Serialization Check

In [72]:

    

%matplotlib inline

import os
import numpy as np
from numpy.random import normal, uniform
import math
import collections
import pandas as pd
from astropy.table import Table

import triangle
import sncosmo
import simulate_lsst as sl
import lc


    

In [2]:

    

lightc = sncosmo.read_lc('sn.dat')
lightc.meta


    

    
Out[2]:





OrderedDict([('SNID', 44), ('IAUC', 'NULL'), ('FAKE', 2), ('RA', 209.882553), ('DECL', -12.400555), ('PIXSIZE', 0.2), ('NXPIX', -9), ('NYPIX', -9), ('SNTYPE', 1), ('NOBS', 83), ('PTROBS_MIN', 73), ('PTROBS_MAX', 155), ('MWEBV', 0.0829484), ('MWEBV_ERR', 0.0132717), ('REDSHIFT_HELIO', 1.06465), ('REDSHIFT_HELIO_ERR', 0.0005), ('REDSHIFT_FINAL', 1.06656), ('REDSHIFT_FINAL_ERR', 0.0005), ('HOSTGAL_OBJID', 29144), ('HOSTGAL_PHOTOZ', 1.20071), ('HOSTGAL_PHOTOZ_ERR', 0.05), ('HOSTGAL_SPECZ', 0.0), ('HOSTGAL_SPECZ_ERR', 0.0), ('HOSTGAL_SNSEP', -999.0), ('HOSTGAL_LOGMASS', -9.0), ('HOSTGAL_LOGMASS_ERR', -9.0), ('HOSTGAL_MAG_u', 999.0), ('HOSTGAL_MAG_g', 999.0), ('HOSTGAL_MAG_r', 999.0), ('HOSTGAL_MAG_i', 999.0), ('HOSTGAL_MAG_z', 999.0), ('HOSTGAL_MAG_Y', 999.0), ('HOSTGAL_SB_FLUXCAL_u', -9.0), ('HOSTGAL_SB_FLUXCAL_g', -9.0), ('HOSTGAL_SB_FLUXCAL_r', -9.0), ('HOSTGAL_SB_FLUXCAL_i', -9.0), ('HOSTGAL_SB_FLUXCAL_z', -9.0), ('HOSTGAL_SB_FLUXCAL_Y', -9.0), ('PEAKMJD', 50915.8), ('SEARCH_TYPE', -9), ('SIM_MODEL_NAME', 'SALT2.Guy10_LAMOPEN'), ('SIM_MODEL_INDEX', 6), ('SIM_TYPE_INDEX', 1), ('SIM_TYPE_NAME', 'Ia'), ('SIM_NON1a', 0), ('SIM_LIBID', 2082), ('SIM_SEARCHEFF_MASK', 3), ('SIM_REDSHIFT_HELIO', 1.06371), ('SIM_REDSHIFT_CMB', 1.06562), ('SIM_VPEC', -0.0), ('SIM_DLMU', 44.2709), ('SIM_RA', 209.882553101), ('SIM_DECL', -12.400554657), ('SIM_MWEBV', 0.0821098), ('SIM_PEAKMJD', 50916.5), ('SIM_SALT2x0', 1.695e-06), ('SIM_SALT2x1', -0.708466), ('SIM_SALT2c', 0.0178018), ('SIM_SALT2mB', 25.0621), ('SIM_SALT2alpha', 0.14), ('SIM_SALT2beta', 3.2), ('SIM_PEAKMAG_u', -9.0), ('SIM_PEAKMAG_g', -9.0), ('SIM_PEAKMAG_r', 26.5509), ('SIM_PEAKMAG_i', 25.0618), ('SIM_PEAKMAG_z', 24.534), ('SIM_PEAKMAG_Y', 24.5804), ('SIM_EXPOSURE_u', 1.0), ('SIM_EXPOSURE_g', 1.0), ('SIM_EXPOSURE_r', 1.0), ('SIM_EXPOSURE_i', 1.0), ('SIM_EXPOSURE_z', 1.0), ('SIM_EXPOSURE_Y', 1.0), ('SIM_GALFRAC_u', -9.0), ('SIM_GALFRAC_g', -9.0), ('SIM_GALFRAC_r', -9.0), ('SIM_GALFRAC_i', -9.0), ('SIM_GALFRAC_z', -9.0), ('SIM_GALFRAC_Y', -9.0)])

In [3]:

    

model = sncosmo.Model(source='salt2-extended')
params = {'t0': lightc.meta['SIM_PEAKMJD'], 'x0': lightc.meta['SIM_SALT2x0'], 'x1': lightc.meta['SIM_SALT2x1'],
          'c': lightc.meta['SIM_SALT2c'], 'z': lightc.meta['SIM_REDSHIFT_HELIO']}
model.set(**params)
vparams = ['t0', 'x0', 'x1', 'c']


    

In [13]:

    

lightc


    

    
Out[13]:




<Table masked=False length=83>

MJD
FIELD
TELESCOPE
PHOTFLAG
PHOTPROB
flux
fluxerr
MAG
MAGERR
PSF_SIG1
PSF_SIG2
PSF_RATIO
SKY_SIG
SKY_SIG_T
RDNOISE
ZEROPT
ZEROPT_ERR
GAIN
SIM_MAGOBS
ZP
ZPSYS
band
float64
int64
string32
int64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
string16
string48
50877.211
2082
LSST
0
0.0
-0.243375
1.27381
99.0
5.0
2.91
0.0
0.0
124.62
-9.0
1.15
35.01
0.005
1.0
99.0
27.5
ab
LSST_r
50877.227
2082
LSST
0
0.0
-0.578643
2.03152
99.0
5.0
2.87
0.0
0.0
188.94
-9.0
1.2
34.94
0.005
1.0
99.0
27.5
ab
LSST_i
50877.238
2082
LSST
0
0.0
0.611568
3.23157
99.0
5.0
2.91
0.0
0.0
201.32
-9.0
1.27
34.52
0.005
1.0
99.0
27.5
ab
LSST_z
50877.246
2082
LSST
0
0.0
-19.0733
13.9148
99.0
5.0
2.91
0.0
0.0
171.38
-9.0
1.12
32.76
0.005
1.0
99.0
27.5
ab
LSST_Y
50881.191
2082
LSST
0
0.0
-0.139555
1.23618
99.0
5.0
1.53
0.0
0.0
217.66
-9.0
1.12
34.95
0.005
1.0
99.0
27.5
ab
LSST_r
50881.207
2082
LSST
0
0.0
3.20314
1.44854
99.0
5.0
1.52
0.0
0.0
242.71
-9.0
1.17
34.89
0.005
1.0
99.0
27.5
ab
LSST_i
50881.219
2082
LSST
0
0.0
-0.18162
1.86808
99.0
5.0
1.51
0.0
0.0
250.5
-9.0
1.35
34.64
0.005
1.0
99.0
27.5
ab
LSST_z
50881.23
2082
LSST
0
0.0
-13.2749
7.24635
99.0
5.0
1.53
0.0
0.0
179.39
-9.0
1.15
32.82
0.005
1.0
99.0
27.5
ab
LSST_Y
50886.281
2082
LSST
0
0.0
-9.08493
17.2941
128.0
0.0
1.97
0.0
0.0
95.02
-9.0
0.61
31.46
0.005
1.0
28.1445
27.5
ab
LSST_Y
50892.191
2082
LSST
0
0.0
-0.305465
1.52099
128.0
0.0
2.29
0.0
0.0
183.94
-9.0
1.12
34.98
0.005
1.0
28.5343
27.5
ab
LSST_r
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
51012.012
2082
LSST
0
0.0
2.72431
2.36954
26.4119
2.21317
2.24
0.0
0.0
195.27
-9.0
1.27
34.54
0.005
1.0
28.0274
27.5
ab
LSST_z
51012.02
2082
LSST
0
0.0
-2.05926
10.4875
128.0
0.0
2.24
0.0
0.0
169.36
-9.0
1.12
32.77
0.005
1.0
27.4699
27.5
ab
LSST_Y
51014.984
2082
LSST
0
0.0
-0.244328
0.726859
128.0
0.0
1.82
0.0
0.0
114.75
-9.0
1.12
35.02
0.005
1.0
30.4503
27.5
ab
LSST_r
51015.0
2082
LSST
0
0.0
-0.638936
1.30243
128.0
0.0
1.82
0.0
0.0
169.46
-9.0
1.12
34.81
0.005
1.0
28.8515
27.5
ab
LSST_i
51015.012
2082
LSST
0
0.0
0.912775
1.94035
27.5991
100.401
1.82
0.0
0.0
196.84
-9.0
1.27
34.54
0.005
1.0
28.0756
27.5
ab
LSST_z
51015.02
2082
LSST
0
0.0
-0.337404
8.64081
128.0
0.0
1.81
0.0
0.0
182.5
-9.0
1.15
32.83
0.005
1.0
27.5007
27.5
ab
LSST_Y
51022.066
2082
LSST
0
0.0
3.32529
5.89412
26.1954
101.805
2.07
0.0
0.0
96.53
-9.0
0.56
32.7
0.005
1.0
28.1929
27.5
ab
LSST_z
51023.027
2082
LSST
0
0.0
-8.91032
17.7638
128.0
0.0
1.96
0.0
0.0
97.2
-9.0
0.61
31.45
0.005
1.0
27.5775
27.5
ab
LSST_Y
51028.043
2082
LSST
0
0.0
16.5022
18.8285
24.4561
103.544
1.95
0.0
0.0
101.6
-9.0
0.61
31.43
0.005
1.0
27.6269
27.5
ab
LSST_Y
51030.039
2082
LSST
0
0.0
-4.52828
10.3872
128.0
0.0
2.21
0.0
0.0
96.05
-9.0
0.43
32.15
0.005
1.0
28.3293
27.5
ab
LSST_z

In [15]:

    

fits_run = lc.LC(model, lightc, vparams)
fits_readfromfile = lc.LC(model, lightc, vparams)


    

In [16]:

    

fits_run.fitOut


    

    




running chi^2 fit






    
Out[16]:





(       errors: OrderedDict([('t0', 0.869705464632716), ('x0', 6.352280833289825e-08), ('x1', 0.5247654629885447), ('c', 0.048283506458290146)])
   parameters: array([  1.06371000e+00,   5.09155364e+04,   1.19022208e-06,
        -1.37443229e+00,  -2.74520718e-02])
      success: True
         ndof: 79
   covariance: array([[  7.56396247e-01,  -1.47415535e-08,   9.27513342e-02,
          8.97227286e-03],
       [ -1.47415535e-08,   4.03514718e-15,  -1.57647791e-08,
          8.09694619e-10],
       [  9.27513342e-02,  -1.57647791e-08,   2.79008873e-01,
          1.00106837e-02],
       [  8.97227286e-03,   8.09694619e-10,   1.00106837e-02,
          2.35188405e-03]])
 vparam_names: ['t0', 'x0', 'x1', 'c']
        chisq: 94.67428083189289
  param_names: ['z', 't0', 'x0', 'x1', 'c']
      message: 'Minimization exited successfully.'
        ncall: 98,
 <sncosmo.models.Model at 0x1099cbd50>)

In [18]:

    

fits_run.mcmcOut


    

    




running MCMC fit






    
Out[18]:





(             vparam_names: ['t0', 'x0', 'x1', 'c']
                   errors: OrderedDict([('t0', 0.96641660746480229), ('x0', 6.1798662228628924e-08), ('x1', 0.57363086895194149), ('c', 0.045134673089685648)])
                  samples: array([[  5.09169383e+04,   1.16404787e-06,  -7.29881838e-01,
          6.09524278e-02],
       [  5.09169383e+04,   1.16404787e-06,  -7.29881838e-01,
          6.09524278e-02],
       [  5.09168300e+04,   1.19136635e-06,  -1.05085648e+00,
          4.73520685e-02],
       ..., 
       [  5.09158371e+04,   1.18020699e-06,  -6.30904921e-01,
         -4.02638873e-03],
       [  5.09159178e+04,   1.17023413e-06,  -5.17086614e-01,
         -6.33549077e-03],
       [  5.09159520e+04,   1.17144264e-06,  -5.76688271e-01,
         -7.85788275e-03]])
               parameters: array([  1.06371000e+00,   5.09156437e+04,   1.19222989e-06,
        -1.24938000e+00,  -1.18653719e-02])
              param_names: ['z', 't0', 'x0', 'x1', 'c']
 mean_acceptance_fraction: 0.58489999999999998
               covariance: array([[  9.33961059e-01,  -1.98132324e-08,   1.27355486e-01,
          8.32272228e-03],
       [ -1.98132324e-08,   3.81907465e-15,  -1.74468062e-08,
          4.14616059e-10],
       [  1.27355486e-01,  -1.74468062e-08,   3.29052374e-01,
          1.05596100e-02],
       [  8.32272228e-03,   4.14616059e-10,   1.05596100e-02,
          2.03713871e-03]]),
 <sncosmo.models.Model at 0x109a287d0>)

In [19]:

    

fits_run.nestOut


    

    




running nest fit
 iter=  1721 logz=-59.785532calls=5612 time=375.322s






    
Out[19]:





