In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import scipy
In [14]:
    mu = 1.25e-8
    gen = 30
    afrDat = pd.read_csv("/home/training/share/MSMC-tutorial-files/results/AFR.msmc2.final.txt", delim_whitespace=True)
    eurDat = pd.read_csv("/home/training/share/MSMC-tutorial-files/results/EUR.msmc2.final.txt", delim_whitespace=True)
    plt.step(afrDat["left_time_boundary"]/mu*gen, (1/afrDat["lambda"])/(2*mu), label="AFR")
    plt.step(eurDat["left_time_boundary"]/mu*gen, (1/eurDat["lambda"])/(2*mu), label="EUR")
    plt.ylim(0,40000)
    plt.xlabel("years ago");
    plt.ylabel("effective population size");
    plt.gca().set_xscale('log')
    plt.legend()
Out[14]:
<matplotlib.legend.Legend at 0x7f69327a7ba8>
In [20]:
    mu = 1.25e-8
    gen = 30
    crossPopDat = pd.read_csv("/home/training/share/MSMC-tutorial-files/results/EUR_AFR.combined.msmc2.final.txt", delim_whitespace=True)
    plt.step(crossPopDat["left_time_boundary"]/mu*gen, 2 * crossPopDat["lambda_01"] / (crossPopDat["lambda_00"] + crossPopDat["lambda_11"]))
    plt.xlim(1000,500000);
    plt.ylim(0, 1.2)
    plt.xlabel("years ago");
    plt.ylabel("relative cross coalescence rate");
    plt.gca().set_xscale('log')
In [22]:
[1,2,3]
Out[22]:
[1, 2, 3]
In [ ]: