In [1]:

    

from pyke import KeplerTargetPixelFile
%matplotlib inline


    

In [2]:

    

import numpy as np


    

In [3]:

    

import matplotlib.pyplot as plt


    

In [4]:

    

from oktopus import UniformPrior, JointPrior


    

In [5]:

    

from pyke import PRFPhotometry, SceneModel
from pyke.utils import KeplerQualityFlags


    

In [6]:

    

tpf = KeplerTargetPixelFile('https://archive.stsci.edu/missions/k2/target_pixel_files/c12/246100000/99000/ktwo246199087-c12_lpd-targ.fits.gz',
                            quality_mask=KeplerQualityFlags.HARDEST_BITMASK)


    

In [7]:

    

tpf.plot()


    

In [8]:

    

prf = tpf.get_prf_model()


    

In [9]:

    

prior_column = UniformPrior(lb=990, ub=996, name='column')
prior_row = UniformPrior(lb=25, ub=30, name='row')
prior_flux = UniformPrior(lb=4e3, ub=2e4, name='flux')
prior_bkg = UniformPrior(lb=1, ub=2e3, name='bkg')


    

In [10]:

    

prior = JointPrior(prior_flux, prior_column, prior_row, prior_bkg)


    

In [11]:

    

scene = SceneModel(prfs=[prf])


    

In [12]:

    

phot = PRFPhotometry(scene_model=scene, prior=prior)


    

In [13]:

    

results = phot.fit(tpf.flux + tpf.flux_bkg)


    

    




  0%|          | 0/3379 [00:00<?, ?it/s]/Users/jvmirca/anaconda3/lib/python3.6/site-packages/autograd/tracer.py:48: RuntimeWarning: invalid value encountered in log
  return f_raw(*args, **kwargs)
 35%|███▍      | 1177/3379 [00:58<01:50, 20.01it/s]/Users/jvmirca/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:1850: RuntimeWarning: invalid value encountered in double_scalars
  tmp2 = (x - v) * (fx - fw)
/Users/jvmirca/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:1851: RuntimeWarning: invalid value encountered in double_scalars
  p = (x - v) * tmp2 - (x - w) * tmp1
/Users/jvmirca/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:1852: RuntimeWarning: invalid value encountered in double_scalars
  tmp2 = 2.0 * (tmp2 - tmp1)
100%|██████████| 3379/3379 [02:41<00:00, 20.96it/s]

In [14]:

    

flux = results[:, 0]
col = results[:, 1]
row = results[:, 2]
bkg = results[:, 3]


    

In [15]:

    

plt.figure(figsize=[14, 4])
plt.plot(tpf.time, flux, 'o', markersize=2)


    

    
Out[15]:





[<matplotlib.lines.Line2D at 0x10ce37470>]






    

In [16]:

    

q = tpf.time < 2956


    

In [17]:

    

plt.figure(figsize=[14, 4])
plt.plot(tpf.time[q], flux[q], 'o', markersize=2)


    

    
Out[17]:





[<matplotlib.lines.Line2D at 0x10ce9da58>]






    

In [18]:

    

plt.figure(figsize=[14, 4])
plt.plot(tpf.time[~q], flux[~q], 'o', markersize=2)
plt.ylim(5400, 6000)


    

    
Out[18]:





(5400, 6000)






    

In [19]:

    

plt.figure(figsize=[14, 4])
plt.plot(tpf.time[q], col[q], 'o', markersize=2)


    

    
Out[19]:





[<matplotlib.lines.Line2D at 0x10cf51f28>]






    

In [20]:

    

plt.figure(figsize=[14, 4])
plt.plot(tpf.time[q], row[q], 'o', markersize=2)


    

    
Out[20]:





[<matplotlib.lines.Line2D at 0x10da04668>]






    

In [21]:

    

np.warnings.filterwarnings('ignore')


    

In [22]:

    

from pyke.lightcurve import SFFCorrector


    

In [70]:

    

sff = SFFCorrector()
lc1 = sff.correct(tpf.time[q], flux[q], col[q], row[q], niters=10, windows=13, polyorder=6)
plt.figure(figsize=[14, 4])
plt.plot(lc1.time, lc1.flux, 'ko', markersize=3)
plt.plot(lc1.time, lc1.flux, 'o', markersize=2)
plt.ylim(.975, 1.025)


    

    




100%|██████████| 13/13 [00:01<00:00,  8.24it/s]






    
Out[70]:





(0.975, 1.025)






    

In [71]:

    

sff._plot_normflux_arclength()
plt.ylim(.9, 1.1)


    

    
Out[71]:





(0.9, 1.1)






    

In [72]:

    

sff._plot_rotated_centroids()


    

    
Out[72]:





<matplotlib.axes._subplots.AxesSubplot at 0x1c23420160>






    

In [73]:

    

lc2 = sff.correct(tpf.time[~q], flux[~q], col[~q], row[~q], niters=10, windows=13)
plt.figure(figsize=[14, 4])
plt.plot(lc2.time, lc2.flux, 'ko', markersize=3)
plt.plot(lc2.time, lc2.flux, 'o', markersize=2)
plt.ylim(.975, 1.025)


    

    




100%|██████████| 13/13 [00:01<00:00,  9.16it/s]






    
Out[73]:





(0.975, 1.025)






    

In [74]:

    

lc = lc1.stitch(lc2)


    

In [75]:

    

plt.figure(figsize=[14, 4])
plt.plot(lc.time, lc.flux, 'ko', markersize=3)
plt.plot(lc.time, lc.flux, 'o', markersize=2)
plt.ylim(.975, 1.025)


    

    
Out[75]:





(0.975, 1.025)






    

In [76]:

    

fold_p1 = lc.fold(1.5108739, phase=2457322.51736 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p1.time, fold_p1.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p1.time, fold_p1.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[76]:





(-0.05, 0.05)






    

In [77]:

    

fold_p2 = lc.fold(2.421818, phase=2457282.80728 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p2.time, fold_p2.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p2.time, fold_p2.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[77]:





(-0.05, 0.05)






    

In [78]:

    

fold_p3 = lc.fold(4.049610, phase=2457670.14165 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p3.time, fold_p3.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p3.time, fold_p3.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[78]:





(-0.05, 0.05)






    

In [79]:

    

fold_p4 = lc.fold(6.099615, phase=2457660.37859 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p4.time, fold_p4.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p4.time, fold_p4.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[79]:





(-0.05, 0.05)






    

In [80]:

    

fold_p5 = lc.fold(9.206690, phase=2457671.39767 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p5.time, fold_p5.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p5.time, fold_p5.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[80]:





(-0.05, 0.05)






    

In [81]:

    

fold_p6 = lc.fold(12.35294, phase=2457665.34937 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p6.time, fold_p6.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p6.time, fold_p6.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[81]:





(-0.05, 0.05)






    

In [82]:

    

fold_p7 = lc.fold(18.767, phase=2457662.55284 - 2454833.0)
plt.figure(figsize=[7, 3])
plt.plot(fold_p7.time, fold_p7.flux, 'ko', markersize=3, alpha=.2)
plt.plot(fold_p7.time, fold_p7.flux, 'o', markersize=2, alpha=.2)
plt.ylim(.975, 1.025)
plt.xlim(-.05, .05)


    

    
Out[82]:





(-0.05, 0.05)






    

In [ ]: