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.

First clone and enter the augur repository:

git clone git@github.com:LSSTDESC/augur.git cd augur # set the variable AUGUR_DIR to the current directory AUGUR_DIR=${PWD}

Start by creating a new anaconda environment from the environment.yml file in the augur directory:

conda env create --name forecasting --file=environment.yml conda activate forecasting

If you already have an existing environment, you can update it with the following command:

conda activate my_env conda env update --name my_env --file=environment.yml --prune

and activate your environment with conda activate [my_env/forecasting].

Now we need to build the cosmosis standard library:

source ${CONDA_PREFIX}/bin/cosmosis-configure cd ${CONDA_PREFIX} cosmosis-build-standard-library # go back to the augur directory cd ${AUGUR_DIR}

We need to let augur know about the location of Firecrown, to do that, run the following command:

FIRECROWN_DIR=$(python -c "import firecrown; print('/'.join(firecrown.__spec__.submodule_search_locations[0].split('/')[0:-1]))")

Now let's set the environment variables that augur needs to know about:

conda env config vars set AUGUR_DIR=${PWD} CSL_DIR=${CONDA_PREFIX}/cosmosis-standard-library FIRECROWN_DIR=${FIRECROWN_DIR}

we now need to reload our environment to make the changes effective:

conda deactivate conda activate forecasting

cd ${AUGUR_DIR} # now install augur via pip in editable mode python -m pip install --no-deps --editable ${PWD}

To install and modify the developer version of firecrown, follow the instructions here.

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 cd augur # 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 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} 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) 

This likelihood object can then be used by cosmosis, cobaya or NumCosmo. For more details follow the examples in the firecrown repository.

Additionally, we can compute the Fisher matrix and Fisher biases via numerical derivatives using the following commands:

from augur.analyze import Analyze ao = Analyze('./examples/config_test.yml') ao.get_fisher_bias() # This command computes the derivates+Fisher matrices+fisher bias print(ao.Fij, ao.bi) # These are the values of the Fisher matrix, Fij, and Fisher biases bi 

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 have augur or the augur installation.
• FIRECROWN_DIR: The directory where you have firecrown or the firecrown installation.

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.