{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "This is a practical guide to using Python to read and analyse data from IRIS. It is meant to complement the existing [IRIS documentation](http://iris.lmsal.com/documents.html) and, in particular, the [Online Guide to IRIS Data Analysis (ITN 26)](http://iris.lmsal.com/itn26/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Why low-level?\n", "\n", "This guide focuses on using simple and efficient methods to read IRIS data, and does not make use of higher-level abstractions or IRIS-specific packages such as [IRISpy](http://docs.sunpy.org/projects/irispy/en/latest/) or [SunPy](http://sunpy.org/). The motivation is two-fold: to detail a \"barebones\" interface when no other packages are available or performance is the goal, and to provide a simple guide while IRISpy is not mature enough and its API changes quickly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Requirements\n", "\n", "To follow this guide only standard python scientific packages are necessary:\n", "\n", "* Numpy\n", "* Scipy\n", "* Matplotlib\n", "\n", "In addition, [Astropy](http://www.astropy.org/) is necessary for its FITS reader and WCS module.\n", "\n", "The recommended way to install all these packages is through the [Anaconda Python Distribution](https://www.anaconda.com/download). Once you have Anaconda installed, you can make sure you have all you need by running:\n", "\n", "```\n", "conda install numpy scipy matplotlib astropy \n", "```\n", "\n", "If you want to follow this tutorial using Jupyter (optional), you should also include the packages `jupyterlab` and `ipympl` above, and build the Jupyter lab extensions with:\n", "\n", "```\n", "jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib\n", "```\n", "\n", "