From 9afcceea66c4a0798e3edf6afccf76ce56e49a26 Mon Sep 17 00:00:00 2001
From: Martijn Vermaat <martijn@vermaat.name>
Date: Tue, 30 Jul 2013 17:16:53 +0200
Subject: [PATCH] Restructuring
Moved notebooks to toplevel dir for easier opening. Created subdirs for
data and images. Harmonized all notebook opening slides.
---
README.md | 74 +
{numpy-intro => data}/populations.txt | 0
{python-intro => data}/short_file.txt | 0
python-intro/fibonacci.py => fibonacci.py | 0
...sion-control-git.ipynb => git-slides.ipynb | 30 +-
.../centralized_vcs.png | Bin
.../distributed_vcs.png | Bin
{numpy-intro => images}/elephant.png | Bin
{version-control-git => images}/local_vcs.png | Bin
images/matplotlib_tick_locators.png | Bin 0 -> 36543 bytes
.../numpy_broadcasting.png | Bin
.../numpy_fancy_indexing.png | Bin
{numpy-intro => images}/numpy_indexing.png | Bin
.../numpy_reductions.png | Bin
...lib-intro.ipynb => matplotlib-slides.ipynb | 62 +-
numpy-intro/numpy-intro.ipynb | 3097 -----------------
numpy-slides.ipynb | 3028 ++++++++++++++++
python-intro/README | 72 -
.../python-intro.ipynb => python-slides.ipynb | 29 +-
19 files changed, 3131 insertions(+), 3261 deletions(-)
rename {numpy-intro => data}/populations.txt (100%)
rename {python-intro => data}/short_file.txt (100%)
rename python-intro/fibonacci.py => fibonacci.py (100%)
rename version-control-git/version-control-git.ipynb => git-slides.ipynb (94%)
rename {version-control-git => images}/centralized_vcs.png (100%)
rename {version-control-git => images}/distributed_vcs.png (100%)
rename {numpy-intro => images}/elephant.png (100%)
rename {version-control-git => images}/local_vcs.png (100%)
create mode 100644 images/matplotlib_tick_locators.png
rename {numpy-intro => images}/numpy_broadcasting.png (100%)
rename {numpy-intro => images}/numpy_fancy_indexing.png (100%)
rename {numpy-intro => images}/numpy_indexing.png (100%)
rename numpy-intro/reductions.png => images/numpy_reductions.png (100%)
rename matplotlib-intro/matplotlib-intro.ipynb => matplotlib-slides.ipynb (82%)
delete mode 100644 numpy-intro/numpy-intro.ipynb
create mode 100644 numpy-slides.ipynb
delete mode 100644 python-intro/README
rename python-intro/python-intro.ipynb => python-slides.ipynb (99%)
diff --git a/README.md b/README.md
index 12e5d36..ac7e33c 100644
--- a/README.md
+++ b/README.md
@@ -7,3 +7,77 @@ Genetics of the Leiden University Medical Center.
See the [Trac Wiki](https://humgenprojects.lumc.nl/trac/programming-course)
for more information.
+
+
+Introduction to Python
+======================
+
+Slides are created as an IPython Notebook (IPython 1.0) and exported to
+reveal.js.
+
+
+Installation in a virtual environment
+-------------------------------------
+
+Required IPython version is 1.0 and you can install it from source in a
+virtual environment like this (on Debian Wheezy):
+
+ mkvirtualenv --no-site-packages programming-course
+
+We need IPython 1.0 which is not yet released, so we get it from GitHub:
+
+ pip install -e git+https://github.com/ipython/ipython#egg=ipython
+
+If IPython 1.0 is released, you can just do this:
+
+ pip install ipython
+
+Some additional packages:
+
+ pip install pyzmq tornado jinja2 pygments sphinx markdown
+ pip install numpy
+
+For matplotlib, we need some development and system packages:
+
+ sudo aptitude install libfreetype6-dev libpng12-dev python-cairo \
+ python-gtk2 python-gtk2-dev
+ mkdir -p $VIRTUAL_ENV/lib/python2.7/dist-packages
+ ln -s /usr/lib/python2.7/dist-packages/{glib,gobject,gtk-2.0*,pygtk.pth} \
+ $VIRTUAL_ENV/lib/python2.7/dist-packages/
+ ln -s /usr/lib/pymodules/python2.7/cairo \
+ $VIRTUAL_ENV/lib/python2.7/dist-packages/
+
+ pip install matplotlib
+ echo "backend : GTKCairo" >> ~/.matplotlib/matplotlibrc
+
+If you want the IPython qtconsole to work, I found the following snippet to
+take PyQT from system packages:
+
+ https://gist.github.com/jlesquembre/2042882
+
+We might want to use the same approach for the PyGTK/PyCAIRO stuff above.
+
+
+Editing the slides
+------------------
+
+ ipython notebook python-intro.ipynb
+
+Some aditional information on editing slides in the Notebook can be found
+here:
+
+http://www.slideviper.oquanta.info/tutorial/slideshow_tutorial_slides.html
+
+
+Exporting the slides
+--------------------
+
+This will create a set of slides to be viewed in a browser:
+
+ ipython nbconvert --FilesWriter.build_directory= --format reveal \
+ python-intro.ipynb
+
+As a reference for students, you could also create a full HTML dump:
+
+ ipython nbconvert --FilesWriter.build_directory= --format full_html \
+ python-intro.ipynb
diff --git a/numpy-intro/populations.txt b/data/populations.txt
similarity index 100%
rename from numpy-intro/populations.txt
rename to data/populations.txt
diff --git a/python-intro/short_file.txt b/data/short_file.txt
similarity index 100%
rename from python-intro/short_file.txt
rename to data/short_file.txt
diff --git a/python-intro/fibonacci.py b/fibonacci.py
similarity index 100%
rename from python-intro/fibonacci.py
rename to fibonacci.py
diff --git a/version-control-git/version-control-git.ipynb b/git-slides.ipynb
similarity index 94%
rename from version-control-git/version-control-git.ipynb
rename to git-slides.ipynb
index 57be69e..06d1e58 100644
--- a/version-control-git/version-control-git.ipynb
+++ b/git-slides.ipynb
@@ -15,26 +15,18 @@
}
},
"source": [
- "\n",
- "<span style=\"font-size: 200%; color: darkblue;\">Version Control with Git</span>\n",
+ "<span style=\"font-size: 200%\">Version control with Git</span>\n",
"===\n",
"\n",
- "<br><br>\n",
- "\n",
- "Martijn Vermaat<br>\n",
- "[m.vermaat.hg@lumc.nl](mailto:m.vermaat.hg@lumc.nl)\n",
+ "<br>\n",
"\n",
- "Zuotian Tatum<br>\n",
- "[z.tatum@lumc.nl](mailto:z.tatum@lumc.nl)\n",
+ "[Zuotian Tatum](mailto:z.tatum@lumc.nl), [Martijn Vermaat](mailto:m.vermaat.hg@lumc.nl)\n",
"\n",
+ "[Department of Human Genetics, Leiden University Medical Center](http://humgen.nl)\n",
"\n",
- "Department of Human Genetics, Leiden University Medical Center<br>\n",
- "[humgen.nl](http://humgen.nl)\n",
+ "License: [Creative Commons Attribution 3.0 License (CC-by)](http://creativecommons.org/licenses/by/3.0)\n",
"\n",
- "Sources:\n",
- "http://git-scm.com/\n",
- "https://teach.github.com/\n",
- "http://try.github.io/"
+ "Sources: [Git homepage](http://git-scm.com/), [GitHub Teaching](https://teach.github.com/), [Try Git](http://try.github.io/)"
]
},
{
@@ -106,7 +98,7 @@
"Local VCS\n",
"---\n",
"\n",
- "<img src=\"files/local_vcs.png\">"
+ "<img src=\"files/images/local_vcs.png\">"
]
},
{
@@ -122,7 +114,7 @@
"\n",
"Centralized VCS\n",
"---\n",
- "<img src=\"files/centralized_vcs.png\">"
+ "<img src=\"files/images/centralized_vcs.png\">"
]
},
{
@@ -138,7 +130,7 @@
"\n",
"Distributed VCS\n",
"---\n",
- "<img src=\"files/distributed_vcs.png\">"
+ "<img src=\"files/images/distributed_vcs.png\">"
]
},
{
@@ -216,7 +208,7 @@
"Git\n",
"===\n",
"\n",
- "To itialize a Git repository\n",
+ "To initialize a Git repository\n",
"---"
]
},
@@ -654,7 +646,7 @@
}
},
"source": [
- "Github and Gitlab\n",
+ "GitHub and Gitlab\n",
"===\n",
"\n",
"TODO."
diff --git a/version-control-git/centralized_vcs.png b/images/centralized_vcs.png
similarity index 100%
rename from version-control-git/centralized_vcs.png
rename to images/centralized_vcs.png
diff --git a/version-control-git/distributed_vcs.png b/images/distributed_vcs.png
similarity index 100%
rename from version-control-git/distributed_vcs.png
rename to images/distributed_vcs.png
diff --git a/numpy-intro/elephant.png b/images/elephant.png
similarity index 100%
rename from numpy-intro/elephant.png
rename to images/elephant.png
diff --git a/version-control-git/local_vcs.png b/images/local_vcs.png
similarity index 100%
rename from version-control-git/local_vcs.png
rename to images/local_vcs.png
diff --git a/images/matplotlib_tick_locators.png b/images/matplotlib_tick_locators.png
new file mode 100644
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diff --git a/numpy-intro/numpy_broadcasting.png b/images/numpy_broadcasting.png
similarity index 100%
rename from numpy-intro/numpy_broadcasting.png
rename to images/numpy_broadcasting.png
diff --git a/numpy-intro/numpy_fancy_indexing.png b/images/numpy_fancy_indexing.png
similarity index 100%
rename from numpy-intro/numpy_fancy_indexing.png
rename to images/numpy_fancy_indexing.png
diff --git a/numpy-intro/numpy_indexing.png b/images/numpy_indexing.png
similarity index 100%
rename from numpy-intro/numpy_indexing.png
rename to images/numpy_indexing.png
diff --git a/numpy-intro/reductions.png b/images/numpy_reductions.png
similarity index 100%
rename from numpy-intro/reductions.png
rename to images/numpy_reductions.png
diff --git a/matplotlib-intro/matplotlib-intro.ipynb b/matplotlib-slides.ipynb
similarity index 82%
rename from matplotlib-intro/matplotlib-intro.ipynb
rename to matplotlib-slides.ipynb
index 4b81a67..44f7cfd 100644
--- a/matplotlib-intro/matplotlib-intro.ipynb
+++ b/matplotlib-slides.ipynb
@@ -15,7 +15,7 @@
}
},
"source": [
- "<span style=\"font-size: 200%\">Introduction to matplotlib</span>\n",
+ "<span style=\"font-size: 200%\">Plotting with matplotlib</span>\n",
"===\n",
"\n",
"<br>\n",
@@ -249,40 +249,11 @@
"Gallery\n",
"---\n",
"\n",
- "The gallery on the matplotlib homepage is **really** useful: [matplotlib.org/gallery.html](http://matplotlib.org/gallery.html)"
+ "The gallery on the matplotlib homepage is **really** useful: [matplotlib.org/gallery.html](http://matplotlib.org/gallery.html)\n",
+ "\n",
+ "<iframe src=\"http://matplotlib.org/gallery.html\" width=\"860\" height=\"400\"></iframe>"
]
},
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "IFrame('http://matplotlib.org/gallery.html', 900, 400)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "html": [
- "\n",
- " <iframe\n",
- " width=\"900\"\n",
- " height=400\"\n",
- " src=\"http://matplotlib.org/gallery.html\"\n",
- " frameborder=\"0\"\n",
- " allowfullscreen\n",
- " ></iframe>\n",
- " "
- ],
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 3,
- "text": [
- "<IPython.lib.display.IFrame at 0x2c362d0>"
- ]
- }
- ],
- "prompt_number": 3
- },
{
"cell_type": "markdown",
"metadata": {
@@ -830,30 +801,11 @@
"---\n",
"\n",
"* Tick locations and appearance can be totally configured.\n",
- "* Major and minor (by default not shown) ticks are separate."
+ "* Major and minor (by default not shown) ticks are separate.\n",
+ "\n",
+ ""
]
},
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "Image(filename='tick_locators.png')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "png": 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NjIzsIElSvnJZRXh4eG89Pb1nw4cPX6doMzAwQGBgII4dO1ahf2OvXLkybGxs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kSuQmCC\n",
- "prompt_number": 29,
- "text": [
- "<IPython.core.display.Image at 0x7f72241fc190>"
- ]
- }
- ],
- "prompt_number": 29
- },
{
"cell_type": "markdown",
"metadata": {
diff --git a/numpy-intro/numpy-intro.ipynb b/numpy-intro/numpy-intro.ipynb
deleted file mode 100644
index aa1c498..0000000
--- a/numpy-intro/numpy-intro.ipynb
+++ /dev/null
@@ -1,3097 +0,0 @@
-{
- "metadata": {
- "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "<span style=\"font-size: 200%\">Introduction to NumPy</span>\n",
- "===\n",
- "\n",
- "<br>\n",
- "\n",
- "[Martijn Vermaat](mailto:m.vermaat.hg@lumc.nl)\n",
- "\n",
- "[Department of Human Genetics, Leiden University Medical Center](http://humgen.nl)\n",
- "\n",
- "Based on: [Python Scientific Lecture Notes](http://scipy-lectures.github.io/)\n",
- "\n",
- "License: [Creative Commons Attribution 3.0 License (CC-by)](http://creativecommons.org/licenses/by/3.0)"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%matplotlib inline\n",
- "\n",
- "from IPython.display import HTML, Image\n",
- "\n",
- "# nbconvert adds a vertical scrollbar to the slides.\n",
- "HTML(\"\"\"\n",
- "<style type=\"text/css\">\n",
- ".reveal {\n",
- " overflow-y: hidden;\n",
- "}\n",
- "</style>\n",
- "\"\"\")"
- ],
- "language": "python",
- "metadata": {
- "slideshow": {
- "slide_type": "skip"
- }
- },
- "outputs": [
- {
- "html": [
- "\n",
- "<style type=\"text/css\">\n",
- ".reveal {\n",
- " overflow-y: hidden;\n",
- "}\n",
- "</style>\n"
- ],
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 141,
- "text": [
- "<IPython.core.display.HTML at 0x42b6a90>"
- ]
- }
- ],
- "prompt_number": 141
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "About NumPy\n",
- "===\n",
- "\n",
- "What is NumPy?\n",
- "---\n",
- "\n",
- "Python has:\n",
- "\n",
- "* Lists (costless insertion and append), dictionaries (fast lookup).\n",
- "* High-level number objects: integers, floating point.\n",
- "\n",
- "NumPy is:\n",
- "\n",
- "* Extension package to Python for multi-dimensional arrays.\n",
- "* Closer to hardware (efficiency).\n",
- "* Designed for scientific computation (convenience)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "About NumPy\n",
- "===\n",
- "\n",
- "Multi-dimensional arrays\n",
- "---\n",
- "\n",
- "Memory-efficient container that provides fast numerical operations.\n",
- "\n",
- "Example data:\n",
- "\n",
- "* Values of an experiment/simulation at discrete time steps.\n",
- "* Signal recorded by a measurement device, e.g. sound wave.\n",
- "* Pixels of an image, grey-level or colour.\n",
- "* 3D data measured at different X-Y-Z positions, e.g. MRI scan.\n",
- "* ..."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "About NumPy\n",
- "===\n",
- "\n",
- "How to use NumPy\n",
- "---\n",
- "\n",
- "### 1) As a normal Python package\n",
- "\n",
- "Convention is to import the package as `np`."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import numpy as np\n",
- "a = np.array([0, 1, 2, 3])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is how we will use NumPy for now.\n",
- "\n",
- "### 2) With PyLab\n",
- "\n",
- "PyLab is a Matlab-like interface in IPython to *NumPy*, *matplotlib*, and *SciPy*.\n",
- "\n",
- "We'll use PyLab later."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "About NumPy\n",
- "===\n",
- "\n",
- "Getting help\n",
- "---\n",
- "\n",
- "Interactive help on any object:\n",
- "\n",
- " >>> help(np.array)\n",
- " Help on built-in function array in module numpy.core.multiarray:\n",
- "\n",
- " array(...)\n",
- " array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)\n",
- " ...\n",
- "\n",
- "Or if you don't know the name:\n",
- "\n",
- " >>> np.lookfor('create array')\n",
- " Search results for 'create array'\n",
- " numpy.array\n",
- " Create an array.\n",
- " numpy.memmap\n",
- " Create a memory-map to an array stored in a *binary* file on disk.\n",
- " ...\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "NumPy arrays\n",
- "===\n",
- "\n",
- "One-dimensional arrays\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([0, 1, 2, 3])\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 3,
- "text": [
- "array([0, 1, 2, 3])"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.ndim"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 4,
- "text": [
- "1"
- ]
- }
- ],
- "prompt_number": 4
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 5,
- "text": [
- "(4,)"
- ]
- }
- ],
- "prompt_number": 5
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "len(a)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 6,
- "text": [
- "4"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "NumPy arrays\n",
- "===\n",
- "\n",
- "Multi-dimensional arrays\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = np.array([[0, 1, 2], [3, 4, 5]])\n",
- "b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 7,
- "text": [
- "array([[0, 1, 2],\n",
- " [3, 4, 5]])"
- ]
- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b.ndim"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 8,
- "text": [
- "2"
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b.shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 9,
- "text": [
- "(2, 3)"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "len(b)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 10,
- "text": [
- "2"
- ]
- }
- ],
- "prompt_number": 10
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "NumPy arrays\n",
- "===\n",
- "\n",
- "Series\n",
- "---\n",
- "\n",
- "Evenly spaced (like the Python `range` builtin):"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.arange(1, 9, 2)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 11,
- "text": [
- "array([1, 3, 5, 7])"
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "By number of points:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.linspace(0, 1, 6)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 12,
- "text": [
- "array([ 0. , 0.2, 0.4, 0.6, 0.8, 1. ])"
- ]
- }
- ],
- "prompt_number": 12
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.linspace(0, 1, 5, endpoint=False)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 13,
- "text": [
- "array([ 0. , 0.2, 0.4, 0.6, 0.8])"
- ]
- }
- ],
- "prompt_number": 13
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "NumPy arrays\n",
- "===\n",
- "\n",
- "Common arrays (1/2)\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.ones((3, 3))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 14,
- "text": [
- "array([[ 1., 1., 1.],\n",
- " [ 1., 1., 1.],\n",
- " [ 1., 1., 1.]])"
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.zeros((2, 2))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 15,
- "text": [
- "array([[ 0., 0.],\n",
- " [ 0., 0.]])"
- ]
- }
- ],
- "prompt_number": 15
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "NumPy arrays\n",
- "===\n",
- "\n",
- "Common arrays (2/2)\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.eye(3)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 16,
- "text": [
- "array([[ 1., 0., 0.],\n",
- " [ 0., 1., 0.],\n",
- " [ 0., 0., 1.]])"
- ]
- }
- ],
- "prompt_number": 16
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.diag(np.array([1, 2, 3, 4]))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 17,
- "text": [
- "array([[1, 0, 0, 0],\n",
- " [0, 2, 0, 0],\n",
- " [0, 0, 3, 0],\n",
- " [0, 0, 0, 4]])"
- ]
- }
- ],
- "prompt_number": 17
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "NumPy arrays\n",
- "===\n",
- "\n",
- "Random numbers\n",
- "---\n",
- "\n",
- "Random samples from a uniform distribution over $[0, 1)$:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.random.rand(4)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 18,
- "text": [
- "array([ 0.69655735, 0.5677686 , 0.9757499 , 0.6363099 ])"
- ]
- }
- ],
- "prompt_number": 18
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Random samples from the normal distribution:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.random.randn(4)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 19,
- "text": [
- "array([ 0.36203176, -2.32303807, -0.08126607, 0.41046712])"
- ]
- }
- ],
- "prompt_number": 19
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Basic data types\n",
- "===\n",
- "\n",
- "Detected from input\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([1, 2, 3])\n",
- "a.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 20,
- "text": [
- "dtype('int64')"
- ]
- }
- ],
- "prompt_number": 20
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = np.array([1., 2., 3.])\n",
- "b.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 21,
- "text": [
- "dtype('float64')"
- ]
- }
- ],
- "prompt_number": 21
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic data types\n",
- "===\n",
- "\n",
- "Default data type is floating point\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.ones((3, 3))\n",
- "a.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 22,
- "text": [
- "dtype('float64')"
- ]
- }
- ],
- "prompt_number": 22
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Explicitely specifying the data type\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = np.ones((3, 3), dtype=int)\n",
- "b.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 23,
- "text": [
- "dtype('int64')"
- ]
- }
- ],
- "prompt_number": 23
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic data types\n",
- "===\n",
- "\n",
- "There are many more\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "d = np.array([1+2j, 3+4j, 5+6*1j])\n",
- "d.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 24,
- "text": [
- "dtype('complex128')"
- ]
- }
- ],
- "prompt_number": 24
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "e = np.array([True, False, False, True])\n",
- "e.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 25,
- "text": [
- "dtype('bool')"
- ]
- }
- ],
- "prompt_number": 25
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "f = np.array(['Bonjour', 'Hello', 'Hallo',])\n",
- "f.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 26,
- "text": [
- "dtype('S7')"
- ]
- }
- ],
- "prompt_number": 26
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "(The `S7` data type is for strings containing $\\leq 7$ characters.)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Indexing and slicing\n",
- "===\n",
- "\n",
- "Indexing on the first dimension\n",
- "---\n",
- "\n",
- "Accessing and assigning to array items is similar to Python lists."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.arange(10)\n",
- "a[0], a[2], a[-1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 27,
- "text": [
- "(0, 2, 9)"
- ]
- }
- ],
- "prompt_number": 27
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = np.diag(np.arange(3))\n",
- "b[0], b[2], b[-1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 28,
- "text": [
- "(array([0, 0, 0]), array([0, 0, 2]), array([0, 0, 2]))"
- ]
- }
- ],
- "prompt_number": 28
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Indexing and slicing\n",
- "===\n",
- "\n",
- "Multidimensional indexing\n",
- "---\n",
- "\n",
- "Indexes can be tuples of integers."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.diag(np.arange(3))\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 29,
- "text": [
- "array([[0, 0, 0],\n",
- " [0, 1, 0],\n",
- " [0, 0, 2]])"
- ]
- }
- ],
- "prompt_number": 29
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[1, 1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 30,
- "text": [
- "1"
- ]
- }
- ],
- "prompt_number": 30
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[2, 1] = 10\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 31,
- "text": [
- "array([[ 0, 0, 0],\n",
- " [ 0, 1, 0],\n",
- " [ 0, 10, 2]])"
- ]
- }
- ],
- "prompt_number": 31
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Indexing and slicing\n",
- "===\n",
- "\n",
- "Slicing\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.arange(10)\n",
- "a[2:9:3]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 32,
- "text": [
- "array([2, 5, 8])"
- ]
- }
- ],
- "prompt_number": 32
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[:4]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 33,
- "text": [
- "array([0, 1, 2, 3])"
- ]
- }
- ],
- "prompt_number": 33
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "By default, start is 0, end is the last and step is 1:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[::2]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 34,
- "text": [
- "array([0, 2, 4, 6, 8])"
- ]
- }
- ],
- "prompt_number": 34
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[3:]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 35,
- "text": [
- "array([3, 4, 5, 6, 7, 8, 9])"
- ]
- }
- ],
- "prompt_number": 35
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Indexing and slicing\n",
- "==="
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "Image(filename='numpy_indexing.png')"
- ],
- "language": "python",
- "metadata": {
- "slideshow": {
- "slide_type": "-"
- }
- },
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "png": 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NVmdYu6ph/jQ2vneT7vPawXAXpg2TcADAl7rDvcDKbEIIIYQQwhoyJgSAK9p5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\na0S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Q0X/q63U4MJ0xxhhjjDEGANj68VaL0WgcS0T/rM/X4UkIY4wxxhhjDADwRK9ua4jo\nZH2/jiCi+n4NxhhjrMEKDAy8RkSthRCSr8fiKRaLRVgsFq1OpzMDeCyu61KfZjqd8PUovOevVx+a\nj5SaGt171/qZAaoecxN4u3/jNuHopHl1roheHf6QMMYY+0MrKyvrDyDE1+OoB9qysjL30uA0AlrR\n7DQAna/H4S0vPKFJ3H+j9B++HkdNtRoa8TyAdF+PgzV8PAlhjDH2h0ZEJQBKfD0OVrXrQ0JO4A80\nCYls5vc9EZ319ThqavTejWG+HgNrHHgSwhhjjLEGr93pa1G+HgNzyy0Ap309CFYn9ZKSt7L/Byik\nTWipSwq0AAAAAElFTkSuQmCC\n",
- "prompt_number": 36,
- "text": [
- "<IPython.core.display.Image at 0x31ff3d0>"
- ]
- }
- ],
- "prompt_number": 36
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Copies and views\n",
- "===\n",
- "\n",
- "* Slicing creates a *view*, not a copy.\n",
- "* Modifying a view also modifies the original.\n",
- "* You can force a copy with `.copy()`."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.arange(10)\n",
- "b = a[::2]\n",
- "a, b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 37,
- "text": [
- "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0, 2, 4, 6, 8]))"
- ]
- }
- ],
- "prompt_number": 37
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b[0] = 12\n",
- "a, b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 38,
- "text": [
- "(array([12, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([12, 2, 4, 6, 8]))"
- ]
- }
- ],
- "prompt_number": 38
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.arange(10)\n",
- "b = a[::2].copy()\n",
- "b[0] = 12\n",
- "a, b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 39,
- "text": [
- "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([12, 2, 4, 6, 8]))"
- ]
- }
- ],
- "prompt_number": 39
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Fancy indexing\n",
- "===\n",
- "\n",
- "Indexing with integer arrays\n",
- "---\n",
- "\n",
- "* Boolean and integer arrays are also called *masks*.\n",
- "* Indexing with a mask creates a copy, not a view."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.random.seed(3)\n",
- "a = np.random.random_integers(0, 20, 15)\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 40,
- "text": [
- "array([10, 3, 8, 0, 19, 10, 11, 9, 10, 6, 0, 20, 12, 7, 14])"
- ]
- }
- ],
- "prompt_number": 40
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[[2, 3, 6, 11]]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 41,
- "text": [
- "array([ 8, 0, 11, 20])"
- ]
- }
- ],
- "prompt_number": 41
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Fancy indexing\n",
- "===\n",
- "\n",
- "Indexing with boolean arrays\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 42,
- "text": [
- "array([10, 3, 8, 0, 19, 10, 11, 9, 10, 6, 0, 20, 12, 7, 14])"
- ]
- }
- ],
- "prompt_number": 42
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a % 3 == 0"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 43,
- "text": [
- "array([False, True, False, True, False, False, False, True, False,\n",
- " True, True, False, True, False, False], dtype=bool)"
- ]
- }
- ],
- "prompt_number": 43
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[a % 3 == 0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 44,
- "text": [
- "array([ 3, 0, 9, 6, 0, 12])"
- ]
- }
- ],
- "prompt_number": 44
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Fancy indexing\n",
- "===\n",
- "\n",
- "Assignment using masks\n",
- "---\n",
- "\n",
- "We can assign a new value to a sub-array by indexing with a mask."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 45,
- "text": [
- "array([10, 3, 8, 0, 19, 10, 11, 9, 10, 6, 0, 20, 12, 7, 14])"
- ]
- }
- ],
- "prompt_number": 45
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[a % 3 == 0] = -1\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 46,
- "text": [
- "array([10, -1, 8, -1, 19, 10, 11, -1, 10, -1, -1, 20, -1, 7, 14])"
- ]
- }
- ],
- "prompt_number": 46
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[[9, 7]] = -2\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 47,
- "text": [
- "array([10, -1, 8, -1, 19, 10, 11, -2, 10, -2, -1, 20, -1, 7, 14])"
- ]
- }
- ],
- "prompt_number": 47
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Fancy indexing\n",
- "==="
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "Image(filename='numpy_fancy_indexing.png')"
- ],
- "language": "python",
- "metadata": {
- "slideshow": {
- "slide_type": "-"
- }
- },
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
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vUqoL6Fa52Tccj637YJhljHloHsrlHCb+SGB1FrjMu3QlRTMVsMkc9dkW4Bwlgf/5rEso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\nMGTIkODw5ORkLFy4EO+++y6+//3vd5rPZrNhw4YNeOaZZzBjx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- "prompt_number": 48,
- "text": [
- "<IPython.core.display.Image at 0x32df990>"
- ]
- }
- ],
- "prompt_number": 48
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "With scalars\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([1, 2, 3, 4])\n",
- "a + 1"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 49,
- "text": [
- "array([2, 3, 4, 5])"
- ]
- }
- ],
- "prompt_number": 49
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "2**a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 50,
- "text": [
- "array([ 2, 4, 8, 16])"
- ]
- }
- ],
- "prompt_number": 50
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "All arithmetic operates elementwise\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = np.ones(4) + 1\n",
- "a, b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 52,
- "text": [
- "(array([1, 2, 3, 4]), array([ 2., 2., 2., 2.]))"
