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%load_ext autoreload
%autoreload 2
%matplotlib inline

execfile ("_ImportScript.py")



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execfile ("_ExploreMockManyNoise.py")









    



/Users/LaurencePeanuts/Documents/Travail/Stanford/Music/Music/beatbox/universe.py:580: RuntimeWarning: divide by zero encountered in power
  self.Power_Spectrum = self.PSnorm*10000*np.power((self.k/self.kstar) ,(-3+(self.n_s-1)))
/Users/LaurencePeanuts/Documents/Travail/Stanford/Music/Music/beatbox/universe.py:414: ComplexWarning: Casting complex values to real discards the imaginary part
  ay_real[zero_ind] = value[zero_ind].astype(np.float)






    



Sigma is 0.000000 arcmin (0.000000 rad) 
-> fwhm is 0.000000 arcmin






    



/Users/LaurencePeanuts/Documents/Travail/Stanford/Music/Music/beatbox/multiverse.py:359: ComplexWarning: Casting complex values to real discards the imaginary part
  R_real[zero_ind,:] = beatbox.Universe.R[zero_ind,:].astype(np.float)






    





<matplotlib.figure.Figure at 0x11058cd10>



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execfile ("_Alpharobustness.py")
print mu.reshape(-1)[max_ind]
print sigma.reshape(-1)[max_ind]



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execfile ('_SigmaDevPlot.py')









    



0.773991392568
69.3



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execfile('_CalcEvidence.py')









    



28.3433684039
1.15276832884e+32



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execfile ('_FigofMerit.py')









    












    



0.00433751804879



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execfile ('_PlotMostProbVals.py')









    



Built potential grid, with dimensions  (41, 41, 41)  and mean value  -0.0 +/- 0.8016978
-0.000291315122425 3.60614010492e-05 -0.00026043655453 -0.000341904406401
-2.26093666278e-06 1.61553534092e-06 -4.11107659914e-07 -4.3471139044e-06



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execfile ('_PlotMargPost.py')



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print beatbox.You.all_simulated_universes[0].fn
print beatbox.You.all_simulated_universes[-1].fn









    



[  6.98149124 -21.47291023  17.22163894   2.37826386  -9.9256234
  13.85993192]
[  6.98149124 -21.47291023  17.22163894   2.37826386  -9.9256234
  13.85993192]



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np.round(100.0*(beatbox.You.all_reconstructed_universes[-1].fn.T-beatbox.You.all_simulated_universes[0].fn)/beatbox.You.all_simulated_universes[0].fn)









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array([-0.,  0., -0., -0.,  0., -0.])



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print 100.*np.mean(np.sqrt(np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]/PS[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]))
print 100.*np.mean(np.sqrt(np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]/PS[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]))
print 100.*np.mean(np.sqrt(np.diag(beatbox.You.inv_A)/PS))









    



0.715874462473
0.0262718337749
0.371073148124



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print 100*np.sqrt(np.mean(realbias**2/PS[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]) )
print 100*np.sqrt(np.mean(imagbias**2/PS[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]) )









    



0.00233004972282
2.20576325252e-05



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print 100*np.sqrt(np.mean(realbias**2/np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]) )
print 100*np.sqrt(np.mean(imagbias**2/np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]) )









    



0.322225977784
0.119049863813



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len(beatbox.Universe.lms)



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len(beatbox.You.all_simulated_universes[0].fn)



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np.log (np.linalg.det(beatbox.You.A))/2.



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beatbox.You.all_reconstructed_universes[0].fn[num]



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np.sqrt(0.000178766944898)



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np.linalg.det(beatbox.You.A)**0.5/(2.*np.pi)**(0.5*len(beatbox.You.all_reconstructed_universes[0].fn))



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beatbox.You.all_reconstructed_universes= np.append(beatbox.You.all_reconstructed_universes, beatbox.Universe())



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beatbox.You.solve_for_3D_potential(beatbox.You.all_simulated_universes[0].ay2ayreal_for_inference(beatbox.You.all_simulated_universes[0].ay), print_alpha = 0)
beatbox.You.all_reconstructed_universes[-1].fn=beatbox.You.reconstrunct_fn
beatbox.You.all_reconstructed_universes[-1].transform_3D_potential_into_alm( usedefault=1, fn=1)



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n=-1
beatbox.You.all_reconstructed_universes[n].rearrange_fn_from_vector_to_grid()
beatbox.You.all_reconstructed_universes[n].evaluate_potential_given_fourier_coefficients()
import matplotlib.pyplot as plt
imgplot = plt.imshow(beatbox.You.all_simulated_universes[-1].phi[:,:,20])
plt.colorbar()



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plt.imshow(beatbox.You.all_reconstructed_universes[n].phi[:,:,20])
plt.colorbar()



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plt.imshow(beatbox.You.all_reconstructed_universes[0].phi[:,:,20]-beatbox.You.all_simulated_universes[0].phi[:,:,20])
plt.colorbar()



