Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
| Authors: |
|
|
|---|---|---|
| Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
| Type: | Resource | |
| Storage: | The size of this resource is 82.4 KB | |
| Created: | Jul 12, 2025 at 2:48 a.m. (UTC) | |
| Last updated: | Aug 16, 2025 at 4:26 a.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
|---|---|
| Views: | 200 |
| Downloads: | 0 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
Abstract
This resource provides code examples for working directly with HydroShare S3 buckets (also referred to as HydroShare cloud storage buckets or simply HydroShare buckets) to access and manage resource files, without the need to download them locally first. Working directly with S3 buckets can offer better performance.
This resource includes the following notebooks:
1- hydroshare_s3_bucket_access_examples.ipynb: Examples for working directly with HydroShare S3 buckets to access and manage resource files.
2- hs_bucket_access_gdal_example.ipynb: Examples for reading raster and shapefile directly from HydroShare S3 buckets using gdal.
3- hs_bucket_access_non_gdal_example.ipynb: Examples of using h5netcdf and xarray for reading netcdf files, rioxarray for reading raster files, and pandas for reading CSV files, all directly from HydroShare S3 buckets.
Subject Keywords
Content
README.md
Using HydroShare Buckets to Access Resource Files
This resource includes the following files:
- hydroshare_s3_bucket_access_examples.ipynb: Examples for working directly with HydroShare S3 buckets to access and manage resource files, without the need to download them locally first.
- hs_bucket_access_gdal_example.ipynb: Examples for reading raster and shapefile directly from HydroShare S3 buckets using
gdal, without the need to download them locally first. - python-modules-direct-read-from-bucket/hs_bucket_access_non_gdal_example.ipynb: Examples of using 1-
h5netcdfandxarrayfor reading netcdf files, 2-rioxarrayto read raster files, and 3-pandasto read CSV files, all directly from HydroShare S3 buckets, without the need to download them locally first. - user_account.py: A utility for reading cached HydroShare account information in any JupyterHub instance accessible by HydroShare. The example notebooks use this utility so that users do not have to enter their hydroshare account information manually to access hydroshare buckets.
- conda_env: A folder containing the CondaEnvironmentSetup.ipynb notebook with instructions and scripts for setting up a custom Conda environment.
- README.md: Explains the contents of this resource.
To use this resource in HydroShare, select Open With on one of the available application servers to launch the corresponding JupyterHub environment. Then, open the desired Jupyter Notebook and follow the instructions.
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment