This page provides more information about the IRIS Python tutorial given at the IRIS-10 meeting in Bengaluru, India. At the workshop, participants are expected to work on the tutorial using Binder, a cloud-based solution that lets you run an instance of Jupyter lab with all the required packages and data files. To start the Binder notebook, follow the link below:

Binder makes it easy to get started with minimal effort, but if you want to actually use IRISPy, you will need to have it installed in your computer. IRISPy relies on several other packages such as SunPy, astropy, and matplotlib. The recommended way to install all of them is via conda (miniconda or Anaconda). You can follow the installation instructions on the IRISPy documentation, or follow all the lines below (they are very similar).

To install the tutorial files, you need to clone its git repository or download a zip file. To clone, run the following in a directory of your choice:

git clone https://github.com/tiagopereira/iris10.git


You will end up with a directory called iris10. In the terminal, go to that directory. If you do not have IRISPy installed, you can create a new conda environment by using the supplied environment.yml file. The following will download and install all required packages into an environment called iris:

conda env create -f environment.yml


Then you need to activate the environment:

conda activate iris


Lastly, you will need to install IRISPy (not available on conda) with pip:

pip install git+https://github.com/tiagopereira/irispy.git


Optionally, if you want to have interactive matplotlib plots in jupyter, you need to run (this operation takes a while):

jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib


Finally, you will need to download the data files used in the tutorial. Assuming you are at the terminal in the directory of iris_tutorials, you can download the data files into iris_tutorials/notebooks with the following:

wget -c http://www.lmsal.com/solarsoft/irisa/data/level2_compressed/2018/01/02/20180102_153155_3610108077/iris_l2_20180102_153155_3610108077_SJI_1400_t000.fits.gz -P notebooks/
wget -c http://www.lmsal.com/solarsoft/irisa/data/level2/2014/09/19/20140919_051712_3860608353/iris_l2_20140919_051712_3860608353_SJI_2832_t000.fits -P notebooks/
wget -c https://folk.uio.no/tiago/aia_20140919_060030_1700_image_lev1.fits -P notebooks/
wget -c https://folk.uio.no/tiago/iris_l2_20180102_153155_3610108077_raster_t000_r00000.fits.bz2 -P notebooks
pbunzip2 notebooks/iris_l2_20180102_153155_3610108077_raster_t000_r00000.fits.bz2


jupyter lab

And then navigate to notebooks/IRIS10.ipynb.