def test_checkplot_pickle_update():
'''
Tests if a checkplot pickle can be made, read back, and updated.
'''
outpath = os.path.join(os.path.dirname(LCPATH),
'test-checkplot.pkl')
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)
# test write
cpf = checkplot.checkplot_pickle(
[gls, pdm],
lcd['rjd'], lcd['aep_000'], lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo']
)
assert os.path.exists(outpath)
# test read back
cpd = checkplot._read_checkplot_picklefile(cpf)
assert isinstance(cpd, dict)
cpdkeys = set(list(cpd.keys()))
testset = {'0-gls', '1-pdm', 'comments', 'externalplots',
'finderchart', 'magseries', 'neighbors', 'normmingap',
'normto', 'objectid', 'objectinfo', 'pfmethods', 'sigclip',
'signals', 'status', 'varinfo'}
assert (testset - cpdkeys) == set()
assert_allclose(cpd['0-gls']['bestperiod'], 1.54289477)
assert_allclose(cpd['1-pdm']['bestperiod'], 3.08578956)
# test update write to pickle
cpd['comments'] = ('this is a test of the checkplot pickle '
'update mechanism. this is only a test.')
cpfupdated = checkplot.checkplot_pickle_update(cpf, cpd)
cpdupdated = checkplot._read_checkplot_picklefile(cpfupdated)
assert cpdupdated['comments'] == cpd['comments']
def test_checkplot_with_multiple_same_pfmethods():
'''
This tests running the same period-finder for different period ranges.
'''
outpath = os.path.join(os.path.dirname(LCPATH), 'test-checkplot.pkl')
lcd, msg = hatlc.read_and_filter_sqlitecurve(LCPATH)
gls_1 = periodbase.pgen_lsp(lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'],
startp=0.01,
endp=0.1)
gls_2 = periodbase.pgen_lsp(lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'],
startp=0.1,
endp=300.0)
pdm_1 = periodbase.stellingwerf_pdm(lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'],
startp=0.01,
endp=0.1)
pdm_2 = periodbase.stellingwerf_pdm(lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'],
startp=0.1,
endp=300.0)
assert isinstance(gls_1, dict)
assert isinstance(gls_2, dict)
assert isinstance(pdm_1, dict)
assert isinstance(pdm_2, dict)
cpf = checkplot.checkplot_pickle([gls_1, gls_2, pdm_1, pdm_2],
lcd['rjd'],
lcd['aep_000'],
lcd['aie_000'],
outfile=outpath,
objectinfo=lcd['objectinfo'])
assert os.path.exists(cpf)
assert os.path.abspath(cpf) == os.path.abspath(outpath)
cpd = checkplot._read_checkplot_picklefile(cpf)
pfmethods = list(cpd['pfmethods'])
assert len(pfmethods) == 4
assert '0-gls' in pfmethods
assert '1-gls' in pfmethods
assert '2-pdm' in pfmethods
assert '3-pdm' in pfmethods
def key_worker(task):
'''
This gets the required keys from the requested file.
'''
cpf, keys = task
cpd = _read_checkplot_picklefile(cpf)
resultkeys = []
for k in keys:
try:
resultkeys.append(dict_get(cpd, k))
except:
resultkeys.append(np.nan)
return resultkeys
def _load_cp_picklefile(hatid):
'''
For the HAT LC corresponding to above sqlitecurve, read in the results from
periodicty analysis of 02_get_EB_checkplots.py.
'''
#TODO: fix varperiod/varepoch to actually be read for general case
## Loading steps to get checkplot dictionary. N.b. this is _already normalized_ mags
detachedEBpath = '../data/detachedEBs/checkplots/checkplot-'
ptail = '.pkl.gz'
cpd = checkplot._read_checkplot_picklefile(detachedEBpath + hatid + ptail)
## checkplot dictionary should have varperiod and varepoch
cpd['varinfo']['varperiod'] = 2.101282
cpd['varinfo']['varepoch'] = 53956.94080
# Results from EB periodicity analysis:
varperiod = cpd['varinfo']['varperiod']
varepoch = cpd['varinfo']['varepoch']
return cpd, varperiod, varepoch
def _subtract_median_EB_signal(hatid, varperiod, varepoch):
'''
Once injected planet periodograms have been run, get the median EB
signal and subtract it to get residuals.
Args:
hatid (str): HAT-XXX-XXXXXXX
varperiod (float): best EB period from PDM in periodicity analysis
varepoch (float): epoch of folded LC
Returns:
residualmags (np array): data + injection - median model
'''
#TODO Propagate errors properly.
#confirm EB period.
injEBpath = '../data/detachedEBs/injs/checkplot-inj-'
ptail = '.pkl.gz'
cpd = checkplot._read_checkplot_picklefile(injEBpath + hatid + ptail)
#unphased LC times, mags, errs
times = cpd['magseries']['times']
mags = cpd['magseries']['mags']
errs = cpd['magseries']['errs']
#phased data, then binned data (blue points)
phase, phasedmags = cpd['pdm'][0]['phase'], cpd['pdm'][0]['phasedmags']
binphase, binphasedmags = cpd['pdm'][0]['binphase'], cpd['pdm'][0][
'binphasedmags']
twophasetimes = varepoch + (
binphase * varperiod
) #times over two phases (stored in binphase originally)
#now extend twophasetimes to beginning and end of baseline
modeltimes = np.array(twophasetimes)
modelmags = binphasedmags
exitf, upf, downf = False, True, True
diff = modeltimes[-1] - modeltimes[0]
while not exitf:
while upf:
onetimeup = np.array(modeltimes[-1] - modeltimes[0] +
twophasetimes)[1:]
onemagup = np.array(binphasedmags[1:])
#oneerrup = np.array() #obnoxiously, these aren't kept thru the phase curve-making. should be needed to propagate uncertainties
modeltimes = np.append(modeltimes, onetimeup)
modelmags = np.append(modelmags, onemagup)
if max(modeltimes) > max(times):
upf = False
k = 1
while downf:
#careful of lengths
onetimedown = np.array(-k * diff + twophasetimes)[:-1]
onemagdown = np.array(binphasedmags[:-1])
#oneerrup = np.array() #not kept thru phase curve making
modeltimes = np.insert(modeltimes, 0, onetimedown)
modelmags = np.insert(modelmags, 0, onemagdown)
k += 1
if min(modeltimes) < min(times):
downf = False
exitf = True
f = interp1d(modeltimes, modelmags, kind='linear')
interpmodelmags = f(times)
residualmags = mags - interpmodelmags # n.b. these mags are the injected ones
#save figure showing model and residuals
plt.close('all')
f, axs = plt.subplots(3, sharex=True, figsize=(15, 8 * 1.5))
axs[0].scatter(times, mags, lw=0, c='black', s=10)
axs[0].set(ylabel='DEB data (w/ planet injected)')
axs[1].scatter(times, interpmodelmags, lw=0, c='black', s=10)
axs[1].set(
ylabel='median model\n(medians from binned phase-folded DEB LC)')
axs[2].scatter(times, residualmags, lw=0, c='black', s=10)
axs[2].set(ylabel='data - model')
f.tight_layout()
f.savefig(
'../data/detachedEBs/plots/4_median_EB_modelLC_and_residuals.png',
dpi=150)
plt.close('all')
print('\nMade figure of median EB model and residuals...\n')
return times, residualmags, errs