Skyrim

Name	Skyrim JSON
Version	0.0.2 JSON
	download
home_page	None
Summary	AI weather models united.
upload_time	2024-05-06 23:07:23
maintainer	None
docs_url	None
author	None
requires_python	>=3.10
license	Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "{}" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2024 Secondlaw AI Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
keywords	pytorch weather forecasting ai ml xarray dl
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            <h1 align="center">
 <a href="https://www.secondlaw.xyz">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="./assets/skyrim_banner_1.png"/>
    <img height="auto" width="90%" src="./assets/skyrim_banner_1.png"/>
  </picture>
 </a>
 <br></br>

</h1>
<p align="center">

🔥 Run state-of-the-art large weather models in less than 2 minutes.

🌪️ Ensemble and fine-tune to push the limits on forecasting.

🌎 Simulate extreme weather events!

</p>

# Getting Started

Skyrim allows you to run any large weather model with a consumer grade GPU.

Until very recently, weather forecasts were run in 100K+ CPU HPC clusters, solving massive numerical models. Within last 2 years, open-source foundation models trained on weather simulation datasets surpassed the skill level of these numerical models.

Our goal is to make these models accessible by providing a well maintained infrastructure.

## Installation

Clone the repo, set an env (either conda or venv) and then run

```bash
pip install .
```

Depending on your use-case (i.e. AWS storage needs or CDS initial conditions), you may need to fill in a `.env` by `cp .env.example .env`.

## Run your first forecast

Skyrim currently supports either running on on [modal](#forecasting-using-modal), on a container –for instance [vast.ai](#vastai-setup) or [bare metal](#bare-metal)(you will need an NVIDIA GPU with at least 24GB and installation can be long).

Modal is the fastest option, it will run forecasts "serverless" so you don't have to worry about the infrastructure.

### Forecasting using Modal:

You will need a [modal](https://modal.com/) key. Run `modal setup` and set it up (<1 min).

Modal comes with $30 free credits and a single forecast costs about 2 cents as of May 2024.

Once you are all good to go, then run:

```bash
modal run skyrim/modal/forecast.py
```

This by default uses `pangu` model to forecast for the next 6 hours, starting from yesterday. It gets initial conditions from [NOAA GFS](https://en.wikipedia.org/wiki/Global_Forecast_System) and writes the forecast to a modal volume. You can choose different dates and weather models as shown in [here](#run-forecasts-with-different-models-initial-conditions-dates).

After you have your forecast, you can explore it by running a notebook (without GPU, so cheap) in modal:

```bash
modal run skyrim/modal/forecast.py::run_analysis
```

This will output a jupyter notebook link that you can follow and access the forecast. For instance, to read the forecast you can run from the notebook the following:

```
import xarray as xr
forecast = xr.open_dataset('/skyrim/outputs/[forecast_id]/[filename], engine='scipy')
```

Once you are done, best is to delete the volume as a daily forecast is about 2GB:

```bash
modal volume rm forecasts /[model_name] -r
```

If you don't want to use modal volume, and want to aggregate results in a bucket (currently only s3), you just have to run:

```bash
modal run skyrim/modal/forecast.py --output_dir s3://skyrim-dev
```

where `skyrim-dev` is the bucket that you want to aggregate the forecasts. By default, `zarr` format is used to store in AWS/GCP so you can read and move only the parts of the forecasts that you need.

### Forecasting with your own GPUs:

If you are running on your own GPUs, installed either via [bare metal](#bare-metal) or via [vast.ai](#vast-ai-setup) then you can just run:

`forecast`

or you can pass in options as such:

`forecast -m graphcast --lead_time 24 --initial_conditions cds --date 20240330`

See [examples](#examples) section for more.✌️

#### Bare metal

1. You will need a NVIDIA GPU with at least 16GB memory, ideally 24GB. We are working on quantization as well so that in the future it would be possible to run simulations with much less compute. Have an environment set with Python +3.10, Pytorch 2.2.2 and CUDA 11.8. Or if easier start with the docker image: `pytorch/pytorch:2.2.2-cuda11.8-cudnn8-devel`.
2. Install conda (miniconda for instance). Then run in that environment:

```bash
conda create -y -n skyenv python=3.10
conda activate skyenv
./build.sh
```

Note: Because we will be building from scratch this can take long (we need to install pytorch extensions through NVIDIA Apex package).

#### vast.ai setup

1. Find a machine you like RTX3090 or above with at least 24GB memory. Make sure you have good bandwith (+500MB/s).
2. Select the instance template from [here](https://cloud.vast.ai/?ref_id=128656&template_id=1883215a8487ec6ea9ad68a7cdb38c5e).
3. Then clone the repo and `pip install . && pip install -r requirements.txt`

## Run forecasts with different models, initial conditions, dates

For each run, you will first pull the initial conditions of your interest (most recent one by default), then the model will run for the desired time step. Initial conditions are pulled from GFS, ECMWF IFS (Operational) or CDS (ERA5 Reanalysis Dataset).

If you are using CDS initial conditions, then you will need a [CDS](https://cds.climate.copernicus.eu/user/login?destination=%2Fcdsapp%23!%2Fdataset%2Freanalysis-era5-single-levels) API key in your `.env` –`cp .env.example` and paste.

## Examples

All examples are from local setup, but you can run them as it is if you just change `forecast` to `modal run skyrim/modal/forecast.py` and also make snake case kebab-case -i.e. `model_name` to `model-name`.

### Example 1: Pick models, initial conditions, lead times

Forecast using `graphcast` model, with ERA5 initial conditions, starting from 2024-04-30T00:00:00 and with a lead time of a week (forecast for the next week, i.e. 168 hours):

```bash
forecast --model_name graphcast --initial_conditions cds --date 20240403 -output_dir s3://skyrim-dev --lead_time 168
```

or in modal:

```bash
modal run skyrim/modal/forecast.py --model-name graphcast --initial-conditions cds --date 20240403 --output-dir s3://skyrim-dev --lead-time 168
```

### Example 2: Store in AWS and then read only what you need

Say you re interested in wind at 37.0344° N, 27.4305 E to see if we can kite tomorrow. If we need wind speed, we need to pull wind vectors at about surface level, these are u10m and v10m [components](http://colaweb.gmu.edu/dev/clim301/lectures/wind/wind-uv) of wind. Here is how you go about it:

```bash
modal run skyrim/modal/forecast.py --output-dir s3://[your_bucket]/[optional_path]  --lead-time 24
```

Then you can read the forecast as below:

```python
import xarray as xr
import pandas as pd
zarr_store_path = "s3://[your_bucket]/[forecast_id]"
forecast = xr.open_dataset(zarr_store_path, engine='zarr') # reads the metadata
df = forecast.sel(lat=37.0344, lon=27.4305, channel=['u10m', 'v10m']).to_pandas()
```

Normally each day is about 2GB but using zarr_store you will only fetch what you need.✌️

## Supported initial conditions and caveats

1. NOAA GFS
2. ECMWF IFS
3. ERA5 Re-analysis Dataset

## Large weather models supported

Currently supported models are:

- [x] [Graphcast](https://arxiv.org/abs/2212.12794)
- [x] [Pangu](https://arxiv.org/abs/2211.02556)
- [x] [Fourcastnet](https://arxiv.org/abs/2202.11214) (v1 & v2)
- [x] [DLWP](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002109)
- [ ] [Fuxi](https://www.nature.com/articles/s41612-023-00512-1)
- [ ] [MetNet-3](https://arxiv.org/abs/2306.06079)

### License

For detailed information regarding licensing, please refer to the license details provided on each model's main homepage, which we link to from each of the corresponding components within our repository.

- **Pangu Weather** : [Original](https://github.com/198808xc/Pangu-Weather), [ECMWF](https://github.com/ecmwf-lab/ai-models-panguweather), [NVIDIA](https://github.com/NVIDIA/earth2mip)

- **FourcastNet** : [Original](https://github.com/NVlabs/FourCastNet), [ECMWF](https://github.com/ecmwf-lab/ai-models-fourcastnetv2),[NVIDIA](https://github.com/NVIDIA/earth2mip)

- **Graphcast** : [Original](https://github.com/google-deepmind/graphcast), [ECMWF](https://github.com/ecmwf-lab/ai-models-graphcast), [NVIDIA](https://github.com/NVIDIA/earth2mip)

- **Fuxi**: [Original](https://github.com/tpys/FuXi)

## Roadmap

- [x] ensemble prediction
- [x] interface to fetch real-time NWP-based predictions, e.g. via ECMWF API.
- [ ] global model performance comparison across various regions and parameters.
- [ ] finetuning api that trains a downstream model on top of features coming from a global/foundation model, that is optimized wrt to a specific criteria and region
- [ ] model quantization and its effect on model efficiency and accuracy.

This README will be updated regularly to reflect the progress and integration of new models or features into the library. It serves as a guide for internal development efforts and aids in prioritizing tasks and milestones.

## Development

All in [here](./CONTRIBUTING.md) ✌️

## Acknowledgements

Skyrim is built on top of NVIDIA's [earth2mip](https://github.com/NVIDIA/earth2mip) and ECMWF's [ai-models](https://github.com/ecmwf-lab/ai-models). Definitely check them out!

## Other Useful Resources

- [🌍 Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)](http://github.com/jaychempan/Awesome-LWMs)
- [Climate Data Store](https://cds.climate.copernicus.eu/)
- [Open Climate Fix](https://github.com/openclimatefix)
- [Herbie](https://github.com/blaylockbk/Herbie)

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "Skyrim",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.10",
    "maintainer_email": "SecondLaw Research <efe@2lw.ai>, SecondLaw Research <murat@2lw.ai>",
    "keywords": "pytorch, weather, forecasting, ai, ml, xarray, dl",
    "author": null,
    "author_email": null,
    "download_url": "https://files.pythonhosted.org/packages/61/aa/b5797d4e997fae523b098018f39bfd6bb64f6937dc9b0c96b760ac773c8f/skyrim-0.0.2.tar.gz",
    "platform": null,
    "description": "<h1 align=\"center\">\n <a href=\"https://www.secondlaw.xyz\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"./assets/skyrim_banner_1.png\"/>\n    <img height=\"auto\" width=\"90%\" src=\"./assets/skyrim_banner_1.png\"/>\n  </picture>\n </a>\n <br></br>\n\n</h1>\n<p align=\"center\">\n\n\ud83d\udd25 Run state-of-the-art large weather models in less than 2 minutes.\n\n\ud83c\udf2a\ufe0f Ensemble and fine-tune to push the limits on forecasting.\n\n\ud83c\udf0e Simulate extreme weather events!\n\n</p>\n\n# Getting Started\n\nSkyrim allows you to run any large weather model with a consumer grade GPU.\n\nUntil very recently, weather forecasts were run in 100K+ CPU HPC clusters, solving massive numerical models. Within last 2 years, open-source foundation models trained on weather simulation datasets surpassed the skill level of these numerical models.\n\nOur goal is to make these models accessible by providing a well maintained infrastructure.\n\n## Installation\n\nClone the repo, set an env (either conda or venv) and then run\n\n```bash\npip install .\n```\n\nDepending on your use-case (i.e. AWS storage needs or CDS initial conditions), you may need to fill in a `.env` by `cp .env.example .env`.\n\n## Run your first forecast\n\nSkyrim currently supports either running on on [modal](#forecasting-using-modal), on a container \u2013for instance [vast.ai](#vastai-setup) or [bare metal](#bare-metal)(you will need an NVIDIA GPU with at least 24GB and installation can be long).\n\nModal is the fastest option, it will run forecasts \"serverless\" so you don't have to worry about the infrastructure.\n\n### Forecasting using Modal:\n\nYou will need a [modal](https://modal.com/) key. Run `modal setup` and set it up (<1 min).\n\nModal comes with $30 free credits and a single forecast costs about 2 cents as of May 2024.\n\nOnce you are all good to go, then run:\n\n```bash\nmodal run skyrim/modal/forecast.py\n```\n\nThis by default uses `pangu` model to forecast for the next 6 hours, starting from yesterday. It gets initial conditions from [NOAA GFS](https://en.wikipedia.org/wiki/Global_Forecast_System) and writes the forecast to a modal volume. You can choose different dates and weather models as shown in [here](#run-forecasts-with-different-models-initial-conditions-dates).\n\nAfter you have your forecast, you can explore it by running a notebook (without GPU, so cheap) in modal:\n\n```bash\nmodal run skyrim/modal/forecast.py::run_analysis\n```\n\nThis will output a jupyter notebook link that you can follow and access the forecast. For instance, to read the forecast you can run from the notebook the following:\n\n```\nimport xarray as xr\nforecast = xr.open_dataset('/skyrim/outputs/[forecast_id]/[filename], engine='scipy')\n```\n\nOnce you are done, best is to delete the volume as a daily forecast is about 2GB:\n\n```bash\nmodal volume rm forecasts /[model_name] -r\n```\n\nIf you don't want to use modal volume, and want to aggregate results in a bucket (currently only s3), you just have to run:\n\n```bash\nmodal run skyrim/modal/forecast.py --output_dir s3://skyrim-dev\n```\n\nwhere `skyrim-dev` is the bucket that you want to aggregate the forecasts. By default, `zarr` format is used to store in AWS/GCP so you can read and move only the parts of the forecasts that you need.\n\n### Forecasting with your own GPUs:\n\nIf you are running on your own GPUs, installed either via [bare metal](#bare-metal) or via [vast.ai](#vast-ai-setup) then you can just run:\n\n`forecast`\n\nor you can pass in options as such:\n\n`forecast -m graphcast --lead_time 24 --initial_conditions cds --date 20240330`\n\nSee [examples](#examples) section for more.\u270c\ufe0f\n\n#### Bare metal\n\n1. You will need a NVIDIA GPU with at least 16GB memory, ideally 24GB. We are working on quantization as well so that in the future it would be possible to run simulations with much less compute. Have an environment set with Python +3.10, Pytorch 2.2.2 and CUDA 11.8. Or if easier start with the docker image: `pytorch/pytorch:2.2.2-cuda11.8-cudnn8-devel`.\n2. Install conda (miniconda for instance). Then run in that environment:\n\n```bash\nconda create -y -n skyenv python=3.10\nconda activate skyenv\n./build.sh\n```\n\nNote: Because we will be building from scratch this can take long (we need to install pytorch extensions through NVIDIA Apex package).\n\n#### vast.ai setup\n\n1. Find a machine you like RTX3090 or above with at least 24GB memory. Make sure you have good bandwith (+500MB/s).\n2. Select the instance template from [here](https://cloud.vast.ai/?ref_id=128656&template_id=1883215a8487ec6ea9ad68a7cdb38c5e).\n3. Then clone the repo and `pip install . && pip install -r requirements.txt`\n\n## Run forecasts with different models, initial conditions, dates\n\nFor each run, you will first pull the initial conditions of your interest (most recent one by default), then the model will run for the desired time step. Initial conditions are pulled from GFS, ECMWF IFS (Operational) or CDS (ERA5 Reanalysis Dataset).\n\nIf you are using CDS initial conditions, then you will need a [CDS](https://cds.climate.copernicus.eu/user/login?destination=%2Fcdsapp%23!%2Fdataset%2Freanalysis-era5-single-levels) API key in your `.env` \u2013`cp .env.example` and paste.\n\n## Examples\n\nAll examples are from local setup, but you can run them as it is if you just change `forecast` to `modal run skyrim/modal/forecast.py` and also make snake case kebab-case -i.e. `model_name` to `model-name`.\n\n### Example 1: Pick models, initial conditions, lead times\n\nForecast using `graphcast` model, with ERA5 initial conditions, starting from 2024-04-30T00:00:00 and with a lead time of a week (forecast for the next week, i.e. 168 hours):\n\n```bash\nforecast --model_name graphcast --initial_conditions cds --date 20240403 -output_dir s3://skyrim-dev --lead_time 168\n```\n\nor in modal:\n\n```bash\nmodal run skyrim/modal/forecast.py --model-name graphcast --initial-conditions cds --date 20240403 --output-dir s3://skyrim-dev --lead-time 168\n```\n\n### Example 2: Store in AWS and then read only what you need\n\nSay you re interested in wind at 37.0344\u00b0 N, 27.4305 E to see if we can kite tomorrow. If we need wind speed, we need to pull wind vectors at about surface level, these are u10m and v10m [components](http://colaweb.gmu.edu/dev/clim301/lectures/wind/wind-uv) of wind. Here is how you go about it:\n\n```bash\nmodal run skyrim/modal/forecast.py --output-dir s3://[your_bucket]/[optional_path]  --lead-time 24\n```\n\nThen you can read the forecast as below:\n\n```python\nimport xarray as xr\nimport pandas as pd\nzarr_store_path = \"s3://[your_bucket]/[forecast_id]\"\nforecast = xr.open_dataset(zarr_store_path, engine='zarr') # reads the metadata\ndf = forecast.sel(lat=37.0344, lon=27.4305, channel=['u10m', 'v10m']).to_pandas()\n```\n\nNormally each day is about 2GB but using zarr_store you will only fetch what you need.\u270c\ufe0f\n\n## Supported initial conditions and caveats\n\n1. NOAA GFS\n2. ECMWF IFS\n3. ERA5 Re-analysis Dataset\n\n## Large weather models supported\n\nCurrently supported models are:\n\n- [x] [Graphcast](https://arxiv.org/abs/2212.12794)\n- [x] [Pangu](https://arxiv.org/abs/2211.02556)\n- [x] [Fourcastnet](https://arxiv.org/abs/2202.11214) (v1 & v2)\n- [x] [DLWP](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002109)\n- [ ] [Fuxi](https://www.nature.com/articles/s41612-023-00512-1)\n- [ ] [MetNet-3](https://arxiv.org/abs/2306.06079)\n\n### License\n\nFor detailed information regarding licensing, please refer to the license details provided on each model's main homepage, which we link to from each of the corresponding components within our repository.\n\n- **Pangu Weather** : [Original](https://github.com/198808xc/Pangu-Weather), [ECMWF](https://github.com/ecmwf-lab/ai-models-panguweather), [NVIDIA](https://github.com/NVIDIA/earth2mip)\n\n- **FourcastNet** : [Original](https://github.com/NVlabs/FourCastNet), [ECMWF](https://github.com/ecmwf-lab/ai-models-fourcastnetv2),[NVIDIA](https://github.com/NVIDIA/earth2mip)\n\n- **Graphcast** : [Original](https://github.com/google-deepmind/graphcast), [ECMWF](https://github.com/ecmwf-lab/ai-models-graphcast), [NVIDIA](https://github.com/NVIDIA/earth2mip)\n\n- **Fuxi**: [Original](https://github.com/tpys/FuXi)\n\n## Roadmap\n\n- [x] ensemble prediction\n- [x] interface to fetch real-time NWP-based predictions, e.g. via ECMWF API.\n- [ ] global model performance comparison across various regions and parameters.