Installation

Dependencies

Required

The core dependencies are:

  • Python 3.6 or later

  • dask

  • distributed

  • numpy

  • pandas

  • xarray

  • toolz

  • affine

  • rasterio (>=1.0.14)

  • pyyaml

Optional

Google Earth Engine (GEE)

To enable any of the capabilities of cedar that relate to the Google Earth Engine, including downloading ARD from the GEE, you will need the following:

  • appmode

  • google-api-python-client

  • earthengine-api

  • google-auth-httplib2

  • google-auth-oauthlib

Command Line Interface

For the command line interface, the following are also required:

  • click

  • click-plugins

  • cligj (>=0.5)

Tests

We use pytest to run the tests in this package, and require:

  • pytest

  • pytest-cov

  • pytest-lazy-fixture

  • coverage

Documentation

Documentation is built using Sphinx and requires:

  • sphinx

  • sphinx_rtd_theme

  • sphinxcontrib-bibtex

  • numpydoc

Instructions

cedar is a pure Python package, but it sits on top of a pile of dependencies that may be difficult to install. The easiest way to install all of these dependencies is using the conda tool.

Conda Package

cedar has been packaged as a Conda package and uploaded to the ceholden Anaconda channel. Installing cedar this way is one of the easiest ways because it will take care of installing the dependencies and the Python code for a release of cedar. Note that this conda package also depends on other packages available in the conda-forge channel, so make sure to include both channels when installing cedar.

An example of how you might want to install cedar using conda is,

$ conda create -n cedar_env -c conda-forge -c ceholden cedar-datacube
$ conda activate cedar_env

From Source

Dependencies

To install cedar from the source code, you will first need to make sure you have installed all the required dependencies.

With conda installed and ready to use, you can install the required dependencies for this library using one of the “environment” files located in the ci/ directory (we use these for our continuous integration tests):

$ conda env create -n cedar -f ci/requirements-py37.yml

With the conda environment created, you can activate it as follows:

$ conda activate cedar

You should now be ready to install cedar.

Install cedar

The sources for cedar can be downloaded from the Github repo. You can either download the source from Github and install it using pip, or use pip to install the source from Github directly.

You can either clone the public repository:

$ git clone git@github.com:ceholden/cedar-datacube

Make sure you have installed the package dependencies before proceeding (see Instructions). Once you have a copy of the source, you can install it with:

$ cd cedar-datacube/
$ pip install -e .

or

$ pip install -e cedar-datacube/

The flag, -e, is recommended to tell pip to make the installation “editable”, meaning that changes you make to the files in the repository will be reflected when you import the Python package. Otherwise you would have to re-install the package with pip for changes to affect the installed package.

Alternatively, you can use pip to install it in one step,

$ pip install git+ssh://git@github.com/ceholden/cedar-datacube.git