Merge pull request #1 from rodluger/dev

Merged dev branch

Author: rodluger

MANIFEST.in

@@ -1,4 +1,5 @@
 include README.md
 include everest/tables/*.csv
 include everest/tables/*.tsv
+include everest/tables/*.info
 include everest/pbs/*.pbs

bin/everest

@@ -13,9 +13,10 @@ import everest
 
 if __name__ == '__main__':
   parser = argparse.ArgumentParser(prog = 'everest', add_help = True)
-  parser.add_argument("epic", type = int, help = 'The EPIC target number (required)')
+  parser.add_argument("epic", type = int, help = 'The EPIC target number or the campaign number (required)')
   parser.add_argument("-a", "--aperture", action = 'store_true', help = 'Plot the aperture used for the photometry')
   parser.add_argument("-c", "--ccd", action = 'store_true', help = 'Plot the location of the target on the CCD')
+  parser.add_argument("-k", "--sky", action = 'store_true', help = 'Plot the location of the target on the sky')
   parser.add_argument("-i", "--interactive", action = 'store_true', help = 'Plot in interactive mode')
   parser.add_argument("-n", "--contamination", action = 'store_true', help = 'Plot the contamination analysis')
   parser.add_argument("-p", "--plot", action = 'store_true', help = 'Plot the everest de-trended light curve (default)')
@@ -28,10 +29,15 @@ if __name__ == '__main__':
   args = parser.parse_args()
 
   # Plot is default
-  if not any([getattr(args, a) for a in ['aperture', 'ccd', 'contamination', 'autocorrelation', 
+  if not any([getattr(args, a) for a in ['aperture', 'ccd', 'sky', 'contamination', 'autocorrelation', 
                                          'postagestamp', 'crossvalidation', 'k2sff', 'k2sc', 'k2varcat']]):
     args.plot = True
 
+  # Is this a campaign number?
+  if args.epic < 20:
+    everest.usertools.Detector(args.epic)
+    quit()
+  
   # Get the data
   star = everest.Everest(args.epic)
 
@@ -40,6 +46,8 @@ if __name__ == '__main__':
     star.aperture()
   if args.ccd:
     star.ccd()
+  if args.sky:
+    star.sky()
   if args.contamination:
     star.contamination()
   if args.crossvalidation:

docs/211069540_everest.jpeg

@@ -0,0 +1,14 @@
+.. automodule:: everest.usertools.detector
+   :members:
+
+.. raw:: html
+
+  <script>
+    (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+    (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+    m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+    })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
+
+    ga('create', 'UA-47070068-2', 'auto');
+    ga('send', 'pageview');
+  </script>

docs/detector.rst

@@ -2,8 +2,8 @@ Known Issues
 ============
 
 As we discuss in the paper, **EVEREST** has certain limitations, particularly when
-it comes to saturated stars and stars in crowded apertures. Below we outline these
-limitations with some examples.
+it comes to saturated stars, stars in crowded apertures, and very variable stars. 
+Below we outline these limitations with some examples.
 
 .. contents::
    :local:
@@ -66,6 +66,24 @@ greater than 2 or 3. Other pipelines are likely to perform better for these targ
           Note that saturated and crowded stars were **not** included \
           when computing the overall performance of **EVEREST** relative to \
           other pipelines (such as in `Figures 10-15 <precision.html>`_ in our paper).
+
+RR Lyrae and very variable stars
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. figure:: 211069540_everest.jpeg
+ :width: 600px
+ :align: center
+ :height: 100px
+ :figclass: align-center
+
+The :py:mod:`everest` pipeline is also likely to fail for very short period
+variable stars, such as RR Lyrae stars. When the stellar variability signal is
+stronger and at a higher frequency than the instrumental signal,
+nearly all the de-trending power comes from the GP, and the resulting CDPP is rather 
+insensitive to the value of the PLD coefficients, leading to poor de-trending. Imperfect
+optimization of the GP can also lead to damping of the stellar variability signal,
+which is evident in the light curve shown above. Consider using the 
+`K2VARCAT catalog <https://archive.stsci.edu/prepds/k2varcat/>`_ for these stars.
 
 Ultrashort-period EBs
 ~~~~~~~~~~~~~~~~~~~~~
@@ -81,7 +99,7 @@ eclipsing binaries. If the eclipses take up a significant fraction of the orbit,
 not much continuum flux to train the model on. It's also likely that the eclipses 
 (particularly the secondaries) may not be properly identified as outliers, in which case
 the GP optimization step will favor a kernel that captures the short timescale, high amplitude
-variability introduced by these eclipses. When this happens, all the de-trending power
+variability introduced by these eclipses. As in the variable star case, all the de-trending power
 comes from the GP, and the resulting CDPP is insensitive to the value of the PLD coefficients,
 which as a result end up taking on effectively random values. This results in light curves
 like the one above, where the eclipses get washed out and the white noise gets inflated

docs/everest_example.jpeg

@@ -30,6 +30,7 @@ code can be found on `github <https://github.com/rodluger/everest>`_.
    :caption: everest.usertools
 
    ccd
+   detector
    selector
    ui
 

docs/everest_example.jpg

@@ -2,7 +2,7 @@ The Paper
 =========
 
 Below we provide links to Python scripts to generate all the figures in the
-`paper <https://github.com/rodluger/everest/blob/master/paper/tex/k2.pdf>`_.
+`paper <http://arxiv.org/abs/1607.00524>`_.
 Most allow the user to generate the figures from scratch, but some load in `.npz`
 files with pre-downloaded and pre-de-trended data. It shouldn't be *too* hard to
 generate that data yourself, though...

docs/issues.rst

@@ -26,10 +26,11 @@ for EPIC 205071984:
 The :py:mod:`everest` command accepts several options, which we list below.
 
 ====================  =================================================================================
-:py:obj:`epic`        The **EPIC** target number (`required`)
+:py:obj:`id`          The **EPIC** target number *or* the *K2* campaign number (`required`)
 :py:obj:`-a`          Plot the aperture used for the photometry
 :py:obj:`-c`          Plot the location of the target on the CCD
 :py:obj:`-i`          Plot in interactive mode
+:py:obj:`-k`          Plot all stars on the *K2* field of view (`interactive`)
 :py:obj:`-n`          Plot the contamination analysis
 :py:obj:`-p`          Plot the **EVEREST** de-trended light curve (`default`)
 :py:obj:`-r`          Plot the autocorrelation/GP fitting result