In [1]:

    

%matplotlib inline 
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = 'retina' # I use a HiDPI screen


    

In [2]:

    

from plot_derived_parameters.plot_function import *


    

In [3]:

    

# load in the data
chainLocation = "analysis/chains/"
planck2015Data = loadChainFolder( 'plot_derived_parameters/chains/CDI_2/' )


    

    




/home/zequnl/anaconda3/lib/python3.6/site-packages/astropy/table/table.py:2622: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.
  result = self.as_array() == other

In [4]:

    

# take a look at what is in this table
planck2015Data.colnames


    

    
Out[4]:





['acceptance',
 'likelihood',
 'omega_b',
 'omega_cdm',
 '100theta_s',
 'tau_reio',
 'P_{RR}^1',
 'P_{RR}^2',
 'P_{II}^1',
 'P_{II}^2',
 'P_{RI}^1',
 'A_planck']

In [18]:

    

# determine 95% percentile isocurvature power 
# (I'm using P_II^2, you could use some other choice)
quantity = planck2015Data["P_{II}^2"]
plt.hist(quantity)
upper_95 = np.percentile(quantity, 95)
plt.xlabel(r"$P_{II}^2$")
plt.axvline(upper_95, color="red", label="95%")
plt.legend()
# plt.yscale("log")


    

    
Out[18]:





<matplotlib.legend.Legend at 0x7fd1b878d4a8>






    

In [19]:

    

# filter by this large isocurvature fraction and get the maximum likelihood sample
filtered = planck2015Data[quantity > upper_95]
filtered[ np.argmax(-filtered["likelihood"]) ]


    

    
Out[19]:




Row index=1492

acceptance
likelihood
omega_b
omega_cdm
100theta_s
tau_reio
P_{RR}^1
P_{RR}^2
P_{II}^1
P_{II}^2
P_{RI}^1
A_planck
int64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
float64
27
5356.33
2.235234
0.1184131
1.041419
0.07000848
2.39496e-09
2.092462e-09
2.39416e-12
7.096496e-09
-4.217695e-12
100.0804

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