- ]
- }
- ],
- "prompt_number": 52
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a - b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 53,
- "text": [
- "array([-1., 0., 1., 2.])"
- ]
- }
- ],
- "prompt_number": 53
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a * b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 54,
- "text": [
- "array([ 2., 4., 6., 8.])"
- ]
- }
- ],
- "prompt_number": 54
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "j = np.arange(5)\n",
- "2**(j + 1) - j"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 60,
- "text": [
- "array([ 2, 3, 6, 13, 28])"
- ]
- }
- ],
- "prompt_number": 60
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "Array multiplication $\\neq$ matrix multiplication!\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "c = np.ones((3, 3))\n",
- "c * c"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 61,
- "text": [
- "array([[ 1., 1., 1.],\n",
- " [ 1., 1., 1.],\n",
- " [ 1., 1., 1.]])"
- ]
- }
- ],
- "prompt_number": 61
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "c.dot(c)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 62,
- "text": [
- "array([[ 3., 3., 3.],\n",
- " [ 3., 3., 3.],\n",
- " [ 3., 3., 3.]])"
- ]
- }
- ],
- "prompt_number": 62
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "Comparisons\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([1, 2, 3, 4])\n",
- "b = np.array([4, 2, 2, 4])\n",
- "a == b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 63,
- "text": [
- "array([False, True, False, True], dtype=bool)"
- ]
- }
- ],
- "prompt_number": 63
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a > b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 64,
- "text": [
- "array([False, False, True, False], dtype=bool)"
- ]
- }
- ],
- "prompt_number": 64
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "Logical operations\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([1, 1, 0, 0], dtype=bool)\n",
- "b = np.array([1, 0, 1, 0], dtype=bool)\n",
- "np.logical_or(a, b)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 65,
- "text": [
- "array([ True, True, True, False], dtype=bool)"
- ]
- }
- ],
- "prompt_number": 65
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.logical_and(a, b)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 67,
- "text": [
- "array([ True, False, False, False], dtype=bool)"
- ]
- }
- ],
- "prompt_number": 67
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "Shape mismatches\n",
- "---\n",
- "\n",
- "Elementwise operations work on arrays with the same shape:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.arange(4) + np.array([1, 2])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "operands could not be broadcast together with shapes (4) (2) ",
- "output_type": "pyerr",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-68-590d12522f07>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4) (2) "
- ]
- }
- ],
- "prompt_number": 68
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Broadcasting? We\u2019ll return to that later."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Elementwise operations\n",
- "===\n",
- "\n",
- "Transposition\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.triu(np.ones((3, 3)), 1)\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 69,
- "text": [
- "array([[ 0., 1., 1.],\n",
- " [ 0., 0., 1.],\n",
- " [ 0., 0., 0.]])"
- ]
- }
- ],
- "prompt_number": 69
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.T"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 70,
- "text": [
- "array([[ 0., 0., 0.],\n",
- " [ 1., 0., 0.],\n",
- " [ 1., 1., 0.]])"
- ]
- }
- ],
- "prompt_number": 70
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Basic reductions\n",
- "===\n",
- "\n",
- "Computing sums\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x = np.array([1, 2, 3, 4])\n",
- "np.sum(x)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 71,
- "text": [
- "10"
- ]
- }
- ],
- "prompt_number": 71
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.sum()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 72,
- "text": [
- "10"
- ]
- }
- ],
- "prompt_number": 72
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic reductions\n",
- "===\n",
- "\n",
- "Sum by rows or columns\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x = np.array([[1, 1], [2, 2]])\n",
- "Image(filename='reductions.png')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "png": 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mZnp5eZWWlmKZ2tvb09LS0tLS+nUmX1/fqKgoIpEIKx0yDNYLm53v78/odEfXnDtXvXNW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\nSSoqKsJVisYHDyj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- "prompt_number": 95,
- "text": [
- "<IPython.core.display.Image at 0x33d9b10>"
- ]
- }
- ],
- "prompt_number": 95
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.sum(axis=0), x.sum(axis=1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 96,
- "text": [
- "(array([3, 3]), array([2, 4]))"
- ]
- }
- ],
- "prompt_number": 96
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic reductions\n",
- "===\n",
- "\n",
- "Axes of higher dimensions\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x = np.random.rand(2, 2, 2)\n",
- "x"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 77,
- "text": [
- "array([[[ 0.64914405, 0.27848728],\n",
- " [ 0.6762549 , 0.59086282]],\n",
- "\n",
- " [[ 0.02398188, 0.55885409],\n",
- " [ 0.25925245, 0.4151012 ]]])"
- ]
- }
- ],
- "prompt_number": 77
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.sum(axis=2)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 78,
- "text": [
- "array([[ 0.92763133, 1.26711772],\n",
- " [ 0.58283597, 0.67435364]])"
- ]
- }
- ],
- "prompt_number": 78
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic reductions\n",
- "===\n",
- "\n",
- "Statistics\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x = np.array([1, 2, 3, 1])\n",
- "y = np.array([[1, 2, 3], [5, 6, 1]])\n",
- "x.mean()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 79,
- "text": [
- "1.75"
- ]
- }
- ],
- "prompt_number": 79
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.median(x)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 82,
- "text": [
- "1.5"
- ]
- }
- ],
- "prompt_number": 82
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The last axis is `-1`:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.median(y, axis=-1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 83,
- "text": [
- "array([ 2., 5.])"
- ]
- }
- ],
- "prompt_number": 83
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.std()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 84,
- "text": [
- "0.82915619758884995"
- ]
- }
- ],
- "prompt_number": 84
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic reductions\n",
- "===\n",
- "\n",
- "Extrema\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x = np.array([1, 3, 2])\n",
- "x.min()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 85,
- "text": [
- "1"
- ]
- }
- ],
- "prompt_number": 85
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.max()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 86,
- "text": [
- "3"
- ]
- }
- ],
- "prompt_number": 86
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can also get their indices:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.argmin()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 87,
- "text": [
- "0"
- ]
- }
- ],
- "prompt_number": 87
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "x.argmax()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 89,
- "text": [
- "1"
- ]
- }
- ],
- "prompt_number": 89
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Basic reductions\n",
- "===\n",
- "\n",
- "Logical operations\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.all([True, True, False])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 90,
- "text": [
- "False"
- ]
- }
- ],
- "prompt_number": 90
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.any([True, True, False])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 91,
- "text": [
- "True"
- ]
- }
- ],
- "prompt_number": 91
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Can be used with comparisons:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.zeros((100, 100))\n",
- "np.any(a != 0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 92,
- "text": [
- "False"
- ]
- }
- ],
- "prompt_number": 92
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([1, 2, 3, 2])\n",
- "b = np.array([2, 2, 3, 2])\n",
- "c = np.array([6, 4, 4, 5])\n",
- "((a <= b) & (b <= c)).all()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 93,
- "text": [
- "True"
- ]
- }
- ],
- "prompt_number": 93
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Broadcasting\n",
- "===\n",
- "\n",
- "- Basic operations on arrays (addition, etc.) are elementwise.\n",
- "- This works on arrays of the same size.\n",
- "\n",
- "**Nevertheless**, it\u2019s also possible to do operations on arrays of different\n",
- "sizes if NumPy can transform these arrays so that they all have\n",
- "the same size.\n",
- "\n",
- "This conversion is called broadcasting."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Broadcasting\n",
- "==="
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "Image(filename='numpy_broadcasting.png')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
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Yh5UrV+7evXvHjh3r1q3DZ7y8vKCd/AQEBwfjqVo5OTnhc5j6VQn79+9///49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\nNydbw0paWtrBwSEgIICshtWrV5N1jJqamjY2Nj+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96BtrstELvahV8yxYsODt27cFBQWc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\n2lobG5u4uLgzZ87gfTUiIiJbt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nw5Pt6xY0d0dPSLFy/+/fdfAZVDYWGhjo5O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\nTmRkZEVFhbm5eX19PbF3QlJScuXKlUwms9GiAHNzc3Fx8YqKC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I/iafkDAPycjh49Sqod\n3MElWPeSupFGo5E6SrSVM0LI1dUVdztIS0uPHj0aIVReXk56AwTrRnKPEHkMpBy0tLT09fX/qxwk\nJCTk5eWb5JOgM8OXFvq/nVrkuI0b/AhJQkJCS0trxIgRzVdeFd8jIJ3OZrPJaCMul4vHFiGE5OTk\n4DoB4KfO1mpqaqZMmYLXwZSSkiJ3RJKECN6JBTvTRHgzLiwsNDU1zczMRAhpaGhs3bq1SQyC2Zpg\nPMI03zZRVVVlY2OD10WVlZUli4y1WA6CxyJ/KAEAAGzbtm0nT57Ex8eOHcNN7IItRC3WjaKtlE6d\nOkUmxe3Zswe3VQnG0OI9QrQxuLi4XLhwAR+fOHECD774ajmI8AYBOg7JxwTXMhXM1jpur1dCS0ur\nZ8+egp171OJyuXFxcXiAKJvNhjUhAeg8mOL/ypqaGltbWzLu5dChQ71798bHZKtTwQpUcEFbUe3H\nmp+fP3HixISEBIQQg8E4d+4caRklMQg2uQluwCKqVrfKykobG5uAgAD849GjR7W0tFopB8EYOnq/\nbGEWDAAAfL+2bNlC1n+aMWMG2W5EcB/qFutGEVZKJ06cWL16NT42MTEhx4IxtHiPEFUMfD7/jz/+\nIMtNzZ8/n8ym+2o5dHS3DI/HE88aGD82sgOE4I1V8LixsVHITSm+Lw0NDXFxcaTvWk9PD/aiAKDz\nEPdfY11d3dSpU0mqdvTo0ZUrV5J3yZqYZF1dhFBhYSE5FkljT3FxsZmZGU7VWCzW7du3BTfzITEI\nfq/gcY8ePUSSsk6ePBmnajQazdXV1cnJSczl0KK8vLxjx47BHwYAP6Ht27eTVG3mzJmCiyoJLljc\nYt0okooRIeTq6krSMzMzs6dPn5JnaMEYWqwbRVUxrl27lqRq8+bNu3LlivjLoUUHDx6sqKggq3SC\ndiOZtmCGT1JxOp3+U6VqXC43NjYWp2o0Gq1Pnz7tXiCg3WpraysqKjq6JRoAyNbaVCPMnj0bL9tF\np9PPnDnTZOkRsgoZmdiNEBJc3LZbt25CxlBRUWFlZZWYmIgQYrPZ7u7ueOHH5jGQ0duCMXTp0oX0\nwrVbQ0PDjBkz/P39EUIMBuPSpUtkI4EmMQjuVyvacmjR58+fx4wZQ5ZfAwD8PI4cObJnzx58PH/+\n/Nu3bwv2HZFKSbBu5PP5JEsRydwbNze3VatW4WNra+unT58K7lOsrKxMQmqxbhRJxbhz587jx4/j\nYycnp+vXrwt2ubRYDg0NDeSe1XFzkLZv375p0yYajWZpaQmXq5DIuhqC6QHpFxVcdeN70e79ghsb\nG+Pj46uqqvCHGBoakiXNxKa6ujo6OhpSNQCoz9b4fP5vv/3m6emJEJKUlLx79+6yZcuanEOaLblc\nLtmZkdyJmUym4Eob7VBbWztlyhS80rSCgsLLly8Fe9WaxCDYbkpiEP5OzOPx5s2b9+zZM4SQlJSU\nh4fHb7/99l8xVFVVkcnQJAZpaWnhM8bm0tPTR48enZOT4+zsDEMgAPipCG4a5uLicu3aNcEUpUmm\nROrG0tJS8oAlfJ+Sp6fnb7/9hucLOTo6Pnr0qMmqdDQajTxHCvatkbpR+L61Y8eO7dq1Cx//9ddf\nFy5caFIZqqurk8diUg4iH3zRnIuLy549exwcHGDtB9Fma4JDW8lx55lL1tF4PF5CQgLew5BOp/fv\n31/Ip6x2qKqqio6OxoX/U3VpAtB24vvDWLVq1Y0bN/Ad98GDBzY2Ns3PMTIyUlVVxXe+hw8fLly4\nECFEdk21srISJovgcrkODg5+fn4IITab/fr166FDhzY/zczMTEJCor6+vrS01M/Pz8TEBCH0/Plz\n/K61tbWQ5bBkyZL79+8jhBgMxuPHj8lalIJGjhypqKiIG2sfPnw4e/ZswXIQPobmUlJSJkyYUF5e\n/vbt29DQUPjDAODncefOHdJ2tnHjxha3eKbRaFZWVl5eXgghDw8P3M5FKqWuXbsOHjxYmBh8fHxm\nz56NZxM5OjpevXq1xdMmTZqEV/54+PChs7MzQsjPzw8/50lLS+Pqut2uXr26bt06fLx9+3aStgli\nsVjm5uZ4hIiHhwfu5iI3CG1t7b59+4r2t8Pn85ctW3bhwoXFixdfvHiR7OkChEGS3urqai6Xi5ME\nMmvrJ0mJ+Xx+UlISftKg0+mDBg0S//94RUVFXFxcY2Ojurp6fX097uID7RYdHR0eHp6ZmamkpNS7\nd28LCwvB4QlETk5OSEhIfHy8hoaGsbHxsGHDRBhDUlJSaGjox48fu3Tp0rt3b3Nz81Y6GLKzsw8f\nPszn84cPH06mSQsvIiIiMjIyMzNTRUWld+/elpaWLe5IweFwgoOD09PTZWVlvxrqT5GtRUZGurq6\nkrudi4uLi4uL4Amenp59+vRhs9krV67cuXMnQmjLli01NTXFxcWXLl3C52zZskWYGDw9PfGjBs6U\nml8WcXFxEhISmpqac+fOxc8Ky5Yt27hxY1xcHL43s1istWvXChNDcHDw5cuX/6fomUwy5ofw9vbu\n2bOnjIyMs7Mz3iBu48aNX758yc3NdXNzE0k5NJeQkGBiYlJbWxsVFWVgYADZGgA/j/r6+hUrVpCF\nhTw8PB49eiR4wpYtW/A20C4uLrgKdXNz69mzp7q6+t9//43PWbNmjZCDx1auXEnGEfj7+/fp00fw\n3aVLl+I8au3atRcvXuTz+S9evFi/fr2hoeGhQ4fwOU5OTsJ0C1RXV//xxx9kJcDbt2/fvXtX8IT9\n+/dPmzYNlwO+I1y6dKlHjx4KCgq7d+/G57i4uIi2c6CxsdHR0fHWrVurV68miycD4SkpKUlLS1dX\nVzc2NqalpWlra5eWluLlW2g0GoVL6otTTk4OGc0rKSmZk5MjuOSppKRkz549OzSA8vLyuLg4Ho/X\nvXv3Xr16xcfHw5XZbp8/f3Zycnrx4oXgi927dz9x4gSuuIi7d+86OTmRtgmEkJ2d3eXLlxUVFYWM\nobS0dOnSpQ8ePBBcUlVFReXgwYPNB5Hhu4+9vX1YWBhCyNHRUSTZWmZm5uLFi5ts4KmtrX3q1KnJ\nkyeTV8rKypYuXeru7t7GUH+WbO3mzZuCvx4Oh9PkBLIh48qVK2/dupWSkpKbm7tixQrBi2nUqFHC\nxHD9+nVyXFVV1TwG8ryyefPm58+f5+XlcTicxYsXCz5PCDkvQrAc6urqmsdAnldWr1597969jx8/\nfvr0SXDI6OzZs4cMGSLalhgzMzMejxcfH4/3KAcA/DyePHkiOE84LS2tyQlkhtiECROmTZv28OFD\nHo+H29TIjRB3c7Xbu3fvBLcGzsjIaHICGetoaGi4fPly3PZHtjxBCKmqqm7YsEGYGDw9PSsqKsiP\nzbcqJoPzf/nlF2tr62fPnnG5XLL1C0JIV1dX8H4hkkR69uzZjx49+uuvv8iUQiAq2traePv1/Pz8\n/Px88rqmpub3OG+tHfLy8shxTU1Nkz3BpaWlOzRbKysri4+P5/P5Ojo6ZEFs0D5VVVW2trYxMTHN\nUzgHB4fAwMCRI0fiV/z8/PBwrSa1X1lZ2Zs3b4SJoaGhwd7evvmHFBUVLVq0SFVVVTBZwlatWoVT\nNRHm/zY2NngRwSYpnL29fWhoKB5S19jY6ODggBvd2hgq5cQxPamxsfH27dttPFlFRSUiImLevHmk\n17JLly5bt251d3cXJobCwkJvb+82nqyvrx8VFWVpaUlaSZWUlE6ePClk02ZDQ8O9e/faeLKGhkZE\nRIS9vT0ZQC8nJ7d7927BfE94YWFh48ePp9FoSUlJkKoB8BMSbMb6qgcPHuzatYs0wbJYrOnTp0dF\nRQk5gAoPkm+j06dPnz17lux1xmAwLCwsoqOjhewP+aZy8PLy2rp1KxkzIyEhMWvWrMjIyBYHHbVP\nbW2tra3t48eP9+/fD6laR1BTU+vbt2+T+ZndunUjWwr98M/3gr0rYlZSUoJ71XR1dSFVE961a9dw\nqsZisc6fP5+Xl/f06VN9fX2EEJfL3bx5MzmTrPrr4OCQk5Pz7Nkz/KDr5+cnZOLk5eVFUrXDhw9n\nZ2f7+vqSLLFJa1pMTIylpSXZ0FJULl68iFM1Npt96dKl/Pz8x48f9+rVCyFUX19P9vB88eIFSdW+\nGmonIY6+NQaDkZub2/bzu3TpcuPGjStXrsTHx8vIyIhk3w9VVVXBycRfpamp+eLFi5qamri4ODU1\nNZG0MLFYLMF1Jr9KUVHR3d29vr4+Li5OTk5OT0+v3Ys+tSggIGDSpElSUlLJycmCW80CAH4eeOWn\nNqLRaNu3b9+2bVtqampFRUX//v1Fsh7DiRMnTpw40fbznZ2dnZ2dMzIy8vPzBwwYIJIcicw9aws6\nnb5nz57du3enpKRUVVX1799ftL0xeDfOkJCQU6dONV+OC4iKqqqqkpJSeXl5VVWVpKSkrKyslJQU\nVZHIysqi/7vnW4eSkJBofZxOxy02VlxcnJCQwOfz9fX1BbfEAO0muL7DkiVLEELW1tbp6el4uk1U\nVBSfz6fRaPHx8fhMGo129OhRTU1NTU3NBQsW4Bk6R44caXuPQisxGBsb47lOXbt23bRpEx6HyeFw\nKisr8UW+efPmAwcOdMQG9CQGW1vbRYsW4YOUlBS8hhZeYvCbQv25srV25zYtrgIiTlJSUiNGjKA2\nBgkJCdFOAMV8fHxsbW0VFBQ4HE6nnVUJAOiEaDQabrWllo6ODrUjAmg0moGBgcg/tqyszMLCIjY2\n9vLly/Pnz4frrUMxGAxFRUXhZ+wI/8AjuGHGD/mNWGFhIR6AahNv6goAACAASURBVGhoSHrIgZC2\nb9++YMGC3Nzc4cOHkxfJdiNsNhu39UdFReFXlJSUyAq6xsbGOFsj77bP77//PmnSpNzcXMFZx2Qu\nMZPJJNdbaGgoTtWcnJxqa2u/aWxF6/bu3bt06dLc3FzBmVOkHEhbjLOzs6mpaVtChWwNUOnJkyfT\np09XV1fncDgiHLoDAABAGEVFRWZmZhwO5969e3Z2dlAg4EeSn5/P4XD4fP7AgQMpz5B/JEOHDm3e\nvXHnzh3yLj74/PkzPhDMk0mWkp2dLUwM/fr169ev33/F0K9fPzIQg0ajmZqabtq06ZdffhHt2IFh\nw4Y1794gMRgZGeEDQ0NDQ0PDtoQK2RqgzIMHD2bNmqWlpZWcnPyTzKUGAIDOLzc318TEJDMz88mT\nJxYWFlAg4Ae7vFNSUhBCgwcPhhE9He369esPHz7Ex7///js++PTpU/NsjUyEqa2tLSkpUVJSElUM\nz549IzPTSAw4LxLbDuwXL158+vRp8xjaGGrnAZsg/1xu3rzp4ODQu3fvlJQUSNUAAKCTyMzMHDVq\nVFZWlq+vL6Rq4Afz+fPnlJQUGo02dOhQSNU62qVLl3777Te8feWsWbPIJr1kFVBlZWVysmB6Rlbf\nFd6jR4+mTZuG1zk3MTHBs8gwsaVqrq6uS5cuxaMuHR0dzczMvjVUyNYABS5evDh//vwBAwYkJiaK\ndlMgAAAA7Zaamjpq1KjCwsLQ0NDRo0dDgYAfSVZW1ocPHxBCRkZGXbp0gQLpUOfOnVuyZAnekmrs\n2LHnz58nb5E8WXC3kvLycnKsoKAgkhg8PDxmzpxZX1+PEOrXrx8ZZChOJ06cWLlyJU7VTE1NT58+\n3UlCxT2cXC4XsjXQglOnTi1dunT48OExMTEdt9YTAACAb/L+/fvRo0dXVFRERUUNHjwYCgT8SDIy\nMtLT0+l0+vDhw2VkZKBAOtT169eXL1+OUxRzc/MXL14Ibq9C1tsQXJ+c9KcxmUyRrPvi7e09e/Zs\nvAz74MGD/fz8xNaZRly4cGH16tX4GG+P2eK1J/5Qb9++vXbtWhqN9q1dJvDU/lM4dOjQ77//PmHC\nhLdv30JpAABAJxEZGTl27Nj6+vqEhISOWGESAAp9+PAhMzOTTqcbGxtTtTvCz+PBgweLFi3CqZqd\nnd2TJ0+aLCNH9ksoLCxsnq1paGgI35QfEBBgb2+P858xY8a8efNG/BtE3bp1iyxe4uDg4OnpSTZw\npjbUixcvzps3T1FRsR27F0C29uPbtWvXxo0bra2thdyoHgAAgAgFBwdPmDCBwWCkpKTAHsHgB5Oa\nmvr582c6nT5ixIgWH5eBCHl7e8+ZMwfPVZs/f767u3vzhQ3Jzu8FBQVkMGRmZiY+0NPTEzKGiIgI\nW1vbmpoahNCkSZN8fHxENbSy7Tw9PR0dHfFA0MWLF9++fbvF5fjFH+rx48eXLl1qbGzcvj3BYfLS\nD+7PP/88cODAjBkz7t+/D6UBAACdxOvXrydPniwnJ5ecnAyrmYMfDIfDycvLYzAYI0aM6IS7V/1g\nkpOTSTeRsrKypaVlk0c+BwcHOp1uZWWlra2dmZlZX19/8ODBPXv25Ofnnzt3Dp+zYsUKYWIoLi62\nsrLCs+CkpaXt7e0fPXokeIKtrW1HD4WNi4ubPXs2nhKmrq5uamraZL/v2bNnUxLqvn37tm7dampq\n6uvr6+npCdka+F98Pn/NmjUnT550dHS8evUqFAgAAHQSeNNLNTU1DocDk3nAD/bskZycXFBQwGQy\nR4wYAUuaicGVK1dwNxFORebPn9/kBDs7OzabzWQyV69evW7dOoTQ3r177969++nTp9raWoRQ7969\np0+fLkwMd+7cKS4uxsfV1dVOTk5NTkhPT+/ouu7SpUt4aUeEUH5+/q+//trkBJytiTlU3Gtia2v7\n+PHjdn8IjIT8MfF4vKVLl548eXLZsmWQqgEAQOeBd77u0aPHhw8fIFUDP1iqlpiYWFhYKCEhMXLk\nSEjVxFPmt2/fbuPJv//+O87WEEKpqak4VdPR0bl165aQk9auX79ObTk0NjbevXu3U4XK5/NXrVp1\n4MCBefPmCZOqIehb+1EtW7YsODjYxcXl0KFDUBoAANBJ3L9/38/Pr0+fPnFxcQwGAwoE/EgSEhLK\ny8slJSWNjY1h9WnxKC4u/uoWYSRtZjKZR44csbGxefnyZUxMjKqq6pAhQxYtWiTkrK3a2lpra+tJ\nkya1ck6LXzF58mS8UqXwy+EWFhaSxUU6ItR2ZI+//fbbjRs3li1bdubMGSE/TTTZGh4kevfuXQo3\nPYyMjEQIPX78OCYmhqoY8GTNW7duNVmHR5xCQkIQQsHBwVu3bt21axdUZAD85L58+ZKYmEh5H/u7\nd+8ojyE+Pp7yGHx9fY2MjCIiIuDKFJXGxkYul0t2/hU/PAittLQU79pECT6fX1tbS2Eh4F6aL1++\nSElJGRsb02g0uDLFQ0VFZefOnd/0T8zMzP5rq+j2YbPZO3bsaMc/nDx58uTJk0USg4aGRlvKod2h\nfpOGhoZZs2Y9evRo/fr1Bw8eFP4DRZOtZWVlMZnMNWvWUHvJslgsMfwOWsdgMFauXEltDHQ6fcOG\nDZCqAQAQQsXFxRkZGT4+PhTGICkpefv27bYP1+kIEhISz549e/bsGbU3CENDQ0jVRKuiooLL5XI4\nHApjoNFonz9/prwcqC0EhJCsrKyRkRFck+BnVltba2dn5+Pjs2vXrq1bt4rkM0WTrfXq1YvL5ebm\n5pKt98TvwIEDf/75Z3R0NIW7i9rb23t4eJSWlop/0VLi4sWLS5YsWbhwIfzBAABw/WxiYnLs2DEK\nY2CxWEePHqW2RY/NZm/atOmff/6hNlv7448/4JoULTk5ucrKSgpvuw0NDeXl5XJychQuflhSUsJi\nsbp06UJhrlhfXz9o0CC4IMHPrLKy0traOiQk5N9//yU7dHeWbA0AAAAAQPzodDqLxerfvz9VAZSV\nlcXGxmprayspKVEVQ2BgoLy8fN++fakKICEhoaioCK5G8DMrKyuzsLCIiYk5d+7c4sWLRfjJkK0B\nAAAAAAAAQDsVFhaamZlxOJybN286ODiI9sMhWwMAAAAAAACA9sjOzjYxMcnKynr48KGNjY3IPx+y\nNQAAAAAAAAD4Zunp6SYmJvn5+S9fvpwwYUJHfAVkawAAAAAAAADwbTgcjomJyZcvX4KCgoYNG9ZB\n3wLZGgAAAAAAAAB8g7i4OFNT07q6uvDw8H79+nXcF0G2BgAAAAAAAABt9e7dO3NzcxqNFhcX16tX\nrw79LjoUNwAAAAAAAAC0hb+/v6mpKZPJTE5O7uhUDbI1AAAAAAAAAGiT58+fW1paysjIpKamampq\niuEbqcnWGhoa2nIal8uFGDo6BgAAaEdt03H1Eo/H4/F41MbQxk/m8/mNjY1w2QAAwM/D1tZWRUXl\n48ePysrK4vlGsc5b+/Dhw65duyIiIjgcjqqq6pAhQ/bs2dN8BRU+n3/8+PE7d+7Exsay2WwjI6O1\na9eKavuCsLCww4cPR0ZGZmRkdO/effjw4fv379fT02tyWn19/Z49e548eZKQkKCoqGhkZLRjx44R\nI0aIJIbExMR9+/ZFRkampqaqqakZGRnt3bt38ODBzR9Zjhw5cv/+/bi4OBkZmaFDh27YsMHS0hL+\nTgAAolVRUbFjx47AwMD4+HhJScl+/fqtXLly3rx5zc98/vz5kSNHIiMja2pqBg4c6ODgsG7dOhqN\nJnwM9fX1+/bte/36dUxMDJ/P79ev38KFC5cvX978w0NDQ/fs2RMREVFWVta/f39bW9tt27YxmSK4\nnX358mXbtm0hISHx8fFSUlL9+/f/448/Wtzn9PHjx8ePH4+Kiqqrqxs4cOCcOXNWr14NF9J3p6Cg\noKysrLq6WlJSUlZWtmvXrgwGQ/xh1NXVJSYm8ng8KSmpvn37ivmrs7Oza2pq6uvrJSQkpKWlu3bt\nKikpCddG51FaWooQsrCwkJCQoCqG4uJihFDPnj3pdMoG5VVUVCCExo8fz2KxqIqhrKwMIdSjR4+k\npCRx/pmIL1t78eLF7Nmz8f8nQig/P//58+c+Pj7Hjh1btWqVYIoyderUJ0+e4B9ra2tfv37t6+u7\nb9++zZs3CxnDtWvXnJ2d6+rq8I+fPn369OnTs2fPbt68OW3aNMFfhoWFRUREBKnNvb29X79+ffHi\nxfnz5wsZg5eX17x58/A1hxDKy8t7+vTpixcvXF1dlyxZQk7jcrk2NjYvX74k9emrV698fX0PHTq0\nbt06qLwAAKLy8eNHW1vbxMREUtuEhoaGhoY+ffr01q1bgmfu2bNnx44dfD4f//ju3bt37975+fl5\nenoK+Yybn58/bdq00NBQ8kp4eHh4eLinp+fTp08F782XL19etmwZGZgQHR0dHR3t6+v7/PlzGRkZ\nYWJISUmxtbVNSUkh2WNwcHBwcLC3t/eVK1cEz/zrr7/+/vtv8mNYWFhYWFhAQIC7u7tIElcgBo2N\njcnJyUVFRYKZW15eXv/+/aWkpMQZCY/HS0hIIE8F4pSTk5OWlkb+orHPnz/37t1bPOO7QFvIy8sj\nhFgsFoVZCq7ZJCQkKKzicKLIZDIpL4eUlBSRtA92umytsbHRyckJp2o6OjpTp0599epVQkJCY2Pj\n2rVrx40bN2jQIHymu7s7TtWkpKRmzpxZXFz89OlTPp+/ffv2OXPm6OjoCJMQL1++HKdqAwcONDEx\n8fT0zMrKqqmpWbRo0dixY1VVVfGZp0+fxqmagoLCzJkzExISQkJC6uvr16xZM336dGEeCBoaGpyc\nnHClrKurO3nyZG9v75SUFC6Xu2rVqrFjxxoaGuIzb926hVM1aWlpBweH7OxsHx8fHo+3ZcuWWbNm\ndevWDeovAIBI/PXXXzhVU1RUnDVrVnJysr+/P5/Pv337tpmZmZOTEz4tMzNz9+7d+MFu0qRJqqqq\n9+7dq62tffLkyZ07d1rsiGu7f/75B6dquMYrKip6/vw5l8v18fHZvXv3nj178Gnl5eUuLi44VRs/\nfry+vr67u3tZWVlQUNDZs2ddXFyEiWHTpk04VVNWVnZwcHj//n1gYCBC6OrVqxMnTvz1119JUvfP\nP//g48mTJysoKNy/f7+urs7Dw8PT01Ow4Q90ZtnZ2SRVk5WVra6u5vF41dXVKSkp5IFEDPh8fkpK\nCiWpWllZGUnVaDSajIxMZWUlzh5TU1NlZGTk5OTgOukMcJby7Nmz5oOwxMbKyurFixccDofNZlMV\nw5YtW/bv3+/r69unTx+qYnBycrp06ZKYUzUktnlrT548+fz5M0KoW7duKSkpx44di4mJwS03XC6X\n9KQhhPbv348Ptm3bdu3atSdPnkyaNAmfduDAAWFiuHbtWk1NDUJozJgxsbGxx48ff/fuHS7xsrIy\nX19ffFptbe2JEyfwsaur6/nz5/39/fX19RFCJSUl58+fFyYGDw+PgoIChFDPnj05HA4uB0VFRYRQ\nfX39s2fPSPVNngb27t175cqVly9fjh49GiFUV1d3+PBhqLwAACJRWFjo4eGBH9fevn175syZN2/e\nTJ48Gb/r7u5Ozjxy5AiezWVpafns2bNr166R8Q5///13k+b5b1JbW3vt2jV87OXldeXKFS8vr+XL\nl+NXPD09yZmurq641W/w4MFv3ry5cOHC8ePH8VuHDx+ura0V5tndy8sLIcRgMCIjI11dXQMCAiws\nLJqXw+HDh/G0uilTpnh5ebm5uZHxDqTeBp0cj8fDzyQIIUNDQyMjo2HDhuFW87Kysi9fvognjIKC\ngvDw8Pz8fEoKIScnB//ZysrKjho1ysjIaNSoUbhfkc/n5+bmwnUCQGfJ2MXzNSNGjHj8+PH+/fuP\nHz+OezCZTCZZ8pIM/YyPj4+JicHHCxcuxAfOzs74wM3NTZgY7Ozs3N3dd+3atXfvXvyKsrKyiooK\nPib9qj4+PjihYrPZs2fPxqEuXrwYv3vjxg1hYhg/frynp+e+ffuOHj2KBw5JSUk1L4eIiIikpKT/\nKofr16/DhQsAEAlpaelnz54dP378wIEDuFkKIUQOBKdJ3Lx5Ex/89ttv+GDZsmX4IDExkQwdbwca\njebp6Xn69Olt27aZmZk1iUFwbgC5CyxYsAC3N//66694vENeXp6Pj0+7Y5CXl3/+/PmxY8cOHTqk\nra3dSjmQ0aHNy+Hdu3epqalwUXV+BQUFuIdWUlJSTU0N34vJ+Jrs7GwxxJCenp6UlIQbkcXfVI/+\n/ywghJCmpiZ+BJKQkMClgRCqqqqC6wSATkJMFYSGhoatra2trS3+kc/ne3t7BwcH4x/J0JFPnz6R\nf0LyKHV1dVJ3lJSUKCkptS8GbW1tbW1te3t7/COXy3V1dc3Ly0MIycrK4h48wRjU1NTI8FwNDQ18\nkJmZKUw5aGpqTp06derUqaQcPD09IyMj8fOKnZ1dkxiYTKaCgkKTGEpKSqqrq6WlpeHyBQAISUZG\nZuLEiRMnTiSvpKWlXb58GR+TBTaqq6tLSkqa1MmqqqoMBgMvivjp0ydjY+P2xSApKTl+/Pjx48eT\nV3Jyck6dOtXkBiFYN5IHazqdrqKigp8sBe8g30pWVtbc3Nzc3Jy8kpycTJrGSDmUlJSQp1hSDqRy\nxveI5stWgc4G50hN2gLIdDVhOmnbDv/h0Gg0HR0dJpMp/jx/xIgRjY2NdXV1gmPb6uvr8QGFA94A\nAE1QsLSLr6+vhoYGXuNRWlr69u3burq6+C3SoCUvL086uwTTs5ycHJHE4ObmpqKigpfwUlNT8/b2\nJtU0iYG0MAk+GRQXF5NaXkje3t7q6urTp0/HDwru7u7du3dvEoOysjLJGEkMIiwHAAAgioqKDAwM\n9PT08BJkW7ZsmTt3bpNKSbBupNFoZP1iUVVKjY2NgwcP7tatG4fDQQj99ttv69evx29VVFSQ3gDS\nnCd4jxBVDLm5uXp6eoaGhhUVFTQabdeuXaSZr8VykJCQIDN8hMkYgdiQnERwuQJyTJYi61AsFqt7\n9+7GxsZaWlpUlQODwZCWliar/DU2NhYWFuLjLl26wHUCwM+brYWFheGhhvhOPHPmTPIWuREK3okF\ndzMQ1fgEPz8/MjB9y5YtY8eObf4VLWZrIrwZBwUFkWrR2dmZdKz9VzkIxiCecRoAgJ9KUlISWRFx\n3Lhxf/75J3mMazFLEayXRFUpZWVlxcbG4mMDA4N//vmH9H4IfkWL9whRxfD+/fu0tDR8bGZm5uLi\nQlrNvloOkK19F0g+JjgEkWRr9fX1wszDbCNtbW1dXV0xrz/ZekNJXFwcnpsqKSnZtWtXuE4A+Hmz\nNQkJCUdHRzxW5PTp00OHDg0PD8dvkUWZBZu7BBeGJu1hQpKXl1+4cCGujNasWTNx4sT09PRWYhCs\n0EUVA5vNXrhwIZ60duTIkREjRsTHx4s5BgAAICoqKmbNmjVhwgSEUGBgoL6+/t27d5tUSv9VLwme\nIIySkhJ7e3srKysajcbhcPT09E6ePNl6DOQeIaqKsaqqas6cObgV7/Xr1wYGBg8fPmxjOUDl/F0g\ne5oLPmAIHv9sm55zudzY2Njy8nL8o76+PiX7zgEAOku25uLicvXqVQ6Hs2bNGoRQXFwcGegiODWL\nnE864hBComrs+ffff69cuZKRkYGn0uH93JrE0GQbFnJMxisKadu2bVeuXElNTcVLn0VERJAF1sRW\nDs0VFhaePXuWwoEZAACqWFtb37lzx8/P78GDBwihvLy8JUuW4LHfglOzWqwbRVUxGhkZubu7k4nN\n5eXlq1evxj1agjG0WDeKqmK0s7O7detWYGAgXlglOzvbyckJp2FiK4cWHTt27MuXL1A/C49k2rgr\nqUkqTqfTKVn2g8JULS4ujgwz7tOnT7sXCGi3mpqaiooKvNoqAID6bA2j0WhkgebAwEB8Mya7Mf5X\nliLafcZYLBbZSujBgwd4aASJgQxTFIxBVlaWLPshml8AnU7WE3v58iXeLZ7EgH/s6HIgsrOzx48f\nX1pa+vTpU/jbAOCnNXXqVLyERkVFBV7XXnCrXFI38vl8ctyjRw/RxjBq1KghQ4bgb7l37x5CSEVF\nhTxDt1g3irxinDlzJt5hpaSk5Pnz5/9VDg0NDXhfgY4oB2L37t1r167dvXs3nu0MhEEW+WwxWxNc\nAvR70e49i3k83vv373GqRqPR+vTpQ5bPEZvq6uro6OjGxsa+ffvCxQlAc2JqPQoPD09ISMjMzBw3\nbhxZoFlOTo5Op/N4PD6fn5iY2K1bN9JsyeVyy8vL8bxtcidmMpmC8wS+lZ+fX1paWlZW1vTp08kO\ng6QBqaysLCsrS09Pr/W+NSHbTd++fZuUlJSVlWVqakoWQCPpX0NDQ2Ji4rhx40gMVVVV9fX1+M5B\nYpCWlhZtxoilp6dPnDiRwWAEBgZC2y0AP4mPHz++e/cuMzNTWVmZtF4xGIwuXbrgbaBiY2MdHByU\nlZVZLBZ+nCV1Y2lpKXnYFaZuzM7ODgkJyczMpNPpZO8yhBDOlBBC79+/x4+S6urquGlPsEWPZE3C\n9K2lpaWFh4dnZmZqaGiQfVOYTKasrCxecyU2NnbKlCnq6uo0Gg1PaiLlUFhYSKY5dVDfmouLy9Gj\nR48dO4YXxwKiytYEh7aSUayCC0X+2Ph8fkJCAp7GT6fT+/btK7hSgHhUVlbGxsbyeLxBgwbBftwA\nUJmtXbly5cyZMwihgQMHkhnkd+/eJb3eQ4cOxf9VVlbGjaaPHz/+9ddfEUJk32oLCwsy5b0dDh48\n6O3tjRBKTEwkW53ev3+fpI69e/dGCJmamuKHkpKSkqCgIDx14dWrV/i0X375RZhyOH/+/JUrVxBC\nz549CwsLwy/evn0bH9DpdJxGjhgxQl5eHlegjx8/njFjhmA5CBlDizgczsSJExUUFHx8fAQbjwEA\nP7aIiIg5c+YghNhs9tSpU/FqGdHR0WSZDVw502g0c3NzXIU+evSIjCHH56irq5MmsHb48OEDXiKf\nRqP98ssv/fr1Qwjl5OT4+fkJxoAQsrS0xFXo48ePly5dihAKCgrCD9lsNhvPuGufkJAQR0dHhJCM\njIydnR1uEXv79i1ZNQTHwGKxTExM3rx5g8sBb59NbhA9evQQeecAj8dzdna+cuXKpUuXyA5vQEhk\nwcOamhoul4v7bMneDD/PcohJSUm44YNOpw8cOFBeXl7MAZSXl8fFxfH5/KFDh+KNE4EwPnz4EBkZ\nmZKSoqura2xsjB9rmyssLAwLC4uPj1dTUxsyZAipYEUiKysrMjIyMTGxR48eQ4YMGTBgQCsn5+Xl\nHT9+nM/nGxkZCS46KKS0tLTIyMi0tDRdXd3hw4eTbY2b+Pjx49u3b9PT02VlZXv37j1hwgRZWdmf\nOlubM2fO2bNn+Xx+XFyctbX13Llzg4KCyD6ndnZ2uDlHWlp6+fLlePfqzZs3NzQ0FBcXnz9/Hp+2\nZcsWYWKYO3cuftR4+PDhvHnzJk+e7OXlRdI2JycnPJCgW7dus2bNwrtgL1u2bMuWLXFxcfgfMplM\nwXbf9pUDftR49+7dlClTZs2a9ebNG7LX6uzZs/FNokuXLkuWLDl8+DBCaMOGDVVVVbm5uVevXsWn\nkeltohIbG2tpadm9e/eXL1+Kv10NAEAhKysrRUXF0tLS2tpaS0vLJUuWFBcXnz17Fr+ro6ODExKE\nkIuLC64Jr169qqenp66uvmvXLvzW6tWrhemOGDNmjJaWVlZWFp/Pt7Ozw4PDT58+jZvzlJSUyJZr\n69atw1Xo06dPt27damhouH//fvzW4sWLhRnBNXnyZDk5ufLy8qqqKnNz8yVLluTm5p47dw6/q6en\nZ2J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sVVXVmjVrGKyElStXjh8/vm/fvkwFsH79+sDAQEjVAAAAgPZ2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\ndtasWatWrWLkqkEW6aanjalUquvXr5MPfdF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- "prompt_number": 98,
- "text": [
- "<IPython.core.display.Image at 0x33d9e10>"
- ]
- }
- ],
- "prompt_number": 98
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Broadcasting\n",
- "===\n",
- "\n",
- "Let's verify\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.tile(np.arange(0, 40, 10), (3, 1)).T\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 106,
- "text": [
- "array([[ 0, 0, 0],\n",
- " [10, 10, 10],\n",
- " [20, 20, 20],\n",
- " [30, 30, 30]])"
- ]
- }
- ],
- "prompt_number": 106
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = np.array([0, 1, 2])\n",
- "b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 107,
- "text": [
- "array([0, 1, 2])"
- ]
- }
- ],
- "prompt_number": 107
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a + b"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 109,
- "text": [
- "array([[ 0, 1, 2],\n",
- " [10, 11, 12],\n",
- " [20, 21, 22],\n",
- " [30, 31, 32]])"
- ]
- }
- ],
- "prompt_number": 109
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Broadcasting\n",
- "===\n",
- "\n",
- "In assignment\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.ones((4, 5))\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 110,
- "text": [
- "array([[ 1., 1., 1., 1., 1.],\n",
- " [ 1., 1., 1., 1., 1.],\n",
- " [ 1., 1., 1., 1., 1.],\n",
- " [ 1., 1., 1., 1., 1.]])"
- ]
- }
- ],
- "prompt_number": 110
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[0] = 2\n",
- "a"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 112,
- "text": [
- "array([[ 2., 2., 2., 2., 2.],\n",
- " [ 1., 1., 1., 1., 1.],\n",
- " [ 1., 1., 1., 1., 1.],\n",
- " [ 1., 1., 1., 1., 1.]])"
- ]
- }
- ],
- "prompt_number": 112
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We assigned an array of dimension 0 to an array of dimension 1."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Array shape manipulation\n",
- "===\n",
- "\n",
- "Flattening\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
- "a.ravel()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 113,
- "text": [
- "array([1, 2, 3, 4, 5, 6])"
- ]
- }
- ],
- "prompt_number": 113
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.T"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 114,
- "text": [
- "array([[1, 4],\n",
- " [2, 5],\n",
- " [3, 6]])"
- ]
- }
- ],
- "prompt_number": 114
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.T.ravel()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 115,
- "text": [
- "array([1, 4, 2, 5, 3, 6])"
- ]
- }
- ],
- "prompt_number": 115
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Array shape manipulation\n",
- "===\n",
- "\n",
- "Reshaping\n",
- "---\n",
- "\n",
- "The inverse of flattening: "
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 117,
- "text": [
- "(2, 3)"
- ]
- }
- ],
- "prompt_number": 117
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a.ravel().reshape((2, 3))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 119,
- "text": [
- "array([[1, 2, 3],\n",
- " [4, 5, 6]])"
- ]
- }
- ],
- "prompt_number": 119
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can create arrays from arrays with different shapes:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "np.arange(36).reshape((6, 6))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 120,
- "text": [
- "array([[ 0, 1, 2, 3, 4, 5],\n",
- " [ 6, 7, 8, 9, 10, 11],\n",
- " [12, 13, 14, 15, 16, 17],\n",
- " [18, 19, 20, 21, 22, 23],\n",
- " [24, 25, 26, 27, 28, 29],\n",
- " [30, 31, 32, 33, 34, 35]])"
- ]
- }
- ],
- "prompt_number": 120
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Array shape manipulation\n",
- "===\n",
- "\n",
- "Dimension shuffling\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a = np.arange(4*3*2).reshape(4, 3, 2)\n",
- "a.shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 122,
- "text": [
- "(4, 3, 2)"
- ]
- }
- ],
- "prompt_number": 122
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "a[0, 2, 1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 123,
- "text": [
- "5"
- ]
- }
- ],
- "prompt_number": 123
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b = a.transpose(1, 2, 0)\n",
- "b.shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 124,
- "text": [
- "(3, 2, 4)"
- ]
- }
- ],
- "prompt_number": 124
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "b[2, 1, 0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 125,
- "text": [
- "5"
- ]
- }
- ],
- "prompt_number": 125
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Loading data files\n",
- "===\n",
- "\n",
- "Text files\n",
- "---"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cat populations.txt | head -5"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "# year\thare\tlynx\tcarrot\r\n",
- "1900\t30e3\t4e3\t48300\r\n",
- "1901\t47.2e3\t6.1e3\t48200\r\n",
- "1902\t70.2e3\t9.8e3\t41500\r\n",
- "1903\t77.4e3\t35.2e3\t38200\r\n"
- ]
- }
- ],
- "prompt_number": 134
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "data = np.loadtxt('populations.txt')\n",
- "data[:5]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 136,
- "text": [
- "array([[ 1900., 30000., 4000., 48300.],\n",
- " [ 1901., 47200., 6100., 48200.],\n",
- " [ 1902., 70200., 9800., 41500.],\n",
- " [ 1903., 77400., 35200., 38200.],\n",
- " [ 1904., 36300., 59400., 40600.]])"
- ]
- }
- ],
- "prompt_number": 136
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Loading data files\n",
- "===\n",
- "\n",
- "Loading images\n",
- "---\n",
- "\n",
- "We can load and show images using matplotlib."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from matplotlib import pyplot as plt\n",
- "img = plt.imread('elephant.png')\n",
- "img.shape, img.dtype"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 166,
- "text": [
- "((200, 300, 3), dtype('float32'))"
- ]
- }
- ],
- "prompt_number": 166
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* This image is 200 by 300 pixels.\n",
- "* The third dimension is the color channel (red, green, blue)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Loading data files\n",
- "===\n",
- "\n",
- "Showing images\n",
- "---\n",
- "\n",
- "Let's show the image together with its individual color channels."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.figure(figsize=(10,8))\n",
- "plt.subplot(121).imshow(img)\n",
- "for i in range(3):\n",
- " plt.subplot(1, 6, i + 4).imshow(img[:,:,i], cmap=plt.cm.coolwarm)\n",
- " plt.title(['Red', 'Green', 'Blue'][i])\n",
- " plt.axis('off')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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gqCoH0xmD8QRjM+q6DipXRxFqYlYUa8ymYHSEwpQ6I93Q7WPyzKuGkDQU6SZBsauLtD8d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JSpGPvMTtSvhS/15Nj8aAwRpRPuaIyhmJQsz1doNUYpSb1YImTAtBAnY6Lr2ZlN0garJNZbOmsR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r6OVzDfKn/gqPfupT7H/4axD/0X/NenadrtzFmoyMmqJforwlHSoQMFo9pE9KUtfQ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DAa0hNaZQN73/XEEJnP53RdzqQbrKU0BoRgebSk2+eNtrooKLSmHyxlabi8vEVIBTGxHzqm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\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7f90a5abc5d0>"
- ]
- }
- ],
- "prompt_number": 227
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We'll come back to matplotlib."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "Loading data files\n",
- "===\n",
- "\n",
- "NumPy's own format\n",
- "---\n",
- "\n",
- "* NumPy has its own binary format.\n",
- "* Not portable but with efficient I/O."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "data = np.ones((3, 3))\n",
- "np.save('pop.npy', data)\n",
- "np.load('pop.npy')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 228,
- "text": [
- "array([[ 1., 1., 1.],\n",
- " [ 1., 1., 1.],\n",
- " [ 1., 1., 1.]])"
- ]
- }
- ],
- "prompt_number": 228
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "Todo\n",
- "===\n",
- "\n",
- "* Exercises"
- ]
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff --git a/numpy-slides.ipynb b/numpy-slides.ipynb
new file mode 100644
index 0000000..0bcea61
--- /dev/null
+++ b/numpy-slides.ipynb
@@ -0,0 +1,3028 @@
+{
+ "metadata": {
+ "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "<span style=\"font-size: 200%\">Working with NumPy arrays</span>\n",
+ "===\n",
+ "\n",
+ "<br>\n",
+ "\n",
+ "[Martijn Vermaat](mailto:m.vermaat.hg@lumc.nl)\n",
+ "\n",
+ "[Department of Human Genetics, Leiden University Medical Center](http://humgen.nl)\n",
+ "\n",
+ "Based on: [Python Scientific Lecture Notes](http://scipy-lectures.github.io/)\n",
+ "\n",
+ "License: [Creative Commons Attribution 3.0 License (CC-by)](http://creativecommons.org/licenses/by/3.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%matplotlib inline\n",
+ "\n",
+ "from IPython.display import HTML, Image\n",
+ "\n",
+ "# nbconvert adds a vertical scrollbar to the slides.\n",
+ "HTML(\"\"\"\n",
+ "<style type=\"text/css\">\n",
+ ".reveal {\n",
+ " overflow-y: hidden;\n",
+ "}\n",
+ "</style>\n",
+ "\"\"\")"
+ ],
+ "language": "python",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [
+ {
+ "html": [
+ "\n",
+ "<style type=\"text/css\">\n",
+ ".reveal {\n",
+ " overflow-y: hidden;\n",
+ "}\n",
+ "</style>\n"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 141,
+ "text": [
+ "<IPython.core.display.HTML at 0x42b6a90>"
+ ]
+ }
+ ],
+ "prompt_number": 141
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "About NumPy\n",
+ "===\n",
+ "\n",
+ "What is NumPy?\n",
+ "---\n",
+ "\n",
+ "Python has:\n",
+ "\n",
+ "* Lists (costless insertion and append), dictionaries (fast lookup).\n",
+ "* High-level number objects: integers, floating point.\n",
+ "\n",
+ "NumPy is:\n",
+ "\n",
+ "* Extension package to Python for multi-dimensional arrays.\n",
+ "* Closer to hardware (efficiency).\n",
+ "* Designed for scientific computation (convenience)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "About NumPy\n",
+ "===\n",
+ "\n",
+ "Multi-dimensional arrays\n",
+ "---\n",
+ "\n",
+ "Memory-efficient container that provides fast numerical operations.\n",
+ "\n",
+ "Example data:\n",
+ "\n",
+ "* Values of an experiment/simulation at discrete time steps.\n",
+ "* Signal recorded by a measurement device, e.g. sound wave.\n",
+ "* Pixels of an image, grey-level or colour.\n",
+ "* 3D data measured at different X-Y-Z positions, e.g. MRI scan.\n",
+ "* ..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "About NumPy\n",
+ "===\n",
+ "\n",
+ "How to use NumPy\n",
+ "---\n",
+ "\n",
+ "### 1) As a normal Python package\n",
+ "\n",
+ "Convention is to import the package as `np`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import numpy as np\n",
+ "a = np.array([0, 1, 2, 3])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is how we will use NumPy for now.\n",
+ "\n",
+ "### 2) With PyLab\n",
+ "\n",
+ "PyLab is a Matlab-like interface in IPython to *NumPy*, *matplotlib*, and *SciPy*.\n",
+ "\n",
+ "We'll use PyLab later."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "About NumPy\n",
+ "===\n",
+ "\n",
+ "Getting help\n",
+ "---\n",
+ "\n",
+ "Interactive help on any object:\n",
+ "\n",
+ " >>> help(np.array)\n",
+ " Help on built-in function array in module numpy.core.multiarray:\n",
+ "\n",
+ " array(...)\n",
+ " array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)\n",
+ " ...\n",
+ "\n",
+ "Or if you don't know the name:\n",
+ "\n",
+ " >>> np.lookfor('create array')\n",
+ " Search results for 'create array'\n",
+ " numpy.array\n",
+ " Create an array.\n",
+ " numpy.memmap\n",
+ " Create a memory-map to an array stored in a *binary* file on disk.\n",
+ " ...\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "NumPy arrays\n",
+ "===\n",
+ "\n",
+ "One-dimensional arrays\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([0, 1, 2, 3])\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 3,
+ "text": [
+ "array([0, 1, 2, 3])"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.ndim"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 4,
+ "text": [
+ "1"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 5,
+ "text": [
+ "(4,)"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(a)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 6,
+ "text": [
+ "4"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "NumPy arrays\n",
+ "===\n",
+ "\n",
+ "Multi-dimensional arrays\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = np.array([[0, 1, 2], [3, 4, 5]])\n",
+ "b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 7,
+ "text": [
+ "array([[0, 1, 2],\n",
+ " [3, 4, 5]])"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b.ndim"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 8,
+ "text": [
+ "2"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 9,
+ "text": [
+ "(2, 3)"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(b)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 10,
+ "text": [
+ "2"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "NumPy arrays\n",
+ "===\n",
+ "\n",
+ "Series\n",
+ "---\n",
+ "\n",
+ "Evenly spaced (like the Python `range` builtin):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.arange(1, 9, 2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 11,
+ "text": [
+ "array([1, 3, 5, 7])"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By number of points:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.linspace(0, 1, 6)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 12,
+ "text": [
+ "array([ 0. , 0.2, 0.4, 0.6, 0.8, 1. ])"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.linspace(0, 1, 5, endpoint=False)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 13,
+ "text": [
+ "array([ 0. , 0.2, 0.4, 0.6, 0.8])"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "NumPy arrays\n",
+ "===\n",
+ "\n",
+ "Common arrays (1/2)\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.ones((3, 3))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ "array([[ 1., 1., 1.],\n",
+ " [ 1., 1., 1.],\n",
+ " [ 1., 1., 1.]])"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.zeros((2, 2))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 15,
+ "text": [
+ "array([[ 0., 0.],\n",
+ " [ 0., 0.]])"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "NumPy arrays\n",
+ "===\n",
+ "\n",
+ "Common arrays (2/2)\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.eye(3)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ "array([[ 1., 0., 0.],\n",
+ " [ 0., 1., 0.],\n",
+ " [ 0., 0., 1.]])"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.diag(np.array([1, 2, 3, 4]))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 17,
+ "text": [
+ "array([[1, 0, 0, 0],\n",
+ " [0, 2, 0, 0],\n",
+ " [0, 0, 3, 0],\n",
+ " [0, 0, 0, 4]])"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "NumPy arrays\n",
+ "===\n",
+ "\n",
+ "Random numbers\n",
+ "---\n",
+ "\n",
+ "Random samples from a uniform distribution over $[0, 1)$:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.random.rand(4)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 18,
+ "text": [
+ "array([ 0.69655735, 0.5677686 , 0.9757499 , 0.6363099 ])"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Random samples from the normal distribution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.random.randn(4)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 19,
+ "text": [
+ "array([ 0.36203176, -2.32303807, -0.08126607, 0.41046712])"
+ ]
+ }
+ ],
+ "prompt_number": 19
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Basic data types\n",
+ "===\n",
+ "\n",
+ "Detected from input\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([1, 2, 3])\n",
+ "a.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 20,
+ "text": [
+ "dtype('int64')"
+ ]
+ }
+ ],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = np.array([1., 2., 3.])\n",
+ "b.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 21,
+ "text": [
+ "dtype('float64')"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic data types\n",
+ "===\n",
+ "\n",
+ "Default data type is floating point\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.ones((3, 3))\n",
+ "a.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 22,
+ "text": [
+ "dtype('float64')"
+ ]
+ }
+ ],
+ "prompt_number": 22
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Explicitely specifying the data type\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = np.ones((3, 3), dtype=int)\n",
+ "b.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 23,
+ "text": [
+ "dtype('int64')"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic data types\n",
+ "===\n",
+ "\n",
+ "There are many more\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "d = np.array([1+2j, 3+4j, 5+6*1j])\n",
+ "d.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 24,
+ "text": [
+ "dtype('complex128')"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "e = np.array([True, False, False, True])\n",
+ "e.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 25,
+ "text": [
+ "dtype('bool')"
+ ]
+ }
+ ],
+ "prompt_number": 25
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "f = np.array(['Bonjour', 'Hello', 'Hallo',])\n",
+ "f.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 26,
+ "text": [
+ "dtype('S7')"
+ ]
+ }
+ ],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "(The `S7` data type is for strings containing $\\leq 7$ characters.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Indexing and slicing\n",
+ "===\n",
+ "\n",
+ "Indexing on the first dimension\n",
+ "---\n",
+ "\n",
+ "Accessing and assigning to array items is similar to Python lists."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.arange(10)\n",
+ "a[0], a[2], a[-1]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 27,
+ "text": [
+ "(0, 2, 9)"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = np.diag(np.arange(3))\n",
+ "b[0], b[2], b[-1]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 28,
+ "text": [
+ "(array([0, 0, 0]), array([0, 0, 2]), array([0, 0, 2]))"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Indexing and slicing\n",
+ "===\n",
+ "\n",
+ "Multidimensional indexing\n",
+ "---\n",
+ "\n",
+ "Indexes can be tuples of integers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.diag(np.arange(3))\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 29,
+ "text": [
+ "array([[0, 0, 0],\n",
+ " [0, 1, 0],\n",
+ " [0, 0, 2]])"
+ ]
+ }
+ ],
+ "prompt_number": 29
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[1, 1]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 30,
+ "text": [
+ "1"
+ ]
+ }
+ ],
+ "prompt_number": 30
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[2, 1] = 10\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 31,
+ "text": [
+ "array([[ 0, 0, 0],\n",
+ " [ 0, 1, 0],\n",
+ " [ 0, 10, 2]])"
+ ]
+ }
+ ],
+ "prompt_number": 31
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Indexing and slicing\n",
+ "===\n",
+ "\n",
+ "Slicing\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.arange(10)\n",
+ "a[2:9:3]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 32,
+ "text": [
+ "array([2, 5, 8])"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[:4]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 33,
+ "text": [
+ "array([0, 1, 2, 3])"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By default, start is 0, end is the last and step is 1:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[::2]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 34,
+ "text": [
+ "array([0, 2, 4, 6, 8])"
+ ]
+ }
+ ],
+ "prompt_number": 34
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[3:]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 35,
+ "text": [
+ "array([3, 4, 5, 6, 7, 8, 9])"
+ ]
+ }
+ ],
+ "prompt_number": 35
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Indexing and slicing\n",
+ "===\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Copies and views\n",
+ "===\n",
+ "\n",
+ "* Slicing creates a *view*, not a copy.\n",
+ "* Modifying a view also modifies the original.\n",
+ "* You can force a copy with `.copy()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.arange(10)\n",
+ "b = a[::2]\n",
+ "a, b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 37,
+ "text": [
+ "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0, 2, 4, 6, 8]))"
+ ]
+ }
+ ],
+ "prompt_number": 37
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b[0] = 12\n",
+ "a, b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 38,
+ "text": [
+ "(array([12, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([12, 2, 4, 6, 8]))"
+ ]
+ }
+ ],
+ "prompt_number": 38
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.arange(10)\n",
+ "b = a[::2].copy()\n",
+ "b[0] = 12\n",
+ "a, b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 39,
+ "text": [
+ "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([12, 2, 4, 6, 8]))"
+ ]
+ }
+ ],
+ "prompt_number": 39
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Fancy indexing\n",
+ "===\n",
+ "\n",
+ "Indexing with integer arrays\n",
+ "---\n",
+ "\n",
+ "* Boolean and integer arrays are also called *masks*.\n",
+ "* Indexing with a mask creates a copy, not a view."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.random.seed(3)\n",
+ "a = np.random.random_integers(0, 20, 15)\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 40,
+ "text": [
+ "array([10, 3, 8, 0, 19, 10, 11, 9, 10, 6, 0, 20, 12, 7, 14])"
+ ]
+ }
+ ],
+ "prompt_number": 40
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[[2, 3, 6, 11]]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 41,
+ "text": [
+ "array([ 8, 0, 11, 20])"
+ ]
+ }
+ ],
+ "prompt_number": 41
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Fancy indexing\n",
+ "===\n",
+ "\n",
+ "Indexing with boolean arrays\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 42,
+ "text": [
+ "array([10, 3, 8, 0, 19, 10, 11, 9, 10, 6, 0, 20, 12, 7, 14])"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a % 3 == 0"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 43,
+ "text": [
+ "array([False, True, False, True, False, False, False, True, False,\n",
+ " True, True, False, True, False, False], dtype=bool)"
+ ]
+ }
+ ],
+ "prompt_number": 43
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[a % 3 == 0]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 44,
+ "text": [
+ "array([ 3, 0, 9, 6, 0, 12])"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Fancy indexing\n",
+ "===\n",
+ "\n",
+ "Assignment using masks\n",
+ "---\n",
+ "\n",
+ "We can assign a new value to a sub-array by indexing with a mask."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 45,
+ "text": [
+ "array([10, 3, 8, 0, 19, 10, 11, 9, 10, 6, 0, 20, 12, 7, 14])"
+ ]
+ }
+ ],
+ "prompt_number": 45
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[a % 3 == 0] = -1\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 46,
+ "text": [
+ "array([10, -1, 8, -1, 19, 10, 11, -1, 10, -1, -1, 20, -1, 7, 14])"
+ ]
+ }
+ ],
+ "prompt_number": 46
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[[9, 7]] = -2\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 47,
+ "text": [
+ "array([10, -1, 8, -1, 19, 10, 11, -2, 10, -2, -1, 20, -1, 7, 14])"
+ ]
+ }
+ ],
+ "prompt_number": 47
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Fancy indexing\n",
+ "===\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "With scalars\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([1, 2, 3, 4])\n",
+ "a + 1"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 49,
+ "text": [
+ "array([2, 3, 4, 5])"
+ ]
+ }
+ ],
+ "prompt_number": 49
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "2**a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 50,
+ "text": [
+ "array([ 2, 4, 8, 16])"
+ ]
+ }
+ ],
+ "prompt_number": 50
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "All arithmetic operates elementwise\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = np.ones(4) + 1\n",
+ "a, b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 52,
+ "text": [
+ "(array([1, 2, 3, 4]), array([ 2., 2., 2., 2.]))"
+ ]
+ }
+ ],
+ "prompt_number": 52
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a - b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 53,
+ "text": [
+ "array([-1., 0., 1., 2.])"
+ ]
+ }
+ ],
+ "prompt_number": 53
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a * b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 54,
+ "text": [
+ "array([ 2., 4., 6., 8.])"
+ ]
+ }
+ ],
+ "prompt_number": 54
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "j = np.arange(5)\n",
+ "2**(j + 1) - j"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 60,
+ "text": [
+ "array([ 2, 3, 6, 13, 28])"
+ ]
+ }
+ ],
+ "prompt_number": 60
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "Array multiplication $\\neq$ matrix multiplication!\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c = np.ones((3, 3))\n",
+ "c * c"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 61,
+ "text": [
+ "array([[ 1., 1., 1.],\n",
+ " [ 1., 1., 1.],\n",
+ " [ 1., 1., 1.]])"
+ ]
+ }
+ ],
+ "prompt_number": 61
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c.dot(c)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 62,
+ "text": [
+ "array([[ 3., 3., 3.],\n",
+ " [ 3., 3., 3.],\n",
+ " [ 3., 3., 3.]])"
+ ]
+ }
+ ],
+ "prompt_number": 62
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "Comparisons\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([1, 2, 3, 4])\n",
+ "b = np.array([4, 2, 2, 4])\n",
+ "a == b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 63,
+ "text": [
+ "array([False, True, False, True], dtype=bool)"
+ ]
+ }
+ ],
+ "prompt_number": 63
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a > b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 64,
+ "text": [
+ "array([False, False, True, False], dtype=bool)"
+ ]
+ }
+ ],
+ "prompt_number": 64
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "Logical operations\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([1, 1, 0, 0], dtype=bool)\n",
+ "b = np.array([1, 0, 1, 0], dtype=bool)\n",
+ "np.logical_or(a, b)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 65,
+ "text": [
+ "array([ True, True, True, False], dtype=bool)"
+ ]
+ }
+ ],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.logical_and(a, b)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 67,
+ "text": [
+ "array([ True, False, False, False], dtype=bool)"
+ ]
+ }
+ ],
+ "prompt_number": 67
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "Shape mismatches\n",
+ "---\n",
+ "\n",
+ "Elementwise operations work on arrays with the same shape:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.arange(4) + np.array([1, 2])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "operands could not be broadcast together with shapes (4) (2) ",
+ "output_type": "pyerr",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m<ipython-input-68-590d12522f07>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4) (2) "
+ ]
+ }
+ ],
+ "prompt_number": 68
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Broadcasting? We\u2019ll return to that later."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Elementwise operations\n",
+ "===\n",
+ "\n",
+ "Transposition\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.triu(np.ones((3, 3)), 1)\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 69,
+ "text": [
+ "array([[ 0., 1., 1.],\n",
+ " [ 0., 0., 1.],\n",
+ " [ 0., 0., 0.]])"
+ ]
+ }
+ ],
+ "prompt_number": 69
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.T"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 70,
+ "text": [
+ "array([[ 0., 0., 0.],\n",
+ " [ 1., 0., 0.],\n",
+ " [ 1., 1., 0.]])"
+ ]
+ }
+ ],
+ "prompt_number": 70
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Basic reductions\n",
+ "===\n",
+ "\n",
+ "Computing sums\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x = np.array([1, 2, 3, 4])\n",
+ "np.sum(x)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 71,
+ "text": [
+ "10"
+ ]
+ }
+ ],
+ "prompt_number": 71
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.sum()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 72,
+ "text": [
+ "10"
+ ]
+ }
+ ],
+ "prompt_number": 72
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic reductions\n",
+ "===\n",
+ "\n",
+ "Sum by rows or columns\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x = np.array([[1, 1], [2, 2]])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.sum(axis=0), x.sum(axis=1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 96,
+ "text": [
+ "(array([3, 3]), array([2, 4]))"
+ ]
+ }
+ ],
+ "prompt_number": 96
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic reductions\n",
+ "===\n",
+ "\n",
+ "Axes of higher dimensions\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x = np.random.rand(2, 2, 2)\n",
+ "x"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 77,
+ "text": [
+ "array([[[ 0.64914405, 0.27848728],\n",
+ " [ 0.6762549 , 0.59086282]],\n",
+ "\n",
+ " [[ 0.02398188, 0.55885409],\n",
+ " [ 0.25925245, 0.4151012 ]]])"
+ ]
+ }
+ ],
+ "prompt_number": 77
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.sum(axis=2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 78,
+ "text": [
+ "array([[ 0.92763133, 1.26711772],\n",
+ " [ 0.58283597, 0.67435364]])"
+ ]
+ }
+ ],
+ "prompt_number": 78
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic reductions\n",
+ "===\n",
+ "\n",
+ "Statistics\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x = np.array([1, 2, 3, 1])\n",
+ "y = np.array([[1, 2, 3], [5, 6, 1]])\n",
+ "x.mean()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 79,
+ "text": [
+ "1.75"
+ ]
+ }
+ ],
+ "prompt_number": 79
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.median(x)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 82,
+ "text": [
+ "1.5"
+ ]
+ }
+ ],
+ "prompt_number": 82
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The last axis is `-1`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.median(y, axis=-1)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 83,
+ "text": [
+ "array([ 2., 5.])"
+ ]
+ }
+ ],
+ "prompt_number": 83
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.std()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 84,
+ "text": [
+ "0.82915619758884995"
+ ]
+ }
+ ],
+ "prompt_number": 84
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic reductions\n",
+ "===\n",
+ "\n",
+ "Extrema\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x = np.array([1, 3, 2])\n",
+ "x.min()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 85,
+ "text": [
+ "1"
+ ]
+ }
+ ],
+ "prompt_number": 85
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.max()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 86,
+ "text": [
+ "3"
+ ]
+ }
+ ],
+ "prompt_number": 86
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also get their indices:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.argmin()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 87,
+ "text": [
+ "0"
+ ]
+ }
+ ],
+ "prompt_number": 87
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "x.argmax()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 89,
+ "text": [
+ "1"
+ ]
+ }
+ ],
+ "prompt_number": 89
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Basic reductions\n",
+ "===\n",
+ "\n",
+ "Logical operations\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.all([True, True, False])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 90,
+ "text": [
+ "False"
+ ]
+ }
+ ],
+ "prompt_number": 90
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.any([True, True, False])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 91,
+ "text": [
+ "True"
+ ]
+ }
+ ],
+ "prompt_number": 91
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Can be used with comparisons:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.zeros((100, 100))\n",
+ "np.any(a != 0)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 92,
+ "text": [
+ "False"
+ ]
+ }
+ ],
+ "prompt_number": 92
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([1, 2, 3, 2])\n",
+ "b = np.array([2, 2, 3, 2])\n",
+ "c = np.array([6, 4, 4, 5])\n",
+ "((a <= b) & (b <= c)).all()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 93,
+ "text": [
+ "True"
+ ]
+ }
+ ],
+ "prompt_number": 93
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Broadcasting\n",
+ "===\n",
+ "\n",
+ "- Basic operations on arrays (addition, etc.) are elementwise.\n",
+ "- This works on arrays of the same size.\n",
+ "\n",
+ "**Nevertheless**, it\u2019s also possible to do operations on arrays of different\n",
+ "sizes if NumPy can transform these arrays so that they all have\n",
+ "the same size.\n",
+ "\n",
+ "This conversion is called broadcasting."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Broadcasting\n",
+ "===\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Broadcasting\n",
+ "===\n",
+ "\n",
+ "Let's verify\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.tile(np.arange(0, 40, 10), (3, 1)).T\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 106,
+ "text": [
+ "array([[ 0, 0, 0],\n",
+ " [10, 10, 10],\n",
+ " [20, 20, 20],\n",
+ " [30, 30, 30]])"
+ ]
+ }
+ ],
+ "prompt_number": 106
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = np.array([0, 1, 2])\n",
+ "b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 107,
+ "text": [
+ "array([0, 1, 2])"
+ ]
+ }
+ ],
+ "prompt_number": 107
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a + b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 109,
+ "text": [
+ "array([[ 0, 1, 2],\n",
+ " [10, 11, 12],\n",
+ " [20, 21, 22],\n",
+ " [30, 31, 32]])"
+ ]
+ }
+ ],
+ "prompt_number": 109
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Broadcasting\n",
+ "===\n",
+ "\n",
+ "In assignment\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.ones((4, 5))\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 110,
+ "text": [
+ "array([[ 1., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 1.]])"
+ ]
+ }
+ ],
+ "prompt_number": 110
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[0] = 2\n",
+ "a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 112,
+ "text": [
+ "array([[ 2., 2., 2., 2., 2.],\n",
+ " [ 1., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 1.],\n",
+ " [ 1., 1., 1., 1., 1.]])"
+ ]
+ }
+ ],
+ "prompt_number": 112
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We assigned an array of dimension 0 to an array of dimension 1."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Array shape manipulation\n",
+ "===\n",
+ "\n",
+ "Flattening\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
+ "a.ravel()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 113,
+ "text": [
+ "array([1, 2, 3, 4, 5, 6])"
+ ]
+ }
+ ],
+ "prompt_number": 113
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.T"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 114,
+ "text": [
+ "array([[1, 4],\n",
+ " [2, 5],\n",
+ " [3, 6]])"
+ ]
+ }
+ ],
+ "prompt_number": 114
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.T.ravel()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 115,
+ "text": [
+ "array([1, 4, 2, 5, 3, 6])"
+ ]
+ }
+ ],
+ "prompt_number": 115
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Array shape manipulation\n",
+ "===\n",
+ "\n",
+ "Reshaping\n",
+ "---\n",
+ "\n",
+ "The inverse of flattening: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 117,
+ "text": [
+ "(2, 3)"
+ ]
+ }
+ ],
+ "prompt_number": 117
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a.ravel().reshape((2, 3))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 119,
+ "text": [
+ "array([[1, 2, 3],\n",
+ " [4, 5, 6]])"
+ ]
+ }
+ ],
+ "prompt_number": 119
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create arrays from arrays with different shapes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "np.arange(36).reshape((6, 6))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 120,
+ "text": [
+ "array([[ 0, 1, 2, 3, 4, 5],\n",
+ " [ 6, 7, 8, 9, 10, 11],\n",
+ " [12, 13, 14, 15, 16, 17],\n",
+ " [18, 19, 20, 21, 22, 23],\n",
+ " [24, 25, 26, 27, 28, 29],\n",
+ " [30, 31, 32, 33, 34, 35]])"
+ ]
+ }
+ ],
+ "prompt_number": 120
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Array shape manipulation\n",
+ "===\n",
+ "\n",
+ "Dimension shuffling\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a = np.arange(4*3*2).reshape(4, 3, 2)\n",
+ "a.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 122,
+ "text": [
+ "(4, 3, 2)"
+ ]
+ }
+ ],
+ "prompt_number": 122
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "a[0, 2, 1]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 123,
+ "text": [
+ "5"
+ ]
+ }
+ ],
+ "prompt_number": 123
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b = a.transpose(1, 2, 0)\n",
+ "b.shape"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 124,
+ "text": [
+ "(3, 2, 4)"
+ ]
+ }
+ ],
+ "prompt_number": 124
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "b[2, 1, 0]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 125,
+ "text": [
+ "5"
+ ]
+ }
+ ],
+ "prompt_number": 125
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Loading data files\n",
+ "===\n",
+ "\n",
+ "Text files\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "cat data/populations.txt | head -5"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "# year\thare\tlynx\tcarrot\r\n",
+ "1900\t30e3\t4e3\t48300\r\n",
+ "1901\t47.2e3\t6.1e3\t48200\r\n",
+ "1902\t70.2e3\t9.8e3\t41500\r\n",
+ "1903\t77.4e3\t35.2e3\t38200\r\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "data = np.loadtxt('data/populations.txt')\n",
+ "data[:5]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 5,
+ "text": [
+ "array([[ 1900., 30000., 4000., 48300.],\n",
+ " [ 1901., 47200., 6100., 48200.],\n",
+ " [ 1902., 70200., 9800., 41500.],\n",
+ " [ 1903., 77400., 35200., 38200.],\n",
+ " [ 1904., 36300., 59400., 40600.]])"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Loading data files\n",
+ "===\n",
+ "\n",
+ "Loading images\n",
+ "---\n",
+ "\n",
+ "We can load and show images using matplotlib."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from matplotlib import pyplot as plt\n",
+ "img = plt.imread('images/elephant.png')\n",
+ "img.shape, img.dtype"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 6,
+ "text": [
+ "((200, 300, 3), dtype('float32'))"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* This image is 200 by 300 pixels.\n",
+ "* The third dimension is the color channel (red, green, blue)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Loading data files\n",
+ "===\n",
+ "\n",
+ "Showing images\n",
+ "---\n",
+ "\n",
+ "Let's show the image together with its individual color channels."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plt.figure(figsize=(10,8))\n",
+ "plt.subplot(121).imshow(img)\n",
+ "for i in range(3):\n",
+ " plt.subplot(1, 6, i + 4).imshow(img[:,:,i], cmap=plt.cm.coolwarm)\n",
+ " plt.title(['Red', 'Green', 'Blue'][i])\n",
+ " plt.axis('off')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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SqnBkmcFmFhGhqDzW0tQCahMNnWDQJRE3dbC2QUN3Z351/Tlr5QDSki0pCK0q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JSpGPvMTtSvhS/15Nj8aAwRpRPuaIyhmJQsz1doNUYpSb1YImTAtBAnY6Lr2ZlN0garJNZbOmsR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r6OVzDfKn/gqPfupT7H/4axD/0X/NenadrtzFmoyMmqJforwlHSoQMFo9pE9KUtfQ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DAa0hNaZQN73/XEEJnP53RdzqQbrKU0BoRgebSk2+eNtrooKLSmHyxlabi8vEVIBTGxHzqm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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x7f90a5abc5d0>"
+ ]
+ }
+ ],
+ "prompt_number": 227
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We'll come back to matplotlib."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "Loading data files\n",
+ "===\n",
+ "\n",
+ "NumPy's own format\n",
+ "---\n",
+ "\n",
+ "* NumPy has its own binary format.\n",
+ "* Not portable but with efficient I/O."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "data = np.ones((3, 3))\n",
+ "np.save('pop.npy', data)\n",
+ "np.load('pop.npy')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 228,
+ "text": [
+ "array([[ 1., 1., 1.],\n",
+ " [ 1., 1., 1.],\n",
+ " [ 1., 1., 1.]])"
+ ]
+ }
+ ],
+ "prompt_number": 228
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Todo\n",
+ "===\n",
+ "\n",
+ "* Exercises"
+ ]
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/python-intro/README b/python-intro/README
deleted file mode 100644
index db2b357..0000000
--- a/python-intro/README
+++ /dev/null
@@ -1,72 +0,0 @@
-Introduction to Python
-======================
-
-Slides are created as an IPython Notebook (IPython 1.0) and exported to
-reveal.js.
-
-
-Installation in a virtual environment
--------------------------------------
-
-Required IPython version is 1.0 and you can install it from source in a
-virtual environment like this (on Debian Wheezy):
-
- mkvirtualenv --no-site-packages programming-course
-
-We need IPython 1.0 which is not yet released, so we get it from GitHub:
-
- pip install -e git+https://github.com/ipython/ipython#egg=ipython
-
-If IPython 1.0 is released, you can just do this:
-
- pip install ipython
-
-Some additional packages:
-
- pip install pyzmq tornado jinja2 pygments sphinx markdown
- pip install numpy
-
-For matplotlib, we need some development and system packages:
-
- sudo aptitude install libfreetype6-dev libpng12-dev python-cairo \
- python-gtk2 python-gtk2-dev
- mkdir -p $VIRTUAL_ENV/lib/python2.7/dist-packages
- ln -s /usr/lib/python2.7/dist-packages/{glib,gobject,gtk-2.0*,pygtk.pth} \
- $VIRTUAL_ENV/lib/python2.7/dist-packages/
- ln -s /usr/lib/pymodules/python2.7/cairo \
- $VIRTUAL_ENV/lib/python2.7/dist-packages/
-
- pip install matplotlib
- echo "backend : GTKCairo" >> ~/.matplotlib/matplotlibrc
-
-If you want the IPython qtconsole to work, I found the following snippet to
-take PyQT from system packages:
-
- https://gist.github.com/jlesquembre/2042882
-
-We might want to use the same approach for the PyGTK/PyCAIRO stuff above.
-
-
-Editing the slides
-------------------
-
- ipython notebook python-intro.ipynb
-
-Some aditional information on editing slides in the Notebook can be found
-here:
-
-http://www.slideviper.oquanta.info/tutorial/slideshow_tutorial_slides.html
-
-
-Exporting the slides
---------------------
-
-This will create a set of slides to be viewed in a browser:
-
- ipython nbconvert --FilesWriter.build_directory= --format reveal \
- python-intro.ipynb
-
-As a reference for students, you could also create a full HTML dump:
-
- ipython nbconvert --FilesWriter.build_directory= --format full_html \
- python-intro.ipynb
diff --git a/python-intro/python-intro.ipynb b/python-slides.ipynb
similarity index 99%
rename from python-intro/python-intro.ipynb
rename to python-slides.ipynb
index 4a3243b..d43c863 100644
--- a/python-intro/python-intro.ipynb
+++ b/python-slides.ipynb
@@ -7,31 +7,24 @@
"worksheets": [
{
"cells": [
- {
- "cell_type": "heading",
- "level": 1,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "<a><span style=\"font-size: 200%\">Introduction to NumPy</span></a>"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
- "slide_type": "-"
+ "slide_type": "slide"
}
},
"source": [
- "Martijn Vermaat<br>\n",
- "[m.vermaat.hg@lumc.nl](mailto:m.vermaat.hg@lumc.nl)\n",
+ "<span style=\"font-size: 200%\">Introduction to Python</span>\n",
+ "===\n",
+ "\n",
+ "<br>\n",
+ "\n",
+ "[Martijn Vermaat](mailto:m.vermaat.hg@lumc.nl)\n",
+ "\n",
+ "[Department of Human Genetics, Leiden University Medical Center](http://humgen.nl)\n",
"\n",
- "Department of Human Genetics, Leiden University Medical Center<br>\n",
- "[humgen.nl](http://humgen.nl)\n",
+ "Based on: [Python Scientific Lecture Notes](http://scipy-lectures.github.io/)\n",
"\n",
"License: [Creative Commons Attribution 3.0 License (CC-by)](http://creativecommons.org/licenses/by/3.0)"
]
@@ -3027,7 +3020,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "for line in open('short_file.txt'):\n",
+ "for line in open('data/short_file.txt'):\n",
" print line"
],
"language": "python",
--
GitLab