def custom_plot(self, name, period, fit_width, allesfit_dir, mode="posterior"):
allesclass = allesfitter.allesclass(allesfit_dir)
baseline_width = fit_width * 24
baseline_to_period = fit_width / period
fig, axes = plt.subplots(2, 3, figsize=(18, 6), gridspec_kw={'height_ratios': [3, 1]}, sharex='col')
fig.subplots_adjust(left=0.5, right=1.5, hspace=0)
style = 'full'
allesclass.plot('lc', name, style, ax=axes[0][0], mode=mode)
axes[0][0].set_title('lc, ' + style)
allesclass.plot('lc', name, style + '_residuals', ax=axes[1][0], mode=mode)
axes[1][0].set_title('')
style = 'phase'
allesclass.plot('lc', name, style, ax=axes[0][1], mode=mode, zoomwindow=baseline_to_period)
axes[0][1].set_title('lc, ' + style)
axes[0][1].set_xlim([- baseline_to_period / 2, baseline_to_period / 2])
allesclass.plot('lc', name, style + '_residuals', ax=axes[1][1], mode=mode, zoomwindow=baseline_to_period)
axes[1][1].set_title('')
axes[1][1].set_xlim([- baseline_to_period / 2, baseline_to_period / 2])
style = 'phasezoom'
allesclass.plot('lc', name, style, ax=axes[0][2], mode=mode, zoomwindow=baseline_width)
axes[0][2].set_title('lc, ' + style)
axes[0][2].set_xlim([- baseline_width / 2, baseline_width / 2])
allesclass.plot('lc', name, style + '_residuals', ax=axes[1][2], mode=mode, zoomwindow=baseline_width)
axes[1][2].set_title('')
axes[1][2].set_xlim([- baseline_width / 2, baseline_width / 2])
fig.savefig(allesfit_dir + '/results/ns_' + mode + '_' + name + '_custom.pdf', bbox_inches='tight')
style = ['phasezoom_occ']
style = ['phase_variations']
#::: plotting settings
import seaborn as sns
sns.set(context='paper',
style='ticks',
palette='deep',
font='sans-serif',
font_scale=1.5,
color_codes=True)
sns.set_style({"xtick.direction": "in", "ytick.direction": "in"})
sns.set_context(rc={'lines.markeredgewidth': 1})
############################################################
#::: plot the fit
############################################################
alles = allesfitter.allesclass('allesfit')
fig = plt.figure(figsize=(12, 6), tight_layout=True)
gs = fig.add_gridspec(2, 3)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[1, 1])
ax4 = fig.add_subplot(gs[1, 2])
alles.plot('Kepler',
'B',
'full',
ax=ax1,
kwargs_data={'rasterized': False},
kwargs_ax={'title': ''})
alles.plot('Kepler',
def prepare_ttv_fit(datadir, ax=None):
'''
this must be run *after* reduce_phot_data()
'''
ax0 = ax
alles = allesfitter.allesclass(datadir)
window = alles.settings['fast_fit_width']
if not os.path.exists( os.path.join(datadir,'ttv_preparation') ): os.makedirs(os.path.join(datadir,'ttv_preparation'))
with open(os.path.join(datadir,'ttv_preparation','ttv_initial_guess_params.csv'),'w') as f:
f.write('')
def plot_all_transits_color_coded():
for inst in alles.settings['inst_phot']:
time = alles.data[inst]['time']
for companion in alles.settings['companions_phot']:
ind = []
for i, t in enumerate(alles.data[companion+'_tmid_observed_transits']):
ind += list( np.where((time >= (t - window/2.)) & (time <= (t + window/2.)))[0] )
ax.plot( alles.data[inst]['time'][ind], alles.data[inst]['flux'][ind], ls='none', marker='.', label=companion )
for companion in alles.settings['companions_phot']:
with open(os.path.join(datadir,'ttv_preparation','ttv_initial_guess_params.csv'),'a') as f:
f.write('#TTV companion '+companion+',,,,,\n')
#----------------------------------------------------------------------
#::: get combined data from all instruments
#----------------------------------------------------------------------
all_times = []
all_flux = []
for inst in alles.settings['inst_phot']:
all_times += list(alles.data[inst]['time'])
all_flux += list(alles.data[inst]['flux'])
ind_sort = np.argsort(all_times)
all_times = np.array(all_times)[ind_sort]
all_flux = np.array(all_flux)[ind_sort]
#----------------------------------------------------------------------
#::: get eclipse window
#----------------------------------------------------------------------
alles.initial_guess_params_median[companion+'_epoch']
eclipse_width = eclipse_width_smart(alles.initial_guess_params_median[companion+'_period'],
alles.initial_guess_params_median[companion+'_rr'],
alles.initial_guess_params_median[companion+'_rsuma'],
alles.initial_guess_params_median[companion+'_cosi'],
alles.initial_guess_params_median[companion+'_f_s'],
alles.initial_guess_params_median[companion+'_f_c'],
)[0]
#----------------------------------------------------------------------
#::: compute tmid, ttv_guess and make per-transit-plots
#----------------------------------------------------------------------
tmids = []
alles.data[companion+'_tmid_observed_transits'] = get_tmid_observed_transits(all_times,alles.initial_guess_params_median[companion+'_epoch'],alles.initial_guess_params_median[companion+'_period'],alles.settings['fast_fit_width'])
N = len(alles.data[companion+'_tmid_observed_transits'])
fig, axes = plt.subplots(N, 1, figsize=(6,4*N), sharey=True, tight_layout=True)
for i, t in enumerate(alles.data[companion+'_tmid_observed_transits']):
ind_tr1 = np.where((all_times >= (t - window/2.)) & (all_times <= (t + window/2.)))[0]
tr_times = all_times[ind_tr1]
tr_flux = all_flux[ind_tr1]
t_exp = np.median(np.diff(tr_times))
N_points_in_eclipse = int(eclipse_width/t_exp)
try:
trend = flatten(tr_times, tr_flux, window_length=eclipse_width/2., method='biweight', return_trend=True)[1]
tmid = np.median( tr_times[ np.argsort(trend)[0:int(N_points_in_eclipse/2.)] ] )
except:
warnings.warn('Install wotan for improved performance of prepare_ttv_fit().')
trend = None
tmid = np.median( tr_times[ np.argsort(tr_times)[0:int(N_points_in_eclipse/2.)] ] )
ttv_guess = tmid - t
tmids.append(tmid)
ax = axes[i]
ax.plot(tr_times, tr_flux, 'b.', rasterized=True)
if trend is not None: ax.plot(tr_times, trend, 'r-')
ax.axvline(t,c='grey',ls='--',label='linear prediction')
ax.axvline(tmid,c='r',ls='--',label='flux minimum')
ax.set(xlabel='Time', ylabel='Flux', xlim=[t-window/2., t+window/2.])
ax.text(0.95,0.95,'Transit '+str(i+1), va='top', ha='right', transform=ax.transAxes)
with open(os.path.join(datadir,'ttv_preparation','ttv_initial_guess_params.csv'),'a') as f:
f.write(companion+'_ttv_transit_'+str(i+1)+','+np.format_float_positional(ttv_guess,4)+',1,uniform '+np.format_float_positional(ttv_guess-0.01,4)+' '+np.format_float_positional(ttv_guess+0.01,4)+',TTV$_\mathrm{'+companion+';'+str(i+1)+'}$,d\n')
axes[0].legend()
fig.savefig(os.path.join(datadir,'ttv_preparation','ttv_preparation_'+companion+'_per_transit.pdf'), bbox_inches='tight')
plt.close(fig)
tmids = np.array(tmids)
#----------------------------------------------------------------------
#::: ttv guess 0-C plot
#----------------------------------------------------------------------
nr = np.array([ int(np.round( (t-tmids[0]) / alles.initial_guess_params_median[companion+'_period'] )) for t in tmids ]) #get corresponding transit number
nr -= int(nr[-1]/2.) #shift into the middle of the data set
period_mean, epoch_mean = np.polyfit(nr, tmids, 1)
fig, axes = plt.subplots(2,1,figsize=(6,8),tight_layout=True,sharex=True)
axes[0].plot(nr, tmids, 'bo', label='Companion '+companion)
axes[0].plot(nr, epoch_mean + nr * period_mean, 'b-')
axes[0].set(xlabel='Nr.', ylabel='Transit mid-time')
axes[0].legend()
axes[1].plot(nr, tmids-(epoch_mean + nr * period_mean), 'bo')
axes[1].axhline(0,c='grey',ls='--')
fig.savefig(os.path.join(datadir,'ttv_preparation','ttv_preparation_'+companion+'_oc.pdf'), bbox_inches='tight')
period_dev = np.abs( (period_mean-alles.initial_guess_params_median[companion+'_period'])/alles.initial_guess_params_median[companion+'_period'] )
epoch_dev = np.abs( (epoch_mean-alles.initial_guess_params_median[companion+'_epoch'])/alles.initial_guess_params_median[companion+'_epoch'] )
print('\nCompanion', companion)
print('Initial guess for mean period and epoch:')
print(np.format_float_positional(alles.initial_guess_params_median[companion+'_period']),
np.format_float_positional(alles.initial_guess_params_median[companion+'_epoch']))
print('New estimate for mean period and epoch:')
print(np.format_float_positional(period_mean,4),
np.format_float_positional(epoch_mean,4))
# print('Deviation from another:')
# print(np.format_float_positional(period_dev,4),
# np.format_float_positional(epoch_dev,4))
if (period_dev > 0.01) or (epoch_dev > 0.01):
print('\n! Consider updating your initial guess to these new estimated mean values.')
print('\n! If you do, then you must rerun this code.')
else:
print('\n! Looks great! You are ready to fit.')
#----------------------------------------------------------------------
#::: full lightcurve plot
#----------------------------------------------------------------------
flux_min = np.nanmin(all_flux)
flux_max = np.nanmax(all_flux)
if ax0 is None:
days = np.max(all_times) - np.min(all_times)
figsizex = np.max( [5, 5*(days/10.)] )
fig, ax = plt.subplots(figsize=(figsizex, 4)) #figsize * 5 for every 20 days
for inst in alles.settings['inst_phot']:
ax.plot(alles.fulldata[inst]['time'], alles.fulldata[inst]['flux'],ls='none',marker='.',color='silver')
# ax.plot(alles.data[inst]['time'], alles.data[inst]['flux'],ls='none',marker='.',label=inst) #color code by instrument
plot_all_transits_color_coded() #color code by companion
ax.plot( alles.data[companion+'_tmid_observed_transits'], np.ones_like(alles.data[companion+'_tmid_observed_transits'])*0.997*flux_min, 'k^', zorder=12 )
for i, tmid in enumerate(alles.data[companion+'_tmid_observed_transits']):
ax.text( tmid, 0.992*flux_min, str(i+1), ha='center', zorder=12 )
ax.axvline( tmid, color='grey', zorder=11 )
ax.set(ylim=[0.99*flux_min, 1.002*flux_max], xlabel='Time (BJD)', ylabel='Realtive Flux', title='Companion '+companion)
ax.legend(loc='best')
fname = os.path.join(datadir,'ttv_preparation','ttv_preparation_'+companion+'.jpg')
fig = plt.gcf()
fig.savefig(fname, bbox_inches='tight' )
plt.close(fig)
def plot_viol(pathdataoutp, pvalthrs=1e-3, boolonlytess=False):
strgtimestmp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
print('allesfitter postprocessing violin plot started at %s...' %
strgtimestmp)
liststrgruns = ['woutTESS', 'alldata']
if boolonlytess:
liststrgruns.append(['TESS'])
numbruns = len(liststrgruns)
indxruns = np.arange(numbruns)
liststrgpara = [[] for i in indxruns]
listlablpara = [[] for i in indxruns]
listobjtalle = [[] for i in indxruns]
for i, strgruns in enumerate(liststrgruns):
pathdata = pathdataoutp + 'allesfits/allesfit_%s' % strgruns
print('Reading from %s...' % pathdata)
config.init(pathdata)
liststrgpara[i] = np.array(config.BASEMENT.fitkeys)
listlablpara[i] = np.array(config.BASEMENT.fitlabels)
# read the chain
listobjtalle[i] = allesfitter.allesclass(pathdata)
liststrgparaconc = np.concatenate(liststrgpara)
liststrgparaconc = np.unique(liststrgparaconc)
listlablparaconc = np.copy(liststrgparaconc)
for k, strgparaconc in enumerate(liststrgparaconc):
for i, strgruns in enumerate(liststrgruns):
if strgparaconc in liststrgpara[i]:
listlablparaconc[k] = listlablpara[i][np.where(
liststrgpara[i] == strgparaconc)[0][0]]
ticklabl = ['w/o TESS', 'w/ TESS']
if boolonlytess:
ticklabl.append(['only TESS'])
xpos = 0.6 * (np.arange(numbruns) + 1.)
for k, strgpara in enumerate(liststrgparaconc):
booltemp = True
for i in indxruns:
if not strgpara in liststrgpara[i]:
booltemp = False
if not booltemp:
continue
figr, axis = plt.subplots(figsize=(5, 4))
chanlist = []
for i in indxruns:
chanlist.append(
(listobjtalle[i].posterior_params[strgpara] -
np.mean(listobjtalle[i].posterior_params[strgpara])) * 24. *
60.)
axis.violinplot(chanlist, xpos, showmedians=True, showextrema=False)
axis.set_xticks(xpos)
axis.set_xticklabels(ticklabl)
if strgpara == 'b_period':
axis.set_ylabel('P [min]')
else:
axis.set_ylabel(listlablparaconc[k])
plt.tight_layout()
path = pathdataoutp + 'viol_%04d.svg' % (k)
print('Writing to %s...' % path)
figr.savefig(path)
plt.close()
listyear = [2021, 2023, 2025]
numbyear = len(listyear)
indxyear = np.arange(numbyear)
timejwst = [[[] for i in indxruns] for k in indxyear]
for k, year in enumerate(listyear):
epocjwst = astropy.time.Time('%d-01-01 00:00:00' % year,
format='iso').jd
for i in indxruns:
epoc = listobjtalle[i].posterior_params['b_epoch']
peri = listobjtalle[i].posterior_params['b_period']
indxtran = (epocjwst - epoc) / peri
indxtran = np.mean(np.rint(indxtran))
if indxtran.size != np.unique(indxtran).size:
raise Exception('')
timejwst[k][i] = epoc + peri * indxtran
timejwst[k][i] -= np.mean(timejwst[k][i])
timejwst[k][i] *= 24. * 60.
listfigr = []
listaxis = []
## temporal evolution
figr, axis = plt.subplots(figsize=(5, 4))
listfigr.append(figr)
listaxis.append(axis)
axis.violinplot([timejwst[k][1] for k in indxyear], listyear)
axis.set_xlabel('Year')
axis.set_ylabel('Transit time residual [min]')
plt.tight_layout()
path = pathdataoutp + 'jwsttime.svg'
print('Writing to %s...' % path)
plt.savefig(path)
plt.close()
## without/with/only TESS prediction comparison
figr, axis = plt.subplots(figsize=(5, 4))
listfigr.append(figr)
listaxis.append(axis)
axis.violinplot(timejwst[1], xpos, points=2000)
axis.set_xticks(xpos)
axis.set_xticklabels(ticklabl)
axis.set_ylabel('Transit time residual in 2023 [min]')
#axis.set_ylim([-300, 300])
plt.tight_layout()
path = pathdataoutp + 'jwstcomp.svg'
print('Writing to %s...' % path)
plt.savefig(path)
plt.close()
return listfigr, listaxis
# all parameter summary
figr, axis = plt.subplots(figsize=(4, 3))
chanlist = []
axis.violinplot(chanlist, xpos, showmedians=True, showextrema=False)
axis.set_xticks(valutick)
axis.set_xticklabels(labltick)
axis.set_ylabel(lablparatemp)
plt.tight_layout()
path = pathdataoutp + 'para_%s.pdf'
print('Writing to %s...' % path)
figr.savefig(path)
plt.close()
# plot p values
## threshold p value to conclude significant difference between posteriors with and without TESS
if pvalthrs is None:
pvalthrs = 1e-6
lablparacomp = [[] for u in indxruns]
for u in indxruns:
lablparacomp[u] = list(
set(lablpara[indxrunsfrst[u]]).intersection(
lablpara[indxrunsseco[u]]))
# post-processing
## calculate the KS test statistic between the posteriors
numbparacomp = len(lablparacomp[u])
pval = np.empty(numbparacomp)
for j in range(numbparacomp):
kosm, pval[j] = scipy.stats.ks_2samp([indxrunsfrst[u]][:, j],
chan[indxrunsseco[u]][:, j])
kosm, pval[j] = scipy.stats.ks_2samp(chan[indxrunsfrst[u]][:, j],
chan[indxrunsseco[u]][:, j])
## find the list of parameters whose posterior with and without TESS are unlikely to be drawn from the same distribution
indxparagood = np.where(pval < pvalthrs)[0]
if indxparagood.size > 0:
figr, axis = plt.subplots(figsize=(12, 5))
indxparacomp = np.arange(numbparacomp)
axis.plot(indxparacomp, pval, ls='', marker='o')
axis.plot(indxparacomp[indxparagood],
pval[indxparagood],
ls='',
marker='o',
color='r')
axis.set_yscale('log')
axis.set_xticks(indxparacomp)
axis.set_xticklabels(lablparacomp[u])
if u == 0:
axis.set_title('Posteriors with TESS vs. without TESS')
if u == 1:
axis.set_title('Posteriors without TESS vs. only TESS')
if u == 2:
axis.set_title('Posteriors with TESS vs. only TESS')
axis.axhline(pvalthrs, ls='--', color='black', alpha=0.3)
plt.tight_layout()
path = pathdataoutp + 'kosm_com%d.pdf' % u
print('Writing to %s...' % path)
figr.savefig(path)
plt.close()
def prepare_ttv_fit(datadir, ax=None):
'''
this must be run *after* reduce_phot_data()
'''
ax0 = ax
alles = allesfitter.allesclass(datadir)
def plot_all_transits_color_coded():
width = alles.settings['fast_fit_width']
for inst in alles.settings['inst_phot']:
time = alles.data[inst]['time']
for companion in alles.settings['companions_phot']:
ind = []
for i, t in enumerate(alles.data[companion +
'_tmid_observed_transits']):
ind += list(
np.where((time >= (t - width / 2.))
& (time <= (t + width / 2.)))[0])
ax.plot(alles.data[inst]['time'][ind],
alles.data[inst]['flux'][ind],
ls='none',
marker='.',
label=companion)
for companion in alles.settings['companions_phot']:
print('#TTV companion ' + companion)
all_times = []
all_flux = []
for inst in alles.settings['inst_phot']:
all_times += list(alles.data[inst]['time'])
all_flux += list(alles.data[inst]['flux'])
alles.data[companion +
'_tmid_observed_transits'] = get_tmid_observed_transits(
all_times,
alles.initial_guess_params_median[companion + '_epoch'],
alles.initial_guess_params_median[companion +
'_period'],
alles.settings['fast_fit_width'])
for i, t in enumerate(alles.data[companion +
'_tmid_observed_transits']):
print(companion + '_ttv_transit_' + str(i + 1) +
',0,1,uniform -0.05 0.05,TTV$_\mathrm{' + companion + ';' +
str(i + 1) + '}$,d')
#::: plot
flux_min = np.nanmin(all_flux)
flux_max = np.nanmax(all_flux)
if ax0 is None:
days = np.max(all_times) - np.min(all_times)
figsizex = np.max([5, 5 * (days / 10.)])
fig, ax = plt.subplots(figsize=(figsizex,
4)) #figsize * 5 for every 20 days
for inst in alles.settings['inst_phot']:
ax.plot(alles.fulldata[inst]['time'],
alles.fulldata[inst]['flux'],
ls='none',
marker='.',
color='silver')
# ax.plot(alles.data[inst]['time'], alles.data[inst]['flux'],ls='none',marker='.',label=inst) #color code by instrument
plot_all_transits_color_coded() #color code by companion
ax.plot(
alles.data[companion + '_tmid_observed_transits'],
np.ones_like(alles.data[companion + '_tmid_observed_transits']) *
0.997 * flux_min,
'k^',
zorder=12)
for i, tmid in enumerate(alles.data[companion +
'_tmid_observed_transits']):
ax.text(tmid, 0.992 * flux_min, str(i + 1), ha='center', zorder=12)
ax.axvline(tmid, color='grey', zorder=11)
ax.set(ylim=[0.99 * flux_min, 1.002 * flux_max],
xlabel='Time (BJD)',
ylabel='Realtive Flux',
title='Companion ' + companion)
ax.legend(loc='best')
if not os.path.exists(os.path.join(datadir, 'results')):
os.makedirs(os.path.join(datadir, 'results'))
fname = os.path.join(datadir, 'results',
'preparation_for_TTV_fit_' + companion + '.jpg')
fig = plt.gcf()
fig.savefig(fname, bbox_inches='tight')
plt.close(fig)
ns_derived_file = object_dir + "/results/ns_derived_table.csv"
ns_file = object_dir + "/results/ns_table.csv"
if not os.path.exists(ns_derived_file) or not os.path.exists(ns_file):
raise ValueError("Bayesian fit posteriors files {" + ns_file + ", " +
ns_derived_file + "} not found")
star_df = pd.read_csv(object_dir + "/params_star.csv")
object_id = star_df.iloc[0]["obj_id"]
ra = star_df.iloc[0]["ra"]
dec = star_df.iloc[0]["dec"]
coords = str(ra) + " " + str(dec)
fit_derived_results = pd.read_csv(object_dir +
"/results/ns_derived_table.csv")
fit_results = pd.read_csv(object_dir + "/results/ns_table.csv")
candidates_count = len(
fit_results[fit_results["#name"].str.contains("_period")])
alles = allesfitter.allesclass(object_dir)
since = Time.now() if args.since is None else Time(args.since, scale='utc')
percentile = 99.7 if args.error_sigma == 3 else 95 if args.error_sigma == 2 else 68
for i in np.arange(0, candidates_count):
period_row = fit_results[fit_results["#name"].str.contains(
"_period")].iloc[i]
period = period_row["median"]
period_distribution = alles.posterior_params[period_row["#name"]]
period_low_err = period - np.percentile(period_distribution,
50 - percentile / 2)
period_up_err = np.percentile(period_distribution,
50 + percentile / 2) - period
name = object_id + "_" + period_row["#name"].replace("_period", "")
epoch_row = fit_results[fit_results["#name"].str.contains(
"_epoch")].iloc[i]
epoch = epoch_row["median"].item()
def custom_plot(name, period, fit_width, allesfit_dir, mode="posterior"):
"""
Creates a custom fit plot from the allesfitter data
@param name: the candidate name
@param period: the final period
@param fit_width: the fit_width window
@param allesfit_dir: the directory where allesfitter data is stred
@param mode: the allesfitter plot model
"""
allesclass = allesfitter.allesclass(allesfit_dir)
baseline_width = fit_width * 24
baseline_to_period = fit_width / period
fig, axes = plt.subplots(2,
3,
figsize=(18, 6),
gridspec_kw={'height_ratios': [3, 1]},
sharex='col')
fig.subplots_adjust(left=0.5, right=1.5, hspace=0)
style = 'full'
allesclass.plot('lc', name, style, ax=axes[0][0], mode=mode)
axes[0][0].set_title('lc, ' + style)
allesclass.plot('lc',
name,
style + '_residuals',
ax=axes[1][0],
mode=mode)
axes[1][0].set_title('')
style = 'phase'
allesclass.plot('lc',
name,
style,
ax=axes[0][1],
mode=mode,
zoomwindow=baseline_to_period)
axes[0][1].set_title('lc, ' + style)
axes[0][1].set_xlim([-baseline_to_period / 2, baseline_to_period / 2])
allesclass.plot('lc',
name,
style + '_residuals',
ax=axes[1][1],
mode=mode,
zoomwindow=baseline_to_period)
axes[1][1].set_title('')
axes[1][1].set_xlim([-baseline_to_period / 2, baseline_to_period / 2])
style = 'phasezoom'
allesclass.plot('lc',
name,
style,
ax=axes[0][2],
mode=mode,
zoomwindow=baseline_width)
axes[0][2].set_title('lc, ' + style)
axes[0][2].set_xlim([-baseline_width / 2, baseline_width / 2])
allesclass.plot('lc',
name,
style + '_residuals',
ax=axes[1][2],
mode=mode,
zoomwindow=baseline_width)
axes[1][2].set_title('')
axes[1][2].set_xlim([-baseline_width / 2, baseline_width / 2])
fig.savefig(allesfit_dir + '/results/ns_' + mode + '_' + name +
'_custom.pdf',
bbox_inches='tight')
style = ['phasezoom_occ']
style = ['phase_variations']
def plot_spots_new(datadir):
alles = allesfitter.allesclass(datadir)
for inst in alles.BASEMENT.settings['inst_phot']:
fig = plt.figure()
plt.subplot(111, projection="aitoff")
plt.grid(True)
for i in [1, 2]:
lons = alles.posterior_params['host_spot_' + str(i) + '_long_' +
inst][0:20]
lats = alles.posterior_params['host_spot_' + str(i) + '_lat_' +
inst][0:20]
sizes = alles.posterior_params['host_spot_' + str(i) + '_size_' +
inst][0:20]
brightnesses = alles.posterior_params['host_spot_' + str(i) +
'_brightness_' + inst][0:20]
lon_list, lat_list, brightness_list = convert_many_points_to_an_area(
lons, lats, sizes, brightnesses)
c = SkyCoord(ra=lon_list * u.deg, dec=lat_list * u.deg)
lon_list_aitoff = c.ra.wrap_at(180 * u.deg).radian
lat_list_aitoff = c.dec.radian
plt.scatter(lon_list_aitoff,
lat_list_aitoff,
c=brightness_list,
vmin=0,
vmax=1)
plt.colorbar(label='Relative spot brightness')
plt.xlabel('Longitude (deg)')
plt.ylabel('Latitude (deg)')
plt.tight_layout()
plt.xticks(ticks=np.deg2rad(
[-150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180]),
labels=[
'', '', r'$270^\circ$', '', '', r'$0^\circ$', '', '',
r'$90^\circ$', '', '', r'$180^\circ$'
])
plt.yticks(ticks=np.deg2rad([-90, -60, -30, 0, 30, 60, 90]),
labels=[
'', r'$-60^\circ$', r'$-30^\circ$', '0', r'$30^\circ$',
r'$60^\circ$', ''
])
fig.savefig(os.path.join(alles.BASEMENT.outdir,
'spots_aitoff_' + inst + '.pdf'),
bbox_inches='tight')
fig = plt.figure()
plt.xlim([0, 360])
plt.ylim([-90, 90])
for i in [1, 2]:
lons = alles.posterior_params['host_spot_' + str(i) + '_long_' +
inst][0:20]
lats = alles.posterior_params['host_spot_' + str(i) + '_lat_' +
inst][0:20]
sizes = alles.posterior_params['host_spot_' + str(i) + '_size_' +
inst][0:20]
brightnesses = alles.posterior_params['host_spot_' + str(i) +
'_brightness_' + inst][0:20]
lon_list, lat_list, brightness_list = convert_many_points_to_an_area(
lons, lats, sizes, brightnesses)
plt.scatter(lon_list, lat_list, c=brightness_list, vmin=0, vmax=1)
plt.colorbar(label='Relative spot brightness')
plt.xlabel('Longitude (deg)')
plt.ylabel('Latitude (deg)')
plt.tight_layout()
fig.savefig(os.path.join(alles.BASEMENT.outdir,
'spots_cartesian_' + inst + '.pdf'),
bbox_inches='tight')
#::: plotting settings
import seaborn as sns
sns.set(context='paper',
style='ticks',
palette='deep',
font='sans-serif',
font_scale=1.5,
color_codes=True)
sns.set_style({"xtick.direction": "in", "ytick.direction": "in"})
sns.set_context(rc={'lines.markeredgewidth': 1})
###############################################################################
#::: plot the fit
###############################################################################
alles = allesfitter.allesclass('allesfit_year_1')
fig, axes = plt.subplots(2,
3,
figsize=(15, 5),
gridspec_kw={'height_ratios': [3, 1]},
sharex='col')
alles.plot('TESS',
'c',
'phasezoom',
ax=axes[0, 0],
kwargs_data={'rasterized': False},
kwargs_ax={'title': ''})
alles.plot('TESS',
'c',
'phasezoom_residuals',
import astropy.units as u
from astropy.time import Time
from lcbuilder.eleanor import TargetData
from sherlockpipe import constants as const
from sherlockpipe import tpfplotter
from sherlockpipe.nbodies.megno import MegnoStabilityCalculator
from sherlockpipe.nbodies.stability_calculator import StabilityCalculator
from sherlockpipe.sherlock import Sherlock
# sherlock = Sherlock(None)
# sherlock = sherlock.filter_multiplanet_ois()
# sherlock.ois.to_csv("multiplanet_ois.csv")
#Allesfitter stuff
alles = allesfitter.allesclass("/mnt/0242522242521AAD/dev/workspaces/git_repositories/sherlockpipe/TIC305048087_[2]_bck/fit_2/")
alles.posterior_samples("lc", "SOI_2_period")
allesfitter.ns_output("/mnt/0242522242521AAD/dev/workspaces/git_repositories/sherlockpipe/run_tests/analysis/TIC142748283_all/fit_0/ttvs_0")
results = pickle.load(open('/mnt/0242522242521AAD/dev/workspaces/git_repositories/sherlockpipe/run_tests/analysis/dietrich/TIC467179528_all/fit_2/results/ns_derived_samples.pickle', 'rb'))
logging.info(results)
#Stability plots
# stability_dir = "/mnt/0242522242521AAD/dev/workspaces/git_repositories/sherlockpipe/run_tests/analysis/dietrich/TIC467179528_all/fit_2/stability_0/"
# df = pandas.read_csv(stability_dir + "stability_megno.csv")
# df = df[(df["megno"] < 3)]
# stability_calc = MegnoStabilityCalculator(5e2)
# for key, row in df.iterrows():
# stability_calc.plot_simulation(row, stability_dir, str(key))
# filename = "/home/martin/Downloads/resimplepulsations/P2.5_R1.28_1354.197892531283.csv"
# df = pandas.read_csv(filename, float_precision='round_trip', sep=',',
def plot_viol(pathbase, liststrgstar, liststrgruns, lablstrgruns, pathimag, pvalthrs=1e-3):
strgtimestmp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
print('allesfitter postprocessing violin plot started at %s...' % strgtimestmp)
# construct global object
gdat = gdatstrt()
# copy unnamed inputs to the global object
#for attr, valu in locals().iter():
for attr, valu in locals().items():
if '__' not in attr and attr != 'gdat':
setattr(gdat, attr, valu)
# runs to be compared for each star
gdat.numbruns = len(liststrgruns)
gdat.indxruns = np.arange(gdat.numbruns)
gdat.pathimag = pathimag
gdat.liststrgstar = liststrgstar
# stars
numbstar = len(liststrgstar)
gdat.indxstar = np.arange(numbstar)
# plotting
gdat.strgplotextn = 'png'
# read parameter keys, labels and posterior from allesfitter output
liststrgpara = [[] for i in gdat.indxruns]
listlablpara = [[] for i in gdat.indxruns]
gdat.listobjtalle = [[[] for m in gdat.indxstar] for i in gdat.indxruns]
for i in gdat.indxruns:
for m in gdat.indxstar:
pathalle = pathbase + '%s/allesfits/allesfit_%s/' % (gdat.liststrgstar[m], gdat.liststrgruns[i])
print('Reading from %s...' % pathalle)
config.init(pathalle)
liststrgpara[i] = np.array(config.BASEMENT.fitkeys)
listlablpara[i] = np.array(config.BASEMENT.fitlabels)
# read the chain
print('pathalle')
print(pathalle)
gdat.listobjtalle[i][m] = allesfitter.allesclass(pathalle)
# concatenate the keys, labels from different runs
gdat.liststrgparaconc = np.concatenate(liststrgpara)
gdat.liststrgparaconc = np.unique(gdat.liststrgparaconc)
gdat.listlablparaconc = np.copy(gdat.liststrgparaconc)
for k, strgparaconc in enumerate(gdat.liststrgparaconc):
for i, strgruns in enumerate(liststrgruns):
if strgparaconc in liststrgpara[i]:
gdat.listlablparaconc[k] = listlablpara[i][np.where(liststrgpara[i] == strgparaconc)[0][0]]
gdat.numbparaconc = len(gdat.liststrgparaconc)
gdat.indxparaconc = np.arange(gdat.numbparaconc)
for k, strgpara in enumerate(gdat.liststrgparaconc):
booltemp = True
for i in gdat.indxruns:
if not strgpara in liststrgpara[i]:
booltemp = False
if not booltemp:
continue
## violin plot
## mid-transit time prediction
plot(gdat, gdat.indxstar, indxpara=np.array([k]), strgtype='paracomp')
## per-star
#for m in gdat.indxstar:
# plot(gdat, indxstar=np.array([m]), indxpara=k, strgtype='paracomp')
# calculate the future evolution of epoch
gdat.listyear = [2021, 2023, 2025]
numbyear = len(gdat.listyear)
gdat.indxyear = np.arange(numbyear)
gdat.timejwst = [[[[] for m in gdat.indxstar] for i in gdat.indxruns] for k in gdat.indxyear]
for k, year in enumerate(gdat.listyear):
epocjwst = astropy.time.Time('%d-01-01 00:00:00' % year, format='iso').jd
for i in gdat.indxruns:
for m in gdat.indxstar:
epoc = gdat.listobjtalle[i][m].posterior_params['b_epoch']
peri = gdat.listobjtalle[i][m].posterior_params['b_period']
indxtran = (epocjwst - epoc) / peri
indxtran = np.mean(np.rint(indxtran))
if indxtran.size != np.unique(indxtran).size:
raise Exception('')
gdat.timejwst[k][i][m] = epoc + peri * indxtran
gdat.timejwst[k][i][m] -= np.mean(gdat.timejwst[k][i][m])
gdat.timejwst[k][i][m] *= 24. * 60.
listfigr = []
listaxis = []
# temporal evolution of mid-transit time prediction
plot(gdat, gdat.indxstar, strgtype='epocevol')
## per-star
#for m in gdat.indxstar:
# plot(gdat, indxstar=np.array([m]), strgtype='epocevol')
## mid-transit time prediction
plot(gdat, gdat.indxstar, strgtype='jwstcomp')
## per-star
#for m in gdat.indxstar:
# plot(gdat, indxstar=np.array([m]), strgtype='jwstcomp')
return listfigr, listaxis
