from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('plt_overlay.pdf')
### log-log data
data_dir = '/Users/jingjing/Work/DK_project/Data/Mine/'
dwarfplanet, exoplanet, browndwarf, star = \
np.loadtxt(data_dir+'dpl.txt'), \
np.loadtxt(data_dir+'epl.txt'), \
np.loadtxt(data_dir+'bd.txt'), \
np.loadtxt(data_dir+'st.txt')
data = [dwarfplanet, exoplanet, browndwarf, star]
### solar planets
# Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune
solarplanet = convert_data( np.loadtxt(data_dir+'spl.txt') )
### mcmc result, spatial_median and trans_1up/down
best_hyper = np.loadtxt('h4_spatial_median.txt', delimiter=',')
trans_best = best_hyper[-3:]
slope_best = best_hyper[1:5]
# find_best.py with spatial_median.txt and find trans_1up/down
trans_1up = trans_best + np.array([0.1245372 , 0.0534476 , 0.04035559])
trans_1down = trans_best - np.array([0.14317994, 0.06079727, 0.03514506 ])
# convert trans best
mearth2mjup = 317.828
mearth2msun = 333060.4
pid = os.getpid() np.random.seed(pid) print 'input', input print 'output', dir print 'top', top print 'random seed', pid ### fix the number of different populations n_pop = 4 ### data #file = '/Users/jingjing/Work/DK_project/Data/Mine/PlanetGroup.txt' file = 'PlanetGroup.txt' dat = np.loadtxt(file) m_exact = (dat[:,1]==0.) dat_fixm = convert_data(dat[m_exact]) M0 = dat_fixm[:,0] dat_varm = convert_data(dat[~m_exact]) ### some static parameter n_fixm = np.shape(dat_fixm)[0] n_varm = np.shape(dat_varm)[0] ### inverse sampling def inverse_hyper(hyper_prob): prob_C0, prob_slope, prob_sigma, prob_trans = \ hyper_prob[0], hyper_prob[1:1+n_pop], hyper_prob[1+n_pop:1+2*n_pop], hyper_prob[1+2*n_pop:3*n_pop] C0 = uniform.ppf(prob_C0,-1.,2.)
def plt_linear(infile, outfile, datadir=""):
pp = PdfPages(outfile)
### data
hyper = np.loadtxt(infile + "hyper.out")
loglike = np.loadtxt(infile + "loglike.out")
repeat = np.loadtxt(infile + "repeat.out")
### split data
c0 = hyper[:, 0]
slope = hyper[:, 1:5]
sigma = hyper[:, 5:9]
trans = hyper[:, 9:12]
### plot
plt.clf()
row = 2
col = 4
f, ((a00, a01, a02, a03), (a10, a11, a12, a13)) = plt.subplots(row, col, figsize=(col * 5, row * 5))
ax = ((a00, a01, a02, a03), (a10, a11, a12, a13))
# repeat
ax[0][0].plot(repeat)
ax[0][0].set_yscale("log")
ax[0][0].set_xlabel("repeat")
# loglike
ax[0][1].plot(loglike)
ax[0][1].set_xlabel("L")
# loglike
ax[0][2].plot(range(len(loglike) / 2, len(loglike)), loglike[len(loglike) / 2 :])
ax[0][1].set_xlabel("L")
# over plot
dat = convert_data(np.loadtxt(datadir + "PlanetGroup.txt"))
ax[0][3].errorbar(dat[:, 0], dat[:, 2], xerr=dat[:, 1], yerr=dat[:, 3], fmt=".")
best_ind = np.argmax(loglike)
hyper_best = hyper[best_ind, :]
trans_best = hyper_best[-3:]
print 10.0 ** trans_best
m_sample = np.linspace(np.min(dat[:, 0]), np.max(dat[:, 0]), 1000)
r_sample = piece_linear(hyper_best, m_sample, prob_R=0.5 * np.ones_like(m_sample))
# r_upper = piece_linear(hyper_best, m_sample, prob_R = 0.84 * np.ones_like(m_sample))
# r_lower = piece_linear(hyper_best, m_sample, prob_R = 0.16 * np.ones_like(m_sample))
r_upper = piece_linear_complex(hyper_best, m_sample, prob_R=0.84 * np.ones_like(m_sample))
r_lower = piece_linear_complex(hyper_best, m_sample, prob_R=0.16 * np.ones_like(m_sample))
ax[0][3].plot(m_sample, r_sample, "r-")
ax[0][3].fill_between(m_sample, r_lower, r_upper, color="grey", alpha=0.2)
r_trans = piece_linear(hyper_best, trans_best, prob_R=0.5 * np.ones_like(trans_best))
ax[0][3].plot(trans_best, r_trans, "rx")
ax[0][3].set_xlabel(r"log10(M [M$_\oplus$])")
ax[0][3].set_ylabel(r"log10(R [R$_\oplus$])")
# C
ax[1][0].plot(c0)
ax[1][0].set_xlabel("c0")
ax[1][0].set_ylim([-1, 1])
# slope
for i in range(4):
ax[1][1].plot(slope[:, i])
ax[1][1].set_xlabel("slope")
# sigma
for i in range(4):
ax[1][2].plot(sigma[:, i])
ax[1][2].set_yscale("log")
ax[1][2].set_xlabel("sigma")
ax[1][2].set_ylim([1e-3, 1e0])
# transition
for i in range(3):
ax[1][3].plot(trans[:, i])
ax[1][3].set_xlabel("transition")
ax[1][3].set_ylim([-4, 6])
pp.savefig()
pp.close()
return None
