def test_pf_kurucz_H2O():
with pt.cd('outputs/'):
pf_data, isotopes, temp = pf.kurucz(
f'{pc.ROOT}tests/inputs/mock_h2opartfn.dat',
outfile='PF_kurucz_H2O.dat')
np.testing.assert_equal(temp, np.arange(10, 51, 10.0))
np.testing.assert_allclose(
pf_data,
np.array([[1.328, 3.349, 6.192, 9.417, 12.962],
[1.33, 3.361, 6.217, 9.457, 13.017],
[1.332, 3.373, 6.24, 9.492, 13.066],
[1.399, 2.945, 5.085, 7.617, 10.476]]))
np.testing.assert_equal(
isotopes, np.array(['1H1H16O', '1H1H17O', '1H1H18O', '1H2H16O']))
pf_read, iso, temp_read = io.read_pf('outputs/PF_kurucz_H2O.dat')
np.testing.assert_allclose(
pf_data,
np.array([[1.328, 3.349, 6.192, 9.417, 12.962],
[1.33, 3.361, 6.217, 9.457, 13.017],
[1.332, 3.373, 6.24, 9.492, 13.066],
[1.399, 2.945, 5.085, 7.617, 10.476]]))
np.testing.assert_equal(temp_read, np.arange(10, 51, 10.0))
np.testing.assert_equal(
iso, np.array(['1H1H16O', '1H1H17O', '1H1H18O', '1H2H16O']))
def main():
cfile = 'run_HATP-41b_tsiaras/mcmc_HATP-41b_tsiaras_w00000-mc.cfg'
folder, cfile = os.path.split(cfile)
_, planet, dset = folder.split('_')
with pt.cd(folder):
pyrat = pb.run(cfile, run_step='init', no_logfile=True)
with np.load(pyrat.ret.mcmcfile) as mcmc:
post, zchain, zmask = mc3.utils.burn(mcmc)
texnames = mcmc['texnames']
bestp = mcmc['bestp']
imol = np.in1d(mcmc['ifree'], pyrat.ret.imol)
posteriors = pyrat.ret.posterior = post
u, uind, uinv = np.unique(posteriors[:, 0],
return_index=True,
return_inverse=True)
nunique = np.size(u)
# Get mean molecular mass:
ZX_ratio = np.zeros(nunique, np.double)
params = pyrat.ret.params
for k in range(nunique):
params[imol] = posteriors[uind[k], imol]
ZX_ratio[k] = ZX(params, pyrat)
iN2 = pyrat.ret.pnames.index('log(N2)')
# Metal mass fraction relative to solar values, i.e., [Z/X]:
posteriors[:, iN2] = ZX_ratio[uinv]
bestp[iN2] = ZX(bestp, pyrat)
texnames[iN2] = r"$[Z/X]$"
posteriors = roll(posteriors, indices=pyrat.ret.imol[:-1])
bestp = roll(bestp, indices=pyrat.ret.imol[:-1])
texnames = roll(texnames, indices=pyrat.ret.imol[:-1])
texnames[4] = texnames[4].replace('f', 'X')
rect = [0.105, 0.11, 0.99, 0.99]
ranges = [(100, 3000), (1.49, 1.68), (-4.5, 2.6), (-5.5, 2.5),
(-7., -0.45)]
plt.figure(316, (6, 6))
plt.clf()
axes, cax = mc3.plots.pairwise(posteriors,
pnames=texnames,
ranges=ranges,
fignum=8,
rect=rect,
nbins=20)
ax = axes[1][2]
plt.text(0.15, 0.125, 'cloudy', rotation=45, transform=ax.transAxes)
plt.text(0.70, 0.25, 'heavy', rotation=45, transform=ax.transAxes)
plt.text(0.75, 0.67, 'deplet', rotation=90, transform=ax.transAxes)
ax = axes[3][3]
plt.text(0.05, 0.30, 'cloudy', rotation=305, transform=ax.transAxes)
plt.text(0.55, 0.75, 'heavy', rotation=0, transform=ax.transAxes)
plt.text(0.65, 0.10, 'deplet', rotation=0, transform=ax.transAxes)
plt.savefig(f'plots/WFC3_{planet}_{dset}_correlation.pdf')
plt.savefig(f'plots/WFC3_{planet}_{dset}_correlation.png', dpi=300)
def test_pf_tips():
expected_temp = np.array([1] + [i for i in range(5, 19, 5)] +
[i for i in range(20, 5001, 10)], float)
with open(pc.ROOT + 'pyratbay/data/tips_2017.pkl', 'rb') as p:
expected_pf = pickle.load(p)['H2O']
with pt.cd('outputs/'):
pf_data, isotopes, temp = pf.tips('H2O', outfile='default')
np.testing.assert_equal(temp, expected_temp)
np.testing.assert_equal(pf_data[0], expected_pf['161'])
assert isotopes == list(expected_pf.keys())
pf_read, iso, temp_read = io.read_pf('outputs/PF_tips_H2O.dat')
np.testing.assert_allclose(pf_read[0, :], expected_pf['161'], rtol=1e-7)
np.testing.assert_allclose(temp_read, expected_temp, rtol=1e-7)
assert list(iso) == list(expected_pf.keys())
def test_pf_exomol_listed_single():
epf = f'{pc.ROOT}tests/outputs/14N-1H4__MockBYTe.pf'
expected_temp = np.arange(1, 6)
expected_pf = np.logspace(0, 1, 5)
mock_pf(epf, expected_temp, expected_pf)
with pt.cd('outputs/'):
pf_data, isotopes, temp = pf.exomol([epf], outfile='default')
np.testing.assert_allclose(pf_data[0, :], expected_pf)
np.testing.assert_allclose(temp, expected_temp)
assert list(isotopes) == ['41111']
pf_read, iso, temp_read = io.read_pf('outputs/PF_exomol_NH4.dat')
np.testing.assert_allclose(pf_read[0, :], expected_pf, rtol=1e-5)
np.testing.assert_allclose(temp_read, expected_temp, rtol=1e-7)
assert list(iso) == ['41111']
def test_pf_exomol_two():
epf1 = f'{pc.ROOT}tests/outputs/14N-1H4__MockBYTe.pf'
epf2 = f'{pc.ROOT}tests/outputs/15N-1H4__MockBYTe.pf'
expected_temp1 = np.arange(1, 6)
expected_temp2 = np.arange(1, 9)
expected_pf1 = np.logspace(0, 1, 5)
expected_pf2 = np.logspace(0, 1, 8)
mock_pf(epf1, expected_temp1, expected_pf1)
mock_pf(epf2, expected_temp2, expected_pf2)
with pt.cd('outputs/'):
pf_data, isotopes, temp = pf.exomol([epf1, epf2], outfile='default')
np.testing.assert_allclose(pf_data[0, :], expected_pf1, rtol=1e-5)
np.testing.assert_allclose(pf_data[1, :], expected_pf2[:5], rtol=1e-5)
np.testing.assert_allclose(temp, expected_temp1)
assert list(isotopes) == ['41111', '51111']
pf_read, iso, temp_read = io.read_pf('outputs/PF_exomol_NH4.dat')
np.testing.assert_allclose(pf_read[0, :], expected_pf1, rtol=1e-5)
np.testing.assert_allclose(pf_read[1, :], expected_pf2[:5], rtol=1e-5)
np.testing.assert_allclose(temp_read, expected_temp1)
assert list(iso) == ['41111', '51111']
def test_pands(capfd):
with pt.cd('outputs/'):
pf.kurucz(f'{ROOT}tests/inputs/mock_h2opartfn.dat', outfile='default')
pb.run('configs/tli_pands_test.cfg')
captured = capfd.readouterr()
caps = [
"Read command-line arguments from configuration file:",
"'configs/tli_pands_test.cfg'",
"Reading input database files:",
"tests/inputs/mock_h2ofastfix.bin",
"There are 1 input database file(s).",
