def get_lc(time1, fluxarr1, quality1, n_chunks, bg_cut, flatlc_window,
smooth_window):
time, lc, xbar, ybar, regnum, lab = optimalAperture(time1,
fluxarr1,
quality1,
qual_cut=False,
return_qual=False,
toss_resat=False,
bg_cut=bg_cut,
skip=None)
m1 = np.isfinite(lc) * np.isfinite(lc)
time = time1[m1]
lc = lc[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time, lc, window=flatlc_window)
cadstep = np.int(np.floor(len(time) / n_chunks)) #600
zpt = len(time) % cadstep
if zpt == cadstep:
zpt = 0
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time,
flatlc,
xbar,
ybar,
cadstep=cadstep,
skip=None)
not_thr = ~thr_cad
corflux = (lc[zpt:][not_thr] / np.median(lc[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4.0
keep = np.abs(corflatflux - 1.) < mad_cut # this might not be used
m2 = np.ones_like(corflatflux, dtype=bool) #corflatflux < 1.1
t1 = time[zpt:][not_thr][m2]
cfflux = extract_lc.medfilt(t1, corflux, window=smooth_window) - 1.0
return time, lc, xbar, ybar, t1, corflux, cfflux
def K2_DetrendRev2(fn,):
#DANCe = fn.split('/')[-1].split('_')[0]
time, flux, xbar, ybar = np.genfromtxt(fn, unpack = True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time,flux,window=3)
zpt = len(time)%300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xbar, ybar, cadstep=300, skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr]/
np.median(flux[zpt:][not_thr])/
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr]/
np.median(flatlc[zpt:][not_thr])/
correction[not_thr])
mad_cut = 1.4826*MAD(corflatflux-1.)*4
keep = np.abs(corflatflux-1.) < mad_cut
#plt.plot(time[zpt:][not_thr],corflatflux,c='r')
#plt.plot(time[zpt:][not_thr][keep],corflatflux[keep],c='b')
#CDPP Calculations
#thr = twohr_cdpp(flux)
#shr = sixhr_cdpp(flux)
#plt.plot(time,flatlc)
#plt.figure(figsize=[15,6])
#plt.plot(time,flux/np.median(flux),'bo',markersize=1)
#plt.plot(time[zpt:][not_thr][keep],corflux[keep],marker='.')
#plt.xlabel('Time [d]')
#plt.ylabel('Normalized Flux')
#plt.title(str(DANCe) + ' 6.5_Hr_CDPP_' + str(shr) + ' | 2.5_Hr_CCPD_' + str(thr))
#plt.savefig(fn.split('.')[-1] + '.png')
#plt.show()
#plt.close('all')
compiled_table = astropy.table.Table()
compiled_table['time'] = time[zpt:][not_thr] #('time', time[zpt:][not_thr])
compiled_table['corflatflux'] = corflatflux
compiled_table['corflux'] = corflux
compiled_table['keep'] = keep
compiled_table['flux'] = flux[zpt:][not_thr]
compiled_table['xbar'] = xbar[zpt:][not_thr]
compiled_table['ybar'] = ybar[zpt:][not_thr]
compiled_table['flatflux'] = flatlc[zpt:][not_thr]
DANCe = fn.split('/')[-1].split('.')[-1]
np.savetxt('/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap2.0_BJM/Detrended_Data/' + DANCe, compiled_tbl, fmt='ascii', delimiter=',')
def K2_DetrendRev4(fn, ):
time, flux, xbar, ybar = np.genfromtxt(fn, unpack=True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time, flux, window=3)
zpt = len(time) % 300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time,
flatlc,
xbar,
ybar,
cadstep=300,
skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4
keep = np.abs(corflatflux - 1.) < mad_cut
# Adds the detrended data to an ascii table.
# To use this in conjunction with the ACF included in this package, you must
# comment out corflatflux, keep, flux, and flatflux, then run the script on
# your data set.
compiled_table = astropy.table.Table()
compiled_table['time'] = time[zpt:][not_thr]
compiled_table['corflatflux'] = corflatflux
compiled_table['corflux'] = corflux
compiled_table['keep'] = keep
compiled_table['flux'] = flux[zpt:][not_thr]
compiled_table['flatflux'] = flatlc[zpt:][not_thr]
compiled_table['xbar'] = xbar[zpt:][not_thr]
compiled_table['ybar'] = ybar[zpt:][not_thr]
# Generates the DANCe # for the file title.
DANCe = fn.split('/')[-1].split('.')[0]
# Saves the detrended data to a file. Need to update the path to the correct folder.
np.savetxt(
'/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap2.0_BJM/Detrended_Data/Detrended_ACF_Data/Raw_Detrended_Data_'
+ DANCe + '.0.dat',
compiled_table,
delimiter=',')
def K2_DetrendRev4(fn,):
time, flux, xbar, ybar = np.genfromtxt(fn, unpack=True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time, flux, window=3)
zpt = len(time) % 300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time, flatlc, xbar, ybar, cadstep=300, skip=1828)
not_thr = ~thr_cad
corflux = flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) / correction[not_thr]
corflatflux = flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) / correction[not_thr]
mad_cut = 1.4826 * MAD(corflatflux - 1.0) * 4
keep = np.abs(corflatflux - 1.0) < mad_cut
# Adds the detrended data to an ascii table.
# To use this in conjunction with the ACF included in this package, you must
# comment out corflatflux, keep, flux, and flatflux, then run the script on
# your data set.
compiled_table = astropy.table.Table()
compiled_table["time"] = time[zpt:][not_thr]
compiled_table["corflatflux"] = corflatflux
compiled_table["corflux"] = corflux
compiled_table["keep"] = keep
compiled_table["flux"] = flux[zpt:][not_thr]
compiled_table["flatflux"] = flatlc[zpt:][not_thr]
compiled_table["xbar"] = xbar[zpt:][not_thr]
compiled_table["ybar"] = ybar[zpt:][not_thr]
# Generates the DANCe # for the file title.
DANCe = fn.split("/")[-1].split(".")[0]
# Saves the detrended data to a file. Need to update the path to the correct folder.
np.savetxt(
"/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap2.0_BJM/Detrended_Data/Detrended_ACF_Data/Raw_Detrended_Data_"
+ DANCe
+ ".0.dat",
compiled_table,
delimiter=",",
)
def K2_Detrend(fn,):
DANCe = fn.split('/')[-1].split('_')[0]
time, flux, xbar, ybar = np.genfromtxt(fn, unpack = True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time,flux,window=3)
zpt = len(time)%300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xbar, ybar, cadstep=300, skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr]/
np.median(flux[zpt:][not_thr])/
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr]/
np.median(flatlc[zpt:][not_thr])/
correction[not_thr])
mad_cut = 1.4826*MAD(corflatflux-1.)*4
keep = np.abs(corflatflux-1.) < mad_cut
plt.plot(time[zpt:][not_thr],corflatflux,c='r')
plt.plot(time[zpt:][not_thr][keep],corflatflux[keep],c='b')
#CDPP Calculations
#thr = twohr_cdpp(flux)
#shr = sixhr_cdpp(flux)
plt.plot(time,flatlc)
plt.figure(figsize=[15,6])
plt.plot(time,flux/np.median(flux),'bo',markersize=1)
plt.plot(time[zpt:][not_thr][keep],corflux[keep],marker='.')
plt.xlabel('Time [d]')
plt.ylabel('Normalized Flux')
#plt.title(str(DANCe) + ' 6.5_Hr_CDPP_' + str(shr) + ' | 2.5_Hr_CCPD_' + str(thr))
#plt.savefig(fn.split('.')[-1] + '.png')
plt.show()
#plt.close('all')
def K2_Detrend(fn, ):
DANCe = fn.split('/')[-1].split('_')[0]
time, flux, xbar, ybar = np.genfromtxt(fn, unpack=True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time, flux, window=3)
zpt = len(time) % 300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time,
flatlc,
xbar,
ybar,
cadstep=300,
skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4
keep = np.abs(corflatflux - 1.) < mad_cut
plt.plot(time[zpt:][not_thr], corflatflux, c='r')
plt.plot(time[zpt:][not_thr][keep], corflatflux[keep], c='b')
#CDPP Calculations
#thr = twohr_cdpp(flux)
#shr = sixhr_cdpp(flux)
plt.plot(time, flatlc)
plt.figure(figsize=[15, 6])
plt.plot(time, flux / np.median(flux), 'bo', markersize=1)
plt.plot(time[zpt:][not_thr][keep], corflux[keep], marker='.')
plt.xlabel('Time [d]')
plt.ylabel('Normalized Flux')
#plt.title(str(DANCe) + ' 6.5_Hr_CDPP_' + str(shr) + ' | 2.5_Hr_CCPD_' + str(thr))
#plt.savefig(fn.split('.')[-1] + '.png')
plt.show()
def K2_DetrendRev3(fn,):
time, flux, xbar, ybar = np.genfromtxt(fn, unpack = True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time,flux,window=3)
zpt = len(time)%300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xbar, ybar, cadstep=300, skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr]/
np.median(flux[zpt:][not_thr])/
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr]/
np.median(flatlc[zpt:][not_thr])/
correction[not_thr])
mad_cut = 1.4826*MAD(corflatflux-1.)*4
keep = np.abs(corflatflux-1.) < mad_cut
# Adds the detrended data to an ascii table.
compiled_table = astropy.table.Table()
compiled_table['time'] = time[zpt:][not_thr]
#compiled_table['corflatflux'] = corflatflux
compiled_table['corflux'] = corflux
#compiled_table['keep'] = keep
#compiled_table['flux'] = flux[zpt:][not_thr]
#compiled_table['flatflux'] = flatlc[zpt:][not_thr]
compiled_table['xbar'] = xbar[zpt:][not_thr]
compiled_table['ybar'] = ybar[zpt:][not_thr]
# Generates the DANCe # for the file title.
DANCe = fn.split('/')[-1].split('.')[0]
# Saves the detrended data to a file. Need to update the path to the correct folder.
np.savetxt('/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap2.0_BJM/Detrended_Data/Detrended_ACF_Data/Raw_Detrended_Data' + DANCe + '.0.dat', compiled_table, delimiter=',')
for i, filename in enumerate(filenames):
#print('{} of {}'.format(i,len(filenames)))
bar.update(i + 1)
time, lc, xbar, ybar, regnum = extract_lc.run_C0_data_extract(filename,
bg_cut=4)
if regnum == 0:
continue
#mask = np.ones(len(lc),dtype=bool)
#mask[range(11,len(lc),12)] = False
#time,lc,xbar,ybar = time[mask], lc[mask], xbar[mask], ybar[mask]
#lets flatten the light curve
flatlc = extract_lc.medfilt(time, lc, window=1.)
zpt = len(time) % 300.
try:
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xbar, ybar, cadstep=300)
except ValueError:
print('{} had an ValueError'.format(filename))
continue
not_thr = ~thr_cad
corflux = (lc[zpt:][not_thr] / np.median(lc[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] /
np.median(flatlc[zpt:][not_thr]) / correction[not_thr])
def K2_DetrendRev4(filename, ap=4.0):
lcs = ascii.read(filename)
outfilename = filename.split("/")[-1][:-4]
logging.info(outfilename)
time = lcs["t"]
x_pos = lcs["x"]
y_pos = lcs["y"]
qual_flux = np.zeros_like(time)
unc_flux = np.ones_like(time)
best_col = "flux_{0:.1f}".format(ap)
flux = lcs[best_col]
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = x_pos[m1]
ybar = y_pos[m1]
flatlc = extract_lc.medfilt(time,flux,window=3)
n_chunks = 6
cadstep = np.int(np.floor(len(time) / n_chunks)) #600
zpt = len(time) % cadstep
if zpt==cadstep:
zpt = 0
logging.info("%d pts %d chunks step=%d zpt=%d ap=%f ",
len(time), n_chunks, cadstep, zpt, ap)
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xbar, ybar, cadstep=cadstep, skip=None)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr]/
np.median(flux[zpt:][not_thr])/
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr]/
np.median(flatlc[zpt:][not_thr])/
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
mad_cut = 1.4826*MAD(corflatflux-1.)*4
keep = np.abs(corflatflux-1.) < mad_cut
# Adds the detrended data to an ascii table.
newtable = {'Dates': time[zpt:][not_thr], 'Flux': flux[zpt:][not_thr], 'Corrflux': corflux, 'Xpos': xbar[zpt:][not_thr], 'Ypos': ybar[zpt:][not_thr]}
ascii.write(newtable, "output_lcs/{0}_detrended.dat".format(outfilename),
names=['Dates','Flux', 'Corrflux','Xpos','Ypos'])
# Create some plots
# Compare to my outputs
sd_lc = ascii.read("../k2spin/output_lcs/{0}_lcs.csv".format(outfilename))
# Raw Lightcurve
plt.clf()
plt.subplot(211)
plt.plot(sd_lc["t"], sd_lc["raw"]/np.median(sd_lc["raw"]), 'c.')
plt.plot(time[zpt:][not_thr], flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]), 'bo', markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim((np.median(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]))-4.5*np.std(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr])),np.median(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]))+
4.5*np.std(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]))))
# Corrected lightcurve
plt.subplot(212)
plt.plot(time[zpt:][not_thr], corflux/np.median(corflux), 'bo', markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim((np.median(corflux/np.median(corflux))-4.5*np.std(corflux/np.median(corflux)),np.median(corflux/np.median(corflux))+4.5*np.std(corflux/np.median(corflux))))
plt.plot(sd_lc["t"], sd_lc["corr"], 'c.')
plt.savefig("plot_outputs/{0}_detrended.png".format(outfilename))
def K2_DetrendRev4(fn, ):
time, flux, xbar, ybar = np.genfromtxt(fn, unpack=True, skip_header=1)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time, flux, window=3)
# zpt = len(time)%300
zpt = len(time) % 327
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
# time, flatlc, xbar, ybar, cadstep=300, skip=1828)
time,
flatlc,
xbar,
ybar,
cadstep=327,
skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4
keep = np.abs(corflatflux - 1.) < mad_cut
# Adds the detrended data to an ascii table.
# To use this in conjunction with the ACF included in this package, you must
# comment out corflatflux, keep, flux, and flatflux, then run the script on
# your data set.
compiled_table = astropy.table.Table()
compiled_table['time'] = time[zpt:][not_thr]
compiled_table['corflatflux'] = corflatflux
compiled_table['corflux'] = corflux
compiled_table['keep'] = keep
compiled_table['flux'] = flux[zpt:][not_thr]
compiled_table['flatflux'] = flatlc[zpt:][not_thr]
compiled_table['xbar'] = xbar[zpt:][not_thr]
compiled_table['ybar'] = ybar[zpt:][not_thr]
# Generates the DANCe # for the file title.
# DANCe = fn.split('/')[-1].split('.')[0]
substr = fn.split('/')[-1]
end = substr.find('.dat')
DANCe = substr[:end]
newtable = {
'Dates': time[zpt:][not_thr],
'Flux': flux[zpt:][not_thr],
'Corrflux': corflux,
'Xpos': xbar[zpt:][not_thr],
'Ypos': ybar[zpt:][not_thr]
}
ascii.write(
newtable,
'/Users/acody/Data/K2/Field_0/M35/Lightcurves_RADec_ap3.0_v4_AMCdetrend/'
+ DANCe + '_detrended.dat',
names=['Dates', 'Flux', 'Corrflux', 'Xpos', 'Ypos'])
# Create some plots
plt.clf()
plt.subplot(211)
plt.plot(time[zpt:][not_thr],
flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]),
'bo',
markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim(
(np.median(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr])) -
4.5 * np.std(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr])),
np.median(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr])) +
4.5 * np.std(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]))))
plt.title = DANCe
plt.subplot(212)
plt.plot(time[zpt:][not_thr],
corflux / np.median(corflux),
'bo',
markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim((np.median(corflux / np.median(corflux)) -
4.5 * np.std(corflux / np.median(corflux)),
np.median(corflux / np.median(corflux)) +
4.5 * np.std(corflux / np.median(corflux))))
plt.savefig(
'/Users/acody/Data/K2/Field_0/M35/Lightcurves_RADec_ap3.0_v4_AMCdetrend/'
+ DANCe + '_detrended.png')
def K2_DetrendRev4(fn,):
time, flux, xbar, ybar = np.genfromtxt(fn, unpack = True, skip_header=1)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time,flux,window=3)
# zpt = len(time)%300
zpt = len(time)%327
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
# time, flatlc, xbar, ybar, cadstep=300, skip=1828)
time, flatlc, xbar, ybar, cadstep=327, skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr]/
np.median(flux[zpt:][not_thr])/
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr]/
np.median(flatlc[zpt:][not_thr])/
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
mad_cut = 1.4826*MAD(corflatflux-1.)*4
keep = np.abs(corflatflux-1.) < mad_cut
# Adds the detrended data to an ascii table.
# To use this in conjunction with the ACF included in this package, you must
# comment out corflatflux, keep, flux, and flatflux, then run the script on
# your data set.
compiled_table = astropy.table.Table()
compiled_table['time'] = time[zpt:][not_thr]
compiled_table['corflatflux'] = corflatflux
compiled_table['corflux'] = corflux
compiled_table['keep'] = keep
compiled_table['flux'] = flux[zpt:][not_thr]
compiled_table['flatflux'] = flatlc[zpt:][not_thr]
compiled_table['xbar'] = xbar[zpt:][not_thr]
compiled_table['ybar'] = ybar[zpt:][not_thr]
# Generates the DANCe # for the file title.
# DANCe = fn.split('/')[-1].split('.')[0]
substr = fn.split('/')[-1]
end = substr.find('.dat')
DANCe = substr[:end]
newtable = {'Dates': time[zpt:][not_thr], 'Flux': flux[zpt:][not_thr], 'Corrflux': corflux, 'Xpos': xbar[zpt:][not_thr], 'Ypos': ybar[zpt:][not_thr]}
ascii.write(newtable, '/Users/acody/Data/K2/Field_0/M35/Lightcurves_RADec_ap3.0_v4_AMCdetrend/'+DANCe + '_detrended.dat', names=['Dates','Flux', 'Corrflux','Xpos','Ypos'])
# Create some plots
plt.clf()
plt.subplot(211)
plt.plot(time[zpt:][not_thr], flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]), 'bo', markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim((np.median(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]))-4.5*np.std(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr])),np.median(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]))+
4.5*np.std(flux[zpt:][not_thr]/np.median(flux[zpt:][not_thr]))))
plt.title = DANCe
plt.subplot(212)
plt.plot(time[zpt:][not_thr], corflux/np.median(corflux), 'bo', markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim((np.median(corflux/np.median(corflux))-4.5*np.std(corflux/np.median(corflux)),np.median(corflux/np.median(corflux))+4.5*np.std(corflux/np.median(corflux))))
plt.savefig('/Users/acody/Data/K2/Field_0/M35/Lightcurves_RADec_ap3.0_v4_AMCdetrend/'+DANCe + '_detrended.png')
def detrendThat(time,
flux,
xpos,
ypos,
ferr=None,
qflags=None,
inpflag=None,
ap=4.0,
cadstep=300):
"""
code to run self flat field on K2 data
Keyword arguments:
time -- time time array
flux -- the array of brightnesses
xpos -- the x-pixel position
ypos -- the y-pixel position
ferr -- flux error, will be assumed to be uniform if None
qflags -- data quality flags
inpflag -- flags to use to explude data, non-zero values are removed
ap -- a random number
"""
if ferr is None:
ferr = np.ones_like(time)
if inpflag is None:
inpflag = np.ones_like(time)
if qflags is None:
qflags = np.zeros_like(time)
# we are going to remove all the bad values
# we should never pass out the time
# this should never trigger
badmask = np.isfinite(flux) * (inpflag > 0.)
time = np.copy(time[badmask])
flux = np.copy(flux[badmask])
ferr = np.copy(ferr[badmask])
xpos = np.copy(xpos[badmask])
ypos = np.copy(ypos[badmask])
flatlc = extract_lc.medfilt(time, flux, window=3)
n_chunks = 6
cadstep = np.int(np.floor(len(time) / n_chunks)) # 600
zpt = len(time) % cadstep
if zpt == cadstep:
zpt = 0
logging.info("%d pts %d chunks step=%d zpt=%d ap=%f ", len(time), n_chunks,
cadstep, zpt, ap)
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time,
flatlc,
xpos,
ypos,
cadstep=cadstep,
skip=None)
not_thr = ~thr_cad
assert len(outflux) == len(correction)
assert len(correction[not_thr]) == len(time[zpt:][not_thr])
corflux = (flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
# this will never trigger
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4
keep = np.abs(corflatflux - 1.) < mad_cut
newflags = np.zeros(len(flux), dtype=bool)
newflags[zpt:][not_thr] = 1
return corflux, corflatflux, correction[not_thr], newflags
def detrendThat(time, flux, xpos, ypos, ferr=None, qflags=None,
inpflag=None,
ap=4.0,
cadstep=300):
"""
code to run self flat field on K2 data
Keyword arguments:
time -- time time array
flux -- the array of brightnesses
xpos -- the x-pixel position
ypos -- the y-pixel position
ferr -- flux error, will be assumed to be uniform if None
qflags -- data quality flags
inpflag -- flags to use to explude data, non-zero values are removed
ap -- a random number
"""
if ferr is None:
ferr = np.ones_like(time)
if inpflag is None:
inpflag = np.ones_like(time)
if qflags is None:
qflags = np.zeros_like(time)
# we are going to remove all the bad values
# we should never pass out the time
# this should never trigger
badmask = np.isfinite(flux) * (inpflag > 0.)
time = np.copy(time[badmask])
flux = np.copy(flux[badmask])
ferr = np.copy(ferr[badmask])
xpos = np.copy(xpos[badmask])
ypos = np.copy(ypos[badmask])
flatlc = extract_lc.medfilt(time, flux, window=3)
n_chunks = 6
cadstep = np.int(np.floor(len(time) / n_chunks)) # 600
zpt = len(time) % cadstep
if zpt == cadstep:
zpt = 0
logging.info("%d pts %d chunks step=%d zpt=%d ap=%f ",
len(time), n_chunks, cadstep, zpt, ap)
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xpos, ypos, cadstep=cadstep, skip=None)
not_thr = ~thr_cad
assert len(outflux) == len(correction)
assert len(correction[not_thr]) == len(time[zpt:][not_thr])
corflux = (flux[zpt:][not_thr] /
np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] /
np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
# this will never trigger
mad_cut = 1.4826*MAD(corflatflux-1.)*4
keep = np.abs(corflatflux-1.) < mad_cut
newflags = np.zeros(len(flux), dtype=bool)
newflags[zpt:][not_thr] = 1
return corflux, corflatflux, correction[not_thr], newflags
def K2_DetrendRev2(fn, ):
#DANCe = fn.split('/')[-1].split('_')[0]
time, flux, xbar, ybar = np.genfromtxt(fn, unpack=True)
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = xbar[m1]
ybar = ybar[m1]
flatlc = extract_lc.medfilt(time, flux, window=3)
zpt = len(time) % 300
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time,
flatlc,
xbar,
ybar,
cadstep=300,
skip=1828)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4
keep = np.abs(corflatflux - 1.) < mad_cut
#plt.plot(time[zpt:][not_thr],corflatflux,c='r')
#plt.plot(time[zpt:][not_thr][keep],corflatflux[keep],c='b')
#CDPP Calculations
#thr = twohr_cdpp(flux)
#shr = sixhr_cdpp(flux)
#plt.plot(time,flatlc)
#plt.figure(figsize=[15,6])
#plt.plot(time,flux/np.median(flux),'bo',markersize=1)
#plt.plot(time[zpt:][not_thr][keep],corflux[keep],marker='.')
#plt.xlabel('Time [d]')
#plt.ylabel('Normalized Flux')
#plt.title(str(DANCe) + ' 6.5_Hr_CDPP_' + str(shr) + ' | 2.5_Hr_CCPD_' + str(thr))
#plt.savefig(fn.split('.')[-1] + '.png')
#plt.show()
#plt.close('all')
compiled_table = astropy.table.Table()
compiled_table['time'] = time[zpt:][
not_thr] #('time', time[zpt:][not_thr])
compiled_table['corflatflux'] = corflatflux
compiled_table['corflux'] = corflux
compiled_table['keep'] = keep
compiled_table['flux'] = flux[zpt:][not_thr]
compiled_table['xbar'] = xbar[zpt:][not_thr]
compiled_table['ybar'] = ybar[zpt:][not_thr]
compiled_table['flatflux'] = flatlc[zpt:][not_thr]
DANCe = fn.split('/')[-1].split('.')[-1]
np.savetxt(
'/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap2.0_BJM/Detrended_Data/'
+ DANCe,
compiled_tbl,
fmt='ascii',
delimiter=',')
def K2_DetrendRev4(filename, ap=4.0):
lcs = ascii.read(filename)
outfilename = filename.split("/")[-1][:-4]
logging.info(outfilename)
time = lcs["t"]
x_pos = lcs["x"]
y_pos = lcs["y"]
qual_flux = np.zeros_like(time)
unc_flux = np.ones_like(time)
best_col = "flux_{0:.1f}".format(ap)
flux = lcs[best_col]
m1 = np.isfinite(flux)
time = time[m1]
flux = flux[m1]
xbar = x_pos[m1]
ybar = y_pos[m1]
flatlc = extract_lc.medfilt(time, flux, window=3)
n_chunks = 6
cadstep = np.int(np.floor(len(time) / n_chunks)) #600
zpt = len(time) % cadstep
if zpt == cadstep:
zpt = 0
logging.info("%d pts %d chunks step=%d zpt=%d ap=%f ", len(time), n_chunks,
cadstep, zpt, ap)
outflux, correction, thr_cad = extract_lc.run_C0_detrend(time,
flatlc,
xbar,
ybar,
cadstep=cadstep,
skip=None)
not_thr = ~thr_cad
corflux = (flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]) /
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr] / np.median(flatlc[zpt:][not_thr]) /
correction[not_thr])
# The 1.4826 and *4 factors make this similar to a 4-sigma cut.
mad_cut = 1.4826 * MAD(corflatflux - 1.) * 4
keep = np.abs(corflatflux - 1.) < mad_cut
# Adds the detrended data to an ascii table.
newtable = {
'Dates': time[zpt:][not_thr],
'Flux': flux[zpt:][not_thr],
'Corrflux': corflux,
'Xpos': xbar[zpt:][not_thr],
'Ypos': ybar[zpt:][not_thr]
}
ascii.write(newtable,
"output_lcs/{0}_detrended.dat".format(outfilename),
names=['Dates', 'Flux', 'Corrflux', 'Xpos', 'Ypos'])
# Create some plots
# Compare to my outputs
sd_lc = ascii.read("../k2spin/output_lcs/{0}_lcs.csv".format(outfilename))
# Raw Lightcurve
plt.clf()
plt.subplot(211)
plt.plot(sd_lc["t"], sd_lc["raw"] / np.median(sd_lc["raw"]), 'c.')
plt.plot(time[zpt:][not_thr],
flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]),
'bo',
markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim(
(np.median(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr])) -
4.5 * np.std(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr])),
np.median(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr])) +
4.5 * np.std(flux[zpt:][not_thr] / np.median(flux[zpt:][not_thr]))))
# Corrected lightcurve
plt.subplot(212)
plt.plot(time[zpt:][not_thr],
corflux / np.median(corflux),
'bo',
markersize=2)
plt.xlabel('Time [d]')
plt.ylabel('Flux/Median flux')
plt.ylim((np.median(corflux / np.median(corflux)) -
4.5 * np.std(corflux / np.median(corflux)),
np.median(corflux / np.median(corflux)) +
4.5 * np.std(corflux / np.median(corflux))))
plt.plot(sd_lc["t"], sd_lc["corr"], 'c.')
plt.savefig("plot_outputs/{0}_detrended.png".format(outfilename))
#print('{} of {}'.format(i,len(filenames)))
bar.update(i+1)
time,lc,xbar,ybar,regnum = extract_lc.run_C0_data_extract(
filename, bg_cut=4)
if regnum == 0:
continue
#mask = np.ones(len(lc),dtype=bool)
#mask[range(11,len(lc),12)] = False
#time,lc,xbar,ybar = time[mask], lc[mask], xbar[mask], ybar[mask]
#lets flatten the light curve
flatlc = extract_lc.medfilt(time,lc,window=1.)
zpt = len(time)%300.
try:
outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time,flatlc,xbar,ybar,cadstep=300)
except ValueError:
print('{} had an ValueError'.format(filename))
continue
not_thr = ~thr_cad
corflux = (lc[zpt:][not_thr]/
np.median(lc[zpt:][not_thr])/
correction[not_thr])
corflatflux = (flatlc[zpt:][not_thr]/