Conversion of all rst to corresponding markdown files

We use git-flavoured markdown

W. Trevor King: I dropped everything from the original 4ba1cf2 except
for the python/sw_engineering/ modifications.

Conflicts:
	LICENSE.md
	python/data_structures/Readme.md
	python/documentation/Readme.md
	python/documentation/close_line.md
	python/flow_control/Readme.md
	python/functions_and_modules/Readme.md
	python/scipy/Readme.md
	shell/Readme.md
	version-control/git/remote/Readme.md

diff --git a/python/sw_engineering/README.md b/python/sw_engineering/README.md
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+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!
+
+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. Today we'll show you
+how to do that.
+
+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.

diff --git a/python/sw_engineering/README.rst b/python/sw_engineering/README.rst
deleted file mode 100644 (file)
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--- a/python/sw_engineering/README.rst
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-====================================
-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!
-
-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. Today we'll show you how to do that.
-
-=========
-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.

diff --git a/python/sw_engineering/dev_notes.md b/python/sw_engineering/dev_notes.md
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index 0000000..123dff6
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+++ b/python/sw_engineering/dev_notes.md
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+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
+
+Students may want an IPython notebook open as a scratch pad.
+
+Lesson Plan
+-----------
+
+We're 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`), then
+    demonstrate putting a function in a file and importing it.
+
+    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). (They may want to develop the function in
+    the IPython notebook and then copy it over.)
+
+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?
+    - Demonstrate a potential test solution comparing output in an `if` and
+      printing a message if something doesn't match.
+    - Explain `assert` and demonstrate
+    - Explain unit tests and show how to 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.

diff --git a/python/sw_engineering/dev_notes.rst b/python/sw_engineering/dev_notes.rst
deleted file mode 100644 (file)
index 4d86738..0000000
--- a/python/sw_engineering/dev_notes.rst
+++ /dev/null
@@ -1,83 +0,0 @@
-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
-
-Students may want an IPython notebook open as a scratch pad.
-
-Lesson Plan
------------
-
-We're 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`), then demonstrate
-   putting a function in a file and importing it.
-
-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). (They may want to develop the function in the IPython
-notebook and then copy it over.)
-
-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?
-
-   - Demonstrate a potential test solution comparing output in an `if` and
-     printing a message if something doesn't match.
-   - Explain `assert` and demonstrate
-   - Explain unit tests and show how to 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.