In [1]:

    

import numpy as np
import pystan
import matplotlib.pyplot as plt
import statsmodels.api as sm
import seaborn as sns


    

    




/home/mldantas/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

In [2]:

    

# Main thread

if __name__ == '__main__':

    # Configuring paths and inputs -------------------------------------------------------------------------------------
    my_data = np.loadtxt('/home/mldantas/Dropbox/DoutoradoIAG/GAMAZOO/logit_3d_dataset.csv', delimiter=',', 
                         dtype=str)

    my_dictionary = {}
    for i in range(len(my_data[0, :])):                                         # Converting numpy array into dictionary
         my_dictionary[my_data[0, i]] = np.array(my_data[0 + 1:, i], dtype=str)

    logit_class = my_dictionary['LOGIT_CLASS(1-UVUP;0-UVWEAK)'].astype(int)
    redshift    = my_dictionary['REDSHIFT'].astype(float)
    sersic_gal  = my_dictionary['SERSIC_GALFIT'].astype(float)
    sersic_sex  = my_dictionary['SERSIC_SEXTRACTOR'].astype(float)
    
    index = np.where(redshift<=0.4)

    x1 = redshift[index]
    x2 = sersic_gal[index]
    y  = logit_class[index]                                             # whether this is a galaxy with uv upturn or not
    n_obs = x1.size

    regression_data = {}
    regression_data['K'] = 4      # number of betas
    regression_data['X'] = sm.add_constant(np.column_stack((x1, x1**2,x2)))
    # regression_data['X'] = sm.add_constant(x1)
    regression_data['N'] = n_obs
    regression_data['Y'] = y
    regression_data['LogN'] = np.log(n_obs)
    
    # Data to be plotted -------------------------------------------------------------------------------------------
#     redshift_plot = np.linspace(x1.min(), x1.max(), 1000)
#     x2 = redshift_plot
#     n_obs2 = redshift_plot.size
#     regression_data['X2'] = sm.add_constant(np.column_stack((x2, x2**2)))
#     regression_data['N2'] = n_obs2

    # Fit: STAN code ---------------------------------------------------------------------------------------------------
    stan_code = """
    data{
        int<lower=0> N;
        int<lower=0> K;
        int Y[N];
        matrix[N,K] X;
        real LogN;
    }

    parameters{
        vector[K] beta;
    }

    transformed parameters{
        vector[N] eta;
        eta = X * beta;
    }

    model{
        Y ~ bernoulli_logit(eta);
    }

    generated quantities{
        real LLi[N];
        real AIC;
        real BIC;
        real LogL;
        vector[N] etanew;
        real<lower=0, upper=1.0> pnew[N];
        etanew = X * beta;
        for (j in 1:N){
            pnew[j] = inv_logit(etanew[j]);
            LLi[j] = bernoulli_lpmf(1|pnew[j]);
        }
        LogL = sum(LLi);
        AIC = -2 * LogL + 2 * K;
        BIC = -2 * LogL + LogN * K;
    }
    """

    fit = pystan.stan(model_code=stan_code, data=regression_data, iter=7000, chains=3, warmup=3000, n_jobs=1)


    

    




INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_380fcba733f6f2ec51b21114e52c2444 NOW.

In [4]:

    

lines = list(range(8)) + [2 * n_obs + 8, 2 * n_obs + 9, 2 * n_obs + 10]
output = str(fit).split('\n')

for i in lines:
    print(output[i])


    

    




Inference for Stan model: anon_model_380fcba733f6f2ec51b21114e52c2444.
3 chains, each with iter=7000; warmup=3000; thin=1; 
post-warmup draws per chain=4000, total post-warmup draws=12000.

               mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat
beta[0]       -2.45  4.4e-3   0.28   -3.0  -2.63  -2.45  -2.26   -1.9 4016.0    1.0
beta[1]       19.43    0.05   3.25   13.1  17.27   19.4  21.57  25.86 3787.0    1.0
beta[2]       -39.6    0.15   9.39 -58.39  -45.8 -39.43 -33.25 -21.67 3949.0    1.0
LLi[2400]     -1.07  7.8e-4   0.06   -1.2  -1.11  -1.07  -1.02  -0.95 6455.0    1.0
AIC          8385.2    2.26 226.15 7944.2 8231.4 8381.9 8535.5 8840.4  10005    1.0
BIC          8408.4    2.26 226.15 7967.3 8254.5 8405.1 8558.7 8863.5  10005    1.0

In [13]:

    

output = str(pystan.misc._print_stanfit(fit, digits_summary=4)).split('\n')


    

In [5]:

    

posteriors = list(fit.extract(u'beta').items()[0])


    

In [6]:

    

betas = posteriors[1]


    

In [7]:

    

print betas.shape


    

    




(12000, 4)

In [8]:

    

beta0 = betas[:,0]
beta1 = betas[:,1]
beta2 = betas[:,2]
beta3 = betas[:,3]


    

In [15]:

    

plt.subplots(1,1, figsize=(20,7), sharey=True)

plot01 = plt.subplot(1,4,1)
sns.kdeplot(beta0, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{0}$", fontsize=25)
plt.ylabel(r"Kernel Density", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.subplot(1,4,2, sharey=plot01)
sns.kdeplot(beta1, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{1}$", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.subplot(1,4,3, sharey=plot01)
sns.kdeplot(beta2, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{2}$", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.subplot(1,4,4, sharey=plot01)
sns.kdeplot(beta3, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{3}$", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.tight_layout()
plt.savefig('./Model/posterios_sharey_3d.pdf', dpi=100)
plt.show()


    

In [19]:

    

plt.subplots(1,1, figsize=(25,10), sharey=True)

plot01 = plt.subplot(1,4,1)
sns.kdeplot(beta0, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{0}$", fontsize=25)
plt.ylabel(r"Kernel Density", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.subplot(1,4,2)
sns.kdeplot(beta1, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{1}$", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.subplot(1,4,3)
sns.kdeplot(beta2, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{2}$", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.subplot(1,4,4)
sns.kdeplot(beta3, shade=True, c='#e6550d')
plt.xlabel(r"$\beta_{3}$", fontsize=25)
plt.tick_params('both', labelsize='20')

plt.tight_layout()
plt.savefig('./Model/posterios_3d.pdf', dpi=100)
plt.show()


    

In [21]:

    

output = np.array(output)


    

In [57]:

    

new_output = output[5:-6] #removing header and footer
print new_output.shape
print new_output.size
print new_output[0].split()[0]


    

    




(9611,)
9611
beta[0]

In [58]:

    

print new_output[7210]
print new_output[9610]
print new_output[-2]
print new_output[4725].split()[1][0:6]
print new_output[4725].split()[1][6:]


    

    




pnew[0]      0.1252  0.0001 0.0105 0.1055  0.118 0.1248 0.1322 0.1466   6049 0.9999
pnew[2400]   0.3448  0.0003 0.0216 0.3026 0.3303 0.3446 0.3591 0.3883   6475    1.0
pnew[2399]     0.159.179e-5 0.0091 0.1326 0.1438 0.1498 0.1562 0.1682   9829 0.9998
-2.456

In [59]:

    

diagnostics = []
for i in range(new_output.size):
    if len(new_output[i].split())<11:
        print i, len(new_output[i].split()),'\n'
        print new_output[i].split(), len(new_output[i].split())
        diagnostics.append(len(new_output[i].split()))
    else:
        continue
print np.unique(diagnostics)


    

    




4806 6 

['AIC', '8.385e3', '2.26092.262e27.944e38.231e38.382e38.536e3', '8.84e3', '10005', '0.9998'] 6
4807 5 

['BIC', '8.408e3', '2.26092.262e27.967e38.255e38.405e38.559e38.864e3', '10005', '0.9998'] 5
4808 5 

['LogL', '-4.189e3', '1.13051.131e2-4.416e3-4.264e3-4.187e3-4.112e3-3.968e3', '10005', '0.9998'] 5
7221 10 

['pnew[11]', '0.16999.573e-5', '0.0097', '0.1512', '0.1633', '0.1697', '0.1764', '0.1892', '10173', '0.9998'] 10
7245 10 

['pnew[35]', '0.15098.472e-5', '0.0088', '0.1339', '0.1448', '0.1508', '0.1568', '0.1684', '10858', '0.9998'] 10
7273 10 

['pnew[63]', '0.14128.972e-5', '0.0089', '0.1242', '0.1351', '0.141', '0.1471', '0.159', '9791', '0.9998'] 10
7286 10 

['pnew[76]', '0.19049.691e-5', '0.0097', '0.1717', '0.1838', '0.1903', '0.1968', '0.21', '10006', '0.9998'] 10
7308 10 

['pnew[98]', '0.18249.301e-5', '0.0097', '0.1634', '0.1758', '0.1823', '0.1888', '0.2018', '10910', '0.9998'] 10
7309 10 

['pnew[99]', '0.13189.846e-5', '0.009', '0.1148', '0.1255', '0.1316', '0.1378', '0.1498', '8298', '0.9998'] 10
7328 10 

['pnew[118]', '0.19949.479e-5', '0.0099', '0.1801', '0.1927', '0.1992', '0.206', '0.2193', '10921', '0.9998'] 10
7343 10 

['pnew[133]', '0.18319.152e-5', '0.0094', '0.1651', '0.1767', '0.183', '0.1894', '0.2022', '10620', '0.9998'] 10
7366 10 

['pnew[156]', '0.1439.049e-5', '0.0089', '0.1259', '0.1368', '0.1428', '0.1489', '0.1609', '9732', '0.9998'] 10
7368 10 

['pnew[158]', '0.19369.782e-5', '0.0098', '0.1749', '0.187', '0.1935', '0.2001', '0.2133', '9946', '0.9998'] 10
7406 10 

['pnew[196]', '0.16928.134e-5', '0.0089', '0.152', '0.1631', '0.1691', '0.1751', '0.1871', '12000', '0.9998'] 10
7409 10 

['pnew[199]', '0.18079.353e-5', '0.0095', '0.1626', '0.1742', '0.1806', '0.187', '0.1998', '10299', '0.9998'] 10
7417 10 

['pnew[207]', '0.15349.351e-5', '0.0091', '0.136', '0.1471', '0.1533', '0.1595', '0.1712', '9458', '0.9998'] 10
7420 10 

['pnew[210]', '0.17848.633e-5', '0.0093', '0.1603', '0.1722', '0.1783', '0.1847', '0.1971', '11682', '0.9998'] 10
7425 10 

['pnew[215]', '0.1449.195e-5', '0.009', '0.1269', '0.1378', '0.1439', '0.1501', '0.162', '9581', '0.9998'] 10
7436 10 

['pnew[226]', '0.16338.068e-5', '0.0088', '0.1464', '0.1573', '0.1632', '0.1692', '0.181', '12000', '0.9998'] 10
7438 10 

['pnew[228]', '0.15338.722e-5', '0.009', '0.136', '0.1471', '0.1532', '0.1592', '0.1709', '10651', '0.9998'] 10
7441 10 

['pnew[231]', '0.16428.075e-5', '0.0088', '0.1473', '0.1582', '0.1641', '0.1701', '0.1819', '12000', '0.9998'] 10
7444 10 

['pnew[234]', '0.15568.996e-5', '0.0091', '0.138', '0.1494', '0.1554', '0.1617', '0.1739', '10292', '0.9998'] 10
7455 10 

['pnew[245]', '0.15858.136e-5', '0.0088', '0.1416', '0.1525', '0.1584', '0.1644', '0.176', '11726', '0.9998'] 10
7476 10 

['pnew[266]', '0.18199.505e-5', '0.0096', '0.1636', '0.1753', '0.1817', '0.1882', '0.2012', '10117', '0.9998'] 10
7484 10 

['pnew[274]', '0.18219.064e-5', '0.0096', '0.1634', '0.1756', '0.182', '0.1885', '0.2012', '11199', '0.9998'] 10
7497 10 

['pnew[287]', '0.20079.501e-5', '0.0098', '0.1816', '0.194', '0.2005', '0.2072', '0.2202', '10637', '0.9998'] 10
7563 10 

['pnew[353]', '0.16568.317e-5', '0.009', '0.1483', '0.1594', '0.1655', '0.1715', '0.1834', '11613', '0.9998'] 10
7566 10 

['pnew[356]', '0.18828.628e-5', '0.0094', '0.1698', '0.1819', '0.1881', '0.1945', '0.2072', '11867', '0.9998'] 10
7573 10 

['pnew[363]', '0.14928.769e-5', '0.0089', '0.1321', '0.1432', '0.1491', '0.1552', '0.167', '10339', '0.9998'] 10
7591 10 

['pnew[381]', '0.16259.963e-5', '0.0094', '0.1446', '0.1559', '0.1624', '0.1688', '0.1812', '8971', '0.9999'] 10
7634 10 

['pnew[424]', '0.16159.509e-5', '0.0095', '0.1431', '0.1551', '0.1613', '0.1679', '0.1806', '9923', '0.9998'] 10
7638 10 

['pnew[428]', '0.20479.868e-5', '0.01', '0.1855', '0.1979', '0.2045', '0.2114', '0.2247', '10229', '0.9998'] 10
7724 10 

['pnew[514]', '0.18418.427e-5', '0.0092', '0.166', '0.1779', '0.184', '0.1903', '0.2027', '12000', '0.9998'] 10
7732 10 

['pnew[522]', '0.16658.157e-5', '0.0089', '0.1493', '0.1604', '0.1664', '0.1724', '0.1844', '12000', '0.9998'] 10
7747 10 

['pnew[537]', '0.18438.438e-5', '0.0092', '0.1662', '0.1781', '0.1842', '0.1905', '0.2029', '12000', '0.9998'] 10
7748 10 

['pnew[538]', '0.16518.386e-5', '0.009', '0.1479', '0.1589', '0.165', '0.171', '0.183', '11480', '0.9998'] 10
7753 10 

['pnew[543]', '0.19439.336e-5', '0.0096', '0.1757', '0.1878', '0.1942', '0.2007', '0.2136', '10603', '0.9998'] 10
7755 10 

['pnew[545]', '0.16418.768e-5', '0.0091', '0.1466', '0.1577', '0.164', '0.1701', '0.1821', '10846', '0.9998'] 10
7772 10 

['pnew[562]', '0.1989.242e-5', '0.0097', '0.1791', '0.1914', '0.1978', '0.2045', '0.2175', '11038', '0.9998'] 10
7781 10 

['pnew[571]', '0.15519.258e-5', '0.0092', '0.1373', '0.1487', '0.1549', '0.1613', '0.1736', '9943', '0.9998'] 10
7788 10 

['pnew[578]', '0.16468.931e-5', '0.0093', '0.1467', '0.1584', '0.1645', '0.1708', '0.1833', '10754', '0.9998'] 10
7789 10 

['pnew[579]', '0.15378.627e-5', '0.0089', '0.1365', '0.1476', '0.1536', '0.1597', '0.1715', '10727', '0.9998'] 10
7798 10 

['pnew[588]', '0.16318.331e-5', '0.0089', '0.146', '0.157', '0.163', '0.169', '0.1809', '11523', '0.9998'] 10
7809 10 

['pnew[599]', '0.17288.339e-5', '0.0091', '0.1551', '0.1667', '0.1727', '0.179', '0.1912', '12000', '0.9998'] 10
7810 10 

['pnew[600]', '0.20289.881e-5', '0.0099', '0.1838', '0.1961', '0.2026', '0.2094', '0.2227', '10076', '0.9998'] 10
7813 10 

['pnew[603]', '0.16899.659e-5', '0.0094', '0.1511', '0.1624', '0.1688', '0.1752', '0.1876', '9403', '0.9998'] 10
7821 10 

['pnew[611]', '0.18098.332e-5', '0.0091', '0.163', '0.1747', '0.1808', '0.187', '0.1993', '12000', '0.9998'] 10
7843 10 

['pnew[633]', '0.15018.513e-5', '0.0089', '0.1333', '0.1441', '0.1501', '0.156', '0.1676', '10827', '0.9998'] 10
7853 10 

['pnew[643]', '0.18869.152e-5', '0.0097', '0.1697', '0.182', '0.1885', '0.1951', '0.2081', '11238', '0.9998'] 10
7862 10 

['pnew[652]', '0.1778.354e-5', '0.0092', '0.1591', '0.1709', '0.1768', '0.1831', '0.1954', '12000', '0.9998'] 10
7876 10 

['pnew[666]', '0.18838.716e-5', '0.0093', '0.1701', '0.182', '0.1881', '0.1945', '0.2071', '11507', '0.9998'] 10
7878 10 

['pnew[668]', '0.16869.062e-5', '0.0093', '0.1509', '0.1621', '0.1685', '0.1748', '0.1872', '10511', '0.9998'] 10
7888 10 

['pnew[678]', '0.14129.318e-5', '0.0089', '0.1241', '0.135', '0.1411', '0.1471', '0.159', '9151', '0.9998'] 10
7964 10 

['pnew[754]', '0.15148.374e-5', '0.0088', '0.1346', '0.1454', '0.1513', '0.1573', '0.1688', '11052', '0.9998'] 10
7965 10 

['pnew[755]', '0.18498.437e-5', '0.0092', '0.1667', '0.1786', '0.1847', '0.1911', '0.2035', '12000', '0.9998'] 10
7973 10 

['pnew[763]', '0.19059.583e-5', '0.0099', '0.1712', '0.1837', '0.1904', '0.1973', '0.2104', '10776', '0.9998'] 10
7977 10 

['pnew[767]', '0.15848.238e-5', '0.0089', '0.1415', '0.1524', '0.1584', '0.1643', '0.176', '11545', '0.9998'] 10
7989 10 

['pnew[779]', '0.16988.199e-5', '0.009', '0.1524', '0.1638', '0.1696', '0.1758', '0.1877', '12000', '0.9998'] 10
7994 10 

['pnew[784]', '0.168.603e-5', '0.009', '0.1427', '0.1537', '0.1599', '0.166', '0.1777', '11001', '0.9998'] 10
7998 10 

['pnew[788]', '0.1919.234e-5', '0.0098', '0.172', '0.1843', '0.1908', '0.1975', '0.2105', '11176', '0.9998'] 10
8004 10 

['pnew[794]', '0.17988.871e-5', '0.0095', '0.1614', '0.1734', '0.1797', '0.1861', '0.1987', '11389', '0.9998'] 10
8015 10 

['pnew[805]', '0.17298.311e-5', '0.0091', '0.1552', '0.1668', '0.1728', '0.179', '0.1913', '12000', '0.9998'] 10
8017 10 

['pnew[807]', '0.13699.484e-5', '0.0089', '0.1201', '0.1307', '0.1368', '0.1429', '0.1549', '8822', '0.9998'] 10
8018 10 

['pnew[808]', '0.1868.521e-5', '0.0093', '0.1677', '0.1797', '0.1859', '0.1923', '0.2048', '12000', '0.9998'] 10
8025 10 

['pnew[815]', '0.16749.757e-5', '0.0097', '0.1486', '0.1608', '0.1672', '0.1739', '0.1868', '9883', '0.9998'] 10
8027 10 

['pnew[817]', '0.15028.426e-5', '0.0088', '0.1333', '0.1442', '0.1501', '0.156', '0.1676', '10933', '0.9998'] 10
8044 10 

['pnew[834]', '0.15988.396e-5', '0.0089', '0.1428', '0.1537', '0.1598', '0.1657', '0.1774', '11323', '0.9998'] 10
8052 10 

['pnew[842]', '0.16768.107e-5', '0.0089', '0.1505', '0.1616', '0.1675', '0.1735', '0.1854', '12000', '0.9998'] 10
8069 10 

['pnew[859]', '0.18369.001e-5', '0.0096', '0.165', '0.1771', '0.1835', '0.19', '0.2027', '11321', '0.9998'] 10
8093 10 

