pcc-sampler 0.1.0

Synthetic complex-valued phase-coherence sample generator

PCC Sampler

pcc-sampler (Phase Coherence Comlex - Sampler) generates synthetic complex-valued image samples where the label is defined by spatial phase coherence rather than amplitude cues.

The package is intentionally small: config in, deterministic complex samples out. It is meant to be imported by experiments that study complex-valued neural networks.

Install

The examples below use the uv command. If uv is installed but not on PATH, run the same commands as python -m uv ....

Install the project environment with uv:

uv sync

Run a quick import check:

uv run python -c "from pcc import load_default_config; print(load_default_config())"

From another local uv project, add this repo in editable mode:

uv add --editable C:\Users\meisa\Projects\pcc

Or add it from a Git URL after publishing the repo:

uv add "pcc-sampler @ git+https://github.com/<owner>/<repo>.git"

Python Usage

from pcc import PCCConfig, PCCDataset

cfg = PCCConfig(
    N=128,
    phase_smooth_sigma=2.0,
    incoh_highpass_sigma=16.0,
    incoh_scale=0.2,
    uniformize_phase_hist=True,
)

dataset = PCCDataset(size=10_000, cfg=cfg, seed=42)
z, amplitude, phase, y = dataset[0]

Returned values:

• z: complex-valued sample, shape (N, N), dtype torch.complex64
• amplitude: amplitude field, shape (N, N), dtype torch.float32
• phase: phase field, shape (N, N), dtype torch.float32
• y: label tensor, where 0 is coherent and 1 is incoherent

YAML Config Usage

Create a YAML file:

N: 128
amp_radial_decay: 2.2
amp_smooth_sigma: 6.0
amp_range: [0.7, 1.4]

phase_smooth_sigma: 2.0
incoh_highpass_sigma: 16.0
incoh_scale: 0.2
global_phase: true
uniformize_phase_hist: true

translate_px: 16
rotate_deg: 30.0
noise_std: 0.1
renorm_amp: false

Load it directly:

from pcc import dataset_from_yaml

dataset = dataset_from_yaml("configs/pcc_phase.yaml", size=50_000, seed=7)

You can also load the built-in default:

from pcc import PCCDataset, load_default_config

cfg = load_default_config()
dataset = PCCDataset(size=1_000, cfg=cfg, seed=0)

Single Sample

from pcc import PCCConfig, make_deterministic_sample

cfg = PCCConfig()
z, amplitude, phase = make_deterministic_sample(cfg, y=0, seed=123, idx=0)

The same (seed, idx, y, config) combination produces the same sample.