From 469efe70e161615b43666638d1865abcfa0442d5 Mon Sep 17 00:00:00 2001 From: "W. Trevor King" Date: Mon, 10 Mar 2014 20:15:18 -0700 Subject: [PATCH 1/1] novice: Copy-paste from origin/master I couldn't find a way to merge these without clobbering a bunch of other files, so this is just a copy-paste of the current status as of e4b84bd (Merge pull request #359 from swcarpentry/ahmadia/dhaine_r_ref, 2014-03-06). --- novice/python/.gitignore | 6 + novice/python/01-numpy.ipynb | 1534 +++++++++++++ novice/python/02-func.ipynb | 1691 ++++++++++++++ novice/python/03-loop.ipynb | 910 ++++++++ novice/python/04-cond.ipynb | 1413 ++++++++++++ novice/python/05-defensive.ipynb | 963 ++++++++ novice/python/06-cmdline.ipynb | 1275 +++++++++++ novice/python/README.txt | 9 + novice/python/argv-list.py | 2 + novice/python/count-stdin.py | 7 + novice/python/gen-inflammation.py | 19 + .../python/img/ave-inflammation-over-time.png | Bin 0 -> 9673 bytes novice/python/img/color-cube.png | Bin 0 -> 28840 bytes novice/python/img/combined-inflammation-2.png | Bin 0 -> 18161 bytes 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novice/python/rectangle.py | 3 + novice/python/small-01.csv | 2 + novice/python/small-02.csv | 2 + novice/python/small-03.csv | 2 + novice/python/spatial-intro.ipynb | 399 ++++ novice/python/swc_bc_coords.csv | 113 + novice/python/sys-version.py | 2 + 75 files changed, 24154 insertions(+) create mode 100644 novice/python/.gitignore create mode 100644 novice/python/01-numpy.ipynb create mode 100644 novice/python/02-func.ipynb create mode 100644 novice/python/03-loop.ipynb create mode 100644 novice/python/04-cond.ipynb create mode 100644 novice/python/05-defensive.ipynb create mode 100644 novice/python/06-cmdline.ipynb create mode 100644 novice/python/README.txt create mode 100644 novice/python/argv-list.py create mode 100644 novice/python/count-stdin.py create mode 100644 novice/python/gen-inflammation.py create mode 100644 novice/python/img/ave-inflammation-over-time.png create mode 100644 novice/python/img/color-cube.png create mode 100644 novice/python/img/combined-inflammation-2.png 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novice/python/inflammation-12.csv create mode 100644 novice/python/readings-01.py create mode 100644 novice/python/readings-02.py create mode 100644 novice/python/readings-03.py create mode 100644 novice/python/readings-04.py create mode 100644 novice/python/readings-05.py create mode 100644 novice/python/readings-06.py create mode 100644 novice/python/rectangle.py create mode 100644 novice/python/small-01.csv create mode 100644 novice/python/small-02.csv create mode 100644 novice/python/small-03.csv create mode 100644 novice/python/spatial-intro.ipynb create mode 100644 novice/python/swc_bc_coords.csv create mode 100644 novice/python/sys-version.py diff --git a/novice/python/.gitignore b/novice/python/.gitignore new file mode 100644 index 0000000..759975e --- /dev/null +++ b/novice/python/.gitignore @@ -0,0 +1,6 @@ +01-numpy.md +02-func.md +03-loop.md +04-cond.md +05-defensive.md +06-cmdline.md diff --git a/novice/python/01-numpy.ipynb b/novice/python/01-numpy.ipynb new file mode 100644 index 0000000..687d29c --- /dev/null +++ b/novice/python/01-numpy.ipynb @@ -0,0 +1,1534 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 2, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Analyzing Patient Data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We are studying inflammation in patients who have been given a new treatment for arthritis,\n", + "and need to analyze the first dozen data sets.\n", + "The data sets are stored in [comma-separated values](../../gloss.html#csv) (CSV) format:\n", + "each row holds information for a single patient,\n", + "and the columns represent successive days.\n", + "The first few rows of our first file look like this:\n", + "\n", + " 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n", + " 0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n", + " 0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n", + " 0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n", + " 0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We want to:\n", + "\n", + "* load that data into memory,\n", + "* calculate the average inflammation per day across all patients, and\n", + "* plot the result.\n", + "\n", + "To do all that, we'll have to learn a little bit about programming." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "objectives" + ] + }, + "source": [ + "#### Objectives\n", + "\n", + "* Explain what a library is, and what libraries are used for.\n", + "* Load a Python library and use the things it contains.\n", + "* Read tabular data from a file into a program.\n", + "* Assign values to variables.\n", + "* Select individual values and subsections from data.\n", + "* Perform operations on arrays of data.\n", + "* Display simple graphs." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Loading Data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Words are useful,\n", + "but what's more useful are the sentences and stories we use them to build.\n", + "Similarly,\n", + "while a lot of powerful tools are built into languages like Python,\n", + "even more lives in the [libraries](../../gloss.html#library) they are used to build.\n", + "\n", + "In order to load our inflammation data,\n", + "we need to [import](../../gloss.html#import) a library called NumPy\n", + "that knows how to operate on matrices:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Importing a library is like getting a piece of lab equipment out of a storage locker\n", + "and setting it up on the bench.\n", + "Once it's done,\n", + "we can ask the library to read our data file for us:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 2, + "text": [ + "array([[ 0., 0., 1., ..., 3., 0., 0.],\n", + " [ 0., 1., 2., ..., 1., 0., 1.],\n", + " [ 0., 1., 1., ..., 2., 1., 1.],\n", + " ..., \n", + " [ 0., 1., 1., ..., 1., 1., 1.],\n", + " [ 0., 0., 0., ..., 0., 2., 0.],\n", + " [ 0., 0., 1., ..., 1., 1., 0.]])" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The expression `numpy.loadtxt(...)` is a [function call](../../gloss.html#function-call)\n", + "that asks Python to run the function `loadtxt` that belongs to the `numpy` library.\n", + "This [dotted notation](../../gloss.html#dotted-notation) is used everywhere in Python\n", + "to refer to the parts of things as `whole.part`.\n", + "\n", + "`numpy.loadtxt` has two [parameters](../../gloss.html#parameter):\n", + "the name of the file we want to read,\n", + "and the [delimiter](../../gloss.html#delimiter) that separates values on a line.\n", + "These both need to be character strings (or [strings](../../gloss.html#string) for short),\n", + "so we put them in quotes.\n", + "\n", + "When we are finished typing and press Shift+Enter,\n", + "the notebook runs our command.\n", + "Since we haven't told it to do anything else with the function's output,\n", + "the notebook displays it.\n", + "In this case,\n", + "that output is the data we just loaded.\n", + "By default,\n", + "only a few rows and columns are shown\n", + "(with `...` displayed to mark missing data).\n", + "To save space,\n", + "Python displays numbers as `1.` instead of `1.0`\n", + "when there's nothing interesting after the decimal point." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Our call to `numpy.loadtxt` read our file,\n", + "but didn't save the data in memory.\n", + "To do that,\n", + "we need to [assign](../../gloss.html#assignment) the array to a [variable](../../gloss.html#variable).\n", + "A variable is just a name for a value,\n", + "such as `x`, `current_temperature`, or `subject_id`.\n", + "We can create a new variable simply by assigning a value to it using `=`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "weight_kg = 55" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Once a variable has a value, we can print it:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print weight_kg" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "55\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "and do arithmetic with it:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'weight in pounds:', 2.2 * weight_kg" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "weight in pounds: 121.0\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can also change a variable's value by assigning it a new one:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "weight_kg = 57.5\n", + "print 'weight in kilograms is now:', weight_kg" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "weight in kilograms is now: 57.5\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "As the example above shows,\n", + "we can print several things at once by separating them with commas.\n", + "\n", + "If we imagine the variable as a sticky note with a name written on it,\n", + "assignment is like putting the sticky note on a particular value:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This means that assigning a value to one variable does *not* change the values of other variables.\n", + "For example,\n", + "let's store the subject's weight in pounds in a variable:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "weight_lb = 2.2 * weight_kg\n", + "print 'weight in kilograms:', weight_kg, 'and in pounds:', weight_lb" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "weight in kilograms: 57.5 and in pounds: 126.5\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Creating" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "and then change `weight_kg`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "weight_kg = 100.0\n", + "print 'weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "weight in kilograms is now: 100.0 and weight in pounds is still: 126.5\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Updating" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Since `weight_lb` doesn't \"remember\" where its value came from,\n", + "it isn't automatically updated when `weight_kg` changes.\n", + "This is different from the way spreadsheets work.\n", + "\n", + "Now that we know how to assign things to variables,\n", + "let's re-run `numpy.loadtxt` and save its result:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This statement doesn't produce any output because assignment doesn't display anything.\n", + "If we want to check that our data has been loaded,\n", + "we can print the variable's value:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 0. 0. 1. ..., 3. 0. 0.]\n", + " [ 0. 1. 2. ..., 1. 0. 1.]\n", + " [ 0. 1. 1. ..., 2. 1. 1.]\n", + " ..., \n", + " [ 0. 1. 1. ..., 1. 1. 1.]\n", + " [ 0. 0. 0. ..., 0. 2. 0.]\n", + " [ 0. 0. 1. ..., 1. 1. 0.]]\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Draw diagrams showing what variables refer to what values after each statement in the following program:\n", + "\n", + " ~~~python\n", + " mass = 47.5\n", + " age = 122\n", + " mass = mass * 2.0\n", + " age = age - 20\n", + " ~~~\n", + "\n", + "1. What does the following program print out?\n", + " ~~~python\n", + " first, second = 'Grace', 'Hopper'\n", + " third, fourth = second, first\n", + " print third, fourth\n", + " ~~~" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Manipulating Data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Now that our data is in memory,\n", + "we can start doing things with it.\n", + "First,\n", + "let's ask what [type](../../gloss.html#data-type) of thing `data` refers to:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print type(data)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The output tells us that `data` currently refers to an N-dimensional array created by the NumPy library.\n", + "We can see what its [shape](../../gloss.html#shape) is like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data.shape" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(60, 40)\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This tells us that `data` has 60 rows and 40 columns.\n", + "`data.shape` is a [member](../../gloss.html#member) of `data`,\n", + "i.e.,\n", + "a value that is stored as part of a larger value.\n", + "We use the same dotted notation for the members of values\n", + "that we use for the functions in libraries\n", + "because they have the same part-and-whole relationship." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "If we want to get a single value from the matrix,\n", + "we must provide an [index](../../gloss.html#index) in square brackets,\n", + "just as we do in math:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'first value in data:', data[0, 0]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "first value in data: 0.0\n" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'middle value in data:', data[30, 20]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "middle value in data: 13.0\n" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The expression `data[30, 20]` may not surprise you,\n", + "but `data[0, 0]` might.\n", + "Programming languages like Fortran and MATLAB start counting at 1,\n", + "because that's what human beings have done for thousands of years.\n", + "Languages in the C family (including C++, Java, Perl, and Python) count from 0\n", + "because that's simpler for computers to do.\n", + "As a result,\n", + "if we have an M×N array in Python,\n", + "its indices go from 0 to M-1 on the first axis\n", + "and 0 to N-1 on the second.\n", + "It takes a bit of getting used to,\n", + "but one way to remember the rule is that\n", + "the index is how many steps we have to take from the start to get the item we want.\n", + "\n", + "> #### In the Corner\n", + ">\n", + "> What may also surprise you is that when Python displays an array,\n", + "> it shows the element with index `[0, 0]` in the upper left corner\n", + "> rather than the lower left.\n", + "> This is consistent with the way mathematicians draw matrices,\n", + "> but different from the Cartesian coordinates.\n", + "> The indices are (row, column) instead of (column, row) for the same reason." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "An index like `[30, 20]` selects a single element of an array,\n", + "but we can select whole sections as well.\n", + "For example,\n", + "we can select the first ten days (columns) of values\n", + "for the first four (rows) patients like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data[0:4, 0:10]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 0. 0. 1. 3. 1. 2. 4. 7. 8. 3.]\n", + " [ 0. 1. 2. 1. 2. 1. 3. 2. 2. 6.]\n", + " [ 0. 1. 1. 3. 3. 2. 6. 2. 5. 9.]\n", + " [ 0. 0. 2. 0. 4. 2. 2. 1. 6. 7.]]\n" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The [slice](../../gloss.html#slice) `0:4` means,\n", + "\"Start at index 0 and go up to, but not including, index 4.\"\n", + "Again,\n", + "the up-to-but-not-including takes a bit of getting used to,\n", + "but the rule is that the difference between the upper and lower bounds is the number of values in the slice.\n", + "\n", + "We don't have to start slices at 0:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data[5:10, 0:10]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 0. 0. 1. 2. 2. 4. 2. 1. 6. 4.]\n", + " [ 0. 0. 2. 2. 4. 2. 2. 5. 5. 8.]\n", + " [ 0. 0. 1. 2. 3. 1. 2. 3. 5. 3.]\n", + " [ 0. 0. 0. 3. 1. 5. 6. 5. 5. 8.]\n", + " [ 0. 1. 1. 2. 1. 3. 5. 3. 5. 8.]]\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "and we don't have to take all the values in the slice---if we provide a [stride](../../gloss.html#stride),\n", + "Python takes values spaced that far apart:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data[0:10:3, 0:10:2]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 0. 1. 1. 4. 8.]\n", + " [ 0. 2. 4. 2. 6.]\n", + " [ 0. 2. 4. 2. 5.]\n", + " [ 0. 1. 1. 5. 5.]]\n" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Here,\n", + "we have taken rows 0, 3, 6, and 9,\n", + "and columns 0, 2, 4, 6, and 8.\n", + "(Again, we always include the lower bound,\n", + "but stop when we reach or cross the upper bound.)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We also don't have to include the upper and lower bound on the slice.\n", + "If we don't include the lower bound,\n", + "Python uses 0 by default;\n", + "if we don't include the upper,\n", + "the slice runs to the end of the axis,\n", + "and if we don't include either\n", + "(i.e., if we just use ':' on its own),\n", + "the slice includes everything:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "small = data[:3, 36:]\n", + "print 'small is:'\n", + "print small" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "small is:\n", + "[[ 2. 3. 0. 0.]\n", + " [ 1. 1. 0. 1.]\n", + " [ 2. 2. 1. 1.]]\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Arrays also know how to perform common mathematical operations on their values.\n", + "If we want to find the average inflammation for all patients on all days,\n", + "for example,\n", + "we can just ask the array for its mean value" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data.mean()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "6.14875\n" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "`mean` is a [method](../../gloss.html#method) of the array,\n", + "i.e.,\n", + "a function that belongs to it\n", + "in the same way that the member `shape` does.\n", + "If variables are nouns, methods are verbs:\n", + "they are what the thing in question knows how to do.\n", + "This is why `data.shape` doesn't need to be called\n", + "(it's just a thing)\n", + "but `data.mean()` does\n", + "(it's an action).\n", + "It is also why we need empty parentheses for `data.mean()`:\n", + "even when we're not passing in any parameters,\n", + "parentheses are how we tell Python to go and do something for us.\n", + "\n", + "NumPy arrays have lots of useful methods:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'maximum inflammation:', data.max()\n", + "print 'minimum inflammation:', data.min()\n", + "print 'standard deviation:', data.std()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "maximum inflammation: 20.0\n", + "minimum inflammation: 0.0\n", + "standard deviation: 4.61383319712\n" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "When analyzing data,\n", + "though,\n", + "we often want to look at partial statistics,\n", + "such as the maximum value per patient\n", + "or the average value per day.\n", + "One way to do this is to select the data we want to create a new temporary array,\n", + "then ask it to do the calculation:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "patient_0 = data[0, :] # 0 on the first axis, everything on the second\n", + "print 'maximum inflammation for patient 0:', patient_0.max()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "maximum inflammation for patient 0: 18.0\n" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We don't actually need to store the row in a variable of its own.\n", + "Instead, we can combine the selection and the method call:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'maximum inflammation for patient 2:', data[2, :].max()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "maximum inflammation for patient 2: 19.0\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "What if we need the maximum inflammation for *all* patients,\n", + "or the average for each day?\n", + "As the diagram below shows,\n", + "we want to perform the operation across an axis:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Operations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "To support this,\n", + "most array methods allow us to specify the axis we want to work on.\n", + "If we ask for the average across axis 0,\n", + "we get:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data.mean(axis=0)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0. 0.45 1.11666667 1.75 2.43333333 3.15\n", + " 3.8 3.88333333 5.23333333 5.51666667 5.95 5.9\n", + " 8.35 7.73333333 8.36666667 9.5 9.58333333\n", + " 10.63333333 11.56666667 12.35 13.25 11.96666667\n", + " 11.03333333 10.16666667 10. 8.66666667 9.15 7.25\n", + " 7.33333333 6.58333333 6.06666667 5.95 5.11666667 3.6\n", + " 3.3 3.56666667 2.48333333 1.5 1.13333333\n", + " 0.56666667]\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "As a quick check,\n", + "we can ask this array what its shape is:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data.mean(axis=0).shape" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(40,)\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The expression `(40,)` tells us we have an N×1 vector,\n", + "so this is the average inflammation per day for all patients.\n", + "If we average across axis 1, we get:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print data.mean(axis=1)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525\n", + " 6.775 5.8 6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55\n", + " 5.775 5.825 6.175 6.1 5.8 6.425 6.05 6.025 6.175 6.55\n", + " 6.175 6.35 6.725 6.125 7.075 5.725 5.925 6.15 6.075 5.75\n", + " 5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15 5.95 6.275 5.7\n", + " 6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]\n" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "which is the average inflammation per patient across all days." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "A subsection of an array is called a [slice](../../gloss.html#slice).\n", + "We can take slices of character strings as well:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "element = 'oxygen'\n", + "print 'first three characters:', element[0:3]\n", + "print 'last three characters:', element[3:6]" + ], + "language": "python", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "first three characters: oxy\n", + "last three characters: gen\n" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "1. What is the value of `element[:4]`?\n", + " What about `element[4:]`?\n", + " Or `element[:]`?\n", + "\n", + "1. What is `element[-1]`?\n", + " What is `element[-2]`?\n", + " Given those answers,\n", + " explain what `element[1:-1]` does.\n", + "\n", + "1. The expression `element[3:3]` produces an [empty string](../../gloss.html#empty-string),\n", + " i.e., a string that contains no characters.\n", + " If `data` holds our array of patient data,\n", + " what does `data[3:3, 4:4]` produce?\n", + " What about `data[3:3, :]`?" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The mathematician Richard Hamming once said,\n", + "\"The purpose of computing is insight, not numbers,\"\n", + "and the best way to develop insight is often to visualize data.\n", + "Visualization deserves an entire lecture (or course) of its own,\n", + "but we can explore a few features of Python's `matplotlib` here.\n", + "First,\n", + "let's tell the IPython Notebook that we want our plots displayed inline,\n", + "rather than in a separate viewing window:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 27 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The `%` at the start of the line signals that this is a command for the notebook,\n", + "rather than a statement in Python.\n", + "Next,\n", + "we will import the `pyplot` module from `matplotlib`\n", + "and use two of its functions to create and display a heat map of our data:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from matplotlib import pyplot\n", + "pyplot.imshow(data)\n", + "pyplot.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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bRP0M5ONA3lL1Wo3stbzivmVAvFGwacyx0kbz1RjQh65JQj0AJgJC2+yEc4nw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\nG5uKPX329IlFj5SQA/+WobfhlJecRNdEdUy1d7jRhzhFbW6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AOmwpZRbodvOma8xDdyTN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ksaZTFbT3l1+5DX8wfMxARVQOMKmgkkfsiKARYNe8wocamMjyoIKGOHm4M9vuQpm0VAlM8Jz+eE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Mj9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\n", + "text": [ + "" + ] + } + ], + "prompt_number": 28 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Blue regions in this heat map are low values, while red shows high values.\n", + "As we can see,\n", + "inflammation rises and falls over a 40-day period.\n", + "Let's take a look at the average inflammation over time:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ave_inflammation = data.mean(axis=0)\n", + "pyplot.plot(ave_inflammation)\n", + "pyplot.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Here,\n", + "we have put the average per day across all patients in the variable `ave_inflammation`,\n", + "then asked `pyplot` to create and display a line graph of those values.\n", + "The result is roughly a linear rise and fall,\n", + "which is suspicious:\n", + "based on other studies,\n", + "we expect a sharper rise and slower fall.\n", + "Let's have a look at two other statistics:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'maximum inflammation per day'\n", + "pyplot.plot(data.max(axis=0))\n", + "pyplot.show()\n", + "\n", + "print 'minimum inflammation per day'\n", + "pyplot.plot(data.min(axis=0))\n", + "pyplot.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "maximum inflammation per day\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "minimum inflammation per day\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The maximum value rises and falls perfectly smoothly,\n", + "while the minimum seems to be a step function.\n", + "Neither result seems particularly likely,\n", + "so either there's a mistake in our calculations\n", + "or something is wrong with our data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Why do all of our plots stop just short of the upper end of our graph?\n", + " Why are the vertical lines in our plot of the minimum inflammation per day not vertical?\n", + "\n", + "1. Create a plot showing the standard deviation of the inflammation data for each day across all patients." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Wrapping Up" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It's very common to create an [alias](../../gloss.html#alias-library) for a library when importing it\n", + "in order to reduce the amount of typing we have to do.\n", + "Here are our three plots side by side using aliases for `numpy` and `pyplot`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "data = np.loadtxt(fname='inflammation-01.csv', delimiter=',')\n", + "\n", + "plt.figure(figsize=(10.0, 3.0))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "plt.ylabel('average')\n", + "plt.plot(data.mean(0))\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "plt.ylabel('max')\n", + "plt.plot(data.max(0))\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "plt.ylabel('min')\n", + "plt.plot(data.min(0))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nRf/+/XnqqacuhDLhXe727Wp1b/t2eZc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fl5CQkL3z1sz7xxfsNF4Nw95j1s7j1TCcM2bt\nPF4Nw15j1s7j1TDsPWadMl4Nw7MxK4eJCCGEEEIIcRlLlVsIIYQQQghhBTJJFkIIIYQQ4jIySRZC\nCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jIySRZCCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jL/\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 31 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The first two lines re-load our libraries as `np` and `plt`,\n", + "which are the aliases most Python programmers use.\n", + "The call to `loadtxt` reads our data,\n", + "and the rest of the program tells the plotting library\n", + "how large we want the figure to be,\n", + "that we're creating three sub-plots,\n", + "what to draw for each one,\n", + "and that we want a tight layout.\n", + "(Perversely,\n", + "if we leave out that call to `plt.tight_layout()`,\n", + "the graphs will actually be squeezed together more closely.)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Modify the program to display the three plots on top of one another instead of side by side." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "keypoints" + ] + }, + "source": [ + "#### Key Points\n", + "\n", + "* Import a library into a program using `import libraryname`.\n", + "* Use the `numpy` library to work with arrays in Python.\n", + "* Use `variable = value` to assign a value to a variable in order to record it in memory.\n", + "* Variables are created on demand whenever a value is assigned to them.\n", + "* Use `print something` to display the value of `something`.\n", + "* The expression `array.shape` gives the shape of an array.\n", + "* Use `array[x, y]` to select a single element from an array.\n", + "* Array indices start at 0, not 1.\n", + "* Use `low:high` to specify a slice that includes the indices from `low` to `high-1`.\n", + "* All the indexing and slicing that works on arrays also works on strings.\n", + "* Use `# some kind of explanation` to add comments to programs.\n", + "* Use `array.mean()`, `array.max()`, and `array.min()` to calculate simple statistics.\n", + "* Use `array.mean(axis=0)` or `array.mean(axis=1)` to calculate statistics across the specified axis.\n", + "* Use the `pyplot` library from `matplotlib` for creating simple visualizations." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Next Steps\n", + "\n", + "Our work so far has convinced us that something's wrong with our first data file.\n", + "We would like to check the other 11 the same way,\n", + "but typing in the same commands repeatedly is tedious and error-prone.\n", + "Since computers don't get bored (that we know of),\n", + "we should create a way to do a complete analysis with a single command,\n", + "and then figure out how to repeat that step once for each file.\n", + "These operations are the subjects of the next two lessons." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/novice/python/02-func.ipynb b/novice/python/02-func.ipynb new file mode 100644 index 0000000..ba59f15 --- /dev/null +++ b/novice/python/02-func.ipynb @@ -0,0 +1,1691 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 2, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Creating Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "If we only had one data set to analyze,\n", + "it would probably be faster to load the file into a spreadsheet\n", + "and use that to plot some simple statistics.\n", + "But we have twelve files to check,\n", + "and may have more in future.\n", + "In this lesson,\n", + "we'll learn how to write a function\n", + "so that we can repeat several operations with a single command." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "objectives" + ] + }, + "source": [ + "#### Objectives\n", + "\n", + "* Define a function that takes parameters.\n", + "* Return a value from a function.\n", + "* Test and debug a function.\n", + "* Explain what a call stack is, and trace changes to the call stack as functions are called.\n", + "* Set default values for function parameters.\n", + "* Explain why we should divide programs into small, single-purpose functions." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Defining a Function" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Let's start by defining a function `fahr_to_kelvin` that converts temperatures from Fahrenheit to Kelvin:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def fahr_to_kelvin(temp):\n", + " return ((temp - 32) * (5/9)) + 273.15" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The definition opens with the word `def`,\n", + "which is followed by the name of the function\n", + "and a parenthesized list of parameter names.\n", + "The [body](../../gloss.html#function-body) of the function—the\n", + "statements that are executed when it runs—is indented below the definition line,\n", + "typically by four spaces.\n", + "\n", + "When we call the function,\n", + "the values we pass to it are assigned to those variables\n", + "so that we can use them inside the function.\n", + "Inside the function,\n", + "we use a [return statement](../../gloss.html#return-statement) to send a result back to whoever asked for it.\n", + "\n", + "Let's try running our function.\n", + "Calling our own function is no different from calling any other function:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'freezing point of water:', fahr_to_kelvin(32)\n", + "print 'boiling point of water:', fahr_to_kelvin(212)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "freezing point of water: 273.15\n", + "boiling point of water: 273.15\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've successfully called the function that we defined,\n", + "and we have access to the value that we returned.\n", + "Unfortunately, the value returned doesn't look right.\n", + "What went wrong?" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Debugging a Function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Debugging* is when we fix a piece of code\n", + "that we know is working incorrectly.\n", + "In this case, we know that `fahr_to_kelvin`\n", + "is giving us the wrong answer,\n", + "so let's find out why.\n", + "\n", + "For big pieces of code,\n", + "there are tools called *debuggers* that aid in this process.\n", + "\n", + "We just have a short function,\n", + "so we'll debug by choosing some parameter value,\n", + "breaking our function into small parts,\n", + "and printing out the value of each part." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# We'll use temp = 212, the boiling point of water, which was incorrect\n", + "print \"212 - 32:\", 212 - 32" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "212 - 32: 180\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print \"(212 - 32) * (5/9):\", (212 - 32) * (5/9)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(212 - 32) * (5/9): 0\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aha! The problem comes when we multiply by `5/9`.\n", + "This is because `5/9` is actually 0." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "5/9" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "0" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Computers store numbers in one of two ways:\n", + "as [integers](../../gloss.html#integer)\n", + "or as [floating-point numbers](../../gloss.html#float) (or floats).\n", + "The first are the numbers we usually count with;\n", + "the second have fractional parts.\n", + "Addition, subtraction and multiplication work on both as we'd expect,\n", + "but division works differently.\n", + "If we divide one integer by another,\n", + "we get the quotient without the remainder:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print '10/3 is:', 10/3" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "10/3 is: 3\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "If either part of the division is a float,\n", + "on the other hand,\n", + "the computer creates a floating-point answer:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print '10.0/3 is:', 10.0/3" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "10.0/3 is: 3.33333333333\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The computer does this for historical reasons:\n", + "integer operations were much faster on early machines,\n", + "and this behavior is actually useful in a lot of situations.\n", + "It's still confusing,\n", + "though,\n", + "so Python 3 produces a floating-point answer when dividing integers if it needs to.\n", + "We're still using Python 2.7 in this class,\n", + "though,\n", + "so if we want `5/9` to give us the right answer,\n", + "we have to write it as `5.0/9`, `5/9.0`, or some other variation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's fix our `fahr_to_kelvin` function with this new knowledge." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def fahr_to_kelvin(temp):\n", + " return ((temp - 32) * (5.0/9.0)) + 273.15\n", + "\n", + "print 'freezing point of water:', fahr_to_kelvin(32)\n", + "print 'boiling point of water:', fahr_to_kelvin(212)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "freezing point of water: 273.15\n", + "boiling point of water: 373.15\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It works!" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Composing Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've seen how to turn Fahrenheit into Kelvin,\n", + "it's easy to turn Kelvin into Celsius:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def kelvin_to_celsius(temp):\n", + " return temp - 273.15\n", + "\n", + "print 'absolute zero in Celsius:', kelvin_to_celsius(0.0)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "absolute zero in Celsius: -273.15\n" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "What about converting Fahrenheit to Celsius?\n", + "We could write out the formula,\n", + "but we don't need to.\n", + "Instead,\n", + "we can [compose](../../gloss.html#function-composition) the two functions we have already created:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def fahr_to_celsius(temp):\n", + " temp_k = fahr_to_kelvin(temp)\n", + " result = kelvin_to_celsius(temp_k)\n", + " return result\n", + "\n", + "print 'freezing point of water in Celsius:', fahr_to_celsius(32.0)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "freezing point of water in Celsius: 0.0\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This is our first taste of how larger programs are built:\n", + "we define basic operations,\n", + "then combine them in ever-large chunks to get the effect we want.\n", + "Real-life functions will usually be larger than the ones shown here—typically half a dozen to a few dozen lines—but\n", + "they shouldn't ever be much longer than that,\n", + "or the next person who reads it won't be able to understand what's going on." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. \"Adding\" two strings produces their concatention:\n", + " `'a' + 'b'` is `'ab'`.\n", + " Write a function called `fence` that takes two parameters called `original` and `wrapper`\n", + " and returns a new string that has the wrapper character at the beginning and end of the original:\n", + "\n", + " ~~~python\n", + " print fence('name', '*')\n", + " *name*\n", + " ~~~\n", + "\n", + "1. If the variable `s` refers to a string,\n", + " then `s[0]` is the string's first character\n", + " and `s[-1]` is its last.\n", + " Write a function called `outer`\n", + " that returns a string made up of just the first and last characters of its input:\n", + "\n", + " ~~~python\n", + " print outer('helium')\n", + " hm\n", + " ~~~" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "The Call Stack" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Let's take a closer look at what happens when we call `fahr_to_celsius(32.0)`.\n", + "To make things clearer,\n", + "we'll start by putting the initial value 32.0 in a variable\n", + "and store the final result in one as well:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "original = 32.0\n", + "final = fahr_to_celsius(original)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The diagram below shows what memory looks like after the first line has been executed:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "When we call `fahr_to_celsius`,\n", + "Python *doesn't* create the variable `temp` right away.\n", + "Instead,\n", + "it creates something called a [stack frame](../../gloss.html#stack-frame)\n", + "to keep track of the variables defined by `fahr_to_kelvin`.\n", + "Initially,\n", + "this stack frame only holds the value of `temp`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "When we call `fahr_to_kelvin` inside `fahr_to_celsius`,\n", + "Python creates another stack frame to hold `fahr_to_kelvin`'s variables:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It does this because there are now two variables in play called `temp`:\n", + "the parameter to `fahr_to_celsius`,\n", + "and the parameter to `fahr_to_kelvin`.\n", + "Having two variables with the same name in the same part of the program would be ambiguous,\n", + "so Python (and every other modern programming language) creates a new stack frame for each function call\n", + "to keep that function's variables separate from those defined by other functions.\n", + "\n", + "When the call to `fahr_to_kelvin` returns a value,\n", + "Python throws away `fahr_to_kelvin`'s stack frame\n", + "and creates a new variable in the stack frame for `fahr_to_celsius` to hold the temperature in Kelvin:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It then calls `kelvin_to_celsius`,\n", + "which means it creates a stack frame to hold that function's variables:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Once again,\n", + "Python throws away that stack frame when `kelvin_to_celsius` is done\n", + "and creates the variable `result` in the stack frame for `fahr_to_celsius`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Finally,\n", + "when `fahr_to_celsius` is done,\n", + "Python throws away *its* stack frame\n", + "and puts its result in a new variable called `final`\n", + "that lives in the stack frame we started with:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This final stack frame is always there;\n", + "it holds the variables we defined outside the functions in our code.\n", + "What it *doesn't* hold is the variables that were in the various stack frames.\n", + "If we try to get the value of `temp` after our functions have finished running,\n", + "Python tells us that there's no such thing:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'final value of temp after all function calls:', temp" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'temp' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m'final value of temp after all function calls:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'temp' is not defined" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "final value of temp after all function calls:" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Why go to all this trouble?\n", + "Well,\n", + "here's a function called `span` that calculates the difference between\n", + "the mininum and maximum values in an array:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy\n", + "\n", + "def span(a):\n", + " diff = a.max() - a.min()\n", + " return diff\n", + "\n", + "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n", + "print 'span of data', span(data)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " span of data 20.0\n" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Notice that `span` assigns a value to a variable called `diff`.\n", + "We might very well use a variable with the same name to hold data:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "diff = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n", + "print 'span of data:', span(diff)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "span of data: 20.0\n" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We don't expect `diff` to have the value 20.0 after this function call,\n", + "so the name `diff` cannot refer to the same thing inside `span` as it does in the main body of our program.\n", + "And yes,\n", + "we could probably choose a different name than `diff` in our main program in this case,\n", + "but we don't want to have to read every line of NumPy to see what variable names its functions use\n", + "before calling any of those functions,\n", + "just in case they change the values of our variables." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The big idea here is [encapsulation](../../gloss.html#encapsulation),\n", + "and it's the key to writing correct, comprehensible programs.\n", + "A function's job is to turn several operations into one\n", + "so that we can think about a single function call\n", + "instead of a dozen or a hundred statements\n", + "each time we want to do something.\n", + "That only works if functions don't interfere with each other;\n", + "if they do,\n", + "we have to pay attention to the details once again,\n", + "which quickly overloads our short-term memory." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. We previously wrote functions called `fence` and `outer`.\n", + " Draw a diagram showing how the call stack changes when we run the following:\n", + " ~~~python\n", + " print outer(fence('carbon', '+'))\n", + " ~~~" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Testing and Documenting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Once we start putting things in functions so that we can re-use them,\n", + "we need to start testing that those functions are working correctly.\n", + "To see how to do this,\n", + "let's write a function to center a dataset around a particular value:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def center(data, desired):\n", + " return (data - data.mean()) + desired" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We could test this on our actual data,\n", + "but since we don't know what the values ought to be,\n", + "it will be hard to tell if the result was correct.\n", + "Instead,\n", + "let's use NumPy to create a matrix of 0's\n", + "and then center that around 3:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "z = numpy.zeros((2,2))\n", + "print center(z, 3)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 3. 3.]\n", + " [ 3. 3.]]\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "That looks right,\n", + "so let's try `center` on our real data:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n", + "print center(data, 0)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[-6.14875 -6.14875 -5.14875 ..., -3.14875 -6.14875 -6.14875]\n", + " [-6.14875 -5.14875 -4.14875 ..., -5.14875 -6.14875 -5.14875]\n", + " [-6.14875 -5.14875 -5.14875 ..., -4.14875 -5.14875 -5.14875]\n", + " ..., \n", + " [-6.14875 -5.14875 -5.14875 ..., -5.14875 -5.14875 -5.14875]\n", + " [-6.14875 -6.14875 -6.14875 ..., -6.14875 -4.14875 -6.14875]\n", + " [-6.14875 -6.14875 -5.14875 ..., -5.14875 -5.14875 -6.14875]]\n" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It's hard to tell from the default output whether the result is correct,\n", + "but there are a few simple tests that will reassure us:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'original min, mean, and max are:', data.min(), data.mean(), data.max()\n", + "centered = center(data, 0)\n", + "print 'min, mean, and and max of centered data are:', centered.min(), centered.mean(), centered.max()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "original min, mean, and max are: 0.0 6.14875 20.0\n", + "min, mean, and and max of centered data are: -6.14875 -3.49054118942e-15 13.85125\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "That seems almost right:\n", + "the original mean was about 6.1,\n", + "so the lower bound from zero is how about -6.1.\n", + "The mean of the centered data isn't quite zero—we'll explore why not in the challenges—but it's pretty close.\n", + "We can even go further and check that the standard deviation hasn't changed:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'std dev before and after:', data.std(), centered.std()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "std dev before and after: 4.61383319712 4.61383319712\n" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Those values look the same,\n", + "but we probably wouldn't notice if they were different in the sixth decimal place.\n", + "Let's do this instead:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'difference in standard deviations before and after:', data.std() - centered.std()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "difference in standard deviations before and after: -3.5527136788e-15\n" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Again,\n", + "the difference is very small.\n", + "It's still possible that our function is wrong,\n", + "but it seems unlikely enough that we should probably get back to doing our analysis.\n", + "We have one more task first, though:\n", + "we should write some [documentation](../../gloss.html#documentation) for our function\n", + "to remind ourselves later what it's for and how to use it.\n", + "\n", + "The usual way to put documentation in software is to add [comments](../../gloss.html#comment) like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# center(data, desired): return a new array containing the original data centered around the desired value.\n", + "def center(data, desired):\n", + " return (data - data.mean()) + desired" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "There's a better way, though.\n", + "If the first thing in a function is a string that isn't assigned to a variable,\n", + "that string is attached to the function as its documentation:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def center(data, desired):\n", + " '''Return a new array containing the original data centered around the desired value.'''\n", + " return (data - data.mean()) + desired" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This is better because we can now ask Python's built-in help system to show us the documentation for the function:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "help(center)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Help on function center in module __main__:\n", + "\n", + "center(data, desired)\n", + " Return a new array containing the original data centered around the desired value.\n", + "\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "A string like this is called a [docstring](../../gloss.html#docstring).\n", + "We don't need to use triple quotes when we write one,\n", + "but if we do,\n", + "we can break the string across multiple lines:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def center(data, desired):\n", + " '''Return a new array containing the original data centered around the desired value.\n", + " Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n", + " return (data - data.mean()) + desired\n", + "\n", + "help(center)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Help on function center in module __main__:\n", + "\n", + "center(data, desired)\n", + " Return a new array containing the original data centered around the desired value.\n", + " Example: center([1, 2, 3], 0) => [-1, 0, 1]\n", + "\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Write a function called `analyze` that takes a filename as a parameter\n", + " and displays the three graphs produced in the [previous lesson](01-numpy.ipynb),\n", + " i.e.,\n", + " `analyze('inflammation-01.csv')` should produce the graphs already shown,\n", + " while `analyze('inflammation-02.csv')` should produce corresponding graphs for the second data set.\n", + " Be sure to give your function a docstring.\n", + "\n", + "2. Write a function `rescale` that takes an array as input\n", + " and returns a corresponding array of values scaled to lie in the range 0.0 to 1.0.\n", + " (If $L$ and $H$ are the lowest and highest values in the original array,\n", + " then the replacement for a value $v$ should be $(v-L) / (H-L)$.)\n", + " Be sure to give the function a docstring.\n", + "\n", + "3. Run the commands `help(numpy.arange)` and `help(numpy.linspace)`\n", + " to see how to use these functions to generate regularly-spaced values,\n", + " then use those values to test your `rescale` function." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Defining Defaults" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We have passed parameters to functions in two ways:\n", + "directly, as in `span(data)`,\n", + "and by name, as in `numpy.loadtxt(fname='something.csv', delimiter=',')`.\n", + "In fact,\n", + "we can pass the filename to `loadtxt` without the `fname=`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "numpy.loadtxt('inflammation-01.csv', delimiter=',')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 25, + "text": [ + "array([[ 0., 0., 1., ..., 3., 0., 0.],\n", + " [ 0., 1., 2., ..., 1., 0., 1.],\n", + " [ 0., 1., 1., ..., 2., 1., 1.],\n", + " ..., \n", + " [ 0., 1., 1., ..., 1., 1., 1.],\n", + " [ 0., 0., 0., ..., 0., 2., 0.],\n", + " [ 0., 0., 1., ..., 1., 1., 0.]])" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "but we still need to say `delimiter=`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "numpy.loadtxt('inflammation-01.csv', ',')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "data type \",\" not understood", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'inflammation-01.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/gwilson/anaconda/lib/python2.7/site-packages/numpy/lib/npyio.pyc\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m 775\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 776\u001b[0m \u001b[0;31m# Make sure we're dealing with a proper dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 778\u001b[0m \u001b[0mdefconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_getconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: data type \",\" not understood" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "To understand what's going on,\n", + "and make our own functions easier to use,\n", + "let's re-define our `center` function like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def center(data, desired=0.0):\n", + " '''Return a new array containing the original data centered around the desired value (0 by default).\n", + " Example: center([1, 2, 3], 0) => [-1, 0, 1]'''\n", + " return (data - data.mean()) + desired" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 27 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The key change is that the second parameter is now written `desired=0.0` instead of just `desired`.\n", + "If we call the function with two arguments,\n", + "it works as it did before:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test_data = numpy.zeros((2, 2))\n", + "print center(test_data, 3)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[[ 3. 3.]\n", + " [ 3. 3.]]\n" + ] + } + ], + "prompt_number": 28 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "But we can also now call it with just one parameter,\n", + "in which case `desired` is automatically assigned the [default value](../../gloss.html#default-parameter-value) of 0.0:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "more_data = 5 + numpy.zeros((2, 2))\n", + "print 'data before centering:', more_data\n", + "print 'centered data:', center(more_data)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "data before centering: [[ 5. 5.]\n", + " [ 5. 5.]]\n", + "centered data: [[ 0. 0.]\n", + " [ 0. 0.]]\n" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This is handy:\n", + "if we usually want a function to work one way,\n", + "but occasionally need it to do something else,\n", + "we can allow people to pass a parameter when they need to\n", + "but provide a default to make the normal case easier.\n", + "The example below shows how Python matches values to parameters:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def display(a=1, b=2, c=3):\n", + " print 'a:', a, 'b:', b, 'c:', c\n", + "\n", + "print 'no parameters:'\n", + "display()\n", + "print 'one parameter:'\n", + "display(55)\n", + "print 'two parameters:'\n", + "display(55, 66)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "no parameters:\n", + "a: 1 b: 2 c: 3\n", + "one parameter:\n", + "a: 55 b: 2 c: 3\n", + "two parameters:\n", + "a: 55 b: 66 c: 3\n" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "As this example shows,\n", + "parameters are matched up from left to right,\n", + "and any that haven't been given a value explicitly get their default value.\n", + "We can override this behavior by naming the value as we pass it in:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'only setting the value of c'\n", + "display(c=77)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "only setting the value of c\n", + "a: 1 b: 2 c: 77\n" + ] + } + ], + "prompt_number": 31 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "With that in hand,\n", + "let's look at the help for `numpy.loadtxt`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "help(numpy.loadtxt)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Help on function loadtxt in module numpy.lib.npyio:\n", + "\n", + "loadtxt(fname, dtype=, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)\n", + " Load data from a text file.\n", + " \n", + " Each row in the text file must have the same number of values.\n", + " \n", + " Parameters\n", + " ----------\n", + " fname : file or str\n", + " File, filename, or generator to read. If the filename extension is\n", + " ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n", + " generators should return byte strings for Python 3k.\n", + " dtype : data-type, optional\n", + " Data-type of the resulting array; default: float. If this is a\n", + " record data-type, the resulting array will be 1-dimensional, and\n", + " each row will be interpreted as an element of the array. In this\n", + " case, the number of columns used must match the number of fields in\n", + " the data-type.\n", + " comments : str, optional\n", + " The character used to indicate the start of a comment;\n", + " default: '#'.\n", + " delimiter : str, optional\n", + " The string used to separate values. By default, this is any\n", + " whitespace.\n", + " converters : dict, optional\n", + " A dictionary mapping column number to a function that will convert\n", + " that column to a float. E.g., if column 0 is a date string:\n", + " ``converters = {0: datestr2num}``. Converters can also be used to\n", + " provide a default value for missing data (but see also `genfromtxt`):\n", + " ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.\n", + " skiprows : int, optional\n", + " Skip the first `skiprows` lines; default: 0.\n", + " usecols : sequence, optional\n", + " Which columns to read, with 0 being the first. For example,\n", + " ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n", + " The default, None, results in all columns being read.\n", + " unpack : bool, optional\n", + " If True, the returned array is transposed, so that arguments may be\n", + " unpacked using ``x, y, z = loadtxt(...)``. When used with a record\n", + " data-type, arrays are returned for each field. Default is False.\n", + " ndmin : int, optional\n", + " The returned array will have at least `ndmin` dimensions.\n", + " Otherwise mono-dimensional axes will be squeezed.\n", + " Legal values: 0 (default), 1 or 2.\n", + " .. versionadded:: 1.6.0\n", + " \n", + " Returns\n", + " -------\n", + " out : ndarray\n", + " Data read from the text file.\n", + " \n", + " See Also\n", + " --------\n", + " load, fromstring, fromregex\n", + " genfromtxt : Load data with missing values handled as specified.\n", + " scipy.io.loadmat : reads MATLAB data files\n", + " \n", + " Notes\n", + " -----\n", + " This function aims to be a fast reader for simply formatted files. The\n", + " `genfromtxt` function provides more sophisticated handling of, e.g.,\n", + " lines with missing values.\n", + " \n", + " Examples\n", + " --------\n", + " >>> from StringIO import StringIO # StringIO behaves like a file object\n", + " >>> c = StringIO(\"0 1\\n2 3\")\n", + " >>> np.loadtxt(c)\n", + " array([[ 0., 1.],\n", + " [ 2., 3.]])\n", + " \n", + " >>> d = StringIO(\"M 21 72\\nF 35 58\")\n", + " >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n", + " ... 'formats': ('S1', 'i4', 'f4')})\n", + " array([('M', 21, 72.0), ('F', 35, 58.0)],\n", + " dtype=[('gender', '|S1'), ('age', '>> c = StringIO(\"1,0,2\\n3,0,4\")\n", + " >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n", + " >>> x\n", + " array([ 1., 3.])\n", + " >>> y\n", + " array([ 2., 4.])\n", + "\n" + ] + } + ], + "prompt_number": 32 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "There's a lot of information here,\n", + "but the most important part is the first couple of lines:\n", + "\n", + "~~~python\n", + "loadtxt(fname, dtype=, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None,\n", + " unpack=False, ndmin=0)\n", + "~~~\n", + "\n", + "This tells us that `loadtxt` has one parameter called `fname` that doesn't have a default value,\n", + "and eight others that do.\n", + "If we call the function like this:\n", + "\n", + "~~~python\n", + "numpy.loadtxt('inflammation-01.csv', ',')\n", + "~~~\n", + "\n", + "then the filename is assigned to `fname` (which is what we want),\n", + "but the delimiter string `','` is assigned to `dtype` rather than `delimiter`,\n", + "because `dtype` is the second parameter in the list.\n", + "That's why we don't have to provide `fname=` for the filename,\n", + "but *do* have to provide `delimiter=` for the second parameter." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Rewrite the `normalize` function so that it scales data to lie between 0.0 and 1.0 by default,\n", + " but will allow the caller to specify lower and upper bounds if they want.\n", + " Compare your implementation to your neighbor's:\n", + " do the two functions always behave the same way?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "keypoints" + ] + }, + "source": [ + "#### Key Points\n", + "\n", + "* Define a function using `def name(...params...)`.\n", + "* The body of a function must be indented.\n", + "* Call a function using `name(...values...)`.\n", + "* Numbers are stored as integers or floating-point numbers.\n", + "* Integer division produces the whole part of the answer (not the fractional part).\n", + "* Each time a function is called, a new stack frame is created on the [call stack](../../gloss.html#call-stack) to hold its parameters and local variables.\n", + "* Python looks for variables in the current stack frame before looking for them at the top level.\n", + "* Use `help(thing)` to view help for something.\n", + "* Put docstrings in functions to provide help for that function.\n", + "* Specify default values for parameters when defining a function using `name=value` in the parameter list.\n", + "* Parameters can be passed by matching based on name, by position, or by omitting them (in which case the default value is used)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Next Steps\n", + "\n", + "We now have a function called `analyze` to visualize a single data set.\n", + "We could use it to explore all 12 of our current data sets like this:\n", + "\n", + "~~~python\n", + "analyze('inflammation-01.csv')\n", + "analyze('inflammation-02.csv')\n", + "...\n", + "analyze('inflammation-12.csv')\n", + "~~~\n", + "\n", + "but the chances of us typing all 12 filenames correctly aren't great,\n", + "and we'll be even worse off if we get another hundred files.\n", + "What we need is a way to tell Python to do something once for each file,\n", + "and that will be the subject of the next lesson." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/novice/python/03-loop.ipynb b/novice/python/03-loop.ipynb new file mode 100644 index 0000000..86cbf69 --- /dev/null +++ b/novice/python/03-loop.ipynb @@ -0,0 +1,910 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 2, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Analyzing Multiple Data Sets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We have created a function called `analyze` that creates graphs of the minimum, average, and maximum daily inflammation rates\n", + "for a single data set:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "def analyze(filename):\n", + " data = np.loadtxt(fname=filename, delimiter=',')\n", + " \n", + " plt.figure(figsize=(10.0, 3.0))\n", + " \n", + " plt.subplot(1, 3, 1)\n", + " plt.ylabel('average')\n", + " plt.plot(data.mean(0))\n", + " \n", + " plt.subplot(1, 3, 2)\n", + " plt.ylabel('max')\n", + " plt.plot(data.max(0))\n", + " \n", + " plt.subplot(1, 3, 3)\n", + " plt.ylabel('min')\n", + " plt.plot(data.min(0))\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "analyze('inflammation-01.csv')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nRf/+/XnqqacuhDLhXe727Wp1b/t2eZc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fl5CQkL3z1sz7xxfsNF4Nw95j1s7j1TCcM2bt\nPF4Nw15j1s7j1TDsPWadMl4Nw7MxK4eJCCGEEEIIcRlLlVsIIYQQQghhBTJJFkIIIYQQ4jIySRZC\nCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jIySRZCCCGEEOIyMkkWQgghhBDiMjJJFkIIIYQQ4jL/\nD8Se36QdbJ39AAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can use it to analyze other data sets one by one:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "analyze('inflammation-02.csv')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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63OPHaV0uozXJrVtTqTh7qDBg7b2fkZGBChUq5PvczZs3UVPhEil66rNjxwJ/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\nP7YtjX37gFOngKFDRSdh9mraNGDhQtr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+ "text": [ + "" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "but we have a dozen data sets right now and more on the way.\n", + "We want to create plots for all our data sets with a single statement.\n", + "To do that,\n", + "we'll have to teach the computer how to repeat things." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "objectives" + ] + }, + "source": [ + "#### Objectives\n", + "\n", + "* Explain what a for loop does.\n", + "* Correctly write for loops to repeat simple calculations.\n", + "* Trace changes to a loop variable as the loop runs.\n", + "* Trace changes to other variables as they are updated by a for loop.\n", + "* Explain what a list is.\n", + "* Create and index lists of simple values.\n", + "* Use a library function to get a list of filenames that match a simple wildcard pattern.\n", + "* Use a for loop to process multiple files." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "For Loops" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Suppose we want to print each character in the word \"lead\" on a line of its own.\n", + "One way is to use four `print` statements:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def print_characters(element):\n", + " print element[0]\n", + " print element[1]\n", + " print element[2]\n", + " print element[3]\n", + "\n", + "print_characters('lead')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "l\n", + "e\n", + "a\n", + "d\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "but that's a bad approach for two reasons:\n", + "\n", + "1. It doesn't scale:\n", + " if we want to print the characters in a string that's hundreds of letters long,\n", + " we'd be better off just typing them in.\n", + "\n", + "1. It's fragile:\n", + " if we give it a longer string,\n", + " it only prints part of the data,\n", + " and if we give it a shorter one,\n", + " it produces an error because we're asking for characters that don't exist." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print_characters('tin')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "string index out of range", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_characters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mprint_characters\u001b[0;34m(element)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint_characters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lead'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: string index out of range" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "t\n", + "i\n", + "n\n" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a better approach:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def print_characters(element):\n", + " for char in element:\n", + " print char\n", + "\n", + "print_characters('lead')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This is shorter---certainly shorter than something that prints every character in a hundred-letter string---and\n", + "more robust as well:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print_characters('oxygen')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The improved version of `print_characters` uses a [for loop](../../gloss.html#for-loop)\n", + "to repeat an operation---in this case, printing---once for each thing in a collection.\n", + "The general form of a loop is:\n", + "\n", + "
