def main(modelid, datestr):
assert modelid in ['allindivtransit', 'tessindivtransit']
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
OVERWRITE = 0
REALID = 'TOI_837'
provenance = 'spoc'
PLOTDIR = os.path.join(
RESULTSDIR, '{}_{}_phot_results'.format(REALID, modelid)
)
PLOTDIR = os.path.join(PLOTDIR, datestr)
summarypath = os.path.join(
PLOTDIR, 'posterior_table_raw_{}.csv'.format(modelid)
)
pklpath = os.path.join(
os.path.expanduser('~'), 'local', 'timmy',
'{}_model_{}_{}.pkl'.format(REALID, modelid, datestr)
)
np.random.seed(42)
##########################################
# get allindivtransit initialized
##########################################
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
x_obs, y_obs, y_err = get_clean_tessphot(provenance, yval, binsize=None,
maskflares=1)
s = np.isfinite(y_obs) & np.isfinite(x_obs) & np.isfinite(y_err)
x_obs, y_obs, y_err = x_obs[s], y_obs[s], y_err[s]
cut_tess = 1
if cut_tess:
x_obs, y_obs, y_err = _subset_cut(x_obs, y_obs, y_err, n=3.5)
ngroups, groupinds = find_lc_timegroups(x_obs, mingap=4.0)
assert ngroups == 5
datasets = OrderedDict()
for ix, g in enumerate(groupinds):
tess_texp = np.nanmedian(np.diff(x_obs[g]))
datasets[f'tess_{ix}'] = [x_obs[g], y_obs[g], y_err[g], tess_texp]
if modelid == 'allindivtransit':
datestrs = ['20200401', '20200426', '20200521', '20200614']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_elsauce_phot(datestr=d)
x_obs -= 2457000 # convert to BTJD
elsauce_texp = np.nanmedian(np.diff(x_obs))
datasets[f'elsauce_{ix}'] = [x_obs, y_obs, y_err, elsauce_texp]
datestrs = ['20200529', '20200614', '20200623']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_astep_phot(datestr=d)
x_obs += 2450000 # convert to BJD_TDB
x_obs -= 2457000 # convert to BTJD
astep_texp = np.nanmedian(np.diff(x_obs))
datasets[f'astep_{ix}'] = [x_obs, y_obs, y_err, astep_texp]
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents, datasets=datasets)
##########################################
# end intiialization
##########################################
if not os.path.exists(summarypath):
m = ModelFitter(modelid, datasets, prior_d, plotdir=PLOTDIR,
pklpath=pklpath, overwrite=OVERWRITE)
# stat_funcsdict = A list of functions or a dict of functions with
# function names as keys used to calculate statistics. By default, the
# mean, standard deviation, simulation standard error, and highest
# posterior density intervals are included.
stat_funcsdict = {
'median': np.nanmedian
}
df = pm.summary(
m.trace,
round_to=10, kind='stats',
stat_funcs=stat_funcsdict,
extend=True
)
df.to_csv(summarypath, index=True)
else:
df = pd.read_csv(summarypath, index_col=0)
fitted_params = [
'period', 't0', 'log_r', 'b', 'u[0]', 'u[1]', 'r_star', 'logg_star'
]
for i in range(5):
fitted_params.append(f'tess_{i}_mean')
fitted_params.append(f'tess_{i}_a1')
fitted_params.append(f'tess_{i}_a2')
if modelid == 'allindivtransit':
for i in range(4):
fitted_params.append(f'elsauce_{i}_mean')
fitted_params.append(f'elsauce_{i}_a1')
fitted_params.append(f'elsauce_{i}_a2')
for i in range(3):
fitted_params.append(f'astep_{i}_mean')
fitted_params.append(f'astep_{i}_a1')
fitted_params.append(f'astep_{i}_a2')
n_fitted = len(fitted_params)
derived_params = [
'r', 'rho_star', 'r_planet', 'a_Rs', 'cosi', 'T_14', 'T_13'
]
n_derived = len(derived_params)
srows = []
for f in fitted_params:
srows.append(f)
for d in derived_params:
srows.append(d)
df = df.loc[srows]
cols = ['median', 'mean', 'sd', 'hpd_3%', 'hpd_97%']
df = df[cols]
print(df)
from timmy.priors import (
LOGG, LOGG_STDEV, RSTAR, RSTAR_STDEV
)
delta_u = 0.15
pr = {
'period': normal_str(
mu=prior_d['period'], sd=1e-1, fmtstr='({:.4f}; {:.4f})'
),
't0': normal_str(
mu=prior_d['t0'], sd=1e-1, fmtstr='({:.6f}; {:.4f})'
),
'log_r': uniform_str(
lower=np.log(1e-2), upper=np.log(1), fmtstr='({:.3f}; {:.3f})'
),
'b': r'$\mathcal{U}(0; 1+R_{\mathrm{p}}/R_\star)$',
#'u[0]': '(2)',
#'u[1]': '(2)',
'u[0]': uniform_str(prior_d['u[0]']-delta_u, prior_d['u[0]']+delta_u,
fmtstr='({:.3f}; {:.3f})') + '$^{(2)}$',
'u[1]': uniform_str(prior_d['u[1]']-delta_u, prior_d['u[1]']+delta_u,
fmtstr='({:.3f}; {:.3f})') + '$^{(2)}$',
'r_star': truncnormal_str(
mu=RSTAR, sd=RSTAR_STDEV, fmtstr='({:.3f}; {:.3f})'
),
'logg_star': normal_str(
mu=LOGG, sd=LOGG_STDEV, fmtstr='({:.3f}; {:.3f})'
)
}
ufmt = '({:.2f}; {:.2f})'
delta_trend = 0.05
for i in range(5):
pr[f'tess_{i}_mean'] = normal_str(mu=prior_d[f'tess_{i}_mean'],
sd=0.01, fmtstr=ufmt)
pr[f'tess_{i}_a1'] = uniform_str(lower=-delta_trend, upper=delta_trend, fmtstr=ufmt)
pr[f'tess_{i}_a2'] = uniform_str(lower=-delta_trend, upper=delta_trend, fmtstr=ufmt)
if modelid == 'allindivtransit':
for i in range(4):
pr[f'elsauce_{i}_mean'] = normal_str(mu=prior_d[f'elsauce_{i}_mean'],
sd=0.01, fmtstr=ufmt)
pr[f'elsauce_{i}_a1'] = uniform_str(lower=-delta_trend, upper=delta_trend, fmtstr=ufmt)
pr[f'elsauce_{i}_a2'] = uniform_str(lower=-delta_trend, upper=delta_trend, fmtstr=ufmt)
for i in range(3):
pr[f'astep_{i}_mean'] = normal_str(mu=prior_d[f'astep_{i}_mean'],
sd=0.01, fmtstr=ufmt)
pr[f'astep_{i}_a1'] = uniform_str(lower=-delta_trend, upper=delta_trend, fmtstr=ufmt)
pr[f'astep_{i}_a2'] = uniform_str(lower=-delta_trend, upper=delta_trend, fmtstr=ufmt)
for d in derived_params:
pr[d] = '--'
# round everything. requires a double transpose because df.round
# operates column-wise
if modelid in ['allindivtransit', 'tessindivtransit']:
round_precision = [7, 7, 5, 4, 3, 3, 3, 3]
n_rp = len(round_precision)
for i in range(n_fitted - n_rp):
round_precision.append(4)
else:
raise NotImplementedError
for d in derived_params:
round_precision.append(2)
df = df.T.round(
decimals=dict(
zip(df.index, round_precision)
)
).T
df['priors'] = list(pr.values())
# units
ud = {
'period': 'd',
't0': 'd',
'log_r': '--',
'b': '--',
'u[0]': '--',
'u[1]': '--',
'r_star': r'$R_\odot$',
'logg_star': 'cgs'
}
for i in range(5):
ud[f'tess_{i}_mean'] = '--'
ud[f'tess_{i}_a1'] = 'd$^{-1}$'
ud[f'tess_{i}_a2'] = 'd$^{-2}$'
if modelid == 'allindivtransit':
for i in range(4):
ud[f'elsauce_{i}_mean'] = '--'
ud[f'elsauce_{i}_a1'] = 'd$^{-1}$'
ud[f'elsauce_{i}_a2'] = 'd$^{-2}$'
for i in range(3):
ud[f'astep_{i}_mean'] = '--'
ud[f'astep_{i}_a1'] = 'd$^{-1}$'
ud[f'astep_{i}_a2'] = 'd$^{-2}$'
ud['r'] = '--'
ud['rho_star'] = 'g$\ $cm$^{-3}$'
ud['r_planet'] = '$R_{\mathrm{Jup}}$'
ud['a_Rs'] = '--'
ud['cosi'] = '--'
ud['T_14'] = 'hr'
ud['T_13'] = 'hr'
df['units'] = list(ud.values())
df = df[
['units', 'priors', 'median', 'mean', 'sd', 'hpd_3%', 'hpd_97%']
]
latexparams = [
#useful
r"$P$",
r"$t_0^{(1)}$",
r"$\log R_{\rm p}/R_\star$",
"$b$",
"$u_1$",
"$u_2$",
"$R_\star$",
"$\log g$"
]
for i in range(5):
latexparams.append('$a_{'+str(i)+'0;\mathrm{TESS}}$')
latexparams.append('$a_{'+str(i)+'1;\mathrm{TESS}}$')
latexparams.append('$a_{'+str(i)+'2;\mathrm{TESS}}$')
if modelid == 'allindivtransit':
for i in range(4):
latexparams.append('$a_{'+str(i)+'0;\mathrm{Sauce}}$')
latexparams.append('$a_{'+str(i)+'1;\mathrm{Sauce}}$')
latexparams.append('$a_{'+str(i)+'2;\mathrm{Sauce}}$')
for i in range(3):
latexparams.append('$a_{'+str(i)+'0;\mathrm{ASTEP}}$')
