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Features. Capable of running ADI, SDI, ADI+SDI with spectral templates to optimize the PSF subtraction.
Library of KLIP-FM capabilties including forward-modelling a PSF, detection algorithms, and spectral extraction. A Forward Model Matched Filter of KLIP is available for GPI as well as post-processing planet detection algorithms. Parallelized with both a quick memory-intensive mode and a slower memory-lite mode. Modularized to support data from multiple instruments.
Currently there are interfaces to, SPHERE, MagAO/VisAO, and Keck/NIRC2. If confused about what a function is doing, read the docstring for it. We have tried our best to document everything. See for update notes.
$ export MKLNUMTHREADS=1As these optimizations may be useful for other python tasks, you may only want MKLNUMTHREADS=1 only when pyKLIP is called,rather than on a system-wide level. By defaulf in parallelized.py, if mkl-service is installed, the originalmaximum number of threads for MKL is saved, and restored to its original value after pyKLIP has finished. You can alsomodify the number of threads MKL uses on a per-code basis by running the following piece of code (assuming mkl-service is installed).
RuntimeError: Attempt to start a new process before the current process has finished its bootstrapping phase. This probably means that you are on Windows and you have forgotten to use the proper idiom in the main module: if name 'main': freezesupport. The 'freezesupport' line can be omitted if the program is not going to be frozen to produce a Windows executable.If so, simply encapsulate the main code of your script within the following if statement if name 'main': as shown below.Note that definitions of new functions or include statements can be placed before this statement. Release Notes Version 2.1. RDI support in forward modeling framework (currently works for DiskFM, support for other FM modules coming). GenericData is more feature rich (better saving, automatic wcs generation). Minor bug fixes and documentation updatesVersion 2.0.1.
Update Python 3 version to Python 3.6Version 2.0. Forgot to update for a long while.
Lots of new changes. Basic KLIP Tutorial with GPIHere, we will explain how to run a simple PSF subtraction using the KLIP algorithm in pyKLIP.
If you are not familiarwith KLIP, we suggest you first read which describes the algorithmin detail. In this tutorial, we assume you are familiar with the terminology in KLIP. We will use GPIdata as an example to explain the process. If you are planning to reduce data from another instrument, seeto learn how to ingest it. We recommend you go through the quick GPI example first.
Other thanreading in the data, all the PSF subtraction steps are the same. MovementIn our exmaple, to build the reference library to build our principal components, we picked PSFs where any potentialastrophysical source will have moved by 1 pixel due to ADI (azimuthal motion) and SDI (radial motion). Decreasingthis number increases the aggressiveness of the reduction as it will allow you to pick PSFs that are closer in time andwavelength. However, you will also suffer more self-subtraction of potential astrophysical sources.We find for GPI data, 1 pixel is good for maximizing the SNR of potential planets in the data. AggressivenessThe aggressiveness of a PSF subtraction is influenced by a number ofparameters described below. There is oftenno one optimal aggressiveness, and there is much to be gained fromboth more aggressive and less aggressive reductions.
A more aggressivereduction will allows you to probe features at closer inner workingangles at the cost of killing fainter or more extended features. Theaggressiveness and the parameters you choose can also be affected bythe geometry and strength of the detection. Edge-on disks are moreresilient to more aggressive reductions while face-on disks will need less aggressivereductions due to the self-subtraction associated with ADI. ::: python import sys sys.
) import P1640spots # if the variables below are not set, default values will be read from P1640.ini # for the tutorial, let's set them explicitly spotfilepath = 'sharedspotfolder/' spotfilesuffix = '-spot' spotfileext = 'csv' for testfile in goodcubes: spotpositions = P1640spots. Getsinglefilespotpositions ( testfile, rotatedspots = False ) P1640spots.
Writespotstofile ( testfile, spotpositions, spotfilepath, spotid = spotfilesuffix, ext = spotfileext, overwrite = False )(For now, only normally-oriented gridspots can be used, but in thefuture you should be able to set rotatedspots=True to fit45deg-rotated grid spots).The default values for the spot file filenames and directories (on Dnahat AMNH) can be found in the P1640.ini config file. I tend to writea separate config file specifically for the reduction and define themagain there, with a custom directory if I want.
An example reductionconfig file will eventually be added to the repo. ::: python import sys sys. Append ( '././././' ) import pyklip.instruments.P1640 as P1640 dataset = P1640.
P1640Data ( filelist, spotdirectory = 'sharedspotfolder/' ) import pyklip.parallelized as parallelized parallelized. Klipdataset ( dataset, outputdir = 'output/', fileprefix = 'woohoo', annuli = 5, subsections = 4, movement = 3, numbasis = 1, 20, 100 , calibrateflux = False, mode = 'SDI' )This will run the KLIP PSF subtraction algorithm. The resulting imagesare stored in the dataset.output field and written as FITS files tothe output directory with the file prefix you provided. The P1640 outputheader format is that the first header stores the KLIP parameters, andthe subsequent headers store copies of the headers from the originalFITS files that were combined in this analysis. One file containing adatacube is written for each KL cutoff specified. Datacube = 'IFSYJHcubecoro.fits' psfcube = 'IFSYJHcubepsf.fits' fitsinfo = 'IFSYJHinfo.fits' wvinfo = 'IFSYJHwavelength.fits' print ( 'read in data' ) dataset = SPHERE. Ifs ( datacube, psfcube, fitsinfo, wvinfo, nanmaskboxsize = 9 ) print ( dataset.
Shape ) # instrumental PSF at each wavelength stored here if you need it parallelized. Klipdataset ( dataset, outputdir = 'path/to/save/dir/', fileprefix = 'myobject', annuli = 9, subsections = 4, movement = 1, numbasis = 1, 20, 50, 100 , mode = 'ADI+SDI', spectrum = 'methane' ).
Datacube = 'IRDISK1K2cubecoro.fits' psfcube = 'IRDISK1K2cubepsf.fits' fitsinfo = 'IRDISK1K2info.fits' print ( 'read in data' ) dataset = SPHERE. Irdis ( datacube, psfcube, fitsinfo, 'K1K2' ) print ( dataset. Shape ) # instrumental PSF at each wavelength stored here if you need it parallelized. Klipdataset ( dataset, outputdir = 'path/to/save/dir/', fileprefix = 'myobject', annuli = 9, subsections = 4, movement = 1, numbasis = 1, 20, 50, 100 , mode = 'ADI+SDI', spectrum = 'methane' ). Import astropy.io.fits as fits import numpy as np from pyklip.instruments.Instrument import GenericData import pyklip.parallelized as parallelized # Read in science images, which are taken at 2 roll angles. For each angle, the files come in as a 3D cube # We want to append the two roll angles together with fits.
Open ( 'data/OS5adi3polxlowfcrandom47Umarollm13degHLCsequencewithplanets.fits' ) as inputhdu1: with fits. Open ( 'data/OS5adi3polxlowfcrandom47Umarollp13degHLCsequencewithplanets.fits' ) as inputhdu2: framesperroll = inputhdu1 0. Shape 0 # the input science data is the combination of the two roll angles. Inputdata = np.
Append ( inputhdu1 0. Data, inputhdu2 0. Data, axis = 0 ) # makes a (2N, y, x) sized cube # generate roll angle lengths for each frame pas = np. Append ( 13 for in range ( framesperroll )- 13 for in range ( framesperroll )) # for each frame, give it a filename inputfilenames = np.
Append ( 'OS5adi3polxlowfcrandom47Umarollm13degHLCsequencewithplanets.fits' for in range ( framesperroll )'OS5adi3polxlowfcrandom47Umarollp13degHLCsequencewithplanets.fits' for in range ( framesperroll )) # all of the files are yet again at (31,31) inputcenters = np. Array ( xcenter, ycenter for in range ( framesperroll. 2 )) # set the inner working angle IWA = 6 # pixels # now let's generate a dataset to reduce for KLIP. This contains data at both roll angles dataset = GenericData ( inputdata, inputcenters, IWA = IWA, parangs = pas, filenames = inputfilenames ) # set up the KLIP parameters and run KLIP numbasis = 1, 5, 10, 20, 50 # number of KL basis vectors to use to model the PSF. We will try several different ones maxnumbasis = 150 # maximum number of most correlated PSFs to do PCA reconstruction with annuli = 3 subsections = 4 # break each annulus into 4 sectors parallelized. Klipdataset ( dataset, outputdir = 'data/', fileprefix = 'pyklipnonoisek150a3s4m1', annuli = annuli, subsections = subsections, numbasis = numbasis, maxnumbasis = maxnumbasis, mode = 'ADI', movement = 1 )If you want to write a new interface into pyKLIP, follow the guide for making your own interface. Calibrating Algorithm Throughput & Generating Contrast CurvesDue to oversubtraction and selfsubtraction (see for a goodexplaination), the shape, flux, and spectrum of the signal of a planet or disk is distoed by PSF subtraction.To calibrate algorithm throughput after KLIP in this tutorial, we will use the standard fake injection technique,which basically is injecting fake planets/disks in the data of known shape, flux, and spectrum to measure thealgorithm throughput.In this tutorial, we will calibrate the throughput of the previous exmaple for thepurpose of generating a contrast curve.
Note that this same general process can be used to character a planet or disk(e.g. Measure astrometry and spectrum of an imaged exoplanet). Import numpy as np import pyklip.klip as klip import pyklip.instruments.GPI as GPI datasetiwa = GPI.
Fpmdiam 'J' / 2 # radius of occulter datasetowa = 1.5 / GPI. Lensletscale # 1.5' is the furtherest out we will go datasetfwhm = 3.5 # fwhm of PSF roughly lowpasssize = 1.
# pixel, corresponds to the sigma of the Gaussian # mask beta Pic b # first get the location of the planet from Wang+ (2016) betapicbsep = 30.11 # pixels betapicbpa = 212.2 # degrees betapicbx = betapicbsep. np. Radians ( betapicbpa )) + datasetcenter 0 betapicby = betapicbsep.
np. Radians ( betapicbpa )) + datasetcenter 1 # now mask the data ydat, xdat = np. Indices ( kl20frame. Shape ) distancefromplanet = np. Sqrt (( xdat - betapicbx ).
2 + ( ydat - betapicby ). 2 ) kl20frame np.
Where ( distancefromplanet. Klhdulist = fits. Open ( 'myobject-withfakes-KLmodes-all.fits' ) datwithfakes = klhdulist 1. Data 1 datwithfakescenters = klhdulist 1. Header 'PSFCENTX' , klhdulist 1. Header 'PSFCENTY' We will measure the flux of each fake in the reduced image using. Our strategy here is to assume the throughput is constant azimuthally, and for each 4 planets at a separation, average their fluxes together to reduce noise.
Note that we need to again specify a WCS header (the one corresponding to the output images) to tell the code where to look for the planet in the image. You can grab that from the header of the reduced image, or we will be lazy here are use the dataset.outputwcs field from our fake dataset, which automatically gets rotated after KLIP. Foward-Model Astrometry and PhotometrypyKLIP offers PSF fitting as the most optimal method for extraction of planet astrometry and photometry.Astrometry/photometry of directly imaged exoplanets is challenging since PSF subtraction algorithms (like pyKLIP)distort the PSF of the planet. Provide a technique toforward model the PSF of a planet through KLIP.Taking this forward model, you could fit it to the data, but you would underestimate your errors because the noise in direct imaging data is correlated(i.e.
Each pixel is not independent). To account for the correlated nature of the noise, we use Gaussianprocess regression to model and account for the correlated nature of the noise. This allows us to obtainaccurate measurements and accurate uncertainties on the planet parameters.The code was originally designed for the Bayesian KLIP-FM Astorometry technique (BKA)that is described in to obtain one milliarcsecondastrometry on ╬▓ Pictoris b.Since then, it has be extended to also do maximum likelihood fitting in a frequentist framework that can alsoobtain uncertainties.
A frequentist framework is appropriate when astrometric calibration uncertainties arealso derived in a frequentist procedure. RequirementsYou need the following pieces of data to forward model the data.
(If you have a model and some data to fit to, you canskip the section on using KLIP-FM to generate a forawrd model PSF.). Data to run PSF subtraction on. A model or data of the instrumental PSF. A good guess of the position of the planet (a center of light centroid routine should get the astrometry to a pixel). For IFS data, an estimate of the spectrum of the planet (it does not need to be very accurate, and 20% errors are fine)If you want to run BKA, you need the additional packages installed, which should be available readily:. Import glob import numpy as np import pyklip.instruments.GPI as GPI # read in the data into a dataset filelist = glob. Glob ( 'path/to/dataset/.fits' ) dataset = GPI.
GPIData ( filelist ) # generate instrumental PSF boxsize = 17 # we want a 17x17 pixel box centered on the instrumental PSF dataset. Generatepsfs ( boxrad = boxsize // 2 ) # this function extracts the satellite spots from the data # now dataset.psfs contains a 37x25x25 spectral cube with the instrumental PSF # normalize the instrumental PSF so the peak flux is unity dataset. Psfs /= ( np. Mean ( dataset.
Reshape ( dataset. Shape 0 // 37, 37 ), axis = 0 ):, None, None )Here is an exmaple using three datacubes from the publicly available GPI data on beta Pic.Note that the wings of the PSF are somewhat noisy, due to the fact the speckle noisein J-band is high near the satellite spots. However, this should still give us an acceptable instrumental PSF.
Forward Modelling the PSF with KLIP-FMWith an estimate of the planet position, the instrumental PSF, and, if applicable, an estimate of the spectrum,we can use the implementation of KLIP-FM and extension toforward model the PSF of a planet through KLIP.First, let us initalize to forward model the planet in our data.For GPI, we are using normalized copies of the satellite spots as our input PSFs, and because of that, we need to pass ina flux conversion value, dnpercontrast, that allows us to scale our guessflux in contrast units to data units. Ifyou are not using normalized PSFs, dnpercontrast should be the factor that scales your input PSF to the flux of theunocculted star. If your input PSF is already scaled to the flux of the stellar PSF, dnpercontrast is optionaland should not actually be passed into the function.
# setup FM guesses # You should change these to be suited to your data! Numbasis = np. Array ( 1, 7, 100 ) # KL basis cutoffs you want to try guesssep = 30.1 # estimate of separation in pixels guesspa = 212.2 # estimate of position angle, in degrees guessflux = 5e-5 # estimated contrast dnpercontrast = yourfluxconversion # factor to scale PSF to star PSF.
For GPI, this is dataset.dnpercontrast guessspec = yourspectrum # should be 1-D array with number of elements = np.size(np.unique(dataset.wvs)) # initialize the FM Planet PSF class import pyklip.fmlib.fmpsf as fmpsf fmclass = fmpsf. FMPlanetPSF ( dataset. Shape, numbasis, guesssep, guesspa, guessflux, dataset.
Unique ( dataset. Wvs ), dnpercontrast, starspt = 'A6', spectrallib = guessspec ).
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