Pleroslzcomplexity

Python usage cheatsheet

Install, prepare inputs and call the five functions.

Install and run¶

python -m pip install lzcomplexity

import lzcomplexity as lz

Python 3.9+.

Choose your symbols¶

Input	Analyzed as
`"ACGT"`	UTF-8 bytes.
`b"ACGT"`	Raw bytes, unchanged.
`bytes([0, 1, 10])`	Three symbols: 0, 1, 10.
`["AC", "GT"]`	Joined text: `"ACGT"`.
`[0, 1, 10]`	Decimal text: `"0110"`.

For state codes 0–255, use bytes(labels); for a NumPy uint8 array, use labels.tobytes(). Input guide.

Continuous signals → symbols¶

Threshold, bin, or encode a signal before analysis:

signal = [-0.2, 0.5, 0.1, -0.7,
          0.8, 0.4, -0.1, -0.3]
symbols = bytes(x > 0 for x in signal)
h = lz.h(symbols, log_base=2)

Here nonpositive → 0, positive → 1.

Factorization¶

count, boundaries = lz.factorization(
    "1000110011101"
)
# count: 5
# boundaries: [0, 1, 2, 5, 9, 13]

Use the returned count. Slice encoded bytes using boundaries. Worked example.

Entropy density h¶

h = lz.h("01010101", log_base=2)
# 0.75

log_base=2 selects bits per input byte. Returns float.

E · effective measure complexity¶

sequence = b"00010111" * 256
E, increments = lz.emc(
    sequence, log_base=2,
    max_block_size=16,
)

Unpack (E, increments). max_block_size sets the maximum range.

NID · compare sequences¶

distance = lz.nid("abcd", "abce")
# 0.25

Returns float. Use the same symbol encoding for both inputs.

Get the complete result¶

result = lz.lz76(
    sequence, log_base=2,
    max_block_size=16,
)
count = result["complexity"]
h = result["h"]
E = result["emc"]["value"]
boundaries = result["factors"]

Full result reference.

Two useful controls¶

Setting	Effect
`log_base=2`	h in bits per input byte.
`max_block_size=16`	E includes ranges up to 16 bytes.

Omit these controls for automatic settings. max_block_size belongs to emc and lz76.