def periodograms(id, x, y, yerr, path, plot=False, savepgram=False):
"""
takes id of the star, returns an array of period measurements and saves the
results.
id: star id.
x, y, yerr: time, flux and error arrays.
path: path where you want to save the output.
"""
ps = np.linspace(2, 100, 1000)
model = LombScargle().fit(x, y, yerr)
pgram = model.periodogram(ps)
# find peaks
peaks = np.array([i for i in range(1, len(ps)-1) if pgram[i-1] <
pgram[i] and pgram[i+1] < pgram[i]])
if len(peaks):
period = ps[pgram==max(pgram[peaks])][0]
else: period = 0
if plot:
plt.clf()
plt.plot(ps, pgram)
plt.axvline(period, color="r")
plt.savefig("{0}/{1}_pgram".format(path, str(int(id)).zfill(4)))
if savepgram:
np.savetxt("{0}/{1}_pgram.txt".format(path, str(int(id)).zfill(4)),
np.transpose((ps, pgram)))
np.savetxt("{0}/{1}_pgram_result.txt".format(path, str(int(id)).zfill(4)),
np.ones(2).T*period)
return period
def calc_p_init(x, y, yerr, id, RESULTS_DIR, which_period="acf"):
print("Calculating ACF")
acf_period, acf, lags = simple_acf(x, y)
err = acf_period * .1
print("acf period, err = ", acf_period, err)
print("Calculating periodogram")
ps = np.arange(.1, 100, .1)
model = LombScargle().fit(x, y, yerr)
pgram = model.periodogram(ps)
plt.clf()
plt.plot(ps, pgram)
plt.savefig(os.path.join(RESULTS_DIR, "{0}_pgram".format(id)))
print("saving figure ", os.path.join(RESULTS_DIR, "{0}_pgram".format(id)))
peaks = np.array([
i for i in range(1,
len(ps) - 1)
if pgram[i - 1] < pgram[i] and pgram[i + 1] < pgram[i]
])
pgram_period = ps[pgram == max(pgram[peaks])][0]
print("pgram period = ", pgram_period, "days")
if which_period == "acf":
p_init, perr = acf_period, err
elif which_period == "pgram":
p_init, perr = pgram_period, pgram_period * .1
else:
print("which_period must equal 'acf' or 'pgram'")
return p_init, perr
def pgram(N, years, fname):
ps = np.linspace(2, 100, 1000) # the period array (in days)
print("Computing periodograms")
# Now compute LS pgrams for a set of LSST light curves & save highest peak
ids = np.arange(N)
periods = np.zeros_like(ids)
for i, id in enumerate(ids):
sid = str(int(id)).zfill(4)
x, y, yerr = np.genfromtxt("simulations/{0}/{1}.txt".format(
fname, sid)).T
m = x < years * 365.25
xt, yt, yerrt = x[m], y[m], yerr[m][m]
model = LombScargle().fit(xt, yt, yerrt) # compute pgram
pgram = model.periodogram(ps)
# find peaks
peaks = np.array([
j for j in range(1,
len(ps) - 1)
if pgram[j - 1] < pgram[j] and pgram[j + 1] < pgram[j]
])
if len(peaks):
period = ps[pgram == max(pgram[peaks])][0]
else:
period = 0
periods[i] = period
np.savetxt(
"results/{0}/{1}_{2}yr_result.txt".format(fname, sid, years),
[period])
np.savetxt("{0}_{1}yr_results.txt".format(fname, years), periods.T)
return periods
def gen_recover_period(prange, ntimes, npers, randtimes = 0.0, rantnormp = 0.0, snrnoise = 0.0, normpropnoise = 0.0, samples = 100):
"""Generate some periodic data and then try to recover the maximum period under various conditions.
prange = period to analyse (kicking off with a single period) with lower bound and upper bound for search
ntimes number of times to generate signal for
npers number of periods (possibly fractional) to scan for
randtimes whether we vary the sample times 0.0 none, 1 probably max
rantnormp variation in times proortion between uniform (0.0) and gaussian (1.0)
snr signal to noise ration of noise to add 0 for None
normpropnoise proportion of noise uniform (0.0) or gaussian (1.0)
samples is the number of samples for the periodogram
Return fractional error"""
lbound, period, ubound = prange
timelist, amps = siggen.siggen(period, ntimes=ntimes, npers=npers, randv=randtimes, unorm=rantnormp, randphase=True)
if snrnoise > 0.0:
amps = noise.noise(amps, snrnoise, normpropnoise)
# OK now lets do our stuff
model = LombScargle().fit(timelist, amps, 0.001)
periods = np.linspace(lbound, ubound, samples)
pgram = model.periodogram(periods)
# Find maximum
maxes = argmaxmin.maxmaxes(periods, pgram)
if len(maxes) == 0:
return 1.0
maxp = periods[maxes[0]]
return abs(maxp - period) / period
def pgram(N, years, fname):
ps = np.linspace(2, 100, 1000) # the period array (in days)
print("Computing periodograms")
# Now compute LS pgrams for a set of LSST light curves & save highest peak
ids = np.arange(N)
periods = np.zeros_like(ids)
for i, id in enumerate(ids):
sid = str(int(id)).zfill(4)
x, y, yerr = np.genfromtxt("simulations/{0}/{1}.txt".format(fname,
sid)).T
m = x < years * 365.25
xt, yt, yerrt = x[m], y[m], yerr[m][m]
model = LombScargle().fit(xt, yt, yerrt) # compute pgram
pgram = model.periodogram(ps)
# find peaks
peaks = np.array([j for j in range(1, len(ps)-1) if pgram[j-1]
< pgram[j] and pgram[j+1] < pgram[j]])
if len(peaks):
period = ps[pgram == max(pgram[peaks])][0]
else:
period = 0
periods[i] = period
data = np.vstack((ids, periods))
f = open("results/{0}_{1}yr_results.txt".format(fname, years), "a")
np.savetxt(f, data.T)
f.close()
return periods
def vbg(campaign):
fnames = glob.glob("data/c%s/*lc.fits" % campaign)
for fname in fnames:
eid = fname[12:21]
print eid
if campaign == 1:
fname = "/Users/angusr/data/K2/c1lcsr4"
# load data
x, y, _ = np.genfromtxt("%s/ep%s.csv" % (fname, str(int(eid))),
delimiter=",").T
elif campaign == 0:
fname = "/Users/angusr/data/K2/c0corcutlcs"
# load data
x, y, _ = np.genfromtxt("%s/ep%scorcut.csv" %
(fname, str(int(eid))),
delimiter=",",
skip_header=1).T
y /= np.median(y)
y -= 1
x *= 24 * 3600 # convert to seconds
# load basis
with h5py.File("data/c1.h5", "r") as f:
basis = f["basis"][:150]
fs = np.arange(1, 300, 4e-2) * 1e-6
ps = 1. / fs
model = LombScargle().fit(x, y, np.ones_like(y) * 1e-5)
s2n = model.periodogram(ps)
# save pgram
plt.clf()
plt.plot(fs, s2n, "k")
plt.savefig("astero/%svbg_pgram" % eid)
np.savetxt("astero/%svbg_pgram.txt" % eid, np.transpose((fs, s2n)))
def calc_p_init(self, clobber=False):
"""
Calculate the ACF and periodogram periods for initialisation.
"""
fname = os.path.join(self.RESULTS_DIR,
"{0}_acf_pgram_results.txt".format(self.kid))
if not clobber and os.path.exists(fname):
print("Previous ACF pgram result found")
df = pd.read_csv(fname)
m = df.N.values == self.kid
acf_period = df.acf_period.values[m]
err = df.acf_period_err.values[m]
pgram_period = df.pgram_period.values[m]
pgram_period_err = df.pgram_period_err.values[m]
else:
print("Calculating ACF")
acf_period, acf, lags, rvar = sa.simple_acf(self.x, self.y)
err = .1 * acf_period
plt.clf()
plt.plot(lags, acf)
plt.axvline(acf_period, color="r")
plt.xlabel("Lags (days)")
plt.ylabel("ACF")
plt.savefig(
os.path.join(self.RESULTS_DIR, "{0}_acf".format(self.kid)))
print("saving figure ",
os.path.join(self.RESULTS_DIR, "{0}_acf".format(self.kid)))
print("Calculating periodogram")
ps = np.arange(.1, 100, .1)
model = LombScargle().fit(self.x, self.y, self.yerr)
pgram = model.periodogram(ps)
plt.clf()
plt.plot(ps, pgram)
plt.savefig(
os.path.join(self.RESULTS_DIR, "{0}_pgram".format(self.kid)))
print("saving figure ",
os.path.join(self.RESULTS_DIR, "{0}_pgram".format(self.kid)))
peaks = np.array([
i for i in range(1,
len(ps) - 1)
if pgram[i - 1] < pgram[i] and pgram[i + 1] < pgram[i]
])
pgram_period = ps[pgram == max(pgram[peaks])][0]
print("pgram period = ", pgram_period, "days")
pgram_period_err = pgram_period * .1
df = pd.DataFrame({
"N": [self.kid],
"acf_period": [acf_period],
"acf_period_err": [err],
"pgram_period": [pgram_period],
"pgram_period_err": [pgram_period_err]
})
df.to_csv(fname)
return acf_period, err, pgram_period, pgram_period_err
def plot_vbg(fname, eid):
# plot andrew's lc
x_vbg, y_vbg, _ = np.genfromtxt("data/ep%s.csv" % eid, delimiter=",").T
x_vbg *= 24 * 3600
y_vbg = y_vbg / np.median(y_vbg) - 1
model = LombScargle().fit(x_vbg, y_vbg, np.ones_like(y_vbg) * 1e-5)
period = 1. / fs
pgram = model.periodogram(period)
plt.clf()
plt.plot(fs, pgram, "k")
plt.xlabel("$\mathrm{Frequency~(}\mu \mathrm{Hz)}$")
plt.ylabel("$\mathrm{Power}$")
plt.savefig("astero/vbg_%spgram" % eid)
def pgram(t, y, dy, fname):
# t *= 24*3600 # convert to seconds
# fs = np.arange(50, 1000, .1)*1e-6 # Hz
# period = 1. / fs
# plt.plot(fs, power)
model = LombScargle().fit(t, y, dy)
period = np.linspace(.25/24, 5./24, 1000)
fs = 1./period
power = model.periodogram(period)
plt.clf()
plt.plot(period*24., power)
print "%s_pgram" % fname
plt.savefig("%s_pgram" % fname)
print period[power==max(power)]*24
def calc_p_init(x, y, yerr, id, RESULTS_DIR, clobber=True):
fname = os.path.join(RESULTS_DIR, "{0}_acf_pgram_results.txt".format(id))
if not clobber and os.path.exists(fname):
print("Previous ACF pgram result found")
df = pd.read_csv(fname)
m = df.N.values == id
acf_period = df.acf_period.values[m]
err = df.acf_period_err.values[m]
pgram_period = df.pgram_period.values[m]
pgram_period_err = df.pgram_period_err.values[m]
else:
print("Calculating ACF")
acf_period, acf, lags, rvar = sa.simple_acf(x, y)
err = .1 * acf_period
# acf_period, err, lags, acf = acf.corr_run(x, y, np.ones_like(y)*1e-5,
# id, RESULTS_DIR)
plt.clf()
plt.plot(lags, acf)
plt.axvline(acf_period, color="r")
plt.xlabel("Lags (days)")
plt.ylabel("ACF")
plt.savefig(os.path.join(RESULTS_DIR, "{0}_acf".format(id)))
print("saving figure ", os.path.join(RESULTS_DIR,
"{0}_acf".format(id)))
print("Calculating periodogram")
ps = np.arange(.1, 100, .1)
model = LombScargle().fit(x, y, yerr)
pgram = model.periodogram(ps)
plt.clf()
plt.plot(ps, pgram)
plt.savefig(os.path.join(RESULTS_DIR, "{0}_pgram".format(id)))
print("saving figure ", os.path.join(RESULTS_DIR,
"{0}_pgram".format(id)))
assert 0
peaks = np.array([i for i in range(1, len(ps)-1) if pgram[i-1] <
pgram[i] and pgram[i+1] < pgram[i]])
