add slideshow cell meta-data
The notebook can now be viewed in slideshow mode
This commit is contained in:
parent
98d9187918
commit
0c0be93f92
@ -2,7 +2,11 @@
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"<div style='background-image: url(\"../images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n",
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" <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px\">\n",
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@ -11,13 +15,8 @@
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" <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\">A super quick crash course</div>\n",
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" </div>\n",
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" </div>\n",
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"</div>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"</div>\n",
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"\n",
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"Seismo-Live: http://seismo-live.org\n",
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"\n",
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"##### Authors:\n",
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@ -28,14 +27,26 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"This notebook is a very quick introduction to Python and in particular its scientific ecosystem in case you have never seen it before. It furthermore grants a possibility to get to know the [IPython/Jupyter notebook](http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261). [See here for the official documentation](http://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb) of the Jupyter notebook - a ton more information can be found online.\n",
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"\n",
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"\n",
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"A lot of motivational writing on *Why Python?* is out there so we will not repeat it here and just condense it to a single sentence: **Python is a good and easy to learn, open-source, general purpose programming language that happens to be very good for many scientific tasks (due to its vast scientific ecosystem).**\n",
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"\n",
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"\n",
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"A lot of motivational writing on *Why Python?* is out there so we will not repeat it here and just condense it to a single sentence: **Python is a good and easy to learn, open-source, general purpose programming language that happens to be very good for many scientific tasks (due to its vast scientific ecosystem).**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"#### Quick Reference on How to Use This Notebook\n",
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"\n",
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"\n",
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@ -57,8 +68,17 @@
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"\n",
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"```bash\n",
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"$ ipython\n",
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"```\n",
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"\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"## Notebook Setup\n",
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"\n",
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"First things first: In many notebooks you will find a cell similar to the following one. **Always execute it!** They do a couple of things:\n",
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@ -71,13 +91,17 @@
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"\n",
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"This essentially makes the notebooks work under Python 2 and Python 3.\n",
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"\n",
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"* Plots look quite a bit nicer (this is optional).\n"
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"* Plots look quite a bit nicer (this is optional)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Plots now appear in the notebook.\n",
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@ -90,7 +114,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"---\n",
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"\n",
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@ -100,6 +128,8 @@
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"\n",
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"If you have any question regarding some specific Python functionality you can consult the official [Python documenation](http://docs.python.org/).\n",
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" \n",
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"### Tutorials\n",
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"\n",
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"Furthermore a large number of Python tutorials, introductions, and books are available online. Here are some examples for those interested in learning more.\n",
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" \n",
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"* [Learn Python The Hard Way](http://learnpythonthehardway.org/book/)\n",
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@ -107,11 +137,23 @@
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"* [The Official Python Tutorial](http://docs.python.org/2/tutorial/index.html)\n",
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"* [Think Python Book](http://www.greenteapress.com/thinkpython/thinkpython.html)\n",
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" \n",
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"### Matlab\n",
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"\n",
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"Some people might be used to Matlab - this helps:\n",
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" \n",
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"* [NumPy for Matlab Users Introdution](http://wiki.scipy.org/NumPy_for_Matlab_Users)\n",
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"* [NumPy for Matlab Users Cheatsheet](http://mathesaurus.sourceforge.net/matlab-numpy.html)\n",
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" \n",
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"* [NumPy for Matlab Users Cheatsheet](http://mathesaurus.sourceforge.net/matlab-numpy.html)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### More\n",
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"\n",
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"Additionally there is an abundance of resources introducing and teaching parts of the scientific Python ecosystem.\n",
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" \n",
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@ -133,7 +175,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Core Python Crash Course\n",
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"\n",
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@ -150,7 +196,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Three basic types of numbers\n",
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@ -170,22 +220,25 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Strings"
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]
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Strings\n",
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"\n",
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"Just enclose something in single or double quotes and it will become a string. On Python 3 it defaults to unicode strings, e.g. non Latin alphabets and other symbols."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# You can use single or double quotes to create strings.\n",
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@ -210,7 +263,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"#### Exercise\n",
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"\n",
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@ -220,7 +277,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"name = \"kasper\"\n",
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@ -229,22 +290,25 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3. Lists"
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]
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3. Lists\n",
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"\n",
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"Python has two main collection types: List and dictionaries. The former is just an ordered collection of objects and is introduced here."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# List use square brackets and are simple ordered collections of things.\n",
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@ -266,7 +330,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### 4. Dictionaries\n",
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"\n",
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@ -276,7 +344,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Dictionaries have named fields and no inherent order. As is\n",
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@ -304,7 +376,10 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### 5. Functions\n",
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@ -315,7 +390,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Functions are defined using the def keyword.\n",
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@ -339,7 +418,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### 6. Imports\n",
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"\n",
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@ -349,7 +432,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Import anything, and use it with the dot accessor.\n",
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@ -369,7 +456,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"How to know what is available?\n",
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"\n",
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@ -380,7 +471,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"print(dir(math))"
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@ -388,7 +483,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"Typing the dot and the TAB will kick off tab-completion."
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]
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@ -396,7 +495,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"math."
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"In the IPython framework you can also use a question mark to view the documentation of modules and functions."
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"math.cos?"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### 7. Control Flow\n",
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"\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"temp = [\"a\", \"b\", \"c\"]\n",
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@ -447,7 +566,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Useful to know is the range() function.\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"The second crucial control flow structure are if/else conditional and they work the same as in any other language."
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"The second crucial control flow structure are ***if/else*** conditional and they work the same as in any other language."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# If/else works as expected.\n",
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" print(\"wait what?\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"List comprehensions are a nice way to write compact loops. Make sure you understand this as it is very common in Python."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# List comprehensions are a nice way to write compact loops.\n",
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"# Make sure you understand this as it is very common in Python.\n",
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"\n",
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"a = list(range(10))\n",
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"print(a)\n",
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"b = [i for i in a if not i % 2]\n",
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@ -503,7 +646,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### 8. Error Messages\n",
|
||||
"\n",
|
||||
@ -513,7 +660,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def do_something(a, b): \n",
|
||||
@ -524,7 +675,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## The Scientific Python Ecosystem\n",
|
||||
"\n",
|
||||
@ -539,7 +694,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### 9. NumPy\n",
|
||||
"\n",
|
||||
@ -549,7 +708,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
@ -571,7 +734,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### 10. SciPy\n",
|
||||
"\n",
|
||||
@ -581,7 +748,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from scipy.interpolate import interp1d\n",
|
||||
@ -595,7 +766,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### 11. Matplotlib\n",
|
||||
"\n",
|
||||
@ -605,7 +780,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
@ -619,7 +798,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Exercises\n",
|
||||
"\n",
|
||||
@ -637,7 +820,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
@ -652,7 +839,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"#### 99 Bottles of Beer\n",
|
||||
"\n",
|
||||
@ -674,7 +865,12 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"scrolled": true,
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"99 bottles of beer on the wall, 99 bottles of beer.\")\n",
|
||||
@ -684,7 +880,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"#### Ceasar Cipher\n",
|
||||
"\n",
|
||||
@ -714,7 +914,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Pnrfne pvcure? V zhpu cersre Pnrfne fnynq!\"\n",
|
||||
@ -739,7 +943,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Encoder ###\n",
|
||||
"Idea: Iterrate over all letters of a sentence and find the key (k) which contains the letter as value (v) in the code dictionary"
|
||||
@ -748,7 +956,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "fragment"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sentence = \"Geophysics with Python is fun!\"\n",
|
||||
@ -763,16 +975,10 @@
|
||||
"\n",
|
||||
"print(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"celltoolbar": "Slideshow",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user