--- /dev/null
+====================================
+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 introduction
+to Python lesson we wrote a function that reads these files but it's stuck
+off in an IPython notebook. We could copy and paste it into a new notebook
+every time we want to use it but that gets tedious and makes it difficult to
+add features to the function. The ideal solution would be to keep the
+function in one spot and use it over and over again from many different places.
+Python modules to the rescue!
+
++Today we're going to move beyond the IPython notebook. Most Python code is
++stored in `.py` files and then used in other `.py` files where it has been
++pulled in using an `import` statement.
+
++=========
++Exercises
++=========
++
++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
++----------
++
++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 text file called `test_animals.py`. Make a function called
++`test_mean` that runs your theoretical mean function through several tests.
++
++Exercise 3
++----------
++
++Write the mean function in `animals.py` and verify that it passes
++your tests.
++
++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
++----------
++
++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.
--- /dev/null
- 1. Demonstrate putting code in a `.py` file and running it.
- 2. Demonstrate importing a module (maybe `math` or `glob`)
+Dev Notes
+=========
+
+Things we want to demonstrate:
+
+- write lots of small tools (functions)
+- document your code
+- test your code, including TDD
+- debugging
+
+ - build in a crash somewhere and explain tracebacks
+ - do an import pdb; pdb.set_trace()
+
+Create a few utilities to work with the `*animal.txt` files from
+introductory Python.
+
+Put all the functionality in one file and make small scripts that import
+from that file, parse command line arguments, and do stuff.
+
+- average number of an animal seen per sighting
+- total number of an animal seen over the season
+- all the sightings on a given day
+
+Students may want an IPython notebook open as a scratch pad.
+
+Lesson Plan
+-----------
+
- Exercise:
++Going to make a command line script that will print the average group
++size in a given file for a given animal.
+
++0. Discuss libraries and re-using code.
++1. Demonstrate importing a module (maybe `math` or `glob`)
++
++Exercise: 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).
++
++2. Go over the exercise and talk about documentation as you do.
++3. How do we know the function works correctly? Try importing and running it
++ on a small file.
++4. But what if we want to make changes and make sure it still works afterward,
++ or we want to make sure it isn't broken when we add new stuff later?
++ Explain unit tests and show how to write one and run with nosetests.
++5. Explain test driven development.
++
++Exercise: 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 text file called `test_animals.py`. Make a function called
++`test_mean` that runs your theoretical mean function through several tests.
++
++6. When going over this with the students, put in a test with an empty list.
++ Also put in tests with lists that contain all ints.
++
++Exercise: Write the mean function in `animals.py` and verify that it passes
++your tests.
++
++7. When going over this with the students do not put in a test for an empty
++ list. The error when running the tests will give a chance to teach
++ tracebacks. Also do not put in any coercion to float so some means will
++ be wrong due to integer truncation. More debugging, `--pdb-failure`.
++8. The last piece we'll need is a function that takes the output of the file
++ reader function and returns only the data relevant to a given animal.
++ Write this function as a live exercise with the students participating,
++ though they can do it on their own if they want.
++
++Exercise: 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: 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.
++
++9. After going over exercises, conclude by showing how to make a command line
++ script that takes a file name and animal name as arguments and prints
++ the average number of animals per sighting.
++10. If time permits, could demonstrate `pdb.set_trace()` somewhere in the
++ script execution.
projects / swc-testing-nose.git / commitdiff