Commit 8f974113 authored by James Sutherland's avatar James Sutherland

JupyterIntro:

  - Add web-based logo image to make it load everywhere.
  - Update a LaTeX link
  - A few other minor edits.

Arrays in Python:
  - Add web-based logo image to make it load everywhere.
  - A few other minor edits.
parent 4f6f2a80
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"# Table of Contents\n",
" <p><div class=\"lev1 toc-item\"><a href=\"#Arrays-in-Python\" data-toc-modified-id=\"Arrays-in-Python-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Arrays in Python</a></div><div class=\"lev2 toc-item\"><a href=\"#Overview\" data-toc-modified-id=\"Overview-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Overview</a></div><div class=\"lev1 toc-item\"><a href=\"#Lists\" data-toc-modified-id=\"Lists-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Lists</a></div><div class=\"lev2 toc-item\"><a href=\"#Operations-on-Lists\" data-toc-modified-id=\"Operations-on-Lists-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Operations on Lists</a></div><div class=\"lev2 toc-item\"><a href=\"#Indexing-and-Slicing-Lists\" data-toc-modified-id=\"Indexing-and-Slicing-Lists-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Indexing and Slicing Lists</a></div><div class=\"lev2 toc-item\"><a href=\"#Loops-and-Lists\" data-toc-modified-id=\"Loops-and-Lists-23\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Loops and Lists</a></div><div class=\"lev3 toc-item\"><a href=\"#Iterating-a-list\" data-toc-modified-id=\"Iterating-a-list-231\"><span class=\"toc-item-num\">2.3.1&nbsp;&nbsp;</span>Iterating a list</a></div><div class=\"lev3 toc-item\"><a href=\"#Index-loops\" data-toc-modified-id=\"Index-loops-232\"><span class=\"toc-item-num\">2.3.2&nbsp;&nbsp;</span>Index loops</a></div><div class=\"lev3 toc-item\"><a href=\"#List-comprehensions\" data-toc-modified-id=\"List-comprehensions-233\"><span class=\"toc-item-num\">2.3.3&nbsp;&nbsp;</span>List comprehensions</a></div><div class=\"lev1 toc-item\"><a href=\"#Tuples\" data-toc-modified-id=\"Tuples-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Tuples</a></div><div class=\"lev1 toc-item\"><a href=\"#Numpy-Arrays\" data-toc-modified-id=\"Numpy-Arrays-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Numpy Arrays</a></div><div class=\"lev2 toc-item\"><a href=\"#Constructing-Numpy-Arrays\" data-toc-modified-id=\"Constructing-Numpy-Arrays-41\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Constructing Numpy Arrays</a></div><div class=\"lev3 toc-item\"><a href=\"#Data-Types\" data-toc-modified-id=\"Data-Types-411\"><span class=\"toc-item-num\">4.1.1&nbsp;&nbsp;</span>Data Types</a></div><div class=\"lev4 toc-item\"><a href=\"#Example\" data-toc-modified-id=\"Example-4111\"><span class=\"toc-item-num\">4.1.1.1&nbsp;&nbsp;</span>Example</a></div><div class=\"lev3 toc-item\"><a href=\"#Arrays-over-specified-ranges\" data-toc-modified-id=\"Arrays-over-specified-ranges-412\"><span class=\"toc-item-num\">4.1.2&nbsp;&nbsp;</span>Arrays over specified ranges</a></div><div class=\"lev3 toc-item\"><a href=\"#Other-Constructors\" data-toc-modified-id=\"Other-Constructors-413\"><span class=\"toc-item-num\">4.1.3&nbsp;&nbsp;</span>Other Constructors</a></div><div class=\"lev2 toc-item\"><a href=\"#Numpy-Matrices\" data-toc-modified-id=\"Numpy-Matrices-42\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Numpy Matrices</a></div><div class=\"lev2 toc-item\"><a href=\"#Manipulating-Numpy-Arrays\" data-toc-modified-id=\"Manipulating-Numpy-Arrays-43\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Manipulating Numpy Arrays</a></div><div class=\"lev3 toc-item\"><a href=\"#Indexing-and-Slicing\" data-toc-modified-id=\"Indexing-and-Slicing-431\"><span class=\"toc-item-num\">4.3.1&nbsp;&nbsp;</span>Indexing and Slicing</a></div><div class=\"lev1 toc-item\"><a href=\"#Mathematical-Operations-on-Numpy-Arrays\" data-toc-modified-id=\"Mathematical-Operations-on-Numpy-Arrays-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Mathematical Operations on Numpy Arrays</a></div><div class=\"lev2 toc-item\"><a href=\"#Array-Multiplication\" data-toc-modified-id=\"Array-Multiplication-51\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Array Multiplication</a></div><div class=\"lev3 toc-item\"><a href=\"#Elemental-Multiplication\" data-toc-modified-id=\"Elemental-Multiplication-511\"><span class=\"toc-item-num\">5.1.1&nbsp;&nbsp;</span>Elemental Multiplication</a></div><div class=\"lev3 toc-item\"><a href=\"#Matrix-Multiplication\" data-toc-modified-id=\"Matrix-Multiplication-512\"><span class=\"toc-item-num\">5.1.2&nbsp;&nbsp;</span>Matrix Multiplication</a></div><div class=\"lev2 toc-item\"><a href=\"#More-useful-stuff\" data-toc-modified-id=\"More-useful-stuff-52\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>More useful stuff</a></div><div class=\"lev1 toc-item\"><a href=\"#Masking-Numpy-Arrays\" data-toc-modified-id=\"Masking-Numpy-Arrays-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Masking Numpy Arrays</a></div>"
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"![](Chem-Engingeering_combined-hrz.jpg)\n",
"![Chemical Engineering Logo](https://sutherland.che.utah.edu/Chem-Engingeering_combined-hrz.jpg)\n",
"\n",
"----\n",
"# Arrays in Python\n",
......@@ -28,6 +38,7 @@
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"<div class=\"alert alert-block alert-info\">\n",
"\n",
"__Note__: if you are a matlab user, you may want to look at\n",
" * This [great cheat sheet](http://mathesaurus.sourceforge.net/matlab-python-xref.pdf) showing common matlab commands and their python counterparts\n",
" * [Numpy for matlab users](http://mathesaurus.sourceforge.net/matlab-numpy.html)"
......@@ -43,7 +54,7 @@
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"## Overview\n",
"In python, there are a few ways to manage collections of information:\n",
" * A [`list`](#Lists) is built-in to the language, and can contain any tipe\n",
" * A [`list`](#Lists) is built-in to the language, and can contain any type\n",
" * A [`tuple`](#Tuples) is also built-in to the language, and is similar to a list, but is immutable.\n",
" * A [numpy array](#Numpy-Arrays) is provided by the [`numpy`](https://docs.scipy.org/doc/numpy/reference) package in python and is useful for holding collections of numbers."
]
......@@ -107,15 +118,14 @@
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"<div class=\"alert alert-block alert-warning\">\n",
"\n",
"___Caution___: copying arrays in python is unique:"
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"a = [1,2,3]\n",
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"fruits = ['apple','banana','grapefruit','orange']"
......@@ -407,6 +413,7 @@
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"<div class=\"alert alert-block alert-info\">\n",
"\n",
"`range(lo,hi)` creates a range of integers from __`lo`__ to __`hi-1`__"
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"<div class=\"alert alert-block alert-info\">\n",
"\n",
"For more information on many other ways of building arrays, see the [numpy docs](https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html)"
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"0.0\n",
"0.444444444444\n",
"0.888888888889\n",
"0.4444444444444444\n",
"0.8888888888888888\n",
"-2.0\n",
"1.77777777778\n",
"2.22222222222\n",
"2.66666666667\n",
"3.11111111111\n",
"3.55555555556\n",
"1.7777777777777777\n",
"2.2222222222222223\n",
"2.6666666666666665\n",
"3.1111111111111107\n",
"3.5555555555555554\n",
"4.0\n"
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"[[ 0.24398388 0.90559418 0.09522744 0.96748697]\n",
" [ 0.98849961 0.86042935 0.90495883 0.66182292]\n",
" [ 0.02990521 0.09611488 0.33834204 0.10196563]\n",
" [ 0.73411202 0.88668081 0.33633824 0.70952783]]\n",
"[[0.03533361 0.13114037 0.42474318 0.62253432]\n",
" [0.75652141 0.64262473 0.42286252 0.67319462]\n",
" [0.30819661 0.56497425 0.8179415 0.47857807]\n",
" [0.21509378 0.27477645 0.37631399 0.66887225]]\n",
"[1 3 5]\n"
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"navigate_menu": true,
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"# Table of Contents\n",
" <p><div class=\"lev1 toc-item\"><a href=\"#An-Introduction-to-Jupyter-Notebooks\" data-toc-modified-id=\"An-Introduction-to-Jupyter-Notebooks-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>An Introduction to Jupyter Notebooks</a></div><div class=\"lev1 toc-item\"><a href=\"#Cells-&amp;-Cell-types\" data-toc-modified-id=\"Cells-&amp;-Cell-types-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Cells &amp; Cell types</a></div><div class=\"lev2 toc-item\"><a href=\"#Edit-Mode-vs.-Command-Mode\" data-toc-modified-id=\"Edit-Mode-vs.-Command-Mode-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Edit Mode vs. Command Mode</a></div><div class=\"lev3 toc-item\"><a href=\"#Getting-Help\" data-toc-modified-id=\"Getting-Help-211\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Getting Help</a></div><div class=\"lev2 toc-item\"><a href=\"#Changing-cell-type\" data-toc-modified-id=\"Changing-cell-type-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Changing cell type</a></div><div class=\"lev2 toc-item\"><a href=\"#Executing-Cells\" data-toc-modified-id=\"Executing-Cells-23\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Executing Cells</a></div><div class=\"lev2 toc-item\"><a href=\"#Navigating-Cells\" data-toc-modified-id=\"Navigating-Cells-24\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Navigating Cells</a></div><div class=\"lev1 toc-item\"><a href=\"#Some-Markdown-Tips\" data-toc-modified-id=\"Some-Markdown-Tips-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Some Markdown Tips</a></div><div class=\"lev2 toc-item\"><a href=\"#Tables\" data-toc-modified-id=\"Tables-31\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Tables</a></div><div class=\"lev2 toc-item\"><a href=\"#Internal-links-in-a-notebook\" data-toc-modified-id=\"Internal-links-in-a-notebook-32\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Internal links in a notebook</a></div><div class=\"lev2 toc-item\"><a href=\"#HTML-in-a-notebook\" data-toc-modified-id=\"HTML-in-a-notebook-33\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>HTML in a notebook</a></div><div class=\"lev2 toc-item\"><a href=\"#LaTeX-in-a-notebook\" data-toc-modified-id=\"LaTeX-in-a-notebook-34\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>LaTeX in a notebook</a></div><div class=\"lev3 toc-item\"><a href=\"#Some-Common-LaTeX-Usage\" data-toc-modified-id=\"Some-Common-LaTeX-Usage-341\"><span class=\"toc-item-num\">3.4.1&nbsp;&nbsp;</span>Some Common LaTeX Usage</a></div><div class=\"lev3 toc-item\"><a href=\"#LaTeX-Macros\" data-toc-modified-id=\"LaTeX-Macros-342\"><span class=\"toc-item-num\">3.4.2&nbsp;&nbsp;</span>LaTeX Macros</a></div><div class=\"lev3 toc-item\"><a href=\"#LaTeX-extensions\" data-toc-modified-id=\"LaTeX-extensions-343\"><span class=\"toc-item-num\">3.4.3&nbsp;&nbsp;</span>LaTeX extensions</a></div><div class=\"lev1 toc-item\"><a href=\"#Debugging-a-notebook\" data-toc-modified-id=\"Debugging-a-notebook-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Debugging a notebook</a></div><div class=\"lev1 toc-item\"><a href=\"#Python-in-a-Jupyter-Notebook\" data-toc-modified-id=\"Python-in-a-Jupyter-Notebook-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Python in a Jupyter Notebook</a></div><div class=\"lev2 toc-item\"><a href=\"#Defining-Functions\" data-toc-modified-id=\"Defining-Functions-51\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Defining Functions</a></div><div class=\"lev2 toc-item\"><a href=\"#Using-Python-Libraries\" data-toc-modified-id=\"Using-Python-Libraries-52\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Using Python Libraries</a></div><div class=\"lev2 toc-item\"><a href=\"#Plotting\" data-toc-modified-id=\"Plotting-53\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Plotting</a></div><div class=\"lev2 toc-item\"><a href=\"#Getting-help-on-python-commands\" data-toc-modified-id=\"Getting-help-on-python-commands-54\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Getting help on python commands</a></div><div class=\"lev1 toc-item\"><a href=\"#Interactive-Notebooks:-Widgets\" data-toc-modified-id=\"Interactive-Notebooks:-Widgets-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Interactive Notebooks: Widgets</a></div><div class=\"lev2 toc-item\"><a href=\"#A-Simple-Example\" data-toc-modified-id=\"A-Simple-Example-61\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>A Simple Example</a></div><div class=\"lev1 toc-item\"><a href=\"#Magics-in-Jupyter-Notebooks\" data-toc-modified-id=\"Magics-in-Jupyter-Notebooks-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Magics in Jupyter Notebooks</a></div><div class=\"lev2 toc-item\"><a href=\"#lsmagic\" data-toc-modified-id=\"lsmagic-71\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>lsmagic</a></div><div class=\"lev2 toc-item\"><a href=\"#timeit\" data-toc-modified-id=\"timeit-72\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>timeit</a></div><div class=\"lev2 toc-item\"><a href=\"#who-and-whos\" data-toc-modified-id=\"who-and-whos-73\"><span class=\"toc-item-num\">7.3&nbsp;&nbsp;</span>who and whos</a></div>"
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......@@ -12,7 +22,7 @@
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"\n",
"![](Chem-Engingeering_combined-hrz.jpg)\n",
"![Chemical Engineering Logo](https://sutherland.che.utah.edu/Chem-Engingeering_combined-hrz.jpg)\n",
"\n",
"----\n",
"# An Introduction to Jupyter Notebooks\n",
......@@ -190,6 +200,7 @@
},
"source": [
"<div class=\"alert alert-block alert-info\">\n",
"\n",
"If you want to display data from Python in tabular format, you may want to use the [pandas](http://pandas.pydata.org/) library."
]
},
......@@ -218,6 +229,7 @@
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"<div class=\"alert alert-block alert-info\">\n",
"\n",
"The heading text is case sensitive, and spaces should be replaced with dashes."
]
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......@@ -243,7 +255,7 @@
"source": [
"## LaTeX in a notebook\n",
"LaTeX is a powerful typesetting language in its own right, but a subset of LaTeX is supported in Jupyter.\n",
"If you are learning LaTeX, [here is a cheat sheet](http://reu.dimacs.rutgers.edu/Symbols.pdf) that you may find useful.\n",
"If you are learning LaTeX, [here is a cheat sheet](https://artofproblemsolving.com/wiki/index.php/LaTeX:Symbols) that you may find useful.\n",
"Built-in to Jupyter notebooks you will find support for inline equations by encapsulating them in single `$` and for displayed equations by encapsulating them in double `$$`"
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......@@ -375,7 +387,6 @@
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......@@ -398,6 +409,7 @@
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"<div class=\"alert alert-block alert-info\">\n",
"\n",
"The colon at the end of the first line, which indicates that the body of the function (indented) comes next."
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Z2MCyZWifPKHYzz/TrVs33nrrrUzHSU7VMm9/EL4FrKhbpqCBpc4YaQAPHSsr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\nIPwfcyRUJVjwC5gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115800438>"
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
......@@ -601,7 +611,6 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "ab378b26-5be2-463a-9099-1822eb87f074"
},
......@@ -633,14 +642,19 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "184cd85706714cd29e5f1a314a66087b"
}
"model_id": "3ab9f0b702cd4eb582bfbd7cbaae590f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=1, description='Frequency:', max=8), Dropdown(description='Function:', o…"
]
},
"metadata": {},
"output_type": "display_data"
......@@ -648,10 +662,10 @@
{
"data": {
"text/plain": [
"<function __main__.plot_it>"
"<function __main__.plot_it(f, w)>"
]
},
"execution_count": 1,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
......@@ -913,7 +927,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"59.9 µs ± 4.67 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
"28.8 µs ± 2.07 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
......@@ -965,14 +979,14 @@
"Variable Type Data/Info\n",
"----------------------------------------\n",
"A ndarray 50x50: 2500 elems, type `float64`, 20000 bytes\n",
"add function <function add at 0x10db4ad08>\n",
"fwidget IntSlider <ipywidgets.widgets.widge<...>er object at 0x10dcb7390>\n",
"interact _InteractFactory <ipywidgets.widgets.inter<...>ry object at 0x10db3fdd8>\n",
"np module <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>\n",
"plot_it function <function plot_it at 0x1188cd598>\n",
"add function <function add at 0x108778c80>\n",
"fwidget IntSlider IntSlider(value=1, descri<...>tion='Frequency:', max=8)\n",
"interact _InteractFactory <ipywidgets.widgets.inter<...>ry object at 0x108771128>\n",
"np module <module 'numpy' from '/an<...>kages/numpy/__init__.py'>\n",
"plot_it function <function plot_it at 0x116403ae8>\n",
"plt module <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>\n",
"rcParams RcParams _internal.classic_mode: F<...>: 0.6\\nytick.right: False\n",
"which Dropdown <ipywidgets.widgets.widge<...>wn object at 0x10dcb7470>\n",
"which Dropdown Dropdown(description='Fun<...>os', 'tan'), value='sin')\n",
"widgets module <module 'ipywidgets' from<...>/ipywidgets/__init__.py'>\n",
"x ndarray 50: 50 elems, type `float64`, 400 bytes\n",
"y ndarray 50: 50 elems, type `float64`, 400 bytes\n",
......@@ -1004,7 +1018,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
"version": "3.7.0"
},
"latex_envs": {
"LaTeX_envs_menu_present": false,
......@@ -1481,7 +1495,7 @@
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_cell": true,
"toc_position": {
"height": "76px",
"left": "0px",
......
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