pgram_period = ps[pgram == max(pgram[peaks])][0]
print("pgram period = ", pgram_period, "days")
pgram_period_err = pgram_period * .1
df = pd.DataFrame({"N": [id], "acf_period": [acf_period],
"acf_period_err": [err],
"pgram_period": [pgram_period],
"pgram_period_err": [pgram_period_err]})
df.to_csv(fname)
return acf_period, err, pgram_period, pgram_period_err
def bases_FFT(eid, nbases):
x, y, basis = read_data(eid, nbases)
fs = np.linspace(1e-6, .7, 1000)
ps = 1. / fs
plt.clf()
cols = np.linspace(.1, .99, len(basis))
freqs = []
for i in range(len(basis)):
model = LombScargle().fit(x, basis[i], np.ones_like(x) * 1e-5)
pgram = model.periodogram(ps)
plt.plot(fs, pgram, color="%s" % cols[i])
freqs.append(fs[pgram == max(pgram)][0])
plt.savefig("all_bases")
freqs = np.array(freqs)
np.savetxt("elc_freqs.txt", np.transpose((np.arange(nbases), freqs)))
def find_spikes_vbg(fnames):
for i, fname in enumerate(fnames):
eid = fname[15:24]
print eid, i, "of", len(fnames)
x, y, _ = np.genfromtxt("../data/c1/ep%s.csv" % eid, delimiter=",").T
x *= 24 * 3600
y /= np.median(y)
fs = np.arange(40, 55, 1e-1) * 1e-6
ps = 1. / fs
model = LombScargle().fit(x, y, np.ones_like(y) * 1e-5)
pg = model.periodogram(ps)
f = h5py.File("spikes/spike_%s_vbg.h5" % eid, "w")
data = f.create_dataset("pgram", (len(fs), 2))
data[:, 0] = fs
data[:, 1] = pg
f.close()
def periodograms(N, plot=False, savepgram=True):
ids = np.arange(N)
periods = []
for id in ids:
print "\n", id, "of", N
# load simulated data
x, y = np.genfromtxt("simulations/%s.txt" % str(int(id)).zfill(4)).T
yerr = np.ones_like(y) * 1e-5
# initialise with acf
try:
p_init = np.genfromtxt("simulations/%s_result.txt"
% int((id)))
except:
corr_run(x, y, yerr, int(id), "simulations",
saveplot=False)
p_init = np.genfromtxt("simulations/%s_result.txt" % int(id))
print "acf period, err = ", p_init
ps = np.linspace(p_init[0]*.1, p_init[0]*4, 1000)
model = LombScargle().fit(x, y, yerr)
pgram = model.periodogram(ps)
# find peaks
peaks = np.array([i for i in range(1, len(ps)-1) if pgram[i-1] <
pgram[i] and pgram[i+1] < pgram[i]])
period = ps[pgram==max(pgram[peaks])][0]
periods.append(period)
print "pgram period = ", period
if plot:
plt.clf()
plt.plot(ps, pgram)
plt.axvline(period, color="r")
plt.savefig("simulations/%s_pgram" % str(int(id)).zfill(4))
if savepgram:
np.savetxt("simulations/%s_pgram.txt" % str(int(id)).zfill(4),
np.transpose((ps, pgram)))
np.savetxt("simulations/periodogram_results.txt",
np.transpose((ids, periods)))
return periods
def trialfor(p1, p2):
"""Try L-S routine for periods p1 and p2, other parameters given by globals"""
global trialfreqs, trialperiods, usegatspy, obstimes, amp1, amp2, usegatspy, phases, maxnum, errbar, errs
global resultvec, rounding
global TWOPI
rs = set()
for p in phases:
sig = amp1 * np.sin(obstimes * TWOPI / p1) + amp2 * np.sin(p + obstimes * TWOPI / p2)
if usegatspy:
model = LombScargle().fit(obstimes, sig, errnar)
pgram = model.periodogram(trialperiods)
else:
pgram = lsas(obstimes, sig, errs, trialfreqs)
maxima = argmaxmin.maxmaxes(trialperiods, pgram)
if len(maxima) > maxnum:
maxima = maxima[0:maxnum]
rs |= set(np.round(trialperiods[maxima], rounding))
rs = list(rs)
rs.sort()
resultvec.append((p1, p2, rs))
def plot_raw(fname, eid):
# read the data
data = fitsio.read("../data/c1/ktwo%s-c01_lpd-lc.fits" % epid)
aps = fitsio.read("../data/c1/ktwo%s-c01_lpd-lc.fits" % epid, 2)
y = data["flux"][:, np.argmin(aps["cdpp6"])]
x = data["time"]
q = data["quality"]
l = np.isfinite(y) * np.isfinite(x) * (q == 0)
y, x = y[l], x[l]
y /= np.median(y)
y -= 1
x *= 24 * 3600
model = LombScargle().fit(x, y, np.ones_like(y) * 1e-5)
fs = np.linspace(1, 280, 1000) * 1e-6
period = 1. / fs
pgram = model.periodogram(period)
plt.clf()
plt.plot(fs * 1e6, pgram, "k")
plt.xlabel("$\\nu\mathrm{(}\mu \mathrm{Hz)}$")
plt.ylabel("$\mathrm{Power}$")
plt.title("$\mathrm{EPIC~%s}$" % eid)
plt.savefig("documents/raw_%s" % eid)
def find_modes(fname, eid, nbasis=150, campaign=1, raw=False):
data = fitsio.read(fname)
aps = fitsio.read(fname, 2)
y = data["flux"][:, np.argmin(aps["cdpp6"])]
x = data["time"]
q = data["quality"]
l = np.isfinite(y) * np.isfinite(x) * (q==0)
y, x = y[l], x[l]
y /= np.median(y)
y -= 1
x *= 24*3600 # convert to seconds
# plot raw data
if raw == True:
plt.clf()
model = LombScargle().fit(x, y, np.ones_like(y)*1e-5)
period = 1. / fs
raw_pgram = model.periodogram(period)
plt.plot(fs, raw_pgram, "k")
plt.savefig("astero/raw_%spgram" % eid)
# load basis
with h5py.File("data/c%s.h5" % campaign, "r") as f:
basis = f["basis"][:150, l]
fs = np.arange(10, 300, 4e-2) * 1e-6
amps2, s2n, w = K2pgram(x, y, basis, fs)
# plot our pgram
plt.clf()
fs *= 1e6
plt.plot(fs, s2n, "k")
plt.xlabel("$\mathrm{Frequency~(}\mu \mathrm{Hz)}$")
plt.ylabel("$\mathrm{Power}$")
plt.savefig("astero/%sastero_pgram" % eid)
# save pgram
np.savetxt("astero/%sastero_pgram.txt" % eid, np.transpose((fs, s2n)))
def periodograms(id, x, y, yerr, path, plot=False, savepgram=False):
"""
takes id of the star, returns an array of period measurements and saves the
results.
id: star id.
x, y, yerr: time, flux and error arrays.
path: path where you want to save the output.
"""
ps = np.linspace(2, 100, 1000)
model = LombScargle().fit(x, y, yerr)
pgram = model.periodogram(ps)
# find peaks
peaks = np.array([
i for i in range(1,
len(ps) - 1)
if pgram[i - 1] < pgram[i] and pgram[i + 1] < pgram[i]
])
if len(peaks):
period = ps[pgram == max(pgram[peaks])][0]
else:
period = 0
if plot:
plt.clf()
plt.plot(ps, pgram)
plt.axvline(period, color="r")
plt.savefig("{0}/{1}_pgram".format(path, str(int(id)).zfill(4)))
if savepgram:
np.savetxt("{0}/{1}_pgram.txt".format(path,
str(int(id)).zfill(4)),
np.transpose((ps, pgram)))
np.savetxt("{0}/{1}_pgram_result.txt".format(path,
str(int(id)).zfill(4)),
np.ones(2).T * period)
return period
fig.add_subplot(gs[0, 1])
]
# Plot the data
ax[0].errorbar(t, mag, dmag, fmt='o', color='#333333')
ax[1].errorbar(phase, mag, dmag, fmt='.', color='#888888')
# Fit and plot the model
model = LombScargle().fit(t, mag, dmag)
model.optimizer.period_range = (0.2, 1.2)
phase = (t / model.best_period) % 1
phasefit = np.linspace(0, 1, 1000)
tfit = model.best_period * phasefit
lines = ax[2].plot(periods, model.periodogram(periods), lw=1)
ax[1].plot(phasefit, model.predict(tfit), c=lines[0].get_color())
ax[1].invert_yaxis()
ax[0].set_xlabel('date of observation (MJD)')
ax[0].set_ylabel('magnitude')
ax[0].set_title('Input Data')
ax[0].invert_yaxis()
ax[1].set_title('Folded Data (P={0:.3f} days)'.format(rrlyrae.period))
ax[1].set_xlabel('phase')
ax[1].yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax[2].set_title('Periodogram')
ax[2].set_xlabel('period (days)')
ax[2].set_ylabel('power')
plt.subplot(3, 1, 2)
print ("calculating fft for long cadence data")
pgram = nufft.nufft3(stops, y, ws)
plt.plot((fs-fs0)*1e6, pgram, "k", alpha=.7)
if len(truths):
for truth in truths:
plt.axvline(truth*1e-6, color="r", linestyle="-.")
plt.ylabel("$\mathrm{FFT}$")
plt.xlabel("$\mathrm{Frequency-%s~(uHz)}$" % (fs0*1e6))
# LombScargle long cadence data
plt.subplot(3, 1, 3)
print ("calculating LombScargle for long cadence data")
periods = 1./fs
model = LombScargle().fit(x, y, yerr)
power = model.periodogram(periods)
plt.plot((fs-fs0)*1e6, power, "k", alpha=.7)
if len(truths):
for truth in truths:
plt.axvline(truth*1e-6, color="r", linestyle="-.")
plt.ylabel("$\mathrm{LS}$")
plt.xlabel("$\mathrm{Frequency-%s~(uHz)}$" % (fs0*1e6))
plt.subplots_adjust(hspace=.4)
# # pwnnyquist long cadence data
# plt.clf()
# plt.subplot(3, 1, 1)
# print ("calculating superpgram for long cadence data")
# x, y, yerr, ivar = load_data(kid, kepler_lc_dir, sc=False)
# starts, stops, centres = real_footprint(x)
# amp2s = spg.superpgram(starts, stops, y, ivar, ws)
fig.add_subplot(gs[1, 1]),
fig.add_subplot(gs[0, 1])]
# Plot the data
ax[0].errorbar(t, mag, dmag, fmt='o', color='#333333')
ax[1].errorbar(phase, mag, dmag, fmt='.', color='#888888')
# Fit and plot the model
model = LombScargle().fit(t, mag, dmag)
model.optimizer.period_range = (0.2, 1.2)
phase = (t / model.best_period) % 1
phasefit = np.linspace(0, 1, 1000)
tfit = model.best_period * phasefit
lines = ax[2].plot(periods, model.periodogram(periods), lw=1)
ax[1].plot(phasefit, model.predict(tfit),
c=lines[0].get_color())
ax[1].invert_yaxis()
ax[0].set_xlabel('date of observation (MJD)')
ax[0].set_ylabel('magnitude')
ax[0].set_title('Input Data')
ax[0].invert_yaxis()
ax[1].set_title('Folded Data (P={0:.3f} days)'.format(rrlyrae.period))
ax[1].set_xlabel('phase')
ax[1].yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax[2].set_title('Periodogram')
ax[2].set_xlabel('period (days)')
print "Conversion error on", integ
sys.exit(12)
except IndexError:
print "Invalid data time column max =", arr.shape[0]
sys.exit(13)
results = np.zeros_like(periods)
for n in xrange(0,iters):
sums = np.zeros_like(timings)
if gaussp is not None:
sums += nr.normal(loc=gmean, scale=gstd, size=len(timings))
if uniformp is not None:
sums += nr.uniform(low=ulow, high=uhigh, size=len(timings))
model = LombScargle().fit(timings, sums, err)
pgram = model.periodogram(periods)
if maxes > 0:
maxima = argmaxmin.maxmaxes(periods, pgram)
if len(maxima) > maxes: maxima = maxima[0:maxes]
results[maxima] += pgram[maxima]
else:
results += pgram
print "Completed iteration", n+1
res = np.array([periods, results])
try:
np.savetxt(outspec, res.transpose())
except IOError as e:
print "Could not save output file", outspec, "error was", e.args[1]
sys.exit(14)
gs = plt.GridSpec(2, 2, left=0.07, right=0.95, wspace=0.15, bottom=0.15)
ax = [
fig.add_subplot(gs[:, 0]),
fig.add_subplot(gs[0, 1]),
fig.add_subplot(gs[1, 1])
]
ax[0].errorbar(phase[mask], mag[mask], dmag[mask], fmt='o', color='#666666')
ax[0].errorbar(phase[~mask], mag[~mask], dmag[~mask], fmt='o', color='#CCCCCC')
ax[0].invert_yaxis()
for fit_offset in [False, True]:
i = int(fit_offset)
model = LombScargle(fit_offset=fit_offset).fit(t[mask], mag[mask],
dmag[mask])
P = model.periodogram(periods)
if fit_offset:
label = 'floating mean'
else:
label = 'standard'
lines = ax[0].plot(phasefit,
model.predict(tfit, period=rrlyrae.period),
label=label)
ax[1 + i].plot(periods, P, lw=1, c=lines[0].get_color())
ax[1 + i].set_title('{0} Periodogram'.format(label.title()))
ax[1 + i].set_ylabel('power')
ax[1 + i].set_ylim(0, 1)
ax[1 + i].set_xlim(0.2, 1.4)
ax[0].legend(loc='upper left')
ax[0].set_xlabel('phase')
def raw_and_vbg():
plotpar = {
'axes.labelsize': 12,
'text.fontsize': 18,
'legend.fontsize': 18,
'xtick.labelsize': 14,
'ytick.labelsize': 14,
'text.usetex': True
}
plt.rcParams.update(plotpar)
# eid = "201545182"
eid = "201183188"
fname = "../data/c1/ktwo%s-c01_lpd-lc.fits" % eid
# load raw data
data = fitsio.read(fname)
aps = fitsio.read(fname, 2)
y = data["flux"][:, np.argmin(aps["cdpp6"])]
x = data["time"]
q = data["quality"]
l = np.isfinite(y) * np.isfinite(x) * (q == 0)
y, x = y[l], x[l]
MAD = np.median(y - np.median(y))
y /= np.median(y)
y -= 1
x *= 24 * 3600 # convert to seconds
fs = np.arange(.1, 300, 4e-2) * 1e-6 # astero
# plot raw data
fig = plt.figure()
ax = fig.add_subplot(211)
ax1 = fig.add_subplot(311)
model = LombScargle().fit(x, y, np.ones_like(y) * 1e-5)
period = 1. / fs
start = time.time()
raw_pgram = model.periodogram(period)
end = time.time()
print("LS time = ", end - start)
ax1.plot(fs[::3] * 1e6, raw_pgram[::3], "k", label="$\mathrm{Raw}$")
ax.set_title("$\mathrm{EPIC~%s}$" % eid)
ax1.set_xlim(10, 280)
ax1.set_ylim(0, .015)
plt.ylabel("$\mathrm{Power}$")
# plt.legend()
# leg1 = Rectangle((0, 0), 0, 0, alpha=0.0)
# plt.legend([leg1], "$\mathrm{Raw}$", handlelength=0)
plt.text(230, .012, "$\mathrm{Raw}$")
ticks = ax1.get_yticks()
ax1.set_yticks(ticks[1:-1])
ax.set_yticklabels(ax.get_yticklabels(), visible=False)
ax.set_xticklabels(ax.get_xticklabels(), visible=False)
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w',
top='off',
bottom='off',
left='off',
right='off')
# load andrew's lcs
ax2 = fig.add_subplot(312)
x, y = np.genfromtxt("../data/c1/ep%s.csv" % eid, skip_header=1).T
x *= 24 * 3600
model = LombScargle().fit(x, y, np.ones_like(y) * 1e-5)
ps = 1. / fs
pgram = model.periodogram(ps)
ax2.plot(fs[::3] * 1e6, pgram[::3], "k", label="$\mathrm{Detrended}$")
plt.text(200, .010, "$\mathrm{VJ14~Detrended}$")
# leg1 = Rectangle((0, 0), 0, 0, alpha=0.0)
# plt.legend([leg1], "$\mathrm{VJ14~Detrended}$", handlelength=0)
ax2.set_xlim(10, 280)
ax2.set_ylim(0, .015)
plt.ylabel("$\mathrm{Power}$")
ticks = ax2.get_yticks()
ax2.set_yticks(ticks[1:-1])
# fig.text(0.04, 0.5, "$\mathrm{Power}$", ha="center", va="top",
# rotation="vertical")
# load sip
fs, s2n = np.genfromtxt("../astero/%sastero_pgram.txt" % str(int(eid))).T
ax3 = fig.add_subplot(313)
if MAD == 0.:
MAD = 1.
plt.plot(fs[::3], s2n[::3] * 10e4 / MAD**2, "k", label="$\mathrm{SIP}$")
# leg1 = Rectangle((0, 0), 0, 0, alpha=0.0)
# plt.legend([leg1], "$\mathrm{SIP}$", handlelength=0)
plt.text(230, 2.2, "$\mathrm{SIP}$")
ax3.set_xlim(10, 280)
plt.ylabel(
"$\mathrm{Relative~(S/N)}^2\mathrm{~(} \\times 10^{4}\mathrm{)}$")
plt.xlabel("$\\nu\mathrm{~(}\mu\mathrm{Hz)}$")
fig.subplots_adjust(hspace=0, bottom=.1)
# ticks = ax3.get_yticks()
# ax3.set_yticks(ticks[1:-1])
print("saving as ../documents/rawvbg_%s.pdf" % eid)
print("saving as poster_rawvbg_%s.pdf" % eid)
plt.savefig("../documents/rawvbg_%s.pdf" % eid)
plt.savefig("poster_rawvbg_%s" % eid, transparent=True)
# compute Fourier transform
sp = np.fft.fft(y)
freq = np.fft.fftfreq(x.shape[-1])
fft = sp.real**2 * np.imag**2
plt.clf()
plt.plot(freq, fft)
plt.savefig("fft")
# Scale back to zero doesn't alter L-S result
day_bjdates -= day_bjdates[0]
sel = day_xrayvs <= xraylevel
bjd = day_bjdates[sel]
dv = day_values[sel]
#dv -= dv.mean() # Need this for ss.lombscargle to work
if plotit:
plt.figure()
plt.plot(bjd, dv)
model = LombScargle().fit(bjd, dv, errorlev)
spectrum = model.periodogram(idrange)
if abss: spectrum = np.abs(spectrum)
np.savetxt(outfile + "_day_%d.ls" % fnum, np.transpose(np.array([idrange, spectrum])))
fnum += 1
bjdates -= bjdates[0]
sel = xrayvs < xraylevel
bjd = bjdates[sel]
dv = values[sel]
#dv -= dv.mean()
if plotit:
plt.figure()
plt.plot(bjd, dv)
model = LombScargle().fit(bjd, dv, errorlev)
spectrum = model.periodogram(allrange)
def calc_p_init(x,
y,
yerr,
id,
RESULTS_DIR="pgram_filtered_results_35",
clobber=True):
fname = os.path.join(RESULTS_DIR, "{0}_acf_pgram_results.txt".format(id))
if not clobber and os.path.exists(fname):
print("Previous ACF pgram result found")
df = pd.read_csv(fname)
m = df.N.values == id
acf_period = df.acf_period.values[m]
err = df.acf_period_err.values[m]
pgram_period = df.pgram_period.values[m]
pgram_period_err = df.pgram_period_err.values[m]
else:
print("Calculating ACF")
acf_period, acf, lags, rvar = sa.simple_acf(x, y)
err = .1 * acf_period
plt.clf()
plt.plot(lags, acf)
plt.axvline(acf_period, color="r")
plt.xlabel("Lags (days)")
plt.ylabel("ACF")
plt.savefig(os.path.join(RESULTS_DIR, "{0}_acf".format(id)))
print("saving figure ", os.path.join(RESULTS_DIR,
"{0}_acf".format(id)))
# Interpolate across gaps
gap_days = 0.02043365
time = np.arange(x[0], x[-1], gap_days)
lin_interp = np.interp(time, x, y)
x, y = time, lin_interp
yerr = np.ones(len(y)) * 1e-5
print("Calculating periodogram")
ps = np.arange(.1, 100, .1)
model = LombScargle().fit(x, y, yerr)
pgram = model.periodogram(ps)
plt.clf()
plt.plot(ps, pgram)
period = 35. # days
fs = 1. / (x[1] - x[0])
lowcut = 1. / period
# pgram = model.periodogram(ps)
yfilt = butter_bandpass_filter(y, lowcut, fs, order=3, plot=False)
# plt.clf()
# plt.plot(x, y, label='Noisy signal')
# plt.plot(x, yfilt, label='{0} day^(-1), {1} days'.format(lowcut,
# period))
# plt.xlabel('time (seconds)')
# plt.grid(True)
# plt.axis('tight')
# plt.legend(loc='upper left')
# plt.savefig("butter_filtered")
# normalise data and variance.
med = np.median(y)
y -= med
var = np.std(y)
print("var = ", var)
y /= var
print("Calculating periodogram")
ps = np.arange(.1, 100, .1)
model = LombScargle().fit(x, yfilt, yerr)
pgram = model.periodogram(ps)
plt.plot(ps, pgram)
plt.savefig(os.path.join(RESULTS_DIR, "{0}_pgram".format(id)))
print("saving figure ",
os.path.join(RESULTS_DIR, "{0}_pgram".format(id)))
peaks = np.array([
i for i in range(1,
len(ps) - 1)
if pgram[i - 1] < pgram[i] and pgram[i + 1] < pgram[i]
])
pgram_period = ps[pgram == max(pgram[peaks])][0]
# Calculate the uncertainty.
_freq = 1. / pgram_period
pgram_freq_err = calc_pgram_uncertainty(x, y, _freq)
print(1. / _freq, "period")
print(_freq, "freq")
print(pgram_freq_err, "pgram_freq_err")
frac_err = pgram_freq_err / _freq
print(frac_err, "frac_err")
pgram_period_err = pgram_period * frac_err
print(pgram_period_err, "pgram_period_err")
print("pgram period = ", pgram_period, "+/-", pgram_period_err, "days")
df = pd.DataFrame({
"N": [id],
"acf_period": [acf_period],
"acf_period_err": [err],
"pgram_period": [pgram_period],
"pgram_period_err": [pgram_period_err]
})
df.to_csv(fname)
return acf_period, err, pgram_period, pgram_period_err
def p_child_plot(x, y, eid):
plotpar = {'axes.labelsize': 15,
'text.fontsize': 15,
'legend.fontsize': 15,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
'text.usetex': True}
plt.rcParams.update(plotpar)
y = y/np.median(y) - 1
# SUBTRACT LINEAR TREND!
plv = np.polyfit(x, y, 1)
print(plv)
poly = plv[1] + x * plv[0]
y -= poly
# compute acf
dt = 0.02043359821692 # time resolution of K2 data (from header)
# for timing: acf takes 6 milliseconds with 3357 data points
# N = 1000
# start = time.time()
# for i in range(N):
# acf = emcee.autocorr.function(y)
# end = time.time()
# print("acf time = ", (end - start)/N * 1e3, "milliseconds")
acf = emcee.autocorr.function(y)
lags = np.arange(len(acf)) * dt
# smooth acf
acf = sps.savgol_filter(acf, 15, 1)
# compute LS periodogram
model = LombScargle().fit(x, y, np.ones_like(y)*1e-5)
fmax = max(lags)/100.
fs = np.linspace(1e-6, fmax, 1000)
ps = 1./fs
# ps = np.linspace(.1, 2, 1000)
# fs = 1./ps
pgram = model.periodogram(ps)
np.savetxt("lspgram_%s.txt" % eid, np.transpose((ps, pgram)))
plt.clf()
plt.subplot(3, 1, 1)
l = x < 2016
plt.plot(x[l], y[l], "k")
plt.plot(x[~l], y[~l], "k")
plt.xlabel("$\mathrm{BJD-2454833~(days)}$")
plt.ylabel("$\mathrm{Normalized~Flux}$")
plt.xlim(min(x), max(x))
plt.title("$\mathrm{EPIC~%s}$" % eid)
# plt.ylim(-.02, .02)
plt.subplot(3, 1, 2)
plt.plot(lags, acf, "k")
plt.ylabel("$\mathrm{Autocorrelation}$")
plt.xlabel("$\mathrm{Time~(days)}$")
acfx, acfy = acf_peak_detect(lags, acf)
plt.axvline(acfx, color=".5", linestyle="--", label="$P_{rot}=%.2f$"
% acfx)
plt.legend()
plt.xlim(min(lags), max(lags))
plt.subplot(3, 1, 3)
# plt.plot(fs, pgram, "k")
# plt.xlim(min(fs), fmax)
plt.plot(1./fs, pgram, "k")
# plt.xlim(0, 2)
plt.xlim(min(1./fs), max(lags))
plt.xlabel("$\mathrm{Period~(days)}$")
# plt.xlabel("$\mathrm{Frequency~(days}^{-1}\mathrm{)}$")
plt.ylabel("$\mathrm{Power}$")
l = fs > 1./100
mx, my = max_peak_detect(fs[l], pgram[l])
px = 1./mx
# plt.axvline(mx, color=".5", linestyle="--", label="$P_{rot}=%.2f$" % px)
plt.axvline(px, color=".5", linestyle="--", label="$P_{max}=%.2f$" % px)
# plt.ylim(0, .6)
plt.legend(loc="best")
plt.subplots_adjust(hspace=.4)
# plt.savefig("vbg_%s" % eid)
# plt.savefig("../documents/rotation_poster_child.pdf")
print("../documents/rotation%s.pdf" % eid)
plt.savefig("../documents/rotation%s.pdf" % eid)
return acfx, px
tfit = rrlyrae.period * phasefit
fig = plt.figure(figsize=(10, 4))
gs = plt.GridSpec(3, 2, left=0.07, right=0.95, bottom=0.15, wspace=0.15, hspace=0.3)
ax = [fig.add_subplot(gs[:, 0]), fig.add_subplot(gs[0, 1]), fig.add_subplot(gs[1, 1]), fig.add_subplot(gs[2, 1])]
# Plot the data
ax[0].errorbar(phase, mag, dmag, fmt="o", color="#AAAAAA")
# Plot the fits
models = [1, 2, 6]
for i, Nterms in enumerate(models):
model = LombScargle(Nterms=Nterms).fit(t, mag, dmag)
P = model.periodogram(periods)
label = "{0} terms".format(Nterms)
if Nterms == 1:
label = label[:-1]
lines = ax[0].plot(phasefit, model.predict(tfit, period=rrlyrae.period), label=label)
ax[1 + i].plot(periods, model.periodogram(periods), c=lines[0].get_color(), lw=1)
ax[1 + i].set_title("{0}-term Periodogram".format(Nterms))
ax[1 + i].set_xlim(0.2, 1.4)
ax[1 + i].set_ylim(0, 1)
ax[2].set_ylabel("power")
ax[3].set_xlabel("period (days)")
for i in [1, 2]:
ax[i].xaxis.set_major_formatter(plt.NullFormatter())
fig = plt.figure(figsize=(10, 4))
gs = plt.GridSpec(3, 2, left=0.07, right=0.95, bottom=0.15,
wspace=0.15, hspace=0.3)
ax = [fig.add_subplot(gs[:, 0]),
fig.add_subplot(gs[0, 1]),
fig.add_subplot(gs[1, 1]),
fig.add_subplot(gs[2, 1])]
# Plot the data
ax[0].errorbar(phase, mag, dmag, fmt='o', color='#AAAAAA')
# Plot the fits
for i, Nterms in enumerate(models):
model = LombScargle(Nterms=Nterms).fit(t, mag, dmag)
P = model.periodogram(periods)
label = "{0} terms".format(Nterms)
if Nterms == 1:
label = label[:-1]
alpha = 1.0 if (Nterms < 20) else 0.5
lines = ax[0].plot(phasefit, model.predict(tfit, period=rrlyrae.period),
label=label, alpha=alpha)
ax[1 + i].plot(periods, model.periodogram(periods),
color=lines[0].get_color(), lw=1)
ax[1 + i].set_title("{0}-term Periodogram".format(Nterms))
ax[1 + i].set_xlim(0.2, 1.4)
ax[1 + i].set_ylim(0, 1)
def grid_over_amps(basis,
flux,
raw_x,
raw_y,
truth,
fs,
amps,
true_a,
flag,
n,
plot=False,
raw=False,
random_amps=True):
# find the threshold level
_, initial_pgram, _ = SIP(raw_x, raw_y, basis, fs)
mx, threshold = peak_detect(fs, initial_pgram)
K2P, rawP, K2a, rawa = [], [], [], []
alla, allp = [], []
all_results = []
for i, a in enumerate(amps):
if random_amps:
if flag == "r":
a = 10**(np.random.uniform(np.log10(1e-5), np.log10(1e-3)))
elif flag == "a":
a = 10**(np.random.uniform(np.log10(1e-5), np.log10(1e-3)))
tf = 1. / truth
print("period = ", truth)
# add lcs together
# plt.clf()
# plt.plot(flux * a, "k.")
noise = np.random.randn(len(flux)) * 50 * 13**.5 * 1e-6
# print 50*13**.5*1e-6, a
fx = flux * a + noise
# plt.plot(fx, "r.")
# plt.savefig("Test")
# assert 0
y = fx + raw_y
SN = np.var(fx) / np.var(raw_y)
if flag == "r":
threshold = .1
elif flag == "a":
threshold = .1
# # Calculate time
# start = time.time()
# amp2s, s2n, w = SIP(raw_x, y, basis, fs[:1000])
# end = time.time()
# print("SIP time = ", (end-start), "s")
# print("for", len(y), "data points and", len(fs), "freqs")
# calculate SIP
amp2s, s2n, w = SIP(raw_x, y, basis, fs)
pgram = s2n
best_f, best_pgram = peak_detect(fs, pgram) # find peaks
print("recovered period", 1. / best_f)
s = 0 # success indicator
alla.append(a)
allp.append(truth)
all_results.append(best_f)
print(tf - threshold * tf, best_f, tf + threshold * tf)
if tf - threshold * tf < best_f and best_f < tf + threshold * tf:
K2P.append(truth)
K2a.append(a)
print("success!", "\n")
s = 1
# calculate periodogram of raw light curve
y = np.array([_y.astype("float64") for _y in y])
raw_x = np.array([_raw_x.astype("float64") for _raw_x in raw_x])
# Calculate time
start = time.time()
model = LombScargle().fit(raw_x, y, np.ones_like(y) * 1e-5)
end = time.time()
print("LS time = ", (end - start) * 1e3, "ms")
print("for", len(y), "data points and", len(fs), "freqs")
assert 0
# # Calculate time
# start = time.time()
# model = LombScargle().fit(raw_x, y, np.ones_like(y)*1e-5)
# end = time.time()
# print("SIP time = ", (end-start)*1e3, "ms")
# print("for", len(y), "data points and", len(fs), "freqs")
# assert 0
model = LombScargle().fit(raw_x, y, np.ones_like(y) * 1e-5)
period = 1. / fs
pg = model.periodogram(period)
best_f2, best_pg2 = peak_detect(fs, pg)
if tf - threshold * tf < best_f2 and best_f2 < tf + threshold * tf:
rawP.append(truth)
rawa.append(a)
if plot:
plt.clf()
plt.subplot(2, 1, 1)
plt.plot(raw_x, y, "k.")
plt.plot(raw_x, fx, color="g")
plt.title("$\mathrm{Amp = %s, P = %.3f}$" % (a, (1. / tf)))
plt.subplot(2, 1, 2)
plt.axvline(best_f, color="r", linestyle="-")
plt.axvline(tf, color="k", linestyle="--")
print("best f = ", best_f)
print("true f = ", tf)
print(tf - threshold * tf, tf + threshold * tf)
c = "b"
if s == 1:
c = "m"
plt.plot(fs, pgram, color=c, label="$\mathrm{SIP$}")
plt.savefig("../injections/sine/%s_%s_result_%s" %
(str(n).zfill(2), str(i).zfill(2), flag))
# n is the period index, i is the amplitude index
print("%s_%s_result_%s" % (str(n).zfill(2), str(i).zfill(2), flag))
# raw_input('enter')
return np.array(K2a), np.array(K2P), np.array(rawa), np.array(rawP), \
np.array(alla), np.array(allp), np.array(all_results)
def LS(x, y, fs):
ps = 1. / fs
model = LombScargle().fit(x, y, np.ones_like(y) * 1e-5)
return model.periodogram(ps)
