05,p95,alpha=0.3,color=colSimp)
+plt.fill_between(simpModMs,p25,p75,alpha=0.3,color=colSimp)
+plt.plot(simpModMs,p50,color=colSimp,ls='--')
+
+
+
+
+
+
+
+plt.ylim(1E-9,1E6)
+plt.xlim(1E6,1E11)
+plt.yscale('log')
+plt.xscale('log')
+plt.xlabel(r'$\mathcal{M}~~({\rm M_{\odot}})$')
+plt.ylabel(r'${\rm d}n / {\rm d} \log_{10} \mathcal{M}/{\rm M_{\odot}}~~({\rm Mpc}^{-3})$')
+plt.tight_layout()
+#plt.savefig('dndlogM_3FreqBins.png')
+
+
+
+
+
+galLinesDir = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f{0}/f{0}_plotting_things/'.format(nFreqs)
+galExtModData = np.genfromtxt('{}/m_lines.dat'.format(galLinesDir))
+galExtModLogMs = np.linspace(5,11,30)
+
+print(type(galExtModData),np.shape(galExtModData))
+
+galExtModMs = [ 10.0**logM for logM in galExtModLogMs ]
+galExtModMs = np.atleast_1d(galExtModMs)
+
+_, p05,p25,p50,p75,p95, _ = getPercentileLines(galExtModData,galExtModMs)
+
+plt.fill_between(galExtModMs,p05,p95,alpha=0.3,color=colGalExt)
+plt.fill_between(galExtModMs,p25,p75,alpha=0.3,color=colGalExt,label='new posterior')
+plt.plot(galExtModMs,p50,color=colGalExt,ls='--')
+
+#plt.savefig('forPaper/mlines_nFreq{}.pdf'.format(nFreqs))
+#plt.savefig('forPaper/mlines_nFreq{}.png'.format(nFreqs))
+
+#plt.show()
+#exit()
+
+galPriorLinesDir = '/home/ADF/middlehr/Documents/PTA/EPTAInterp/results/gal_model/f{0}/f{0}_plotting_things_prior/'.format(nFreqs)
+galExtModPrior = np.genfromtxt('{}/m_linesprior.dat'.format(galPriorLinesDir))
+p01,p05,p25,p50,p75,p95,p99= getPercentileLines(galExtModPrior,galExtModMs)
+plt.plot(galExtModMs, p01, color='k', alpha=0.7, ls=':',label='newly computed prior')
+plt.plot(galExtModMs, p99, color='k', alpha=0.7, ls=':')
+
+
+
+"""
+galModRunLoc = '../../runs/galaxyModel/'
+galModData = np.genfromtxt('{}/m_lines.dat'.format(galModRunLoc))
+galModLogMs = np.genfromtxt('{}/mc.txt'.format(galModRunLoc))
+galModMs = [ 10.0**logM for logM in galModLogMs ]
+
+p05,p25,p50,p75,p95 = confBands.getPercentiles(galModMs,galModData)
+
+plt.fill_between(galModMs,p05,p95,alpha=0.3,color=colGal)
+plt.fill_between(galModMs,p25,p75,alpha=0.3,color=colGal)
+plt.plot(galModMs,p50,color=colGal,ls='--')
+"""
+
+
+
+galPriorLinesDir = '/home/ADF/middlehr/repositories/PTAInference/runs/galaxyModel_ext/'
+galExtModPrior = np.genfromtxt('{}/m_linesprior.dat'.format(galPriorLinesDir))
+p01,p05,p25,p50,p75,p95,p99= getPercentileLines(galExtModPrior,galExtModMs)
+plt.plot(galExtModMs, p01, color='k', alpha=0.7, ls='-.',label='M21 prior')
+plt.plot(galExtModMs, p99, color='k', alpha=0.7, ls='-.')
+
+
+
+
+galLinesDir = '/home/ADF/middlehr/repositories/PTAInference/runs/galaxyModel_ext/'
+galExtModData = np.genfromtxt('{}/m_lines.dat'.format(galLinesDir))
+
+_, p05,p25,p50,p75,p95, _ = getPercentileLines(galExtModData,galExtModMs)
+
+plt.fill_between(galExtModMs,p05,p95,alpha=0.2,color='b',label='M21 posterior')
+plt.fill_between(galExtModMs,p25,p75,alpha=0.2,color='b')
+plt.plot(galExtModMs,p50,color='b',ls='--')
+
+plt.legend()
+
+plt.ylim(1E-9,1E6)
+plt.xlim(1E6,1E11)
+plt.yscale('log')
+plt.xscale('log')
+plt.xlabel(r'$\mathcal{M}~~({\rm M_{\odot}})$')
+plt.ylabel(r'${\rm d}n / {\rm d} \log_{10} \mathcal{M}/{\rm M_{\odot}}~~({\rm Mpc}^{-3})$')
+plt.tight_layout()
+plt.savefig('forPaper/mlines_nFreq{}.pdf'.format(nFreqs))
+plt.savefig('forPaper/mlines_nFreq{}.png'.format(nFreqs))
+
+plt.show()