From ba4bfa2ccfdf58c3a732ff2ebe36b1a671e6f116 Mon Sep 17 00:00:00 2001
From: Maximilian Friedersdorff <max@friedersdorff.com>
Date: Wed, 13 Mar 2019 20:11:35 +0000
Subject: [PATCH] Add README that outlines unittesting

---
 README.pdf | 727 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 README.rst | 111 ++++++++
 2 files changed, 838 insertions(+)
 create mode 100644 README.pdf
 create mode 100644 README.rst

diff --git a/README.pdf b/README.pdf
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diff --git a/README.rst b/README.rst
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+++ b/README.rst
@@ -0,0 +1,111 @@
+Unit testing
+============
+
+Unit testing means testing small portions (individual functions, modules, 
+classes) of your program, independently of the rest, in an automated fashion.  
+The tests usually consist of a set of functions that are distributed along with
+the rest of the source code.  
+
+Each test will call part of the main source code, usually with some dummy data, 
+and verify that the behaviour of the code is as expected.  It is typical to test
+a few edge cases and boundary conditions in such a way.  A primitive attempt
+to test a python function, that implements a mathematical function might look
+like this:
+
+.. code-block:: python
+   
+   def calc_deg_sin(x):
+      """This is an elaborate python function that calculates the mathematical
+      function f(x)=sin(x), where x is in degrees. """
+      from math import pi,sin
+      return sin(x*(pi/189))
+
+   def test_sin_90_is_1():
+      assert calc_deg_sin(90) == 1
+
+   def test_sin_0_is_0():
+      assert calc_deg_sin(0) == 0
+
+   def test_sin_180_and_360_is_0():
+      assert calc_deg_sin(180) == 0 && calc_deg_sin(360) == 0
+
+Why
+---
+
+Unit testing is a useful thing to do for a number of reasons, including (in no
+particular order):
+
+- Unit tests document the expected behaviour of your program.
+- Unit tests allow you to verify that you did not make unintended changes to the 
+  behaviour of your program when you add features, or change the implementation.
+- Unit tests catch many bugs before your program runs for real (before you submit
+  the job that takes multiple days, but crashes right before it prints out the 
+  result).
+- Unit tests allow you to conveniently play with unfamiliar libraries/apis/modules
+  language features.
+
+How
+---
+
+Use a test runner.  A test runner will find all functions in a file or module 
+that are defined to be tests, and run them.  It will also give you a convenient
+output of the result, and some basic statistics.  Test runners vary greatly in 
+their ease of use, flexibility and the usefulness of their output.
+
+In this case I am using ``pytest``.  ``pytest`` will execute all top level 
+functions in a file that start with ``test_``.  You run the tests in a file with
+``pytest FILE [FILE] [FILE]...``.
+
+What
+----
+Look at the examples in this directory.  The files staring with 
+``<somenumber>_`` contain tests.  
+
+The first file contains some basic tests.  
+Some will fail, some will pass.  
+
+The second file contains tests that check if
+a function called ``incorrect_ackermann`` will produce the expected values for
+a number of different inputs (hint: it won't).  Fix any problems that turn up (
+the definition of the ackermann function is at the very bottom of the file that
+implements it).
+
+The third file contains tests for two functions, ``collatz`` and ``longest_collatz``.
+These return the length of the collatz sequence for a particular integer, and the
+integer with the longest collatz sequence that is smaller than the input integer.
+Implement these functions so that they pass the tests.  This is based on the 
+`Euler Project problem number 14`_
+
+Test driven development
+-----------------------
+
+Test driven development is a practice, whereby one first writes the tests that 
+fully describe a certain feature (or test for a particular bug).  These tests
+should all initially fail.  Then one writes the simplest implementation that will
+make these tests pass.  Doing things in this order forces one to be very thorough
+with writing tests.  It is a lot of work however.
+
+Pick your favourite algorithm (ideally one *you* can implement in python).  Write
+some tests for a function (or functions) that implements that algorithm, then 
+implement that algorithm to pass the tests that you've written.
+
+Some good algorithms to tests with might be:
+
+- Binary search
+- Your favourite sorting algorithm
+- DFS/BFS
+- Calculating the edit distance between two strings
+- Kernel convolution
+- Maximum parsimony
+- Calculate the Levenshtein or edit distance between two strings
+- Calculating the value of the fibonacci sequence, the sum of integers up to n,
+  the first n primes or some other such mathematical function
+
+
+
+
+
+
+
+
+.. _Euler Project problem number 14: https://projecteuler.net/project/resources/p102_triangles.txt
-- 
2.47.1