['pnew[883]', '0.17578.929e-5', '0.0093', '0.1579', '0.1693', '0.1756', '0.1819', '0.1945', '10823', '0.9998'] 10
8112 10 

['pnew[902]', '0.15948.423e-5', '0.0089', '0.1424', '0.1532', '0.1593', '0.1653', '0.177', '11267', '0.9998'] 10
8115 10 

['pnew[905]', '0.19459.552e-5', '0.0097', '0.1758', '0.1879', '0.1943', '0.2009', '0.2139', '10278', '0.9998'] 10
8157 10 

['pnew[947]', '0.15739.383e-5', '0.0091', '0.1397', '0.1509', '0.1572', '0.1634', '0.1753', '9506', '0.9998'] 10
8181 10 

['pnew[971]', '0.15378.301e-5', '0.0088', '0.1368', '0.1477', '0.1536', '0.1595', '0.1711', '11268', '0.9998'] 10
8186 10 

['pnew[976]', '0.14779.632e-5', '0.0093', '0.1301', '0.1413', '0.1475', '0.154', '0.1663', '9226', '0.9998'] 10
8201 10 

['pnew[991]', '0.15319.624e-5', '0.0092', '0.1354', '0.1467', '0.153', '0.1591', '0.1711', '9150', '0.9998'] 10
8205 10 

['pnew[995]', '0.14288.913e-5', '0.0089', '0.1258', '0.1367', '0.1426', '0.1487', '0.1606', '9917', '0.9998'] 10
8226 10 

['pnew[1016]', '0.19489.919e-5', '0.0102', '0.1749', '0.1879', '0.1946', '0.2017', '0.215', '10494', '0.9998'] 10
8231 10 

['pnew[1021]', '0.19339.096e-5', '0.0097', '0.1745', '0.1868', '0.1931', '0.1998', '0.2128', '11346', '0.9998'] 10
8237 10 

['pnew[1027]', '0.14648.674e-5', '0.0088', '0.1295', '0.1404', '0.1463', '0.1524', '0.164', '10387', '0.9998'] 10
8241 10 

['pnew[1031]', '0.18468.529e-5', '0.0093', '0.1664', '0.1783', '0.1845', '0.1909', '0.2035', '12000', '0.9998'] 10
8253 10 

['pnew[1043]', '0.17288.384e-5', '0.0091', '0.1551', '0.1667', '0.1727', '0.179', '0.1912', '11876', '0.9998'] 10
8271 10 

['pnew[1061]', '0.18949.304e-5', '0.0095', '0.1711', '0.1829', '0.1893', '0.1957', '0.2087', '10521', '0.9998'] 10
8280 10 

['pnew[1070]', '0.16099.601e-5', '0.0093', '0.1431', '0.1545', '0.1608', '0.1671', '0.1792', '9328', '0.9998'] 10
8288 10 

['pnew[1078]', '0.18569.654e-5', '0.0099', '0.1662', '0.1788', '0.1855', '0.1922', '0.2054', '10589', '0.9998'] 10
8322 10 

['pnew[1112]', '0.19919.335e-5', '0.0097', '0.1801', '0.1925', '0.1989', '0.2057', '0.2187', '10906', '0.9998'] 10
8339 10 

['pnew[1129]', '0.14029.953e-5', '0.0092', '0.1226', '0.1337', '0.14', '0.1463', '0.1583', '8543', '0.9998'] 10
8347 10 

['pnew[1137]', '0.16018.063e-5', '0.0088', '0.1433', '0.1541', '0.16', '0.1659', '0.1777', '12000', '0.9998'] 10
8352 10 

['pnew[1142]', '0.18438.415e-5', '0.0092', '0.1663', '0.1781', '0.1841', '0.1904', '0.2028', '12000', '0.9998'] 10
8380 10 

['pnew[1170]', '0.15589.727e-5', '0.0093', '0.1379', '0.1493', '0.1556', '0.1619', '0.174', '9096', '0.9998'] 10
8389 10 

['pnew[1179]', '0.16998.187e-5', '0.009', '0.1526', '0.1639', '0.1697', '0.1759', '0.1878', '12000', '0.9998'] 10
8393 10 

['pnew[1183]', '0.14749.663e-5', '0.0092', '0.1299', '0.1411', '0.1473', '0.1535', '0.1654', '8985', '0.9998'] 10
8395 10 

['pnew[1185]', '0.15189.961e-5', '0.0093', '0.1338', '0.1453', '0.1517', '0.1579', '0.1702', '8791', '0.9998'] 10
8399 10 

['pnew[1189]', '0.17058.172e-5', '0.009', '0.1532', '0.1646', '0.1704', '0.1765', '0.1886', '12000', '0.9998'] 10
8422 10 

['pnew[1212]', '0.14029.379e-5', '0.0089', '0.1232', '0.134', '0.1402', '0.1462', '0.158', '9058', '0.9998'] 10
8431 10 

['pnew[1221]', '0.15738.518e-5', '0.009', '0.1401', '0.1512', '0.1572', '0.1634', '0.1753', '11046', '0.9998'] 10
8439 10 

['pnew[1229]', '0.1389.768e-5', '0.009', '0.121', '0.1318', '0.1379', '0.1441', '0.1562', '8541', '0.9998'] 10
8464 10 

['pnew[1254]', '0.18829.759e-5', '0.01', '0.1688', '0.1814', '0.1882', '0.195', '0.2083', '10540', '0.9998'] 10
8473 10 

['pnew[1263]', '0.20489.879e-5', '0.01', '0.1856', '0.198', '0.2046', '0.2115', '0.2248', '10214', '0.9998'] 10
8475 10 

['pnew[1265]', '0.18558.773e-5', '0.0093', '0.1673', '0.1792', '0.1854', '0.1917', '0.2042', '11294', '0.9998'] 10
8507 10 

['pnew[1297]', '0.15948.164e-5', '0.0088', '0.1426', '0.1533', '0.1593', '0.1652', '0.1769', '11706', '0.9998'] 10
8552 10 

['pnew[1342]', '0.17118.187e-5', '0.009', '0.1537', '0.165', '0.171', '0.177', '0.189', '12000', '0.9998'] 10
8558 10 

['pnew[1348]', '0.17618.233e-5', '0.009', '0.1586', '0.17', '0.176', '0.1821', '0.1942', '12000', '0.9998'] 10
8566 10 

['pnew[1356]', '0.16239.667e-5', '0.0093', '0.1445', '0.1558', '0.1622', '0.1685', '0.1807', '9279', '0.9998'] 10
8578 10 

['pnew[1368]', '0.17188.809e-5', '0.0092', '0.1542', '0.1655', '0.1717', '0.178', '0.1903', '10935', '0.9998'] 10
8585 10 

['pnew[1375]', '0.15788.156e-5', '0.0088', '0.1409', '0.1517', '0.1576', '0.1636', '0.1752', '11661', '0.9998'] 10
8591 10 

['pnew[1381]', '0.15529.725e-5', '0.0094', '0.1371', '0.1488', '0.155', '0.1616', '0.1741', '9431', '0.9998'] 10
8595 10 

['pnew[1385]', '0.19899.325e-5', '0.0097', '0.18', '0.1923', '0.1987', '0.2054', '0.2185', '10900', '0.9998'] 10
8611 10 

['pnew[1401]', '0.17538.448e-5', '0.0092', '0.1574', '0.1691', '0.1752', '0.1815', '0.1938', '11861', '0.9998'] 10
8636 10 

['pnew[1426]', '0.1849.156e-5', '0.0094', '0.1659', '0.1776', '0.1839', '0.1903', '0.2031', '10631', '0.9998'] 10
8641 10 

['pnew[1431]', '0.1658.087e-5', '0.0089', '0.148', '0.159', '0.1649', '0.1709', '0.1827', '12000', '0.9998'] 10
8648 10 

['pnew[1438]', '0.17359.783e-5', '0.0095', '0.1555', '0.167', '0.1734', '0.1798', '0.1925', '9338', '0.9998'] 10
8657 10 

['pnew[1447]', '0.16458.231e-5', '0.0089', '0.1473', '0.1585', '0.1644', '0.1705', '0.1825', '11804', '0.9998'] 10
8661 10 

['pnew[1451]', '0.18278.378e-5', '0.0092', '0.1649', '0.1765', '0.1826', '0.1889', '0.2012', '12000', '0.9998'] 10
8674 10 

['pnew[1464]', '0.18049.477e-5', '0.0098', '0.1614', '0.1738', '0.1804', '0.187', '0.1999', '10644', '0.9998'] 10
8684 10 

['pnew[1474]', '0.17699.133e-5', '0.0096', '0.1584', '0.1704', '0.1768', '0.1833', '0.196', '10949', '0.9998'] 10
8715 10 

['pnew[1505]', '0.14019.098e-5', '0.0089', '0.1232', '0.134', '0.14', '0.1461', '0.158', '9585', '0.9998'] 10
8728 10 

['pnew[1518]', '0.17218.166e-5', '0.0089', '0.1548', '0.1661', '0.172', '0.178', '0.1901', '12000', '0.9998'] 10
8734 10 

['pnew[1524]', '0.16568.119e-5', '0.0089', '0.1486', '0.1596', '0.1655', '0.1715', '0.1834', '12000', '0.9998'] 10
8738 10 

['pnew[1528]', '0.15159.602e-5', '0.0092', '0.1339', '0.1452', '0.1515', '0.1576', '0.1696', '9140', '0.9998'] 10
8741 10 

['pnew[1531]', '0.18969.674e-5', '0.0097', '0.171', '0.1831', '0.1895', '0.196', '0.2092', '10015', '0.9998'] 10
8751 10 

['pnew[1541]', '0.1889.152e-5', '0.0097', '0.1691', '0.1814', '0.1879', '0.1945', '0.2074', '11226', '0.9998'] 10
8753 10 

