DigCod Python Scripts for TI-Nspire

This repository contains a collection of Python scripts designed for use with the TI-Nspire calculator.

Note: You may have to use a TI-nspire CX II, the older version TI-nspire CX may not support python scripts. It requires OS 5.20 or greater, the older TI-nspire CX can only be updated up to version ~ 4.5. You might take a look at Ndless

Usage

To use these scripts on your TI-Nspire calculator:

1. Connect your Handheld to the computer.
2. Open the TI-Nspire Student Software.
3. Create a new Python script within the software.
4. Copy and paste the content of each .py script into the new Python script you created.
5. Use the "File -> Save to Handheld" function to transfer the script to your calculator.

Cracked version of Students-software can be found here.

Alternatively:

1. Open Chrome (it's required to use Chrome for this method).*1
2. Navigate to TI-Nspire Navigator.
3. Choose the option to transfer files. *2

*1 This method doesn't work for linux. The only option for linux is AFAIK through a VM.

*2 Apparently you have to rename the .py to .tns to create a supported file type, but then you receive another error message when opening the file. So you prolly have to use the Students Software.

Running Scripts

To execute a script on the TI-Nspire:

1. Navigate to the script on your handheld.
2. Press ctrl+r to run the script.
3. Follow the on-screen prompts to input numbers and observe the output.

TODO

• Implement an option to abort the running script e.g. with ctrl+c
• Make the main-loops consistent, so every script has the same structure
• Rewrite the scripts in program editor, so they can also be used on the older CX-Version (will prolly never happen)
• Would be cool if all scripts are combined in a single script. E.g. a menu in which you can select a script and if the script has run through, you jump back to the menu.

Additional scripts TODO

• Make a RSA script based on the multiplicative_inverse.py that takes the given keys as input and calculates everything else
• Enhance the binary_decimal.py so it solves the exercise in the exam 1:1

Informationstheorie:

• Entscheidungsgehalt
• Informationsgehalt
• Entropie
• Redundanz der Quelle
• Mittlere Codewortlänge
• Redundanz des Codes
• Maximierung Entropie

Kanalmatrix:

• Berechnung der Kanalmatrix
• Berechnung der Eingangswahrscheinlichkeiten
• Berechnung der Ausgangswahrscheinlichkeiten
• Entropie am Kanaleingang H(X)
• Entropie am Kanalausgang H(Y)
• Transinformation
• Maximale Symbolrate

Blockcodes:

• Hammingdistanz
• Anzahl möglicher/gültiger Codewörter
• Dichtgepacktheit eines Codes
• Schnelle Mehrfachaddition