In this lesson, you will learn how to install a conda environment designed specifically for this class called
earth-analytics-python from a .yaml file.
At the end of this activity, you will be able to:
- Install a new environment in Anaconda
- View a list of the available environments in Anaconda
What You Need
You should have
Bash and the Anaconda distribution of
Python 3.x setup on your computer and an
earth-analytics working directory. Be sure you have:
- Completed the setup for
- Created a
earth-analyticsdirectory on your computer
Set up A Conda Python Environment
Install the Earth Lab Conda Environment
Anaconda allows you to have different environments installed on your computer to access different versions of
Python and different libraries. Sometimes libraries conflict which causes errors and packages not to work.
To avoid conflicts, we created an environment specifically for this earth analytics course that contains all of the spatial libraries that you will need.
Data Tip: For more information about conda environments check out the conda documentation.
To install the earth lab environment, you will need to follow these steps:
- Fork and clone a Github repository from
earth-analyticsdirectory. This repository contains a file called
environment.yamlthat is needed for the installation. (For instructions on forking/cloning repositories, see the section below on Fork and Clone Github Repository).
- Copy the
environment.yamlfile into your
earth-analyticsdirectory using your file manager. You can also copy the file using
Bashif you prefer (e.g.
cd earth-analytics-python-env, then
cp environment.yaml ..to move the file up to the parent directory
- Open the Terminal on your computer (e.g. Git Bash for Windows or Terminal on a Mac/Linux).
- In the Terminal, navigate to the
cd ~, then
- Then, type in the Terminal:
conda env create -f environment.yml
Note that it takes a bit of time to run this setup, as it needs to download and install each library, and that you need to have internet access for this to run!
Data Tip: The instructions above will only work if you run them in the directory where you placed the environment.yml file
Once the environment is installed, always make sure that the earth-analytics-python environment is activated before doing work for this class.
See the instructions further down on this page to Activate a Conda Environment. Once the environment is activated, the name of the activated environment will appear in parentheses on the left side of your terminal.
Fork and Clone Github Repository
This section provides the basic steps for forking a
Github repository (i.e. copying an existing repository to your
Github account) and for cloning a forked repository (i.e. downloading your forked repository locally to your computer). For a more detailed explanation of these steps, see the lesson on Get Files from Github.com.
Fork a Repository on Github.com
fork an existing
Github repository from the main
Github.com page of the repository that you want to copy (example:
On the main
Github.com page of the repository, you will see a button on the top right that says
Click on the
Fork button and select your
Github.com account as the home of the forked repository.
Clone a Repository to Local Computer
To download your forked copy of the
earth-analytics-python-env repository to your computer, open the Terminal and change directories to your
earth-analytics directory (e.g.
cd ~, then
Then, run the command
git clone followed by the URL to your fork on
https://github.com/your-username/earth-analytics-python-env). Be sure to change
your-username to your
Github account username.
cd ~ cd earth-analytics git clone https://github.com/your-username/earth-analytics-python-env
About the Conda Environment
What is a .yaml File?
When you work with Anaconda, you can create custom lists that tell Anaconda where to install libraries from, and in what order. You can even specify a particular version. You write this list using yaml(Yet Another Markup Language). This is an alternative to using
pip to install
In previous steps, you used a custom .yaml list to install all of the
Python libraries that you will need to complete the lessons in this course. This .yaml list is customized to install libraries from the repositories and in an order that minimizes conflicts.
If you run into any issues installing the environment from the yaml, let us know!
Let’s take a minute to explore the conda environment for this course! Here’s what part of the .yaml file looks like:
name: earth-analytics-python channels: - conda-forge dependencies: # Core scientific python libraries - numpy - matplotlib - python=3.6 - pyqt - seaborn # spatial libraries - rasterstats - geopy - cartopy - geopandas
Notice at the top of the file there is the environment name. This file has a few key parts:
NAME: the name of the environment that you will call when you wish to activate the environment. The name earth-analytics-python is defined in the environment.yml file.
Channels: this lists where packages will be installed from. There are many options including conda, conda-forge and pip. You will be predominately using conda-forge for all of the earth-analytics courses.
Dependencies: Dependencies are all of the things that you need installed in order for the environment to be complete. In the example, python version 3.6 is specified because this is because it works best with gdal. The order in which the libraries should be installed is also specified.
Manage Your Conda Environment
You can have different
Python environments on your computer. Anaconda allows you to easily jump between environments using a set of commands that you run in your terminal.
View a List of All Installed Conda Environments
You can see a list of all installed conda environments by typing:
conda info --envs
If you want to
Jupyter Notebook to use a particular environment that you have setup on your computer, you need to activate it.
For example, if a
Python package such as
geopandas is only installed in the earth-analytics-python environment, and not the default anaconda environment, you will not be able to import it to
Jupyter Notebook, unless you have the earth-analytics-python environment activated.
Activate a Conda Environment
To activate an environment, use the Terminal to navigate to your earth-analytics directory (e.g.
cd to the directory). Then, type the following command to activate the environment (e.g. earth-analytics-python):
conda activate earth-analytics-python
For older installations of Anaconda (versions prior to 4.6) on Mac, Linux, and Git Bash for Windows, type:
source activate earth-analytics-python
Windows Users: The lessons on this website assume that Windows users are using Git Bash as their primary terminal. If you need to activate a conda environment using the Command Prompt, you will need to use the following command:
Once the environment is activated, the name of the activated environment will appear in parentheses on the left side of your terminal.
Data Tip: Note that after you restart the Terminal, the earth-analytics-python environment is no longer active. You will need to activate the earth-analytics-python environment each time you start the Terminal by running the appropriate command provided above for your operating system.
Deactivate a Conda Environment
If needed, you can deactivate a conda environment. Deactivating the environment switches you back to the default environment in your computer.
Mac and Linux Instructions:
Source deactivate earth-analytics-python
Delete a Conda Environment
If you ever want to delete an envrionment, you must first deactivate that environment and then type:
conda env remove --name myenv
myenv with the name of the environment that you want to remove.
Remember to never delete your root environment.