['pnew[1543]', '0.16328.068e-5', '0.0088', '0.1463', '0.1572', '0.1631', '0.169', '0.1809', '12000', '0.9998'] 10
8755 10 

['pnew[1545]', '0.14239.887e-5', '0.0093', '0.1248', '0.1359', '0.142', '0.1485', '0.1609', '8769', '0.9998'] 10
8764 10 

['pnew[1554]', '0.17178.876e-5', '0.0092', '0.1539', '0.1653', '0.1715', '0.1778', '0.1901', '10830', '0.9998'] 10
8802 10 

['pnew[1592]', '0.14828.789e-5', '0.0089', '0.1311', '0.1421', '0.1481', '0.1542', '0.1661', '10272', '0.9998'] 10
8803 10 

['pnew[1593]', '0.16858.121e-5', '0.0089', '0.1514', '0.1625', '0.1685', '0.1745', '0.1863', '12000', '0.9998'] 10
8804 10 

['pnew[1594]', '0.19999.457e-5', '0.0099', '0.1807', '0.1932', '0.1997', '0.2065', '0.2198', '10907', '0.9998'] 10
8814 10 

['pnew[1604]', '0.16089.086e-5', '0.0093', '0.1429', '0.1545', '0.1607', '0.167', '0.1795', '10397', '0.9998'] 10
8827 10 

['pnew[1617]', '0.17338.867e-5', '0.0092', '0.1555', '0.1669', '0.1731', '0.1794', '0.1918', '10872', '0.9998'] 10
8830 10 

['pnew[1620]', '0.17399.172e-5', '0.0094', '0.156', '0.1674', '0.1738', '0.1801', '0.1927', '10444', '0.9998'] 10
8834 10 

['pnew[1624]', '0.19068.773e-5', '0.0094', '0.1722', '0.1842', '0.1904', '0.1969', '0.2096', '11512', '0.9998'] 10
8842 10 

['pnew[1632]', '0.18228.596e-5', '0.0094', '0.164', '0.176', '0.1821', '0.1885', '0.2009', '11839', '0.9998'] 10
8863 10 

['pnew[1653]', '0.16129.992e-5', '0.0097', '0.1425', '0.1546', '0.161', '0.1678', '0.1806', '9409', '0.9998'] 10
8872 10 

['pnew[1662]', '0.14499.693e-5', '0.0091', '0.1274', '0.1386', '0.1448', '0.151', '0.163', '8895', '0.9998'] 10
8897 10 

['pnew[1687]', '0.19839.561e-5', '0.0097', '0.1795', '0.1917', '0.1981', '0.2047', '0.2179', '10385', '0.9998'] 10
8915 10 

['pnew[1705]', '0.15889.872e-5', '0.0094', '0.1409', '0.1523', '0.1587', '0.165', '0.1772', '9003', '0.9998'] 10
8917 10 

['pnew[1707]', '0.15878.323e-5', '0.0089', '0.1416', '0.1527', '0.1586', '0.1647', '0.1765', '11416', '0.9998'] 10
8933 10 

['pnew[1723]', '0.18648.471e-5', '0.0093', '0.1683', '0.1802', '0.1863', '0.1927', '0.2051', '12000', '0.9998'] 10
8942 10 

['pnew[1732]', '0.14379.982e-5', '0.0093', '0.1261', '0.1373', '0.1437', '0.1499', '0.162', '8606', '0.9998'] 10
8965 10 

['pnew[1755]', '0.14958.955e-5', '0.009', '0.1324', '0.1434', '0.1494', '0.1556', '0.1676', '10094', '0.9998'] 10
8988 10 

['pnew[1778]', '0.20379.781e-5', '0.0099', '0.1846', '0.1969', '0.2035', '0.2103', '0.2236', '10310', '0.9998'] 10
8991 10 

['pnew[1781]', '0.19469.166e-5', '0.0097', '0.1756', '0.188', '0.1944', '0.2011', '0.2141', '11268', '0.9998'] 10
8992 10 

['pnew[1782]', '0.20419.805e-5', '0.0101', '0.1846', '0.1973', '0.2039', '0.2108', '0.2242', '10513', '0.9998'] 10
8997 10 

['pnew[1787]', '0.18468.604e-5', '0.0093', '0.1665', '0.1784', '0.1845', '0.1908', '0.2032', '11587', '0.9998'] 10
9013 10 

['pnew[1803]', '0.16898.162e-5', '0.0089', '0.1516', '0.1629', '0.1687', '0.1748', '0.1868', '12000', '0.9998'] 10
9043 10 

['pnew[1833]', '0.18149.803e-5', '0.01', '0.162', '0.1745', '0.1813', '0.188', '0.2011', '10312', '0.9998'] 10
9061 10 

['pnew[1851]', '0.17468.228e-5', '0.009', '0.157', '0.1685', '0.1744', '0.1806', '0.1927', '12000', '0.9998'] 10
9096 10 

['pnew[1886]', '0.15828.147e-5', '0.0088', '0.1414', '0.1522', '0.1581', '0.1641', '0.1757', '11696', '0.9998'] 10
9111 10 

['pnew[1901]', '0.19459.708e-5', '0.01', '0.1749', '0.1877', '0.1944', '0.2013', '0.2146', '10703', '0.9998'] 10
9115 10 

['pnew[1905]', '0.18588.471e-5', '0.0093', '0.1675', '0.1795', '0.1856', '0.192', '0.2045', '12000', '0.9998'] 10
9123 10 

['pnew[1913]', '0.16718.315e-5', '0.009', '0.1496', '0.161', '0.167', '0.1731', '0.1852', '11768', '0.9998'] 10
9125 10 

['pnew[1915]', '0.15348.658e-5', '0.009', '0.1362', '0.1472', '0.1533', '0.1594', '0.171', '10742', '0.9998'] 10
9143 10 

['pnew[1933]', '0.16898.202e-5', '0.009', '0.1516', '0.1628', '0.1688', '0.1748', '0.1868', '12000', '0.9998'] 10
9174 10 

['pnew[1964]', '0.18058.374e-5', '0.0092', '0.1626', '0.1743', '0.1803', '0.1866', '0.1989', '12000', '0.9998'] 10
9223 10 

['pnew[2013]', '0.1688.904e-5', '0.0092', '0.1503', '0.1616', '0.1679', '0.1741', '0.1864', '10724', '0.9998'] 10
9228 10 

['pnew[2018]', '0.16029.929e-5', '0.0096', '0.1416', '0.1537', '0.16', '0.1667', '0.1795', '9430', '0.9998'] 10
9236 10 

['pnew[2026]', '0.15678.835e-5', '0.0091', '0.1392', '0.1505', '0.1565', '0.1628', '0.1749', '10556', '0.9998'] 10
9246 10 

['pnew[2036]', '0.18348.764e-5', '0.0095', '0.1649', '0.177', '0.1833', '0.1897', '0.2023', '11632', '0.9998'] 10
9273 10 

['pnew[2063]', '0.16188.063e-5', '0.0088', '0.1449', '0.1558', '0.1617', '0.1677', '0.1795', '12000', '0.9998'] 10
9281 10 

['pnew[2071]', '0.18278.762e-5', '0.0094', '0.1643', '0.1764', '0.1826', '0.189', '0.2016', '11617', '0.9998'] 10
9284 10 

['pnew[2074]', '0.1679.566e-5', '0.0093', '0.1492', '0.1605', '0.1669', '0.1731', '0.1855', '9477', '0.9998'] 10
9291 10 

['pnew[2081]', '0.18218.904e-5', '0.0093', '0.1641', '0.1757', '0.1819', '0.1882', '0.201', '10983', '0.9998'] 10
9297 10 

['pnew[2087]', '0.16538.721e-5', '0.0091', '0.1478', '0.1589', '0.1652', '0.1713', '0.1834', '10941', '0.9998'] 10
9307 10 

['pnew[2097]', '0.14868.588e-5', '0.0089', '0.1317', '0.1425', '0.1485', '0.1545', '0.1661', '10666', '0.9998'] 10
9318 10 

['pnew[2108]', '0.18158.406e-5', '0.0092', '0.1635', '0.1753', '0.1813', '0.1877', '0.2', '12000', '0.9998'] 10
9324 10 

['pnew[2114]', '0.15748.372e-5', '0.0089', '0.1404', '0.1513', '0.1574', '0.1633', '0.175', '11290', '0.9998'] 10
9339 10 

['pnew[2129]', '0.13679.529e-5', '0.0089', '0.1199', '0.1305', '0.1366', '0.1426', '0.1545', '8782', '0.9998'] 10
9358 10 

['pnew[2148]', '0.17368.256e-5', '0.009', '0.156', '0.1675', '0.1734', '0.1797', '0.1918', '12000', '0.9998'] 10
9374 10 

['pnew[2164]', '0.15668.604e-5', '0.009', '0.1394', '0.1505', '0.1565', '0.1626', '0.1746', '10885', '0.9998'] 10
9395 10 

['pnew[2185]', '0.16968.402e-5', '0.0091', '0.152', '0.1634', '0.1695', '0.1757', '0.1879', '11726', '0.9998'] 10
9398 10 

['pnew[2188]', '0.19388.982e-5', '0.0095', '0.1753', '0.1873', '0.1936', '0.2001', '0.2129', '11244', '0.9998'] 10
9413 10 

['pnew[2203]', '0.19949.957e-5', '0.0099', '0.1803', '0.1927', '0.1992', '0.2059', '0.2193', '9848', '0.9998'] 10
9418 10 

['pnew[2208]', '0.19018.687e-5', '0.0094', '0.1717', '0.1838', '0.19', '0.1964', '0.2091', '11744', '0.9998'] 10
9440 10 

['pnew[2230]', '0.1848.876e-5', '0.0093', '0.1659', '0.1776', '0.1838', '0.1902', '0.2028', '11072', '0.9998'] 10
9450 10 

['pnew[2240]', '0.15548.825e-5', '0.009', '0.138', '0.1492', '0.1552', '0.1614', '0.1735', '10513', '0.9998'] 10
9501 10 