latexparams.append('$a_{'+str(i)+'1;\mathrm{ASTEP}}$')
latexparams.append('$a_{'+str(i)+'2;\mathrm{ASTEP}}$')
from billy.convenience import flatten
dlatexparams = [
r"$R_{\rm p}/R_\star$",
r"$\rho_\star$",
r"$R_{\rm p}$",
"$a/R_\star$",
'$\cos i$',
'$T_{14}$',
'$T_{13}$'
]
latexparams = flatten([latexparams, dlatexparams])
df.index = latexparams
outpath = os.path.join(PLOTDIR,
'posterior_table_clean_{}.csv'.format(modelid))
df.to_csv(outpath, float_format='%.12f', na_rep='NaN')
print('made {}'.format(outpath))
# df.to_latex is dumb with float formatting.
outpath = os.path.join(PLOTDIR,
'posterior_table_clean_{}.tex'.format(modelid))
df.to_csv(outpath, sep=',', line_terminator=' \\\\\n',
float_format='%.12f', na_rep='NaN')
with open(outpath, 'r') as f:
lines = f.readlines()
for ix, l in enumerate(lines):
# replace commas with latex ampersands
thisline = deepcopy(l.replace(',', ' & '))
# replace quotes with nada
thisline = thisline.replace('"', '')
# replace }0 with },0
thisline = thisline.replace('}0', '},0')
thisline = thisline.replace('}1', '},1')
thisline = thisline.replace('}2', '},2')
if ix == 0:
lines[ix] = thisline
continue
# iteratively replace stupid trailing zeros with whitespace
while re.search("0{2,10}\ ", thisline) is not None:
r = re.search("0{2,10}\ ", thisline)
thisline = thisline.replace(
thisline[r.start():r.end()],
' '
)
lines[ix] = thisline
with open(outpath, 'w') as f:
f.writelines(lines)
print('made {}'.format(outpath))
def main(modelid):
make_threadsafe = 0
cut_tess = 1
fitindiv = 1
phaseplot = 1
grounddepth = 1
cornerplot = 1
subsetcorner = 1
N_samples = 30000 # took 2h 20m, but Rhat=1.0 for all
# N_samples = 2000 # took 16m, 14s. but Rhat=1.01 for b, rp/rs, + a few a1/a2s
target_accept = 0.9
OVERWRITE = 1
REALID = 'TOI_837'
PLOTDIR = os.path.join(RESULTSDIR,
'{}_{}_phot_results'.format(REALID, modelid))
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
datestr = '20200711'
PLOTDIR = os.path.join(PLOTDIR, datestr)
##########################################
assert modelid in ['allindivtransit']
print(42 * '#')
print(modelid)
print(42 * '#')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
pklpath = os.path.join(os.path.expanduser('~'), 'local', 'timmy',
f'{REALID}_model_{modelid}_{datestr}.pkl')
np.random.seed(42)
# get tess data
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
x_obs, y_obs, y_err = get_clean_tessphot(provenance,
yval,
binsize=None,
maskflares=1)
#y_flat, y_trend = detrend_tessphot(x_obs, y_obs, y_err)
s = np.isfinite(y_obs) & np.isfinite(x_obs) & np.isfinite(y_err)
x_obs, y_obs, y_err = x_obs[s], y_obs[s], y_err[s]
if cut_tess:
x_obs, y_obs, y_err = _subset_cut(x_obs, y_obs, y_err, n=3.5)
ngroups, groupinds = find_lc_timegroups(x_obs, mingap=4.0)
assert ngroups == 5
datasets = OrderedDict()
for ix, g in enumerate(groupinds):
tess_texp = np.nanmedian(np.diff(x_obs[g]))
datasets[f'tess_{ix}'] = [x_obs[g], y_obs[g], y_err[g], tess_texp]
datestrs = ['20200401', '20200426', '20200521', '20200614']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_elsauce_phot(datestr=d)
x_obs -= 2457000 # convert to BTJD
elsauce_texp = np.nanmedian(np.diff(x_obs))
datasets[f'elsauce_{ix}'] = [x_obs, y_obs, y_err, elsauce_texp]
datestrs = ['20200529', '20200614', '20200623']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_astep_phot(datestr=d)
x_obs += 2450000 # convert to BJD_TDB
x_obs -= 2457000 # convert to BTJD
astep_texp = np.nanmedian(np.diff(x_obs))
datasets[f'astep_{ix}'] = [x_obs, y_obs, y_err, astep_texp]
