Datasets:
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dataset_info:
features:
- name: sample_id
dtype: string
- name: energy
sequence: float32
length: 3384
- name: n_freqs
dtype: int32
- name: n_dirs
dtype: int32
- name: source
dtype: string
- name: station
dtype: string
- name: n_anchors
dtype: int32
- name: anchors_json
dtype: string
- name: Hs
dtype: float32
- name: Tp
dtype: float32
- name: Dp
dtype: float32
- name: total_energy
dtype: float32
splits:
- name: train
num_examples: 37412
- name: validation
num_examples: 15625
- name: test
num_examples: 15411
license: cc-by-4.0
task_categories:
- image-to-image
tags:
- oceanography
- wave-spectrum
- compression
- diffusion-model
---
# 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
```python
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
```python
# 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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