The code in this repo is used to produce the Dark Energy Survey (DES) 1 YR mock data (in the format of CosmoMC) .
To use this code, you need to have the code MGCosmoMC.
To install this code on your machine, type the following commands on the terminal
git clone https://github.com/alexzucca90/DES_mock_dataThis code allows the user to run MGCosmoMC and produce the theoretical predictions for the DES two-point correlation functions from a desired model. Then the mock data is obtained by adding gaussian multivariate noise from the covariance matrix of the DES data. The dependence on the cosmological model is ignored here.
To create the DES mock dataset, first run MGCosmoMC using the files provided in the folder mgcosmomc. To do so, copy/paste (and replace if needed) the MGCosmoMC file with the one provided in this repo. Then recompile MGCosmoMC using
make cosmomcfrom your MGCosmoMC directory.
You can now run MGCosmoMC with the DES_mock.ini initializing file. Type
./cosmomc DES_mock.iniMake sure to fix your model parameters in params_CMB_defaults.ini, so that you are sure that you are getting the theoretical predictions for your desired model.
MGCosmoMC will run a likelihood test and will return a set of output files containing the theoretical predictions for your model and the inverse covariance. Put them in this directory (or in the input directory). You can now run this code.
Type
python create_DES_mock_data.py -t theory -i invcov -o outputwhere theory is a string that declares the theoretical predictions file (such as this file ), invcov is the string declaring the file for the inverse covariance matrix (see this file ) and output is the name of the output file.
The code will return a set of data files in the output directory that need to be inserted in the MGCosmoMC directory data/DES/. They contain the mock two-point correlation functions. Make sure to edit the file data/DES/DES_1YR_final.dataset if you want to use this data.