\n",
+      "for variable in collection:\n",
+      "    do things with variable\n",
+      "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can call the [loop variable](../../gloss.html#loop-variable) anything we like,\n", + "but there must be a colon at the end of the line starting the loop,\n", + "and we must indent the body of the loop.\n", + "\n", + "Here's another loop that repeatedly updates a variable:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "length = 0\n", + "for vowel in 'aeiou':\n", + " length = length + 1\n", + "print 'There are', length, 'vowels'" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It's worth tracing the execution of this little program step by step.\n", + "Since there are five characters in `'aeiou'`,\n", + "the statement on line 3 will be executed five times.\n", + "The first time around,\n", + "`length` is zero (the value assigned to it on line 1)\n", + "and `vowel` is `'a'`.\n", + "The statement adds 1 to the old value of `length`,\n", + "producing 1,\n", + "and updates `length` to refer to that new value.\n", + "The next time around,\n", + "`vowel` is `'e'` and `length` is 1,\n", + "so `length` is updated to be 2.\n", + "After three more updates,\n", + "`length` is 5;\n", + "since there is nothing left in `'aeiou'` for Python to process,\n", + "the loop finishes\n", + "and the `print` statement on line 4 tells us our final answer.\n", + "\n", + "Note that a loop variable is just a variable that's being used to record progress in a loop.\n", + "It still exists after the loop is over,\n", + "and we can re-use variables previously defined as loop variables as well:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "letter = 'z'\n", + "for letter in 'abc':\n", + " print letter\n", + "print 'after the loop, letter is', letter" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Note also that finding the length of a string is such a common operation\n", + "that Python actually has a built-in function to do it called `len`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print len('aeiou')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "`len` is much faster than any function we could write ourselves,\n", + "and much easier to read than a two-line loop;\n", + "it will also give us the length of many other things that we haven't met yet,\n", + "so we should always use it when we can." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Python has a built-in function called `range` that creates a list of numbers:\n", + " `range(3)` produces `[0, 1, 2]`, `range(2, 5)` produces `[2, 3, 4]`, and `range(2, 10, 3)` produces `[2, 5, 8]`.\n", + " Using `range`,\n", + " write a function that prints the $N$ natural numbers:\n", + " ~~~python\n", + " print_N(3)\n", + " 1\n", + " 2\n", + " 3\n", + " ~~~\n", + "\n", + "1. Exponentiation is built into Python:\n", + " ~~~python\n", + " print 2**4\n", + " 16\n", + " ~~~\n", + " It also has a function called `pow` that calculates the same value.\n", + " Write a function called `expo` that uses a loop to calculate the same result.\n", + "\n", + "1. Python's strings have methods, just like NumPy's arrays.\n", + " One of these is called `reverse`:\n", + " ~~~python\n", + " print 'Newton'.reverse()\n", + " notweN\n", + " ~~~\n", + " Write a function called `rev` that does the same thing:\n", + " ~~~python\n", + " print rev('Newton')\n", + " notweN\n", + " ~~~\n", + " As always, be sure to include a docstring." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Lists" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Just as a `for` loop is a way to do operations many times,\n", + "a list is a way to store many values.\n", + "Unlike NumPy arrays,\n", + "there are built into the language.\n", + "We create a list by putting values inside square brackets:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "odds = [1, 3, 5, 7]\n", + "print 'odds are:', odds" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We select individual elements from lists by indexing them:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'first and last:', odds[0], odds[-1]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "and if we loop over a list,\n", + "the loop variable is assigned elements one at a time:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for number in odds:\n", + " print number" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "There is one important difference between lists and strings:\n", + "we can change the values in a list,\n", + "but we cannot change the characters in a string.\n", + "For example:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "names = ['Newton', 'Darwing', 'Turing'] # typo in Darwin's name\n", + "print 'names is originally:', names\n", + "names[1] = 'Darwin' # correct the name\n", + "print 'final value of names:', names" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "works, but:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "name = 'Bell'\n", + "name[0] = 'b'" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "does not.\n", + "\n", + "> #### Ch-Ch-Ch-Changes\n", + ">\n", + "> Data that can be changed is called [mutable](../../gloss.html#mutable),\n", + "> while data that cannot be is called [immutable](../../gloss.html#immutable).\n", + "> Like strings,\n", + "> numbers are immutable:\n", + "> there's no way to make the number 0 have the value 1 or vice versa\n", + "> (at least, not in Python—there actually *are* languages that will let people do this,\n", + "> with predictably confusing results).\n", + "> Lists and arrays,\n", + "> on the other hand,\n", + "> are mutable:\n", + "> both can be modified after they have been created.\n", + ">\n", + "> Programs that modify data in place can be harder to understand than ones that don't\n", + "> because readers may have to mentally sum up many lines of code\n", + "> in order to figure out what the value of something actually is.\n", + "> On the other hand,\n", + "> programs that modify data in place instead of creating copies that are almost identical to the original\n", + "> every time they want to make a small change\n", + "> are much more efficient.\n", + "\n", + "There are many ways to change the contents of in lists besides assigning to elements:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "odds.append(11)\n", + "print 'odds after adding a value:', odds" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "del odds[0]\n", + "print 'odds after removing the first element:', odds" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "odds.reverse()\n", + "print 'odds after reversing:', odds" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Write a function called `total` that calculates the sum of the values in a list.\n", + " (Python has a built-in function called `sum` that does this for you.\n", + " Please don't use it for this exercise.)" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Processing Multiple Files" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We now have almost everything we need to process all our data files.\n", + "The only thing that's missing is a library with a rather unpleasant name:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import glob" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The `glob` library contains a single function, also called `glob`,\n", + "that finds files whose names match a pattern.\n", + "We provide those patterns as strings:\n", + "the character `*` matches zero or more characters,\n", + "while `?` matches any one character.\n", + "We can use this to get the names of all the IPython Notebooks we have created so far:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print glob.glob('*.ipynb')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "['01-numpy.ipynb', '02-func.ipynb', '03-loop.ipynb', '04-cond.ipynb', '05-defensive.ipynb', '06-cmdline.ipynb', 'spatial-intro.ipynb']\n" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "or to get the names of all our CSV data files:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print glob.glob('*.csv')" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "['inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv', 'small-01.csv', 'small-02.csv', 'small-03.csv', 'swc_bc_coords.csv']\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "As these examples show,\n", + "`glob.glob`'s result is a list of strings,\n", + "which means we can loop over it\n", + "to do something with each filename in turn.\n", + "In our case,\n", + "the \"something\" we want is our `analyze` function.\n", + "Let's test it by analyzing the first three files in the list:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "filenames = glob.glob('*.csv')\n", + "filenames = filenames[0:3]\n", + "for f in filenames:\n", + " print f\n", + " analyze(f)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "inflammation-01.csv\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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SncRedu9WJaApKbqTeFdB7/081yvbtm1L0aJFeeihhzAMg48++oj09HSCg4Pp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\nRf/+/XnqqacuhDLhXe727Wp1b/t2eZc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63OPHaV0uozXJrVtTqTh7qDBg7b2fkZGBChUq5PvczZs3UVPhEil66rNjxwJ/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\nP7YtjX37gFOngKFDRSdh9mraNGDhQtr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cfz0cPKhe1AbS2QQFKXAl2e12Z7d+y/k5b1rB5RvKwa9yc3rtNfj+e/jkE91J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\noky5nmGoX3rPPGPK5YQo0N13w1dfqR6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+ "text": [ + "" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Sure enough,\n", + "the maxima of these data sets show exactly the same ramp as the first,\n", + "and their minima show the same staircase structure." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Write a function called `analyze_all` that takes a filename pattern as its sole argument\n", + " and runs `analyze` for each file whose name matches the pattern." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Key Points\n", + "\n", + "* Use `for variable in collection` to process the elements of a collection one at a time.\n", + "* The body of a for loop must be indented.\n", + "* Use `len(thing)` to determine the length of something that contains other values.\n", + "* `[value1, value2, value3, ...]` creates a list.\n", + "* Lists are indexed and sliced in the same way as strings and arrays.\n", + "* Lists are mutable (i.e., their values can be changed in place).\n", + "* Strings are immutable (i.e., the characters in them cannot be changed).\n", + "* Use `glob.glob(pattern)` to create a list of files whose names match a pattern.\n", + "* Use `*` in a pattern to match zero or more characters, and `?` to match any single character." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Next Steps\n", + "\n", + "We have now solved our original problem:\n", + "we can analyze any number of data files with a single command.\n", + "More importantly,\n", + "we have met two of the most important ideas in programming:\n", + "\n", + "1. Use functions to make code easier to re-use and easier to understand.\n", + "1. Use lists and arrays to store related values, and loops to repeat operations on them.\n", + "\n", + "We have one more big idea to introduce,\n", + "and then we will be able to go back and create a heat map\n", + "like the one we initially used to display our first data set." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/novice/python/04-cond.ipynb b/novice/python/04-cond.ipynb new file mode 100644 index 0000000..c247d96 --- /dev/null +++ b/novice/python/04-cond.ipynb @@ -0,0 +1,1413 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 2, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Making Choices" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Our previous lessons have shown us how to manipulate data,\n", + "define our own functions,\n", + "and repeat things.\n", + "However,\n", + "the programs we have written so far always do the same things,\n", + "regardless of what data they're given.\n", + "We want programs to make choices based on the values they are manipulating.\n", + "To help us see what decisions they're making,\n", + "we'll start by looking at how computers manipulate images." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "objectives" + ] + }, + "source": [ + "#### Objectives\n", + "\n", + "* Create a simple \"image\" made out of colored blocks.\n", + "* Explain how the RGB model represents colors.\n", + "* Explain the similarities and differences between tuples and lists.\n", + "* Write conditional statements including `if`, `elif`, and `else` branches.\n", + "* Correctly evaluate expressions containing `and` and `or`.\n", + "* Correctly write and interpret code containing nested loops and conditionals.\n", + "* Explain the advantages of putting frequently-modified code in a function." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Image Grids" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Let's start by creating some simple heat maps of our own\n", + "using a library called `ipythonblocks`.\n", + "The first step is to create our own \"image\":" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from ipythonblocks import ImageGrid" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unlike the `import` statements we have seen earlier,\n", + "this one doesn't load the entire `ipythonblocks` library.\n", + "Instead,\n", + "it just loads `ImageGrid` from that library,\n", + "since that's the only thing we need (for now).\n", + "\n", + "Once we have `ImageGrid` loaded,\n", + "we can use it to create a very simple grid of colored cells:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "grid = ImageGrid(5, 3)\n", + "grid.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Just like a NumPy array,\n", + "an `ImageGrid` has some properties that hold information about it:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'grid width:', grid.width\n", + "print 'grid height:', grid.height\n", + "print 'grid lines on:', grid.lines_on" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "grid width: 5\n", + "grid height: 3\n", + "grid lines on: True\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The obvious thing to do with a grid like this is color in its cells,\n", + "but in order to do that,\n", + "we need to know how computers represent color.\n", + "The most common schemes are [RGB](../../gloss.html#rgb),\n", + "which is short for \"red, green, blue\".\n", + "RGB is an [additive color model](../../gloss.html#additive-color-model):\n", + "every shade is some combination of red, green, and blue intensities.\n", + "We can think of these three values as being the axes in a cube:\n", + "\n", + "\"RGB\n", + "\n", + "An RGB color is an example of a multi-part value:\n", + "like a Cartesian coordinate,\n", + "it is one thing with several parts.\n", + "We can represent such a value in Python using a [tuple](../../gloss.html#tuple),\n", + "which we write using parentheses instead of the square brackets used for a list:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "position = (12.3, 45.6)\n", + "print 'position is:', position\n", + "color = (10, 20, 30)\n", + "print 'color is:', color" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "position is: (12.3, 45.6)\n", + "color is: (10, 20, 30)\n" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can select elements from tuples using indexing,\n", + "just as we do with lists and arrays:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'first element of color is:', color[0]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "first element of color is: 10\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Unlike lists and arrays,\n", + "though,\n", + "tuples cannot be changed after they are created—in technical terms,\n", + "they are [immutable](../../gloss.html#immutable):" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "color[0] = 40\n", + "print 'first element of color after change:', color[0]" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcolor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'first element of color after change:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "If a tuple represents an RGB color,\n", + "its red, green, and blue components can take on values between 0 and 255.\n", + "The upper bound may seem odd,\n", + "but it's the largest number that can be represented in an 8-bit byte\n", + "(i.e., 28-1).\n", + "This makes it easy for computers to manipulate colors,\n", + "while providing fine enough gradations to fool most human eyes,\n", + "most of the time.\n", + "\n", + "Let's see what a few RGB colors actually look like:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "row = ImageGrid(8, 1)\n", + "row[0, 0] = (0, 0, 0) # no color => black\n", + "row[1, 0] = (255, 255, 255) # all colors => white\n", + "row[2, 0] = (255, 0, 0) # all red\n", + "row[3, 0] = (0, 255, 0) # all green\n", + "row[4, 0] = (0, 0, 255) # all blue\n", + "row[5, 0] = (255, 255, 0) # red and green\n", + "row[6, 0] = (255, 0, 255) # red and blue\n", + "row[7, 0] = (0, 255, 255) # green and blue\n", + "row.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Simple color values like `(0,255,0)` are easy enough to decipher with a bit of practice,\n", + "but what color is `(214,90,127)`?\n", + "To help us,\n", + "`ipythonblocks` provides a function called `show_color`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from ipythonblocks import show_color\n", + "show_color(214, 90, 127)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "It also provides a table of standard colors:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from ipythonblocks import colors\n", + "c = ImageGrid(3, 2)\n", + "c[0, 0] = colors['Fuchsia']\n", + "c[0, 1] = colors['Salmon']\n", + "c[1, 0] = colors['Orchid']\n", + "c[1, 1] = colors['Lavender']\n", + "c[2, 0] = colors['LimeGreen']\n", + "c[2, 1] = colors['HotPink']\n", + "c.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Fill in the `____` in the code below to create a bar that changes color from dark blue to black.\n", + "\n", + " ~~~python\n", + " bar = ImageGrid(10, 1)\n", + " for x in range(10):\n", + " bar[x, 0] = (0, 0, ____)\n", + " bar.show()\n", + " ~~~\n", + "\n", + "2. Why do computers use red, green, and blue as their primary colors?" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Conditionals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The other thing we need in order to create a heat map of our own\n", + "is a way to pick a color based on a data value.\n", + "The tool Python gives us for doing this is called a [conditional statement](../../gloss.html#conditional-statement),\n", + "and looks like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "num = 37\n", + "if num > 100:\n", + " print 'greater'\n", + "else:\n", + " print 'not greater'\n", + "print 'done'" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "not greater\n", + "done\n" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The second line of this code uses the keyword `if` to tell Python that we want to make a choice.\n", + "If the test that follows it is true,\n", + "the body of the `if`\n", + "(i.e., the lines indented underneath it) are executed.\n", + "If the test is false,\n", + "the body of the `else` is executed instead.\n", + "Only one or the other is ever executed:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Executing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Conditional statements don't have to include an `else`.\n", + "If there isn't one,\n", + "Python simply does nothing if the test is false:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "num = 53\n", + "print 'before conditional...'\n", + "if num > 100:\n", + " print '53 is greater than 100'\n", + "print '...after conditional'" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "before conditional...\n", + "...after conditional\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can also chain several tests together using `elif`,\n", + "which is short for \"else if\".\n", + "This makes it simple to write a function that returns the sign of a number:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def sign(num):\n", + " if num > 0:\n", + " return 1\n", + " elif num == 0:\n", + " return 0\n", + " else:\n", + " return -1\n", + "\n", + "print 'sign of -3:', sign(-3)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "sign of -3: -1\n" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "One important thing to notice the code above is that we use a double equals sign `==` to test for equality\n", + "rather than a single equals sign\n", + "because the latter is used to mean assignment.\n", + "This convention was inherited from C,\n", + "and while many other programming languages work the same way,\n", + "it does take a bit of getting used to...\n", + "\n", + "We can also combine tests using `and` and `or`.\n", + "`and` is only true if both parts are true:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "if (1 > 0) and (-1 > 0):\n", + " print 'both parts are true'\n", + "else:\n", + " print 'one part is not true'" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "one part is not true\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "while `or` is true if either part is true:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "if (1 < 0) or ('left' < 'right'):\n", + " print 'at least one test is true'" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "at least one test is true\n" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "In this case,\n", + "\"either\" means \"either or both\", not \"either one or the other but not both\"." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. `True` and `False` aren't the only values in Python that are true and false.\n", + " In fact, *any* value can be used in an `if` or `elif`.\n", + " After reading and running the code below,\n", + " explain what the rule is for which values are considered true and which are considered false.\n", + " (Note that if the body of a conditional is a single statement, we can write it on the same line as the `if`.)\n", + " \n", + " ~~~python\n", + " if '': print 'empty string is true'\n", + " if 'word': print 'word is true'\n", + " if []: print 'empty list is true'\n", + " if [1, 2, 3]: print 'non-empty list is true'\n", + " if 0: print 'zero is true'\n", + " if 1: print 'one is true'\n", + " ~~~\n", + "\n", + "2. Write a function called `near` that returns `True` if its first parameter is within 10% of its second\n", + " and `False` otherwise.\n", + " Compare your implementation with your partner's:\n", + " do you return the same answer for all possible pairs of numbers?" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Nesting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Another thing to realize is that `if` statements can be combined with loops\n", + "just as easily as they can be combined with functions.\n", + "For example,\n", + "if we want to sum the positive numbers in a list,\n", + "we can write this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "numbers = [-5, 3, 2, -1, 9, 6]\n", + "total = 0\n", + "for n in numbers:\n", + " if n >= 0:\n", + " total = total + n\n", + "print 'sum of positive values:', total" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "sum of positive values: 20\n" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We could equally well calculate the positive and negative sums in a single loop:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pos_total = 0\n", + "neg_total = 0\n", + "for n in numbers:\n", + " if n >= 0:\n", + " pos_total = pos_total + n\n", + " else:\n", + " neg_total = neg_total + n\n", + "print 'negative and positive sums are:', neg_total, pos_total" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "negative and positive sums are: -6 20\n" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can even put one loop inside another:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for consonant in 'bcd':\n", + " for vowel in 'ae':\n", + " print consonant + vowel" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "ba\n", + "be\n", + "ca\n", + "ce\n", + "da\n", + "de\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "As the diagram below shows,\n", + "the [inner loop](../../gloss.html#inner-loop) runs from start to finish\n", + "each time the [outer loop](../../gloss.html#outer-loop) runs once:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can combine nesting and conditionals to create patterns in an image:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "square = ImageGrid(5, 5)\n", + "for x in range(square.width):\n", + " for y in range(square.height):\n", + " if x < y:\n", + " square[x, y] = colors['Fuchsia']\n", + " elif x == y:\n", + " square[x, y] = colors['Olive']\n", + " else:\n", + " square[x, y] = colors['SlateGray']\n", + "square.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This is our first hand-made data visualization:\n", + "the colors show where `x` is less than, equal to, or greater than `y`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Will changing the nesting of the loops in the code above—i.e.,\n", + " wrapping the Y-axis loop around the X-axis loop—change the final image?\n", + " Why or why not?\n", + "\n", + "2. Python (and most other languages in the C family) provides [in-place operators](../../gloss.html#in-place-operator)\n", + " that work like this:\n", + " \n", + " ~~~python\n", + " x = 1 # original value\n", + " x += 1 # add one to x, assigning result back to x\n", + " x *= 3 # multiply x by 3\n", + " print x\n", + " 6\n", + " ~~~\n", + " \n", + " Rewrite the code that sums the positive and negative numbers in a list\n", + " using in-place operators.\n", + " Do you think the result is more or less readable than the original?" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Creating a Heat Map" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The last step is to turn our data into something we can see.\n", + "As in previous lessons,\n", + "the first step is to get the data into memory:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "data = np.loadtxt(fname='inflammation-01.csv', delimiter=',')\n", + "print 'data shape:', data.shape" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "data shape: (60, 40)\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The second is to create an image grid that is the same size as the data:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "width, height = data.shape\n", + "heatmap = ImageGrid(width, height)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 25 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "(The first line of the code above takes advantage of a neat trick:\n", + "we can unpack the values in a tuple by assigning it to\n", + "as many variables as it has entries.)\n", + "\n", + "The third step is to decide *how* we are going to color the cells in the heat map.\n", + "To keep things simple,\n", + "we will use red, green, and blue as our colors,\n", + "and compare data values to the data set's mean.\n", + "Here's the code:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "for x in range(width):\n", + " for y in range(height):\n", + " if data[x, y] < data.mean():\n", + " heatmap[x, y] = colors['Red']\n", + " elif data[x, y] == data.mean():\n", + " heatmap[x, y] = colors['Green']\n", + " else:\n", + " heatmap[x, y] = colors['Blue']\n", + "heatmap.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This may be what we asked for,\n", + "but both the image and the code are hideous:\n", + "\n", + "1. It's too large for us to view the whole thing at once on a small laptop screen.\n", + "2. Our first heatmap had time along the X axis; this seems to have time along the Y axis.\n", + "3. Red against blue is pretty hard on the eyes.\n", + "4. The heatmap only shows two colors because none of the (integer) measurements has exactly the same value as the (fractional) mean.\n", + "5. We are calculating the mean of `data` either once or twice each time we go through the loop. That means that on a 40×60 data set, we are performing the same calculation 2400 times.\n", + "\n", + "Here's how we can improve it:\n", + "\n", + "1. We can give `ImageGrid` an optional parameter `block_size` to set the size of each block.\n", + "2. We can transpose our data before creating the grid.\n", + "3. We can pick better colors (I'm personally fond of orchid, fuchsia, and hot pink).\n", + "4. Instead of checking if values are exactly equal to the mean, we can see if they are close to it.\n", + "5. We can calculate the mean once, before we start our loops, and use that value over and over.\n", + "\n", + "Our modified code looks like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "flipped = data.transpose()\n", + "width, height = flipped.shape\n", + "heatmap = ImageGrid(width, height, block_size=5)\n", + "center = flipped.mean()\n", + "for x in range(width):\n", + " for y in range(height):\n", + " if flipped[x, y] < (0.8 * center):\n", + " heatmap[x, y] = colors['Orchid']\n", + " elif flipped[x, y] > (1.2 * center):\n", + " heatmap[x, y] = colors['HotPink']\n", + " else:\n", + " heatmap[x, y] = colors['Fuchsia']\n", + "heatmap.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "That's a bit better—but now the contrast between the colors isn't great enough.\n", + "And there still aren't very many fuchsia cells:\n", + "we may want to widen the band around the mean that gets that color.\n", + "\n", + "We could rewrite our loop a third time,\n", + "but the right thing to do is to put our code in a function\n", + "so that we can experiment with bands and colors more easily." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def make_heatmap(values, low_color, mid_color, high_color, low_band, high_band, block_size):\n", + " '''Make a 3-colored heatmap from a 2D array of data.'''\n", + " width, height = values.shape\n", + " result = ImageGrid(width, height, block_size=block_size)\n", + " center = values.mean()\n", + " for x in range(width):\n", + " for y in range(height):\n", + " if values[x, y] < low_band * center:\n", + " result[x, y] = low_color\n", + " elif values[x, y] > high_band * center:\n", + " result[x, y] = high_color\n", + " else:\n", + " result[x, y] = mid_color\n", + " return result" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 28 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "To test this function,\n", + "we'll run it with the settings we just used:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "h = make_heatmap(flipped, colors['Orchid'], colors['Fuchsia'], colors['HotPink'], 0.8, 1.2, 5)\n", + "h.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "That seems right,\n", + "so let's widen the band and use more dramatic colors:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "h = make_heatmap(flipped, colors['Gray'], colors['YellowGreen'], colors['SpringGreen'], 0.5, 1.5, 5)\n", + "h.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We'll probably want to experiment a bit more before publishing,\n", + "but writing a function has made experimenting easy.\n", + "We can make it even easier by re-defining our function one more time\n", + "to give the parameters default values.\n", + "While we're at it,\n", + "let's put the low and high bands at the front,\n", + "since they're more likely to change than our color choices:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def make_heatmap(values,\n", + " low_band=0.5, high_band=1.5,\n", + " low_color=colors['Gray'], mid_color=colors['YellowGreen'], high_color=colors['SpringGreen'],\n", + " block_size=5):\n", + " '''Make a 3-colored heatmap from a 2D array of data.\n", + " Default color scheme is gray to green.'''\n", + " width, height = values.shape\n", + " result = ImageGrid(width, height, block_size=block_size)\n", + " center = values.mean()\n", + " for x in range(width):\n", + " for y in range(height):\n", + " if values[x, y] < low_band * center:\n", + " result[x, y] = low_color\n", + " elif values[x, y] > high_band * center:\n", + " result[x, y] = high_color\n", + " else:\n", + " result[x, y] = mid_color\n", + " return result" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 31 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Once default values are added,\n", + "the function's first line is too long to fit comfortably on our screen.\n", + "Rather than breaking it wherever it hits the right edge of the screen,\n", + "we have divided the parameters into logical groups to make it more readable.\n", + "\n", + "Again,\n", + "our first test is to re-run it with the same values as before\n", + "(which we give it in a different order,\n", + "since we've changed the order of parameters):" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "h = make_heatmap(flipped, 0.5, 1.5, colors['Gray'], colors['YellowGreen'], colors['SpringGreen'], 5)\n", + "h.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 32 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can now leave out everything except the data being visualized,\n", + "or provide the data and the bands\n", + "and re-use the default colors and block size:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "h = make_heatmap(flipped, 0.4, 1.6)\n", + "h.show()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "html": [ + "
" + ], + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + } + ], + "prompt_number": 33 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can now explore our data with just a few keystrokes,\n", + "which means we can concentrate on our science\n", + "and not on our programming." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Why did we transpose our data outside our heat map function?\n", + " Why not have the function perform the transpose?\n", + "\n", + "2. Why does the heat map function return the grid rather than displaying it immediately?\n", + " Do you think this is a good or bad design choice?\n", + "\n", + "3. Explain what the overall effect of this code is:\n", + " ~~~\n", + " temp = left\n", + " left = right\n", + " right = temp\n", + " ~~~\n", + " Compare it to:\n", + " ~~~\n", + " left, right = right, left\n", + " ~~~\n", + " Do they always do the same thing?\n", + " Which do you find easier to read?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "keypoints" + ] + }, + "source": [ + "#### Key Points\n", + "\n", + "* Use the `ImageGrid` class from the `ipythonblocks` library to create simple \"images\" made of colored blocks.\n", + "* Specify colors use (red, green, blue) triples, each component of which is an integer in the range 0..255.\n", + "* Use `if condition` to start a conditional statement, `elif condition` to provide additional tests, and `else` to provide a default.\n", + "* The bodies of the branches of conditional statements must be indented.\n", + "* Use `==` to test for equality.\n", + "* `X and Y` is only true if both X and Y are true.\n", + "* `X or Y` is true if either X or Y, or both, are true.\n", + "* Zero, the empty string, and the empty list are considered false; all other numbers, strings, and lists are considered true.\n", + "* Nest loops to operate on multi-dimensional data.\n", + "* Put code whose parameters change frequently in a function, then call it with different parameter values to customize its behavior." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Next Steps\n", + "\n", + "Our final heatmap function is 17 lines long,\n", + "which means that if there's a 95% chance of each line being correct,\n", + "the odds of the whole function being right are only 41%.\n", + "Before we go any further,\n", + "we need to learn how to test whether our code is doing what we want it to do,\n", + "and that will be the subject of the next lesson." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/novice/python/05-defensive.ipynb b/novice/python/05-defensive.ipynb new file mode 100644 index 0000000..7327ee5 --- /dev/null +++ b/novice/python/05-defensive.ipynb @@ -0,0 +1,963 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 2, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Defensive Programming" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Our previous lessons have introduced the basic tools of programming:\n", + "variables and lists,\n", + "file I/O,\n", + "loops,\n", + "conditionals,\n", + "and functions.\n", + "What they *haven't* done is show us how to tell\n", + "whether a program is getting the right answer,\n", + "and how to tell if it's *still* getting the right answer\n", + "as we make changes to it.\n", + "\n", + "To achieve that,\n", + "we need to:\n", + "\n", + "* write programs that check their own operation,\n", + "* write and run tests for widely-used functions, and\n", + "* make sure we know what \"correct\" actually means.\n", + "\n", + "The good news is,\n", + "doing these things will speed up our programming,\n", + "not slow it down.\n", + "As in real carpentry—the kind done with lumber—the time saved\n", + "by measuring carefully before cutting a piece of wood\n", + "is much greater than the time that measuring takes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Objectives\n", + "\n", + "* Explain what an assertion is.\n", + "* Add assertions to programs that correctly check the program's state.\n", + "* Correctly add precondition and postcondition assertions to functions.\n", + "* Explain what test-driven development is, and use it when creating new functions.\n", + "* Explain why variables should be initialized using actual data values rather than arbitrary constants.\n", + "* Debug code containing an error systematically." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Assertions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The first step toward getting the right answers from our programs\n", + "is to assume that mistakes *will* happen\n", + "and to guard against them.\n", + "This is called [defensive programming](../../gloss.html#defensive-programming),\n", + "and the most common way to do it is to add [assertions](../../gloss.html#assertion) to our code\n", + "so that it checks itself as it runs.\n", + "An assertion is simply a statement that something must be true at a certain point in a program.\n", + "When Python sees one,\n", + "it checks that the assertion's condition.\n", + "If it's true,\n", + "Python does nothing,\n", + "but if it's false,\n", + "Python halts the program immediately\n", + "and prints the error message provided.\n", + "For example,\n", + "this piece of code halts as soon as the loop encounters a value that isn't positive:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "numbers = [1.5, 2.3, 0.7, -0.001, 4.4]\n", + "total = 0.0\n", + "for n in numbers:\n", + " assert n >= 0.0, 'Data should only contain positive values'\n", + " total += n\n", + "print 'total is:', total" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Data should only contain positive values", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Data should only contain positive values'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'total is:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: Data should only contain positive values" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Programs like the Firefox browser are full of assertions:\n", + "10-20% of the code they contain\n", + "are there to check that the other 80-90% are working correctly.\n", + "Broadly speaking,\n", + "assertions fall into three categories:\n", + "\n", + "- A [precondition](../../gloss.html#precondition) is something that must be true\n", + " at the start of a function in order for it to work correctly.\n", + "- A [postcondition](../../gloss.html#postcondition) is something that\n", + " the function guarantees is true when it finishes.\n", + "- An [invariant](../../gloss.html#invariant) is something that is always true\n", + " at a particular point inside a piece of code.\n", + "\n", + "For example,\n", + "suppose we are representing rectangles using a tuple of four coordinates `(x0, y0, x1, y1)`.\n", + "In order to do some calculations,\n", + "we need to normalize the rectangle so that it is at the origin\n", + "and 1.0 units long on its longest axis.\n", + "This function does that,\n", + "but checks that its input is correctly formatted and that its result makes sense:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def normalize_rectangle(rect):\n", + " '''Normalizes a rectangle so that it is at the origin and 1.0 units long on its longest axis.'''\n", + " assert len(rect) == 4, 'Rectangles must contain 4 coordinates'\n", + " x0, y0, x1, y1 = rect\n", + " assert x0 < x1, 'Invalid X coordinates'\n", + " assert y0 < y1, 'Invalid Y coordinates'\n", + "\n", + " dx = x1 - x0\n", + " dy = y1 - y0\n", + " if dx > dy:\n", + " scaled = float(dx) / dy\n", + " upper_x, upper_y = 1.0, scaled\n", + " else:\n", + " scaled = float(dx) / dy\n", + " upper_x, upper_y = scaled, 1.0\n", + "\n", + " assert 0 < upper_x <= 1.0, 'Calculated upper X coordinate invalid'\n", + " assert 0 < upper_y <= 1.0, 'Calculated upper Y coordinate invalid'\n", + "\n", + " return (0, 0, upper_x, upper_y)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The preconditions on lines 2, 4, and 5 catch invalid inputs:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print normalize_rectangle( (0.0, 1.0, 2.0) ) # missing the fourth coordinate" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Rectangles must contain 4 coordinates", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mnormalize_rectangle\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;31m# missing the fourth coordinate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[0;34m(rect)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnormalize_rectangle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m'''Normalizes a rectangle so that it is at the origin and 1.0 units long on its longest axis.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Rectangles must contain 4 coordinates'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mx0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Invalid X coordinates'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: Rectangles must contain 4 coordinates" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print normalize_rectangle( (4.0, 2.0, 1.0, 5.0) ) # X axis inverted" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Invalid X coordinates", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mnormalize_rectangle\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;31m# X axis inverted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[0;34m(rect)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Rectangles must contain 4 coordinates'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mx0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Invalid X coordinates'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0my0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Invalid Y coordinates'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: Invalid X coordinates" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The post-conditions help us catch bugs by telling us when our calculations cannot have been correct.