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markdownlint-disable MD002 MD033 MD041 MD053 --\u003e\n\u003cdiv align=\"center\"\u003e\n\n![Earth2Studio Banner](https://huggingface.co/datasets/NickGeneva/Earth2StudioAssets/raw/main/0.2.0/earth2studio_banner.png)\n\n[![python version][e2studio_python_img]][e2studio_python_url]\n[![license][e2studio_license_img]][e2studio_license_url]\n[![coverage][e2studio_cov_img]][e2studio_cov_url]\n[![mypy][e2studio_mypy_img]][e2studio_mypy_url]\n[![format][e2studio_format_img]][e2studio_format_url]\n[![ruff][e2studio_ruff_img]][e2studio_ruff_url]\n[![uv][e2studio_uv_img]][e2studio_uv_url]\n\nEarth2Studio is a Python-based package designed to get users up and running\nwith AI weather and climate models *fast*.\nOur mission is to enable everyone to build, research and explore AI driven meteorology.\n\n\u003c!-- markdownlint-disable MD036 --\u003e\n**- Earth2Studio Documentation -**\n\u003c!-- markdownlint-enable MD036 --\u003e\n\n[Install][e2studio_install_url] | [User-Guide][e2studio_userguide_url] |\n[Examples][e2studio_examples_url] | [API][e2studio_api_url]\n\n![Earth2Studio Banner](https://huggingface.co/datasets/NickGeneva/Earth2StudioAssets/raw/main/0.2.0/earth2studio_feature_banner.png)\n\n\u003c/div\u003e\n\n## Quick start\n\nInstall Earth2Studio:\n\n```bash\npip install earth2studio[dlwp]\n```\n\nRun a deterministic weather prediction in just a few lines of code:\n\n```python\nfrom earth2studio.models.px import DLWP\nfrom earth2studio.data import GFS\nfrom earth2studio.io import NetCDF4Backend\nfrom earth2studio.run import deterministic as run\n\nmodel = DLWP.load_model(DLWP.load_default_package())\nds = GFS()\nio = NetCDF4Backend(\"output.nc\")\n\nrun([\"2024-01-01\"], 10, model, ds, io)\n```\n\n## Features\n\nEarth2Studio provides access to pre-trained AI weather models and inference\nfeatures through an easy to use and extendable Python interface.\nThis package focuses on supplying users the tools to build their own\nworkflows, pipelines, APIs, packages, etc. via modular components including:\n\n- Collection of pre-trained weather/climate prediction models\n- Collection of pre-trained diagnostic weather models\n- Variety of online and on-prem data sources for initialization, scoring, analysis, etc.\n- IO utilities for exporting predicted data to user friendly formats\n- Suite of perturbation methods for building ensemble predictions\n- Sample workflows and examples for common tasks / use cases\n- Seamless integration into other Nvidia packages including [PhysicsNeMo][physicsnemo_repo_url]\n\nFor a more complete list of feature set, be sure to view the [documentation][e2studio_docs_url].\nDon't see what you need?\nGreat news, extension and customization are at the heart of our [design][e2studio_customization_url].\n\n## Contributors\n\nCheck out the [Contributing](CONTRIBUTING.md) document for details about the technical\nrequirements and the userguide for higher level philosophy, structure, and design.\n\n## License\n\nEarth2Studio is provided under the Apache License 2.0, please see\n[LICENSE file][e2studio_license_url] for full license text.\n\n\u003c!-- Badge links --\u003e\n\n[e2studio_python_img]: https://img.shields.io/badge/Python-3.10%20|%203.11%20|%203.12%20|%203.13-blue?style=flat-square\u0026logo=python\n[e2studio_license_img]: https://img.shields.io/badge/License-Apache%202.0-green?style=flat-square\n[e2studio_format_img]: https://img.shields.io/badge/Code%20Style-Black-black?style=flat-square\n[e2studio_mypy_img]: https://img.shields.io/badge/mypy-Checked-blue?style=flat-square\u0026labelColor=grey\n[e2studio_cov_img]: https://img.shields.io/codecov/c/github/nickgeneva/earth2studio?style=flat-square\u0026logo=codecov\n[e2studio_ruff_img]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\u0026style=flat-square\n[e2studio_uv_img]: https://img.shields.io/endpoint?url=https%3A%2F%2Fraw.githubusercontent.com%2Fastral-sh%2Fuv%2Fmain%2Fassets%2Fbadge%2Fv0.json\u0026style=flat-square\n\n[e2studio_python_url]: https://www.python.org/downloads/\n[e2studio_license_url]: ./LICENSE\n[e2studio_format_url]: https://github.com/psf/black\n[e2studio_cov_url]: ./test/\n[e2studio_mypy_url]: https://mypy-lang.org/\n[e2studio_ruff_url]: https://github.com/astral-sh/ruff\n[e2studio_uv_url]: https://github.com/astral-sh/uv\n\n\u003c!-- Doc links --\u003e\n[e2studio_docs_url]: https://nvidia.github.io/earth2studio/\n[e2studio_install_url]: https://nvidia.github.io/earth2studio/userguide/about/install.html\n[e2studio_userguide_url]: https://nvidia.github.io/earth2studio/userguide/\n[e2studio_examples_url]: https://nvidia.github.io/earth2studio/examples/\n[e2studio_api_url]: https://nvidia.github.io/earth2studio/modules/\n[e2studio_customization_url]: https://nvidia.github.io/earth2studio/examples/extend/index.html\n\n\u003c!-- Misc links --\u003e\n[physicsnemo_repo_url]: https://github.com/NVIDIA/physicsnemo\n","funding_links":[],"categories":["Building","Climate Change","🔬 Domain-Specific Applications"],"sub_categories":["Workflows","Earth and Climate Modeling","🌍 Earth \u0026 Climate Science"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNVIDIA%2Fearth2studio","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FNVIDIA%2Fearth2studio","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNVIDIA%2Fearth2studio/lists"}