"Initial TLI wavelength (um): 2.500 ( 4000.000 cm-1)",
"Final TLI wavelength (um): 2.501 ( 3998.401 cm-1)",
"Database (1/1): 'Partridge & Schwenke (1997)' (H2O molecule)",
"Number of temperatures: 5",
"Number of isotopes: 4",
"Process P&S H2O database between records 38 and 10,220.",
"[9625 207 219 132]",
"Writing 10,183 transition lines.",
"/pands_H2O_2.500-2.501um_test.tli'.",
]
for cap in caps:
assert cap in captured.out
def test_pf_kurucz_TiO():
with pt.cd('outputs/'):
pf_data, isotopes, temp = pf.kurucz(
f'{pc.ROOT}tests/inputs/mock_tiopart.dat', outfile='default')
np.testing.assert_allclose(
pf_data,
np.array([[28.829, 54.866, 81.572, 110.039, 141.079],
[28.97, 55.151, 82.002, 110.625, 141.834],
[29.107, 55.425, 82.417, 111.19, 142.564],
[29.24, 55.692, 82.821, 111.741, 143.273],
[29.369, 55.95, 83.212, 112.273, 143.96]]))
np.testing.assert_equal(temp, np.arange(10, 51, 10.0))
np.testing.assert_equal(isotopes, np.array(['66', '76', '86', '96', '06']))
pf_read, iso, temp_read = io.read_pf('outputs/PF_kurucz_TiO.dat')
np.testing.assert_allclose(
pf_read,
np.array([[28.829, 54.866, 81.572, 110.039, 141.079],
[28.97, 55.151, 82.002, 110.625, 141.834],
[29.107, 55.425, 82.417, 111.19, 142.564],
[29.24, 55.692, 82.821, 111.741, 143.273],
[29.369, 55.95, 83.212, 112.273, 143.96]]))
np.testing.assert_equal(temp_read, np.arange(10, 51, 10.0))
np.testing.assert_equal(iso, np.array(['66', '76', '86', '96', '06']))
'run_feng2020/mcmc_WASP43b_integrated_phase0.81.cfg',
'run_feng2020/mcmc_WASP43b_integrated_phase0.88.cfg',
'run_feng2020/mcmc_WASP43b_integrated_phase0.94.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.06.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.12.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.19.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.25.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.31.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.38.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.44.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.50.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.56.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.62.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.69.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.75.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.81.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.88.cfg',
'run_feng2020/mcmc_WASP43b_resolved_phase0.94.cfg',
]
for cfg in retrievals:
with pt.cd(os.path.dirname(cfg)):
pyrat = pb.run(os.path.basename(cfg), run_step='init', no_logfile=True)
mcmc = np.load(pyrat.ret.mcmcfile)
pyrat.ret.posterior, _, _ = mc3.utils.burn(mcmc)
pyrat.percentile_spectrum()
pfile = pyrat.ret.mcmcfile.replace('.npz', '.pickle')
io.save_pyrat(pyrat, pfile)
mcmc.close()
