print(B.kepid)
import numpy as np
kepid = np.random.choice(kepio.get_id(cata_path))
filedir = kepio.pathfinder(kepid, data_path, '*')
filenames = glob.glob(filedir)
all_time = []
all_flux = []
for i in range(len(filenames)):
#read into cadence
instr = fits.open(filenames[i])
tstart, tstop, bjdref, cadence = kepio.timekeys(instr, filenames[i])
#reduce lc
intime, nordata = kepreduce.reduce_lc(instr, filenames[i])
#calculte runing stddev
stddev = kepstat.running_frac_std(intime, nordata, 6.5 / 24) * 1.0e6
#cdpp
cdpp = stddev / math.sqrt(6.5 * 3600.0 / cadence)
# filter cdpp
for i in range(len(cdpp)):
if cdpp[i] > np.median(cdpp) * 10.0:
cdpp[i] = cdpp[i - 1]
#calculte RMS cdpp
rms = kepstat.rms(cdpp, np.zeros(len(stddev)))
rs = np.random.RandomState(seed=13)
flux1 = np.zeros(nordata.size)+ np.median(nordata)
errors1 = rms*1e-06*np.ones_like(nordata)
#add gaussian noise
flux1 += errors1*rs.randn(len(nordata))
all_time.append(intime)
def kepstddev(infile,outfile,datacol,timescale,clobber,verbose,logfile,status,cmdLine=False):
# startup parameters
status = 0
labelsize = 44
ticksize = 36
xsize = 16
ysize = 6
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPSTDDEV -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+str(datacol)+' '
call += 'timescale='+str(timescale)+' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber='+overwrite+ ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPSTDDEV started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# clobber output file
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPSTDDEV: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# filter input data table
if status == 0:
work1 = numpy.array([table.field('time'), table.field(datacol)])
work1 = numpy.rot90(work1,3)
work1 = work1[~numpy.isnan(work1).any(1)]
# read table columns
if status == 0:
intime = work1[:,1] + bjdref
indata = work1[:,0]
# calculate STDDEV in units of ppm
if status == 0:
stddev = running_frac_std(intime,indata,timescale/24) * 1.0e6
astddev = numpy.std(indata) * 1.0e6
cdpp = stddev / sqrt(timescale * 3600.0 / cadence)
# filter cdpp
if status == 0:
for i in range(len(cdpp)):
if cdpp[i] > median(cdpp) * 10.0: cdpp[i] = cdpp[i-1]
# calculate median STDDEV
if status == 0:
medcdpp = ones((len(cdpp)),dtype='float32') * median(cdpp[:])
# print '\nMedian %.1fhr standard deviation = %d ppm' % (timescale, median(stddev[:]))
print('\nStandard deviation = %d ppm' % astddev)
# calculate median STDDEV
if status == 0:
medcdpp = ones((len(cdpp)),dtype='float32') * median(cdpp[:])
print('Median %.1fhr CDPP = %d ppm' % (timescale, median(cdpp[:])))
# calculate RMS STDDEV
if status == 0:
rms, status = kepstat.rms(cdpp,zeros(len(stddev)),logfile,verbose)
rmscdpp = ones((len(cdpp)),dtype='float32') * rms
print(' RMS %.1fhr CDPP = %d ppm\n' % (timescale, rms))
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
ptime = intime - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = copy(cdpp)
nrm = math.ceil(math.log10(median(cdpp))) - 1.0
# pout = pout / 10**nrm
# ylab = '%.1fhr $\sigma$ (10$^%d$ ppm)' % (timescale,nrm)
ylab = '%.1fhr $\sigma$ (ppm)' % timescale
# data limits
xmin = ptime.min()
xmax = ptime.max()
ymin = pout.min()
ymax = pout.max()
xr = xmax - xmin
yr = ymax - ymin
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot style
if status == 0:
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 36,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 32,
'ytick.labelsize': 36}
pylab.rcParams.update(params)
except:
pass
# define size of plot on monitor screen
pylab.figure(figsize=[xsize,ysize])
# delete any fossil plots in the matplotlib window
pylab.clf()
# position first axes inside the plotting window
ax = pylab.axes([0.07,0.15,0.92,0.83])
# force tick labels to be absolute rather than relative
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
ax.yaxis.set_major_locator(MaxNLocator(5))
# rotate y labels by 90 deg
labels = ax.get_yticklabels()
pylab.setp(labels, 'rotation', 90,fontsize=36)
# plot flux vs time
ltime = array([],dtype='float64')
ldata = array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for i in range(1,len(ptime)-1):
dt = ptime[i] - ptime[i-1]
if dt < work1:
ltime = append(ltime,ptime[i])
ldata = append(ldata,pout[i])
else:
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = array([],dtype='float64')
ldata = array([],dtype='float32')
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(ptime,pout,fc='#ffff00',linewidth=0.0,alpha=0.2)
# plot median CDPP
# pylab.plot(intime - intime0,medcdpp / 10**nrm,color='r',linestyle='-',linewidth=2.0)
# pylab.plot(intime - intime0,medcdpp,color='r',linestyle='-',linewidth=2.0)
# plot RMS CDPP
# pylab.plot(intime - intime0,rmscdpp / 10**nrm,color='r',linestyle='--',linewidth=2.0)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin - yr * 0.01 <= 0.0:
pylab.ylim(1.0e-10, ymax + yr * 0.01)
else:
pylab.ylim(ymin - yr * 0.01, ymax + yr * 0.01)
# plot labels
pylab.xlabel(xlab, {'color' : 'k'})
pylab.ylabel(ylab, {'color' : 'k'})
# make grid on plot
pylab.grid()
# render plot
if status == 0:
if cmdLine:
pylab.show(block=True)
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# add NaNs back into data
if status == 0:
n = 0
work1 = array([],dtype='float32')
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
for i in range(len(table.field(0))):
if isfinite(table.field('time')[i]) and isfinite(table.field(datacol)[i]):
work1 = append(work1,cdpp[n])
n += 1
else:
work1 = append(work1,nan)
# write output file
if status == 0:
status = kepkey.new('MCDPP%d' % (timescale * 10.0),medcdpp[0],
'Median %.1fhr CDPP (ppm)' % timescale,
instr[1],outfile,logfile,verbose)
status = kepkey.new('RCDPP%d' % (timescale * 10.0),rmscdpp[0],
'RMS %.1fhr CDPP (ppm)' % timescale,
instr[1],outfile,logfile,verbose)
colname = 'CDPP_%d' % (timescale * 10)
col1 = pyfits.Column(name=colname,format='E13.7',array=work1)
cols = instr[1].data.columns + col1
instr[1] = pyfits.new_table(cols,header=instr[1].header)
instr.writeto(outfile)
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# close FITS
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
if (status == 0):
message = 'KEPSTDDEV completed at'
else:
message = '\nKEPSTDDEV aborted at'
kepmsg.clock(message,logfile,verbose)
def GetCDPP(time, trial_lc, npoly, nsig, niter, winsize, stepsize, timescale,
logfile, verbose, status):
# detrend data: find limits of each time step
if status == 0:
npts = len(time)
tstep1 = []
tstep2 = []
work = time[0]
while work <= time[-1]:
tstep1.append(work)
tstep2.append(
array([work + winsize, time[-1]], dtype='float64').min())
work += stepsize
# detrend data: find cadence limits of each time step
if status == 0:
cstep1 = []
cstep2 = []
for n in range(len(tstep1)):
for i in range(len(time) - 1):
if time[i] <= tstep1[n] and time[i + 1] > tstep1[n]:
for j in range(i, len(time) - 1):
if time[j] < tstep2[n] and time[j + 1] >= tstep2[n]:
cstep1.append(i)
cstep2.append(j + 1)
# detrend data: loop over each time step, fit data, determine rms
if status == 0:
fitarray = zeros((npts, len(cstep1)), dtype='float32')
fitarray[:, :] = numpy.nan
masterfit = trial_lc * 0.0
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
timeSeries = time[cstep1[i]:cstep2[i] + 1] - time[cstep1[i]]
dataSeries = trial_lc[cstep1[i]:cstep2[i] + 1]
pinit = [dataSeries.mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit, dtype='float32')
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,timeSeries,dataSeries,None,nsig,nsig,niter,
logfile,verbose)
fitarray[cstep1[i]:cstep2[i] + 1, i] = 0.0
for j in range(len(coeffs)):
fitarray[cstep1[i]:cstep2[i] + 1,
i] += coeffs[j] * timeSeries**j
except:
for j in range(cstep1[i], cstep2[i] + 1):
fitarray[cstep1[i]:cstep2[i] + 1, i] = 0.0
# message = 'WARNING -- KEPFLATTEN: could not fit range '
# message += str(time[cstep1[i]]) + '-' + str(time[cstep2[i]])
# kepmsg.warn(None,message)
# detrend data: find mean fit for each timestamp
if status == 0:
for i in range(npts):
masterfit[i] = nanmean(fitarray[i, :])
masterfit[-1] = masterfit[-4] #fudge
masterfit[-2] = masterfit[-4] #fudge
masterfit[-3] = masterfit[-4] #fudge
# detrend data: normalize light curve
if status == 0:
trial_lc = trial_lc / masterfit
# calculate STDDEV in units of ppm
if status == 0:
stddev = kepstat.running_frac_std(time, trial_lc,
timescale / 24) * 1.0e6
# calculate median STDDEV
if status == 0:
medstddev = ones((len(stddev)), dtype='float32') * median(stddev)
# print '\nMedian %.1fhr STDDEV = %d ppm' % (timescale, median(stddev))
return median(stddev), stddev, status
def kepstddev(infile,outfile,datacol,timescale,clobber,verbose,logfile,status,cmdLine=False):
# startup parameters
status = 0
labelsize = 44
ticksize = 36
xsize = 16
ysize = 6
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPSTDDEV -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+str(datacol)+' '
call += 'timescale='+str(timescale)+' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber='+overwrite+ ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPSTDDEV started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# clobber output file
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPSTDDEV: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# filter input data table
if status == 0:
work1 = numpy.array([table.field('time'), table.field(datacol)])
work1 = numpy.rot90(work1,3)
work1 = work1[~numpy.isnan(work1).any(1)]
# read table columns
if status == 0:
intime = work1[:,1] + bjdref
indata = work1[:,0]
# calculate STDDEV in units of ppm
if status == 0:
stddev = running_frac_std(intime,indata,timescale/24) * 1.0e6
astddev = numpy.std(indata) * 1.0e6
cdpp = stddev / sqrt(timescale * 3600.0 / cadence)
# filter cdpp
if status == 0:
for i in range(len(cdpp)):
if cdpp[i] > median(cdpp) * 10.0: cdpp[i] = cdpp[i-1]
# calculate median STDDEV
if status == 0:
medcdpp = ones((len(cdpp)),dtype='float32') * median(cdpp[:])
# print '\nMedian %.1fhr standard deviation = %d ppm' % (timescale, median(stddev[:]))
print '\nStandard deviation = %d ppm' % astddev
# calculate median STDDEV
if status == 0:
medcdpp = ones((len(cdpp)),dtype='float32') * median(cdpp[:])
print 'Median %.1fhr CDPP = %d ppm' % (timescale, median(cdpp[:]))
# calculate RMS STDDEV
if status == 0:
rms, status = kepstat.rms(cdpp,zeros(len(stddev)),logfile,verbose)
rmscdpp = ones((len(cdpp)),dtype='float32') * rms
print ' RMS %.1fhr CDPP = %d ppm\n' % (timescale, rms)
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
ptime = intime - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = copy(cdpp)
nrm = math.ceil(math.log10(median(cdpp))) - 1.0
# pout = pout / 10**nrm
# ylab = '%.1fhr $\sigma$ (10$^%d$ ppm)' % (timescale,nrm)
ylab = '%.1fhr $\sigma$ (ppm)' % timescale
# data limits
xmin = ptime.min()
xmax = ptime.max()
ymin = pout.min()
ymax = pout.max()
xr = xmax - xmin
yr = ymax - ymin
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot style
if status == 0:
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 36,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 32,
'ytick.labelsize': 36}
pylab.rcParams.update(params)
except:
pass
# define size of plot on monitor screen
pylab.figure(figsize=[xsize,ysize])
# delete any fossil plots in the matplotlib window
pylab.clf()
# position first axes inside the plotting window
ax = pylab.axes([0.07,0.15,0.92,0.83])
# force tick labels to be absolute rather than relative
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
ax.yaxis.set_major_locator(MaxNLocator(5))
# rotate y labels by 90 deg
labels = ax.get_yticklabels()
pylab.setp(labels, 'rotation', 90,fontsize=36)
# plot flux vs time
ltime = array([],dtype='float64')
ldata = array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for i in range(1,len(ptime)-1):
dt = ptime[i] - ptime[i-1]
if dt < work1:
ltime = append(ltime,ptime[i])
ldata = append(ldata,pout[i])
else:
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = array([],dtype='float64')
ldata = array([],dtype='float32')
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(ptime,pout,fc='#ffff00',linewidth=0.0,alpha=0.2)
# plot median CDPP
# pylab.plot(intime - intime0,medcdpp / 10**nrm,color='r',linestyle='-',linewidth=2.0)
# pylab.plot(intime - intime0,medcdpp,color='r',linestyle='-',linewidth=2.0)
# plot RMS CDPP
# pylab.plot(intime - intime0,rmscdpp / 10**nrm,color='r',linestyle='--',linewidth=2.0)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin - yr * 0.01 <= 0.0:
pylab.ylim(1.0e-10, ymax + yr * 0.01)
else:
pylab.ylim(ymin - yr * 0.01, ymax + yr * 0.01)
# plot labels
pylab.xlabel(xlab, {'color' : 'k'})
pylab.ylabel(ylab, {'color' : 'k'})
# make grid on plot
pylab.grid()
# render plot
if status == 0:
if cmdLine:
pylab.show(block=True)
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# add NaNs back into data
if status == 0:
n = 0
work1 = array([],dtype='float32')
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
for i in range(len(table.field(0))):
if isfinite(table.field('time')[i]) and isfinite(table.field(datacol)[i]):
work1 = append(work1,cdpp[n])
n += 1
else:
work1 = append(work1,nan)
# write output file
if status == 0:
status = kepkey.new('MCDPP%d' % (timescale * 10.0),medcdpp[0],
'Median %.1fhr CDPP (ppm)' % timescale,
instr[1],outfile,logfile,verbose)
status = kepkey.new('RCDPP%d' % (timescale * 10.0),rmscdpp[0],
'RMS %.1fhr CDPP (ppm)' % timescale,
instr[1],outfile,logfile,verbose)
colname = 'CDPP_%d' % (timescale * 10)
col1 = pyfits.Column(name=colname,format='E13.7',array=work1)
cols = instr[1].data.columns + col1
instr[1] = pyfits.new_table(cols,header=instr[1].header)
instr.writeto(outfile)
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# close FITS
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
if (status == 0):
message = 'KEPSTDDEV completed at'
else:
message = '\nKEPSTDDEV aborted at'
kepmsg.clock(message,logfile,verbose)
def rmsestimation(quarter, timescale):
"""
do stastitical analysis of rms cdpp(expected snr) of certain quarter
"""
#read data from keplerstellar catalog and match lightcurve in lc database
cata_path = '../catalog/cumulative.csv'
data_path = '/scratch/kepler_data/'
kepid = kepio.get_id(cata_path)
all_rms = []
own_kid = []
for i, k_id in enumerate(kepid[:100]):
#print('This is '+str(kepid[i]))
#lc path here
file_dir = kepio.pathfinder(k_id, data_path, quarter)
try:
filename = glob.glob(file_dir)
name = filename[0]
#open file and read time keys
instr = fits.open(name)
tstart, tstop, bjdref, cadence = kepio.timekeys(instr, filename)
#read lc
hdu = instr[1]
time = hdu.data.TIME
time = time + bjdref - 2454900
flux = hdu.data.PDCSAP_FLUX
#filter data
work1 = np.array([time, flux])
work1 = np.rot90(work1, 3)
work1 = work1[~np.isnan(work1).any(1)]
intime = work1[:, 1]
indata = work1[:, 0]
#split lc
intime, indata = keputils.split(intime, indata, gap_width=0.75)
#calculate breaking points
bkspaces = np.logspace(np.log10(0.5), np.log10(20), num=20)
#calculate spline to every data points
spline = kepspline.choose_kepler_spline(intime,
indata,
bkspaces,
penalty_coeff=1.0,
verbose=False)[0]
if spline is None:
raise ValueError("faied to fit spline")
#flatten the data array
intime = np.concatenate(intime).ravel()
indata = np.concatenate(indata).ravel()
spline = np.concatenate(spline).ravel()
#normalized flux using spline
nordata = indata / spline
#calculte runing stddev
stddev = kepstat.running_frac_std(intime, nordata,
timescale / 24) * 1.0e6
#cdpp
cdpp = stddev / math.sqrt(timescale * 3600.0 / cadence)
# filter cdpp
for i in range(len(cdpp)):
if cdpp[i] > np.median(cdpp) * 10.0:
cdpp[i] = cdpp[i - 1]
#calculte RMS cdpp
rms = kepstat.rms(cdpp, np.zeros(len(stddev)))
rmscdpp = np.ones((len(cdpp)), dtype='float32') * rms
if rms > 200:
plt.figure(figsize=(10, 8))
#plt.hist(cdpp, bins =25, color = 'gold', fill = True, edgecolor = 'black', linewidth = 2.0)
#plt.ylabel('Count')
#plt.xlabel('CDPP/6.5hrs(ppm)')
#plt.savefig("./test/KOIQ"+str(quarter)+str(k_id)+"cdpp.png")
plt.scatter(intime, nordata, marker='.')
plt.savefig("./test/KOIQ" + str(quarter) + str(k_id) +
"display_lc.png")
# print('%d has RMS %.1fhr CDPP = %d ppm\n' % (k_id,timescale, rms))
all_rms.append(rms)
own_kid.append(k_id)
except IndexError:
pass
c, low, upp = sigmaclip(all_rms, 3, 3)
plt.figure(figsize=(10, 8))
plt.hist(c,
bins=25,
range=(low, upp),
fill=True,
edgecolor='black',
linewidth=2.0)
plt.ylabel('Count')
plt.xlabel('RMSCDPP(ppm)')
#plt.savefig("./result/KOIQ"+str(quarter)+"rms.png")
result = np.transpose([own_kid, all_rms])
def GetCDPP(time,trial_lc,npoly,nsig,niter,winsize,stepsize,timescale,logfile,verbose,status):
# detrend data: find limits of each time step
if status == 0:
npts = len(time)
tstep1 = []; tstep2 = []
work = time[0]
while work <= time[-1]:
tstep1.append(work)
tstep2.append(array([work+winsize,time[-1]],dtype='float64').min())
work += stepsize
# detrend data: find cadence limits of each time step
if status == 0:
cstep1 = []; cstep2 = []
for n in range(len(tstep1)):
for i in range(len(time)-1):
if time[i] <= tstep1[n] and time[i+1] > tstep1[n]:
for j in range(i,len(time)-1):
if time[j] < tstep2[n] and time[j+1] >= tstep2[n]:
cstep1.append(i)
cstep2.append(j+1)
# detrend data: loop over each time step, fit data, determine rms
if status == 0:
fitarray = zeros((npts,len(cstep1)),dtype='float32')
fitarray[:,:] = numpy.nan
masterfit = trial_lc * 0.0
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
timeSeries = time[cstep1[i]:cstep2[i]+1]-time[cstep1[i]]
dataSeries = trial_lc[cstep1[i]:cstep2[i]+1]
pinit = [dataSeries.mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit,dtype='float32')
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,timeSeries,dataSeries,None,nsig,nsig,niter,
logfile,verbose)
fitarray[cstep1[i]:cstep2[i]+1,i] = 0.0
for j in range(len(coeffs)):
fitarray[cstep1[i]:cstep2[i]+1,i] += coeffs[j] * timeSeries**j
except:
for j in range(cstep1[i],cstep2[i]+1):
fitarray[cstep1[i]:cstep2[i]+1,i] = 0.0
# message = 'WARNING -- KEPFLATTEN: could not fit range '
# message += str(time[cstep1[i]]) + '-' + str(time[cstep2[i]])
# kepmsg.warn(None,message)
# detrend data: find mean fit for each timestamp
if status == 0:
for i in range(npts):
masterfit[i] = nanmean(fitarray[i,:])
masterfit[-1] = masterfit[-4] #fudge
masterfit[-2] = masterfit[-4] #fudge
masterfit[-3] = masterfit[-4] #fudge
# detrend data: normalize light curve
if status == 0:
trial_lc = trial_lc / masterfit
# calculate STDDEV in units of ppm
if status == 0:
stddev = kepstat.running_frac_std(time,trial_lc,timescale/24) * 1.0e6
# calculate median STDDEV
if status == 0:
medstddev = ones((len(stddev)),dtype='float32') * median(stddev)
# print '\nMedian %.1fhr STDDEV = %d ppm' % (timescale, median(stddev))
return median(stddev), stddev, status