sompz_y6

Setting up the environment

You need to set up a Python 3 sompz enviroment and a Jupyter interpreter with the packages listed in requierments.txt and the twopoint, which is not currently avaible in conda. In a terminal inside the sompz_y6 folder, run:

$ module load python $ conda config --append channels conda-forge
$ conda create --name sompz python=3.10.9 ipykernel --file requirements.txt
$ conda activate sompz
$ pip install twopoint
$ python -m ipykernel install --user --name sompz --display-name SOMPZ \

If you running at nersc, you need to also need the following 2 commands to make your mpi4py work properly

$ module swap PrgEnv-${PE_ENV,,} PrgEnv-gnu
$ MPICC="cc -shared" pip install --force-reinstall --no-cache-dir --no-binary=mpi4py mpi4py\

You only need to run the commands above once. Now, when you want to use the sompz enviroment just run

$ conda activate sompz

or select the SOMPZ kernel in your Jupyter notebook.

Train Phase

The train_ files are reference files to train the deep and wide SOMs

• Train deep som
  • Use deep catalog
• Train wide some
  • Use subsample of the wide catalog

Assign Phase

The assign_ files are reference files to assign the deep, balrog and wide catalogs to their respective SOMs

• Assign catalogs to deep som: deep and balrog
• Assign catalogs to wide som: wide and balrog

Redshifts Estimation

The compute_redshifts.ipynb notebook contains all the steps to estimate the N(z)s for the wide catalog

• Read deep, wide and balrog catalogs
• Create dataframes for each catalog and add cell assignment information
• Compute pcchat (transfer matrix) using balrog assignments in deep and wide
• Compute pzc and pzchat
• Compute N(z)s
• Save everything in h5 file (input for the 2pt pipeline)

Papers to Cite

Please cite the following papers if you use this code in your research:

1. A. Campos et al. (DES Collaboration) - Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method (in preparation)
2. C. Sánchez, M. Raveri, A. Alarcon, G. Bernstein - Propagating sample variance uncertainties in redshift calibration: simulations, theory, and application to the COSMOS2015 data
3. R. Buchs, et al. - Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing
4. J. Myles, A. Alarcon, et al. (DES Collaboration) - Dark Energy Survey Year 3 results: redshift calibration of the weak lensing source galaxies