(        niter: 1722
       errors: OrderedDict([('t0', 0.92234910684464666), ('x0', 6.3086027646714359e-08), ('x1', 0.55882972699104072), ('c', 0.05059971608094839)])
   param_dict: OrderedDict([('z', 1.0637099999999999), ('t0', 50915.586386077928), ('x0', 1.1846365119066584e-06), ('x1', -1.2725691849184311), ('c', -0.016387962565982316)])
   parameters: array([  1.06371000e+00,   5.09155864e+04,   1.18463651e-06,
        -1.27256918e+00,  -1.63879626e-02])
       bounds: {'c': (-0.3, 0.3), 'x1': (-3.0, 3.0), 'x0': (0.0, 1.4249497311936984e-05), 't0': (50774.025500000011, 51071.31319999999)}
            h: 10.409304125663482
     logprior: array([ -4.61016602,  -4.62016602,  -4.63016602, ..., -21.82517019,
       -21.82517019, -21.82517019])
         logz: -59.777783297675832
         ndof: 79
        ncall: 5612
 vparam_names: ['t0', 'x0', 'x1', 'c']
      weights: array([  0.00000000e+00,   0.00000000e+00,   0.00000000e+00, ...,
         7.53874994e-05,   7.41112576e-05,   7.44992683e-05])
      samples: array([[  5.09068339e+04,   1.40227088e-05,  -8.25662223e-01,
         -2.49403336e-01],
       [  5.09251962e+04,   1.13631238e-05,  -1.45712075e+00,
         -2.01863503e-01],
       [  5.09288517e+04,   1.04907047e-05,   3.52519891e-01,
         -4.76533692e-02],
       ..., 
       [  5.09154322e+04,   1.21978393e-06,  -1.47553988e+00,
         -2.09912769e-02],
       [  5.09159262e+04,   1.19225771e-06,  -1.42583909e+00,
         -2.10510009e-02],
       [  5.09155912e+04,   1.18366107e-06,  -1.40916650e+00,
         -1.52150577e-02]])
         time: 375.3223900794983
      logzerr: 0.3226345320275479
   covariance: array([[  8.50727875e-01,  -1.46016862e-08,   1.35466840e-01,
          1.31806008e-02],
       [ -1.46016862e-08,   3.97984688e-15,  -1.90053393e-08,
          6.80506024e-10],
       [  1.35466840e-01,  -1.90053393e-08,   3.12290664e-01,
          1.08423019e-02],
       [  1.31806008e-02,   6.80506024e-10,   1.08423019e-02,
          2.56033127e-03]])
         logl: array([ -5.61571061e+04,  -4.85330366e+04,  -3.72343526e+04, ...,
        -4.74454822e+01,  -4.74625562e+01,  -4.74573344e+01]),
 <sncosmo.models.Model at 0x1099cbf50>)

In [20]:

    

fits_run.writeFits('serial_test.hdf5', 'sn')


    

In [21]:

    

fits_readfromfile.fitOut, fits_readfromfile.mcmcOut, fits_readfromfile.nestOut = fits_readfromfile.readFits('serial_test.hdf5', 'sn')


    

In [22]:

    

fits_readfromfile.plotLC()


    

    
Out[22]:












    

In [49]:

    

fits_run.fitRes


    

    
Out[49]:





       errors: OrderedDict([('t0', 0.869705464632716), ('x0', 6.352280833289825e-08), ('x1', 0.5247654629885447), ('c', 0.048283506458290146)])
   parameters: array([  1.06371000e+00,   5.09155364e+04,   1.19022208e-06,
        -1.37443229e+00,  -2.74520718e-02])
      success: True
         ndof: 79
   covariance: array([[  7.56396247e-01,  -1.47415535e-08,   9.27513342e-02,
          8.97227286e-03],
       [ -1.47415535e-08,   4.03514718e-15,  -1.57647791e-08,
          8.09694619e-10],
       [  9.27513342e-02,  -1.57647791e-08,   2.79008873e-01,
          1.00106837e-02],
       [  8.97227286e-03,   8.09694619e-10,   1.00106837e-02,
          2.35188405e-03]])
 vparam_names: ['t0', 'x0', 'x1', 'c']
        chisq: 94.67428083189289
  param_names: ['z', 't0', 'x0', 'x1', 'c']
      message: 'Minimization exited successfully.'
        ncall: 98

In [48]:

    

fits_run.fitRes.errors == fits_readfromfile.fitRes.errors


    

    
Out[48]:





True

In [50]:

    

np.allclose(fits_run.fitRes.parameters, fits_readfromfile.fitRes.parameters)


    

    
Out[50]:





True

In [73]:

    

np.isclose(fits_run.fitRes.ndof, fits_readfromfile.fitRes.ndof)


    

    
Out[73]:





True

In [51]:

    

np.allclose(fits_run.fitRes.covariance, fits_readfromfile.fitRes.covariance)


    

    
Out[51]:





True

In [74]:

    

np.isclose(fits_run.fitRes.chisq, fits_readfromfile.fitRes.chisq)


    

    
Out[74]:





True

In [75]:

    

np.isclose(fits_run.fitRes.ncall, fits_readfromfile.fitRes.ncall)


    

    
Out[75]:





True

In [52]:

    

fits_run.mcmcRes


    

    
Out[52]:





             vparam_names: ['t0', 'x0', 'x1', 'c']
                   errors: OrderedDict([('t0', 0.96641660746480229), ('x0', 6.1798662228628924e-08), ('x1', 0.57363086895194149), ('c', 0.045134673089685648)])
                  samples: array([[  5.09169383e+04,   1.16404787e-06,  -7.29881838e-01,
          6.09524278e-02],
       [  5.09169383e+04,   1.16404787e-06,  -7.29881838e-01,
          6.09524278e-02],
       [  5.09168300e+04,   1.19136635e-06,  -1.05085648e+00,
          4.73520685e-02],
       ..., 
       [  5.09158371e+04,   1.18020699e-06,  -6.30904921e-01,
         -4.02638873e-03],
       [  5.09159178e+04,   1.17023413e-06,  -5.17086614e-01,
         -6.33549077e-03],
       [  5.09159520e+04,   1.17144264e-06,  -5.76688271e-01,
         -7.85788275e-03]])
               parameters: array([  1.06371000e+00,   5.09156437e+04,   1.19222989e-06,
        -1.24938000e+00,  -1.18653719e-02])
              param_names: ['z', 't0', 'x0', 'x1', 'c']
 mean_acceptance_fraction: 0.58489999999999998
               covariance: array([[  9.33961059e-01,  -1.98132324e-08,   1.27355486e-01,
          8.32272228e-03],
       [ -1.98132324e-08,   3.81907465e-15,  -1.74468062e-08,
          4.14616059e-10],
       [  1.27355486e-01,  -1.74468062e-08,   3.29052374e-01,
          1.05596100e-02],
       [  8.32272228e-03,   4.14616059e-10,   1.05596100e-02,
          2.03713871e-03]])

In [53]:

    

fits_run.mcmcRes.errors == fits_readfromfile.mcmcRes.errors


    

    
Out[53]:





True

In [56]:

    

np.allclose(fits_run.mcmcRes.samples, fits_readfromfile.mcmcRes.samples)


    

    
Out[56]:





True

In [57]:

    

np.allclose(fits_run.mcmcRes.parameters, fits_readfromfile.mcmcRes.parameters)


    

    
Out[57]:





True

In [77]:

    

np.isclose(fits_run.mcmcRes.mean_acceptance_fraction,
           fits_readfromfile.mcmcRes.mean_acceptance_fraction)


    

    
Out[77]:





True

In [58]:

    

np.allclose(fits_run.mcmcRes.covariance, fits_readfromfile.mcmcRes.covariance)


    

    
Out[58]:





True

In [59]:

    

fits_run.nestRes


    

    
Out[59]:





