Datasets:
sample_id string | energy list | n_freqs int32 | n_dirs int32 | source string | station string | n_anchors int32 | anchors_json string | Hs float32 | Tp float32 | Dp float32 | total_energy float32 |
|---|---|---|---|---|---|---|---|---|---|---|---|
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t10 | [
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0... | 47 | 72 | ndbc | 41001 | 2 | [{"mu_freq": 0.14503937168848485, "mu_dir": 186.4982547916937, "peak_energy": 3.570914714304976, "sigma_freq": 0.02872749417358217, "sigma_dir": 32.28734954120909, "rho": 0.1760177174422073, "alpha_freq": 0.15346178247364983, "alpha_dir": 0.02889178079068871}, {"mu_freq": 0.12325362111616237, "mu_dir": 13.2496199196035... | 2.740766 | 7.142857 | 185 | 0.469487 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t1001 | [
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0... | 47 | 72 | ndbc | 41001 | 3 | [{"mu_freq": 0.1325245891851744, "mu_dir": 342.36423334748855, "peak_energy": 2.564869431959154, "sigma_freq": 0.024501859107454022, "sigma_dir": 28.791856203041945, "rho": -0.16987052256470728, "alpha_freq": 0.19221156898827738, "alpha_dir": 0.13678078918933206}, {"mu_freq": 0.13647671713326043, "mu_dir": 320.18793284... | 2.79063 | 8.333334 | 340 | 0.486726 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t1003 | [
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0... | 47 | 72 | ndbc | 41001 | 3 | [{"mu_freq": 0.139024134968941, "mu_dir": 322.6883225670723, "peak_energy": 4.119118525431002, "sigma_freq": 0.026611448830649493, "sigma_dir": 31.79082561250818, "rho": -0.07042087811450395, "alpha_freq": 0.21529128794783922, "alpha_dir": -0.02475057435367246}, {"mu_freq": 0.13620925216616114, "mu_dir": 342.3950058738... | 2.855357 | 7.692308 | 310 | 0.509566 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t0 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 2 | "[{\"mu_freq\": 0.1473047760196059, \"mu_dir\": 181.56276851293845, \"peak_energy\": 3.4982946024158(...TRUNCATED) | 2.761628 | 7.142857 | 180 | 0.476662 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t1000 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 2 | "[{\"mu_freq\": 0.14404770471240813, \"mu_dir\": 314.813117133184, \"peak_energy\": 3.49886942289547(...TRUNCATED) | 2.924584 | 7.692308 | 295 | 0.534575 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t1002 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 3 | "[{\"mu_freq\": 0.12896739037631014, \"mu_dir\": 344.8652356637826, \"peak_energy\": 4.4881365612805(...TRUNCATED) | 2.828292 | 9.090909 | 345 | 0.499952 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t1004 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 3 | "[{\"mu_freq\": 0.1435685194797933, \"mu_dir\": 326.123481605264, \"peak_energy\": 3.998945400267267(...TRUNCATED) | 2.79883 | 7.142857 | 320 | 0.489591 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t100 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 3 | "[{\"mu_freq\": 0.12900439661430088, \"mu_dir\": 151.57336844057775, \"peak_energy\": 0.653470135037(...TRUNCATED) | 1.285612 | 8.333334 | 150 | 0.1033 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t1005 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 3 | "[{\"mu_freq\": 0.1444116402512313, \"mu_dir\": 318.87326868935304, \"peak_energy\": 4.6493589645110(...TRUNCATED) | 2.874564 | 7.692308 | 310 | 0.516445 |
ndbc_nc_cman_NDBC_41001_202301_D6_v00_t101 | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED) | 47 | 72 | ndbc | 41001 | 3 | "[{\"mu_freq\": 0.12989120006789442, \"mu_dir\": 117.90587600785146, \"peak_energy\": 0.504201327742(...TRUNCATED) | 1.201817 | 9.090909 | 110 | 0.090273 |
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ATLAS-WDS: Wave Directional Spectrum Dataset
海浪方向谱压缩回传训练数据集。
数据格式
每条记录包含一个 47×72 能量矩阵（展平为 3384 维 float32 数组） 及对应的 斜高斯锚点参数。
快速加载
from datasets import load_dataset
import numpy as np, json
ds = load_dataset("wuff-mann/ATLAS-WDS", split="train", streaming=True)
for sample in ds:
# 还原能量矩阵
energy = np.array(sample["energy"], dtype=np.float32).reshape(
sample["n_freqs"], sample["n_dirs"]) # (47, 72)
# 锚点参数
anchors = json.loads(sample["anchors_json"])
# 物理参数
Hs, Tp, Dp = sample["Hs"], sample["Tp"], sample["Dp"]
三阶段训练使用
# Stage 1: cLDM 预训练 — 只用能量矩阵
for sample in ds:
matrix = np.array(sample["energy"]).reshape(47, 72)
# Stage 2: Swin 编码器 — 矩阵 + 锚点
for sample in ds:
matrix = np.array(sample["energy"]).reshape(47, 72)
anchors = json.loads(sample["anchors_json"])
# Stage 3: 端到端对齐 — 仅真实数据
ds_real = ds.filter(lambda x: x["source"] != "synthetic")
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