# -*- coding: utf-8 -*-
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as pl
from plot_setup import setup, SQUARE_FIGSIZE, COLORS, savefig
from twod_a import log_p_gauss, run_mcmc
setup() # initialize the plotting styles
np.random.seed(42)
sigs = 2.0 ** np.arange(-2, 6)
if __name__ == "__main__":
acc_fracs = np.empty_like(sigs)
for i, sig in enumerate(sigs):
_, acc_fracs[i] = run_mcmc(log_p_gauss, np.array([0.0, 0.0]),
prop_sigma=sig)
fig, ax = pl.subplots(1, 1, figsize=SQUARE_FIGSIZE)
ax.axhline(0.25, color=COLORS["DATA"])
ax.plot(sigs, acc_fracs, ".-", color=COLORS["MODEL_2"])
ax.set_xscale("log")
ax.set_xlabel(r"$\sigma_q$")
ax.set_ylabel("acceptance fraction")
ax.set_xlim(0.2, 40)
savefig(fig, "tuning.pdf")
from twod_a import run_mcmc, log_p_gauss
from plot_setup import setup, COLORS, SQUARE_FIGSIZE, savefig
setup() # initialize the plotting styles
np.random.seed(42)
x0 = [-4.0, 5.0]
s = SQUARE_FIGSIZE
s[0] *= 2
s[1] *= 0.8
fig, axes = pl.subplots(1, 3, sharex=True, sharey=True, figsize=s)
for n, ax in zip([0.0, -1.0, 2.0], axes):
chain, _ = run_mcmc(log_p_gauss,
np.array(x0),
nsteps=2e3,
prop_sigma=10**n)
ax.plot(chain[:, 0], chain[:, 1], "o-", color=COLORS["DATA"], ms=2)
ax.plot(x0[0], x0[1], "o", color=COLORS["MODEL_1"])
ax.set_xlim(-6.3, 6.3)
ax.set_ylim(-6.3, 6.3)
ax.set_xlabel("$x$")
ax.annotate(r"$\sigma_q = 10^{{{0:.0f}}}$".format(n), (1, 0),
xycoords="axes fraction",
xytext=(-5, 5),
textcoords="offset points",
ha="right",
va="bottom")
ax.yaxis.set_major_locator(pl.MaxNLocator(5))
ax.xaxis.set_major_locator(pl.MaxNLocator(5))
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import corner
import numpy as np
from twod_a import run_mcmc
from plot_setup import setup, savefig
setup() # initialize the plotting styles
np.random.seed(42)
def log_p_uniform(x):
if 3 <= x[0] <= 7 and 1 <= x[1] <= 9:
return 0.0
return -np.inf
chain, _ = run_mcmc(log_p_uniform, np.array([5.0, 5.0]), nsteps=1e5)
fig = corner.corner(chain, labels=["$x$", "$y$"],
range=[(2.5, 7.5), (0.5, 9.5)],
plot_density=False, plot_contours=False)
savefig(fig, "twod_b.pdf", dpi=300)
from twod_a import run_mcmc, log_p_gauss
from plot_setup import setup, COLORS, SQUARE_FIGSIZE, savefig
setup() # initialize the plotting styles
np.random.seed(42)
x0 = [-4.0, 5.0]
s = SQUARE_FIGSIZE
s[0] *= 2
s[1] *= 0.8
fig, axes = pl.subplots(1, 3, sharex=True, sharey=True, figsize=s)
for n, ax in zip([0.0, -1.0, 2.0], axes):
chain, _ = run_mcmc(log_p_gauss, np.array(x0), nsteps=2e3,
prop_sigma=10**n)
ax.plot(chain[:, 0], chain[:, 1], "o-", color=COLORS["DATA"], ms=2)
ax.plot(x0[0], x0[1], "o", color=COLORS["MODEL_1"])
ax.set_xlim(-6.3, 6.3)
ax.set_ylim(-6.3, 6.3)
ax.set_xlabel("$x$")
ax.annotate(r"$\sigma_q = 10^{{{0:.0f}}}$".format(n),
(1, 0), xycoords="axes fraction",
xytext=(-5, 5), textcoords="offset points",
ha="right", va="bottom")
ax.yaxis.set_major_locator(pl.MaxNLocator(5))
ax.xaxis.set_major_locator(pl.MaxNLocator(5))
axes[0].set_ylabel("$y$")
savefig(fig, "MH_sigma.pdf")
