From 85ed9896d15a012c3b572ea01886e46a36155690 Mon Sep 17 00:00:00 2001
From: Jennifer Rinker <rink@win.dtu.dk>
Date: Mon, 25 Sep 2017 16:39:39 +0200
Subject: [PATCH] initial upload of demo notebook
---
demo_notebook.ipynb | 433 ++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 433 insertions(+)
create mode 100644 demo_notebook.ipynb
diff --git a/demo_notebook.ipynb b/demo_notebook.ipynb
new file mode 100644
index 0000000..6db1613
--- /dev/null
+++ b/demo_notebook.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Jupyter notebooks are extremely cool ways to demo your code or work interactively. They are separated into cells, just like Mathematica. You enter insert mode by clicking \"enter\" when a cell is selected, and you exit by hitting the escape key. You evaluate a cell using shift-enter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cell types"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can control the type of cell in Cell -> Cell Type, and choose whether your cell is\n",
+ "- text\n",
+ "- code \n",
+ "\n",
+ "Text cells can be formatted following Markdown conventions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Random cool things"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = 'Hello, world!'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the output of a cell in the last line is automatically printed if it's a variable or unassigned output:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "8"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x = 4\n",
+ "2 * x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are a bunch of \"magic\" commands that can really up your game."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/json": {
+ "cell": {
+ "!": "OSMagics",
+ "HTML": "Other",
+ "SVG": "Other",
+ "bash": "Other",
+ "capture": "ExecutionMagics",
+ "cmd": "Other",
+ "debug": "ExecutionMagics",
+ "file": "Other",
+ "html": "DisplayMagics",
+ "javascript": "DisplayMagics",
+ "js": "DisplayMagics",
+ "latex": "DisplayMagics",
+ "markdown": "DisplayMagics",
+ "perl": "Other",
+ "prun": "ExecutionMagics",
+ "pypy": "Other",
+ "python": "Other",
+ "python2": "Other",
+ "python3": "Other",
+ "ruby": "Other",
+ "script": "ScriptMagics",
+ "sh": "Other",
+ "svg": "DisplayMagics",
+ "sx": "OSMagics",
+ "system": "OSMagics",
+ "time": "ExecutionMagics",
+ "timeit": "ExecutionMagics",
+ "writefile": "OSMagics"
+ },
+ "line": {
+ "alias": "OSMagics",
+ "alias_magic": "BasicMagics",
+ "autocall": "AutoMagics",
+ "automagic": "AutoMagics",
+ "autosave": "KernelMagics",
+ "bookmark": "OSMagics",
+ "cd": "OSMagics",
+ "clear": "KernelMagics",
+ "cls": "KernelMagics",
+ "colors": "BasicMagics",
+ "config": "ConfigMagics",
+ "connect_info": "KernelMagics",
+ "copy": "Other",
+ "ddir": "Other",
+ "debug": "ExecutionMagics",
+ "dhist": "OSMagics",
+ "dirs": "OSMagics",
+ "doctest_mode": "BasicMagics",
+ "echo": "Other",
+ "ed": "Other",
+ "edit": "KernelMagics",
+ "env": "OSMagics",
+ "gui": "BasicMagics",
+ "hist": "Other",
+ "history": "HistoryMagics",
+ "killbgscripts": "ScriptMagics",
+ "ldir": "Other",
+ "less": "KernelMagics",
+ "load": "CodeMagics",
+ "load_ext": "ExtensionMagics",
+ "loadpy": "CodeMagics",
+ "logoff": "LoggingMagics",
+ "logon": "LoggingMagics",
+ "logstart": "LoggingMagics",
+ "logstate": "LoggingMagics",
+ "logstop": "LoggingMagics",
+ "ls": "Other",
+ "lsmagic": "BasicMagics",
+ "macro": "ExecutionMagics",
+ "magic": "BasicMagics",
+ "matplotlib": "PylabMagics",
+ "mkdir": "Other",
+ "more": "KernelMagics",
+ "notebook": "BasicMagics",
+ "page": "BasicMagics",
+ "pastebin": "CodeMagics",
+ "pdb": "ExecutionMagics",
+ "pdef": "NamespaceMagics",
+ "pdoc": "NamespaceMagics",
+ "pfile": "NamespaceMagics",
+ "pinfo": "NamespaceMagics",
+ "pinfo2": "NamespaceMagics",
+ "pip": "BasicMagics",
+ "popd": "OSMagics",
+ "pprint": "BasicMagics",
+ "precision": "BasicMagics",
+ "profile": "BasicMagics",
+ "prun": "ExecutionMagics",
+ "psearch": "NamespaceMagics",
+ "psource": "NamespaceMagics",
+ "pushd": "OSMagics",
+ "pwd": "OSMagics",
+ "pycat": "OSMagics",
+ "pylab": "PylabMagics",
+ "qtconsole": "KernelMagics",
+ "quickref": "BasicMagics",
+ "recall": "HistoryMagics",
+ "rehashx": "OSMagics",
+ "reload_ext": "ExtensionMagics",
+ "ren": "Other",
+ "rep": "Other",
+ "rerun": "HistoryMagics",
+ "reset": "NamespaceMagics",
+ "reset_selective": "NamespaceMagics",
+ "rmdir": "Other",
+ "run": "ExecutionMagics",
+ "save": "CodeMagics",
+ "sc": "OSMagics",
+ "set_env": "OSMagics",
+ "store": "StoreMagics",
+ "sx": "OSMagics",
+ "system": "OSMagics",
+ "tb": "ExecutionMagics",
+ "time": "ExecutionMagics",
+ "timeit": "ExecutionMagics",
+ "unalias": "OSMagics",
+ "unload_ext": "ExtensionMagics",
+ "who": "NamespaceMagics",
+ "who_ls": "NamespaceMagics",
+ "whos": "NamespaceMagics",
+ "xdel": "NamespaceMagics",
+ "xmode": "BasicMagics"
+ }
+ },
+ "text/plain": [
+ "Available line magics:\n",
+ "%alias %alias_magic %autocall %automagic %autosave %bookmark %cd %clear %cls %colors %config %connect_info %copy %ddir %debug %dhist %dirs %doctest_mode %echo %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %macro %magic %matplotlib %mkdir %more %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %ren %rep %rerun %reset %reset_selective %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
+ "\n",
+ "Available cell magics:\n",
+ "%%! %%HTML %%SVG %%bash %%capture %%cmd %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
+ "\n",
+ "Automagic is ON, % prefix IS NOT needed for line magics."
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%lsmagic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For example, let's test how long a silly function takes..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "def silly_function():\n",
+ " np.random.randint(0, high=100, size=int(1e6))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.7 ms ± 19.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "silly_function()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exercise"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define $x$ and $y$ below such that $x$ is a numpy array from 1 to 9 (inclusive) and $y=1/x$. Then, run your cell and the next cell and look at the resulting plot. Does it make sense? Run your mouse over the plot. Notice anything cool?\n",
+ "\n",
+ "__HINT__: Google \"numpy arange function\" to figure out how to define $x$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "# x = ???\n",
+ "# y = ???"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import plotly\n",
+ "from plotly.graph_objs import Scatter, Layout\n",
+ "\n",
+ "plotly.offline.init_notebook_mode(connected=True)\n",
+ "\n",
+ "plotly.offline.iplot({\n",
+ " \"data\": [Scatter(x=x, y=y)],\n",
+ " \"layout\": Layout(title=\"Hello, world!\")\n",
+ "})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "x = np.arange(1, 10)\n",
+ "y = 1/x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
+ ],
+ "text/vnd.plotly.v1+html": [
+ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "data": [
+ {
+ "type": "scatter",
+ "x": [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9
+ ],
+ "y": [
+ 1,
+ 0.5,
+ 0.3333333333333333,
+ 0.25,
+ 0.2,
+ 0.16666666666666666,
+ 0.14285714285714285,
+ 0.125,
+ 0.1111111111111111
+ ]
+ }
+ ],
+ "layout": {
+ "title": "Hello, world!"
+ }
+ },
+ "text/html": [
+ "<div id=\"c43ec9be-ef5d-4689-b6ab-37f4d26f7e7a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"c43ec9be-ef5d-4689-b6ab-37f4d26f7e7a\", [{\"type\": \"scatter\", \"x\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"y\": [1.0, 0.5, 0.3333333333333333, 0.25, 0.2, 0.16666666666666666, 0.14285714285714285, 0.125, 0.1111111111111111]}], {\"title\": \"Hello, world!\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
+ ],
+ "text/vnd.plotly.v1+html": [
+ "<div id=\"c43ec9be-ef5d-4689-b6ab-37f4d26f7e7a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"c43ec9be-ef5d-4689-b6ab-37f4d26f7e7a\", [{\"type\": \"scatter\", \"x\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"y\": [1.0, 0.5, 0.3333333333333333, 0.25, 0.2, 0.16666666666666666, 0.14285714285714285, 0.125, 0.1111111111111111]}], {\"title\": \"Hello, world!\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly\n",
+ "from plotly.graph_objs import Scatter, Layout\n",
+ "\n",
+ "plotly.offline.init_notebook_mode(connected=True)\n",
+ "\n",
+ "plotly.offline.iplot({\n",
+ " \"data\": [Scatter(x=x, y=y)],\n",
+ " \"layout\": Layout(title=\"Hello, world!\")\n",
+ "})"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
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