YangyangTan/4Dphi4

4D phi4 Wolff FAHMC configurations

This dataset contains 4D scalar phi4 lattice configurations generated with a Wolff + Fourier-Accelerated HMC sampler.

Files

File	Lattice	Shape of cfgs	dtype
cfgs_wolff_fahmc_k=0.145_l=0.9_8^4.npz	8^4	(8, 8, 8, 8, 5120)	float64
cfgs_wolff_fahmc_k=0.145_l=0.9_16^4.npz	16^4	(16, 16, 16, 16, 5120)	float64

Each .npz file contains:

• cfgs: field configurations, with samples on the last axis.
• kappa: hopping parameter.
• lambda: quartic coupling.
• N: lattice size.
• n_samples: number of stored configurations.
• epsilon_final: final HMC step size.
• acc_rate: production HMC acceptance rate.

In Python, one configuration is cfgs[:, :, :, :, i], where i is the zero-based sample index.

Download

Install the Hugging Face Hub client:

pip install -U huggingface_hub

Download one file:

hf download YangyangTan/4Dphi4 \
  "cfgs_wolff_fahmc_k=0.145_l=0.9_8^4.npz" \
  --repo-type dataset \
  --local-dir .

Load with NumPy

import numpy as np

path = "cfgs_wolff_fahmc_k=0.145_l=0.9_8^4.npz"
data = np.load(path)

cfgs = data["cfgs"]
kappa = data["kappa"].item()
lam = data["lambda"].item()
N = data["N"].item()

phi0 = cfgs[:, :, :, :, 0]
print(cfgs.shape, cfgs.dtype)
print(kappa, lam, N)

You can also download directly from Python:

from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    repo_id="YangyangTan/4Dphi4",
    filename="cfgs_wolff_fahmc_k=0.145_l=0.9_8^4.npz",
    repo_type="dataset",
)

data = np.load(path)
cfgs = data["cfgs"]