        niter: 1722
       errors: OrderedDict([('t0', 0.92234910684464666), ('x0', 6.3086027646714359e-08), ('x1', 0.55882972699104072), ('c', 0.05059971608094839)])
   param_dict: OrderedDict([('z', 1.0637099999999999), ('t0', 50915.586386077928), ('x0', 1.1846365119066584e-06), ('x1', -1.2725691849184311), ('c', -0.016387962565982316)])
   parameters: array([  1.06371000e+00,   5.09155864e+04,   1.18463651e-06,
        -1.27256918e+00,  -1.63879626e-02])
       bounds: {'c': (-0.3, 0.3), 'x1': (-3.0, 3.0), 'x0': (0.0, 1.4249497311936984e-05), 't0': (50774.025500000011, 51071.31319999999)}
            h: 10.409304125663482
     logprior: array([ -4.61016602,  -4.62016602,  -4.63016602, ..., -21.82517019,
       -21.82517019, -21.82517019])
         logz: -59.777783297675832
         ndof: 79
        ncall: 5612
 vparam_names: ['t0', 'x0', 'x1', 'c']
      weights: array([  0.00000000e+00,   0.00000000e+00,   0.00000000e+00, ...,
         7.53874994e-05,   7.41112576e-05,   7.44992683e-05])
      samples: array([[  5.09068339e+04,   1.40227088e-05,  -8.25662223e-01,
         -2.49403336e-01],
       [  5.09251962e+04,   1.13631238e-05,  -1.45712075e+00,
         -2.01863503e-01],
       [  5.09288517e+04,   1.04907047e-05,   3.52519891e-01,
         -4.76533692e-02],
       ..., 
       [  5.09154322e+04,   1.21978393e-06,  -1.47553988e+00,
         -2.09912769e-02],
       [  5.09159262e+04,   1.19225771e-06,  -1.42583909e+00,
         -2.10510009e-02],
       [  5.09155912e+04,   1.18366107e-06,  -1.40916650e+00,
         -1.52150577e-02]])
         time: 375.3223900794983
      logzerr: 0.3226345320275479
   covariance: array([[  8.50727875e-01,  -1.46016862e-08,   1.35466840e-01,
          1.31806008e-02],
       [ -1.46016862e-08,   3.97984688e-15,  -1.90053393e-08,
          6.80506024e-10],
       [  1.35466840e-01,  -1.90053393e-08,   3.12290664e-01,
          1.08423019e-02],
       [  1.31806008e-02,   6.80506024e-10,   1.08423019e-02,
          2.56033127e-03]])
         logl: array([ -5.61571061e+04,  -4.85330366e+04,  -3.72343526e+04, ...,
        -4.74454822e+01,  -4.74625562e+01,  -4.74573344e+01])

In [78]:

    

np.isclose(fits_run.nestRes.niter, fits_readfromfile.nestRes.niter)


    

    
Out[78]:





True

In [60]:

    

fits_run.nestRes.errors == fits_readfromfile.nestRes.errors


    

    
Out[60]:





True

In [61]:

    

fits_run.nestRes.param_dict == fits_readfromfile.nestRes.param_dict


    

    
Out[61]:





False

In [71]:

    

fits_run.nestRes.param_dict, fits_readfromfile.nestRes.param_dict


    

    
Out[71]:





(OrderedDict([('z', 1.0637099999999999), ('t0', 50915.586386077928), ('x0', 1.1846365119066584e-06), ('x1', -1.2725691849184311), ('c', -0.016387962565982316)]),
 OrderedDict([('t0', 50915.586386077928), ('x0', 1.1846365119066584e-06), ('x1', -1.2725691849184311), ('c', -0.016387962565982316), ('z', 1.0637099999999999)]))

In [63]:

    

np.allclose(fits_run.nestRes.parameters, fits_readfromfile.nestRes.parameters)


    

    
Out[63]:





True

In [64]:

    

fits_run.nestRes.bounds == fits_readfromfile.nestRes.bounds


    

    
Out[64]:





True

In [79]:

    

np.isclose(fits_run.nestRes.h, fits_readfromfile.nestRes.h)


    

    
Out[79]:





True

In [65]:

    

np.allclose(fits_run.nestRes.logprior, fits_readfromfile.nestRes.logprior)


    

    
Out[65]:





True

In [80]:

    

np.isclose(fits_run.nestRes.logz, fits_readfromfile.nestRes.logz)


    

    
Out[80]:





True

In [81]:

    

np.isclose(fits_run.nestRes.ndof, fits_readfromfile.nestRes.ndof)


    

    
Out[81]:





True

In [82]:

    

np.isclose(fits_run.nestRes.ncall, fits_readfromfile.nestRes.ncall)


    

    
Out[82]:





True

In [66]:

    

np.allclose(fits_run.nestRes.weights, fits_readfromfile.nestRes.weights)


    

    
Out[66]:





True

In [67]:

    

np.allclose(fits_run.nestRes.samples, fits_readfromfile.nestRes.samples)


    

    
Out[67]:





True

In [83]:

    

np.isclose(fits_run.nestRes.time, fits_readfromfile.nestRes.time)


    

    
Out[83]:





True

In [84]:

    

np.isclose(fits_run.nestRes.logzerr, fits_readfromfile.nestRes.logzerr)


    

    
Out[84]:





True

In [68]:

    

np.allclose(fits_run.nestRes.covariance, fits_readfromfile.nestRes.covariance)


    

    
Out[68]:





True

In [69]:

    

np.allclose(fits_run.nestRes.logl, fits_readfromfile.nestRes.logl)


    

    
Out[69]:





True

In [ ]: