Simulating observations of the galaxy-galaxy strong lensing population as expected to be seen in upcoming surveys.

The code is open access, but please email me to tell him that you are using it. If you use any of the software or ideas in this repo in your own research, please cit "Collett (2015)."

The documentation is deliberately sparse - I'd much rather you email me asking to collaborate than use the code as a black box.

-- Tom Collett.

If you don't want to simulate lenses, but do want to know the properties of future samples, we make these available for each of the surveys. These are space-separated text files, with one lens per line. The columns are explained at the start of each file).

The files are:

    Euclid_lenses.txt
    LSSTa_lenses.txt
    LSSTb_lenses.txt
    LSSTc_lenses.txt
    DESa_lenses.txt
    DESb_lenses.txt
    DESc_lenses.txt

• a refers to lenses discoverable in the full co-add.
• b refers to lenses discoverable in the best single epoch imaging.
• c refers to lenses discoverable in the optimally stacked coadd.

Install LensPop via pip with:

   pip install git+git://github.com/jiwoncpark/LensPop.git@master#egg=lenspop

Check that it's been installed properly by running:

python
import lenspop

First clone the repo with

   git clone "https://github.com/jiwoncpark/LensPop.git"

and then set up your path with

   python setup.py develop

You can then push to a remote fork and submit pull requests as needed.

You'll need to make a few folders to put things in:

   mkdir idealisedlenses # for storing output .pkl data files
   mkdir LensStats # for storing output figures

The code is mostly python, so should work out of the box (if you have standard astrophysical libraries installed already - if you have any problems install anaconda python) except the deflection angles code which must be compiled using:

   cd pylens
   f2py -c -m powerlaw powerlaw.f

To see that everything runs, run the following example scripts in order. It shouldn't take more than 3 minutes in total.

    # Generate the lens population
    python MakeLensPop.py
    # Observe
    python ModelAll.py CFHT 0.0001
    python ModelAll.py CFHTa 0.0001
    python ModelAll.py Euclid 0.0001
    python ModelAll.py DES 0.0001
    python ModelAll.py LSST 0.0001
    # Make results
    python MakeResults.py CFHT
    python MakeResults.py CFHTa
    python MakeResults.py Euclid
    python MakeResults.py DES
    python MakeResults.py LSST
    # Make figures
    python MakeFigure34567.py

There is also a demo script on using LensPop to get multi-filter magnitudes of a lensed quasar system, taken from OM10. Simply run:

    python paint_lensed_system.py

Timings are based on my ~2011 intel i5 desktop. First generate idealized lenspopulation:

    python MakeLensPop.py

To make all the lenses on the sky, edit this script to use fsky=1 - this will take about 7 hours.

Now observe the idealized lens population (with very loose limits on SN and maglims):

    python ModelAll.py CFHT 0.1 (~8 hrs, 0.1 is fraction of sky, not survey fraction)
    python ModelAll.py Euclid 0.1 (~9 hrs)
    python ModelAll.py DES 0.1 (~16 hrs)
    python ModelAll.py LSST 0.1 (~22 hrs)

You can of course cut down shot noise by simulating more sky (up to 1), but it'll take longer. The 0.1 is hard coded into the next part (but can be changed trivially).

Now Make Results:

    python MakeResults.py CFHT
    python MakeResults.py DES
    python MakeResults.py LSST
    python MakeResults.py Euclid

Finally, make the figures showing what you've found:

    python MakeFigure34567.py

If your finder uses gaussianised coadds (or single epoch images), simply open ModelAll.py and add your finder at line ~180.

Simply find somewhere to put all the data (it'll be a lot!), and uncomment Lines 190 through 210.

Hack the code (it should be moderately easy) or email me: thomas.collett@port.ac.uk. This will be a huge amount of data [tens of terabytes for all the lenses in LSST, and that's before simulating non-lenses], so plan accordingly!