https://github.com/samapriya/education-research
https://github.com/samapriya/education-research
Last synced: 5 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/samapriya/education-research
- Owner: samapriya
- License: apache-2.0
- Created: 2020-01-16T02:53:03.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-25T16:22:13.000Z (over 5 years ago)
- Last Synced: 2024-12-13T22:33:30.622Z (6 months ago)
- Language: Jupyter Notebook
- Size: 98.3 MB
- Stars: 3
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Planet training workshop
[](https://mybinder.org/v2/gh/samapriya/education-research/master)
This repo contains all materials used on Planet's training workshops based on [training-workshop materials]. Below you will find environment set up instructions, decks and other learning resources to have you started on using Planet's APIs and tools.
# Development Environment Setup
## Getting the repo
This is a public Planet repository so you don't have to SSH authenticate to clone to your local environment. You can simply use
```
git clone https://github.com/samapriya/education-research.git
```
## The Basics
The following tools are regularly used in [Planet School's guides from developers page](https://developers.planet.com/planetschool/):
* Python 3x (Note: Python 2.7 is in legacy support)
* cURL
* [requests](https://2.python-requests.org//en/master/)
* [retrying](https://pypi.org/project/retrying/)
* [jq](https://stedolan.github.io/jq/)
* [geojsonio-cli](https://github.com/mapbox/geojsonio-cli)
If you choose to follow along with code here, you may find it useful to install these libraries in your development environment. You can do this by yourself both installing each library individually,
```bash
$ pip install requests
$ pip install retrying
$ pip install jq
# Requires Node.js
$ npm install -g geojsonio-cli
```
or installing from our pre-defined requirements file (it install a bit more than what shown above)
```bash
pip install -r requirements.txt --user
```
# Working with Planet Data
When working with datasets like Planet's satellite imagery, here are a few useful Python libraries to know:
* [Rasterio](https://rasterio.readthedocs.io/en/stable/)
* [Fiona](https://fiona.readthedocs.io/en/latest/manual.html)
* [NumPy](https://numpy.org/)
* [MatPlotLib](https://matplotlib.org/)
**Rasterio** is a free and open source library for working with geospatial raster imagery. Rasterio is used extensively in the Python code that you'll find through Planet School, as well as in most Jupyter Notebooks in Planet's open source Notebook collection.
**Fiona** is a sibling library to Rasterio used when working with geospatial vector data. While less frequently used in Planet's developer resources, Fiona can still come in handy when, for example, creating or manipulating an AOI (Area of Interest) vector dataset.
**NumPy** and **MatPlotLib** can be used to manipulate, plot, and display raster data that has been loaded via Rasterio.
# Learning resources
Visit our learning hub: Planet's [Developer Center](https://developers.planet.com/). Explore Planet's API docs, references, tutorials, and developer tools: everything you need to start building with Planet today.
_All tailored training decks will be available here once the workhops kick off_. Below you can find some links to already publicly available resources
Some useful links:
* [Data API docs](https://developers.planet.com/docs/data/)
* [Orders API docs](https://developers.planet.com/docs/orders/)
* [Tiles API docs](https://developers.planet.com/docs/data/tile-services/)
* [Public APIs changelog](https://developers.planet.com/changelog/)
* [Planet Python Client](https://github.com/planetlabs/planet-client-python)
* [Planet integrations (QGIS plug-in)](https://developers.planet.com/integrations/)
* [Quickstart guides](https://developers.planet.com/docs/quickstart/)
* [Planet school](https://developers.planet.com/planetschool/)
* [Jupyter notebooks](https://github.com/planetlabs/notebooks)