added readme from library section that @jiffyclub taught in berkeley.

diff --git a/python/testing/Readme.md b/python/testing/Readme.md
index 562892bb2a725039f2abf9f389b793927ff337b4..5a3dfdba72032afff49c663512fdae350d68b426 100644 (file)
--- a/python/testing/Readme.md
+++ b/python/testing/Readme.md
@@ -543,8 +543,7 @@ 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]])
 ```
-Building a Library of Code you Trust
-====================================
+# Building a Library of Code you Trust
 
 Suppose we’re going to be dealing a lot with these animal count files,
 and doing many different kinds of analysis with them. In the
@@ -560,19 +559,16 @@ stored in \`.py\` files and then used in other \`.py\` files where it
 has been pulled in using an \`import\` statement. Today we’ll show you
 how to do that.
 
-Exercises
-=========
+## Exercises
 
-Exercise 1
-----------
+### Exercise 1
 
 Make a new text file called \`animals.py\`. Copy the file reading
 function from yesterday’s IPython notebook into the file and modify it
 so that it returns the columns of the file as lists (instead of printing
 certain lines).
 
-Exercise 2
-----------
+### Exercise 2
 
 We’re going to make a function to calculate the mean of all the values
 in a list, but we’re going to write the tests for it first. Make a new
@@ -580,20 +576,17 @@ text file called \`test\_animals.py\`. Make a function called
 \`test\_mean\` that runs your theoretical mean function through several
 tests.
 
-Exercise 3
-----------
+### Exercise 3
 
 Write the mean function in \`animals.py\` and verify that it passes your
 tests.
 
-Exercise 4
-----------
+### Exercise 4
 
 Write tests for a function that will take a file name and animal name as
 arguments, and return the average number of animals per sighting.
 
-Exercise 5
-----------
+### Exercise 5
 
 Write a function that takes a file name and animal name and returns the
 average number of animals per sighting. Make sure it passes your tests.