['pnew[2291]', '0.15848.196e-5', '0.0088', '0.1416', '0.1524', '0.1583', '0.1643', '0.1759', '11617', '0.9998'] 10
9509 10 

['pnew[2299]', '0.17279.666e-5', '0.0098', '0.1539', '0.166', '0.1726', '0.1793', '0.1922', '10175', '0.9998'] 10
9514 10 

['pnew[2304]', '0.19169.042e-5', '0.0095', '0.1732', '0.1852', '0.1914', '0.1979', '0.2107', '11009', '0.9998'] 10
9517 10 

['pnew[2307]', '0.18228.961e-5', '0.0095', '0.1636', '0.1757', '0.1821', '0.1886', '0.2013', '11336', '0.9998'] 10
9525 10 

['pnew[2315]', '0.13279.895e-5', '0.009', '0.1156', '0.1264', '0.1326', '0.1388', '0.1508', '8355', '0.9998'] 10
9533 10 

['pnew[2323]', '0.16118.765e-5', '0.0091', '0.1437', '0.1548', '0.161', '0.1672', '0.179', '10789', '0.9998'] 10
9536 10 

['pnew[2326]', '0.19459.054e-5', '0.0097', '0.1757', '0.188', '0.1944', '0.201', '0.214', '11375', '0.9998'] 10
9562 10 

['pnew[2352]', '0.18749.166e-5', '0.0097', '0.1685', '0.1809', '0.1873', '0.194', '0.2068', '11198', '0.9998'] 10
9584 10 

['pnew[2374]', '0.18779.397e-5', '0.0098', '0.1686', '0.181', '0.1876', '0.1943', '0.2073', '10929', '0.9998'] 10
9609 10 

['pnew[2399]', '0.159.179e-5', '0.0091', '0.1326', '0.1438', '0.1498', '0.1562', '0.1682', '9829', '0.9998'] 10
[ 5  6 10]

In [41]:

    

header_fit = output[4].split()
print header_fit


    

    




['mean', 'se_mean', 'sd', '2.5%', '25%', '50%', '75%', '97.5%', 'n_eff', 'Rhat']

In [42]:

    

header_addendum = 'parameter'
header_fit = [header_addendum] + header_fit
print header_fit


    

    




['parameter', 'mean', 'se_mean', 'sd', '2.5%', '25%', '50%', '75%', '97.5%', 'n_eff', 'Rhat']

In [83]:

    

# this is to fix lines where numbers are not separated - which is the case of positions 2 and 3 many times
new_data = header_fit
for i in range(new_output.size):
    if len(new_output[i].split())==11: #the length of the list must be 11, in which case we connect them directly
        new_output_i = np.array(new_output[i].split()).reshape(1,11)
        new_data = np.vstack((new_data, new_output_i))
        
    elif len(new_output[i].split())==5:
        list_temp = new_output[i].split()
        new_list_temp = []
        for j in range(len(list_temp)):
            if list_temp[0] == 'BIC':
                if j==2:
                    new_list_temp.append(list_temp[j][0:6])
                    new_list_temp.append(list_temp[j][6:13])
                    new_list_temp.append(list_temp[j][13:20])
                    new_list_temp.append(list_temp[j][20:27])
                    new_list_temp.append(list_temp[j][27:34])
                    new_list_temp.append(list_temp[j][34:41])
                    new_list_temp.append(list_temp[j][41:48])
                else:
                    new_list_temp.append(list_temp[j])

            elif list_temp[0] == 'LogL':
                if j==2:
                    new_list_temp.append(list_temp[j][0:6])
                    new_list_temp.append(list_temp[j][6:13])
                    new_list_temp.append(list_temp[j][13:21])
                    new_list_temp.append(list_temp[j][21:29])
                    new_list_temp.append(list_temp[j][29:37])
                    new_list_temp.append(list_temp[j][37:45])
                    new_list_temp.append(list_temp[j][45:53])
                else:
                    new_list_temp.append(list_temp[j])
            print new_list_temp
        fixed_row = np.array(new_list_temp)
        fixed_row = fixed_row.reshape(1,11)
        new_data = np.vstack((new_data, fixed_row))
        
    elif len(new_output[i].split())==6:
        list_temp = new_output[i].split()
        new_list_temp = []
        for j in range(len(list_temp)):
            if j==2:
                new_list_temp.append(list_temp[j][0:6])
                new_list_temp.append(list_temp[j][6:13])
                new_list_temp.append(list_temp[j][13:20])
                new_list_temp.append(list_temp[j][20:27])
                new_list_temp.append(list_temp[j][27:34])
                new_list_temp.append(list_temp[j][34:41])
            else:
                new_list_temp.append(list_temp[j])
            print new_list_temp 
        fixed_row = np.array(new_list_temp)
        fixed_row = fixed_row.reshape(1,11)
        new_data = np.vstack((new_data, fixed_row))
       
    
    elif len(new_output[i].split())==10: # if the length is 10, we must split these lines
        list_temp = new_output[i].split()
        new_list_temp = []
        for j in range(len(list_temp)):
            if j==1:
                if (list_temp[j][0:6][-1]=='.'): 
                    new_list_temp.append(list_temp[j][0:4])
                    new_list_temp.append(list_temp[j][4:])
                elif list_temp[j][6:][0]=='.':
                    new_list_temp.append(list_temp[j][0:5])
                    new_list_temp.append(list_temp[j][5:])
                else:
                    new_list_temp.append(list_temp[j][0:6])
                    new_list_temp.append(list_temp[j][6:])
            else:
                new_list_temp.append(list_temp[j])
        print new_list_temp
        fixed_row = np.array(new_list_temp)
        fixed_row = fixed_row.reshape(1,11)
        new_data = np.vstack((new_data, fixed_row))


    

    




['AIC']
['AIC', '8.385e3']
['AIC', '8.385e3', '2.2609', '2.262e2', '7.944e3', '8.231e3', '8.382e3', '8.536e3']
['AIC', '8.385e3', '2.2609', '2.262e2', '7.944e3', '8.231e3', '8.382e3', '8.536e3', '8.84e3']
['AIC', '8.385e3', '2.2609', '2.262e2', '7.944e3', '8.231e3', '8.382e3', '8.536e3', '8.84e3', '10005']
['AIC', '8.385e3', '2.2609', '2.262e2', '7.944e3', '8.231e3', '8.382e3', '8.536e3', '8.84e3', '10005', '0.9998']
['BIC']
['BIC', '8.408e3']
['BIC', '8.408e3', '2.2609', '2.262e2', '7.967e3', '8.255e3', '8.405e3', '8.559e3', '8.864e3']
['BIC', '8.408e3', '2.2609', '2.262e2', '7.967e3', '8.255e3', '8.405e3', '8.559e3', '8.864e3', '10005']
['BIC', '8.408e3', '2.2609', '2.262e2', '7.967e3', '8.255e3', '8.405e3', '8.559e3', '8.864e3', '10005', '0.9998']
['LogL']
['LogL', '-4.189e3']
['LogL', '-4.189e3', '1.1305', '1.131e2', '-4.416e3', '-4.264e3', '-4.187e3', '-4.112e3', '-3.968e3']
['LogL', '-4.189e3', '1.1305', '1.131e2', '-4.416e3', '-4.264e3', '-4.187e3', '-4.112e3', '-3.968e3', '10005']
['LogL', '-4.189e3', '1.1305', '1.131e2', '-4.416e3', '-4.264e3', '-4.187e3', '-4.112e3', '-3.968e3', '10005', '0.9998']
['pnew[11]', '0.1699', '9.573e-5', '0.0097', '0.1512', '0.1633', '0.1697', '0.1764', '0.1892', '10173', '0.9998']
['pnew[35]', '0.1509', '8.472e-5', '0.0088', '0.1339', '0.1448', '0.1508', '0.1568', '0.1684', '10858', '0.9998']
['pnew[63]', '0.1412', '8.972e-5', '0.0089', '0.1242', '0.1351', '0.141', '0.1471', '0.159', '9791', '0.9998']
['pnew[76]', '0.1904', '9.691e-5', '0.0097', '0.1717', '0.1838', '0.1903', '0.1968', '0.21', '10006', '0.9998']
['pnew[98]', '0.1824', '9.301e-5', '0.0097', '0.1634', '0.1758', '0.1823', '0.1888', '0.2018', '10910', '0.9998']
['pnew[99]', '0.1318', '9.846e-5', '0.009', '0.1148', '0.1255', '0.1316', '0.1378', '0.1498', '8298', '0.9998']
['pnew[118]', '0.1994', '9.479e-5', '0.0099', '0.1801', '0.1927', '0.1992', '0.206', '0.2193', '10921', '0.9998']
['pnew[133]', '0.1831', '9.152e-5', '0.0094', '0.1651', '0.1767', '0.183', '0.1894', '0.2022', '10620', '0.9998']
['pnew[156]', '0.143', '9.049e-5', '0.0089', '0.1259', '0.1368', '0.1428', '0.1489', '0.1609', '9732', '0.9998']
['pnew[158]', '0.1936', '9.782e-5', '0.0098', '0.1749', '0.187', '0.1935', '0.2001', '0.2133', '9946', '0.9998']
['pnew[196]', '0.1692', '8.134e-5', '0.0089', '0.152', '0.1631', '0.1691', '0.1751', '0.1871', '12000', '0.9998']
['pnew[199]', '0.1807', '9.353e-5', '0.0095', '0.1626', '0.1742', '0.1806', '0.187', '0.1998', '10299', '0.9998']
['pnew[207]', '0.1534', '9.351e-5', '0.0091', '0.136', '0.1471', '0.1533', '0.1595', '0.1712', '9458', '0.9998']
['pnew[210]', '0.1784', '8.633e-5', '0.0093', '0.1603', '0.1722', '0.1783', '0.1847', '0.1971', '11682', '0.9998']
['pnew[215]', '0.144', '9.195e-5', '0.009', '0.1269', '0.1378', '0.1439', '0.1501', '0.162', '9581', '0.9998']
['pnew[226]', '0.1633', '8.068e-5', '0.0088', '0.1464', '0.1573', '0.1632', '0.1692', '0.181', '12000', '0.9998']
['pnew[228]', '0.1533', '8.722e-5', '0.009', '0.136', '0.1471', '0.1532', '0.1592', '0.1709', '10651', '0.9998']
['pnew[231]', '0.1642', '8.075e-5', '0.0088', '0.1473', '0.1582', '0.1641', '0.1701', '0.1819', '12000', '0.9998']
['pnew[234]', '0.1556', '8.996e-5', '0.0091', '0.138', '0.1494', '0.1554', '0.1617', '0.1739', '10292', '0.9998']
['pnew[245]', '0.1585', '8.136e-5', '0.0088', '0.1416', '0.1525', '0.1584', '0.1644', '0.176', '11726', '0.9998']
['pnew[266]', '0.1819', '9.505e-5', '0.0096', '0.1636', '0.1753', '0.1817', '0.1882', '0.2012', '10117', '0.9998']
['pnew[274]', '0.1821', '9.064e-5', '0.0096', '0.1634', '0.1756', '0.182', '0.1885', '0.2012', '11199', '0.9998']
['pnew[287]', '0.2007', '9.501e-5', '0.0098', '0.1816', '0.194', '0.2005', '0.2072', '0.2202', '10637', '0.9998']
['pnew[353]', '0.1656', '8.317e-5', '0.009', '0.1483', '0.1594', '0.1655', '0.1715', '0.1834', '11613', '0.9998']
['pnew[356]', '0.1882', '8.628e-5', '0.0094', '0.1698', '0.1819', '0.1881', '0.1945', '0.2072', '11867', '0.9998']
['pnew[363]', '0.1492', '8.769e-5', '0.0089', '0.1321', '0.1432', '0.1491', '0.1552', '0.167', '10339', '0.9998']
['pnew[381]', '0.1625', '9.963e-5', '0.0094', '0.1446', '0.1559', '0.1624', '0.1688', '0.1812', '8971', '0.9999']
['pnew[424]', '0.1615', '9.509e-5', '0.0095', '0.1431', '0.1551', '0.1613', '0.1679', '0.1806', '9923', '0.9998']
['pnew[428]', '0.2047', '9.868e-5', '0.01', '0.1855', '0.1979', '0.2045', '0.2114', '0.2247', '10229', '0.9998']
['pnew[514]', '0.1841', '8.427e-5', '0.0092', '0.166', '0.1779', '0.184', '0.1903', '0.2027', '12000', '0.9998']
['pnew[522]', '0.1665', '8.157e-5', '0.0089', '0.1493', '0.1604', '0.1664', '0.1724', '0.1844', '12000', '0.9998']
['pnew[537]', '0.1843', '8.438e-5', '0.0092', '0.1662', '0.1781', '0.1842', '0.1905', '0.2029', '12000', '0.9998']
['pnew[538]', '0.1651', '8.386e-5', '0.009', '0.1479', '0.1589', '0.165', '0.171', '0.183', '11480', '0.9998']
['pnew[543]', '0.1943', '9.336e-5', '0.0096', '0.1757', '0.1878', '0.1942', '0.2007', '0.2136', '10603', '0.9998']
['pnew[545]', '0.1641', '8.768e-5', '0.0091', '0.1466', '0.1577', '0.164', '0.1701', '0.1821', '10846', '0.9998']
['pnew[562]', '0.198', '9.242e-5', '0.0097', '0.1791', '0.1914', '0.1978', '0.2045', '0.2175', '11038', '0.9998']
['pnew[571]', '0.1551', '9.258e-5', '0.0092', '0.1373', '0.1487', '0.1549', '0.1613', '0.1736', '9943', '0.9998']
['pnew[578]', '0.1646', '8.931e-5', '0.0093', '0.1467', '0.1584', '0.1645', '0.1708', '0.1833', '10754', '0.9998']
['pnew[579]', '0.1537', '8.627e-5', '0.0089', '0.1365', '0.1476', '0.1536', '0.1597', '0.1715', '10727', '0.9998']
['pnew[588]', '0.1631', '8.331e-5', '0.0089', '0.146', '0.157', '0.163', '0.169', '0.1809', '11523', '0.9998']
['pnew[599]', '0.1728', '8.339e-5', '0.0091', '0.1551', '0.1667', '0.1727', '0.179', '0.1912', '12000', '0.9998']
['pnew[600]', '0.2028', '9.881e-5', '0.0099', '0.1838', '0.1961', '0.2026', '0.2094', '0.2227', '10076', '0.9998']
['pnew[603]', '0.1689', '9.659e-5', '0.0094', '0.1511', '0.1624', '0.1688', '0.1752', '0.1876', '9403', '0.9998']
['pnew[611]', '0.1809', '8.332e-5', '0.0091', '0.163', '0.1747', '0.1808', '0.187', '0.1993', '12000', '0.9998']
['pnew[633]', '0.1501', '8.513e-5', '0.0089', '0.1333', '0.1441', '0.1501', '0.156', '0.1676', '10827', '0.9998']
['pnew[643]', '0.1886', '9.152e-5', '0.0097', '0.1697', '0.182', '0.1885', '0.1951', '0.2081', '11238', '0.9998']
['pnew[652]', '0.177', '8.354e-5', '0.0092', '0.1591', '0.1709', '0.1768', '0.1831', '0.1954', '12000', '0.9998']
['pnew[666]', '0.1883', '8.716e-5', '0.0093', '0.1701', '0.182', '0.1881', '0.1945', '0.2071', '11507', '0.9998']
['pnew[668]', '0.1686', '9.062e-5', '0.0093', '0.1509', '0.1621', '0.1685', '0.1748', '0.1872', '10511', '0.9998']
['pnew[678]', '0.1412', '9.318e-5', '0.0089', '0.1241', '0.135', '0.1411', '0.1471', '0.159', '9151', '0.9998']
['pnew[754]', '0.1514', '8.374e-5', '0.0088', '0.1346', '0.1454', '0.1513', '0.1573', '0.1688', '11052', '0.9998']
['pnew[755]', '0.1849', '8.437e-5', '0.0092', '0.1667', '0.1786', '0.1847', '0.1911', '0.2035', '12000', '0.9998']
['pnew[763]', '0.1905', '9.583e-5', '0.0099', '0.1712', '0.1837', '0.1904', '0.1973', '0.2104', '10776', '0.9998']
['pnew[767]', '0.1584', '8.238e-5', '0.0089', '0.1415', '0.1524', '0.1584', '0.1643', '0.176', '11545', '0.9998']
['pnew[779]', '0.1698', '8.199e-5', '0.009', '0.1524', '0.1638', '0.1696', '0.1758', '0.1877', '12000', '0.9998']
['pnew[784]', '0.16', '8.603e-5', '0.009', '0.1427', '0.1537', '0.1599', '0.166', '0.1777', '11001', '0.9998']
['pnew[788]', '0.191', '9.234e-5', '0.0098', '0.172', '0.1843', '0.1908', '0.1975', '0.2105', '11176', '0.9998']
['pnew[794]', '0.1798', '8.871e-5', '0.0095', '0.1614', '0.1734', '0.1797', '0.1861', '0.1987', '11389', '0.9998']
['pnew[805]', '0.1729', '8.311e-5', '0.0091', '0.1552', '0.1668', '0.1728', '0.179', '0.1913', '12000', '0.9998']
['pnew[807]', '0.1369', '9.484e-5', '0.0089', '0.1201', '0.1307', '0.1368', '0.1429', '0.1549', '8822', '0.9998']
['pnew[808]', '0.186', '8.521e-5', '0.0093', '0.1677', '0.1797', '0.1859', '0.1923', '0.2048', '12000', '0.9998']
['pnew[815]', '0.1674', '9.757e-5', '0.0097', '0.1486', '0.1608', '0.1672', '0.1739', '0.1868', '9883', '0.9998']
['pnew[817]', '0.1502', '8.426e-5', '0.0088', '0.1333', '0.1442', '0.1501', '0.156', '0.1676', '10933', '0.9998']
['pnew[834]', '0.1598', '8.396e-5', '0.0089', '0.1428', '0.1537', '0.1598', '0.1657', '0.1774', '11323', '0.9998']
['pnew[842]', '0.1676', '8.107e-5', '0.0089', '0.1505', '0.1616', '0.1675', '0.1735', '0.1854', '12000', '0.9998']
['pnew[859]', '0.1836', '9.001e-5', '0.0096', '0.165', '0.1771', '0.1835', '0.19', '0.2027', '11321', '0.9998']
['pnew[883]', '0.1757', '8.929e-5', '0.0093', '0.1579', '0.1693', '0.1756', '0.1819', '0.1945', '10823', '0.9998']
['pnew[902]', '0.1594', '8.423e-5', '0.0089', '0.1424', '0.1532', '0.1593', '0.1653', '0.177', '11267', '0.9998']
['pnew[905]', '0.1945', '9.552e-5', '0.0097', '0.1758', '0.1879', '0.1943', '0.2009', '0.2139', '10278', '0.9998']
['pnew[947]', '0.1573', '9.383e-5', '0.0091', '0.1397', '0.1509', '0.1572', '0.1634', '0.1753', '9506', '0.9998']
['pnew[971]', '0.1537', '8.301e-5', '0.0088', '0.1368', '0.1477', '0.1536', '0.1595', '0.1711', '11268', '0.9998']
['pnew[976]', '0.1477', '9.632e-5', '0.0093', '0.1301', '0.1413', '0.1475', '0.154', '0.1663', '9226', '0.9998']
['pnew[991]', '0.1531', '9.624e-5', '0.0092', '0.1354', '0.1467', '0.153', '0.1591', '0.1711', '9150', '0.9998']
['pnew[995]', '0.1428', '8.913e-5', '0.0089', '0.1258', '0.1367', '0.1426', '0.1487', '0.1606', '9917', '0.9998']
['pnew[1016]', '0.1948', '9.919e-5', '0.0102', '0.1749', '0.1879', '0.1946', '0.2017', '0.215', '10494', '0.9998']
['pnew[1021]', '0.1933', '9.096e-5', '0.0097', '0.1745', '0.1868', '0.1931', '0.1998', '0.2128', '11346', '0.9998']
['pnew[1027]', '0.1464', '8.674e-5', '0.0088', '0.1295', '0.1404', '0.1463', '0.1524', '0.164', '10387', '0.9998']
['pnew[1031]', '0.1846', '8.529e-5', '0.0093', '0.1664', '0.1783', '0.1845', '0.1909', '0.2035', '12000', '0.9998']
['pnew[1043]', '0.1728', '8.384e-5', '0.0091', '0.1551', '0.1667', '0.1727', '0.179', '0.1912', '11876', '0.9998']
['pnew[1061]', '0.1894', '9.304e-5', '0.0095', '0.1711', '0.1829', '0.1893', '0.1957', '0.2087', '10521', '0.9998']
['pnew[1070]', '0.1609', '9.601e-5', '0.0093', '0.1431', '0.1545', '0.1608', '0.1671', '0.1792', '9328', '0.9998']
['pnew[1078]', '0.1856', '9.654e-5', '0.0099', '0.1662', '0.1788', '0.1855', '0.1922', '0.2054', '10589', '0.9998']
['pnew[1112]', '0.1991', '9.335e-5', '0.0097', '0.1801', '0.1925', '0.1989', '0.2057', '0.2187', '10906', '0.9998']
['pnew[1129]', '0.1402', '9.953e-5', '0.0092', '0.1226', '0.1337', '0.14', '0.1463', '0.1583', '8543', '0.9998']
['pnew[1137]', '0.1601', '8.063e-5', '0.0088', '0.1433', '0.1541', '0.16', '0.1659', '0.1777', '12000', '0.9998']
['pnew[1142]', '0.1843', '8.415e-5', '0.0092', '0.1663', '0.1781', '0.1841', '0.1904', '0.2028', '12000', '0.9998']
['pnew[1170]', '0.1558', '9.727e-5', '0.0093', '0.1379', '0.1493', '0.1556', '0.1619', '0.174', '9096', '0.9998']
['pnew[1179]', '0.1699', '8.187e-5', '0.009', '0.1526', '0.1639', '0.1697', '0.1759', '0.1878', '12000', '0.9998']
['pnew[1183]', '0.1474', '9.663e-5', '0.0092', '0.1299', '0.1411', '0.1473', '0.1535', '0.1654', '8985', '0.9998']
['pnew[1185]', '0.1518', '9.961e-5', '0.0093', '0.1338', '0.1453', '0.1517', '0.1579', '0.1702', '8791', '0.9998']
['pnew[1189]', '0.1705', '8.172e-5', '0.009', '0.1532', '0.1646', '0.1704', '0.1765', '0.1886', '12000', '0.9998']
['pnew[1212]', '0.1402', '9.379e-5', '0.0089', '0.1232', '0.134', '0.1402', '0.1462', '0.158', '9058', '0.9998']
['pnew[1221]', '0.1573', '8.518e-5', '0.009', '0.1401', '0.1512', '0.1572', '0.1634', '0.1753', '11046', '0.9998']
['pnew[1229]', '0.138', '9.768e-5', '0.009', '0.121', '0.1318', '0.1379', '0.1441', '0.1562', '8541', '0.9998']
['pnew[1254]', '0.1882', '9.759e-5', '0.01', '0.1688', '0.1814', '0.1882', '0.195', '0.2083', '10540', '0.9998']
['pnew[1263]', '0.2048', '9.879e-5', '0.01', '0.1856', '0.198', '0.2046', '0.2115', '0.2248', '10214', '0.9998']
['pnew[1265]', '0.1855', '8.773e-5', '0.0093', '0.1673', '0.1792', '0.1854', '0.1917', '0.2042', '11294', '0.9998']
['pnew[1297]', '0.1594', '8.164e-5', '0.0088', '0.1426', '0.1533', '0.1593', '0.1652', '0.1769', '11706', '0.9998']
['pnew[1342]', '0.1711', '8.187e-5', '0.009', '0.1537', '0.165', '0.171', '0.177', '0.189', '12000', '0.9998']
['pnew[1348]', '0.1761', '8.233e-5', '0.009', '0.1586', '0.17', '0.176', '0.1821', '0.1942', '12000', '0.9998']
['pnew[1356]', '0.1623', '9.667e-5', '0.0093', '0.1445', '0.1558', '0.1622', '0.1685', '0.1807', '9279', '0.9998']
['pnew[1368]', '0.1718', '8.809e-5', '0.0092', '0.1542', '0.1655', '0.1717', '0.178', '0.1903', '10935', '0.9998']
['pnew[1375]', '0.1578', '8.156e-5', '0.0088', '0.1409', '0.1517', '0.1576', '0.1636', '0.1752', '11661', '0.9998']
['pnew[1381]', '0.1552', '9.725e-5', '0.0094', '0.1371', '0.1488', '0.155', '0.1616', '0.1741', '9431', '0.9998']
['pnew[1385]', '0.1989', '9.325e-5', '0.0097', '0.18', '0.1923', '0.1987', '0.2054', '0.2185', '10900', '0.9998']
['pnew[1401]', '0.1753', '8.448e-5', '0.0092', '0.1574', '0.1691', '0.1752', '0.1815', '0.1938', '11861', '0.9998']
['pnew[1426]', '0.184', '9.156e-5', '0.0094', '0.1659', '0.1776', '0.1839', '0.1903', '0.2031', '10631', '0.9998']
['pnew[1431]', '0.165', '8.087e-5', '0.0089', '0.148', '0.159', '0.1649', '0.1709', '0.1827', '12000', '0.9998']
['pnew[1438]', '0.1735', '9.783e-5', '0.0095', '0.1555', '0.167', '0.1734', '0.1798', '0.1925', '9338', '0.9998']
['pnew[1447]', '0.1645', '8.231e-5', '0.0089', '0.1473', '0.1585', '0.1644', '0.1705', '0.1825', '11804', '0.9998']
['pnew[1451]', '0.1827', '8.378e-5', '0.0092', '0.1649', '0.1765', '0.1826', '0.1889', '0.2012', '12000', '0.9998']
['pnew[1464]', '0.1804', '9.477e-5', '0.0098', '0.1614', '0.1738', '0.1804', '0.187', '0.1999', '10644', '0.9998']
['pnew[1474]', '0.1769', '9.133e-5', '0.0096', '0.1584', '0.1704', '0.1768', '0.1833', '0.196', '10949', '0.9998']
['pnew[1505]', '0.1401', '9.098e-5', '0.0089', '0.1232', '0.134', '0.14', '0.1461', '0.158', '9585', '0.9998']
['pnew[1518]', '0.1721', '8.166e-5', '0.0089', '0.1548', '0.1661', '0.172', '0.178', '0.1901', '12000', '0.9998']
['pnew[1524]', '0.1656', '8.119e-5', '0.0089', '0.1486', '0.1596', '0.1655', '0.1715', '0.1834', '12000', '0.9998']
['pnew[1528]', '0.1515', '9.602e-5', '0.0092', '0.1339', '0.1452', '0.1515', '0.1576', '0.1696', '9140', '0.9998']
['pnew[1531]', '0.1896', '9.674e-5', '0.0097', '0.171', '0.1831', '0.1895', '0.196', '0.2092', '10015', '0.9998']
['pnew[1541]', '0.188', '9.152e-5', '0.0097', '0.1691', '0.1814', '0.1879', '0.1945', '0.2074', '11226', '0.9998']
['pnew[1543]', '0.1632', '8.068e-5', '0.0088', '0.1463', '0.1572', '0.1631', '0.169', '0.1809', '12000', '0.9998']
['pnew[1545]', '0.1423', '9.887e-5', '0.0093', '0.1248', '0.1359', '0.142', '0.1485', '0.1609', '8769', '0.9998']
['pnew[1554]', '0.1717', '8.876e-5', '0.0092', '0.1539', '0.1653', '0.1715', '0.1778', '0.1901', '10830', '0.9998']
['pnew[1592]', '0.1482', '8.789e-5', '0.0089', '0.1311', '0.1421', '0.1481', '0.1542', '0.1661', '10272', '0.9998']
['pnew[1593]', '0.1685', '8.121e-5', '0.0089', '0.1514', '0.1625', '0.1685', '0.1745', '0.1863', '12000', '0.9998']
['pnew[1594]', '0.1999', '9.457e-5', '0.0099', '0.1807', '0.1932', '0.1997', '0.2065', '0.2198', '10907', '0.9998']
['pnew[1604]', '0.1608', '9.086e-5', '0.0093', '0.1429', '0.1545', '0.1607', '0.167', '0.1795', '10397', '0.9998']
['pnew[1617]', '0.1733', '8.867e-5', '0.0092', '0.1555', '0.1669', '0.1731', '0.1794', '0.1918', '10872', '0.9998']
['pnew[1620]', '0.1739', '9.172e-5', '0.0094', '0.156', '0.1674', '0.1738', '0.1801', '0.1927', '10444', '0.9998']
['pnew[1624]', '0.1906', '8.773e-5', '0.0094', '0.1722', '0.1842', '0.1904', '0.1969', '0.2096', '11512', '0.9998']
['pnew[1632]', '0.1822', '8.596e-5', '0.0094', '0.164', '0.176', '0.1821', '0.1885', '0.2009', '11839', '0.9998']
['pnew[1653]', '0.1612', '9.992e-5', '0.0097', '0.1425', '0.1546', '0.161', '0.1678', '0.1806', '9409', '0.9998']
['pnew[1662]', '0.1449', '9.693e-5', '0.0091', '0.1274', '0.1386', '0.1448', '0.151', '0.163', '8895', '0.9998']
['pnew[1687]', '0.1983', '9.561e-5', '0.0097', '0.1795', '0.1917', '0.1981', '0.2047', '0.2179', '10385', '0.9998']
['pnew[1705]', '0.1588', '9.872e-5', '0.0094', '0.1409', '0.1523', '0.1587', '0.165', '0.1772', '9003', '0.9998']
['pnew[1707]', '0.1587', '8.323e-5', '0.0089', '0.1416', '0.1527', '0.1586', '0.1647', '0.1765', '11416', '0.9998']
['pnew[1723]', '0.1864', '8.471e-5', '0.0093', '0.1683', '0.1802', '0.1863', '0.1927', '0.2051', '12000', '0.9998']
['pnew[1732]', '0.1437', '9.982e-5', '0.0093', '0.1261', '0.1373', '0.1437', '0.1499', '0.162', '8606', '0.9998']
['pnew[1755]', '0.1495', '8.955e-5', '0.009', '0.1324', '0.1434', '0.1494', '0.1556', '0.1676', '10094', '0.9998']
['pnew[1778]', '0.2037', '9.781e-5', '0.0099', '0.1846', '0.1969', '0.2035', '0.2103', '0.2236', '10310', '0.9998']
['pnew[1781]', '0.1946', '9.166e-5', '0.0097', '0.1756', '0.188', '0.1944', '0.2011', '0.2141', '11268', '0.9998']
['pnew[1782]', '0.2041', '9.805e-5', '0.0101', '0.1846', '0.1973', '0.2039', '0.2108', '0.2242', '10513', '0.9998']
['pnew[1787]', '0.1846', '8.604e-5', '0.0093', '0.1665', '0.1784', '0.1845', '0.1908', '0.2032', '11587', '0.9998']
['pnew[1803]', '0.1689', '8.162e-5', '0.0089', '0.1516', '0.1629', '0.1687', '0.1748', '0.1868', '12000', '0.9998']
['pnew[1833]', '0.1814', '9.803e-5', '0.01', '0.162', '0.1745', '0.1813', '0.188', '0.2011', '10312', '0.9998']
['pnew[1851]', '0.1746', '8.228e-5', '0.009', '0.157', '0.1685', '0.1744', '0.1806', '0.1927', '12000', '0.9998']
['pnew[1886]', '0.1582', '8.147e-5', '0.0088', '0.1414', '0.1522', '0.1581', '0.1641', '0.1757', '11696', '0.9998']
['pnew[1901]', '0.1945', '9.708e-5', '0.01', '0.1749', '0.1877', '0.1944', '0.2013', '0.2146', '10703', '0.9998']
['pnew[1905]', '0.1858', '8.471e-5', '0.0093', '0.1675', '0.1795', '0.1856', '0.192', '0.2045', '12000', '0.9998']
['pnew[1913]', '0.1671', '8.315e-5', '0.009', '0.1496', '0.161', '0.167', '0.1731', '0.1852', '11768', '0.9998']
['pnew[1915]', '0.1534', '8.658e-5', '0.009', '0.1362', '0.1472', '0.1533', '0.1594', '0.171', '10742', '0.9998']
['pnew[1933]', '0.1689', '8.202e-5', '0.009', '0.1516', '0.1628', '0.1688', '0.1748', '0.1868', '12000', '0.9998']
['pnew[1964]', '0.1805', '8.374e-5', '0.0092', '0.1626', '0.1743', '0.1803', '0.1866', '0.1989', '12000', '0.9998']
['pnew[2013]', '0.168', '8.904e-5', '0.0092', '0.1503', '0.1616', '0.1679', '0.1741', '0.1864', '10724', '0.9998']
['pnew[2018]', '0.1602', '9.929e-5', '0.0096', '0.1416', '0.1537', '0.16', '0.1667', '0.1795', '9430', '0.9998']
['pnew[2026]', '0.1567', '8.835e-5', '0.0091', '0.1392', '0.1505', '0.1565', '0.1628', '0.1749', '10556', '0.9998']
['pnew[2036]', '0.1834', '8.764e-5', '0.0095', '0.1649', '0.177', '0.1833', '0.1897', '0.2023', '11632', '0.9998']
['pnew[2063]', '0.1618', '8.063e-5', '0.0088', '0.1449', '0.1558', '0.1617', '0.1677', '0.1795', '12000', '0.9998']
['pnew[2071]', '0.1827', '8.762e-5', '0.0094', '0.1643', '0.1764', '0.1826', '0.189', '0.2016', '11617', '0.9998']
['pnew[2074]', '0.167', '9.566e-5', '0.0093', '0.1492', '0.1605', '0.1669', '0.1731', '0.1855', '9477', '0.9998']
['pnew[2081]', '0.1821', '8.904e-5', '0.0093', '0.1641', '0.1757', '0.1819', '0.1882', '0.201', '10983', '0.9998']
['pnew[2087]', '0.1653', '8.721e-5', '0.0091', '0.1478', '0.1589', '0.1652', '0.1713', '0.1834', '10941', '0.9998']
['pnew[2097]', '0.1486', '8.588e-5', '0.0089', '0.1317', '0.1425', '0.1485', '0.1545', '0.1661', '10666', '0.9998']
['pnew[2108]', '0.1815', '8.406e-5', '0.0092', '0.1635', '0.1753', '0.1813', '0.1877', '0.2', '12000', '0.9998']
['pnew[2114]', '0.1574', '8.372e-5', '0.0089', '0.1404', '0.1513', '0.1574', '0.1633', '0.175', '11290', '0.9998']
['pnew[2129]', '0.1367', '9.529e-5', '0.0089', '0.1199', '0.1305', '0.1366', '0.1426', '0.1545', '8782', '0.9998']
['pnew[2148]', '0.1736', '8.256e-5', '0.009', '0.156', '0.1675', '0.1734', '0.1797', '0.1918', '12000', '0.9998']
['pnew[2164]', '0.1566', '8.604e-5', '0.009', '0.1394', '0.1505', '0.1565', '0.1626', '0.1746', '10885', '0.9998']
['pnew[2185]', '0.1696', '8.402e-5', '0.0091', '0.152', '0.1634', '0.1695', '0.1757', '0.1879', '11726', '0.9998']
['pnew[2188]', '0.1938', '8.982e-5', '0.0095', '0.1753', '0.1873', '0.1936', '0.2001', '0.2129', '11244', '0.9998']
['pnew[2203]', '0.1994', '9.957e-5', '0.0099', '0.1803', '0.1927', '0.1992', '0.2059', '0.2193', '9848', '0.9998']
['pnew[2208]', '0.1901', '8.687e-5', '0.0094', '0.1717', '0.1838', '0.19', '0.1964', '0.2091', '11744', '0.9998']
['pnew[2230]', '0.184', '8.876e-5', '0.0093', '0.1659', '0.1776', '0.1838', '0.1902', '0.2028', '11072', '0.9998']
['pnew[2240]', '0.1554', '8.825e-5', '0.009', '0.138', '0.1492', '0.1552', '0.1614', '0.1735', '10513', '0.9998']
['pnew[2291]', '0.1584', '8.196e-5', '0.0088', '0.1416', '0.1524', '0.1583', '0.1643', '0.1759', '11617', '0.9998']
['pnew[2299]', '0.1727', '9.666e-5', '0.0098', '0.1539', '0.166', '0.1726', '0.1793', '0.1922', '10175', '0.9998']
['pnew[2304]', '0.1916', '9.042e-5', '0.0095', '0.1732', '0.1852', '0.1914', '0.1979', '0.2107', '11009', '0.9998']
['pnew[2307]', '0.1822', '8.961e-5', '0.0095', '0.1636', '0.1757', '0.1821', '0.1886', '0.2013', '11336', '0.9998']
['pnew[2315]', '0.1327', '9.895e-5', '0.009', '0.1156', '0.1264', '0.1326', '0.1388', '0.1508', '8355', '0.9998']
['pnew[2323]', '0.1611', '8.765e-5', '0.0091', '0.1437', '0.1548', '0.161', '0.1672', '0.179', '10789', '0.9998']
['pnew[2326]', '0.1945', '9.054e-5', '0.0097', '0.1757', '0.188', '0.1944', '0.201', '0.214', '11375', '0.9998']
['pnew[2352]', '0.1874', '9.166e-5', '0.0097', '0.1685', '0.1809', '0.1873', '0.194', '0.2068', '11198', '0.9998']
['pnew[2374]', '0.1877', '9.397e-5', '0.0098', '0.1686', '0.181', '0.1876', '0.1943', '0.2073', '10929', '0.9998']
['pnew[2399]', '0.15', '9.179e-5', '0.0091', '0.1326', '0.1438', '0.1498', '0.1562', '0.1682', '9829', '0.9998']

In [84]:

    

import pandas as pd


    

In [85]:

    

new_dataframe = pd.DataFrame(new_data)
new_dataframe.to_csv('./Model/fit_results_3d.csv', sep=',', header=False, index=False)


    

In [86]:

    

print new_data.shape
print new_data[-1]


    

    




(9612, 11)
['pnew[2400]' '0.3448' '0.0003' '0.0216' '0.3026' '0.3303' '0.3446'
 '0.3591' '0.3883' '6475' '1.0']

In [87]:

    

betas = {}
betas['beta0'] = beta0
betas['beta1'] = beta1
betas['beta2'] = beta2
betas['beta3'] = beta3


    

In [88]:

    

betas_dataframe = pd.DataFrame(betas)
betas_dataframe.to_csv('./Model/betas_3d.csv', sep=',', header=True, index=False)


    

In [89]:

    

pnew = list(fit.extract(u'pnew').items()[0])


    

In [90]:

    

model_results = {}
model_results['pnew'] = pnew[1][0]
model_results['redshift'] = x1
model_results['SERSIC_GALFIT'] = x2
model_results['SERSIC_SEXTRACTOR'] = sersic_sex[index]


    

In [91]:

    

model_dataframe = pd.DataFrame(model_results)
model_dataframe.to_csv('./Model/model_prob_3d.csv', sep=',', header=True, index=False)


    

In [ ]: