The data file is in fits format and called 
      2pt_NG_mcal_1110.fits

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

Here is a dictionary for the chains:
Each file is named in the following way:

<Datasets_Model>".txt"

Replace <Datasets_Model> by the name convetion below

e.g.: s_l3.txt

** When you use the chains, please use the last column, the weights [see the python snippet below]. Note that the sequence of samples is not a random walk**


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

There is a letter corresponding to each dataset:

s = DES shear only
wg = DES wtheta + galaxy-galaxy lensing
d = DES 3x2
pnl = Planck with no lensing
e = Planck + BAO + SN
dprime = DES 3x2 but using only large scales
eprime = BAO + SN

So, e.g., dpnl = DES 3x2 combined with Planck no lensing

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

Finally, the models considered were

l = LCDM
w = wCDM
The 3 at the end means that the 3 neutrinos were taken to be degenerate [appropriate to the mass range to which we are sensitive]

**************************
A simple python script that reads in omegam and sigma8
and plots using chainconsumer [https://samreay.github.io/ChainConsumer/] is:

import numpy as np
import os
import sys
sys.path.append('/Library/Python/2.7/site-packages')
from chainconsumer import ChainConsumer
import matplotlib.pyplot as plt
c = ChainConsumer()
names=['$\\Omega_m$','$S_8$']
file = 'd_l3.txt'
f =open(file)
ch=[]
w=[]
for i,line in enumerate(f):
        words=line.split()
        om=float(words[0])  $$ as described in the first line, omegam is the 1st entry on each line
        sigma8=float(words[26]) ## as described on the first line, sigma8 is the 27 entry in each line
        s8=(om/0.3)**.5*sigma8
        ch.append([om,s8])
        w.append(float(words[28]))
w=np.array(w)
ch=np.array(ch)
f.close()
c.add_chain(ch,parameters=names,weights=w,name=r'\textbf{DES Y1 All}')
## Then after adding 2 more chains
c.configure(plot_hists=False,sigma2d=False,kde=1.5, colors=["g", "r","b"], linewidths=1.2, legend_kwargs={"loc": "upper right", "fontsize": 10},
            legend_color_text=True, legend_location=(-1, 0),diagonal_tick_labels=False)
fig = c.plotter.plot(legend='t',figsize="column")
fig.savefig('omegams8.pdf', bbox_inches="tight", dpi=300, transparent=True, pad_inches=0.05)