In [1]:

    

test_name = "test6"


    

In [2]:

    

pi_list = range(9)
sigma_list = range(9)

f = open("{}.data".format(test_name), 'w')

for (k_pi, pi) in enumerate(pi_list):
    for (k_sigma, sigma) in enumerate(sigma_list):
        
        k = k_pi*len(sigma_list)+k_sigma
        if (sigma < 3.0):
            if (pi < 3.0):
                f.write("{} 4.0\n".format(k))
            elif (pi < 6.0):
                f.write("{} -2.0\n".format(k))
            else:
                f.write("{} 5.0\n".format(k))
                        
        elif (sigma < 6.0):
            if (pi < 3.0):
                f.write("{} -8.0\n".format(k))
            elif (pi < 6.0):
                f.write("{} 10.0\n".format(k))
            else:
                f.write("{} -3.0\n".format(k))
                
        else:
            if (pi < 3.0):
                f.write("{} 1.0\n".format(k))
            elif (pi < 6.0):
                f.write("{} -7.0\n".format(k))
            else:
                f.write("{} 6.0\n".format(k))

f.close()


    

In [3]:

    

f = open("{}.cov".format(test_name), 'w')

for i in range(80):
    f.write("{} {} 1.0\n".format(i, i))
    f.write("{} {} 0.25\n".format(i, i + 1))
f.write("80 80 1.0\n")
f.close()


    

In [4]:

    

f = open("{}.rebin".format(test_name), 'w')

f.write("pi")
for i in range(10):
    f.write(" {:.1f}".format(i))
f.write(" sigma")
for i in range(10):
    f.write(" {:.1f}".format(i))
f.write("\n")

f.write("R0 pi")
for i in range(0, 10, 3):
    f.write(" {:.1f}".format(i))
f.write(" sigma")
for i in range(0, 10, 3):
    f.write(" {:.1f}".format(i))

f.close()


    

In [39]:

    

cov_mat = np.zeros(81*81, dtype=float).reshape((81,81))

for i in range(80):
    cov_mat[i, i] = 1.0
    cov_mat[i, i+1] = 0.25
    cov_mat[i+1, i] = 0.25
cov_mat[-1,-1] = 1.0

inv_cov_mat = np.linalg.inv(cov_mat)
transformation_matrix = np.zeros(81*9, dtype=float).reshape(81,9)

for i in range(9):
    for j in range(81):
        if i == 0 and j in [0, 1, 2, 9, 10, 11, 18, 19, 20]:
            transformation_matrix[j,i] = 1.0
        elif i == 1 and j in [3, 4, 5, 12, 13, 14, 21, 22, 23]:
            transformation_matrix[j,i] = 1.0
        elif i == 2 and j in [6, 7, 8, 15, 16, 17, 24, 25, 26]:
            transformation_matrix[j,i] = 1.0
        elif i == 3 and j in [27, 28, 29, 36, 37, 38, 45, 46, 47]:
            transformation_matrix[j,i] = 1.0
        elif i == 4 and j in [30, 31, 32, 39, 40, 41, 48, 49, 50]:
            transformation_matrix[j,i] = 1.0
        elif i == 5 and j in [33, 34, 35, 42, 43, 44, 51, 52, 53]:
            transformation_matrix[j,i] = 1.0
        elif i == 6 and j in [54, 55, 56, 63, 64, 65, 72, 73, 74]:
            transformation_matrix[j,i] = 1.0
        elif i == 7 and j in [57, 58, 59, 66, 67, 68, 75, 76, 77]:
            transformation_matrix[j,i] = 1.0
        elif i == 8 and j in [60, 61, 62, 69, 70, 71, 78, 79, 80]:
            transformation_matrix[j,i] = 1.0
            
inv_cov_mat_new = np.dot(transformation_matrix.transpose(), np.dot(inv_cov_mat, transformation_matrix))
cov_mat_new = np.linalg.inv(inv_cov_mat_new)
        
print np.dot(inv_cov_mat, transformation_matrix)


    

    




[[  8.61555103e-01  -1.65744992e-02   3.18858335e-04  -3.10956239e-16
    5.98214083e-18  -1.15083746e-19   1.12231687e-31  -2.15910046e-33
    4.15365295e-35]
 [  5.53779590e-01   6.62979970e-02  -1.27543334e-03   1.24382496e-15
   -2.39285633e-17   4.60334984e-19  -4.48926749e-31   8.63640183e-33
   -1.66146118e-34]
 [  9.23326538e-01  -2.48617489e-01   4.78287502e-03  -4.66434359e-15
    8.97321125e-17  -1.72625619e-18   1.68347531e-30  -3.23865069e-32
    6.23047942e-34]
 [ -2.47085742e-01   9.28171958e-01  -1.78560667e-02   1.74135494e-14
   -3.34999886e-16   6.44468978e-18  -6.28497449e-30   1.20909626e-31
   -2.32604565e-33]
 [  6.50164295e-02   5.35929657e-01   6.66413920e-02  -6.49898540e-14
    1.25026743e-15  -2.40525029e-17   2.34564226e-29  -4.51251996e-31
    8.68113466e-33]
 [ -1.29799761e-02   9.28109413e-01  -2.48709501e-01   2.42545867e-13
   -4.66606985e-15   8.97653220e-17  -8.75407161e-29   1.68409836e-30
   -3.23984930e-32]
 [ -1.30965251e-02  -2.48367309e-01   9.28196613e-01  -9.05193613e-13
    1.74140120e-14  -3.35008785e-16   3.26706442e-28  -6.28514143e-30
    1.20912837e-31]
 [  6.53660766e-02   6.53598245e-02   5.35923051e-01   3.37822859e-12
   -6.49899780e-14   1.25026982e-15  -1.21928505e-27   2.34564674e-29
   -4.51252856e-31]
 [ -2.48367781e-01  -1.30719885e-02   9.28111183e-01  -1.26077207e-11
    2.42545900e-13  -4.66607049e-15   4.55043376e-27  -8.75407281e-29
    1.68409859e-30]
 [  9.28105049e-01  -1.30718705e-02  -2.48367784e-01   4.70526543e-11
   -9.05193622e-13   1.74140121e-14  -1.69824500e-26   3.26706445e-28
   -6.28514149e-30]
 [  5.35947586e-01   6.53594704e-02   6.53599516e-02  -1.75602897e-10
    3.37822859e-12  -6.49899780e-14   6.33793662e-26  -1.21928505e-27
    2.34564674e-29]
 [  9.28104609e-01  -2.48366011e-01  -1.30720225e-02   6.55358932e-10
   -1.26077207e-11   2.42545900e-13  -2.36535015e-25   4.55043376e-27
   -8.75407281e-29]
 [ -2.48366020e-01   9.28104575e-01  -1.30718614e-02  -2.44583283e-09
    4.70526543e-11  -9.05193622e-13   8.82760693e-25  -1.69824500e-26
    3.26706445e-28]
 [  6.53594704e-02   5.35947713e-01   6.53594680e-02   9.12797239e-09
   -1.75602897e-10   3.37822859e-12  -3.29450776e-24   6.33793662e-26
   -1.21928505e-27]
 [ -1.30718620e-02   9.28104574e-01  -2.48366011e-01  -3.40660567e-08
    6.55358932e-10  -1.26077207e-11   1.22952703e-23  -2.36535015e-25
    4.55043376e-27]
 [ -1.30720224e-02  -2.48366011e-01   9.28104574e-01   1.27136255e-07
   -2.44583283e-09   4.70526543e-11  -4.58865736e-23   8.82760693e-25
   -1.69824500e-26]
 [  6.53599516e-02   6.53594680e-02   5.35947713e-01  -4.74478962e-07
    9.12797239e-09  -1.75602897e-10   1.71251024e-22  -3.29450776e-24
    6.33793662e-26]
 [ -2.48367784e-01  -1.30718614e-02   9.28104574e-01   1.77077959e-06
   -3.40660567e-08   6.55358932e-10  -6.39117523e-22   1.22952703e-23
   -2.36535015e-25]
 [  9.28111184e-01  -1.30720226e-02  -2.48366011e-01  -6.60863940e-06
    1.27136255e-07  -2.44583283e-09   2.38521907e-21  -4.58865736e-23
    8.82760693e-25]
 [  5.35923049e-01   6.53599516e-02   6.53594680e-02   2.46637780e-05
   -4.74478962e-07   9.12797239e-09  -8.90175874e-21   1.71251024e-22
   -3.29450776e-24]
 [  9.28196622e-01  -2.48367784e-01  -1.30718614e-02  -9.20464727e-05
    1.77077959e-06  -3.40660567e-08   3.32218159e-20  -6.39117523e-22
    1.22952703e-23]
 [ -2.48709535e-01   9.28111184e-01  -1.30720226e-02   3.43522113e-04
   -6.60863940e-06   1.27136255e-07  -1.23985505e-19   2.38521907e-21
   -4.58865736e-23]
 [  6.66415191e-02   5.35923049e-01   6.53599516e-02  -1.28204198e-03
    2.46637780e-05  -4.74478962e-07   4.62720204e-19  -8.90175874e-21
    1.71251024e-22]
 [ -1.78565412e-02   9.28196622e-01  -2.48367784e-01   4.78464580e-03
   -9.20464727e-05   1.77077959e-06  -1.72689531e-18   3.32218159e-20
   -6.39117523e-22]
 [  4.78464580e-03  -2.48709535e-01   9.28111184e-01  -1.78565412e-02
    3.43522113e-04  -6.60863940e-06   6.44486103e-18  -1.23985505e-19
    2.38521907e-21]
 [ -1.28204198e-03   6.66415191e-02   5.35923049e-01   6.66415191e-02
   -1.28204198e-03   2.46637780e-05  -2.40525488e-17   4.62720204e-19
   -8.90175874e-21]
 [  3.43522113e-04  -1.78565412e-02   9.28196622e-01  -2.48709535e-01
    4.78464580e-03  -9.20464727e-05   8.97653343e-17  -1.72689531e-18
    3.32218159e-20]
 [ -9.20464727e-05   4.78464580e-03  -2.48709535e-01   9.28196622e-01
   -1.78565412e-02   3.43522113e-04  -3.35008788e-16   6.44486103e-18
   -1.23985505e-19]
 [  2.46637780e-05  -1.28204198e-03   6.66415191e-02   5.35923049e-01
    6.66415191e-02  -1.28204198e-03   1.25026982e-15  -2.40525488e-17
    4.62720204e-19]
 [ -6.60863940e-06   3.43522113e-04  -1.78565412e-02   9.28111184e-01
   -2.48709535e-01   4.78464580e-03  -4.66607049e-15   8.97653343e-17
   -1.72689531e-18]
 [  1.77077959e-06  -9.20464727e-05   4.78464580e-03  -2.48367784e-01
    9.28196622e-01  -1.78565412e-02   1.74140121e-14  -3.35008788e-16
    6.44486103e-18]
 [ -4.74478962e-07   2.46637780e-05  -1.28204198e-03   6.53599516e-02
    5.35923049e-01   6.66415191e-02  -6.49899780e-14   1.25026982e-15
   -2.40525488e-17]
 [  1.27136255e-07  -6.60863940e-06   3.43522113e-04  -1.30720226e-02
    9.28111184e-01  -2.48709535e-01   2.42545900e-13  -4.66607049e-15
    8.97653343e-17]
 [ -3.40660567e-08   1.77077959e-06  -9.20464727e-05  -1.30718614e-02
   -2.48367784e-01   9.28196622e-01  -9.05193622e-13   1.74140121e-14
   -3.35008788e-16]
 [  9.12797239e-09  -4.74478962e-07   2.46637780e-05   6.53594680e-02
    6.53599516e-02   5.35923049e-01   3.37822859e-12  -6.49899780e-14
    1.25026982e-15]
 [ -2.44583283e-09   1.27136255e-07  -6.60863940e-06  -2.48366011e-01
   -1.30720226e-02   9.28111184e-01  -1.26077207e-11   2.42545900e-13
   -4.66607049e-15]
 [  6.55358932e-10  -3.40660567e-08   1.77077959e-06   9.28104574e-01
   -1.30718614e-02  -2.48367784e-01   4.70526543e-11  -9.05193622e-13
    1.74140121e-14]
 [ -1.75602897e-10   9.12797239e-09  -4.74478962e-07   5.35947713e-01
    6.53594680e-02   6.53599516e-02  -1.75602897e-10   3.37822859e-12
   -6.49899780e-14]
 [  4.70526543e-11  -2.44583283e-09   1.27136255e-07   9.28104574e-01
   -2.48366011e-01  -1.30720226e-02   6.55358932e-10  -1.26077207e-11
    2.42545900e-13]
 [ -1.26077207e-11   6.55358932e-10  -3.40660567e-08  -2.48366011e-01
    9.28104574e-01  -1.30718614e-02  -2.44583283e-09   4.70526543e-11
   -9.05193622e-13]
 [  3.37822859e-12  -1.75602897e-10   9.12797239e-09   6.53594680e-02
    5.35947713e-01   6.53594680e-02   9.12797239e-09  -1.75602897e-10
    3.37822859e-12]
 [ -9.05193622e-13   4.70526543e-11  -2.44583283e-09  -1.30718614e-02
    9.28104574e-01  -2.48366011e-01  -3.40660567e-08   6.55358932e-10
   -1.26077207e-11]
 [  2.42545900e-13  -1.26077207e-11   6.55358932e-10  -1.30720226e-02
   -2.48366011e-01   9.28104574e-01   1.27136255e-07  -2.44583283e-09
    4.70526543e-11]
 [ -6.49899780e-14   3.37822859e-12  -1.75602897e-10   6.53599516e-02
    6.53594680e-02   5.35947713e-01  -4.74478962e-07   9.12797239e-09
   -1.75602897e-10]
 [  1.74140121e-14  -9.05193622e-13   4.70526543e-11  -2.48367784e-01
   -1.30718614e-02   9.28104574e-01   1.77077959e-06  -3.40660567e-08
    6.55358932e-10]
 [ -4.66607049e-15   2.42545900e-13  -1.26077207e-11   9.28111184e-01
   -1.30720226e-02  -2.48366011e-01  -6.60863940e-06   1.27136255e-07
   -2.44583283e-09]
 [  1.25026982e-15  -6.49899780e-14   3.37822859e-12   5.35923049e-01
    6.53599516e-02   6.53594680e-02   2.46637780e-05  -4.74478962e-07
    9.12797239e-09]
 [ -3.35008788e-16   1.74140121e-14  -9.05193622e-13   9.28196622e-01
   -2.48367784e-01  -1.30718614e-02  -9.20464727e-05   1.77077959e-06
   -3.40660567e-08]
 [  8.97653343e-17  -4.66607049e-15   2.42545900e-13  -2.48709535e-01
    9.28111184e-01  -1.30720226e-02   3.43522113e-04  -6.60863940e-06
    1.27136255e-07]
 [ -2.40525488e-17   1.25026982e-15  -6.49899780e-14   6.66415191e-02
    5.35923049e-01   6.53599516e-02  -1.28204198e-03   2.46637780e-05
   -4.74478962e-07]
 [  6.44486103e-18  -3.35008788e-16   1.74140121e-14  -1.78565412e-02
    9.28196622e-01  -2.48367784e-01   4.78464580e-03  -9.20464727e-05
    1.77077959e-06]
 [ -1.72689531e-18   8.97653343e-17  -4.66607049e-15   4.78464580e-03
   -2.48709535e-01   9.28111184e-01  -1.78565412e-02   3.43522113e-04
   -6.60863940e-06]
 [  4.62720204e-19  -2.40525488e-17   1.25026982e-15  -1.28204198e-03
    6.66415191e-02   5.35923049e-01   6.66415191e-02  -1.28204198e-03
    2.46637780e-05]
 [ -1.23985505e-19   6.44486103e-18  -3.35008788e-16   3.43522113e-04
   -1.78565412e-02   9.28196622e-01  -2.48709535e-01   4.78464580e-03
   -9.20464727e-05]
 [  3.32218159e-20  -1.72689531e-18   8.97653343e-17  -9.20464727e-05
    4.78464580e-03  -2.48709535e-01   9.28196622e-01  -1.78565412e-02
    3.43522113e-04]
 [ -8.90175874e-21   4.62720204e-19  -2.40525488e-17   2.46637780e-05
   -1.28204198e-03   6.66415191e-02   5.35923049e-01   6.66415191e-02
   -1.28204198e-03]
 [  2.38521907e-21  -1.23985505e-19   6.44486103e-18  -6.60863940e-06
    3.43522113e-04  -1.78565412e-02   9.28111184e-01  -2.48709535e-01
    4.78464580e-03]
 [ -6.39117523e-22   3.32218159e-20  -1.72689531e-18   1.77077959e-06
   -9.20464727e-05   4.78464580e-03  -2.48367784e-01   9.28196622e-01
   -1.78565412e-02]
 [  1.71251024e-22  -8.90175874e-21   4.62720204e-19  -4.74478962e-07
    2.46637780e-05  -1.28204198e-03   6.53599516e-02   5.35923049e-01
    6.66415191e-02]
 [ -4.58865736e-23   2.38521907e-21  -1.23985505e-19   1.27136255e-07
   -6.60863940e-06   3.43522113e-04  -1.30720226e-02   9.28111184e-01
   -2.48709535e-01]
 [  1.22952703e-23  -6.39117523e-22   3.32218159e-20  -3.40660567e-08
    1.77077959e-06  -9.20464727e-05  -1.30718614e-02  -2.48367784e-01
