def interpolate(time, flux, approx_duration):
min_gap = approx_duration / gap_days
# p.close(5)
# p.figure(5)
# p.subplot(2,1,1)
# p.plot(time, flux, 'k.')
""" Calculate noise properties """
flux_filt = filter.filt1d(flux, 10, 5)
med_noise, sig_noise = filter.medsig(flux - flux_filt)
"""
Find gaps greater than 1 (1.1) data points and linear interp with
noise
"""
diff1 = time[1:] - time[:-1]
diff1 = scipy.append(diff1, gap_days)
gap_find = diff1 > min_gap * gap_days * 0.9 # 1.1*gap_days
gap_times = time[gap_find]
time_index = scipy.r_[0 : len(time) : 1]
fill_arr_t = scipy.zeros(1)
fill_arr_f = scipy.zeros(1)
# fill_arr_nan = scipy.zeros(1)
print("Filling gaps...")
for m in scipy.arange(len(gap_times)):
time_start = time[time_index[gap_find][m]]
flux_start = flux[time_index[gap_find][m]]
time_end = time[time_index[gap_find][m] + 1]
flux_end = flux[time_index[gap_find][m] + 1]
span = time_end - time_start
if span < 2.0 * gap_days:
fill = scipy.array([time_start, time_start + gap_days, time_end])
else:
fill = scipy.r_[time_start:time_end:gap_days]
fill = fill[1:-1]
if time_end - fill.max() > 1.1 * gap_days: # 1.1*gap_days*min_gap:
fill = scipy.append(fill, fill.max() + gap_days)
f = scipy.interpolate.interp1d([time_start, time_end], [flux_start, flux_end])
gap_new = f(fill)
if sig_noise > 0:
gap_new += normal(0, sig_noise, len(fill))
fill_arr_t = scipy.append(fill_arr_t, fill)
fill_arr_f = scipy.append(fill_arr_f, gap_new)
# fill_arr_nan = scipy.append(fill_arr_nan, scipy.ones(len(fill))*scipy.nan)
# combine time and flux arrays with their filled sections
fill_arr_t = fill_arr_t[1:]
fill_arr_f = fill_arr_f[1:]
# fill_arr_nan = fill_arr_nan[1:]
fill_arr_t = scipy.append(fill_arr_t, time)
fill_arr_f = scipy.append(fill_arr_f, flux)
# fill_arr_nan = scipy.append(fill_arr_nan, flux_base)
if len(fill_arr_t) == 0:
print("*************** empty time array ***************")
return False, atpy.Table(), 0, 0
# put in table and sort
tf = atpy.Table()
tf.add_column("time", fill_arr_t)
tf.add_column("flux", fill_arr_f)
# tf.add_column('flux_pdc', fill_arr_nan)
tf.sort("time")
# for i in range(0, len(gap_times)):
# p.axvline(gap_times[i], color = 'y')
# p.subplot(2,1,2)
# p.plot(fill_arr_t, fill_arr_f, 'k.')
return tf.time, tf.flux
def load_lc(quarter_load, kid_x = None, index = None, tr_out = False, tr_type = False):
# read in tableset of LCs for all Qs, normalise and quarter join
dir_lc = '/Users/angusr/angusr/data2/Q%s_public' %quarter_load + '/Light_Curves'
print 'Reading in LCs...'
tset, found = kt.GetLc(id = kid_x, dir = dir_lc, tr_out = tr_out, \
filename = index.filename[(index.keplerid == kid_x) * (index.quarter <= qmax) * (index.quarter >= qmin)])
if found == 0:
print '****not found****'
return False, atpy.Table(), 0, 0
# find number of Quarters and max(time) per quarter
if tr_out == False: lc = tset[0]
else: lc = tset[1]
quarters = scipy.array(list(set(lc.Q)))
tablen = len(quarters)
qt_max = scipy.zeros(tablen)
for z in scipy.arange(len(quarters)):
qt_max[z] = max(lc.TIME[lc.Q == quarters[z]])
qt_max = scipy.append(0,qt_max)
finite = scipy.where((scipy.isfinite(lc.TIME) == True) * (scipy.isfinite(lc.SAP_FLUX) == True) * \
(scipy.isfinite(lc.PDCSAP_FLUX) == True))
# keep unchanged set for table
flux_base = lc.SAP_FLUX[finite]
q_arr_base = lc.Q[finite]
for qt in quarters:
flux_base[q_arr_base == qt] = (flux_base[q_arr_base == qt] / scipy.median(flux_base[q_arr_base == qt])) - 1.0
time = lc.TIME
flux_raw = lc.SAP_FLUX
flux = lc.PDCSAP_FLUX
if tr_out == True:
print 'Removing eclipses...'
# remove in-eclipse regions
phase, inTr = kt.TransitPhase(tset)
npl, nobs = inTr.shape
for ipl in scipy.arange(npl):
trlist = inTr[ipl,:].astype(bool)
time[trlist] = scipy.nan
flux_raw[trlist] = scipy.nan
flux[trlist] = scipy.nan
if tr_type == 'EB':
print 'Removing EB transits...'
phase, inTr = EBTransitPhase(tset, kid_x)
npl, nobs = inTr.shape
for ipl in scipy.arange(npl):
trlist = inTr[ipl,:].astype(bool)
time[trlist] = scipy.nan
flux_raw[trlist] = scipy.nan
flux[trlist] = scipy.nan
pylab.plot(time, flux, 'r.')
finite_tr = scipy.where((scipy.isfinite(time) == True) * \
(scipy.isfinite(flux) == True) * (scipy.isfinite(flux_raw) == True) * (scipy.isfinite(lc.Q) == True))
time = time[finite_tr]
flux = flux[finite_tr]
flux_raw = flux_raw[finite_tr]
quarter_arr = lc.Q[finite_tr]
# median normalise each quarter
for qt in quarters:
flux[quarter_arr == qt] = (flux[quarter_arr == qt] / scipy.median(flux[quarter_arr == qt])) - 1.0
flux_raw[quarter_arr == qt] = (flux_raw[quarter_arr == qt] / scipy.median(flux_raw[quarter_arr == qt])) - 1.0
# fit 1d polynomial and remove >5sig outliers
coeff = scipy.polyfit(time,flux, 1)
p = scipy.poly1d(coeff)
med, sig = filter.medsig(flux - p(time))
sigclip = abs(flux - p(time)) <= 5*sig
time = time[sigclip]
flux = flux[sigclip]
flux_base = flux_base[sigclip]
# to get noise properties
flux_filt = filter.filt1d(flux, 10, 5)
med_noise, sig_noise = filter.medsig(flux-flux_filt)
# find gaps greater than 1 (1.1) data points and linear interp with noise
diff1 = time[1:] - time[:-1]
diff1 = scipy.append(diff1, gap_days)
gap_find = diff1 > 1.1*gap_days
gap_times = time[gap_find]
time_index = scipy.r_[0:len(time):1]
fill_arr_t = scipy.zeros(1)
fill_arr_f = scipy.zeros(1)
fill_arr_nan = scipy.zeros(1)
print 'Filling gaps...'
for m in scipy.arange(len(gap_times)):
time_start = time[time_index[gap_find][m]]
flux_start = flux[time_index[gap_find][m]]
time_end = time[time_index[gap_find][m] + 1]
flux_end = flux[time_index[gap_find][m] + 1]
span = time_end - time_start
if span < 2.0*gap_days:
fill = scipy.array([time_start, time_start+gap_days, time_end])
else: fill = scipy.r_[time_start: time_end: gap_days]
fill = fill[1:-1]
if time_end - fill.max() > 1.1*gap_days: fill = scipy.append(fill, fill.max()+gap_days)
f = scipy.interpolate.interp1d([time_start, time_end], [flux_start, flux_end])
gap_new = f(fill)
if sig_noise > 0: gap_new += normal(0,sig_noise,len(fill))
fill_arr_t = scipy.append(fill_arr_t, fill)
fill_arr_f = scipy.append(fill_arr_f, gap_new)
fill_arr_nan = scipy.append(fill_arr_nan, scipy.ones(len(fill))*scipy.nan)
# combine time and flux arrays with their filled sections
fill_arr_t = fill_arr_t[1:]
fill_arr_f = fill_arr_f[1:]
fill_arr_nan = fill_arr_nan[1:]
fill_arr_t = scipy.append(fill_arr_t, time)
fill_arr_f = scipy.append(fill_arr_f, flux)
fill_arr_nan = scipy.append(fill_arr_nan, flux_base)
if len(fill_arr_t) == 0:
print '*************** empty time array ***************'
return False, atpy.Table(), 0, 0
# put in table and sort
tf = atpy.Table()
tf.add_column('time', fill_arr_t)
tf.add_column('flux', fill_arr_f)
tf.add_column('flux_pdc', fill_arr_nan)
tf.sort('time')
return True, tf, qt_max, tablen
def interpolate(time, flux, approx_duration):
min_gap = approx_duration/gap_days
# p.close(5)
# p.figure(5)
# p.subplot(2,1,1)
# p.plot(time, flux, 'k.')
''' Calculate noise properties '''
flux_filt = filter.filt1d(flux, 10, 5)
med_noise, sig_noise = filter.medsig(flux-flux_filt)
''' find gaps greater than 1 (1.1) data points and linear interp with noise'''
diff1 = time[1:] - time[:-1]
diff1 = scipy.append(diff1, gap_days)
gap_find = diff1 > min_gap*gap_days*0.9 #1.1*gap_days
gap_times = time[gap_find]
time_index = scipy.r_[0:len(time):1]
fill_arr_t = scipy.zeros(1)
fill_arr_f = scipy.zeros(1)
#fill_arr_nan = scipy.zeros(1)
print 'Filling gaps...'
for m in scipy.arange(len(gap_times)):
time_start = time[time_index[gap_find][m]]
flux_start = flux[time_index[gap_find][m]]
time_end = time[time_index[gap_find][m] + 1]
flux_end = flux[time_index[gap_find][m] + 1]
span = time_end - time_start
if span < 2.0*gap_days:
fill = scipy.array([time_start, time_start+gap_days, time_end])
else: fill = scipy.r_[time_start: time_end: gap_days]
fill = fill[1:-1]
if time_end - fill.max() > 1.1*gap_days: #1.1*gap_days*min_gap:
fill = scipy.append(fill, fill.max()+gap_days)
f = scipy.interpolate.interp1d([time_start, time_end], [flux_start, flux_end])
gap_new = f(fill)
if sig_noise > 0: gap_new += normal(0,sig_noise,len(fill))
fill_arr_t = scipy.append(fill_arr_t, fill)
fill_arr_f = scipy.append(fill_arr_f, gap_new)
#fill_arr_nan = scipy.append(fill_arr_nan, scipy.ones(len(fill))*scipy.nan)
# combine time and flux arrays with their filled sections
fill_arr_t = fill_arr_t[1:]
fill_arr_f = fill_arr_f[1:]
#fill_arr_nan = fill_arr_nan[1:]
fill_arr_t = scipy.append(fill_arr_t, time)
fill_arr_f = scipy.append(fill_arr_f, flux)
#fill_arr_nan = scipy.append(fill_arr_nan, flux_base)
if len(fill_arr_t) == 0:
print '*************** empty time array ***************'
return False, atpy.Table(), 0, 0
# put in table and sort
tf = atpy.Table()
tf.add_column('time', fill_arr_t)
tf.add_column('flux', fill_arr_f)
# tf.add_column('flux_pdc', fill_arr_nan)
tf.sort('time')
# for i in range(0, len(gap_times)):
# p.axvline(gap_times[i], color = 'y')
# p.subplot(2,1,2)
# p.plot(fill_arr_t, fill_arr_f, 'k.')
return tf.time, tf.flux
