ASDF (Advanced Scientific Data Format) SciPy Tutorial 2022

To run all the workshop notebooks on your own computer, please be sure your machine is configured with the packages in the installation check file. These packages are the ones we use to verify that the notebooks are working as expected.

These instructions describe setup using git and Miniconda. It is not strictly necessary to use either of these.

If you have any problems with any of these steps, please check if your problem has already been reported as an issue. If not, please create a new issue to ask your question.

0. (Only for Windows) Install WSL

If you are using Windows, we now recommend using the Windows Subsystem for Linux (WSL) instead of using native Windows tools. WSL is now fully supported by Microsoft and tends to result in fewer install headaches, and lets you use tools that were developed for Linux seamlessly in windows. While you still may be able to use the Windows-native installation of Miniconda, these instructions focus on the WSL approach for the above reasons.

To install WSL, you should follow the instructions provided by Microsoft:

https://docs.microsoft.com/en-us/windows/wsl/install

While you may choose an alternative Linux distribution from the default Ubuntu, the instructions below have been tested on Ubuntu, so unless you have a specific reason, we suggest you stick with the default. Once you reach the point in the instructions with a working Linux terminal prompt, you can proceed to step 1 of these instructions.

Optional While you can run a WSL terminal with the command prompt built into Windows, it's rather bare-bones and you may not have the best experience. For WSL on Windows you'll probably want to install Windows Terminal to have a terminal experience closer to what you'd see on Mac or Linux.

1. Install Miniconda (if needed)

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 like pip, zlib etc. If you have already installed Miniconda or Anaconda, 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 that all packages work correctly.

(On native Windows, you might also need additional compilers, although this should not be necessary in WSL).

2. Open the conda command prompt

Miniconda includes an environment manager called conda. An environment manager allows you to have multiple installations of Python, including packages and versions, installed on your computer. You can create, export, list, remove, and update environments that have different versions of Python and / or packages installed in them. For this workshop, we will configure an environment using the conda command line utility.

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 $SHELL

On Windows, open the Anaconda Prompt terminal app.

(An alternative to using conda is mamba which is a reimplementation of the conda package manager in C++.)

Note: you will need conda version 4.6 or later. You can update your conda installation with conda update conda

3. Install Git (if needed)

At the prompt, check whether Git is already installed:

git --version

If the output shows a Git version, skip to the next step. Otherwise, install Git:

conda install git

4. Clone this repository, or download and extract a ZIP file, from GitHub

If using git, clone the workshop repository using git:

git clone https://github.com/asdf-format/scipy2022tutorial.git

If you elect not to use git, you can download the ZIP file by opening the green Code button at https://github.com/asdf-format/scipy2022tutorial and selecting Download ZIP.

5. Create a conda environment for the workshop

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 a conda environment for this workshop using a yml file . The python version and all needed packages are listed in environment.yml.

Open a terminal window using the appropriate one for your operating system.

Now navigate to this directory in the terminal:

cd scipy2022tutorial

And finally, on any platform, to install and activate the asdf-tutorial-env environment, type:

conda env create --file 00_install/environment.yml
conda activate asdf-tutorial-env

The name of the new conda environment created above should now be displayed next to the terminal prompt: (asdf-tutorial-env)

6. Check Installation

In the terminal you used in the preceding step, run the verify_install.py script to check the Python environment and some required dependencies:

python 00_install/verify_install.py

If the script reports that some versions do not match for a specific package (for example numpy), check first whether the package was installed using conda or pip by using conda list <package>:

conda list numpy

If the package was installed with pip, the Build and Channel columns will include pypi:

# packages in environment at /opt/miniconda3/envs/test:
#
# Name                    Version                   Build  Channel
numpy                     1.22.4                   pypi_0    pypi

and then you can upgrade with pip install --upgrade <package>:

pip install --upgrade numpy

If you need a pre-release version from PyPI, add --pre to the pip install command.

Otherwise, if the package is installed with conda, it will show something similar to the following:

# packages in environment at /opt/miniconda3/envs/test:
#
# Name                    Version                   Build  Channel
numpy                     1.22.3          py310hdcd3fac_0
numpy-base                1.22.3          py310hfd2de13_0

and you can update with conda update <package>:

conda update numpy

7. Starting Jupyter Notebook

Making sure your terminal is in the scipy2022tutorial directory (you can use pwd to check), you can then start the Jupyter server on your local computer, with which you can view the Jupyter notebooks:

jupyter notebook

If successful, your browser will open a new page / tab pointing to localhost, showing a listing of the current directory (including subdirectories).

Click into one of the notebook directories, double-click on a notebook, and wait for it to launch. In the top right corner, if you see a blue Kernel Ready message appear and disappear, then all is well.

If you see a red Kernel Error in the top right corner, click on it and scroll down to see the error message. If it says FileNotFoundError, shut down the notebook server on your terminal and run this command:

python -m ipykernel install --user

Now, try running jupyter notebook again as above, and the Kernel Error should be fixed. You can try running the first cell (usually an import) to check.

Additional Resources

• Set up git
• Conda Users Guide
• Instructions for keeping this repo up to date