Skip to main content

10D emulator for the nonlinear matter power spectrum built on Goku simulations

Project description

GokuNEmu: A Neural Network Emulator Based on the Goku Simulation Suite

GokuNEmu is a neural network (NN) emulator for the nonlinear matter power spectrum, trained on simulations from the Goku suite using the T2N-MusE emulation technique.


Installation

We recommend installing GokuNEmu via pip:

pip install gokunemu

Note for Intel Mac users:
You may need to install (if not yet) pytorch via conda before installing GokuNEmu due to potential compatibility issues with pip wheels:

conda install -c conda-forge pytorch

Usage

Example notebooks are provided in the examples/ directory:

  • example.ipynb: Demonstrates how to use GokuNEmu for predicting the nonlinear matter power spectrum.
  • speed_benchmark.ipynb: Benchmarks the runtime performance.

Training data

The data used as the training set for the emulator are available at https://github.com/astro-YYH/T2N-MusE.


Citation

If you use GokuNEmu, please cite:


License

This project is licensed under the MIT License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gokunemu-0.1.8.tar.gz (21.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gokunemu-0.1.8-py3-none-any.whl (21.0 MB view details)

Uploaded Python 3

File details

Details for the file gokunemu-0.1.8.tar.gz.

File metadata

  • Download URL: gokunemu-0.1.8.tar.gz
  • Upload date:
  • Size: 21.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for gokunemu-0.1.8.tar.gz
Algorithm Hash digest
SHA256 bdfdbf0adb5107b801238f509fa712b9404852b68fbcccdc6781fc4cc8d306c4
MD5 5063964c64be20eddc1c8af36e6931da
BLAKE2b-256 81c5d969764a549dae11cf1ddb0577f6e05f9218fc6e896524d8848ff0f98456

See more details on using hashes here.

File details

Details for the file gokunemu-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: gokunemu-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 21.0 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.2

File hashes

Hashes for gokunemu-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 5fc7f0e8749050d9142c7f91de97ef861807e3e133f37c03541ccbf1f715cc1d
MD5 050171735f78fa175a1871dba5581470
BLAKE2b-256 9edac719e16575456fecbee6950f5a8bd0114f626aad8ac4f8f407ee4863743d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page