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print (beatbox.You.all_reconstructed_universes[-1].fn-beatbox.You.all_simulated_universes[0].fn)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]
print (beatbox.You.all_reconstructed_universes[-1].fn-beatbox.You.all_simulated_universes[0].fn)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]



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realbias = (beatbox.You.all_reconstructed_universes[-1].fn-beatbox.You.all_simulated_universes[0].fn)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]
imagbias = (beatbox.You.all_reconstructed_universes[-1].fn-beatbox.You.all_simulated_universes[0].fn)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]



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print np.mean(realbias), np.std(realbias), np.max(realbias), np.min(realbias)
print np.mean(imagbias), np.std(imagbias), np.max(imagbias), np.min(imagbias)



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n, bins, patches = plt.hist(np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)], 20, normed=0, facecolor='green', alpha=0.75)

n, bins, patches = plt.hist(np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):], 20, normed=0, facecolor='yellow', alpha=0.75)

plt.savefig('RobustnessAnalysis/rob_plt_lmax'+str(beatbox.Universe.truncated_lmax)+'_lmin'+str(beatbox.Universe.truncated_lmin)+'_nmax'+str(beatbox.Universe.truncated_nmax)+'_nmin'+str(beatbox.Universe.truncated_nmin)+'/A_histogram.png')

#plt.axis([0, 52, 0, 0.2])



plt.show()



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#print np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]
#print np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]
print np.mean(np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]), np.std(np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]), np.max(np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)]), np.min(np.diag(beatbox.You.inv_A)[:(len(beatbox.You.all_reconstructed_universes[-1].fn)/2)])
print np.mean(np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]), np.std(np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]), np.max(np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):]), np.min(np.diag(beatbox.You.inv_A)[(len(beatbox.You.all_reconstructed_universes[-1].fn)/2):])



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n=2
hp.mollview(hp.alm2map(MockUniverse[n].alm-beatbox.You.all_simulated_universes[0].alm,256),  rot=(-90,0,0), title="CMB graviational potential     fluctuations as seen from inside the LSS, l_max=%d, Tmap diff" % beatbox.You.truncated_lmax)
hp.mollview(hp.alm2map(beatbox.You.all_reconstructed_universes[n].alm-beatbox.You.all_simulated_universes[0].alm,256),  rot=(-90,0,0), title="CMB graviational potential     fluctuations as seen from inside the LSS, l_max=%d, Tmap diff" % beatbox.You.truncated_lmax)



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beatbox.You.all_simulated_universes[0].show_CMB_T_map(from_perspective_of="observer")
MockUniverse[n].show_CMB_T_map(from_perspective_of="observer")
beatbox.You.all_reconstructed_universes[n].show_CMB_T_map(from_perspective_of="observer")



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n=3
np.savetxt( "scratch/foryashar/true_alm.txt", beatbox.You.all_simulated_universes[0].alm)
np.savetxt( "scratch/foryashar/Mock_alm.txt", MockUniverse[n].alm)
np.savetxt( "scratch/foryashar/rec_alm.txt", beatbox.You.all_reconstructed_universes[n].alm)
np.savetxt( "scratch/foryashar/true_ay.txt", beatbox.You.all_simulated_universes[0].ay2ayreal_for_inference(beatbox.You.all_simulated_universes[0].ay))
np.savetxt( "scratch/foryashar/Mock_ay.txt", MockUniverse[n].ay2ayreal_for_inference(MockUniverse[n].ay))
np.savetxt( "scratch/foryashar/rec_ay.txt", beatbox.You.all_reconstructed_universes[n].ay2ayreal_for_inference(beatbox.You.all_reconstructed_universes[n].ay))
np.savetxt( "scratch/foryashar/A.txt",beatbox.You.A)
np.savetxt( "scratch/foryashar/invA.txt",beatbox.You.inv_A)
np.savetxt( "scratch/foryashar/C_yy.txt",beatbox.You.C_yy)
np.savetxt( "scratch/foryashar/invC_yy.txt",beatbox.You.inv_Cyy)
ind = np.where(beatbox.Universe.kfilter>0)
PS = np.zeros(2*len(ind[1]))
PS[:len(ind[1])] = (beatbox.You.all_simulated_universes[-1].Power_Spectrum[ind])
PS[len(ind[1]):] = (beatbox.You.all_simulated_universes[-1].Power_Spectrum[ind])
inv_Cf=np.diag(1./PS)
np.savetxt( "scratch/foryashar/prior_inv_Cf.txt",inv_Cf)
np.savetxt( "scratch/foryashar/true_fn.txt", beatbox.You.all_simulated_universes[0].fn)
np.savetxt( "scratch/foryashar/rec_fn.txt", beatbox.You.all_reconstructed_universes[n].fn)
np.savetxt( "scratch/foryashar/R.txt", beatbox.You.R_real)



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