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents, datasets=datasets)
m = ModelFitter(modelid,
datasets,
prior_d,
plotdir=PLOTDIR,
pklpath=pklpath,
overwrite=OVERWRITE,
N_samples=N_samples,
target_accept=target_accept)
print(pm.summary(m.trace, var_names=list(prior_d.keys())))
summdf = pm.summary(m.trace,
var_names=list(prior_d.keys()),
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
rp_limit = np.percentile(m.trace.r_planet, 1 - 0.9973)
print(42 * '-')
print(f'Rp limit: {rp_limit:.3f} Rjup')
print(42 * '-')
if make_threadsafe:
pass
else:
if grounddepth:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_grounddepth.png')
tp.plot_grounddepth(m,
summdf,
outpath,
modelid=modelid,
showerror=0)
if subsetcorner:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_subsetcorner.png')
tp.plot_subsetcorner(m, outpath)
if phaseplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_phaseplot.png')
tp.plot_phasefold(m, summdf, outpath, modelid=modelid, inppt=1)
if fitindiv:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_fitindiv.png')
tp.plot_fitindiv(m, summdf, outpath, modelid=modelid)
if cornerplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_cornerplot.png')
tp.plot_cornerplot(prior_d, m, outpath)
def main(modelid):
make_threadsafe = 0
cut_tess = 1
fitindiv = 1
phaseplot = 1
cornerplot = 1
subsetcorner = 1
grounddepth = 1
N_samples = 30000
target_accept = 0.9
OVERWRITE = 1
REALID = 'TOI_837'
PLOTDIR = os.path.join(RESULTSDIR,
'{}_{}_phot_results'.format(REALID, modelid))
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
datestr = '20200711'
PLOTDIR = os.path.join(PLOTDIR, datestr)
##########################################
assert modelid in ['tessindivtransit']
print(42 * '#')
print(modelid)
print(42 * '#')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
pklpath = os.path.join(os.path.expanduser('~'), 'local', 'timmy',
f'{REALID}_model_{modelid}_{datestr}.pkl')
np.random.seed(42)
# get tess data
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
x_obs, y_obs, y_err = get_clean_tessphot(provenance,
yval,
binsize=None,
maskflares=1)
#y_flat, y_trend = detrend_tessphot(x_obs, y_obs, y_err)
s = np.isfinite(y_obs) & np.isfinite(x_obs) & np.isfinite(y_err)
x_obs, y_obs, y_err = x_obs[s], y_obs[s], y_err[s]
if cut_tess:
x_obs, y_obs, y_err = _subset_cut(x_obs, y_obs, y_err, n=3.5)
ngroups, groupinds = find_lc_timegroups(x_obs, mingap=4.0)
assert ngroups == 5
datasets = OrderedDict()
for ix, g in enumerate(groupinds):
tess_texp = np.nanmedian(np.diff(x_obs[g]))
datasets[f'tess_{ix}'] = [x_obs[g], y_obs[g], y_err[g], tess_texp]
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents, datasets=datasets)
m = ModelFitter(modelid,
datasets,
prior_d,
plotdir=PLOTDIR,
pklpath=pklpath,
overwrite=OVERWRITE,
N_samples=N_samples,
target_accept=target_accept)
print(pm.summary(m.trace, var_names=list(prior_d.keys())))
summdf = pm.summary(m.trace,
var_names=list(prior_d.keys()),
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
printparams = ['r_planet', 'b']
print(42 * '-')
for p in printparams:
med = np.percentile(m.trace[p], 50)
up = np.percentile(m.trace[p], 84)
low = np.percentile(m.trace[p], 36)
print(f'{p} limit: {med:.3f} +{up-med:.3f} -{med-low:.3f}')
print(42 * '-')
if make_threadsafe:
pass
else:
# if grounddepth:
# outpath = join(PLOTDIR, f'{REALID}_{modelid}_grounddepth.png')
# tp.plot_grounddepth(m, summdf, outpath, modelid=modelid,
# showerror=0)
if subsetcorner:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_subsetcorner.png')
tp.plot_subsetcorner(m, outpath)
if phaseplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_phaseplot.png')
tp.plot_phasefold(m, summdf, outpath, modelid=modelid, inppt=1)
if fitindiv:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_fitindiv.png')
tp.plot_fitindiv(m, summdf, outpath, modelid=modelid)
if cornerplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_cornerplot.png')
tp.plot_cornerplot(prior_d, m, outpath)
def main(modelid):
make_threadsafe = 0
cut_tess = 1
fitindiv = 0
phaseplot = 0
grounddepth = 0
cornerplot = 0
subsetcorner = 0
N_samples = 30000 # took 2h 20m, but Rhat=1.0 for all
# N_samples = 100 # testing
target_accept = 0.9
OVERWRITE = 1
REALID = 'TOI_837'
PLOTDIR = os.path.join(
RESULTSDIR, '{}_{}_phot_results'.format(REALID, modelid)
)
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
datestr = '20200805'
PLOTDIR = os.path.join(PLOTDIR, datestr)
##########################################
assert modelid in ['evenindivtransit', 'oddindivtransit']
print(42*'#')
print(modelid)
print(42*'#')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
pklpath = os.path.join(
os.path.expanduser('~'), 'local', 'timmy',
f'{REALID}_model_{modelid}_{datestr}.pkl'
)
np.random.seed(42)
# get tess data
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
x_obs, y_obs, y_err = get_clean_tessphot(provenance, yval, binsize=None,
maskflares=1)
s = np.isfinite(y_obs) & np.isfinite(x_obs) & np.isfinite(y_err)
x_obs, y_obs, y_err = x_obs[s], y_obs[s], y_err[s]
if cut_tess:
if 'even' in modelid:
onlyeven = True
onlyodd = False
elif 'odd' in modelid:
onlyeven = False
onlyodd = True
else:
raise NotImplementedError
x_obs, y_obs, y_err = _subset_cut(
x_obs, y_obs, y_err, n=3.5, onlyeven=onlyeven, onlyodd=onlyodd
)
ngroups, groupinds = find_lc_timegroups(x_obs, mingap=4.0)
assert ngroups in [2,3]
datasets = OrderedDict()
for ix, g in enumerate(groupinds):
tess_texp = np.nanmedian(np.diff(x_obs[g]))
datasets[f'tess_{ix}'] = [x_obs[g], y_obs[g], y_err[g], tess_texp]
# see /doc/20200805_ephemeris_counting.txt
if 'even' in modelid:
datestrs = ['20200401', '20200521']
elif 'odd' in modelid:
datestrs = ['20200426', '20200614']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_elsauce_phot(datestr=d)
x_obs -= 2457000 # convert to BTJD
elsauce_texp = np.nanmedian(np.diff(x_obs))
datasets[f'elsauce_{ix}'] = [x_obs, y_obs, y_err, elsauce_texp]
if 'even' in modelid:
datestrs = ['20200623']
elif 'odd' in modelid:
datestrs = ['20200529', '20200614']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_astep_phot(datestr=d)
x_obs += 2450000 # convert to BJD_TDB
x_obs -= 2457000 # convert to BTJD
astep_texp = np.nanmedian(np.diff(x_obs))
datasets[f'astep_{ix}'] = [x_obs, y_obs, y_err, astep_texp]
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents, datasets=datasets)
m = ModelFitter(modelid, datasets, prior_d, plotdir=PLOTDIR,
pklpath=pklpath, overwrite=OVERWRITE, N_samples=N_samples,
target_accept=target_accept)
print(pm.summary(m.trace, var_names=list(prior_d.keys())))
summdf = pm.summary(m.trace, var_names=list(prior_d.keys()), round_to=10,
kind='stats', stat_funcs={'median':np.nanmedian},
extend=True)
printparams = ['r_planet', 'b', 'log_r', 'log_r_sq']
print(42*'-')
print(modelid)
for p in printparams:
if p != 'log_r_sq':
med = np.percentile(m.trace[p], 50)
up = np.percentile(m.trace[p], 84)
low = np.percentile(m.trace[p], 36)
else:
med = np.percentile(np.exp(m.trace['log_r'])**2, 50)
up = np.percentile(np.exp(m.trace['log_r'])**2, 84)
low = np.percentile(np.exp(m.trace['log_r'])**2, 36)
print(f'{p} limit: {med:.6f} +{up-med:.6f} -{med-low:.6f}')
print(42*'-')
if make_threadsafe:
pass
else:
if grounddepth:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_grounddepth.png')
tp.plot_grounddepth(m, summdf, outpath, modelid=modelid,
showerror=0)
if subsetcorner:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_subsetcorner.png')
tp.plot_subsetcorner(m, outpath)
if phaseplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_phaseplot.png')
tp.plot_phasefold(m, summdf, outpath, modelid=modelid, inppt=1)
if fitindiv:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_fitindiv.png')
tp.plot_fitindiv(m, summdf, outpath, modelid=modelid)
if cornerplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_cornerplot.png')
tp.plot_cornerplot(prior_d, m, outpath)
def main(modelid):
make_threadsafe = 0
cut_tess = 1
phaseplot = 1
grounddepth = 1
fittedzoom = 0
cornerplot = 0
OVERWRITE = 1
REALID = 'TOI_837'
PLOTDIR = os.path.join(RESULTSDIR,
'{}_{}_phot_results'.format(REALID, modelid))
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
PLOTDIR = os.path.join(PLOTDIR, '20200617')
##########################################
assert modelid in [
'alltransit', 'alltransit_quad', 'alltransit_quaddepthvar'
]
print(42 * '#')
print(modelid)
print(42 * '#')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
pklpath = os.path.join(os.path.expanduser('~'), 'local', 'timmy',
'{}_model_{}.pkl'.format(REALID, modelid))
np.random.seed(42)
# get tess data
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
x_obs, y_obs, y_err = get_clean_tessphot(provenance,
yval,
binsize=None,
maskflares=1)
y_flat, y_trend = detrend_tessphot(x_obs, y_obs, y_err)
s = np.isfinite(y_flat) & np.isfinite(x_obs) & np.isfinite(y_err)
x_obs, y_flat, y_err = x_obs[s], y_flat[s], y_err[s]
if cut_tess:
x_obs, y_flat, y_err = _subset_cut(x_obs, y_flat, y_err)
tess_texp = np.nanmedian(np.diff(x_obs))
datasets = OrderedDict()
datasets['tess'] = [x_obs, y_flat, y_err, tess_texp]
datestrs = ['20200401', '20200426', '20200521', '20200614']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_elsauce_phot(datestr=d)
x_obs -= 2457000 # convert to BTJD
elsauce_texp = np.nanmedian(np.diff(x_obs))
datasets[f'elsauce_{ix}'] = [x_obs, y_obs, y_err, elsauce_texp]
# note: we're fitting the detrended data
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents, datasets=datasets)
m = ModelFitter(modelid,
datasets,
prior_d,
plotdir=PLOTDIR,
pklpath=pklpath,
overwrite=OVERWRITE)
print(pm.summary(m.trace, var_names=list(prior_d.keys())))
summdf = pm.summary(m.trace,
var_names=list(prior_d.keys()),
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
if modelid == 'alltransit_quaddepthvar':
var_names = [
'tess_roughdepth', 'elsauce_0_roughdepth', 'elsauce_1_roughdepth',
'elsauce_2_roughdepth', 'elsauce_3_roughdepth'
]
ddf = pm.summary(m.trace,
var_names=var_names,
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
print(ddf)
if make_threadsafe:
pass
else:
if cornerplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_cornerplot.png')
tp.plot_cornerplot(prior_d, m, outpath)
if phaseplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_phaseplot.png')
tp.plot_phasefold(m, summdf, outpath, modelid=modelid, inppt=1)
if fittedzoom:
outpath = join(PLOTDIR,
'{}_{}_fittedzoom.png'.format(REALID, modelid))
tp.plot_fitted_zoom(m, summdf, outpath, modelid=modelid)
if grounddepth:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_grounddepth.png')
tp.plot_grounddepth(m, summdf, outpath, modelid=modelid)
def main(modelid):
seldate = '20200426'
bp = 'Rband'
