Python package for the GQP mock challenge. The package makes it easy to read in forward modeled DESI-like spectra and photometry and run SED fitting. The package is being actively developed so there will be constant updates! Also, we want this package to be developed as openly as possible so please feel free to contribute via pull requests. [toc]
04/30/2020: LGal data updated. Now with version flag. We're starting with v1.0
! As long as you git pull and install updates, there shouldn't be any issues.
The main goals of the mock challenge is to simultaneously fit DESI-like spectra and photometry of the mock challenge galaxies. For examples running SED fits for photometry or spectra + photometry, check out run/eg.sh
.
For examples of submitting SED fit jobs to NERSC
see slurm scripts in run/cori/
. We are coordinating job submissions using this spreadsheet.
module load python
conda init
This will append several lines into your ~/.bashrc
file. After you've done this once you do not need to run this again and you can directly activate the conda environment. For details see the nersc documentation
First, add the following to your ~/.bashrc
or ~/.bashrc.ext
:
export GQPMC_DIR="\SOME_LOCAL_DIRECTORY\"
export HDF5_USE_FILE_LOCKING=FALSE
This defines the $GQPMC_DIR
environment, which is used in the package and address an HDF5
i/o issue on NERSC
. Then run source ~/.bashrc
or source ~/.bashrc.ext
on the command line so that the changes take effect.
Now we're going to symlink to the LGal directory and the directory with the mini-Mock Challenge (mini_mocha
) in the desi project directory so that we have access to the data.
# go to $GQPMC_DIR
cd $GQPMC_DIR
ln -s /global/cfs/cdirs/desi/mocks/LGal_spectra/ Lgal
ln -s /global/cfs/cdirs/desi/mocks/TNG_spectra/ tng
ln -s /global/cfs/cdirs/desi/mocks/gqp_mini_mocha/ mini_mocha
Your symlinks should point to the proper directory. If the symlinks are bad, fix the symlink, referring to this: updating_symlink
You need to install FSPS
if you want to use the iFSPS
fitter: https://github.com/cconroy20/fsps. See below for some notes on installing FSPS
on NERSC
. (You probably want to install FSPS
)
With the data all set up, we can now install the package:
# create conda environment
conda create -n gqp python=3.7 jupyter ipython pip
# install dependencies
pip install h5py
pip install pytest
pip install astropy
pip install emcee
pip install speclite
pip install fsps
pip install multiprocessing
pip install configobj
pip install sqlalchemy
pip install corner
# clone the repo
git clone https://github.com/changhoonhahn/gqp_mc.git
# go to project directory
cd gqp_mc
# install package
python setup.py install
# test the package
pytest
# create ofiles directory
cd run/cori
mkdir ofiles
You're all set. Now you can activate the conda environment by
conda activate gqp
Above is one way to setup the package on nersc. See also Rita's notebook, which details how to install the package.
If you want to use the CIGALE photometry fitter, you need also to install CIGALE https://cigale.lam.fr. See below some notes on installing CIGALE on NERSC.
-
Multiprocessing installation might raise following error:
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
You can neglect this error, as multiprocessing package has been integrated to python default packages for python 3.X.
-
If you encounter following error while installing the packages:
ERROR: Could not install packages due to an EnvironmentError: [Errno 30] Read-only file system: '/global/common/cori_cle7/software/python/3.7-anaconda-2019.10/lib/python3.7/site-packages/...'
follow this link.
Follow instructions in https://github.com/cconroy20/fsps/ and https://github.com/cconroy20/fsps/blob/master/doc/INSTALL except when compiling the code:
-
modify the
src/Makefile
and comment out line 10F90=gfortran
and uncomment line 13
F90=ifort
-
If you get the following error message:
autosps.f90(21): error #6353: A RETURN statement is invalid in the main program. RETURN
modify
RETURN
in line 21 ofautosps.f90
toSTOP
and rerunmake
Before compiling add DECam* filters to the CIGALE filter directory
cigale/database_builder/filters
Compile CIGALE (tested on v2018)
python setup.py build python setup.py develop
Add CIGALE to your python path
export PYTHONPATH='${PYTHONPATH}:/your_directory/cigalev2018/'