def test_checkplot_twolsp_png():
'''
Tests if a two-LSP checkplot PNG can be made.
'''
outpath = os.path.join(os.path.dirname(LCPATH),
'test-twolsp-checkplot.png')
lcd, msg = hatlc.read_and_filter_sqlitecurve(LCPATH)
gls = periodbase.pgen_lsp(lcd['rjd'], lcd['aep_000'], lcd['aie_000'])
assert isinstance(gls, dict)
assert_allclose(gls['bestperiod'], 1.54289477)
pdm = periodbase.stellingwerf_pdm(lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'])
assert isinstance(pdm, dict)
assert_allclose(pdm['bestperiod'], 3.08578956)
cpf = checkplot.twolsp_checkplot_png(
gls, pdm,
lcd['rjd'], lcd['aep_000'], lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo']
)
assert os.path.exists(outpath)
def run_asas_periodograms(times,
mags,
errs,
outdir='../results/ASAS_lightcurves/',
outname='WASP-18b_BLS_GLS.png'):
blsdict = kbls.bls_parallel_pfind(times,
mags,
errs,
magsarefluxes=False,
startp=0.5,
endp=1.5,
maxtransitduration=0.3,
nworkers=8,
sigclip=[15, 3])
gls = periodbase.pgen_lsp(times,
mags,
errs,
magsarefluxes=False,
startp=0.5,
endp=1.5,
nworkers=8,
sigclip=[15, 3])
outpath = outdir + outname
cpf = checkplot.twolsp_checkplot_png(blsdict,
gls,
times,
mags,
errs,
outfile=outpath,
objectinfo=None)
def do_period_finding_fitslc(lcpath,
fluxap='TFA2',
period_min=0.5,
outdir=None):
if not outdir:
outdir = os.path.dirname(lcpath)
outfile = os.path.basename(lcpath).replace('.fits',
'_spdm_blsp_checkplot.png')
outpath = os.path.join(outdir, outfile)
if os.path.exists(outpath):
print('found & skipped {}'.format(outpath))
return
hdulist = fits.open(lcpath)
hdr, lc = hdulist[0].header, hdulist[1].data
times, mags, errs = (lc['TMID_BJD'], lc[fluxap], np.ones_like(lc[fluxap]) *
np.nanmedian(lc[fluxap]) / 1000)
#glsp = periodbase.pgen_lsp(times,mags,errs)
spdm = periodbase.stellingwerf_pdm(times, mags, errs)
blsp = periodbase.bls_parallel_pfind(times,
mags,
errs,
startp=period_min,
get_stats=False)
objectinfo = {}
keys = ['objectid', 'ra', 'decl', 'pmra', 'pmdecl', 'teff', 'gmag']
hdrkeys = [
'Gaia-ID', 'RA[deg]', 'Dec[deg]', 'PM_RA[mas/yr]', 'PM_Dec[mas/year]',
'teff_val', 'phot_g_mean_mag'
]
for k, hk in zip(keys, hdrkeys):
if hk in hdr:
objectinfo[k] = hdr[hk]
else:
objectinfo[k] = np.nan
cp = checkplot.twolsp_checkplot_png(blsp,
spdm,
times,
mags,
errs,
objectinfo=objectinfo,
outfile=outpath,
sigclip=[50., 5.],
plotdpi=100,
phasebin=3e-2,
phasems=6.0,
phasebinms=12.0,
unphasedms=6.0)
print('did {}'.format(outpath))
def allvar_periodogram_checkplot(a):
ap = int(a['ap'])
time, flux, err = a['STIME'], a[f'SPCA{ap}'], a[f'SPCAE{ap}']
spdm = periodbase.stellingwerf_pdm(time,
flux,
err,
magsarefluxes=True,
startp=0.05,
endp=30,
sigclip=5.0,
nworkers=nworkers)
lsp = periodbase.pgen_lsp(time,
flux,
err,
magsarefluxes=True,
startp=0.05,
endp=30,
autofreq=True,
sigclip=5.0,
nworkers=nworkers)
objectinfo = {}
keys = ['objectid', 'ra', 'decl', 'pmra', 'pmdecl', 'teff', 'gmag']
hdrkeys = [
'Gaia-ID', 'RA_OBJ', 'DEC_OBJ', 'PM_RA[mas/yr]', 'PM_Dec[mas/year]',
'teff_val', 'phot_g_mean_mag'
]
hdr = a['dtr_infos'][0][0]
for k, hk in zip(keys, hdrkeys):
if hk in hdr:
objectinfo[k] = hdr[hk]
else:
objectinfo[k] = np.nan
import matplotlib as mpl
mpl.rcParams['axes.titlesize'] = 'xx-large'
mpl.rcParams['axes.labelsize'] = 'xx-large'
fig = checkplot.twolsp_checkplot_png(lsp,
spdm,
time,
flux,
err,
magsarefluxes=True,
objectinfo=objectinfo,
varepoch='min',
sigclip=None,
plotdpi=100,
phasebin=3e-2,
phasems=6.0,
phasebinms=14.0,
unphasedms=6.0,
figsize=(30, 24),
returnfigure=True,
circleoverlay=APSIZEDICT[ap] * 21,
yticksize=20,
trimylim=True)
axs = fig.get_axes()
for ax in axs:
ax.get_yaxis().set_tick_params(which='both',
direction='in',
labelsize='xx-large')
ax.get_xaxis().set_tick_params(which='both',
direction='in',
labelsize='xx-large')
return fig, lsp, spdm, objectinfo
def test_twolsp_checkplot_png_varepoch_handling(capsys):
'''This tests the various different ways to give varepoch
to twolsp_checkplot_png.
Uses the py.test capsys fixture to capture stdout and see if we reported the
correct epoch being used.
'''
outpath = os.path.join(os.path.dirname(LCPATH),
'test-checkplot.png')
lcd, msg = hatlc.read_and_filter_sqlitecurve(LCPATH)
gls = periodbase.pgen_lsp(lcd['rjd'], lcd['aep_000'], lcd['aie_000'])
pdm = periodbase.stellingwerf_pdm(lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'])
assert isinstance(gls, dict)
assert_allclose(gls['bestperiod'], 1.54289477)
# 1. usual handling where epoch is None
# should use min(times) as epoch all the time
cpf = checkplot.twolsp_checkplot_png(
gls, pdm,
lcd['rjd'], lcd['aep_000'], lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo'],
varepoch=None
)
assert os.path.exists(outpath)
# capture the stdout
captured = capsys.readouterr()
# see if we used the correct periods and epochs
splitout = captured.out.split('\n')
# plot output lines
plotoutlines = [x for x in splitout if 'plotting phased LC' in x]
# these are the periods and epochs to match per line
lookfor = ['period 1.542895, epoch 56092.64056',
'period 0.771304, epoch 56092.64056',
'period 0.514234, epoch 56092.64056',
'period 3.085790, epoch 56092.64056',
'period 1.542895, epoch 56092.64056',
'period 6.157824, epoch 56092.64056']
for expected, plotline in zip(lookfor,plotoutlines):
assert expected in plotline
# 2. handle varepoch = 'min'
cpf = checkplot.twolsp_checkplot_png(
gls, pdm,
lcd['rjd'], lcd['aep_000'], lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo'],
varepoch='min'
)
assert os.path.exists(outpath)
# capture the stdout
captured = capsys.readouterr()
# see if we used the correct periods and epochs
splitout = captured.out.split('\n')
# plot output lines
plotoutlines = [x for x in splitout if 'plotting phased LC' in x]
# these are the periods and epochs to match per line
lookfor = ['period 1.542895, epoch 56341.11968',
'period 0.771304, epoch 56856.46618',
'period 0.514234, epoch 56889.88470',
'period 3.085790, epoch 56828.62835',
'period 1.542895, epoch 56341.11968',
'period 6.157824, epoch 56154.33367']
for expected, plotline in zip(lookfor,plotoutlines):
assert expected in plotline
# 3. handle varepoch = some float
cpf = checkplot.twolsp_checkplot_png(
gls, pdm,
lcd['rjd'], lcd['aep_000'], lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo'],
varepoch=56000.5
)
assert os.path.exists(outpath)
# capture the stdout
captured = capsys.readouterr()
# see if we used the correct periods and epochs
splitout = captured.out.split('\n')
# plot output lines
plotoutlines = [x for x in splitout if 'plotting phased LC' in x]
# these are the periods and epochs to match per line
lookfor = ['period 1.542895, epoch 56000.50000',
'period 0.771304, epoch 56000.50000',
'period 0.514234, epoch 56000.50000',
'period 3.085790, epoch 56000.50000',
'period 1.542895, epoch 56000.50000',
'period 6.157824, epoch 56000.50000']
for expected, plotline in zip(lookfor,plotoutlines):
assert expected in plotline
# 4. handle varepoch = list of floats
cpf = checkplot.twolsp_checkplot_png(
gls, pdm,
lcd['rjd'], lcd['aep_000'], lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo'],
varepoch=[[56000.1,56000.2,56000.3],
[56000.4,56000.5,56000.6]]
)
assert os.path.exists(outpath)
# capture the stdout
captured = capsys.readouterr()
# see if we used the correct periods and epochs
splitout = captured.out.split('\n')
# plot output lines
plotoutlines = [x for x in splitout if 'plotting phased LC' in x]
# these are the periods and epochs to match per line
lookfor = ['period 1.542895, epoch 56000.10000',
'period 0.771304, epoch 56000.20000',
'period 0.514234, epoch 56000.30000',
'period 3.085790, epoch 56000.40000',
'period 1.542895, epoch 56000.50000',
'period 6.157824, epoch 56000.60000']
for expected, plotline in zip(lookfor,plotoutlines):
assert expected in plotline
def _periodicity_analysis(hatid,
times,
mags,
errs,
normlcd,
varperiod=None,
isresidual=False):
'''
Given times, magnitudes, and errors, run Phase Dispersion Minimization and
Box Least Squares, then write the phase-folded LCs, periodograms, and
header data to a checkplot pickle.
Typically, in 03_EB_processing.py, mags will be (data+injected planet), or
(data + injected planet - EB model).
Args:
hatid (str): HAT-XXX-XXXXXXX
times (np.array): times of measurement (typically RJD)
mags (np.array): measured magnitudes at observing times
errs (np.array): measured error on mags
normlcd: lightcurve dictionary from early reading / astrobase parsing
Keyword Args:
varperiod (float, default None): EB period, used to set upper and lower
bounds of frequency search.
isresidual (bool, default False): if `mags` is from residuals, use
this. It will do a denser frequency search, at >~2x the EB period.
Returns:
nothing
'''
#TODO: fix args, add docs
if not varperiod:
#injection has happened
smallest_p = 0.5
cpwritepre = '../data/detachedEBs/injs/checkplot-inj-'
elif isinstance(varperiod, float) and isresidual:
smallest_p = varperiod * 2. + 0.1
cpwritepre = '../data/detachedEBs/injres/checkplot-injresiduals-'
else:
raise ValueError('smallest_p never properly assigned')
biggest_p = min((times[-1] - times[0]) / 2.01, 100.)
print('\nStellingwerf...\n')
spdmp = periodbase.stellingwerf_pdm(
times,
mags,
errs,
autofreq=True,
startp=smallest_p,
endp=biggest_p,
normalize=False,
stepsize=1.0e-5,
phasebinsize=0.03,
mindetperbin=9,
nbestpeaks=5,
periodepsilon=0.1, # 0.1days
sigclip=None, # no sigma clipping
nworkers=None)
varinfo = {
'objectisvar': True,
'vartags': None,
'varisperiodic': True,
'varperiod': spdmp['bestperiod'],
'varepoch': None
}
print('\nBLS...\n')
blsp = periodbase.bls_parallel_pfind(times,
mags,
errs,
startp=smallest_p,
endp=biggest_p,
stepsize=1.0e-5,
mintransitduration=0.01,
maxtransitduration=0.6,
nphasebins=200,
autofreq=False,
nbestpeaks=5,
periodepsilon=0.1,
nworkers=None,
sigclip=None)
lspinfolist = [spdmp, blsp]
cp = checkplot.checkplot_pickle(lspinfolist,
times,
mags,
errs,
nperiodstouse=3,
objectinfo=normlcd['objectinfo'],
varinfo=varinfo,
findercmap='gray_r',
normto='globalmedian',
normmingap=4.,
outfile=cpwritepre + hatid + '.pkl.gz',
sigclip=[10., -3.],
varepoch='min',
phasewrap=True,
phasesort=True,
phasebin=0.002,
plotxlim=[-0.6, 0.6],
plotdpi=150,
returndict=False,
pickleprotocol=3,
greenhighlight=False,
xgridlines=[-0.5, 0., 0.5])
#since we don't have easy checkplot_pickle to png written yet, use twolsp_checkplot_png
#TODO: remove any png writing once happy with output
if not varperiod:
injpath = '../data/detachedEBs/plots/3_checkplot-inj-'
elif isinstance(varperiod, float) and isresidual:
injpath = '../data/detachedEBs/plots/5_checkplot-injresiduals-'
else:
raise ValueError('injpath never properly assigned')
checkplot.twolsp_checkplot_png(spdmp,
blsp,
times,
mags,
errs,
objectinfo=normlcd['objectinfo'],
findercmap='gray_r',
normto='globalmedian',
normmingap=4.,
outfile=injpath + hatid + '.png',
sigclip=[10., -3.],
varepoch='min',
phasewrap=True,
phasesort=True,
phasebin=0.002,
plotxlim=[-0.6, 0.6],
plotdpi=150)
plt.close('all')
