simvx.core.noise

Noise generation — FastNoiseLite-inspired procedural noise, pure NumPy vectorized.

Supports Perlin, Simplex, Value, and Cellular (Worley) noise with fractal layering (FBM, Ridged, Ping-Pong). All implementations accept and return NumPy arrays with no Python loops over pixels.

Examples: from simvx.core.noise import FastNoiseLite, NoiseType, FractalType

noise = FastNoiseLite(seed=42)
noise.noise_type = NoiseType.SIMPLEX
noise.fractal_type = FractalType.FBM
noise.fractal_octaves = 4

val = noise.get_noise_2d(10.5, 20.3)
image = noise.get_image(512, 512, scale=0.02)  # HxW float array in [-1, 1]

Module Contents

Classes

NoiseType	Noise algorithm selection.
FractalType	Fractal layering mode.
FastNoiseLite	Procedural noise generator inspired by FastNoiseLite.

Data

__all__

API

simvx.core.noise.__all__

[‘FastNoiseLite’, ‘NoiseType’, ‘FractalType’]

class simvx.core.noise.NoiseType

Bases: enum.IntEnum

Noise algorithm selection.

Initialization

Initialize self. See help(type(self)) for accurate signature.

PERLIN

0

SIMPLEX

1

VALUE

2

CELLULAR

3

__abs__()

__add__()

__and__()

__bool__()

__ceil__()

__delattr__()

__dir__()

__divmod__()

__eq__()

__float__()

__floor__()

__floordiv__()

__format__()

__ge__()

__getattribute__()

__getnewargs__()

__getstate__()

__gt__()

__hash__()

__index__()

__int__()

__invert__()

__le__()

__lshift__()

__lt__()

__mod__()

__mul__()

__ne__()

__neg__()

__new__()

__or__()

__pos__()

__pow__()

__radd__()

__rand__()

__rdivmod__()

__reduce__()

__reduce_ex__()

__repr__()

__rfloordiv__()

__rlshift__()

__rmod__()

__rmul__()

__ror__()

__round__()

__rpow__()

__rrshift__()

__rshift__()

__rsub__()

__rtruediv__()

__rxor__()

__setattr__()

__sizeof__()

__str__()

__sub__()

__subclasshook__()

__truediv__()

__trunc__()

__xor__()

as_integer_ratio()

bit_count()

bit_length()

conjugate()

class denominator

class imag

is_integer()

class numerator

class real

to_bytes()

__deepcopy__(memo)

__copy__()

name()

value()

class simvx.core.noise.FractalType

Bases: enum.IntEnum

Fractal layering mode.

Initialization

Initialize self. See help(type(self)) for accurate signature.

NONE

0

FBM

1

RIDGED

2

PING_PONG

3

__abs__()

__add__()

__and__()

__bool__()

__ceil__()

__delattr__()

__dir__()

__divmod__()

__eq__()

__float__()

__floor__()

__floordiv__()

__format__()

__ge__()

__getattribute__()

__getnewargs__()

__getstate__()

__gt__()

__hash__()

__index__()

__int__()

__invert__()

__le__()

__lshift__()

__lt__()

__mod__()

__mul__()

__ne__()

__neg__()

__new__()

__or__()

__pos__()

__pow__()

__radd__()

__rand__()

__rdivmod__()

__reduce__()

__reduce_ex__()

__repr__()

__rfloordiv__()

__rlshift__()

__rmod__()

__rmul__()

__ror__()

__round__()

__rpow__()

__rrshift__()

__rshift__()

__rsub__()

__rtruediv__()

__rxor__()

__setattr__()

__sizeof__()

__str__()

__sub__()

__subclasshook__()

__truediv__()

__trunc__()

__xor__()

as_integer_ratio()

bit_count()

bit_length()

conjugate()

class denominator

class imag

is_integer()

class numerator

class real

to_bytes()

__deepcopy__(memo)

__copy__()

name()

value()

class simvx.core.noise.FastNoiseLite(seed: int = 0, noise_type: simvx.core.noise.NoiseType = NoiseType.PERLIN, frequency: float = 0.01)

Procedural noise generator inspired by FastNoiseLite.

Pure NumPy implementation — all evaluation is vectorized. Supports Perlin, Simplex, Value, and Cellular noise with optional fractal layering.

Args: seed: Random seed for permutation table generation. noise_type: Base noise algorithm (default PERLIN). frequency: Coordinate multiplier — lower values produce larger features.

Examples: noise = FastNoiseLite(seed=42, noise_type=NoiseType.SIMPLEX) noise.fractal_type = FractalType.FBM noise.fractal_octaves = 5 image = noise.get_image(256, 256, scale=0.05)

Initialization

property seed: int

get_noise_2d(x: float, y: float) → float

Evaluate noise at a single 2D point. Returns float in ~[-1, 1].

get_noise_3d(x: float, y: float, z: float) → float

Evaluate noise at a single 3D point. Returns float in ~[-1, 1].

get_noise_2d_array(xs: numpy.ndarray, ys: numpy.ndarray) → numpy.ndarray

Evaluate noise at arrays of 2D points. Returns array in ~[-1, 1].

get_noise_3d_array(xs: numpy.ndarray, ys: numpy.ndarray, zs: numpy.ndarray) → numpy.ndarray

Evaluate noise at arrays of 3D points. Returns array in ~[-1, 1].

get_image(width: int, height: int, scale: float = 1.0) → numpy.ndarray

Generate an HxW noise image. Returns float32 array in ~[-1, 1].

Args: width: Image width in pixels. height: Image height in pixels. scale: Coordinate scale multiplier (applied on top of frequency).