m^T C^-1 m values and scaling Civ

gistfile1.txt

FIRST, FIND NO PEAK IN LF, NOISE PARAMETERS E-21 TO E-23

CODE:

""""
vary_x_samples125 = np.logspace(-8, -12, num=40) #num = 40
sigma125 = np.logspace(-21, -23, num=40)

Sij = vary_x_samples125[:, None, None] * norm_matrix[1][None, :, :]
newSij = (1e21)*Sij   # multiply S_ij by 1e12
Nij = sigma125[:, None, None] * id_mat[None, :, :]
newNij = (1e21)*Nij
# Format 7/4pi * param * P_3(M) where param is the parameter we vary, C_l
# Sij.shape = (40, 3072, 3072)
Cij = newSij + newNij
#invCij = np.linalg.inv(Cij)
logdetC = np.linalg.slogdet(Cij)  # returns sign and determinant; use logdetC[1]
# model_fit_terms = m^T C^-1 m
#
# model_fit_terms = np.array([np.dot(tempval.T , np.dot(invCij[i] , tempval) ) 
# for i in range(invCij.shape[0])])
#
model_fit_terms = np.array([np.dot(tempp.T , np.linalg.solve(Cij[i], tempp) ) for i in range(Cij.shape[0]) ]) 
print "m^T c^-1 m terms are"
print model_fit_terms 
print "******************"
print "logdetC terms are"
print logdetC[1] 
print "******************"
print "Npix2pi term is"
print Npix2pi
""""

OUTPUT:



m^T c^-1 m terms are
[  9.45804805e+06   1.01483017e+07   1.10268075e+07   1.20673659e+07
   1.32689374e+07   1.46133094e+07   1.61825120e+07   1.79600153e+07
   1.99559126e+07   2.22256705e+07   2.47646632e+07   2.76424156e+07
   3.08855750e+07   3.45339903e+07   3.86587049e+07   4.33120503e+07
   4.85288998e+07   5.44131447e+07   6.10398347e+07   6.84991954e+07
   7.68939251e+07   8.63276615e+07   9.69909206e+07   1.08947713e+08
   1.22424263e+08   1.37576408e+08   1.54634394e+08   1.73828119e+08
   1.95434208e+08   2.19747753e+08   2.47115351e+08   2.77910405e+08
   3.12543652e+08   3.51552581e+08   3.95426692e+08   4.44811316e+08
   5.00390442e+08   5.62931595e+08   6.33312474e+08   7.12509516e+08]
******************
logdetC terms are
[   234.83595269   -125.48871852   -486.73629436   -848.73844692
  -1211.04772189  -1573.36162977  -1936.79205007  -2300.35381787
  -2662.82613924  -3026.67180777  -3389.29604324  -3752.32106286
  -4115.94072189  -4479.40823086  -4842.79499263  -5205.87784784
  -5569.53277853  -5933.18379399  -6296.59554129  -6660.37091899
  -7023.75150763  -7387.0944891   -7750.90452509  -8114.35002254
  -8477.84033831  -8841.49715615  -9205.03206405  -9568.51393667
  -9932.08545533 -10295.68073606 -10659.20465258 -11022.79171886
 -11386.33372681 -11749.98089605 -12113.47607432 -12477.02136508
 -12840.61843452 -13204.16794796 -13567.75960538 -13931.32768536]
******************
Npix2pi term is
19301.9452637


**********************************************
**********************************************

SECOND, REDUCE NOISE TO E-22 TO E-24, FIND A PEAK IN LF

CODE:
""""
vary_x_samples123 = np.logspace(-8, -12, num=40) #num = 40
sigma123 = np.logspace(-22, -24, num=40)
#
# Plot noise sigma from e-22 to e-24
#
Sij = vary_x_samples123 [:, None, None] * norm_matrix[1][None, :, :]
newSij = (1e22)*Sij   # multiply S_ij by 1e12
Nij = sigma123[:, None, None] * id_mat[None, :, :]
newNij = (1e22)*Nij
# Format 7/4pi * param * P_3(M) where param is the parameter we vary, C_l
# Sij.shape = (40, 3072, 3072)
Cij = newSij + newNij
#invCij = np.linalg.inv(Cij)
logdetC = np.linalg.slogdet(Cij)  # returns sign and determinant; use logdetC[1]
# model_fit_terms = m^T C^-1 m
#
# model_fit_terms = np.array([np.dot(tempval.T , np.dot(invCij[i] , tempval) ) 
# for i in range(invCij.shape[0])])
#
model_fit_terms = np.array([np.dot(tempp.T , np.linalg.solve(Cij[i], tempp) ) for i in range(Cij.shape[0]) ]) 

print "m^T c^-1 m terms are"
print model_fit_terms 
print "******************"
print "logdetC terms are"
print logdetC[1] 
print "******************"
print "Npix2pi term is"
print Npix2pi
""""

OUTPUT


m^T c^-1 m terms are
[  1.16534465e+06   5.03576814e+06   1.18842066e+06   3.78456972e+06
   2.07230699e+06   1.16453569e+06  -7.41348447e+05  -3.92254528e+06
  -9.93526825e+06  -2.29984553e+07  -5.63527744e+07  -3.44097793e+08
   2.13719373e+08   1.10203181e+08   9.28265870e+07   8.26463737e+07
   8.06037248e+07   8.18050050e+07   8.54633954e+07   8.98256378e+07
   9.79018046e+07   1.05962829e+08   1.15393283e+08   1.26566259e+08
   1.39139646e+08   1.53686968e+08   1.70516270e+08   1.89068539e+08
   2.10547663e+08   2.34462101e+08   2.61421501e+08   2.92204648e+08
   3.26641827e+08   3.65300144e+08   4.09114198e+08   4.58308672e+08
   5.13744893e+08   5.76396127e+08   6.46599075e+08   7.25782771e+08]
******************
logdetC terms are
[  -410.62898964   -672.91913446   -926.22503662  -1169.34164618
  -1445.18261905  -1738.08384536  -2079.4620371   -2399.02656433
  -2735.89491794  -3080.90524843  -3433.09248454  -3787.30516381
  -4142.67275289  -4502.70581932  -4854.37183367  -5211.64589642
  -5574.580628    -5931.36044619  -6295.41523156  -6660.63863576
  -7019.91756434  -7381.27969271  -7740.57086979  -8102.90872247
  -8466.51398412  -8828.15412569  -9191.80550759  -9554.54294936
  -9917.99595215 -10281.00471672 -10644.32030792 -11007.88053013
 -11371.45867768 -11734.62772477 -12098.20386412 -12461.47921611
 -12824.82116055 -13188.76508825 -13552.04849419 -13915.45159908]
******************
Npix2pi term is
19301.9452637