    9.28196622e-01]
 [ -3.29450776e-24   1.71251024e-22  -8.90175874e-21   9.12797239e-09
   -4.74478962e-07   2.46637780e-05   6.53594680e-02   6.53599516e-02
    5.35923049e-01]
 [  8.82760693e-25  -4.58865736e-23   2.38521907e-21  -2.44583283e-09
    1.27136255e-07  -6.60863940e-06  -2.48366011e-01  -1.30720226e-02
    9.28111184e-01]
 [ -2.36535015e-25   1.22952703e-23  -6.39117523e-22   6.55358932e-10
   -3.40660567e-08   1.77077959e-06   9.28104574e-01  -1.30718614e-02
   -2.48367784e-01]
 [  6.33793662e-26  -3.29450776e-24   1.71251024e-22  -1.75602897e-10
    9.12797239e-09  -4.74478962e-07   5.35947713e-01   6.53594680e-02
    6.53599516e-02]
 [ -1.69824500e-26   8.82760693e-25  -4.58865736e-23   4.70526543e-11
   -2.44583283e-09   1.27136255e-07   9.28104574e-01  -2.48366011e-01
   -1.30720224e-02]
 [  4.55043376e-27  -2.36535015e-25   1.22952703e-23  -1.26077207e-11
    6.55358932e-10  -3.40660567e-08  -2.48366011e-01   9.28104574e-01
   -1.30718620e-02]
 [ -1.21928505e-27   6.33793662e-26  -3.29450776e-24   3.37822859e-12
   -1.75602897e-10   9.12797239e-09   6.53594680e-02   5.35947713e-01
    6.53594704e-02]
 [  3.26706445e-28  -1.69824500e-26   8.82760693e-25  -9.05193622e-13
    4.70526543e-11  -2.44583283e-09  -1.30718614e-02   9.28104575e-01
   -2.48366020e-01]
 [ -8.75407281e-29   4.55043376e-27  -2.36535015e-25   2.42545900e-13
   -1.26077207e-11   6.55358932e-10  -1.30720225e-02  -2.48366011e-01
    9.28104609e-01]
 [  2.34564674e-29  -1.21928505e-27   6.33793662e-26  -6.49899780e-14
    3.37822859e-12  -1.75602897e-10   6.53599516e-02   6.53594704e-02
    5.35947586e-01]
 [ -6.28514149e-30   3.26706445e-28  -1.69824500e-26   1.74140121e-14
   -9.05193622e-13   4.70526543e-11  -2.48367784e-01  -1.30718705e-02
    9.28105049e-01]
 [  1.68409859e-30  -8.75407281e-29   4.55043376e-27  -4.66607049e-15
    2.42545900e-13  -1.26077207e-11   9.28111183e-01  -1.30719885e-02
   -2.48367781e-01]
 [ -4.51252856e-31   2.34564674e-29  -1.21928505e-27   1.25026982e-15
   -6.49899780e-14   3.37822859e-12   5.35923051e-01   6.53598245e-02
    6.53660766e-02]
 [  1.20912837e-31  -6.28514143e-30   3.26706442e-28  -3.35008785e-16
    1.74140120e-14  -9.05193613e-13   9.28196613e-01  -2.48367309e-01
   -1.30965251e-02]
 [ -3.23984930e-32   1.68409836e-30  -8.75407161e-29   8.97653220e-17
   -4.66606985e-15   2.42545867e-13  -2.48709501e-01   9.28109413e-01
   -1.29799761e-02]
 [  8.68113466e-33  -4.51251996e-31   2.34564226e-29  -2.40525029e-17
    1.25026743e-15  -6.49898540e-14   6.66413920e-02   5.35929657e-01
    6.50164295e-02]
 [ -2.32604565e-33   1.20909626e-31  -6.28497449e-30   6.44468978e-18
   -3.34999886e-16   1.74135494e-14  -1.78560667e-02   9.28171958e-01
   -2.47085742e-01]
 [  6.23047942e-34  -3.23865069e-32   1.68347531e-30  -1.72625619e-18
    8.97321125e-17  -4.66434359e-15   4.78287502e-03  -2.48617489e-01
    9.23326538e-01]
 [ -1.66146118e-34   8.63640183e-33  -4.48926749e-31   4.60334984e-19
   -2.39285633e-17   1.24382496e-15  -1.27543334e-03   6.62979970e-02
    5.53779590e-01]
 [  4.15365295e-35  -2.15910046e-33   1.12231687e-31  -1.15083746e-19
    5.98214083e-18  -3.10956239e-16   3.18858335e-04  -1.65744992e-02
    8.61555103e-01]]

In [40]:

    

pi_list = range(0, 9, 3)
sigma_list = range(0, 9, 3)

f = open("{}.sol".format(test_name), 'w')

f.write("R0 data")
for (k_pi, pi) in enumerate(pi_list):
    for (k_sigma, sigma) in enumerate(sigma_list):
        if (sigma < 3.0):
            if (pi < 3.0):
                f.write(" 4.0000")
            elif (pi < 6.0):
                f.write(" -2.0000")
            else:
                f.write(" 5.0000")
                        
        elif (sigma < 6.0):
            if (pi < 3.0):
                f.write(" -8.0000")
            elif (pi < 6.0):
                f.write(" 10.0000")
            else:
                f.write(" -3.0000")
                
        else:
            if (pi < 3.0):
                f.write(" 1.0000")
            elif (pi < 6.0):
                f.write(" -7.0000")
            else:
                f.write(" 6.0000")
                
f.write(" cov")
for i in range(9):
    for j in range(9):
        f.write(" {:.4f}".format(cov_mat_new[i,j]))
                
    
f.close()


    

In [41]:

    

cov_mat = np.zeros(81*81, dtype=float).reshape((81,81))

for i in range(80):
    cov_mat[i, i] = 1.0
    cov_mat[i, i+1] = 0.25
    cov_mat[i+1, i] = 0.25
cov_mat[-1,-1] = 1.0

print cov_mat


    

    




[[ 1.    0.25  0.   ...,  0.    0.    0.  ]
 [ 0.25  1.    0.25 ...,  0.    0.    0.  ]
 [ 0.    0.25  1.   ...,  0.    0.    0.  ]
 ..., 
 [ 0.    0.    0.   ...,  1.    0.25  0.  ]
 [ 0.    0.    0.   ...,  0.25  1.    0.25]
 [ 0.    0.    0.   ...,  0.    0.25  1.  ]]

In [42]:

    

inv_cov_mat = np.linalg.inv(cov_mat)
print inv_cov_mat


    

    




[[  1.07179677e+00  -2.87187079e-01   7.69515459e-02 ...,   2.61844210e-45
   -6.98251228e-46   1.74562807e-46]
 [ -2.87187079e-01   1.14874832e+00  -3.07806183e-01 ...,  -1.04737684e-44
    2.79300491e-45  -6.98251228e-46]
 [  7.69515459e-02  -3.07806183e-01   1.15427319e+00 ...,   3.92766316e-44
   -1.04737684e-44   2.61844210e-45]
 ..., 
 [  2.61844210e-45  -1.04737684e-44   3.92766316e-44 ...,   1.15427319e+00
   -3.07806183e-01   7.69515459e-02]
 [ -6.98251228e-46   2.79300491e-45  -1.04737684e-44 ...,  -3.07806183e-01
    1.14874832e+00  -2.87187079e-01]
 [  1.74562807e-46  -6.98251228e-46   2.61844210e-45 ...,   7.69515459e-02
   -2.87187079e-01   1.07179677e+00]]

In [1]:

    

transformation_matrix = np.zeros(81*9, dtype=float).reshape(81,9)

for i in range(9):
    for j in range(81):
        if i == 0 and j in [0, 1, 2, 9, 10, 11, 18, 19, 20]:
            transformation_matrix[j,i] = 1.0
        elif i == 1 and j in [3, 4, 5, 12, 13, 14, 21, 22, 23]:
            transformation_matrix[j,i] = 1.0
        elif i == 2 and j in [6, 7, 8, 15, 16, 17, 24, 25, 26]:
            transformation_matrix[j,i] = 1.0
        elif i == 3 and j in [27, 28, 29, 36, 36, 38, 45, 46, 47]:
            transformation_matrix[j,i] = 1.0
        elif i == 4 and j in [30, 31, 32, 39, 40, 41, 48, 49, 50]:
            transformation_matrix[j,i] = 1.0
        elif i == 5 and j in [33, 34, 35, 42, 43, 44, 51, 52, 53]:
            transformation_matrix[j,i] = 1.0
        elif i == 6 and j in [54, 55, 56, 63, 64, 65, 72, 73, 74]:
            transformation_matrix[j,i] = 1.0
        elif i == 7 and j in [57, 58, 59, 66, 67, 68, 75, 76, 77]:
            transformation_matrix[j,i] = 1.0
        elif i == 8 and j in [60, 61, 62, 69, 70, 71, 78, 79, 80]:
            transformation_matrix[j,i] = 1.0
        
            
plt.figure()
plt.subplot(111)
plt.contour(transformation_matrix)
plt.show()


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-88e128d347fb> in <module>()
     25 plt.figure()
     26 plt.subplot(111)
---> 27 plt.contour(tranf_matrix)
     28 plt.show()

NameError: name 'tranf_matrix' is not defined

In [44]:

    

inv_cov_mat_new = np.dot(transformation_matrix.transpose(), np.dot(inv_cov_mat, transformation_matrix))
print inv_cov_mat_new


    

    




[[  7.12304933e+00  -5.91052257e-01  -3.88331959e-01  -7.39906317e-05
    1.42342675e-06  -2.73837222e-08   2.67050861e-20  -5.13749414e-22
    9.88345290e-24]
 [ -5.91052257e-01   7.17659874e+00  -5.92082435e-01   3.84608942e-03
   -7.39908073e-05   1.42342675e-06  -1.38815073e-18   2.67050861e-20
   -5.13749414e-22]
 [ -3.88331959e-01  -5.92082435e-01   7.17661856e+00  -1.99922659e-01
    3.84609855e-03  -7.39908073e-05   7.21571327e-17  -1.38815073e-18
    2.67050861e-20]
 [ -7.39906317e-05   3.84608942e-03  -1.99922659e-01   7.25942368e+00
   -6.57442284e-01  -4.53672084e-01  -7.39906317e-05   1.42342337e-06
   -2.73836572e-08]
 [  1.42342675e-06  -7.39908073e-05   3.84609855e-03  -6.57442284e-01
    7.17661857e+00  -5.92082816e-01   3.84609855e-03  -7.39908073e-05
    1.42342675e-06]
 [ -2.73837222e-08   1.42342675e-06  -7.39908073e-05  -4.53672084e-01
   -5.92082816e-01   7.17661857e+00  -1.99923134e-01   3.84609855e-03
   -7.39908073e-05]
 [  2.67050861e-20  -1.38815073e-18   7.21571327e-17  -7.39906317e-05
    3.84609855e-03  -1.99923134e-01   7.17661856e+00  -5.92082435e-01
   -3.88331959e-01]
 [ -5.13749414e-22   2.67050861e-20  -1.38815073e-18   1.42342337e-06
   -7.39908073e-05   3.84609855e-03  -5.92082435e-01   7.17659874e+00
   -5.91052257e-01]
 [  9.88345290e-24  -5.13749414e-22   2.67050861e-20  -2.73836572e-08
    1.42342675e-06  -7.39908073e-05  -3.88331959e-01  -5.91052257e-01
    7.12304933e+00]]

In [45]:

    

cov_mat_new = np.linalg.inv(inv_cov_mat_new)
print cov_mat_new.shape
print cov_mat_new


    

    




(9, 9)
[[  1.41893335e-01   1.24043830e-02   8.70793491e-03   2.37408804e-04
    1.85542694e-05   1.66392876e-05   4.60139613e-07   3.14843797e-08
    2.78682720e-08]
 [  1.24043830e-02   1.41381543e-01   1.23428727e-02   2.68214295e-04
    2.09618187e-05   1.87983543e-05   5.19846022e-07   3.55697033e-08
    3.14843797e-08]
 [  8.70793491e-03   1.23428727e-02   1.40939926e-01   3.91991277e-03
    3.06353921e-04   2.74735204e-04   7.59747372e-06   5.19846022e-07
    4.60139613e-07]
 [  2.37408804e-04   2.68214295e-04   3.91991277e-03   1.39715844e-01
    1.36189726e-02   9.96343385e-03   2.74144793e-04   1.87579563e-05
    1.66035295e-05]
 [  1.85542694e-05   2.09618187e-05   3.06353921e-04   1.36189726e-02
    1.41624328e-01   1.25528600e-02   2.76399247e-04   1.89122140e-05
    1.67400701e-05]
 [  1.66392876e-05   1.87983543e-05   2.74735204e-04   9.96343385e-03
    1.25528600e-02   1.41117035e-01   3.95988376e-03   2.70949252e-04
    2.39829640e-04]
 [  4.60139613e-07   5.19846022e-07   7.59747372e-06   2.74144793e-04
    2.76399247e-04   3.95988376e-03   1.40941066e-01   1.23429507e-02
    8.70800395e-03]
 [  3.14843797e-08   3.55697033e-08   5.19846022e-07   1.87579563e-05
    1.89122140e-05   2.70949252e-04   1.23429507e-02   1.41381549e-01
    1.24043877e-02]
 [  2.78682720e-08   3.14843797e-08   4.60139613e-07   1.66035295e-05
    1.67400701e-05   2.39829640e-04   8.70800395e-03   1.24043877e-02
    1.41893339e-01]]

In [46]:

    

plt.figure()
plt.subplot(121)
plt.contourf(inv_cov_mat)
plt.subplot(122)
plt.contourf(inv_cov_mat_new)
plt.show()


    

In [ ]:

    




    

In [ ]: