What's new in PyKE 3.1?

Utility functions

PyKE has included two convinience functions to convert between module.output to channel and vice-versa:


In [1]:
%matplotlib inline
import matplotlib.pyplot as plt

In [2]:
from pyke.utils import module_output_to_channel, channel_to_module_output


/Users/jvmirca/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:1405: UserWarning: 
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

  warnings.warn(_use_error_msg)

In [3]:
module_output_to_channel(module=19, output=3)


Out[3]:
67

In [4]:
channel_to_module_output(67)


Out[4]:
(19, 3)

PyKE 3.1 includes KeplerQualityFlags class which encodes the meaning of the Kepler QUALITY bitmask flags as documented in the Kepler Archive Manual (Table 2.3):


In [5]:
from pyke.utils import KeplerQualityFlags

In [6]:
KeplerQualityFlags.decode(1)


Out[6]:
['Attitude tweak']

It also can handle multiple flags:


In [7]:
KeplerQualityFlags.decode(1 + 1024 + 1048576)


Out[7]:
['Attitude tweak', 'Sudden sensitivity dropout', 'Thruster firing']

A few quality flags are already computed:


In [8]:
KeplerQualityFlags.decode(KeplerQualityFlags.DEFAULT_BITMASK)


Out[8]:
['Attitude tweak',
 'Safe mode',
 'Coarse point',
 'Earth point',
 'Desaturation event',
 'Cosmic ray in optimal aperture',
 'Manual exclude',
 'No data',
 'Thruster firing']

In [9]:
KeplerQualityFlags.decode(KeplerQualityFlags.CONSERVATIVE_BITMASK)


Out[9]:
['Attitude tweak',
 'Safe mode',
 'Coarse point',
 'Earth point',
 'Desaturation event',
 'Cosmic ray in optimal aperture',
 'Manual exclude',
 'Sudden sensitivity dropout',
 'Cosmic ray in collateral data',
 'No data',
 'Possible thruster firing',
 'Thruster firing']

Target Pixel File (TPF)

PyKE 3.1 includes class called KeplerTargetPixelFile which is used to handle target pixel files:


In [10]:
from pyke import KeplerTargetPixelFile

A KeplerTargetPixelFile can be instantiated either from a local file or a url:


In [11]:
tpf = KeplerTargetPixelFile('https://archive.stsci.edu/missions/k2/target_pixel_files/c14/'
                            '200100000/82000/ktwo200182949-c14_lpd-targ.fits.gz')

Additionally, we can mask out cadences that are flagged using the quality_bitmask argument in the constructor:


In [12]:
tpf = KeplerTargetPixelFile('https://archive.stsci.edu/missions/k2/target_pixel_files/c14/'
                            '200100000/82000/ktwo200182949-c14_lpd-targ.fits.gz',
                            quality_bitmask=KeplerQualityFlags.CONSERVATIVE_BITMASK)

Furthermore, we can mask out pixel values using the aperture_mask argument. The default behaviour is to use all pixels that have real values. This argument can also get a string value 'kepler-pipeline', in which case the default aperture used by Kepler's pipeline is applied.


In [13]:
tpf = KeplerTargetPixelFile('https://archive.stsci.edu/missions/k2/target_pixel_files/c14/'
                            '200100000/82000/ktwo200182949-c14_lpd-targ.fits.gz',
                            aperture_mask='kepler-pipeline',
                            quality_bitmask=KeplerQualityFlags.CONSERVATIVE_BITMASK)

In [14]:
tpf.aperture_mask


Out[14]:
array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ..., 
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]], dtype=bool)

The TPF objects stores both data and a few metadata information, e.g., channel number, EPIC number, reference column and row, module, and shape. The whole header is also available:


In [15]:
tpf.header(ext=0)


Out[15]:
SIMPLE  =                    T / conforms to FITS standards                     
BITPIX  =                    8 / array data type                                
NAXIS   =                    0 / number of array dimensions                     
EXTEND  =                    T / file contains extensions                       
NEXTEND =                    2 / number of standard extensions                  
EXTNAME = 'PRIMARY '           / name of extension                              
EXTVER  =                    1 / extension version number (not format version)  
ORIGIN  = 'NASA/Ames'          / institution responsible for creating this file 
DATE    = '2017-10-30'         / file creation date.                            
CREATOR = '472097 TargetPixelExporterPipelineModule' / pipeline job and program 
PROCVER = 'svn+ssh://murzim/repo/soc/tags/release/9.3.75 r62933' / SW version   
FILEVER = '6.1     '           / file format version                            
TIMVERSN= 'OGIP/93-003'        / OGIP memo number for file format               
TELESCOP= 'Kepler  '           / telescope                                      
INSTRUME= 'Kepler Photometer'  / detector type                                  
OBJECT  = 'EPIC 200182949'     / string version of target id                    
KEPLERID=            200182949 / unique Kepler target identifier                
CHANNEL =                    1 / CCD channel                                    
MODULE  =                    2 / CCD module                                     
OUTPUT  =                    1 / CCD output                                     
CAMPAIGN=                   14 / Observing campaign number                      
DATA_REL=                   20 / data release version number                    
OBSMODE = 'long cadence'       / observing mode                                 
MISSION = 'K2      '           / Mission name                                   
TTABLEID=                   92 / target table id                                
RADESYS = 'ICRS    '           / reference frame of celestial coordinates       
RA_OBJ  =           165.706242 / [deg] right ascension                          
DEC_OBJ =             2.769444 / [deg] declination                              
EQUINOX =               2000.0 / equinox of celestial coordinate system         
PMRA    =                      / [arcsec/yr] RA proper motion                   
PMDEC   =                      / [arcsec/yr] Dec proper motion                  
PMTOTAL =                      / [arcsec/yr] total proper motion                
PARALLAX=                      / [arcsec] parallax                              
GLON    =                      / [deg] galactic longitude                       
GLAT    =                      / [deg] galactic latitude                        
GMAG    =                      / [mag] SDSS g band magnitude                    
RMAG    =                      / [mag] SDSS r band magnitude                    
IMAG    =                      / [mag] SDSS i band magnitude                    
ZMAG    =                      / [mag] SDSS z band magnitude                    
JMAG    =                      / [mag] J band magnitude from 2MASS              
HMAG    =                      / [mag] H band magnitude from 2MASS              
KMAG    =                      / [mag] K band magnitude from 2MASS              
KEPMAG  =                      / [mag] Kepler magnitude (Kp)                    
GRCOLOR =                      / [mag] (g-r) color, SDSS bands                  
JKCOLOR =                      / [mag] (J-K) color, 2MASS bands                 
GKCOLOR =                      / [mag] (g-K) color, SDSS g - 2MASS K            
TEFF    =                      / [K] Effective temperature                      
LOGG    =                      / [cm/s2] log10 surface gravity                  
FEH     =                      / [log10([Fe/H])]  metallicity                   
EBMINUSV=                      / [mag] E(B-V) reddening                         
AV      =                      / [mag] A_v extinction                           
RADIUS  =                      / [solar radii] stellar radius                   
TMINDEX =                      / unique 2MASS catalog ID                        
CHECKSUM= 'QkmmQijlQijlQijl'   / HDU checksum updated 2017-10-30T20:42:50Z      

The pixel fluxes time series can be accessed using the flux property:


In [16]:
tpf.flux.shape


Out[16]:
(3209, 35, 35)

This shows that our TPF is a 35 x 35 image recorded over 3209 cadences.

One can visualize the pixel data at a given cadence using the plot method:


In [17]:
tpf.plot(frame=1)


We can perform aperture photometry using the method to_lightcurve:


In [18]:
lc = tpf.to_lightcurve()

In [19]:
plt.figure(figsize=[17, 4])
plt.plot(lc.time, lc.flux)


Out[19]:
[<matplotlib.lines.Line2D at 0x129c079e8>]

Light Curves

Let's see how the previous light curve compares against the 'SAP_FLUX' produced by Kepler's pipeline. For that, we are going to explore the KeplerLightCurveFile class:


In [20]:
from pyke.lightcurve import KeplerLightCurveFile

In [21]:
klc = KeplerLightCurveFile('https://archive.stsci.edu/missions/k2/lightcurves/'
                           'c14/200100000/82000/ktwo200182949-c14_llc.fits',
                           quality_bitmask=KeplerQualityFlags.CONSERVATIVE_BITMASK)

In [22]:
sap_lc = klc.SAP_FLUX

In [23]:
plt.figure(figsize=[17, 4])
plt.plot(lc.time, lc.flux)
plt.plot(sap_lc.time, sap_lc.flux)
plt.ylabel('Flux (e-/s)')
plt.xlabel('Time (BJD - 2454833)')


Out[23]:
<matplotlib.text.Text at 0x12a0af400>

Now, let's correct this light curve using by fitting cotrending basis vectors. That can be achived either with the KeplerCBVCorrector class or the compute_cotrended_lightcurve in KeplerLightCurveFile. Let's try the latter:


In [24]:
klc_corrected = klc.compute_cotrended_lightcurve(cbvs=range(1, 17))

In [25]:
plt.figure(figsize=[17, 4])
plt.plot(klc_corrected.time, klc_corrected.flux)
plt.ylabel('Flux (e-/s)')
plt.xlabel('Time (BJD - 2454833)')


Out[25]:
<matplotlib.text.Text at 0x12a1c1c50>

In [26]:
pdcsap_lc = klc.PDCSAP_FLUX

In [27]:
plt.figure(figsize=[17, 4])
plt.plot(klc_corrected.time, klc_corrected.flux)
plt.plot(pdcsap_lc.time, pdcsap_lc.flux)
plt.ylabel('Flux (e-/s)')
plt.xlabel('Time (BJD - 2454833)')


Out[27]:
<matplotlib.text.Text at 0x12a0bc0f0>

Pixel Response Function (PRF) Photometry

PyKE 3.1 also includes tools to perform PRF Photometry:


In [28]:
from pyke.prf import PRFPhotometry, SceneModel, SimpleKeplerPRF

For that, let's create a SceneModel which will be fitted to the object of the following TPF:


In [29]:
tpf = KeplerTargetPixelFile('https://archive.stsci.edu/missions/k2/target_pixel_files/c14/'
                            '201500000/43000/ktwo201543306-c14_lpd-targ.fits.gz',
                            quality_bitmask=KeplerQualityFlags.CONSERVATIVE_BITMASK)

In [30]:
tpf.plot(frame=100)



In [31]:
scene = SceneModel(prfs=[SimpleKeplerPRF(channel=tpf.channel, shape=tpf.shape[1:],
                                         column=tpf.column, row=tpf.row)])

We also need to define prior distributions on the parameters of our SceneModel model. Those parameters are the flux, center positions of the target, and a constant background level. We can do that with oktopus:


In [32]:
from oktopus.prior import UniformPrior

In [33]:
unif_prior = UniformPrior(lb=[0, 1090., 706., 0.],
                          ub=[1e5, 1096., 712., 1e5])

In [34]:
scene.plot(*unif_prior.mean)



In [35]:
prf_phot = PRFPhotometry(scene_model=scene, prior=unif_prior)

In [36]:
results = prf_phot.fit(tpf.flux + tpf.flux_bkg)


  0%|          | 0/3633 [00:00<?, ?it/s]/Users/jvmirca/anaconda3/lib/python3.6/site-packages/autograd/core.py:82: RuntimeWarning: invalid value encountered in log
  result_value = self.fun(*argvals, **kwargs)
/Users/jvmirca/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:1927: RuntimeWarning: invalid value encountered in double_scalars
  tmp2 = (x - v) * (fx - fw)
/Users/jvmirca/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:1928: 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:1929: RuntimeWarning: invalid value encountered in double_scalars
  tmp2 = 2.0 * (tmp2 - tmp1)
100%|██████████| 3633/3633 [02:49<00:00, 21.46it/s]

In [37]:
plt.imshow(prf_phot.residuals[1], origin='lower')
plt.colorbar()


Out[37]:
<matplotlib.colorbar.Colorbar at 0x12aed6be0>

In [38]:
flux = results[:, 0]
xcenter = results[:, 1]
ycenter = results[:, 2]
bkg_density = results[:, 3]

In [39]:
plt.figure(figsize=[17, 4])
plt.plot(tpf.time, flux)
plt.ylabel('Flux (e-/s)')
plt.xlabel('Time (BJD - 2454833)')


Out[39]:
<matplotlib.text.Text at 0x12b2960f0>

In [40]:
plt.figure(figsize=[17, 4])
plt.plot(tpf.time, xcenter)
plt.ylabel('Column position')
plt.xlabel('Time (BJD - 2454833)')


Out[40]:
<matplotlib.text.Text at 0x128a809b0>

In [41]:
plt.figure(figsize=[17, 4])
plt.plot(tpf.time, ycenter)
plt.ylabel('Row position')
plt.xlabel('Time (BJD - 2454833)')


Out[41]:
<matplotlib.text.Text at 0x1299983c8>

In [42]:
plt.figure(figsize=[17, 4])
plt.plot(tpf.time, bkg_density)
plt.ylabel('Background density')
plt.xlabel('Time (BJD - 2454833)')


Out[42]:
<matplotlib.text.Text at 0x12b2bb278>

For more examples on PRF photometry, please see our tutorials page: http://pyke.keplerscience.org/tutorials/index.html