def brew_LATTE_FFI(tic, indir, syspath, transit_list, simple, BLS, model, save, DV, sectors, sectors_all, alltime, allflux_normal, allflux_small, allflux, all_md, allfbkg, allfbkg_t, start_sec, end_sec, in_sec, X1_list, X4_list, apmask_list, arrshape_list, tpf_filt_list, t_list, bkg_list, tpf_list, ra, dec, upper_axis_lim_final, lower_axis_lim_final, args):
'''
This function that runs LATTE - makes the plots, saves them, runs the BLS model and the pyaneti model before
making a PHT DV report (if told to do so.)
This function is very similar to brew_LATTE -
except that it is designed to analyse the FFIs and therefore the data download is different.
Parameters
----------
tic : str
target TIC ID
indir : str
path to directory where all the plots and data will be saved.
transit_list : list
list of the transit-like events
simple : boolean
whether or not to run the simple version
BLS : boolean
whether or not to run the BLS routine
model : boolean
whether or not to model the transit using pyaneti
save : boolean
whether or not to save the figures and data
DV : boolean
whether or not to write and save a DV report
sectors_all : list
all the sectors in which the target has been/ will be observed
alltime : list
times
allflux_normal : list
normalized flux extracted with the larger aperture (PCA corrected)
allflux_small : list
normalized flux extracted with the smaller aperture (PCA corrected)
allflux : list
normalized detrended flux extracted with the larger aperture
all_md : list
times of the momentum dumps
allfbkg : list
background flux
allfbkg_t : list
times used to plot the background
start_sec : list
times of the start of the sector
end_sec : list
times of the end of the sector
in_sec : list
the sectors for which data was downloaded
X1_list : list
flux vs time for each pixel (for each sector)
X4_list : list
PCA corrected flux vs time for each pixel (for each sector)
apmask_list : list
aperture masks from the pipeline
arrshape_list : list
list of the shape of the array (for each sector)
tpf_filt_list : list
list of the filtered (masked) corrected target pixel data - from X4. (for each sector)
t_list : list
list of the time arrays (for each sector)
bkg_list : list
the flux that was used to normalise each pixel - i.e. what is used to make the background plot colour for each pixel.
tpf_list : list
list of the target pixel files (for each sector)
ra : float
right ascension of the target star
dec : float
declination of the target star
'''
# ----------------------------------------
# SAVE THE DATA FILES
# ----------------------------------------
if (save == True) or (DV == True):
save = True
# if this folder doesn't exist then create it. These are the folder where the images, data and reports for each TIC ID will be stored.
newpath = '{}/{}'.format(indir,tic)
if not exists(newpath):
os.makedirs(newpath)
# save the data used as a text file - these often come in use later for a quick re-analysis.
with open('{}/{}/{}_data.txt'.format(indir, tic, tic), "w") as f:
# get rid of nan values first using a mask
good_mask = np.isfinite(np.array(alltime)) * np.isfinite(np.array(allflux))
alltime_ar = np.array(alltime)[good_mask]
allflux_ar = np.array(allflux)[good_mask]
allflux_err_ar = allflux_ar * 0.001
# save data
writer = csv.writer(f, delimiter='\t')
writer.writerow(['time', 'flux', 'flux_err'])
writer.writerows(zip(alltime_ar,allflux_ar,allflux_err_ar))
'''
if the modelling option was also chose, save another data file with slightly different formatting to be called by Pyaneti.
Pyaneti requires a very specific data format.
Furhermore, in order for Pyaneti to run more efficiently (it has a Bayesian backend which scales with number of data points)
we create a cutout of the times around the times of the marked transit events.
'''
if model == True:
with open('{}/{}/{}_data_pyaneti.dat'.format(indir, tic, tic), "w") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerow(['#time', 'flux', 'flux_err'])
# if the dip separations are too small, then don't create cut outs and save the whole dataset
if (len(transit_list) > 1) and ((transit_list[1] - transit_list[0]) < 2): # if there are LOTS of transit events on short period (if so it's probably a TOI but let's keep it here as a condition)
with open('{}/{}/{}_data_pyaneti.dat'.format(indir, tic, tic), "a") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(zip(alltime_ar,allflux_ar,allflux_err_ar)) # save all the data
# else create a cut out of the data around the time of the transit event
else:
for transit in transit_list:
# save the data
# get rid of nan values first - this is used for the pyaneti code
pyaneti_mask = (alltime_ar > (transit - 1)) * (alltime_ar < (transit + 1))
with open('{}/{}/{}_data_pyaneti.dat'.format(indir, tic, tic), "a") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(zip(alltime_ar[pyaneti_mask],allflux_ar[pyaneti_mask],allflux_err_ar[pyaneti_mask]))
# ------------------------------------------
# START PLOTTING
# ------------------------------------------
# create a plot of the fulllighcurves with the momentum dumps (MDs) marked and a zoom-in of the marked transits
utils.plot_full_md(tic, indir, alltime, allflux, all_md, alltime, allflux, transit_list, upper_axis_lim_final, lower_axis_lim_final, args)
# get the sectors that have a transit marked in them
transit_sec = utils.transit_sec(in_sec,start_sec, end_sec, transit_list)
# -----------
# plot the background flux at the time of the transit event.
utils.plot_background(tic, indir, allfbkg_t, allfbkg, transit_list, args)
print ("Background plots... done.")
# -----------
if simple == True:
print ("Simple option was selected, therefore end analysis here.")
sys.exit('')
# -----------
# plot the LC using different aperture sizes
utils.plot_aperturesize(tic,indir,alltime, alltime, alltime, allflux_normal, allflux_normal, allflux_small, transit_list, args)
print ("Aperture size plots... done.")
# -----------
'''
Plot the average pixel brightness of the cut-out around the target star and the corresponding SDSS field of view.
Both are oriented so that North is pointing upwards.
The former also shows the nearby stars with TESS magnitude brighter than 17. Queried from GAIA using astroquery.
The function returns the TESS magnitude, effective temperature (K), the radius of the star (solar radii)and the
mass of the star (solar mass) (also output from astroquery)- this is a useful input for the Pyaneti modelling
'''
if args.mpi == False:
tessmag, teff, srad, mstar,vmag, logg, plx, c_id = utils.plot_TESS_stars(tic,indir, transit_sec, tpf_list, args)
if tessmag == -111:
tessmag, teff, srad, mstar,vmag, logg, plx, c_id = utils.plot_TESS_stars_not_proj(tic,indir, transit_list, transit_sec, tpf_list, args)
args.mpi = True
else:
tessmag, teff, srad, mstar, vmag, logg, plx, c_id = utils.plot_TESS_stars_not_proj(tic,indir, transit_list, transit_sec, tpf_list, args)
# keep track of whether astroquery is working (sometimes the site is down and we don't want this to stop us from running the code)
astroquery_corrupt = False
if tessmag == -999:
astroquery_corrupt = True
print ("Star Aperture plots... failed.")
else:
print ("Star Aperture plots... done.")
# -----------
# If more than one transit has been marked by the user, the LC is phase folded based on the period of the separation of the first two maarked peaks.
# These plots are saved but do not feature in the DV report.
# needs to know a period so can only do this if more than one transit has been marked.
if len(transit_list) > 1:
# find the period
period = transit_list[1] - transit_list[0]
t0 = transit_list[0] # time of the first marking
# calculate the phase
# phased = np.array([-0.5+( ( t - t0-0.5*period) % period) / period for t in alltime])
# phased plot where odd and even are different colours
phased = (np.array(alltime) - t0) % period
phased2 = (np.array(alltime) - (t0)) % (period*2)
index = phased>0.5*period
index2 = phased2 >0.5*(period *2)
phased[index] -= period
phased2[index2] -= (period *2)
fig, ax = plt.subplots(figsize=(5.55,5))
ax.plot(phased,np.array(allflux), 'k.', markersize=5)
ax.plot(phased2,np.array(allflux), 'r.', markersize=5)
#ax.plot(phased, allflux,marker='o',color = 'navy', alpha = 0.7, lw = 0, markersize = 2, label = 'binning = 7', markerfacecolor='white')
plt.title("Phase folded LC")
ax.set_xlabel("Phase (days)")
ax.set_ylabel("Normalized Flux")
plt.plot()
if save == True:
plt.savefig('{}/{}/{}_phase_folded.png'.format(indir, tic, tic), format='png')
if args.noshow == False:
plt.show()
print ("Phase folded plot... done.")
else:
print ("\n Only one transit marked - therefore can't be phase folded. \n")
# ------------
'''
In order to create the plots that compare the in and out of transit average flux
we need to create TPF masks for the in and out of transit.
This couldn't be done at the time of the extraction of the TPF arrays as the transit times
had not yet been defined.
'''
oot_list = [] # out of transit
intr_list = [] # in transit
X1_list_n = []
X4_list_n = []
bkg_list_n = []
apmask_list_n = []
arrshape_list_n = []
t_list_n = []
tpf_filt_list_n = []
tpf_list_n = []
T0_list = [] # list of the transit times - make a new list to ensure that thet are in the same order.
for T0 in transit_list:
for idx,t in enumerate(t_list):
# these times were found to give good approximations for out of transit and in transit
# Note: this will NOT be 'perfect' for all events, as it depends on the transit duration which the code doesnt' know at this time but it's a good approximation.
if (T0 > np.nanmin(t)) and (T0 < np.nanmax(t)): # if T0 is within the time... (should be)
oot_list.append((abs(T0-np.array(t)) < 0.55) * (abs(T0-np.array(t)) < 0.3))
intr_list.append(abs(T0-np.array(t)) < 0.1)
X1_list_n.append(X1_list[idx])
X4_list_n.append(X4_list[idx])
bkg_list_n.append(bkg_list[idx])
apmask_list_n.append(apmask_list[idx])
arrshape_list_n.append(arrshape_list[idx])
t_list_n.append(t)
tpf_filt_list_n.append(tpf_filt_list[idx])
tpf_list_n.append(tpf_list[idx])
T0_list.append(T0)
X1_list = X1_list_n
X4_list = X4_list_n
bkg_list = bkg_list_n
apmask_list = apmask_list_n
arrshape_list = arrshape_list_n
t_list = t_list_n
tpf_filt_list = tpf_filt_list_n
tpf_list = tpf_list_n
# ------------
# Plot the in and out of transit flux comparison
# By default the images are NOT orented north - this is because the reprojectio takes longer to run and for a simple
# analysis to chekc whether the brightest pixel moves during the transit this is not required.
# The orientation towards north can be defined in the command line with '--north'.
if args.north == True:
utils.plot_in_out_TPF_proj(tic, indir, X4_list, oot_list, t_list, intr_list, T0_list, tpf_filt_list, tpf_list, args)
print ("In and out of aperture flux comparison with reprojection... done. ")
else:
utils.plot_in_out_TPF(tic, indir, X4_list, oot_list, t_list, intr_list, T0_list, tpf_filt_list, args)
print ("In and out of aperture flux comparison... done.")
# ------------
# For each pixel in the TPF, extract and plot a lightcurve around the time of the marked transit event.
utils.plot_pixel_level_LC(tic, indir, X1_list, X4_list, oot_list, intr_list, bkg_list, tpf_list, apmask_list, arrshape_list, t_list, T0_list, args)
print ("Pixel level LCs plot... done.")
# ------------
# If the BLS option is chose, a BLS search is run. The LCs are first detrended and smoothed using a moving average.
# The corrected and uncorrected LCs are saves as a single plot for comparison and to verify that the correction worked well - saved but do not feature in the DV report.
if BLS == True:
print ("Running BLS")
bls_stats1, bls_stats2 = utils.data_bls_FFI(tic, indir, alltime, allflux, args)
# ------------
# ------------
print ("Periodogram plot...", end =" ")
mass_ast, radius_ast, logg_ast, numax, deltanu = utils.plot_periodogram(tic, indir, alltime, allflux, 0, -999, -999, args)
print ("done.")
# ------------
print ("Evolutionary tracks plot...", end =" ")
utils.eep_target(tic, indir, syspath, teff, srad, args)
print ("done.")
# ------------
'''
If the modelling option is selected (in the GUI), model the transit event using Pyaneti (Barragan et al 2018)
which uses an Bayesian approach with an MCMC sampling to best fit and model the transit.
The code runs slightly differently depending on whether one or multiple transits have been marked.
This is because with multiple transits the code has information about the possible orbital period.
Need to ensure that the code has compiled correctly on the users computer.
SKIP FOR NOW - this will become available in the next version of LATTE
'''
# first check if Pyaneti is even installed...
if os.path.exists("{}/pyaneti_LATTE.py".format(syspath)):
if model == True:
print ("Running Pyaneti modelling - this could take a while so be patient...")
# the code is usually run through the command line so call it using the os.system function.
transit_list_model = ("{}".format(str(np.asarray(transit_list)))[1:-1]) # change the list into a string and get rid of the brackets
os.system("python3 {}/pyaneti_LATTE.py {} {} {} {} {} {} {}".format(syspath, tic, indir, syspath, mstar, teff, srad, transit_list_model))
else:
print ("Pyaneti has not been installed so you can't model anything yet. Ask Nora or Oscar for the LATTE version of the Pyaneti code.")
model = False
# SKIP until HERE
# ------------
# Finally, create a DV report which summarises all of the plots and tables.
if DV == True:
from LATTE import LATTE_DV as ldv
if BLS == True:
ldv.LATTE_DV(tic, indir, syspath, transit_list, sectors_all, ra, dec, tessmag, teff, srad, mstar, vmag, logg, mass_ast, radius_ast, logg_ast, numax, deltanu, plx, c_id, bls_stats1, bls_stats2, False, astroquery_corrupt, FFI = True, bls = True, model = model, mpi = args.mpi)
else:
ldv.LATTE_DV(tic, indir, syspath, transit_list, sectors_all, ra, dec, tessmag, teff, srad, mstar, vmag, logg, mass_ast, radius_ast, logg_ast, numax, deltanu, plx, c_id, [0], [0], False, astroquery_corrupt, FFI = True, bls = False, model = model, mpi = args.mpi)
else:
print ("\n Complete! \n ")
def brew_LATTE(tic, indir, syspath, transit_list, simple, BLS, model, save, DV, sectors, sectors_all, alltime, allflux, allflux_err, all_md, alltimebinned, allfluxbinned, allx1, allx2, ally1, ally2, alltime12, allfbkg, start_sec, end_sec, in_sec, upper_axis_lim_final, lower_axis_lim_final, tessmag, teff, srad, ra, dec, input_numax, input_analysis_window, url_list, args):
'''
This function combines all the results from LATTE and calls all the different functions -
it makes the plots, saves them, runs the BLS model and the pyaneti model before making a PHT DV report (if this option is selected.)
Parameters
----------
tic : str
target TIC ID
indir : str
path to directory where all the plots and data will be saved.
transit_list : list
list of the transit-like events
simple : boolean
whether or not to run the simple version
BLS : boolean
whether or not to run the BLS routine
model : boolean
whether or not to model the transit using pyaneti
save : boolean
whether or not to save the figures and data
DV : boolean
whether or not to write and save a DV report
sectors_all : list
all the sectors in which the target has been/ will be observed
alltime : list
times (not binned)
allflux : list
normalized flux (not binned)
allflux_err : list
normalized flux errors (not binned)
all_md : list
times of the momentum dumps
alltimebinned : list
binned time
allfluxbinned : list
normalized binned flux
allx1 : list
CCD column position of target’s flux-weighted centroid. In x direction
allx2 : list
The CCD column local motion differential velocity aberration (DVA), pointing drift, and thermal effects. In x direction
ally1 : list
CCD column position of target’s flux-weighted centroid. In y direction
ally2 : list
The CCD column local motion differential velocity aberration (DVA), pointing drift, and thermal effects. In y direction
alltimel2 : list
time used for the x and y centroid position plottin
allfbkg : list
background flux
start_sec : list
times of the start of the sector
end_sec : list
times of the end of the sector
in_sec : list
the sectors for which data was downloaded
tessmag : list
TESS magnitude of the target star
teff : float
effective temperature of the tagret star (K)
srad : float
radius of the target star (solar radii)
ra : float
the right ascension of the target stars
dec : float
the declination of the target star
'''
# -------------------
# SAVE THE DATA FILES
# -------------------
if (save == True) or (DV == True):
save = True
# if this folder doesn't exist then create it. These are the folder where the images, data and reports for each TIC ID will be stored.
newpath = '{}/{}'.format(indir,tic)
if not exists(newpath):
os.makedirs(newpath)
# save the data used as a text file - these often come in use later for a quick re-analysis.
with open('{}/{}/{}_data.txt'.format(indir, tic, tic), "w") as f:
# get rid of nan values first using a mask
good_mask = np.isfinite(np.array(alltime)) * np.isfinite(np.array(allflux)) * np.isfinite(np.array(allflux_err))
alltime_ar = np.array(alltime)[good_mask]
allflux_ar = np.array(allflux)[good_mask]
allflux_err_ar = np.array(allflux_err)[good_mask]
# save
writer = csv.writer(f, delimiter='\t')
writer.writerow(['time', 'flux', 'flux_err'])
writer.writerows(zip(alltime_ar,allflux_ar,allflux_err_ar))
'''
if the modelling option was also chose, save another data file with slightly different formatting to be called by Pyaneti.
Pyaneti requires a very specific data format.
Furhermore, in order for Pyaneti to run more efficiently (it has a Bayesian backend which scales with number of data points)
we create a cutout of the times around the times of the marked transit events.
'''
if len(transit_list) != 0:
if model == True:
with open('{}/{}/{}_data_pyaneti.dat'.format(indir, tic, tic), "w") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerow(['#time', 'flux', 'flux_err'])
# If the dip separations are too small, then don't create cut outs and save the whole dataset
if (len(transit_list) > 1) and ((transit_list[1] - transit_list[0]) < 2): # if there are LOTS of transit events on short period (if so it's probably a TOI but let's keep it here as a condition)
with open('{}/{}/{}_data_pyaneti.dat'.format(indir, tic, tic), "a") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(zip(alltime_ar,allflux_ar,allflux_err_ar)) # save all the data
# else create a cut out of the data around the time of the transit events
else:
for transit in transit_list:
# save the data
# get rid of nan values first - this is used for the pyaneti code
pyaneti_mask = (alltime_ar > (transit - 1)) * (alltime_ar < (transit + 1))
with open('{}/{}/{}_data_pyaneti.dat'.format(indir, tic, tic), "a") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(zip(alltime_ar[pyaneti_mask],allflux_ar[pyaneti_mask],allflux_err_ar[pyaneti_mask]))
# -----------------------------------
# START PLOTTING - calls functions from LATTEutils.py
# -----------------------------------
if len(transit_list) != 0: # this is always the case unless the asteroseismic only option ws chosen
# create a plot of the fulllighcurves with the momentum dumps (MDs) marked and a zoom-in of the marked transits
# this plit is saved but not shown (as already shown in the interact part fo the code)
utils.plot_full_md(tic, indir, alltime,allflux,all_md,alltimebinned,allfluxbinned, transit_list, upper_axis_lim_final, lower_axis_lim_final, args)
# Get a list of the sectors that have transit marked in them
# this is so that we no longer have to loop through all of the sectors, and can focus on the ones which are important.
transit_sec = utils.transit_sec(in_sec, start_sec, end_sec, transit_list)
# -----------
# plot how the centroids moved during the transit event
utils.plot_centroid(tic, indir,alltime12, allx1, ally1, allx2, ally2, transit_list, args)
# plot the background flux at the time of the transit event.
utils.plot_background(tic, indir,alltime, allfbkg, transit_list, args)
print ("Centroid and background plots... done.")
# -----------
# if the 'simple' option is chosen in the GUI, then the code will end here - this is designed to provide a quick analysis requiring no TPFs.
if simple == True:
print ("Simple option was selected, therefore end analysis here.")
sys.exit('')
# -----------
# call function to extract the Target Pixel File information
# this is needed in order to extract the LCs in different aperture sizes.
# the data is extracted using the open source Lightkurve package as they a built in function to extract LCs using different aperture sizes
#TESS_unbinned_t_l, TESS_binned_t_l, small_binned_t_l, TESS_unbinned_l, TESS_binned_l, small_binned_l, tpf_list = utils.download_tpf_lightkurve(indir, transit_list, sectors, tic)
print ("\n Start downloading of the target pixel files - this can take a little while (up to a minute) as the files are large \n")
X1_list, X4_list, oot_list, intr_list, bkg_list, apmask_list, arrshape_list, t_list, T0_list, tpf_filt_list,TESS_unbinned_t_l, TESS_binned_t_l, small_binned_t_l, TESS_unbinned_l, TESS_binned_l, small_binned_l, tpf_list = utils.download_tpf(indir, transit_sec, transit_list, tic, url_list)
# if the TPF wasn't corrupt then make the TPF files (only very ocassionally corrupt but don't want code to crash if it is corrrupt)
if (TESS_unbinned_t_l[0] != -111):
tpf_corrupt = False
# plot the LCs using two different aperture sizes.
utils.plot_aperturesize(tic,indir,TESS_unbinned_t_l, TESS_binned_t_l, small_binned_t_l, TESS_unbinned_l, TESS_binned_l, small_binned_l, transit_list, args)
print ("Aperture size plots... done.")
# ------------
'''
Plot the average pixel brightness of the cut-out around the target star and the corresponding SDSS field of view.
Both are oriented so that North is pointing upwards.
The former also shows the nearby stars with TESS magnitude brighter than 17. Queried from GAIA using astroquery.
The function returns the mass of the star (also output from astroquery)- this is a useful input for the Pyaneti modelling
'''
if args.mpi == False:
test_astroquery, _, _, mstar, vmag, logg, plx, c_id = utils.plot_TESS_stars(tic,indir, transit_sec, tpf_list, args)
if test_astroquery == -111:
tessmag, teff, srad, mstar, vmag, logg, plx, c_id = utils.plot_TESS_stars_not_proj(tic,indir, transit_list, transit_sec, tpf_list, args)
args.mpi = True
else:
test_astroquery, _, _, mstar, vmag, logg, plx, c_id = utils.plot_TESS_stars_not_proj(tic,indir, transit_list, transit_sec, tpf_list, args)
# keep track of whether astroquery is working (sometimes the site is down and we don't want this to stop us from running the code)
astroquery_corrupt = False
if test_astroquery == -999:
astroquery_corrupt = True
print ("Star Aperture plots... failed.")
else:
print ("Star Aperture plots... done.")
# ------------
# Download the Target Pixel File using the raw MAST data - this comes in a different format as the TPFs extracted using Lightkurve
# This data is then corrected using Principal Component Analysis is orderto get rid of systematics.
#X1_list, X4_list, oot_list, intr_list, bkg_list, apmask_list, arrshape_list, t_list, T0_list, tpf_filt_list = utils.download_tpf_mast(indir, transit_sec, transit_list, tic)
# ------------
'''
plot the in and out of transit flux comparison.
By default the images are NOT oriented north - this is because the reprojection takes longer to run and for a simple
analysis to check whether the brightest pixel moves during the transit this is not required.
The orientation towards north can be defined in the command line with '--north'.
'''
if args.north == True:
utils.plot_in_out_TPF_proj(tic, indir, X4_list, oot_list, t_list, intr_list, T0_list, tpf_filt_list, tpf_list, args)
print ("In and out of aperture flux comparison with reprojection... done. ")
else:
utils.plot_in_out_TPF(tic, indir, X4_list, oot_list, t_list, intr_list, T0_list, tpf_filt_list, args)
print ("In and out of aperture flux comparison... done.")
# ------------
# For each pixel in the TPF, extract and plot a lightcurve around the time of the marked transit event.
utils.plot_pixel_level_LC(tic, indir, X1_list, X4_list, oot_list, intr_list, bkg_list, tpf_list, apmask_list, arrshape_list, t_list, T0_list, args)
print ("Pixel level LCs plot... done.")
# ------------
else:
tpf_corrupt = True
mstar = 1 # need to define mstar otherwise pyaneti will complain - just make it one as an approximation.
tessmag = np.nan
teff = np.nan
srad = np.nan
vmag = np.nan
logg = np.nan
plx = np.nan
c_id = np.nan
astroquery_corrupt = True
# ------------
# end of plots that require target pixel files
# ------------
# If more than one transit has been marked by the user, the LC is phase folded based on the period of the separation of the first two maarked peaks.
# These plots are saved but do not feature in the DV report.
if len (transit_list) > 1: # needs to know a period so can only do this if more than one transit has been marked.
period = transit_list[1] - transit_list[0]
t0 = transit_list[0] # time of the first marking
# calculate the phase
phased = np.array([-0.5+( ( t - t0-0.5*period) % period) / period for t in alltimebinned])
fig, ax = plt.subplots(figsize=(5.55,5))
ax.plot(phased, allfluxbinned, marker='.',color = 'k', alpha = 1, lw = 0, markersize = 4, label = 'None', markerfacecolor='k')
#ax.plot(phased, allflux,marker='o',color = 'navy', alpha = 0.7, lw = 0, markersize = 2, label = 'binning = 7', markerfacecolor='white')
plt.title("Phase folded LC")
ax.set_xlabel("Phase (days)")
ax.set_ylabel("Normalized Flux")
plt.plot()
if save == True:
plt.savefig('{}/{}/{}_phase_folded.png'.format(indir, tic, tic), format='png')
if args.noshow == False:
plt.show()
print ("Phase folded plot... done.")
else:
print ("\n Only one transit marked - therefore can't be phase folded. \n")
# ------------
'''
Plot LCs of the six closest TESS target stars. This allows us to check whether the transit-like events
also appear in other nearby LCs which would be a sign that this is caused by a background event.
'''
# get the tic IDs of the six nearest stars
if args.FFI == False:
ticids, distance, target_ra, target_dec = utils.nn_ticids(indir, transit_sec, tic)
# download the data for these stars
alltime_nn, allflux_nn, all_md_nn, alltimebinned_nn, allfluxbinned_nn,outtics,tessmag_list, distance = utils.download_data_neighbours(indir, transit_sec[0], ticids, distance)
# plot the LCs
utils.plot_nn(tic, indir,alltime_nn, allflux_nn, alltimebinned_nn, allfluxbinned_nn, transit_list, outtics, tessmag_list, distance, args)
else:
target_ra = ra
target_dec = dec
distance = None
print ("Nearest neighbour plot... done.")
# ------------
# if the BLS option is chose, a BLS search is run. The LCs are first detrended and smoothed using a moving average.
# The corrected and uncorrected LCs are saves as a single plot for comparison and to verify that the correction worked well - saved but do not feature in the DV report.
if BLS == True:
print ("Running BLS algorithm...", end =" ")
bls_stats1, bls_stats2 = utils.data_bls(tic, indir, alltime, allflux, allfluxbinned, alltimebinned, args)
print ("done.")
else:
from astroquery.mast import Catalogs
#plot the main LC with only one panel and no transit events marked
utils.plot_full_md_notransits(tic, indir, alltime, allflux, all_md, alltimebinned, allfluxbinned, upper_axis_lim_final, lower_axis_lim_final, args)
target_ra = ra
target_dec = dec
tpf_corrupt = False
# get the star information that would otherwise come from the plot_TESS_stars function
starName = "TIC " + str(tic)
radSearch = 5/60 #radius in degrees
# this function depends on astroquery working, and sometimes it doesn't.
# for when it doesn't work (or simply can't connect to it), just skip plotting the other TESS stars.
try:
astroquery_corrupt = False
catalogData = Catalogs.query_object(starName, radius = radSearch, catalog = "TIC")
except:
astroquery_corrupt = True
print ("Currently cannot connect to Astroquery.")
# return values that we know aren't real so that we can tell the code that the plotting didn't work
return -999, -999, -999, 1, -999,-999,-999,-999
# ra and dec of the target star
ra = catalogData[0]['ra']
dec = catalogData[0]['dec']
# while we have the astroquery loaded, let's collect some other information about the star
# these paramaters can help us find out what type of star we have with just a glance
vmag = catalogData['Vmag'][0] # v magnitude (this migth be more useful than the TESS mag for things such as osbevring)
logg = catalogData['logg'][0] # logg of the star
mstar = catalogData['mass'][0] # mass of the star
plx = catalogData['plx'][0] # parallax
# sometimes these values aren't accessible through astroquery - so we shoudl just quickly check.
if not np.isfinite(vmag): vmag = '--' # this is what will appear in the table of the report to indicate that it's unknown
if not np.isfinite(logg): logg = '--'
if not np.isfinite(mstar): mass = '--'
if not np.isfinite(plx): plx = '--'
# sometimes it's useufl to know if the star has another name
# check whether it was osberved by one of these four large surveys
catalogs = ['HIP', 'TYC', 'TWOMASS', 'GAIA']
for cat in catalogs:
c_id = str(catalogData[0][cat])
if c_id != '--':
cat_id = "{} {}".format(cat,c_id)
break
else:
continue
tessmag = catalogData['Tmag'][0]
teff = catalogData['Teff'][0]
srad = catalogData['rad'][0]
c_id = c_id
# ------------
# period analysis
# always make a peridoogram? QUestion for later.
print ("Periodogram plot...", end =" ")
mass_ast, radius_ast, logg_ast, numax, deltanu = utils.plot_periodogram(tic, indir, alltime, allflux, teff, input_numax, input_analysis_window, args)
print ("done.")
# ------------
# stellar evolutionary tracks
print ("Evolutionary tracks plot...", end =" ")
utils.eep_target(tic, indir, syspath, teff, srad, args)
print ("done.")
# ------------
# SKIP FROM HERE....
'''
NOTE: CURRENTLY ONLY WORKS ON NORA'S COMPUTER - WILL BE AVAILABLE IN NEXT RELEASE SO PLEASE SKIP THIS PART OF THE CODE
If the modelling option is selected (in the GUI), model the transit event using Pyaneti (Barragan et al 2018)
which uses an Bayesian approach with an MCMC sampling to best fit and model the transit.
The code runs slightly differently depending on whether one or multiple transits have been marked.
This is because with multiple transits the code has information about the possible orbital period.
Need to ensure that the code has compiled correctly on the users computer.
Reason why is doesn't work else where: the priors need to be set up ver carefully, and this has not been tested enough to know
it can be automated to work reliably. Also, this code requires a fortran backend, which has not yet been included in LATTE.
---> we're working on implementing this as it will be very useful.
'''
# First check if Pyaneti is installed...
if os.path.exists("{}/pyaneti_LATTE.py".format(syspath)):
if model == True:
print ("Running Pyaneti modelling - this could take a while so be patient...")
transit_list_model = ("{}".format(str(np.asarray(transit_list)))[1:-1]) # change the list into a string and get rid of the brackets
# the code is usually run through the command line so call it using the os.system function.
os.system("python3 {}/pyaneti_LATTE.py {} {} {} {} {} {} {}".format(syspath, tic, indir, syspath, mstar, teff, srad, transit_list_model))
else:
#print ("Pyaneti has not been installed so you can't model anything yet. Contact Nora or Oscar for the LATTE version of the Pyaneti code.")
model = False
# ... UNTIL HERE
# ------------
# Finally, create a DV report which summarises all of the plots and tables.
if DV == True:
from LATTE import LATTE_DV as ldv
if BLS == True:
ldv.LATTE_DV(tic, indir, syspath, transit_list, sectors_all, target_ra, target_dec, tessmag, teff, srad, mstar, vmag, logg, mass_ast, radius_ast, logg_ast, numax, deltanu, plx, c_id, bls_stats1, bls_stats2, tpf_corrupt, astroquery_corrupt, FFI = args.FFI, bls = True, model = model, mpi = args.mpi)
else:
ldv.LATTE_DV(tic, indir, syspath, transit_list, sectors_all, target_ra, target_dec, tessmag, teff, srad, mstar, vmag, logg, mass_ast, radius_ast, logg_ast, numax, deltanu, plx, c_id, [0], [0], tpf_corrupt, astroquery_corrupt, FFI = args.FFI, bls = False, model = model, mpi = args.mpi)