metadata
pretty_name: 4D phi4 Wolff FAHMC configurations
tags:
- lattice-field-theory
- phi4
- monte-carlo
- hmc
- wolff
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"]