Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/wekeo/learn-olci
Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 OLCI sensor for marine applications
https://github.com/wekeo/learn-olci
copernicus earth-observation eumetsat jupyter-notebook ocean ocean-colour python remote-sensing sentinel-3
Last synced: 4 days ago
JSON representation
Repository of WEkEO Jupyter Notebooks for learning about the Sentinel-3 OLCI sensor for marine applications
- Host: GitHub
- URL: https://github.com/wekeo/learn-olci
- Owner: wekeo
- License: other
- Created: 2022-02-20T15:50:35.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-02-04T15:03:38.000Z (17 days ago)
- Last Synced: 2025-02-04T15:22:21.739Z (17 days ago)
- Topics: copernicus, earth-observation, eumetsat, jupyter-notebook, ocean, ocean-colour, python, remote-sensing, sentinel-3
- Language: Jupyter Notebook
- Homepage: https://user.eumetsat.int/data/themes/marine
- Size: 47.8 MB
- Stars: 14
- Watchers: 3
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Learn OLCI
[](https://www.anaconda.com/products/distribution)
[](LICENSE.txt)
[](https://gitlab.eumetsat.int/eumetlab/oceans/ocean-training/sensors/learn-olci)
[](https://user.eumetsat.int/data/themes/marine)
[](https://mybinder.org/v2/git/https%3A%2F%2Fgitlab.eumetsat.int%2Feumetlab%2Foceans%2Focean-training%2Fsensors%2Flearn-olci/HEAD?labpath=Index.ipynb)
[](https://jupyterhub.prod.wekeo2.eu/hub/user-redirect/lab/tree/public/wekeo4oceans/learn-olci/Index.ipynb)
[](https://code.insula.destine.eu/hub/)
## Overview
This software was developed for EUMETSAT under contract EUM/CO/21/4600002620, funded by the
European Union under the Copernicus component of the EU Space Programme.
The **learn-olci** module consists of a collection of Python-based Jupyter Notebooks
design to demonstrate the capability of the Ocean and Land Colour Instrument (OLCI),
carried by the Sentinel-3 satellites, and to help users begin to work with its data
at level-1B and level-2.
For any questions about this repository, please contact [email protected].
## Ownership
This software and all associated intellectual property rights (IPRs) are owned by the European Union.
## License
This code is licensed under an MIT license. See file LICENSE.txt for details on
the usage and distribution terms. No dependencies are distributed as part of this
package. Copyright 2024 European Union.
All product names, logos, and brands are property of their respective owners.
All company, product and service names used in this website are for identification
purposes only.
## Authors
* [**Ben Loveday**](mailto://[email protected]) - [EUMETSAT](http://www.eumetsat.int)
* [**Hayley Evers-King**](mailto://[email protected]) - [EUMETSAT](http://www.eumetsat.int)
Please see the AUTHORS.txt file for more information on contributors.
## Prerequisites
You will require `Jupyter Notebook` to run this code. We recommend that you install
the latest [Anaconda Python distribution](https://www.anaconda.com/) for your
operating system. Anaconda Python distributions include Jupyter Notebook.
## Dependencies
|item|version|licence|package info|
|---|---|---|---|
|bokeh|3.2.1|BSD-3|https://anaconda.org/conda-forge/bokeh|
|cartopy|0.23.0|LGPL-3|https://scitools.org.uk/cartopy/docs/latest/copyright.html|
|cmocean|4.0.3|MIT|https://anaconda.org/conda-forge/cmocean|
|dask|2024.6.0|BSD-3|https://anaconda.org/conda-forge/dask|
|distributed|2024.6.0|BSD-3|https://anaconda.org/conda-forge/distributed/|
|hda|2.16|Apache-2.0|https://pypi.org/project/hda|
|ipywidgets|8.1.3|BSD-3|https://anaconda.org/conda-forge/ipywidgets|
|jupyterlab|4.2.2|BSD-3|https://anaconda.org/conda-forge/jupyterlab|
|matplotlib|3.8.4|PSFL|https://matplotlib.org/stable/users/project/license.html|
|netcdf4|1.7.1|MIT|https://anaconda.org/conda-forge/netcdf4|
|python|3.10.14|PSF|https://docs.python.org/3/license.html|
|scipy|1.13.0|BSD-3|https://anaconda.org/conda-forge/scipy|
|xarray|2024.6.0|Apache-2.0|https://anaconda.org/conda-forge/xarray|
|eumartools|0.0.1|MIT|https://anaconda.org/cmts/eumartools|
|eumdac|2.2.2|MIT|https://anaconda.org/eumetsat/eumdac|
|scikit-image|0.22.0|BSD-3|https://anaconda.org/conda-forge/scikit-image|
|shapely|2.0.3|BSD-3|https://anaconda.org/conda-forge/shapely|
|beautifulsoup4|4.12.3|MIT|https://anaconda.org/conda-forge/beautifulsoup4|
|cdsapi|0.6.1|Apache-2.0|https://anaconda.org/conda-forge/cdsapi|
|ephem|4.1.5|LGPL|https://anaconda.org/conda-forge/ephem|
|pandas|2.2.1|BSD-3|https://anaconda.org/conda-forge/pandas|
|urllib3|2.2.1|MIT|https://anaconda.org/conda-forge/urllib3/|
## Included components
None
## Installation
The simplest and best way to install these packages is via Git. Users can clone this
repository by running the following commands from either their [terminal](https://tinyurl.com/2s44595a)
(on Linux/OSx), or from the [Anaconda prompt](https://docs.anaconda.com/anaconda/user-guide/getting-started/).
You can usually find your terminal in the start menu of most Linux distributions
and in the Applications/Utilities folder on OSx. Alternatively, you should be
able to find/open your Anaconda prompt from your start menu (or dock, or via running
the Anaconda Navigator). Once you have opened a terminal/prompt, you should navigate
to the directory where you want to put the code. Once you are in the correct directory,
you should run the following command;
`git clone --recurse-submodules --remote-submodules https://gitlab.eumetsat.int/eumetlab/oceans/ocean-training/sensors/learn-olci.git`
This will make a local copy of all the relevant files.
*Note: If you find that you are missing packages, you should check that you ran
`git clone` with both the `--recurse-submodules` and `--remote-submodules` options.*
*Note: if you are using an older version of git, you may find that your submodules are empty.
In this case, you need to remove the folder and re-run the line above with `--recursive` added to the end*
*Note: in some rare Anaconda instances, Git is not installed by default. To correct
this, you can install Git using `conda install git` from the Anaconda prompt (Windows)
or in your terminal (OSx/Linux).*
## Usage
This collection supports Python 3.10. Although many options are possible, the
authors highly recommend that users install the appropriate Anaconda package
for their operating system. In order to ensure that you have all the required
dependencies, we recommend that you build a suitable Python environment, as
discussed below.
### Python environments
Python allows users to create specific environments that suit their applications.
This tutorials included in this collection require a number of non-standard
packages - e.g. those that are not included by default in Anaconda. In this
directory, users will find a *environment.yaml* file which can be used to
construct an environment that will install all the required packages.
To construct the environment, you should open either **terminal** (Linux/OSx)
or an **Anaconda prompt** window and navigate to repository folder you downloaded
in the **Installation** section above. In this folder there is a file called
**environment.yml**. This contains all the information we need to install the relevant
packages.
Older versions of the conda package manager can be very slow, so we will install a new "solver" that
speeds things up. To do this, from the Anaconda prompt (Windows) or in the terminal (OSx/Linux)
you can run:
`conda install -n base conda-libmamba-solver`
Once the line above is run, to create out Python environment, we run:
`conda env create -f environment.yml --solver=libmamba`
This will create a Python environment called **cmts_learn_olci**. The environment
won't be activated by default. To activate it, run:
`conda activate cmts_learn_olci`
Now you are ready to go!
*Note: remember that you may need to reactivate the environment in every
new window instance*
*Note: if you get a warning that "solver" is not a valid conda argument, you can
skip the libmamba install and run:* `conda env create -f environment.yml`
*Note: as you need to install libmamba solver in the conda base environment, this may not always be
possible on cloud systems.*
### Running Jupyter Lab
This module is based around a series of [Jupyter Notebooks](https://jupyter.org/), designed to be run in Jupyter Lab.
Jupyter Notebooks support high-level interactive learning by allowing us to combine code, text description and data
visualisations. If you have not worked with `Jupyter Notebooks` before, please look at the [Introduction to Python
and Project Jupyter](./working-with-python/Intro_to_Python_and_Jupyter.ipynb) module to get a short introduction to
their usage and benefits.
To run Jupyter Notebook, open a terminal or Anaconda prompt and make sure you have activated
the correct environment. Again, navigate to the repository folder. Now you can run Jupyter using:
`jupyter lab` or `jupyter-lab`, depending on your operating system.
This should open Jupyter Lab in a browser window. On occasion, Jupyter may not
be able to open a window and will give you a URL to past in your browser. Please do
so, if required.
*Note: Jupyter Lab is not able to find antyhing that is 'above' it in a directory
tree, and you will unable to navigate to these. So make sure you run the line above
from the correct directory!*
Now you can run the notebooks! We recommend you start with the [Index](./Index.ipynb) module.
### Running on cloud platforms
If you are running on a remote Jupyter Hub (e.g. WEkEO or Insula) you will need to perform some additional steps to
ensure that you have the right python environment loaded in your notebook. When running locally, as long you have activated
the correct environment, Jupyter will load it into your the "kernel" which runs your code by default. On cloud systems, we
have to add the kernel to the system and apply it manually when we run.
To add an environment to a kernel you should first build the environment and activate it as described above. Once you have
done this, you can add your environment to a kernel from the command line as follows:
`python -m ipykernel install --name cmts_learn_olci --user`
You should now be able to select the kernel from the menu bar in the top right hand side of any notebook you run.
*Note: it sometimes takes a few seconds for the kernel to register in the notebook itself*
*Note: the above does not apply to Binder, which will load the environment supplied with the Git repository*
### Collaborating, contributing and issues
If you would like to collaborate on a part of this code base or contribute to it
please contact us on [email protected]. If you are have issues and
need help, or you have found something that doesn't work, then please contact us
at [email protected]. We welcome your feedback!