Augur is a DESC forecasting and inference validation tool. The name comes from the official diviners in ancient Rome whose function was to divine whether the gods approved of a proposed undertaking by observing the behavior of birds. Firecrown is the bird species of choice in DESC.
Augur is a wrapper to firecrown that generates synthetic datasets of abitrary complexity and then calls inference engine to either generate full MCMC or a simple second-order derivative at fiducal model to generate a Fisher matrix forecast.
As always, you can force installation through pip like
pip install augur/
or actually inside the augur directory running
python setup.py install
This step-by-step installaion shows you how to get a working environment with firecrown and augur that you can hack away efficiently.
Start by creating a new anaconda environment:
conda create --name forecasting
conda activate forecasting
Next install firecrown and augur.
Install a repo version of firecrown:
git clone git@github.com:LSSTDESC/firecrown.git
cd firecrown
The current version of augur relies on firecrown v0.5 or lower so in the same directory you can do:
git checkout tags/v0.5
pip install . -e
Now run a pytest to see if things work.
Next repeat the same with augur:
git clone git@github.com:LSSTDESC/augur.git
cd augur
pip install --no-deps -e .
You are now ready to try a simple forecast as outlined in the next section.
Because Augur depends upon Firecrown, and Firecrown requires installation using Conda, we use Conda to install Augur.
# clone the Augur repository
git clone git@github.com:LSSTDESC/augur.git
# conda env update, when run as suggested, is able to create a new environment, as
# well as updating an existing environment.
conda env update -f augur/environment.yml
conda activate forecasting
# The following line loads the firecrown module from the environment, and queries
# it to find the installation location.
FIRECROWN_DIR=$(python -c "import firecrown; print('/'.join(firecrown.__spec__.submodule_search_locations[0].split('/')[0:-1]))")
# We define some environment variables that will be defined whenever you activate
# the conda environment.
conda env config vars set AUGUR_DIR=${PWD}/augur CSL_DIR=${CONDA_PREFIX}/cosmosis-standard-library FIRECROWN_DIR=${FIRECROWN_DIR}
# The command above does not immediately define the environment variables.
# They are made available on every fresh activation of the environment.
# So we have to deactivate and then reactivate...
conda deactivate
conda activate forecasting
# Now we can finish building the CosmoSIS Standard Library.
source ${CONDA_PREFIX}/bin/cosmosis-configure
# We want to put the CSL into the same directory as conda environment upon which it depends
cd ${CONDA_PREFIX}
cosmosis-build-standard-library
# Now change directory into the Augur repository
cd ${AUGUR_DIR}
# And finally make an editable (developer) installation of firecrown into the conda environment
python -m pip install --no-deps --editable ${PWD}
augur has changed from its initial version and currently only contains a
generate stage where it creates the firecrown-likelihood object and fiducial data vector as specified by the configuration file passed to this stage.
The user can create configuration files to fit their specific purposes following the example configuration files in the examples subdirectory. We show a quick example of how to obtain a likelihood object from an example configuration file.
from augur.generate import generate
lk = generate('./examples/config_test.yml', return_all_outputs=False, force_read=False)
This likelihood object can then be used by cosmosis, cobaya or NumCosmo. For more details follow the examples in the firecrown repository.
We also include example configuration files for cosmosis and cobaya to reproduce the results from the LSST DESC Science Requirements Document.
Note: It is left to the discretion of the user which inference framework to use. In order to run the chains the user can decide whether they want to install cosmosis, cobaya, or NumCosmo, or a combination of them.
First, you will need to have cosmosis-standard-library installed. Follow the instructions here to install cosmosis.
To run the example chain you can just do:
cd examples
cosmosis srd_y1_3x2_cosmosis.ini
By default, the results from this run will be saved at:
${AUGUR_DIR}/output/SRD_y1_3x2pt_samples.txt
Feel free to modify the paths indicated in this configuration file as needed or define the following environment variables:
CSL_DIR: The path to the directory where the cosmosis standard library can be found. It should contain the cosmosis modules.AUGUR_DIR: The directory where you haveauguror theaugurinstallation.FIRECROWN_DIR: The directory where you havefirecrownor thefirecrowninstallation.
Similarly to the case of cosmosis, first you need to have cobaya installed in order to be able to run the cobaya example. Please follow the instructions here to learn how to install cobaya.
To run the example chain you can just do:
cd examples
cobaya-run cobaya_mcmc.yaml
By default the outputs will be saved at ./examples/cobaya_evaluate_output.