'run_jwst_09TP_01Q',
'run_jwst_16TP_16Q',
]
modes = [
'integrated',
'resolved',
]
nmodels = len(model_names)
nmodes = len(modes)
nphase = len(obs_phase)
k = 2
model = f'mcmc_model_WASP43b_{modes[0]}_phase{obs_phase[0]:.2f}.cfg'
with pt.cd(model_names[k]):
pyrat = pb.run(model, run_step='init', no_logfile=True)
nspec = pyrat.obs.ndata
nlayers = pyrat.atm.nlayers
nprofiles = 5
data = np.zeros((nmodels, nmodes, nphase, nspec))
uncerts = np.zeros((nmodels, nmodes, nphase, nspec))
temp_posteriors = np.zeros((nmodels, nmodes, nphase, nprofiles, nlayers))
posteriors = np.zeros((nmodels, nmodes, nphase), dtype=object)
#for k,j,i in product(range(nmodels), range(nmodes), range(nphase)):
for j, i in product(range(nmodes), range(nphase)):
if k != 2 and i > 8:
continue
model = f'mcmc_model_WASP43b_{modes[j]}_phase{obs_phase[i]:.2f}.cfg'
'run_WASP-101b_wakeford',
'run_WASP-103b_kreidberg',
'run_XO-1b_tsiaras',
]
ntargets = len(targets)
# Extract values:
names = [None] * ntargets
flat_chisq = np.zeros(ntargets)
flat_rchisq = np.zeros(ntargets)
flat_bic = np.zeros(ntargets)
for i in range(ntargets):
cfile = [f for f in os.listdir(targets[i]) if f.endswith(".cfg")][0]
with cd(targets[i]):
pyrat = pb.run(cfile, run_step='dry', no_logfile=True)
data = pyrat.obs.data
uncert = pyrat.obs.uncert
indparams = [data]
params = np.array([np.mean(data)])
pstep = np.array([1.0])
mc3_fit = mc3.fit(data, uncert, sfit, params, indparams, pstep)
dof = len(data) - len(params)
names[i] = cfile.split('_')[1] + ' ' + cfile.split('_')[2]
flat_chisq[i] = mc3_fit['best_chisq']
flat_rchisq[i] = mc3_fit['best_chisq'] / dof
flat_bic[i] = mc3_fit['best_chisq'] + len(params) * np.log(len(data))
def main():
# Only those with ADI > 3:
cfiles = [
'run_HATP-18b_tsiaras/mcmc_HATP-18b_tsiaras_w00000-0c.cfg',
'run_HATP-32b_tsiaras/mcmc_HATP-32b_tsiaras_w00000-0c.cfg',
'run_HATP-38b_bruno/mcmc_HATP-38b_bruno_w00000-0c.cfg',
'run_HATP-41b_tsiaras/mcmc_HATP-41b_tsiaras_w00000-0c.cfg',
'run_K2-018b_benneke/mcmc_K2-018b_benneke_w00000-0c.cfg',
'run_WASP-043b_stevenson/mcmc_WASP-043b_stevenson_w00000-0c.cfg',
'run_WASP-063b_kilpatrick/mcmc_WASP-063b_kilpatrick_w000h0-0c.cfg',
'run_WASP-069b_tsiaras/mcmc_WASP-069b_tsiaras_w00000-0c.cfg',
'run_WASP-103b_kreidberg/mcmc_WASP-103b_kreidberg_w00000-0c.cfg',
'run_XO-1b_tsiaras/mcmc_XO-1b_tsiaras_w00000-0c.cfg',
]
ntargets = len(cfiles)
names = np.array([None for _ in range(ntargets)])
posteriors = [None for _ in range(ntargets)]
teq = np.zeros(ntargets)
for i in range(ntargets):
folder, cfile = os.path.split(cfiles[i])
_, planet, dset = folder.split('_')
name = f'{planet} {dset.capitalize()}'
names[i] = name.replace('-0', '-')
with pt.cd(folder):
pyrat = pb.run(cfile, run_step='init', no_logfile=True)
teq[i], _ = pa.equilibrium_temp(pyrat.phy.tstar, pyrat.phy.rstar,
pyrat.phy.smaxis)
with np.load(pyrat.ret.mcmcfile) as mcmc:
posteriors[i], zchain, zmask = mc3.utils.burn(mcmc)
tposteriors = np.array([post[:, 0] for post in posteriors])
ci1 = np.zeros((2, ntargets))
for i in range(ntargets):
pdf, xpdf, HPDmin = mc3.stats.cred_region(tposteriors[i])
ci1[:, i] = -np.amin(xpdf[pdf > HPDmin]), np.amax(xpdf[pdf > HPDmin])
# Print some stats:
decimals = [0, 3, 2, 2, 2, 2]
split = [None, None, -4, None, None, None, None, -2, -4, None]
table = []
for j, posterior in enumerate(posteriors):
texts = []
for i in range(len(posterior.T)):
dec = decimals[i]
text = ''
pdf, xpdf, hpd_min = mc3.stats.cred_region(posterior[:, i])
if i == 2 and split[j] is not None:
x = xpdf[xpdf < split[j]]
p = pdf[xpdf < split[j]]
mode = x[np.argmax(p)]
lo = mode - np.amin(x[p > hpd_min])
hi = -(mode - np.amax(x[p > hpd_min]))
text = f'${mode:.{dec}f}_{{-{lo:.{dec}f}}}^{{+{hi:.{dec}f}}}$ and '
pdf = pdf[xpdf > split[j]]
xpdf = xpdf[xpdf > split[j]]
mode = xpdf[np.argmax(pdf)]
lo = mode - np.amin(xpdf[pdf > hpd_min])
hi = -(mode - np.amax(xpdf[pdf > hpd_min]))
text += f'${mode:.{dec}f}_{{-{lo:.{dec}f}}}^{{+{hi:.{dec}f}}}$'
texts.append(text)
text = " & ".join(texts)
table.append(text)
table_text = " \\\\\n".join(table)
print(table_text)
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Temperatures:
lw = 2.0
fs = 14
y = np.zeros(ntargets)
plt.figure(22, (6.5, 4.75))
plt.clf()
plt.subplots_adjust(0.12, 0.1, 0.99, 0.99)
ax = plt.subplot(111)
plt.errorbar(teq,
y,
yerr=ci1,
capsize=5.0,
capthick=lw,
lw=lw,
ls='none',
color='orangered',
label='This work')
plt.plot(teq, teq, "o", color='mediumblue')
plt.ylim(ymin=0)
ax.set_xlim(200, 2560)
ax.tick_params(labelsize=fs - 2, direction='in', which='both')
for xt, name in zip(teq, names):
name = name.split()[0]
plt.text(xt - 61,
xt + 45,
name.split()[0],
va='bottom',
rotation=90,
fontsize=fs - 4)
ax.set_xlabel('Equilibrium temperature (K)', fontsize=fs)
ax.set_ylabel('Retrieved temperature (K)', fontsize=fs)
plt.savefig('plots/WFC3_sample_temperature.pdf')
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# H2O abundances:
idx_H2O = 2
H2O_posts = [post[:, idx_H2O] for post in posteriors]
nx = 1
ny = ntargets
rect = [0.1, 0.06, 0.95, 0.99]
margin = 0.0
ranges = [(-12, 0) for _ in range(ntargets)]
pnames = ['' for _ in range(ntargets)]
fs = 13
plt.figure(32, (6, 8))
plt.clf()
axes = [
mc3.plots.subplotter(rect, margin, i + 1, nx, ny)
for i in range(ntargets)
]
for i in range(ntargets):
mc3.plots.histogram(H2O_posts[i],
pnames=pnames[i:i + 1],
quantile=0.683,
ranges=ranges[i:i + 1],
nbins=60,
axes=axes[i:i + 1],
lw=2.0,
fs=fs - 1,
theme='blue')
plt.text(0.02, 0.75, names[i], transform=axes[i].transAxes)
if i < ntargets - 1:
axes[i].set_xticklabels([])
axes[i].set_yticks([])