\n- [ ] finetuning api that trains a downstream model on top of features coming from a global/foundation model, that is optimized wrt to a specific criteria and region\n- [ ] model quantization and its effect on model efficiency and accuracy.\n\nThis README will be updated regularly to reflect the progress and integration of new models or features into the library. It serves as a guide for internal development efforts and aids in prioritizing tasks and milestones.\n\n## Development\n\nAll in [here](./CONTRIBUTING.md) \u270c\ufe0f\n\n## Acknowledgements\n\nSkyrim is built on top of NVIDIA's [earth2mip](https://github.com/NVIDIA/earth2mip) and ECMWF's [ai-models](https://github.com/ecmwf-lab/ai-models). Definitely check them out!\n\n## Other Useful Resources\n\n- [\ud83c\udf0d Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)](http://github.com/jaychempan/Awesome-LWMs)\n- [Climate Data Store](https://cds.climate.copernicus.eu/)\n- [Open Climate Fix](https://github.com/openclimatefix)\n- [Herbie](https://github.com/blaylockbk/Herbie)\n",
    "bugtrack_url": null,
    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.  \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\"  \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.  2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.  4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:  (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and  (b) You must cause any modified files to carry prominent notices stating that You changed the files; and  (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and  (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.  You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.  5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.  6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.  7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.  8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.  9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.  END OF TERMS AND CONDITIONS  APPENDIX: How to apply the Apache License to your work.  To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"{}\" replaced with your own identifying information. (Don't include the brackets!)  The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives.  Copyright 2024 Secondlaw AI  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at  http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
    "summary": "AI weather models united.",
    "version": "0.0.2",
    "project_urls": {
        "Docs": "https://github.com/secondlaw-ai/skyrim",
        "Homepage": "https://github.com/secondlaw-ai/skyrim",
        "Tracker": "https://github.com/secondlaw-ai/skyrim/issues"
    },
    "split_keywords": [
        "pytorch",
        " weather",
        " forecasting",
        " ai",
        " ml",
        " xarray",
        " dl"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "a0eae6476674d9d6d4f00b0baeb31fd41d5696ac3dfcec45538b5f59b1c3663d",
                "md5": "a4c10c73b09581d40b84fc27e297673a",
                "sha256": "b6f73b8ab565987d11d8ef178f51b171a7dd8aab4a6b1e1f19e3f117a310bdd1"
            },
            "downloads": -1,
            "filename": "Skyrim-0.0.2-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "a4c10c73b09581d40b84fc27e297673a",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.10",
            "size": 42952,
            "upload_time": "2024-05-06T23:07:20",
            "upload_time_iso_8601": "2024-05-06T23:07:20.358337Z",
            "url": "https://files.pythonhosted.org/packages/a0/ea/e6476674d9d6d4f00b0baeb31fd41d5696ac3dfcec45538b5f59b1c3663d/Skyrim-0.0.2-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "61aab5797d4e997fae523b098018f39bfd6bb64f6937dc9b0c96b760ac773c8f",
                "md5": "be0c955a31e4bfee6e2188ac8ffcf75d",
                "sha256": "53d1a19b9ddd6a1aef69812b092d392fc09cd735a2220ded245d46a8735cae35"
            },
            "downloads": -1,
            "filename": "skyrim-0.0.2.tar.gz",
            "has_sig": false,
            "md5_digest": "be0c955a31e4bfee6e2188ac8ffcf75d",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.10",
            "size": 4936605,
            "upload_time": "2024-05-06T23:07:23",
            "upload_time_iso_8601": "2024-05-06T23:07:23.460393Z",
            "url": "https://files.pythonhosted.org/packages/61/aa/b5797d4e997fae523b098018f39bfd6bb64f6937dc9b0c96b760ac773c8f/skyrim-0.0.2.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-05-06 23:07:23",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "secondlaw-ai",
    "github_project": "skyrim",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "requirements": [],
    "lcname": "skyrim"
}