desilike is an attempt to provide a common framework for writing DESI likelihoods, that can be imported in common cosmological inference codes (Cobaya, CosmoSIS, MontePython).
desilike has the following structure:
- root directory: definition of parameters, base calculator classes, differentiation and Fisher routines, installation routines
- theories: e.g. BAO, full-shape theory models
- observables: e.g. power spectrum, correlation function
- likelihoods: e.g. Gaussian likelihood of observables, a few external likelihoods (Pantheon, Planck)
- bindings: automatic linkage with cobaya, cosmosis, montepython
- emulators: emulate e.g. full-shape theory models, to speed up inference
- samples: define chains, profiles data structures and plotting routines
- samplers: many samplers for posterior sampling
- profilers: profilers for posterior profiling
samples, samplers and profilers are provided for self-contained sampling / profiling of provided likelihoods. Example notebooks presenting most use cases are provided in directory nb/.
blackjax profilers. Clean ups.
Documentation in construction on Read the Docs, desilike docs. See in particular getting started.
Only strict requirements are:
- numpy
- scipy
- pyyaml
- mpi4py
- cosmoprimo (currently with pyclass to compute DESI fiducial cosmology)
Should be made optional in the future:
- mpi4py
- pyclass (by extending TabulatedDESI to power spectra)
Simply run:
python -m pip install git+https://github.com/cosmodesi/desilike
If you wish to use plotting routines (getdist, anesthetic), and tabulate for pretty tables:
python -m pip install git+https://github.com/cosmodesi/desilike#egg=desilike[plotting]
If you addtionally wish to be able to use analytic marginalization with jax:
python -m pip install git+https://github.com/cosmodesi/desilike#egg=desilike[plotting,jax]
First:
git clone https://github.com/cosmodesi/desilike.git
To install the code:
pip install --user
Or in development mode (any change to Python code will take place immediately) (Note the .):
pip install --user -e .
You may want to avoid installing dependencies in your local $HOME (in particular if you load the cosmodesi environment):
pip install --no-deps --user -e .
More information on pip: https://pip.pypa.io/en/stable/cli/pip_install/
Just define your calculator (most commonly your likelihood), then in a python script:
from desilike import Installer
Installer(user=True)(likelihood)
desilike is free software distributed under a BSD3 license. For details see the LICENSE.
- Stephen Chen, Mark Maus, Martin White for velocileptors wrapper: https://github.com/sfschen/velocileptors, https://github.com/martinjameswhite/CobayaLSS
- Pierre Zhang, Cullan Howlett, Yan Xiang Lai for pybird wrapper: https://github.com/pierrexyz/pybird, https://github.com/CullanHowlett/pybird
- Hernan E. Noriega, Alejandro Aviles for folps wrapper: https://github.com/henoriega/FOLPS-nu
- Samuel Brieden, Hector Gil-Marin, Mark Maus for ShapeFit: https://arxiv.org/abs/2106.07641
- Stephen Chen, Mark Maus for Taylor expansion emulator: https://github.com/sfschen/velocileptors_shapefit
- Stephen Chen, Joe DeRose for MLP emulator: https://github.com/sfschen/EmulateLSS
- Pat McDonald, Eva Maria Mueller, Antony Lewis for thoughts
- Pat McDonald, Edmond Chaussidon, Uendert Andrade, Daniel Forero Sanchez, Batia Friedman-Shaw, Svyatoslav Trusov, Nathan Findlay, Enrique Paillas, Vincenzo Aronica for early debugging and feedback
- Ruiyang Zhao for systematics templates
- Benedict Bahr-Kalus for turnover scale analysis: https://arxiv.org/pdf/2302.07484.pdf
- Rodrigo Calderón for Pantheon+ with/out SH0ES and Union3 likelihoods
- Cobaya, CosmoSIS bindings inspired by firecrown: https://github.com/LSSTDESC/firecrown
- Inspiration from Cobaya: https://github.com/CobayaSampler/cobaya