axes[i].set_xlabel('$\\log_{10}(X_{\\rm H2O})$', fontsize=fs)
axes[4].set_ylabel('Marginal posterior density' + 15 * ' ', fontsize=fs)
plt.setp(axes[i].xaxis.get_majorticklabels(), rotation=0)
plt.savefig('plots/WFC3_sample_H2O_abundance.pdf')
nprofiles = 3
nlayers = 61
nwave = 17048
data = np.zeros((nmodes, nphase, nspec))
uncerts = np.zeros((nmodes, nphase, nspec))
tpost = np.zeros((nmodes, nphase, nprofiles, nlayers))
posteriors = np.zeros((nmodes, nphase), dtype=object)
median_fr = np.zeros((nmodes, nphase, nwave))
hi_fr = np.zeros((nmodes, nphase, nwave))
lo_fr = np.zeros((nmodes, nphase, nwave))
for j, i in product(range(nmodes), range(nphase)):
pickle_file = f'MCMC_WASP43b_{modes[j]}_phase{obs_phase[i]:.2f}.pickle'
print(f'{modes[j]}: phase {obs_phase[i]:.2f}')
with pt.cd('run_feng2020/'):
pyrat = io.load_pyrat(pickle_file)
posteriors[j, i] = pyrat.ret.posterior[:, -4:]
rprs = pyrat.phy.rplanet / pyrat.phy.rstar
starflux = pyrat.spec.starflux
median_fr[j, i] = pyrat.ret.spec_median / starflux * rprs**2
lo_fr[j, i] = pyrat.ret.spec_low1 / starflux * rprs**2
hi_fr[j, i] = pyrat.ret.spec_high1 / starflux * rprs**2
data[j, i] = pyrat.obs.data
uncerts[j, i] = pyrat.obs.uncert
ifree = pyrat.ret.pstep[pyrat.ret.itemp] > 0
itemp = np.arange(np.sum(ifree))
tpost[j, i] = pa.temperature_posterior(pyrat.ret.posterior[:, itemp],
pyrat.atm.tmodel,
'run_WASP-063b_kilpatrick/mcmc_WASP-063b_kilpatrick_w000h0-mc.cfg',
'run_WASP-063b_tsiaras/mcmc_WASP-063b_tsiaras_w000h0-mc.cfg',
'run_WASP-067b_bruno/mcmc_WASP-067b_bruno_w00000-mc.cfg',
'run_WASP-067b_tsiaras/mcmc_WASP-067b_tsiaras_w00000-mc.cfg',
'run_WASP-069b_tsiaras/mcmc_WASP-069b_tsiaras_w00000-mc.cfg',
'run_WASP-074b_tsiaras/mcmc_WASP-074b_tsiaras_w00000-mc.cfg',
'run_WASP-080b_tsiaras/mcmc_WASP-080b_tsiaras_w00000-mc.cfg',
'run_WASP-101b_tsiaras/mcmc_WASP-101b_tsiaras_w00000-mc.cfg',
'run_WASP-101b_wakeford/mcmc_WASP-101b_wakeford_w00000-mc.cfg',
'run_WASP-103b_kreidberg/mcmc_WASP-103b_kreidberg_wmdm00-mc.cfg',
'run_XO-1b_tsiaras/mcmc_XO-1b_tsiaras_w00000-mc.cfg',
]
ntargets = len(cfiles)
for cfile in cfiles:
folder, cfg = os.path.split(cfile)
_, planet, dset = folder.split('_')
with pt.cd(folder):
pyrat = pb.run(cfg, run_step='init', no_logfile=True)
with np.load(pyrat.ret.mcmcfile) as mcmc:
post, zchain, zmask = mc3.utils.burn(mcmc)
accept_rate = mcmc['acceptance_rate']
pyrat.ret.posterior = post
# I want to evaluate less than ~1e5 unique samples:
nmax = int(1e5 / (6 * 0.01 * accept_rate))
pyrat.percentile_spectrum(nmax)
pfile = pyrat.ret.mcmcfile.replace('.npz', '.pickle')
io.save_pyrat(pyrat, pfile)
def test_pf_tips_outfile(outfile):
with pt.cd('outputs/'):
pf_data, isotopes, temp = pf.tips('HCN', outfile=outfile)
assert 'PF_tips_HCN.dat' in os.listdir('outputs/')
os.remove('outputs/PF_tips_HCN.dat')