\n", + "For example,\n", + "if we normalize a rectangle that is taller than it is wide everything seems OK:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print normalize_rectangle( (0.0, 0.0, 1.0, 5.0) )" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "(0, 0, 0.2, 1.0)\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "but if we normalize one that's wider than it is tall,\n", + "the assertion is triggered:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print normalize_rectangle( (0.0, 0.0, 5.0, 1.0) )" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Calculated upper Y coordinate invalid", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mnormalize_rectangle\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mnormalize_rectangle\u001b[0;34m(rect)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mupper_x\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Calculated upper X coordinate invalid'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mupper_y\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Calculated upper Y coordinate invalid'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupper_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupper_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: Calculated upper Y coordinate invalid" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Re-reading our function,\n", + "we realize that line 10 should divide `dy` by `dx` rather than `dx` by `dy`.\n", + "(You can display line numbers by typing Ctrl-M, then L.)\n", + "If we had left out the assertion at the end of the function,\n", + "we would have created and returned something that had the right shape as a valid answer,\n", + "but wasn't.\n", + "Detecting and debugging that would almost certainly have taken more time in the long run\n", + "than writing the assertion.\n", + "\n", + "But assertions aren't just about catching errors:\n", + "they also help people understand programs.\n", + "Each assertion gives the person reading the program\n", + "a chance to check (consciously or otherwise)\n", + "that their understanding matches what the code is doing.\n", + "\n", + "Most good programmers follow two rules when adding assertions to their code.\n", + "The first is, \"[fail early, fail often](../../rules.html#fail-early-fail-often)\".\n", + "The greater the distance between when and where an error occurs and when it's noticed,\n", + "the harder the error will be to debug,\n", + "so good code catches mistakes as early as possible.\n", + "\n", + "The second rule is, \"[turn bugs into assertions or tests](../../rules.html#turn-bugs-into-assertions-or-tests)\".\n", + "If you made a mistake in a piece of code,\n", + "the odds are good that you have made other mistakes nearby,\n", + "or will make the same mistake (or a related one)\n", + "the next time you change it.\n", + "Writing assertions to check that you haven't [regressed](../../gloss.html#regression)\n", + "(i.e., haven't re-introduced an old problem)\n", + "can save a lot of time in the long run,\n", + "and helps to warn people who are reading the code\n", + "(including your future self)\n", + "that this bit is tricky." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Suppose you are writing a function called `average` that calculates the average of the numbers in a list.\n", + " What pre-conditions and post-conditions would you write for it?\n", + " Compare your answer to your neighbor's:\n", + " can you think of a function that will past your tests but not hers or vice versa?\n", + "\n", + "2. Explain in words what the assertions in this code check,\n", + " and for each one,\n", + " give an example of input that will make that assertion fail.\n", + " \n", + " ~~~\n", + " def running(values):\n", + " assert len(values) > 0\n", + " result = [values[0]]\n", + " for v in values[1:]:\n", + " assert result[-1] >= 0\n", + " result.append(result[-1] + v)\n", + " assert result[-1] >= result[0]\n", + " return result\n", + " ~~~" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Test-Driven Development" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "An assertion checks that something is true at a particular point in the program.\n", + "The next step is to check the overall behavior of a piece of code,\n", + "i.e.,\n", + "to make sure that it produces the right output when it's given a particular input.\n", + "For example,\n", + "suppose we need to find where two or more time series overlap.\n", + "The range of each time series is represented as a pair of numbers,\n", + "which are the time the interval started and ended.\n", + "The output is the largest range that they all include:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "\"Overlapping" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Most novice programmers would solve this problem like this:\n", + "\n", + "1. Write a function `range_overlap`.\n", + "2. Call it interactively on two or three different inputs.\n", + "3. If it produces the wrong answer, fix the function and re-run that test.\n", + "\n", + "This clearly works—after all, thousands of scientists are doing it right now—but\n", + "there's a better way:\n", + "\n", + "1. Write a short function for each test.\n", + "2. Write a `range_overlap` function that should pass those tests.\n", + "3. If `range_overlap` produces any wrong answers, fix it and re-run the test functions.\n", + "\n", + "Writing the tests *before* writing the function they exercise\n", + "is called [test-driven development](../../gloss.html#test-driven-development) (TDD).\n", + "Its advocates believe it produces better code faster because:\n", + "\n", + "1. If people write tests after writing the thing to be tested,\n", + " they are subject to confirmation bias,\n", + " i.e.,\n", + " they subconsciously write tests to show that their code is correct,\n", + " rather than to find errors.\n", + "2. Writing tests helps programmers figure out what the function is actually supposed to do.\n", + "\n", + "Here are three test functions for `range_overlap`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)\n", + "assert range_overlap([ (0.0, 1.0), (0.0, 2.0) ]) == (0.0, 1.0)\n", + "assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The error is actually reassuring:\n", + "we haven't written `range_overlap` yet,\n", + "so if the tests passed,\n", + "it would be a sign that someone else had\n", + "and that we were accidentally using their function.\n", + "\n", + "And as a bonus of writing these tests,\n", + "we've implicitly defined what our input and output look like:\n", + "we expect a list of pairs as input,\n", + "and produce a single pair as output.\n", + "\n", + "Something important is missing, though.\n", + "We don't have any tests for the case where the ranges don't overlap at all:\n", + "\n", + "~~~\n", + "assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == ???\n", + "~~~\n", + "\n", + "What should `range_overlap` do in this case:\n", + "fail with an error message,\n", + "produce a special value like `(0.0, 0.0)` to signal that there's no overlap,\n", + "or something else?\n", + "Any actual implementation of the function will do one of these things;\n", + "writing the tests first helps us figure out which is best\n", + "*before* we're emotionally invested in whatever we happened to write\n", + "before we realized there was an issue.\n", + "\n", + "And what about this case?\n", + "\n", + "~~~\n", + "assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == ???\n", + "~~~\n", + "\n", + "Do two segments that touch at their endpoints overlap or not?\n", + "Mathematicians usually say \"yes\",\n", + "but engineers usually say \"no\".\n", + "The best answer is \"whatever is most useful in the rest of our program\",\n", + "but again,\n", + "any actual implementation of `range_overlap` is going to do *something*,\n", + "and whatever it is ought to be consistent with what it does when there's no overlap at all.\n", + "\n", + "Since we're planning to use the range this function returns\n", + "as the X axis in a time series chart,\n", + "we decide that:\n", + "\n", + "1. every overlap has to have non-zero width, and\n", + "2. we will return the special value `None` when there's no overlap.\n", + "\n", + "`None` is built into Python,\n", + "and means \"nothing here\".\n", + "(Other languages often call the equivalent value `null` or `nil`).\n", + "With that decision made,\n", + "we can finish writing our last two tests:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == None\n", + "assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == None" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m5.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Again,\n", + "we get an error because we haven't written our function,\n", + "but we're now ready to do so:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def range_overlap(ranges):\n", + " '''Return common overlap among a set of [low, high] ranges.'''\n", + " lowest = 0.0\n", + " highest = 1.0\n", + " for (low, high) in ranges:\n", + " lowest = max(lowest, low)\n", + " highest = min(highest, high)\n", + " return (lowest, highest)" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "(Take a moment to think about why we use `max` to raise `lowest`\n", + "and `min` to lower `highest`.)\n", + "We'd now like to re-run our tests,\n", + "but they're scattered across three different cells.\n", + "To make running them easier,\n", + "let's put them all in a function:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def test_range_overlap():\n", + " assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)\n", + " assert range_overlap([ (0.0, 1.0), (0.0, 2.0) ]) == (0.0, 1.0)\n", + " assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)\n", + " assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == None\n", + " assert range_overlap([ (0.0, 1.0), (1.0, 2.0) ]) == None" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "We can now test `range_overlap` with a single function call:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test_range_overlap()" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_range_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_range_overlap\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m5.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mrange_overlap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The first of the tests that was supposed to produce `None` fails,\n", + "so we know there's something wrong with our function.\n", + "What we *don't* know,\n", + "though,\n", + "is whether the last of our five tests passed or failed,\n", + "because Python halted the program as soon as it spotted the first error.\n", + "Still,\n", + "some information is better than none,\n", + "and if we trace the behavior of the function with that input,\n", + "we realize that we're initializing `lowest` and `highest` to 0.0 and 1.0 respectively,\n", + "regardless of the input values.\n", + "This violates another important rule of programming:\n", + "\"[always initialize from data](../../rules.html#always-initialize-from-data)\".\n", + "We'll leave it as an exercise to fix `range_overlap`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Fix `range_overlap`. Re-run `test_range_overlap` after each change you make." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Debugging" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Once testing has uncovered problems,\n", + "the next step is to fix them.\n", + "Many novices do this by making more-or-less random changes to their code\n", + "until it seems to produce the right answer,\n", + "but that's very inefficient\n", + "(and the result is usually only correct for the one case they're testing).\n", + "The more experienced a programmer is,\n", + "the more systematically they debug,\n", + "and most follow some variation on the rules explained below.\n", + "\n", + "#### Know What It's Supposed to Do\n", + "\n", + "The first step in debugging something is to\n", + "[know what it's supposed to do](../../rules.html#know-what-its-supposed-to-do).\n", + "\"My program doesn't work\" isn't good enough:\n", + "in order to diagnose and fix problems,\n", + "we need to be able to tell correct output from incorrect.\n", + "If we can write a test case for the failing case—i.e.,\n", + "if we can assert that with *these* inputs,\n", + "the function should produce *that* result—\n", + "then we're ready to start debugging.\n", + "If we can't,\n", + "then we need to figure out how we're going to know when we've fixed things.\n", + "\n", + "But writing test cases for scientific software is frequently harder than\n", + "writing test cases for commercial applications,\n", + "because if we knew what the output of the scientific code was supposed to be,\n", + "we wouldn't be running the software:\n", + "we'd be writing up our results and moving on to the next program.\n", + "In practice,\n", + "scientists tend to do the following:\n", + "\n", + "1. *Test with simplified data.*\n", + " Before doing statistics on a real data set,\n", + " we should try calculating statistics for a single record,\n", + " for two identical records,\n", + " for two records whose values are one step apart,\n", + " or for some other case where we can calculate the right answer by hand.\n", + "\n", + "2. *Test a simplified case.*\n", + " If our program is supposed to simulate\n", + " magnetic eddies in rapidly-rotating blobs of supercooled helium,\n", + " our first test should be a blob of helium that isn't rotating,\n", + " and isn't being subjected to any external electromagnetic fields.\n", + " Similarly,\n", + " if we're looking at the effects of climate change on speciation,\n", + " our first test should hold temperature, precipitation, and other factors constant.\n", + "\n", + "3. *Compare to an oracle.*\n", + " A [test oracle](../../gloss.html#test-oracle) is something—experimental data,\n", + " an older program whose results are trusted,\n", + " or even a human expert—against which we can compare the results of our new program.\n", + " If we have a test oracle,\n", + " we should store its output for particular cases\n", + " so that we can compare it with our new results as often as we like\n", + " without re-running that program.\n", + "\n", + "4. *Check conservation laws.*\n", + " Mass, energy, and other quantitites are conserved in physical systems,\n", + " so they should be in programs as well.\n", + " Similarly,\n", + " if we are analyzing patient data,\n", + " the number of records should either stay the same or decrease\n", + " as we move from one analysis to the next\n", + " (since we might throw away outliers or records with missing values).\n", + " If \"new\" patients start appearing out of nowhere as we move through our pipeline,\n", + " it's probably a sign that something is wrong.\n", + "\n", + "5. *Visualize.*\n", + " Data analysts frequently use simple visualizations to check both\n", + " the science they're doing\n", + " and the correctness of their code\n", + " (just as we did in the [opening lesson](01-numpy.html) of this tutorial).\n", + " This should only be used for debugging as a last resort,\n", + " though,\n", + " since it's very hard to compare two visualizations automatically.\n", + "\n", + "#### Make It Fail Every Time\n", + "\n", + "We can only debug something when it fails,\n", + "so the second step is always to find a test case that\n", + "[makes it fail every time](../../rules.html#make-it-fail-every-time).\n", + "The \"every time\" part is important because\n", + "few things are more frustrating than debugging an intermittent problem:\n", + "if we have to call a function a dozen times to get a single failure,\n", + "the odds are good that we'll scroll past the failure when it actually occurs.\n", + "\n", + "As part of this,\n", + "it's always important to check that our code is \"plugged in\",\n", + "i.e.,\n", + "that we're actually exercising the problem that we think we are.\n", + "Every programmer has spent hours chasing a bug,\n", + "only to realize that they were actually calling their code on the wrong data set\n", + "or with the wrong configuration parameters,\n", + "or are using the wrong version of the software entirely.\n", + "Mistakes like these are particularly likely to happen when we're tired,\n", + "frustrated,\n", + "and up against a deadline,\n", + "which is one of the reasons late-night (or overnight) coding sessions\n", + "are almost never worthwhile.\n", + "\n", + "#### Make It Fail Fast\n", + "\n", + "If it takes 20 minutes for the bug to surface,\n", + "we can only do three experiments an hour.\n", + "That doesn't must mean we'll get less data in more time:\n", + "we're also more likely to be distracted by other things as we wait for our program to fail,\n", + "which means the time we *are* spending on the problem is less focused.\n", + "It's therefore critical to [make it fail fast](../../rules.html#make-it-fail-fast).\n", + "\n", + "As well as making the program fail fast in time,\n", + "we want to make it fail fast in space,\n", + "i.e.,\n", + "we want to localize the failure to the smallest possible region of code:\n", + "\n", + "1. The smaller the gap between cause and effect,\n", + " the easier the connection is to find.\n", + " Many programmers therefore use a divide and conquer strategy to find bugs,\n", + " i.e.,\n", + " if the output of a function is wrong,\n", + " they check whether things are OK in the middle,\n", + " then concentrate on either the first or second half,\n", + " and so on.\n", + "\n", + "2. N things can interact in N2/2 different ways,\n", + " so every line of code that *isn't* run as part of a test\n", + " means more than one thing we don't need to worry about.\n", + "\n", + "#### Change One Thing at a Time, For a Reason\n", + "\n", + "Replacing random chunks of code is unlikely to do much good.\n", + "(After all,\n", + "if you got it wrong the first time,\n", + "you'll probably get it wrong the second and third as well.)\n", + "Good programmers therefore\n", + "[change one thing at a time, for a reason](../../rules.html#change-one-thing-at-a-time)\n", + "They are either trying to gather more information\n", + "(\"is the bug still there if we change the order of the loops?\")\n", + "or test a fix\n", + "(\"can we make the bug go away by sorting our data before processing it?\").\n", + " \n", + "Every time we make a change,\n", + "however small,\n", + "we should re-run our tests immediately,\n", + "because the more things we change at once,\n", + "the harder it is to know what's responsible for what\n", + "(those N2 interactions again).\n", + "And we should re-run *all* of our tests:\n", + "more than half of fixes made to code introduce (or re-introduce) bugs,\n", + "so re-running all of our tests tells us whether we have [regressed](../../gloss.html#regression).\n", + "\n", + "#### Keep Track of What You've Done\n", + "\n", + "Good scientists keep track of what they've done\n", + "so that they can reproduce their work,\n", + "and so that they don't waste time repeating the same experiments\n", + "or running ones whose results won't be interesting.\n", + "Similarly,\n", + "debugging works best when we\n", + "[keep track of what we've done](../../rules.html#keep-track-of-what-youve-done)\n", + "and how well it worked.\n", + "If we find ourselves asking,\n", + "\"Did left followed by right with an odd number of lines cause the crash?\n", + "Or was it right followed by left?\n", + "Or was I using an even number of lines?\"\n", + "then it's time to step away from the computer,\n", + "take a deep breath,\n", + "and start working more systematically.\n", + " \n", + "Records are particularly useful when the time comes to ask for help.\n", + "People are more likely to listen to us\n", + "when we can explain clearly what we did,\n", + "and we're better able to give them the information they need to be useful.\n", + "\n", + "> #### Version Control Revisited\n", + ">\n", + "> Version control is often used to reset software to a known state during debugging,\n", + "> and to explore recent changes to code that might be responsible for bugs.\n", + "> In particular,\n", + "> most version control systems have a `blame` command\n", + "> that will show who last changed particular lines of code...\n", + "\n", + "#### Be Humble\n", + "\n", + "And speaking of help:\n", + "if we can't find a bug in 10 minutes,\n", + "we should [be humble](../../rules.html#be-humble) and ask for help.\n", + "Just explaining the problem aloud is often useful,\n", + "since hearing what we're thinking helps us spot inconsistencies and hidden assumptions.\n", + "\n", + "Asking for help also helps alleviate confirmation bias.\n", + "If we have just spent an hour writing a complicated program,\n", + "we want it to work,\n", + "so we're likely to keep telling ourselves why it should,\n", + "rather than searching for the reason it doesn't.\n", + "People who aren't emotionally invested in the code can be more objective,\n", + "which is why they're often able to spot the simple mistakes we have overlooked.\n", + "\n", + "Part of being humble is learning from our mistakes.\n", + "Programmers tend to get the same things wrong over and over:\n", + "either they don't understand the language and libraries they're working with,\n", + "or their model of how things work is wrong.\n", + "In either case,\n", + "taking note of why the error occurred\n", + "and checking for it next time\n", + "quickly turns into not making the mistake at all.\n", + "\n", + "And that is what makes us most productive in the long run.\n", + "As the saying goes,\n", + "\"[A week of hard work can sometimes save you an hour of thought](../../rules.html#week-hard-work-hour-thought).\"\n", + "If we train ourselves to avoid making some kinds of mistakes,\n", + "to break our code into modular, testable chunks,\n", + "and to turn every assumption (or mistake) into an assertion,\n", + "it will actually take us *less* time to produce working programs,\n", + "not more." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "keypoints" + ] + }, + "source": [ + "#### Key Points\n", + "\n", + "* Program defensively, i.e., assume that errors are going to arise, and write code to detect them when they do.\n", + "* Put assertions in programs to check their state as they run, and to help readers understand how those programs are supposed to work.\n", + "* Use preconditions to check that the inputs to a function are safe to use.\n", + "* Use postconditions to check that the output from a function is safe to use.\n", + "* Write tests before writing code in order to help determine exactly what that code is supposed to do.\n", + "* Know what code is supposed to do *before* trying to debug it.\n", + "* Make it fail every time.\n", + "* Make it fail fast.\n", + "* Change one thing at a time, and for a reason.\n", + "* Keep track of what you've done.\n", + "* Be humble." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "#### Next Steps\n", + "\n", + "We have now seen the basics of building and testing Python code in the IPython Notebook.\n", + "The last thing we need to learn is how to build command-line programs\n", + "that we can use in pipelines and shell scripts,\n", + "so that we can integrate our tools with other people's work.\n", + "This will be the subject of our next and final lesson." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/novice/python/06-cmdline.ipynb b/novice/python/06-cmdline.ipynb new file mode 100644 index 0000000..981ce92 --- /dev/null +++ b/novice/python/06-cmdline.ipynb @@ -0,0 +1,1275 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 2, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Command-Line Programs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The IPython Notebook and other interactive tools are great for prototyping code and exploring data,\n", + "but sooner or later we will want to use our program in a pipeline\n", + "or run it in a shell script to process thousands of data files.\n", + "In order to do that,\n", + "we need to make our programs work like other Unix command-line tools.\n", + "For example,\n", + "we may want a program that reads a data set\n", + "and prints the average inflammation per patient:\n", + "\n", + "~~~\n", + "$ python readings.py --mean inflammation-01.csv\n", + "5.45\n", + "5.425\n", + "6.1\n", + "...\n", + "6.4\n", + "7.05\n", + "5.9\n", + "~~~\n", + "\n", + "but we might also want to look at the minimum of the first four lines\n", + "\n", + "~~~\n", + "$ head -4 inflammation-01.csv | python readings.py --min\n", + "~~~\n", + "\n", + "or the maximum inflammations in several files one after another:\n", + "\n", + "~~~\n", + "$ python readings.py --max inflammation-*.csv\n", + "~~~\n", + "\n", + "Our overall requirements are:\n", + "\n", + "1. If no filename is given on the command line, read data from [standard input](../../gloss.html#standard-input).\n", + "2. If one or more filenames are given, read data from them and report statistics for each file separately.\n", + "3. Use the `--min`, `--mean`, or `--max` flag to determine what statistic to print.\n", + "\n", + "To make this work,\n", + "we need to know how to handle command-line arguments in a program,\n", + "and how to get at standard input.\n", + "We'll tackle these questions in turn below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "objectives" + ] + }, + "source": [ + "#### Objectives\n", + "\n", + "* Use the values of command-line arguments in a program.\n", + "* Handle flags and files separately in a command-line program.\n", + "* Read data from standard input in a program so that it can be used in a pipeline." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Command-Line Arguments" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Using the text editor of your choice,\n", + "save the following in a text file:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat sys-version.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "print 'version is', sys.version\r\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The first line imports a library called `sys`,\n", + "which is short for \"system\".\n", + "It defines values such as `sys.version`,\n", + "which describes which version of Python we are running.\n", + "We can run this script from within the IPython Notebook like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run sys-version.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "version is 2.7.5 |Anaconda 1.8.0 (x86_64)| (default, Oct 24 2013, 07:02:20) \n", + "[GCC 4.0.1 (Apple Inc. build 5493)]\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "or like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ipython sys-version.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "version is 2.7.5 |Anaconda 1.8.0 (x86_64)| (default, Oct 24 2013, 07:02:20) \r\n", + "[GCC 4.0.1 (Apple Inc. build 5493)]\r\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The first method, `%run`,\n", + "uses a special command in the IPython Notebook to run a program in a `.py` file.\n", + "The second method is more general:\n", + "the exclamation mark `!` tells the Notebook to run a shell command,\n", + "and it just so happens that the command we run is `ipython` with the name of the script." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Here's another script that does something more interesting:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat argv-list.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "print 'sys.argv is', sys.argv\r\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The strange name `argv` stands for \"argument values\".\n", + "Whenever Python runs a program,\n", + "it takes all of the values given on the command line\n", + "and puts them in the list `sys.argv`\n", + "so that the program can determine what they were.\n", + "If we run this program with no arguments:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ipython argv-list.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "sys.argv is ['/Users/gwilson/s/bc/python/novice/argv-list.py']\r\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "the only thing in the list is the full path to our script,\n", + "which is always `sys.argv[0]`.\n", + "If we run it with a few arguments, however:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ipython argv-list.py first second third" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "sys.argv is ['/Users/gwilson/s/bc/python/novice/argv-list.py', 'first', 'second', 'third']\r\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "then Python adds each of those arguments to that magic list." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "With this in hand,\n", + "let's build a version of `readings.py` that always prints the per-patient mean of a single data file.\n", + "The first step is to write a function that outlines our implementation,\n", + "and a placeholder for the function that does the actual work.\n", + "By convention this function is usually called `main`,\n", + "though we can call it whatever we want:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat readings-01.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "import numpy as np\r\n", + "\r\n", + "def main():\r\n", + " script = sys.argv[0]\r\n", + " filename = sys.argv[1]\r\n", + " data = np.loadtxt(filename, delimiter=',')\r\n", + " for m in data.mean(axis=1):\r\n", + " print m\r\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This function gets the name of the script from `sys.argv[0]`,\n", + "because that's where it's always put,\n", + "and the name of the file to process from `sys.argv[1]`.\n", + "Here's a simple test:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run readings-01.py inflammation-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "There is no output because we have defined a function,\n", + "but haven't actually called it.\n", + "Let's add a call to `main`:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat readings-02.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "import numpy as np\r\n", + "\r\n", + "def main():\r\n", + " script = sys.argv[0]\r\n", + " filename = sys.argv[1]\r\n", + " data = np.loadtxt(filename, delimiter=',')\r\n", + " for m in data.mean(axis=1):\r\n", + " print m\r\n", + "\r\n", + "main()\r\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "and run that:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run readings-02.py inflammation-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "5.45\n", + "5.425\n", + "6.1\n", + "5.9\n", + "5.55\n", + "6.225\n", + "5.975\n", + "6.65\n", + "6.625\n", + "6.525\n", + "6.775\n", + "5.8\n", + "6.225\n", + "5.75\n", + "5.225\n", + "6.3\n", + "6.55\n", + "5.7\n", + "5.85\n", + "6.55\n", + "5.775\n", + "5.825\n", + "6.175\n", + "6.1\n", + "5.8\n", + "6.425\n", + "6.05\n", + "6.025\n", + "6.175\n", + "6.55\n", + "6.175\n", + "6.35\n", + "6.725\n", + "6.125\n", + "7.075\n", + "5.725\n", + "5.925\n", + "6.15\n", + "6.075\n", + "5.75\n", + "5.975\n", + "5.725\n", + "6.3\n", + "5.9\n", + "6.75\n", + "5.925\n", + "7.225\n", + "6.15\n", + "5.95\n", + "6.275\n", + "5.7\n", + "6.1\n", + "6.825\n", + "5.975\n", + "6.725\n", + "5.7\n", + "6.25\n", + "6.4\n", + "7.05\n", + "5.9\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> #### The Right Way to Do It\n", + ">\n", + "> If our programs can take complex parameters or multiple filenames,\n", + "> we shouldn't handle `sys.argv` directly.\n", + "> Instead,\n", + "> we should use Python's `argparse` library,\n", + "> which handles common cases in a systematic way,\n", + "> and also makes it easy for us to provide sensible error messages for our users." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Write a command-line program that does addition and subtraction:\n", + " ~~~\n", + " python arith.py 1 + 2\n", + " 3\n", + " python arith.py 3 - 4\n", + " -1\n", + " ~~~\n", + "\n", + " What goes wrong if you try to add multiplication using '*' to the program?\n", + "\n", + "2. Using the `glob` module introduced [03-loop.ipynb](earlier),\n", + " write a simple version of `ls` that shows files in the current directory with a particular suffix:\n", + " ~~~\n", + " python my_ls.py py\n", + " left.py\n", + " right.py\n", + " zero.py\n", + " ~~~" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Handling Multiple Files" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The next step is to teach our program how to handle multiple files.\n", + "Since 60 lines of output per file is a lot to page through,\n", + "we'll start by creating three smaller files,\n", + "each of which has three days of data for two patients:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ls small-*.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "small-01.csv small-02.csv small-03.csv\r\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat small-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0,0,1\r\n", + "0,1,2\r\n" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run readings-02.py small-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0.333333333333\n", + "1.0\n" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Using small data files as input also allows us to check our results more easily:\n", + "here,\n", + "for example,\n", + "we can see that our program is calculating the mean correctly for each line,\n", + "whereas we were really taking it on faith before.\n", + "This is yet another rule of programming:\n", + "\"[test the simple things first](../../rules.html#test-simple-first)\".\n", + "\n", + "We want our program to process each file separately,\n", + "so we need a looop that executes once for each filename.\n", + "If we specify the files on the command line,\n", + "the filenames will be in `sys.argv`,\n", + "but we need to be careful:\n", + "`sys.argv[0]` will always be the name of our script,\n", + "rather than the name of a file.\n", + "We also need to handle an unknown number of filenames,\n", + "since our program could be run for any number of files.\n", + "\n", + "The solution to both problems is to loop over the contents of `sys.argv[1:]`.\n", + "The '1' tells Python to start the slice at location 1,\n", + "so the program's name isn't included;\n", + "since we've left off the upper bound,\n", + "the slice runs to the end of the list,\n", + "and includes all the filenames.\n", + "Here's our changed program:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat readings-03.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "import numpy as np\r\n", + "\r\n", + "def main():\r\n", + " script = sys.argv[0]\r\n", + " for filename in sys.argv[1:]:\r\n", + " data = np.loadtxt(filename, delimiter=',')\r\n", + " for m in data.mean(axis=1):\r\n", + " print m\r\n", + "\r\n", + "main()\r\n" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "and here it is in action:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run readings-03.py small-01.csv small-02.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0.333333333333\n", + "1.0\n", + "13.6666666667\n", + "11.0\n" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Note:\n", + "at this point,\n", + "we have created three versions of our script called `readings-01.py`,\n", + "`readings-02.py`, and `readings-03.py`.\n", + "We wouldn't do this in real life:\n", + "instead,\n", + "we would have one file called `readings.py` that we committed to version control\n", + "every time we got an enhancement working.\n", + "For teaching,\n", + "though,\n", + "we need all the successive versions side by side." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Write a program called `check.py` that takes the names of one or more inflammation data files as arguments\n", + " and checks that all the files have the same number of rows and columns.\n", + " What is the best way to test your program?" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Handling Command-Line Flags" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The next step is to teach our program to pay attention to the `--min`, `--mean`, and `--max` flags.\n", + "These always appear before the names of the files,\n", + "so we could just do this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat readings-04.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "import numpy as np\r\n", + "\r\n", + "def main():\r\n", + " script = sys.argv[0]\r\n", + " action = sys.argv[1]\r\n", + " filenames = sys.argv[2:]\r\n", + "\r\n", + " for f in filenames:\r\n", + " data = np.loadtxt(f, delimiter=',')\r\n", + "\r\n", + " if action == '--min':\r\n", + " values = data.min(axis=1)\r\n", + " elif action == '--mean':\r\n", + " values = data.mean(axis=1)\r\n", + " elif action == '--max':\r\n", + " values = data.max(axis=1)\r\n", + "\r\n", + " for m in values:\r\n", + " print m\r\n", + "\r\n", + "main()\r\n" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This works:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run readings-04.py --max small-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1.0\n", + "2.0\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "but there are seveal things wrong with it:\n", + "\n", + "1. `main` is too large to read comfortably.\n", + "\n", + "2. If `action` isn't one of the three recognized flags,\n", + " the program loads each file but does nothing with it\n", + " (because none of the branches in the conditional match).\n", + " [Silent failures](../../gloss.html#silent-failure) like this\n", + " are always hard to debug.\n", + "\n", + "This version pulls the processing of each file out of the loop into a function of its own.\n", + "It also checks that `action` is one of the allowed flags\n", + "before doing any processing,\n", + "so that the program fails fast:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat readings-05.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "import numpy as np\r\n", + "\r\n", + "def main():\r\n", + " script = sys.argv[0]\r\n", + " action = sys.argv[1]\r\n", + " filenames = sys.argv[2:]\r\n", + " assert action in ['--min', '--mean', '--max'], \\\r\n", + " 'Action is not one of --min, --mean, or --max: ' + action\r\n", + " for f in filenames:\r\n", + " process(f, action)\r\n", + "\r\n", + "def process(filename, action):\r\n", + " data = np.loadtxt(filename, delimiter=',')\r\n", + "\r\n", + " if action == '--min':\r\n", + " values = data.min(axis=1)\r\n", + " elif action == '--mean':\r\n", + " values = data.mean(axis=1)\r\n", + " elif action == '--max':\r\n", + " values = data.max(axis=1)\r\n", + "\r\n", + " for m in values:\r\n", + " print m\r\n", + "\r\n", + "main()\r\n" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This is four lines longer than its predecessor,\n", + "but broken into more digestible chunks of 8 and 12 lines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python has a module named [argparse](http://docs.python.org/dev/library/argparse.html)\n", + "that helps handle complex command-line flags. We will not cover this module in this lesson\n", + "but you can go to Tshepang Lekhonkhobe's [Argparse tutorial](http://docs.python.org/dev/howto/argparse.html)\n", + "that is part of Python's Official Documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Rewrite this program so that it uses `-n`, `-m`, and `-x` instead of `--min`, `--mean`, and `--max` respectively.\n", + " Is the code easier to read?\n", + " Is the program easier to understand?\n", + "\n", + "2. Separately,\n", + " modify the program so that if no parameters are given\n", + " (i.e., no action is specified and no filenames are given),\n", + " it prints a message explaining how it should be used.\n", + "\n", + "3. Separately,\n", + " modify the program so that if no action is given\n", + " it displays the means of the data." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": { + "cell_tags": [] + }, + "source": [ + "Handling Standard Input" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "The next thing our program has to do is read data from standard input if no filenames are given\n", + "so that we can put it in a pipeline,\n", + "redirect input to it,\n", + "and so on.\n", + "Let's experiment in another script:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!cat count-stdin.py" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "import sys\r\n", + "\r\n", + "count = 0\r\n", + "for line in sys.stdin:\r\n", + " count += 1\r\n", + "\r\n", + "print count, 'lines in standard input'\r\n" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "This little program reads lines from a special \"file\" called `sys.stdin`,\n", + "which is automatically connected to the program's standard input.\n", + "We don't have to open it—Python and the operating system\n", + "take care of that when the program starts up—\n", + "but we can do almost anything with it that we could do to a regular file.\n", + "Let's try running it as if it were a regular command-line program:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ipython count-stdin.py < small-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "2 lines in standard input\r\n" + ] + } + ], + "prompt_number": 21 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "What if we run it using `%run`?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%run count-stdin.py < fractal_1.txt" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0 lines in standard input\n" + ] + } + ], + "prompt_number": 22 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "As you can see,\n", + "`%run` doesn't understand file redirection:\n", + "that's a shell thing.\n", + "\n", + "A common mistake is to try to run something that reads from standard input like this:\n", + "\n", + "~~~\n", + "!ipython count_stdin.py fractal_1.txt\n", + "~~~\n", + "\n", + "i.e., to forget the `<` character that redirect the file to standard input.\n", + "In this case,\n", + "there's nothing in standard input,\n", + "so the program waits at the start of the loop for someone to type something on the keyboard.\n", + "Since there's no way for us to do this,\n", + "our program is stuck,\n", + "and we have to halt it using the `Interrupt` option from the `Kernel` menu in the Notebook.\n", + "\n", + "We now need to rewrite the program so that it loads data from `sys.stdin` if no filenames are provided.\n", + "Luckily,\n", + "`numpy.loadtxt` can handle either a filename or an open file as its first parameter,\n", + "so we don't actually need to change `process`.\n", + "That leaves `main`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "~~~\n", + "def main():\n", + " script = sys.argv[0]\n", + " action = sys.argv[1]\n", + " filenames = sys.argv[2:]\n", + " assert action in ['--min', '--mean', '--max'], \\\n", + " 'Action is not one of --min, --mean, or --max: ' + action\n", + " if len(filenames) == 0:\n", + " process(sys.stdin, action)\n", + " else:\n", + " for f in filenames:\n", + " process(f, action)\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Let's try it out\n", + "(we'll see in a moment why we send the output through `head`):" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ipython readings-06.py --mean < small-01.csv | head -10" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[TerminalIPythonApp] CRITICAL | Bad config encountered during initialization:\r\n", + "[TerminalIPythonApp] CRITICAL | Unrecognized flag: '--mean'\r\n", + "=========\r\n", + " IPython\r\n", + "=========\r\n", + "\r\n", + "Tools for Interactive Computing in Python\r\n", + "=========================================\r\n", + "\r\n", + " A Python shell with automatic history (input and output), dynamic object\r\n", + " introspection, easier configuration, command completion, access to the\r\n", + " system shell and more. IPython can also be embedded in running programs.\r\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "Whoops:\n", + "why are we getting IPython's help rather than the line-by-line average of our data?\n", + "The answer is that IPython has a hard time telling\n", + "which command-line arguments are meant for it,\n", + "and which are meant for the program it's running.\n", + "To make our meaning clear,\n", + "we have to use `--` (a double dash)\n", + "to separate the two:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "!ipython readings-06.py -- --mean < small-01.csv" + ], + "language": "python", + "metadata": { + "cell_tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0.333333333333\r\n", + "1.0\r\n" + ] + } + ], + "prompt_number": 24 + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [] + }, + "source": [ + "That's better.\n", + "In fact,\n", + "that's done:\n", + "the program now does everything we set out to do." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "challenges" + ] + }, + "source": [ + "#### Challenges\n", + "\n", + "1. Write a program called `line-count.py` that works like the Unix `wc` command:\n", + " * If no filenames are given, it reports the number of lines in standard input.\n", + " * If one or more filenames are given, it reports the number of lines in each, followed by the total number of lines." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_tags": [ + "keypoints" + ] + }, + "source": [ + "#### Key Points\n", + "\n", + "* The `sys` library connects a Python program to the system it is running on.\n", + "* The list `sys.argv` contains the command-line arguments that a program was run with.\n", + "* Avoid silent failures.\n", + "* The \"file\" `sys.stdin` connects to a program's standard input.\n", + "* The \"file\" `sys.stdout` connects to a program's standard output." + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/novice/python/README.txt b/novice/python/README.txt new file mode 100644 index 0000000..b43514d --- /dev/null +++ b/novice/python/README.txt @@ -0,0 +1,9 @@ +# Resources + +* `inflammation-*.csv`: data files used in notebooks. +* `readings-*.py`: successive versions of command-line script. +* `small-*.csv`: small data files used to test command-line script. +* `argv-list.py`: example command-line script. +* `count-stdin.py`: example command-line script. +* `sys-version.py`: example command-line script. +* `img/*`: images used in notebooks diff --git a/novice/python/argv-list.py b/novice/python/argv-list.py new file mode 100644 index 0000000..26fe990 --- /dev/null +++ b/novice/python/argv-list.py @@ -0,0 +1,2 @@ +import sys +print 'sys.argv is', sys.argv diff --git a/novice/python/count-stdin.py b/novice/python/count-stdin.py new file mode 100644 index 0000000..994a9ad --- /dev/null +++ b/novice/python/count-stdin.py @@ -0,0 +1,7 @@ +import sys + +count = 0 +for line in sys.stdin: + count += 1 + +print count, 'lines in standard input' diff --git a/novice/python/gen-inflammation.py b/novice/python/gen-inflammation.py new file mode 100644 index 0000000..05be9d9 --- /dev/null +++ b/novice/python/gen-inflammation.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python + +'''Generate pseudo-random patient inflammation data for use in Python lessons.''' + +import sys +import random + +n_patients = 60 +n_days = 40 +n_range = 20 + +middle = n_days / 2 + +for p in range(n_patients): + vals = [] + for d in range(n_days): + upper = max(n_range - abs(d - middle), 0) + vals.append(random.randint(upper/4, upper)) + print ','.join([str(v) for v in vals]) diff --git a/novice/python/img/ave-inflammation-over-time.png b/novice/python/img/ave-inflammation-over-time.png new file mode 100644 index 0000000000000000000000000000000000000000..1e45780f28fa53822d56f51b2cfd7b1b7a511b54 GIT binary patch literal 9673 zcmZX41z3||xb|R_q=?cX100fybPFSeDJc!oC^@=^3XJZQP*OmUkgh4COG-exr3O-? zIiKhM&-u@Fey(fm+rE8oKF|H!&;7g+FW@Q^q>Q8>5Qsue6{ZaW;kg0VS0qHhfAzO2 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0000000..26a36ac --- /dev/null +++ b/novice/python/index.md @@ -0,0 +1,34 @@ +--- +layout: lesson +root: ../.. +title: Programming with Python +level: novice +--- +The best way to learn how to program is to do something useful, +so this introduction to Python is built around a common scientific task: +data analysis. + +Our real goal isn't to teach you Python, +but to teach you the basic concepts that all programming depends on. +We use Python in our lessons because: + +1. we have to use *something* for examples; +2. it's free, well-documented, and runs almost everywhere; +3. it has a large (and growing) user base among scientists; and +4. experience shows that it's easier for novices to pick up than most other languages. + +But the two most important things are +to use whatever language your colleagues are using, +so that you can share you work with them easily, +and to use that language *well*. + +

+ +1. [Analyzing Patient Data](01-numpy.html) +2. [Creating Functions](02-func.html) +3. [Analyzing Multiple Data Sets](03-loop.html) +4. [Making Choices](04-cond.html) +5. [Defensive Programming](05-defensive.html) +6. [Command-Line Programs](06-cmdline.html) + +
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index 0000000..ffa9ed2 --- /dev/null +++ b/novice/python/readings-02.py @@ -0,0 +1,11 @@ +import sys +import numpy as np + +def main(): + script = sys.argv[0] + filename = sys.argv[1] + data = np.loadtxt(filename, delimiter=',') + for m in data.mean(axis=1): + print m + +main() diff --git a/novice/python/readings-03.py b/novice/python/readings-03.py new file mode 100644 index 0000000..6ca7a4c --- /dev/null +++ b/novice/python/readings-03.py @@ -0,0 +1,11 @@ +import sys +import numpy as np + +def main(): + script = sys.argv[0] + for filename in sys.argv[1:]: + data = np.loadtxt(filename, delimiter=',') + for m in data.mean(axis=1): + print m + +main() diff --git a/novice/python/readings-04.py b/novice/python/readings-04.py new file mode 100644 index 0000000..404588d --- /dev/null +++ b/novice/python/readings-04.py @@ -0,0 +1,22 @@ +import sys +import numpy as np + +def main(): + script = sys.argv[0] + action = sys.argv[1] + filenames = sys.argv[2:] + + for f in filenames: + data = np.loadtxt(f, delimiter=',') + + if action == '--min': + values = data.min(axis=1) + elif action == '--mean': + values = data.mean(axis=1) + elif action == '--max': + values = data.max(axis=1) + + for m in values: + print m + +main() diff --git a/novice/python/readings-05.py b/novice/python/readings-05.py new file mode 100644 index 0000000..b1bc32c --- /dev/null +++ b/novice/python/readings-05.py @@ -0,0 +1,26 @@ +import sys +import numpy as np + +def main(): + script = sys.argv[0] + action = sys.argv[1] + filenames = sys.argv[2:] + assert action in ['--min', '--mean', '--max'], \ + 'Action is not one of --min, --mean, or --max: ' + action + for f in filenames: + process(f, action) + +def process(filename, action): + data = np.loadtxt(filename, delimiter=',') + + if action == '--min': + values = data.min(axis=1) + elif action == '--mean': + values = data.mean(axis=1) + elif action == '--max': + values = data.max(axis=1) + + for m in values: + print m + +main() diff --git a/novice/python/readings-06.py b/novice/python/readings-06.py new file mode 100644 index 0000000..9ec3687 --- /dev/null +++ b/novice/python/readings-06.py @@ -0,0 +1,29 @@ +import sys +import numpy as np + +def main(): + script = sys.argv[0] + action = sys.argv[1] + filenames = sys.argv[2:] + assert action in ['--min', '--mean', '--max'], \ + 'Action is not one of --min, --mean, or --max: ' + action + if len(filenames) == 0: + process(sys.stdin, action) + else: + for f in filenames: + process(f, action) + +def process(filename, action): + data = np.loadtxt(filename, delimiter=',') + + if action == '--min': + values = data.min(axis=1) + elif action == '--mean': + values = data.mean(axis=1) + elif action == '--max': + values = data.max(axis=1) + + for m in values: + print m + +main() diff --git a/novice/python/rectangle.py b/novice/python/rectangle.py new file mode 100644 index 0000000..7cd2bb7 --- /dev/null +++ b/novice/python/rectangle.py @@ -0,0 +1,3 @@ +def rectangle_area(coords): + x0, y0, x1, y1 = coords + return (x1 - x0) * (x1 - y0) diff --git a/novice/python/small-01.csv b/novice/python/small-01.csv new file mode 100644 index 0000000..4d65327 --- /dev/null +++ b/novice/python/small-01.csv @@ -0,0 +1,2 @@ +0,0,1 +0,1,2 diff --git a/novice/python/small-02.csv b/novice/python/small-02.csv new file mode 100644 index 0000000..8fa62db --- /dev/null +++ b/novice/python/small-02.csv @@ -0,0 +1,2 @@ +9,17,15 +20,8,5 diff --git a/novice/python/small-03.csv b/novice/python/small-03.csv new file mode 100644 index 0000000..ea233a3 --- /dev/null +++ b/novice/python/small-03.csv @@ -0,0 +1,2 @@ +0,2,0 +1,1,0 diff --git a/novice/python/spatial-intro.ipynb b/novice/python/spatial-intro.ipynb new file mode 100644 index 0000000..93b2e5d --- /dev/null +++ b/novice/python/spatial-intro.ipynb @@ -0,0 +1,399 @@ + +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A note to students and instructors\n", + "\n", + "This lesson requires the [Basemap](http://matplotlib.org/basemap) toolkit for Matplotlib. This library is not distributed with Matplotlib directly. If you are using Continuum's Anaconda distribution, you can obtain the library using:\n", + "\n", + " conda install basemap\n", + " \n", + "If you are using Enthought Canopy and have the full version or an academic license, Basemap should already be installed on your system. Otherwise, you will need to follow the [installation instructions](http://matplotlib.org/basemap/users/installing.html) on the Basemap documentation. Using one of the two scientific distributions is preferred in most instances. " + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Visualizing spatial data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Original materials by Joshua Adelman; modified by Randy Olson" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are examining some simple spatial coordinate data, specifically the location of all of the previous Software Carpentry bootcamps. The data set is stored in [comma-separated values](../../gloss.html#csv) (CSV) format. After the header line (marked with a `#`), each row contains the latitude and longitude for each bootcamp, separated by a comma. \n", + "\n", + " # Latitude, Longitude\n", + " 43.661476,-79.395189\n", + " 39.332604,-76.623190\n", + " 45.703255, 13.718013\n", + " 43.661476,-79.395189\n", + " 39.166381,-86.526621\n", + " ...\n", + " \n", + "We want to:\n", + "\n", + "* Load the data into our analysis environment\n", + "* Inspect the data\n", + "* Visualize it in a meaningful context\n", + "\n", + "To do this, we'll begin to delve into working with Python and do a bit of programming." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Objectives\n", + "\n", + "* Explain what a library is, and what libraries are used for.\n", + "* Load a Python library and use the things it contains.\n", + "* Read tabular data from a file into a program.\n", + "* Display simple visualizations of the data" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Loading the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to work with the coordinates stored in the file, we need to [import](../../gloss.html#import) a library called NumPy that is designed to easily handle arrays of data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's very common to create an [alias](../../gloss.html#alias-library) for a library when importing it\n", + "in order to reduce the amount of typing we have to do. We can now refer to this library in the code as `np` instead of typing out `numpy` each time we want to use it.\n", + "\n", + "We can now ask numpy to read our data file:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lat, lon = np.loadtxt('swc_bc_coords.csv', delimiter=',', unpack=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The expression `np.loadtxt(...)` means,\n", + "\"Run the function `loadtxt` that belongs to the `numpy` library.\"\n", + "This [dotted notation](../../gloss.html#dotted-notation) is used everywhere in Python\n", + "to refer to the parts of larger things.\n", + "\n", + "`np.loadtxt` has three [parameters](../../gloss.html#parameter):\n", + "the name of the file we want to read,\n", + "and the [delimiter](../../gloss.html#delimiter) that separates values on a line.\n", + "These both need to be character strings (or [strings](../../gloss.html#string) for short),\n", + "so we put them in quotes.\n", + "Finally, passing the `unpack` paramter the boolean value, `True` tells `np.loadtxt` to take the first and second column of data and [assign](../../gloss.html#assignment) them to the [variables](../../gloss.html#variable) `lat` and `lon`, respectively.\n", + "A variable is just a name for some data.\n", + "Also note that `np.loadtxt` automatically skipped the line with the header information, since it recognizes that \n", + "this line is a [comment](../../gloss.html#comment) and does not contain numerical data.\n", + "\n", + "When we are finished typing and press Shift+Enter,\n", + "the notebook runs our command." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`lat` and `lon` now contain our data, which we can inspect by just executing a cell with the name of a variable:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lat" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "array([ 43.661476, 39.332604, 45.703255, 43.661476, 39.166381,\n", + " 36.802151, 37.808381, 41.790113, 41.744949, 51.559882,\n", + " 42.727288, 54.980095, 53.523454, 49.261715, 39.32758 ,\n", + " 48.831673, 42.359133, 43.47013 , 44.632261, 43.783551,\n", + " 53.948193, 59.939959, 40.808078, 40.428267, 37.875928,\n", + " 49.261715, 37.8695 , 54.980095, 34.141411, 38.831513,\n", + " 51.757137, 43.261328, 38.648056, 32.89533 , 34.227425,\n", + " 21.300662, 55.945328, 30.283599, 49.261715, 41.790113,\n", + " 45.417417, 43.469128, 49.261715, 48.264934, 43.647118,\n", + " 48.53698 , 40.808078, 37.228384, 49.261715, -33.773636,\n", + " -37.825328, 47.655965, 37.875928, 38.031441, 33.900058,\n", + " 41.744949, 22.3101 , 32.236358, 51.524789, -33.929492,\n", + " 53.467102, 37.8695 , 53.478349, 48.82629 , 39.291389,\n", + " 43.07718 , 52.33399 , 54.32707 , 39.07141 , 37.42949 ,\n", + " 37.875928, 43.64712 , 51.759865, 38.54926 , 36.00803 ,\n", + " 50.060833, 36.00283 , 40.03131 , 42.388889, 53.52345 ,\n", + " 50.937716, 42.35076 , 41.789722, 49.276765, 32.887151,\n", + " 41.790113, 42.3625 , 30.283599, -43.523333, 35.20859 ,\n", + " 59.939959, 30.538978, 39.166381, 51.377743, 37.228384,\n", + " 41.7408 , 41.70522 , 47.655 , 40.443322, 44.968657,\n", + " 38.958455, 32.30192 , 43.07718 , 41.66293 , 51.457971,\n", + " 43.468889, 42.724085, -34.919159, 49.261111, -37.9083 ,\n", + " 34.052778, 41.526667])" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The array is a type of container defined by numpy to hold values. We will discuss how to manipulate arrays in more detail in another lesson.\n", + "For now let's just make a simple plot of the data. For this, we will use another library called `matplotlib`. First, let's tell the IPython Notebook that we want our plots displayed inline, rather than in a separate viewing window:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `%` at the start of the line signals that this is a command for the notebook,\n", + "rather than a statement in Python.\n", + "Next,\n", + "we will import the `pyplot` module from `matplotlib` and use one of the commands it defines to make plot a point for each latitude, longitude pair of data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from matplotlib import pyplot\n", + "pyplot.plot(lon, lat, 'o')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + 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wejzpeRy3b9+O7OxsAEB2dnZA3tu7774bN998c7/qCuR77qlOQFm/m1FRUZg0\naRIAYPjw4UhISEBjY6Osx9PvC65t2bIFM2fOBOD9Qq6e7dHR0QG9wKuurg4pKSkwmUz47LPPAACN\njY2KqlHpx7KwsBDJyclYvHix66Op0i7ka2xsxNixYxVTT08ajQb33nsvpkyZgk2bNgEAWlpaoNPp\nAAA6nQ4tLS2BLNHFW11Ke88B5f5unjx5EocOHcK0adNkPZ6Dnr3Tk7cLuV555RXMnj0bgPOirrCw\nMGRlZcm12wHpT4093Xrrraivr8fNN9+MgwcPYu7cuTh69Kji6gy0vi7ke/LJJ/Hiiy8CAFavXo3f\n/OY32Lx5s8ftBHKml9Jnmf3tb3/DmDFjcPr0aZjNZhiNRrfnNRqNIv8N16orkDUr9XfzwoULmDdv\nHtavX48RI0b0qkPK8ZQt9CsqKvp8/q233sLOnTvx8ccfu9qio6NRX1/v+r6hoQF6vR7R0dGuIaCu\n9ujoaJ/X6ElYWBjCwsIAAJMnT4bBYEBNTY3Pahxsnf4+lj31t+YlS5a4/nB5qtkXtfVXz3rq6+vd\nelGBNmbMGADOda4eeOABVFVVQafTobm5GVFRUWhqasLo0aMDXKWTt7qU9p53P15K+d28cuUK5s2b\nh0ceeQRz584FIO/x9MvwTllZGdatW4eSkhKEh4e72ufMmYN3330XdrsddXV1rgu5oqKicMMNN2D/\n/v0QQuBPf/qT6x/vD93H+L777jt0dHQAAE6cOIGamhr89Kc/xZgxYwJaY886lXosAef5jy5//etf\nXbMnvNUcKFOmTEFNTQ1OnjwJu92O4uJizJkzJ2D1dPfDDz/g+++/BwBcvHgR5eXlmDBhAubMmYOt\nW7cCALZu3er399Ybb3Up7T1X2u+mEAKLFy9GYmIinnrqKVe7rMfTJ6ege4iNjRW33XabmDRpkpg0\naZJ48sknXc9ZrVZhMBjEuHHjRFlZmav9iy++EOPHjxcGg0EsXbrU5zW+//77Qq/Xi/DwcKHT6cSM\nGTOEEEL85S9/EUlJSWLSpEli8uTJYseOHQGrsa86hVDOsezpkUceERMmTBATJ04UP//5z0Vzc/M1\naw6UnTt3ivj4eGEwGMQrr7wS6HJcTpw4IZKTk0VycrJISkpy1dba2iqmT58u4uLihNlsFmfPnvV7\nbQsWLBBjxowRoaGhQq/Xiy1btvRZV6De8551bt68WXG/m59++qnQaDQiOTnZlZe7du2S9Xjy4iwi\nIhXh7RLtiNGiAAAAL0lEQVSJiFSEoU9EpCIMfSIiFWHoExGpCEOfiEhFGPpERCrC0CciUhGGPhGR\nivx/oRVlsekb2EUAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Exercise 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the dots with a different color according to the continent they would be on." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While matplotlib provides a simple facility for visualizing numerical data in a variety of ways, we will use a supplementary toolkit called *Basemap* that enhances matplotlib to specifically deal with spatial data. We need to import this library and can do so using: " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from mpl_toolkits.basemap import Basemap" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a Basemap object that will allow us to project the coordinates onto map. For this example we are going to use a [Robinson Projection](http://en.wikipedia.org/wiki/Robinson_projection)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "basemap_graph = Basemap(projection='robin', lat_0=0.0, lon_0=0.0)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters `lat_0` and `lon_0` define the center of the map. Now let's add some features to our map using methods defined by the `bm` object. We will also use the object itself to get the coordinates of the bootcamps in the projection given our original longitudes and latitudes. We will also tell pyplot to make the figure 12 inches by 12 inches to make it more legible." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pyplot.figure(figsize=(12,12))\n", + "basemap_graph.drawcoastlines()\n", + "basemap_graph.drawcountries()\n", + "basemap_graph.fillcontinents()\n", + "basemap_graph.drawmeridians(np.arange(-180,180,20))\n", + "basemap_graph.drawparallels(np.arange(-90,90,20))\n", + "\n", + "x, y = basemap_graph(lon, lat)\n", + "basemap_graph.plot(x, y, 'o', markersize=4, color='red')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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jMTIyYuTIkQQEBKCtrS2p0Xrv3j2OHDnCtm3b+PDDDxk1ahRDhw6VTF/D3wuN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\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y6NAh5s+f/8w6ZWVl7Nixg1mzZqFQKKisrEShUNC1a1fatGnDV199hZeXF4MGDRLtvv8r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\n5GXx4sUEBATg6ek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+ "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final line of the above code cell mimics matplotlib's built-in `plot` method to plot our projected coordinates onto the map. \n", + "\n", + "With just a handful of lines of code, you see that we can create a rich visualization of our data." + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Exercise 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Integrate the coloring scheme from Exercise 1 into the Basemap projection.\n", + "2. Try out a different projection that better shows the boot camp locations in North America. Here is the list of projections in Basemap: http://matplotlib.org/basemap/users/mapsetup.html" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + } + ], + "metadata": {} + } + ] +} diff --git a/novice/python/swc_bc_coords.csv b/novice/python/swc_bc_coords.csv new file mode 100644 index 0000000..c040eaf --- /dev/null +++ b/novice/python/swc_bc_coords.csv @@ -0,0 +1,113 @@ +# Latitude, Longitude + 43.661476,-79.395189 + 39.332604,-76.623190 + 45.703255, 13.718013 + 43.661476,-79.395189 + 39.166381,-86.526621 + 36.802151,-121.788163 + 37.808381,-122.267579 + 41.790113,-87.600732 + 41.744949,-111.804294 + 51.559882, -0.133458 + 42.727288,-84.482106 + 54.980095, -1.614614 + 53.523454,-113.525995 + 49.261715,-123.253430 + 39.327580,-76.620758 + 48.831673, 2.355623 + 42.359133,-71.093201 + 43.470130,-80.535771 + 44.632261,-63.580263 + 43.783551,-79.186397 + 53.948193, -1.052914 + 59.939959, 10.721750 + 40.808078,-73.963568 + 40.428267,-86.914325 + 37.875928,-122.250042 + 49.261715,-123.253430 + 37.869500,-122.258949 + 54.980095, -1.614614 + 34.141411,-118.124900 + 38.831513,-77.308746 + 51.757137, -1.256905 + 43.261328,-79.920248 + 38.648056,-90.305096 + 32.895330,-117.242188 + 34.227425,-77.879179 + 21.300662,-157.819165 + 55.945328, -3.191184 + 30.283599,-97.734428 + 49.261715,-123.253430 + 41.790113,-87.600732 + 45.417417,-73.948928 + 43.469128,-80.539865 + 49.261715,-123.253430 + 48.264934, 11.669121 + 43.647118,-79.394300 + 48.536980, 9.058935 + 40.808078,-73.963568 + 37.228384,-80.423417 + 49.261715,-123.253430 +-33.773636,151.112005 +-37.825328,144.951621 + 47.655965,-122.309377 + 37.875928,-122.250042 + 38.031441,-78.499356 + 33.900058, 35.482727 + 41.744949,-111.804294 + 22.310100, 39.125900 + 32.236358,-110.949540 + 51.524789, -0.133578 +-33.929492, 18.865391 + 53.467102, -2.233958 + 37.869500,-122.258949 + 53.478349, -2.241605 + 48.826290, 2.346420 + 39.291389,-76.625000 + 43.077180,-89.403990 + 52.333990, 4.863050 + 54.327070, 10.182650 + 39.071410,-77.464270 + 37.429490,-122.171860 + 37.875928,-122.250042 + 43.647120,-79.394300 + 51.759865, -1.258648 + 38.549260,-121.767090 + 36.008030,-78.923230 + 50.060833, 19.932778 + 36.002830,-78.938270 + 40.031310,-105.245900 + 42.388889,-72.527778 + 53.523450,-113.526000 + 50.937716, -1.395599 + 42.350760,-71.062740 + 41.789722,-87.599722 + 49.276765,-122.917957 + 32.887151,-117.246212 + 41.790113,-87.600732 + 42.362500,-71.085000 + 30.283599,-97.734428 +-43.523333,172.581944 + 35.208590,-97.445660 + 59.939959, 10.721750 + 30.538978,114.321928 + 39.166381,-86.526621 + 51.377743, -2.326378 + 37.228384,-80.423417 + 41.740800,-111.814160 + 41.705220,-86.235310 + 47.655000,-122.303333 + 40.443322,-79.943583 + 44.968657,-93.274570 + 38.958455,-95.243284 + 32.301920,-90.873352 + 43.077180,-89.403990 + 41.662930,-91.562030 + 51.457971, -2.601474 + 43.468889,-80.540000 + 42.724085,-84.476265 +-34.919159,138.604140 + 49.261111,-123.253056 +-37.908300,145.138000 + 34.052778,-118.255833 + 41.526667,-70.663056 diff --git a/novice/python/sys-version.py b/novice/python/sys-version.py new file mode 100644 index 0000000..f23ef3e --- /dev/null +++ b/novice/python/sys-version.py @@ -0,0 +1,2 @@ +import sys +print 'version is', sys.version -- 2.26.2