Q: How do we use constraint propagation to solve the naked twins problem?
A: Constraint Propagation has been used applying the naked-twins technique implementing the following steps:

1. Start searching for boxes with 2 digits in length in all units.
2. Select only the naked_twins candidates: the constrain is that naked-twins are when there are just two candidates being looked for in a unit.
3. Apply the naked-twins technique for all the units eliminating the naked-twins values, if possible. As a result some boxes are left with only a single digit, if we are lucky, or other new naked-twins pairs could appear.
4. This new function can be used with eliminate and only_choise functions, to enforce our constraint propagation strategy in order to solve sudoku.

Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: For my understanding, the diagonal sudoku is a regular sudoku with one more constraint: among the two main diagonals, the numbers 1 to 9 should all appear exactly once. Then I reuse all the functions already implemented, adding the 2 diagonal units to the list of units, in order to enforce the constraint propagation.

This project requires Python 3.

We recommend students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. Please try using the environment we provided in the Anaconda lesson of the Nanodegree.

Optionally, you can also install pygame if you want to see your visualization. If you've followed our instructions for setting up our conda environment, you should be all set.

If not, please see how to download pygame here.

• solution.py - You'll fill this in as part of your solution.
• solution_test.py - Do not modify this. You can test your solution by running python solution_test.py.
• PySudoku.py - Do not modify this. This is code for visualizing your solution.
• visualize.py - Do not modify this. This is code for visualizing your solution.

To visualize your solution, please only assign values to the values_dict using the assign_values function provided in solution.py

Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback.

The setup is simple. If you have not installed the client tool already, then you may do so with the command pip install udacity-pa.

To submit your code to the project assistant, run udacity submit from within the top-level directory of this project. You will be prompted for a username and password. If you login using google or facebook, visit [this link](https://project-assistant.udacity.com/auth_tokens/jwt_login for alternate login instructions.

This process will create a zipfile in your top-level directory named sudoku-.zip. This is the file that you should submit to the Udacity reviews system.