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 = 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 = 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):
assert modelid == 'rv'
make_threadsafe = 0
phaseplot = 0
cornerplot = 1
OVERWRITE = 1
REALID = 'TOI_837'
PLOTDIR = os.path.join(RESULTSDIR,
'{}_{}_fitting_results'.format(REALID, modelid))
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
PLOTDIR = os.path.join(PLOTDIR, '20200525')
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
print(42 * '#')
print(modelid)
print(42 * '#')
pklpath = os.path.join(os.path.expanduser('~'), 'local', 'timmy',
'{}_model_{}.pkl'.format(REALID, modelid))
np.random.seed(42)
rv_df = get_clean_rv_data()
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents)
m = ModelFitter(modelid,
rv_df,
prior_d,
plotdir=PLOTDIR,
pklpath=pklpath,
overwrite=OVERWRITE)
var_names = [
'r_star', 'logg_star', 'log_K', 'period', 'ecc', 'omega', 'phase',
't0', 'S_tot', 'ell', 'means', 'sigmas'
]
print(pm.summary(m.trace, var_names=var_names))
summdf = pm.summary(m.trace,
var_names=var_names,
round_to=10,
kind='stats',
stat_funcs={'median': np.nanmedian},
extend=True)
if make_threadsafe:
pass
else:
if cornerplot:
outpath = join(PLOTDIR,
'{}_{}_cornerplot.png'.format(REALID, modelid))
tp.plot_cornerplot(prior_d, m, outpath)
import IPython
IPython.embed()
#FIXME: save the SAMPLES from the trace (rather than the trace itself,
#b/c of the local functions definined within the model context)
assert 0
#FIXME plots below
if phaseplot:
outpath = join(PLOTDIR,
'{}_{}_phaseplot.png'.format(REALID, modelid))
tp.plot_phasefold(m, summdf, outpath)
assert 0
if sampleplot:
outpath = join(PLOTDIR,
'{}_{}_sampleplot.png'.format(REALID, modelid))
tp.plot_sampleplot(m, outpath, N_samples=100)
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):
make_threadsafe = 0
phaseplot = 0
cornerplot = 0
fittedzoom = 0
grounddepth = 1
writevespa = 0
sampleplot = 0
OVERWRITE = 1
REALID = 'TOI_837'
provenance = 'spoc' # could be "cdips"
yval = 'PDCSAP_FLUX' # could be SAP_FLUX
PLOTDIR = os.path.join(RESULTSDIR,
'{}_{}_phot_results'.format(REALID, modelid))
if not os.path.exists(PLOTDIR):
os.mkdir(PLOTDIR)
PLOTDIR = os.path.join(PLOTDIR, '20200518')
##########################################
assert modelid == 'transit'
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)
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 writevespa:
_write_vespa(x_obs, y_flat, y_err)
return
# note: we're fitting the detrended data
mp = ModelParser(modelid)
prior_d = initialize_prior_d(mp.modelcomponents)
data_df = pd.DataFrame({'x_obs': x_obs, 'y_obs': y_flat, 'y_err': y_err})
m = ModelFitter(modelid,
data_df,
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 make_threadsafe:
pass
else:
if fittedzoom:
outpath = join(PLOTDIR,
'{}_{}_fittedzoom.png'.format(REALID, modelid))
tp.plot_fitted_zoom(m, summdf, outpath)
if grounddepth:
outpath = join(PLOTDIR,
'{}_{}_grounddepth.png'.format(REALID, modelid))
tp.plot_grounddepth(m, summdf, outpath)
if phaseplot:
outpath = join(PLOTDIR,
'{}_{}_phaseplot.png'.format(REALID, modelid))
tp.plot_phasefold(m, summdf, outpath)
if cornerplot:
outpath = join(PLOTDIR,
'{}_{}_cornerplot.png'.format(REALID, modelid))
tp.plot_cornerplot(prior_d, m, outpath)
# NOTE: following are deprecated
if sampleplot:
outpath = join(PLOTDIR,
'{}_{}_sampleplot.png'.format(REALID, modelid))
tp.plot_sampleplot(m, outpath, N_samples=100)
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)