To run all the demo notebooks on your own computer, you will need
a Python environment configured with the required packages (and data).
These instructions describe setup using git and Miniconda.
If you have any problems with any of these steps, please feel free to submit an issue or send an email to Cami at [email protected].
For the commands shown, % (and anything to the left of it) represents
the terminal prompt. You do not need to copy it; instead only copy the
command to the right of %.
Miniconda is a free minimal installer for conda. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib, and a few others. If you have either Miniconda or the full Anaconda already installed, you can skip to the next step.
In a terminal window, check if Miniconda is already installed.
% conda info
If Miniconda is not already installed, follow these instructions for your operating system: https://conda.io/projects/conda/en/latest/user-guide/install/index.html. Please be sure to install a 64-bit version of Miniconda to ensure all packages work correctly.
(On native Windows, you might also need additional compilers, although this should not be necessary in WSL).
Open a terminal window and verify that conda is working:
% conda info
If you are having trouble, check your shell in a terminal window:
% echo $SHELL
then run the initialization if needed, in that same terminal window:
% conda init `basename $SHELL`
You should open a new terminal window after conda init is run.
It is advisable to update your conda to the latest version. We recommend a minimum version of 23.10.0. Check your conda version with:
% conda --version
Update it with:
% conda update conda
or
% conda update -n base conda
At the prompt opened in the previous step, enter this command to see whether git is already installed and accessible to this shell:
% git --version
If the output shows a git version, proceed to the next step. Otherwise install git by entering the following command and following the prompts:
% conda install git
If using git, clone the workshop repository using
git:
% git clone https://github.com/spacetelescope/jdaviz_demo.git
If you elect not to use git, you can download
the ZIP file by opening the green Code button at
https://github.com/spacetelescope/jdaviz_demo and selecting
Download ZIP.
Miniconda includes an environment manager called conda. Environments allow you to have multiple sets of Python packages installed at the same time, making reproducibility and upgrades easier. You can create, export, list, remove, and update environments that have different versions of Python and/or packages installed in them.
Create and activate a new Python 3.12 environment for the workshop called jdaviz:
% conda create -n jdaviz python=3.12 -y
% conda activate jdaviz
The name of the new conda environment created above should be displayed next
to the terminal prompt: (jdaviz) %
Jdaviz comes with a series of packages like astropy, matplotlib, Jupyter, and others. To be able to work with instrument footprints we will need to install pysiaf and for data sonification we will need strauss. Here are three versions of the same command (you do not have to run all three). I would recommend using the last one since there are a couple of interesting things we can look at in the not-yet released version.
Here is the basic command:
% pip install jdaviz pysiaf strauss
If (like me) you have a large number of conda environments with various
versions of the same packages, you might want to add --no-cache-dir
to be sure to get the latest versions of the required packages for the
latest jdaviz. The command will look like this:
% pip install jdaviz pysiaf strauss --no-cache-dir
If you want to use the developer version of jdaviz, you can install directly from github with the following commands:
% pip install pysiaf strauss --no-cache-dir
% pip install git+https://github.com/spacetelescope/jdaviz.git --no-cache-dir
To check your installation, run jupyter lab from your command line when
in the jdaviz environment. This should launch a Jupyter lab instance in
your default browser. Open a new notebook and in a code cell type:
import jdaviz
print(jdaviz.__version__)
Note that the first import is quite slow because of the dependencies that need to be imported too. Thank you for being patient.
The script download_data.py will download the necessary data for this
demo from MAST. To download the data, run
% python download_data.py