**Example**:
- nosetests test_mean.py
+ nosetests -v test_mean.py
# Test Driven Development
## A TDD Example
-Say you want to write a fib() function which generates values of the
-Fibonacci sequence of given indexes. You would - of course - start by
-writing the test, possibly testing a single value:
-
-```python
-from nose.tools import assert_equal
-
-from pisa import fib
-
-def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-```
-
-You would *then* go ahead and write the actual function:
-
-```python
-def fib(n):
- # you snarky so-and-so
- return 1
-```
-
-And that is it right?! Well, not quite. This implementation fails for
-most other values. Adding tests we see that:
-
-```python
-def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-
-def test_fib2():
- obs = fib(0)
- exp = 0
- assert_equal(obs, exp)
-
- obs = fib(1)
- exp = 1
- assert_equal(obs, exp)
-```
-
-This extra test now requires that we bother to implement at least the
-initial values:
-
-```python
-def fib(n):
- # a little better
- if n == 0 or n == 1:
- return n
- return 1
-```
-
-However, this function still falls over for `2 < n`. Time for more
-tests!
-
-```python
-def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-
-def test_fib2():
- obs = fib(0)
- exp = 0
- assert_equal(obs, exp)
-
- obs = fib(1)
- exp = 1
- assert_equal(obs, exp)
-
-
-def test_fib3():
- obs = fib(3)
- exp = 2
- assert_equal(obs, exp)
-
- obs = fib(6)
- exp = 8
- assert_equal(obs, exp)
-```
-
-At this point, we had better go ahead and try do the right thing...
-
-```python
-def fib(n):
- # finally, some math
- if n == 0 or n == 1:
- return n
- else:
- return fib(n - 1) + fib(n - 2)
-```
-
-Here it becomes very tempting to take an extended coffee break or
-possibly a power lunch. But then you remember those pesky negative
-numbers and floats. Perhaps the right thing to do here is to just be
-undefined.
-
-```python
-def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-
-def test_fib2():
- obs = fib(0)
- exp = 0
- assert_equal(obs, exp)
-
- obs = fib(1)
- exp = 1
- assert_equal(obs, exp)
-
-
-def test_fib3():
- obs = fib(3)
- exp = 2
- assert_equal(obs, exp)
-
- obs = fib(6)
- exp = 8
- assert_equal(obs, exp)
-
-
-def test_fib3():
- obs = fib(13.37)
- exp = NotImplemented
- assert_equal(obs, exp)
-
- obs = fib(-9000)
- exp = NotImplemented
- assert_equal(obs, exp)
-```
-
-This means that it is time to add the appropriate case to the function
-itself:
-
-```python
-def fib(n):
- # sequence and you shall find
- if n < 0 or int(n) != n:
- return NotImplemented
- elif n == 0 or n == 1:
- return n
- else:
- return fib(n - 1) + fib(n - 2)
-```
-
-# Quality Assurance Exercise
-
-Can you think of other tests to make for the fibonacci function? I promise there
-are at least two.
-
-Implement one new test in test_fib.py, run nosetests, and if it fails, implement
-a more robust function for that case.
-
-And thus - finally - we have a robust function together with working
-tests!
-
-# Exercise
-
-**The Problem:** In 2D or 3D, we have two points (p1 and p2) which
-define a line segment. Additionally there exists experimental data which
-can be anywhere in the domain. Find the data point which is closest to
-the line segment.
-
-In the `close_line.py` file there are four different implementations
-which all solve this problem. [You can read more about them
-here.](http://inscight.org/2012/03/31/evolution_of_a_solution/) However,
-there are no tests! Please write from scratch a `test_close_line.py`
-file which tests the closest\_data\_to\_line() functions.
-
-*Hint:* you can use one implementation function to test another. Below
-is some sample data to help you get started.
-
-
-> -
-
-```python
-import numpy as np
-
-p1 = np.array([0.0, 0.0])
-p2 = np.array([1.0, 1.0])
-data = np.array([[0.3, 0.6], [0.25, 0.5], [1.0, 0.75]])
-```
+++ /dev/null
-import numpy as np
-from scipy.optimize import fmin
-
-#
-# Attempt 1
-#
-
-def point_on_line1(x, p1, p2):
- y = p1[1] + (x - p1[0])*(p2[1] - p1[1]) / (p2[0] - p1[0])
- return np.array([x, y])
-
-
-def dist_from_line1(x, pdata, p1, p2):
- pline = point_on_line1(x, p1, p2)
- return np.sqrt(np.sum((pline - pdata)**2))
-
-
-def closest_data_to_line1(data, p1, p2):
- dists = np.empty(len(data), dtype=float)
- for i, pdata in enumerate(data):
- x = fmin(dist_from_line1, p1[0], (pdata, p1, p2), disp=False)[0]
- dists[i] = dist_from_line1(x, pdata, p1, p2)
- imin = np.argmin(dists)
- return imin, data[imin]
-
-
-#
-# Attempt 2
-#
-
-def dist_from_line2(pdata, p1, p2):
- a = np.sqrt(np.sum((p1 - pdata)**2))
- b = np.sqrt(np.sum((p2 - pdata)**2))
- c = np.sqrt(np.sum((p2 - p1)**2))
- h = a * np.sqrt(1.0 - ((a**2 + c**2 - b**2) / (2.0 * a * c))**2)
- return h
-
-def closest_data_to_line2(data, p1, p2):
- dists = np.empty(len(data), dtype=float)
- for i, pdata in enumerate(data):
- dists[i] = dist_from_line2(pdata, p1, p2)
- imin = np.argmin(dists)
- return imin, data[imin]
-
-#
-# Attempt 3
-#
-
-def perimeter3(pdata, p1, p2):
- a = np.sqrt(np.sum((p1 - pdata)**2))
- b = np.sqrt(np.sum((p2 - pdata)**2))
- c = np.sqrt(np.sum((p2 - p1)**2))
- return (a + b + c)
-
-def closest_data_to_line3(data, p1, p2):
- peris = np.empty(len(data), dtype=float)
- for i, pdata in enumerate(data):
- peris[i] = perimeter3(pdata, p1, p2)
- imin = np.argmin(peris)
- return imin, data[imin]
-
-#
-# Attempt 4
-#
-
-def closest_data_to_line4(data, p1, p2):
- return data[np.argmin(np.sqrt(np.sum((p1 - data)**2, axis=1)) + \
- np.sqrt(np.sum((p2 - data)**2, axis=1)))]
-
projects / swc-testing-nose.git / commitdiff
