def plot_timing(data):
fig = on_key.figure()
ax = fig.add_subplot(1,1,1)
for fct_name in data:
N = data[fct_name][:,0]
T = data[fct_name][:,1:]
mean = np.mean(T, axis=1)
std = np.std(T, axis=1)
assert(len(N) == len(mean))
assert(len(N) == len(std))
ax.errorbar(N, mean, yerr=std, label=fct_name)
del fct_name
ax.grid(True)
ax.legend(loc='best')
ax.set_xlabel('N')
ax.set_ylabel('Duration [s]')
ax.set_xscale('log', basex=2)
ax.set_yscale('log')
xlims = ax.get_xlim()
ax.set_xlim(xlims[0]/2, xlims[1]*2)
ax.set_title('Scaling of different sorting algorithms implemented in Rust 1.2')
on_key.show()
def main():
import matplotlib.pyplot as plt
import on_key
r = np.linspace(0.0, 10.0, 1000)
css = []
css.append(0)
css.append(1)
css.append(2)
css.append(3)
css.append(4)
css.append(5)
css.append(6)
fig = on_key.figure()
axprops = dict()
ax1 = fig.add_subplot(211, **axprops)
axprops['sharex'] = ax1
plt.setp(ax1.get_xticklabels(), visible=False)
ax2 = fig.add_subplot(212, **axprops)
plt.subplots_adjust(hspace=0.0)
ax1.grid(True)
ax2.grid(True)
for cs in css:
HS_U = HS_Fitting_Function_Xe_Potential(r, cs)
HS_E = HS_Fitting_Function_Xe_Field(r, cs)
indices = np.where(r > HS_Xe_rmax[cs])
HS_U[indices] = -float(cs) / r[indices]
HS_E[indices] = -float(cs) / (r[indices]*r[indices])
#HS_U[indices] = 0.0
#HS_E[indices] = 0.0
ax1.plot(r, HS_U, label = str(cs) + "+")
ax2.plot(r, HS_E, label = str(cs) + "+")
ax2.legend(loc='best')
ax1.set_ylabel("Potentiel energy (Hartree)")
ax2.set_ylabel("Electric field (atomic units)")
ax2.set_xlabel("Distance (Bohr)")
ax1.set_ylim((-5.0, 0.0))
ax2.set_ylim((-0.6, 0.0))
plt.show()
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import sys, os, glob
import numpy
import matplotlib.pyplot as plt
import on_key
globber = glob.glob(os.path.join("output", "*"))
globber.sort()
fig = on_key.figure()
colors = ["b", "r", "m", "c", "g", "y"]
symbols = ["-", "--", ":", "-."]
line_width = 2
fi = 0
for prng_file in globber:
# Skip folders
if not os.path.isfile(prng_file) or prng_file == "output/make_run.log":
continue
print "prng_file =", prng_file
data = numpy.loadtxt(prng_file, dtype=float)
plt.hist(data, bins=100, label=prng_file.replace("output/", ""), alpha=0.5)