N_samples = 12000 # nb: 7.5mins, decent but not perfect convergence
# seldate = '20200614'
# bp = 'Bband'
# Nsamples = 12000 # 7min: gets rhat = 1.0 for all parameters.
cornerplot = 1
phaseplot = 0
grounddepth = 0
fittedzoom = 0
make_threadsafe = 0
cut_tess = 1
OVERWRITE = 1
REALID = 'TOI_837'
PLOTDIR = os.path.join(RESULTSDIR,
f'{REALID}_{modelid}_{seldate}_{bp}_phot_results')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
PLOTDIR = os.path.join(PLOTDIR, f'20200617_{bp}')
##########################################
assert modelid in ['onetransit']
print(42 * '#')
print(modelid)
print(42 * '#')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
pklpath = os.path.join(os.path.expanduser('~'), 'local', 'timmy',
f'{REALID}_model_{modelid}_{seldate}_{bp}.pkl')
np.random.seed(42)
# get tess data
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
x_obs, y_obs, y_err = get_clean_tessphot(provenance,
yval,
binsize=None,
maskflares=1)
y_flat, y_trend = detrend_tessphot(x_obs, y_obs, y_err)
s = np.isfinite(y_flat) & np.isfinite(x_obs) & np.isfinite(y_err)
x_obs, y_flat, y_err = x_obs[s], y_flat[s], y_err[s]
if cut_tess:
x_obs, y_flat, y_err = _subset_cut(x_obs, y_flat, y_err)
tess_texp = np.nanmedian(np.diff(x_obs))
datasets = OrderedDict()
datasets['tess'] = [x_obs, y_flat, y_err, tess_texp]
datestrs = ['20200401', '20200426', '20200521', '20200614']
for ix, d in enumerate(datestrs):
x_obs, y_obs, y_err = get_elsauce_phot(datestr=d)
x_obs -= 2457000 # convert to BTJD
elsauce_texp = np.nanmedian(np.diff(x_obs))
datasets[f'elsauce_{ix}'] = [x_obs, y_obs, y_err, elsauce_texp]
if seldate not in datestrs:
raise NotImplementedError
else:
usedata = OrderedDict()
tra_ind = int(np.argwhere(np.array(datestrs) == seldate))
usedata[f'elsauce_{tra_ind}'] = (deepcopy(
datasets[f'elsauce_{tra_ind}']))
# note: we're fitting the detrended data
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents, datasets=usedata)
m = ModelFitter(modelid,
usedata,
prior_d,
plotdir=PLOTDIR,
pklpath=pklpath,
overwrite=OVERWRITE,
N_samples=N_samples)
print(pm.summary(m.trace, var_names=list(prior_d.keys())))
summdf = pm.summary(m.trace,
var_names=list(prior_d.keys()),
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
var_names = ['roughdepth']
ddf = pm.summary(m.trace,
var_names=var_names,
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
print(ddf)
d_1sig = np.percentile(m.trace.roughdepth, 50 - (68 / 2))
d_2sig = np.percentile(m.trace.roughdepth, 50 - (95 / 2))
print(f'1 sigma depth lower limit: {d_1sig:.6f}')
print(f'2 sigma depth lower limit: {d_2sig:.6f}')
if make_threadsafe:
pass
else:
if cornerplot:
outpath = join(PLOTDIR, f'{REALID}_{modelid}_cornerplot.png')
prior_d['roughdepth'] = 4600e-6
tp.plot_cornerplot(prior_d, m, outpath)