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5111d13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | from __future__ import annotations
import numpy as np
import torch
def multiscale_batch(batch: int, length: int, seed: int) -> torch.Tensor:
rng = np.random.default_rng(seed)
time = np.arange(length, dtype=np.float32)[None]
values = np.zeros((batch, length), dtype=np.float32)
for low, high, scale in [(6, 12, 0.45), (20, 40, 0.35), (70, 110, 0.55)]:
periods = rng.uniform(low, high, size=(batch, 1))
phases = rng.uniform(0, 2 * np.pi, size=(batch, 1))
amplitudes = rng.uniform(0.5, 1.0, size=(batch, 1)) * scale
values += amplitudes * np.sin(2 * np.pi * time / periods + phases)
values += rng.normal(0, 0.015, size=values.shape)
return torch.from_numpy(values).float().unsqueeze(2)
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