In [108]:

    

from __future__ import division, print_function
%matplotlib inline


    

In [109]:

    

import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table


    

In [110]:

    

from martinsff import martinsff
import extract_lc


    

In [111]:

    

#from os import listdir
#from os.path import isfile, join
#onlyfiles = [ f for f in listdir('/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Light_Curves/dat_files/') if isfile(join('/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Light_Curves/dat_files/',f)) ]


    

In [112]:

    

#for row in onlyfiles:
    #fn = row
    #time, flux, xbar, ybar = np.genfromtxt(fn, unpack = True)

fn = '/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap3.0_BJM/J06101557+2436535_xy_v2_ap3.0.dat'
time, flux, xbar, ybar = np.genfromtxt(fn, unpack = True)
DANCe = 'J06101557+2436535'


    

In [113]:

    

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


    

In [114]:

    

outflux, correction, thr_cad = extract_lc.run_C0_detrend(
time, flatlc, xbar, ybar, cadstep=300)


    

In [115]:

    

plt.plot(time,flatlc)


    

    
Out[115]:





[<matplotlib.lines.Line2D at 0x114e26f10>]






    

In [118]:

    

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])


    

In [119]:

    

from astropy.stats import median_absolute_deviation as MAD
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')


    

    
Out[119]:





[<matplotlib.lines.Line2D at 0x116e83fd0>]






    

In [120]:

    

from scipy import stats

# 2.5 hr cdpp function
def twohr_cdpp(flux):
    bin_sigma = []
    for k in range(len(flux)-5):
        bin_sigma.append(stats.nanstd(flux[k:k+5])/np.sqrt(5.))
    cdpp = stats.nanmedian(bin_sigma)
    return cdpp

# 6.5 hr cdpp function
def sixhr_cdpp(flux):
    bin_sigma = []
    for k in range(len(flux)-13):
        bin_sigma.append(stats.nanstd(flux[k:k+13])/np.sqrt(13.))
    cdpp = stats.nanmedian(bin_sigma)
    return cdpp


    

In [121]:

    

thr = twohr_cdpp(flux)
shr = sixhr_cdpp(flux)


    

In [122]:

    

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('/Users/bryanmann/Documents/NASA_Kepler_2.0/All_Light_Curves/Lightcurves_RADec_ap3.0_BJM/J06101557+2436535_xy_v2_ap3.0.png')


    

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