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cosmopower-jax-0.2.2
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مشاهده بیشترتوضیحات
Differentiable cosmological emulators
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cosmopower-jax-0.2.2 |
| نام | cosmopower-jax |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Davide Piras |
| ایمیل نویسنده | davide.piras@unige.ch |
| آدرس صفحه اصلی | https://github.com/dpiras/cosmopower-jax |
| آدرس اینترنتی | https://pypi.org/project/cosmopower-jax/ |
| مجوز | GNU General Public License v3.0 (GPLv3) |
# CosmoPower-JAX
<p align="center">
<img src="https://user-images.githubusercontent.com/25639122/235351711-39be2b50-dbcb-4964-adbf-f38ffc74ef5f.jpeg" width="300" height="202.5"
alt="CPJ_logo"/>
</p>
`CosmoPower-JAX` in an extension of the [CosmoPower](https://github.com/alessiospuriomancini/cosmopower) framework to emulate cosmological power spectra in a differentiable way. With `CosmoPower-JAX` you can efficiently run Hamiltonian Monte Carlo with hundreds of parameters (for example, nuisance parameters describing systematic effects), on CPUs and GPUs, in a fraction of the time which would be required with traditional methods. We provide some examples on how to use the neural emulators below, and more applications in our paper (coming soon).
Of course, with `CosmoPower-JAX` you can also obtain efficient and differentiable predictions of cosmological power spectra. We show how to achieve this in less than 5 lines of code below.
## Installation
To install `CosmoPower-JAX`, you can simply use `pip`:
pip install cosmopower-jax
We recommend doing it in a fresh `conda` environment, to avoid clashes (e.g. `conda create -n cpj python=3.9 && conda activate cpj`).
Alternatively, you can:
git clone https://github.com/dpiras/cosmopower-jax.git
cd cosmopower-jax
pip install .
The latter will also give you access to a Jupyter notebook with some examples.
## Usage & example
After the installation, getting a cosmological power spectrum prediction is as simple as (e.g. for the CMB temperature power spectrum):
import numpy as np
from cosmopower_jax.cosmopower_jax import CosmoPowerJAX as CPJ
# omega_b, omega_cdm, h, tau, n_s, ln10^10A_s
cosmo_params = np.array([0.025, 0.11, 0.68, 0.1, 0.97, 3.1])
emulator = CPJ(probe='cmb_tt')
emulator_predictions = emulator.predict(cosmo_params)
Similarly, we can also compute derivatives like:
emulator_derivatives = emulator.derivative(cosmo_params)
We provide a full walkthrough in the accompanying [Jupyter notebook](https://github.com/dpiras/cosmopower-jax/blob/main/notebooks/emulators_example.ipynb), and we describe `CosmoPower-JAX` in detail in the release paper. We currently do not provide the code to train a neural-network model in JAX; if you would like to re-train a JAX-based neural network on different data, [raise an issue](https://github.com/dpiras/cosmopower-jax/issues) or contact [Davide Piras](mailto:davide.piras@unige.ch).
## Contributing and contacts
Feel free to [fork](https://github.com/dpiras/cosmopower-jax/fork) this repository to work on it; otherwise, please [raise an issue](https://github.com/dpiras/cosmopower-jax/issues) or contact [Davide Piras](mailto:davide.piras@unige.ch).
## Citation
If you use `CosmoPower-JAX` in your work, please cite both papers as follows:
@article{SpurioMancini2022,
title={CosmoPower: emulating cosmological power spectra for
accelerated Bayesian inference from next-generation surveys},
volume={511},
ISSN={1365-2966},
url={http://dx.doi.org/10.1093/mnras/stac064},
DOI={10.1093/mnras/stac064},
number={2},
journal={Monthly Notices of the Royal Astronomical Society},
publisher={Oxford University Press (OUP)},
author={Spurio Mancini, Alessio and Piras, Davide and
Alsing, Justin and Joachimi, Benjamin and Hobson, Michael P},
year={2022},
month={Jan},
pages={1771–1788}
}
@article{Piras23,
title={CosmoPower-JAX: high-dimensional Bayesian inference with
differentiable cosmological emulators},
volume={TBC},
ISSN={TBC},
url={TBC},
DOI={TBC},
number={TBC},
journal={TBC},
publisher={TBC},
author={Piras, Davide and Spurio Mancini, Alessio},
year={2023},
month={TBC},
pages={TBC}
}
## License
`CosmoPower-JAX` is released under the GPL-3 license - see [LICENSE](https://github.com/dpiras/cosmopower-jax/blob/main/LICENSE)-, subject to
the non-commercial use condition - see [LICENSE_EXT](https://github.com/dpiras/cosmopower-jax/blob/main/LICENSE_EXT).
CosmoPower-JAX
Copyright (C) 2023 Davide Piras & contributors
This program is released under the GPL-3 license (see LICENSE),
subject to a non-commercial use condition (see LICENSE_EXT).
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
نیازمندی
| مقدار | نام |
|---|---|
| - | jax |
| - | jaxlib |
| >=3.1.2 | matplotlib |
| >=1.17.4 | numpy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9 | Python |
نحوه نصب
نصب پکیج whl cosmopower-jax-0.2.2:
pip install cosmopower-jax-0.2.2.whl
نصب پکیج tar.gz cosmopower-jax-0.2.2:
pip install cosmopower-jax-0.2.2.tar.gz