Binary_tree_encoder_decoder_Huffman

Plain Python (no libraries) to create a convertion table using binary tree to convert (encrypt and decrypt) strings to bits based on character's frequency.

Bellow, you can see the printed results for a data_sample based on average letter occurrence in brazilian portuguese (with the least common letters "w" and "y" appearing 1000 times):

Frequency for each character:

• 0.00869% "w"
• 0.00869% "y"
• 0.01739% "k"
• 0.18259% "x"
• 0.34780% "j"
• 0.40866% "z"
• 0.88688% "f"
• 0.90427% "b"
• 1.04339% "q"
• 1.11295% "h"
• 1.13034% "g"
• 1.45205% "v"
• 2.19111% "p"
• 2.41718% "l"
• 3.37362% "c"
• 3.77358% "t"
• 4.02574% "u"
• 4.12138% "m"
• 4.33875% "d"
• 4.39092% "n"
• 5.37345% "i"
• 5.67777% "r"
• 6.79071% "s"
• 9.32962% "o"
• 10.92948% "e"
• 12.72063% "a"
• 13.04234% " "

Conversion_table: (From the most to the least frequent character

Printables

• 000 o
• 010 e
• 100 a
• 101
• 0011 i
• 0110 r
• 1100 s
• 00101 l
• 01111 c
• 11010 t
• 11011 u
• 11100 m
• 11110 d
• 11111 n
• 001000 h
• 001001 g
• 011100 v
• 111011 p
• 0111010 f
• 0111011 b
• 1110101 q
• 11101000 z
• 111010011 j
• 1110100101 x
• 11101001000 k
• 111010010010 w
• 111010010011 y

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Encoding to standard 8 bits...

Encoding binary tree (encryption)...

Decoding encrypted bits...

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ANALYSIS FOR data_input:

data_input:

• data_input type: <class 'str'>
• data_input size: 11,501,000b (bytes or characters)
• Binary size: 92,008,000 bits (100.0%) 8.00 bits per character.
• Cryptography size: 46,628,000 bits ( 50.7%) 4.05 bits (average) per character.

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CONCLUSION:

You saved 5.40972MB from 10.96821MB using this encryption method! [Finished in 11.3s]