Quick Start

Installation

pip install boofun

Development:

git clone https://github.com/GabbyTab/boofun.git
cd boofun
pip install -e ".[dev]"

Creating Functions

import boofun as bf

# From truth table (list or numpy array)
xor_2 = bf.create([0, 1, 1, 0])

# From callable (your own function)
f = bf.create(lambda x: x[0] and x[1], n=3)

# From file
f = bf.load("function.json")
f = bf.create("function.bf")  # Aaronson format

# Built-in families (use descriptive names)
maj_5 = bf.majority(5)
par_4 = bf.parity(4)
dic_3 = bf.dictator(3, i=0)
tribes_2_6 = bf.tribes(2, 6)
ltf = bf.weighted_majority([3, 2, 1, 1, 1])

Flexible Input Types

bf.create() auto-detects input type:

bf.create([0, 1, 1, 0])           # list → truth table
bf.create(np.array([0, 1, 1, 0])) # numpy → truth table
bf.create(lambda x: x[0] ^ x[1], n=2)  # callable
bf.create({frozenset(): 1, frozenset({0}): 1})  # dict → polynomial
bf.create("x0 & x1")              # string → symbolic
bf.create({(0,1), (1,0)})         # set of tuples → which inputs are True

# From files
bf.load("func.json")   # JSON with metadata
bf.load("func.bf")     # Aaronson .bf format
bf.load("func.cnf")    # DIMACS CNF

Evaluation

Functions are callable. Evaluation is flexible:

maj_5 = bf.majority(5)

# Callable syntax (preferred)
maj_5([1, 1, 0, 0, 1])  # → True (majority satisfied)
maj_5(7)                # → True (7 = 00111, 3 ones)

# Equivalent .evaluate() method
maj_5.evaluate([1, 1, 0, 0, 1])

# All input formats work
maj_5(3)                    # Integer index (binary: 00011)
maj_5([0, 1, 1, 0, 0])      # List of bits
maj_5((0, 1, 1, 0, 0))      # Tuple
maj_5(np.array([0,1,1,0,0]))  # NumPy array

Analysis

maj_5 = bf.majority(5)

# Fourier analysis
maj_5.fourier()           # Fourier coefficients
maj_5.influences()        # Per-variable
maj_5.total_influence()   # I[f]
maj_5.noise_stability(0.9)
maj_5.degree()            # Fourier degree

# Query complexity
from boofun.analysis import complexity
complexity.D(maj_5)       # Decision tree depth D(f)
complexity.s(maj_5)       # Max sensitivity s(f)

maj_5.analyze()  # Dict with all metrics

Properties

f.is_linear()
f.is_monotone()
f.is_balanced()
f.is_junta(2)

Representations

f.get_representation('truth_table')
f.get_representation('anf')
f.get_representation('fourier_expansion')

Visualization

Requires matplotlib:

viz = bf.BooleanFunctionVisualizer(f)
viz.plot_influences()
viz.plot_fourier_spectrum()

Next