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41dd7e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | """FID adapter for the augmented state of the heterogeneous waves dataset.
The raw state is 392-D = concat(u_coarse[196], v_hat_coarse[196]). The image and the physical diagnostics read
the observed displacement field u only (the velocity is a model input, not the observable). This adapter slices the first 196
coordinates and otherwise delegates to ``HeterogeneousWavesAdapter`` (identical reconstruction,
clip rule, physical diagnostics) -- no numerical difference from the plain-u adapter once sliced.
"""
from __future__ import annotations
import numpy as np
from .adapters import register_adapter
from .heterogeneous_waves import HeterogeneousWavesAdapter
_D_U = 196
@register_adapter("heterogeneous_waves_augmented_coarse")
class HeterogeneousWavesAugmentedAdapter(HeterogeneousWavesAdapter):
def to_fields(self, states_raw: np.ndarray) -> np.ndarray:
x = np.asarray(states_raw, dtype=np.float64)
if x.ndim == 1:
x = x[None]
if x.shape[1] not in (_D_U, 2 * _D_U):
raise ValueError(f"expected {_D_U}-D or {2 * _D_U}-D (augmented) states, got D={x.shape[1]}")
return super().to_fields(x[:, :_D_U])
def _moments(self, v: np.ndarray) -> tuple[float, float]:
v = np.asarray(v, np.float64)
if v.shape[1] == 2 * _D_U:
v = v[:, :_D_U]
return super()._moments(v)
def card(self) -> dict:
c = super().card()
c.update({"state": "392-D augmented state (196-D coarse displacement u + 196-D finite-difference "
"coarse velocity v_hat); this adapter reads only the first 196 (displacement) "
"coordinates -- identical reconstruction to heterogeneous_waves_coarse"})
return c
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