def test2(kepid):
filedir = [
kepio.pathfinder(kepid, '/scratch/kepler_data/', q) for q in range(18)
]
with Pool(5) as p:
q2 = p.map(process2, filedir)
return q2
import kepreduce
from transit_basic import *
instr = fits.open('/scratch/kepler_data/kepler_Q16/kplr008123937-2013098041711_llc.fits')
filename = '/scratch/kepler_data/kepler_Q16/kplr008123937-2013098041711_llc.fits'
print(kepio.timekeys(instr, filename))
cata_path = '../catalog/cumulative.csv'
data_path = '/scratch/kepler_data/'
B = LcNoiseSampler(cata_path, data_path)
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)):
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])