Skip to main content

LINNA

https://img.shields.io/pypi/v/linna.svg https://github.com/chto/linna/actions/workflows/check.yml/badge.svg Documentation Status https://img.shields.io/badge/arXiv-<INDEX>-<COLOR>.svg

Linna (Likelihood Inference Neural Network Accelerator) is a tool to accelerate Bayesian posterior inferences using artificial neural networks.

  • Linna automatically builds training data, trains the neural network, and produces a Markov chain that samples the posterior.

  • Reduces the runtime of survey cosmological analyses of the Dark Energy Survey by a factor 8-50.

  • Linna is verified to enable accurate and efficicient sampling for Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST) year 10 multi-probe analyses.

  • Linna is explicity verified for the following three multi-probe analysese:
    • 3x2pt, a joint analysis of galaxy clustering, galaxy–galaxy lensing, and cosmic shear.

    • 4x2pt+N, a joint analysis of cluster–galaxy cross correlations, cluster lensing, cluster clustering, and cluster abundances.

    • 6x2pt+N, a joint analysis of data vectors in 3x2pt and 4x2pt+N.

Documentation

Read the docs at https://linna.readthedocs.io/en/latest/readme.html#documentation

Installation

git clone https://github.com/chto/linna.git
cd linna
python setup.py install

Example

For example, if you want to sample a 33 dimensional gaussian spaces, you can do

import numpy as np
import matplotlib.pyplot as plt
from linna.main import ml_sampler
from linna.util import *
#Define gaussian
ndim = 33
init =  np.random.uniform(size=ndim)
means = np.random.uniform(size=ndim)
cov = np.diag(0.1*np.random.uniform(size=ndim))
priors = []
for i in range(ndim):
    priors.append({
        'param': 'test_{0}'.format(i),
        'dist': 'flat',
        'arg1': -5.,
        'arg2': 5.
    })
def theory(x, outdirs):
    x_new = deepcopy(x[1])
    return x_new
#LINNA
nwalkers = 4 #Number of mcmc walker
pool = None
outdir = os.path.abspath(os.getcwd())+"/out/2dgaussian/"
chain, logprob = ml_sampler(outdir, theory, priors, means, cov, init, pool, nwalkers, gpunode=None, nepoch=101)

History

0.0.1 (2022-01-18)

  • First release on PyPI.

Metadata

Release files for linna 0.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for linna 0.0.3
File Size Uploaded
linna-0.0.3.tar.gz 980.6 kB Details

Release files / linna-0.0.3.tar.gz

Download URL linna-0.0.3.tar.gz
Size 980.6 kB
Tags Source
SHA-256 checksum
How to use checksums
be00314ba9b4009ba65c112233f3bfd3982f5e7ad50bbab7e51fa3310506783c
BLAKE2b-256 checksum
How to use checksums
e26ce3861cbe57a1b9c48ca69cae313ce85b75c16d48282e6aed36b2bc7caa68
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.2 importlib-metadata/4.8.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.7

Release history Release notifications | RSS feed

This release

0.0.3 This release

1 release file

0.0.2

1 release file

0.0.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page