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plot_variance_subplots.py
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plot_variance_subplots.py
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import glob
from pylab import *
# import colors
from palettable.colorbrewer.qualitative import Set2_7
colors = Set2_7.mpl_colors
params = {
'axes.labelsize': 8,
'font.size': 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
def plot_data(ax, min_gen, max_gen):
# now all plot function should be applied to ax
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.tick_params(axis='x', direction='out')
ax.tick_params(axis='y', length=0)
# offset the spines
for spine in ax.spines.values():
spine.set_position(('outward', 5))
ax.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
# put the grid behind
ax.set_axisbelow(True)
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(min_gen, max_gen)
ax.set_ylim(-5000, 300)
# change xticks to set_xticks
ax.set_xticks(np.arange(min_gen, max_gen, 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')
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()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
plot_data(ax1, 0, 500)
plot_data(ax2, 0, 110)
fig.savefig('variance_subplot_bis.png')