plot_variance_matplotlib.py

import glob
from pylab import *
import brewer2mpl

 # brewer2mpl.get_map args: set name  set type  number of colors
bmap = brewer2mpl.get_map('Set2', 'qualitative', 7)
colors = bmap.mpl_colors
 
params = {
    'axes.labelsize': 8,
    'text.fontsize': 8,
    'legend.fontsize': 10,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'text.usetex': False,
    'figure.figsize': [4.5, 4.5]
}
rcParams.update(params)


def load(dir):
    f_list = glob.glob(dir + '/*/*/bestfit.dat')
    num_lines = sum(1 for line in open(f_list[0]))
    i = 0;
    data = np.zeros((len(f_list), num_lines)) 
    for f in f_list:
        data[i, :] = np.loadtxt(f)[:,1]
        i += 1
    return data

def perc(data):
    median = np.zeros(data.shape[1])
    perc_25 = np.zeros(data.shape[1])
    perc_75 = np.zeros(data.shape[1])
    for i in range(0, len(median)):
        median[i] = np.median(data[:, i])
        perc_25[i] = np.percentile(data[:, i], 25)
        perc_75[i] = np.percentile(data[:, i], 75)
    return median, perc_25, perc_75

data_low_mut = load('data/low_mut')
data_high_mut = load('data/high_mut')

n_generations = data_low_mut.shape[1]
x = np.arange(0, n_generations)

med_low_mut, perc_25_low_mut, perc_75_low_mut = perc(data_low_mut)
med_high_mut, perc_25_high_mut, perc_75_high_mut = perc(data_high_mut)

fig = figure() # no frame
ax = fig.add_subplot(111)

# now all plot function should be applied to ax
ax.fill_between(x, perc_25_low_mut, perc_75_low_mut, alpha=0.25, linewidth=0, color=colors[0]) 
ax.fill_between(x, perc_25_high_mut, perc_75_high_mut, alpha=0.25, linewidth=0, color=colors[1])
ax.plot(x, med_low_mut, linewidth=2, color=colors[0])
ax.plot(x, med_high_mut, linewidth=2, linestyle='--', color=colors[1])

# change xlim to set_xlim
ax.set_xlim(-5, 400)
ax.set_ylim(-5000, 300)

#change xticks to set_xticks
ax.set_xticks(np.arange(0, 500, 100))

legend = ax.legend(["Low mutation rate", "High Mutation rate"], loc=4);
frame = legend.get_frame()
frame.set_facecolor('1.0')
frame.set_edgecolor('1.0')

fig.savefig('variance_matplotlib.png')