label='$N=%i, \mu=%.2f$, $\sigma=%.2f$' %
(len(outside_dist_dH), NLSSmu, NLSSsigma),
color='white')
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Frequency')
plt.title(
'Histogram of luminosity distance diviation for supernovae of \ncluster origin and not of cluster origin'
)
plt.legend()
plt.show()
within_dist_dH.sort()
outside_dist_dH.sort()
plt.plot(within_dist_dH,
fun.cumfreq(within_dist_dH),
label="Cluster Origin, (N=%i)" % len(within_dist_dH),
color='blue')
plt.plot(outside_dist_dH,
fun.cumfreq(outside_dist_dH),
label="Not cluster origin (N=%i)" % len(outside_dist_dH),
color='green')
emptyNLSS = plt.hist([],
range=[-1, 1],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(within_dist_dH, outside_dist_dH)[0],
ks_2samp(within_dist_dH, outside_dist_dH)[1]),
color='white')
plt.legend()
#plt.axis([-0.5, 0.5, 0, 1])
Hinmean = sum(Hin) / len(Hin)
Hinerror = fun.stdev(Hin, Hinmean) / np.sqrt(len(Hin))
print('Hubble Constant within distance to cluster: ', Hinmean, ' pm ',
Hinerror)
Hout = [fun.HubbleLCDM(x[2], fun.D(x[0])) for x in SNeout]
Houtmean = sum(Hout) / len(Hout)
Houterror = fun.stdev(Hout, Houtmean) / np.sqrt(len(Hout))
print('Hubble Constant within distance to cluster: ', Houtmean, ' pm ',
Houterror)
####CUMFREQ
cin.sort()
cout.sort()
plt.plot(cin,
fun.cumfreq(cin),
label="Cluster Origin, (N=%i)" % len(cin),
color='blue')
plt.plot(cout,
fun.cumfreq(cout),
label="Not cluster origin (N=%i)" % len(cout),
color='green')
emptyNLSS = plt.hist([],
range=[-1, 1],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(cin, cout)[0], ks_2samp(cin, cout)[1]),
color='white')
plt.legend()
#plt.axis([-0.5, 0.5, 0, 1])
plt.xlabel('$\delta d_L$')
print(ks_2samp(dHin, dHout))
'''
####H values
Hin = [fun.HubbleLCDM(x[2], fun.D(x[0])) for x in SNein]
Hinmean = sum(Hin)/len(Hin)
Hinerror = fun.stdev(Hin, Hinmean)/np.sqrt(len(Hin))
print('Hubble Constant within distance to cluster: ', Hinmean, ' pm ', Hinerror)
Hout = [fun.HubbleLCDM(x[2], fun.D(x[0])) for x in SNeout]
Houtmean = sum(Hout)/len(Hout)
Houterror = fun.stdev(Hout, Houtmean)/np.sqrt(len(Hout))
print('Hubble Constant within distance to cluster: ', Houtmean, ' pm ', Houterror)
'''
####CUMFREQ
dHin.sort()
dHout.sort()
plt.plot(dHin, fun.cumfreq(dHin), label="Cluster Origin, (N=%i)"%len(dHin), color='blue')
plt.plot(dHout, fun.cumfreq(dHout), label="Not cluster origin (N=%i)"%len(dHout), color='green')
emptyNLSS = plt.hist([], range=[-1,1], alpha=0.5, label='Stat=%.3f \np-val=%.4f'%(ks_2samp(dHin, dHout)[0], ks_2samp(dHin, dHout)[1]), color='white')
plt.legend()
#plt.axis([-0.5, 0.5, 0, 1])
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Cumulative frequency')
plt.title('Cumulative frequency graph for SN of cluster and non-cluster origin')
plt.show()
Hin = [fun.HubbleLCDM(x[2], fun.D(x[0])) for x in SNein]
Hinmean = sum(Hin)/len(Hin)
Hinerror = fun.stdev(Hin, Hinmean)/np.sqrt(len(Hin))
print('Hubble Constant within distance to cluster: ', Hinmean, ' pm ', Hinerror)
Hout = [fun.HubbleLCDM(x[2], fun.D(x[0])) for x in SNeout]
Houtmean = sum(Hout)/len(Hout)
Houterror = fun.stdev(Hout, Houtmean)/np.sqrt(len(Hout))
print('Hubble Constant within distance to cluster: ', Houtmean, ' pm ', Houterror)
'''
####CUMFREQ
dHin.sort()
dHout.sort()
plt.plot(dHin,
fun.cumfreq(dHin),
label="Cluster Origin, (N=%i)" % len(dHin),
color='blue')
plt.plot(dHout,
fun.cumfreq(dHout),
label="Not cluster origin (N=%i)" % len(dHout),
color='green')
emptyNLSS = plt.hist([],
range=[-1, 1],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(dHin, dHout)[0], ks_2samp(dHin, dHout)[1]),
color='white')
plt.legend()
#plt.axis([-0.5, 0.5, 0, 1])
plt.xlabel('% Deviation from expected distance')
alpha=0.5,
label='$N=%i, \mu=%.2f$, $\sigma=%.2f$' %
(len(small_dH), smallmu, smallsigma),
color='white')
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Frequency')
plt.title(
'Histogram of deviation from expected distance for small and its corresponding dipole supernovae'
)
plt.legend()
plt.show()
large_dH.sort()
small_dH.sort()
plt.plot(large_dH, fun.cumfreq(large_dH), label="Maximal H0", color='blue')
plt.plot(small_dH, fun.cumfreq(small_dH), label="Minimal H0", color='red')
emptyNLSS = plt.hist(
[],
range=[-0.3, 0.3],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(large_dH, small_dH)[0], ks_2samp(large_dH, small_dH)[1]),
color='white')
plt.xlabel('$\delta d_L$')
plt.ylabel('Frequency')
plt.title(
'Cumulative frequency for the deviations \nof SNe found in the area of maximal and minimal expansion.'
)
plt.legend()
plt.show()
alpha=0.5,
label='$N=%i, \mu=%.2f$, $\sigma=%.2f$' %
(len(SGC_dH), SGCmu, SGCsigma),
color='white')
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Frequency')
plt.title(
'Histogram of deviation from expected distance for SGC and its corresponding dipole supernovae'
)
plt.legend()
plt.show()
NGC_dH.sort()
SGC_dH.sort()
plt.plot(NGC_dH, fun.cumfreq(NGC_dH), label="NGC", color='blue')
plt.plot(SGC_dH, fun.cumfreq(SGC_dH), label="SGC", color='red')
emptyNLSS = plt.hist(
[],
range=[-0.3, 0.3],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(NGC_dH, SGC_dH)[0], ks_2samp(NGC_dH, SGC_dH)[1]),
color='white')
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Frequency')
plt.title(
'Cumulative frequency for the deviations in the SGC and its corresponding dipole.'
)
plt.legend()
plt.show()
sys.path.insert(0, 'C:\Python34\masters\graphs')
import functions as fun
##Create two Gaussian distributions of random numbers, differing
##only in a standard deviation of 0.2.
dist1 = np.random.normal(0, 1, 500)
dist2 = np.random.normal(0, 0.8, 2000)
print(ks_2samp(dist1, dist2)) ##Perform a KS test
dist1.sort() ##Sort the two distributions from low to high to create
dist2.sort() ##a cumulative frequency plot
##Plotting
plt.plot(dist1, fun.cumfreq(dist1), label="Distribution 1", color='blue')
plt.plot(dist2, fun.cumfreq(dist2), label="Distribution 2", color='red')
emptyNLSS = plt.hist([],
range=[-0.3, 0.3],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(dist1, dist2)[0], ks_2samp(dist1, dist2)[1]),
color='white')
plt.xlabel('Value')
plt.ylabel('Cumulative Frequency')
plt.legend()
plt.show()
##Empirical p-value determination
bothdist = np.append(dist1, dist2) ##Parent distribution
trials = 10000
label='$N=%i, \mu=%.2f$, $\sigma=%.2f$' %
(len(SGC_dH), SGCmu, SGCsigma),
color='white')
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Frequency')
plt.title(
'Histogram of deviation from expected distance for supernovae\n from maximal and minimal accelerating regions'
)
plt.legend()
plt.show()
NGC_dH.sort()
SGC_dH.sort()
plt.plot(NGC_dH,
fun.cumfreq(NGC_dH),
label="Maximal acceleration",
color='blue')
plt.plot(SGC_dH,
fun.cumfreq(SGC_dH),
label="Minimal acceleration",
color='red')
plt.xlabel('% Deviation from expected distance')
plt.ylabel('Frequency')
plt.title(
'Cumulative frequency for the for supernovae\n from maximal and minimal accelerating regions'
)
plt.legend()
plt.show()
#########EQUITORIAL COORDINATE
while i < len(NGCred):
NGC_dH.append((fun.D(fun.HubbleIntegrate(NGCred[i])) - NGCdist[i]) /
fun.D(fun.HubbleIntegrate(NGCred[i])))
i += 1
i = 0
while i < len(SGCred):
SGC_dH.append((fun.D(fun.HubbleIntegrate(SGCred[i])) - SGCdist[i]) /
fun.D(fun.HubbleIntegrate(SGCred[i])))
i += 1
print(ks_2samp(NGC_dH, SGC_dH))
NGC_dH.sort()
SGC_dH.sort()
plt.plot(NGC_dH, fun.cumfreq(NGC_dH), label="Maximal expansion", color='blue')
plt.plot(SGC_dH, fun.cumfreq(SGC_dH), label="Minimal expansion", color='red')
emptyNLSS = plt.hist(
[],
range=[-0.3, 0.3],
alpha=0.5,
label='Stat=%.3f \np-val=%.4f' %
(ks_2samp(NGC_dH, SGC_dH)[0], ks_2samp(NGC_dH, SGC_dH)[1]),
color='white')
plt.xlabel('$\delta d_L$')
plt.ylabel('Cumulative Frequency')
plt.title(
'Cumulative frequency for the deviations in the\n maximal and minimal rate of expansion'
)
plt.legend()
plt.show()