# ras-stac
[![CI](https://github.com/fema-ffrd/rashdf/actions/workflows/continuous-integration.yml/badge.svg?branch=main)](https://github.com/fema-ffrd/ras-stac/actions/workflows/continuous-integration.yml)
[![Release](https://github.com/fema-ffrd/ras-stac/actions/workflows/release.yml/badge.svg)](https://github.com/fema-ffrd/ras-stac/actions/workflows/release.yml)
[![PyPI version](https://badge.fury.io/py/ras-stac.svg)](https://badge.fury.io/py/ras-stac)
Utilities for making SpatioTemporal Asset Catalogs of HEC-RAS models
This repository contains code for developing STAC items from HEC-RAS models. Current activities focus on creating items for geometry files `g**.hdf` stored in AWS S3. More to come.
*Source code largely ported from [ffrd-stac](https://github.com/arc-pts/ffrd-stac/blob/204e1ec85068936856b317fa9446da3c4da5d8d4/ffrd_stac/rasmeta.py).*
## Getting Started
1. For local development, create a `.env` file using the `example.env` file.
2. Start a minio service and load data using the [cloud-mock](https://github.com/fema-ffrd/cloud-mock) repository.
3. Run the `populate-sample-data.sh` script to test set-up, connetivity, and view a sample stac catalog created using this library.
**NOTE** It is recommended that ras-stac not be run in a container for testing and development due to networking issues that complicate use of these tools, when using minio.
Raw data
{
"_id": null,
"home_page": null,
"name": "ras-stac",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "Seth Lawler <slawler@dewberry.com>",
"keywords": "hec-ras, catalog, STAC",
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/eb/c2/01820134f90d3a106cb69e656ec52a35d617047d5a19ed91317b19e0950b/ras_stac-0.1.0b1.tar.gz",
"platform": null,
"description": "# ras-stac\n[![CI](https://github.com/fema-ffrd/rashdf/actions/workflows/continuous-integration.yml/badge.svg?branch=main)](https://github.com/fema-ffrd/ras-stac/actions/workflows/continuous-integration.yml)\n[![Release](https://github.com/fema-ffrd/ras-stac/actions/workflows/release.yml/badge.svg)](https://github.com/fema-ffrd/ras-stac/actions/workflows/release.yml)\n[![PyPI version](https://badge.fury.io/py/ras-stac.svg)](https://badge.fury.io/py/ras-stac)\n\nUtilities for making SpatioTemporal Asset Catalogs of HEC-RAS models\n\nThis repository contains code for developing STAC items from HEC-RAS models. Current activities focus on creating items for geometry files `g**.hdf` stored in AWS S3. More to come. \n\n*Source code largely ported from [ffrd-stac](https://github.com/arc-pts/ffrd-stac/blob/204e1ec85068936856b317fa9446da3c4da5d8d4/ffrd_stac/rasmeta.py).*\n\n\n## Getting Started\n\n1. For local development, create a `.env` file using the `example.env` file.\n\n2. Start a minio service and load data using the [cloud-mock](https://github.com/fema-ffrd/cloud-mock) repository.\n\n3. Run the `populate-sample-data.sh` script to test set-up, connetivity, and view a sample stac catalog created using this library.\n\n\n**NOTE** It is recommended that ras-stac not be run in a container for testing and development due to networking issues that complicate use of these tools, when using minio. \n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Create SpatioTemporal Asset Catalog (STAC) objects from HEC-RAS model data.",
"version": "0.1.0b1",
"project_urls": {
"repository": "https://github.com/fema-ffrd/ras-stac"
},
"split_keywords": [
"hec-ras",
" catalog",
" stac"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "453ca73dca4ebe01ab89ee3bfe5cf8966a39d4d07bc7d0fbf7f8fbfa1c654025",
"md5": "c182844b6ce295a4a0d795786cbb4162",
"sha256": "23553615a98971db1015881b50de4db58ad8d8cbdcf9315240613171c916de5c"
},
"downloads": -1,
"filename": "ras_stac-0.1.0b1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c182844b6ce295a4a0d795786cbb4162",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 19658,
"upload_time": "2024-05-13T15:43:38",
"upload_time_iso_8601": "2024-05-13T15:43:38.750857Z",
"url": "https://files.pythonhosted.org/packages/45/3c/a73dca4ebe01ab89ee3bfe5cf8966a39d4d07bc7d0fbf7f8fbfa1c654025/ras_stac-0.1.0b1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ebc201820134f90d3a106cb69e656ec52a35d617047d5a19ed91317b19e0950b",
"md5": "e8ef7e6b11435d9e8f90771d74cb44bf",
"sha256": "866133c15f040c3af9490a171f557bc41d4ec44763d04aa2e24e5ace110e3e8b"
},
"downloads": -1,
"filename": "ras_stac-0.1.0b1.tar.gz",
"has_sig": false,
"md5_digest": "e8ef7e6b11435d9e8f90771d74cb44bf",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 16052,
"upload_time": "2024-05-13T15:43:39",
"upload_time_iso_8601": "2024-05-13T15:43:39.987018Z",
"url": "https://files.pythonhosted.org/packages/eb/c2/01820134f90d3a106cb69e656ec52a35d617047d5a19ed91317b19e0950b/ras_stac-0.1.0b1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-05-13 15:43:39",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "fema-ffrd",
"github_project": "ras-stac",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "s3fs",
"specs": [
[
"==",
"2024.2.0"
]
]
},
{
"name": "boto3",
"specs": [
[
"==",
"1.34.34"
]
]
},
{
"name": "botocore",
"specs": [
[
"==",
"1.34.34"
]
]
},
{
"name": "fsspec",
"specs": [
[
"==",
"2024.2.0"
]
]
},
{
"name": "h5py",
"specs": [
[
"==",
"3.10.0"
]
]
},
{
"name": "mypy-boto3-s3",
"specs": [
[
"==",
"1.34.14"
]
]
},
{
"name": "numpy",
"specs": [
[
"==",
"1.21.5"
]
]
},
{
"name": "papipyplug",
"specs": [
[
"==",
"2024.3.4"
]
]
},
{
"name": "python-dotenv",
"specs": [
[
"==",
"1.0.1"
]
]
},
{
"name": "pyproj",
"specs": [
[
"==",
"3.6.1"
]
]
},
{
"name": "pystac",
"specs": [
[
"==",
"1.9.0"
]
]
},
{
"name": "shapely",
"specs": [
[
"==",
"2.0.2"
]
]
},
{
"name": "rasterio",
"specs": [
[
"==",
"1.3.9"
]
]
},
{
"name": "rashdf",
"specs": [
[
"==",
"0.1.0"
]
]
}
],
"lcname": "ras-stac"
}