@wraps(function)

def function_timer(*args, **kwargs):

t0 = time.time()

result = function(*args, **kwargs)

t1 = time.time()

print ("Total time running %s: %s seconds" %

(function.func_name, str(t1-t0))

)

return result

"""

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])

#np.savetxt("./result/KOIQ"+str(quarter)+"rms.txt",result)