odc-tools | ODC features that DEA

by opendatacube Python Version: 0.0.0 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | odc-tools Summary

odc-tools is a Python library. odc-tools has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However odc-tools build file is not available. You can install using 'pip install odc-tools' or download it from GitHub, PyPI.

ODC features that DEA is experimenting with or prototyping with the intention of being integrated into odc-core in the future

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Quality

Security

License

Reuse

Support

odc-tools has a low active ecosystem.

It has 51 star(s) with 25 fork(s). There are 21 watchers for this library.

It had no major release in the last 6 months.

There are 29 open issues and 80 have been closed. On average issues are closed in 97 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of odc-tools is 0.0.0

Quality

odc-tools has 0 bugs and 0 code smells.

Security

odc-tools has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

odc-tools code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

odc-tools is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

odc-tools releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

odc-tools has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

It has 8925 lines of code, 614 functions and 104 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed odc-tools and discovered the below as its top functions. This is intended to give you an instant insight into odc-tools implemented functionality, and help decide if they suit your requirements.

Compute geomedian using geomedian
Split chunks into chunks
Extract chunks and shapes from a numpy array
Get the chunks for all bands
Saves a GeoRaster to dst
Retrieves credentials from a session
Try to automatically find region name
Create a boto session
Load enum data
Download an S3 file
Broadcast an action to a pool
Fetch object from s3
Load a set of enum values
Compute the band mads
Process a URI tile
Colorize an image
Select a region on a map
Read pixels from urls
Get an object from s3
A wrapper for os walk
Create a S3 bucket request
Generate an asynchronous stream for dask - compute
Compute the geometric mean of a given dataset
Generate geomedian
Build the UI
Convenience function for creating products

Get all kandi verified functions for this library.

odc-tools Key Features

No Key Features are available at this moment for odc-tools.

odc-tools Examples and Code Snippets

No Code Snippets are available at this moment for odc-tools.

Community Discussions

No Community Discussions are available at this moment for odc-tools.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install odc-tools

This repository provides a number of small [libraries](https://github.com/opendatacube/odc-tools/tree/develop/libs) and [CLI tools](https://github.com/opendatacube/odc-tools/tree/develop/apps).
odc.algo algorithms (GeoMedian wrapper is here)
odc.stats large scale processing framework (Moved to [odc-stats](http://github.com/opendatacube/odc-stats))
odc.ui tools for data visualization in notebook/lab
odc.stac STAC to ODC conversion tools (Moved to [odc-stac](https://github.com/opendatacube/odc-stac))
odc.dscache experimental key-value store where key=UUID, value=Dataset (moved to [odc-dscache](https://github.com/opendatacube/odc-dscache))
odc.io common IO utilities, used by apps mainly
odc-cloud[ASYNC,AZURE,THREDDS] cloud crawling support package
odc.aws AWS/S3 utilities, used by apps mainly
odc.aio faster concurrent fetching from S3 with async, used by apps odc-cloud[ASYNC]
odc.{thredds,azure} internal libs for cloud IO odc-cloud[THREDDS,AZURE]
Cloud tools depend on aiobotocore package which has a dependency on a specific version of botocore. Another package we use, boto3, also depends on a specific version of botocore. As a result having both aiobotocore and boto3 in one environment can be a bit tricky. The easiest way to solve this, is to install aiobotocore[awscli,boto3] before anything else, which will pull in a compatible version of boto3 and awscli into the environment. The specific version of aiobotocore is not relevant, but it is needed in practice to limit pip/conda package resolution search.
For cloud (AWS only) ` pip install odc-apps-cloud `
For cloud (GCP, THREDDS and AWS) ` pip install odc-apps-cloud[GCP,THREDDS] `
For dc-index-from-tar (indexing to datacube from tar archive) ` pip install odc-apps-dc-tools `

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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