File size: 3,262 Bytes
570b87b | 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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | """Encode discrete sources into HOA-7 (Ambix SN3D) coefficient vectors."""
from __future__ import annotations
from typing import Sequence
import numpy as np
from .basis import MAX_ORDER, N_CHANNELS, sh_sn3d, sh_sn3d_batch, unit_vector
def encode_points(
azimuths: Sequence[float] | np.ndarray,
elevations: Sequence[float] | np.ndarray,
gains: Sequence[float] | np.ndarray | None = None,
*,
degrees: bool = True,
max_order: int = MAX_ORDER,
) -> np.ndarray:
"""Encode point / plane-wave sources on the sphere into HOA coeffs.
Each source at (az, el) with gain g contributes g * Y(az, el).
Returns shape (n_channels,) float64. Time-domain: call per sample
with complex gains or use encode_plane_waves for signals.
"""
az = np.atleast_1d(np.asarray(azimuths, dtype=np.float64))
el = np.atleast_1d(np.asarray(elevations, dtype=np.float64))
if az.shape != el.shape:
raise ValueError("azimuths and elevations must match")
if gains is None:
g = np.ones(az.shape, dtype=np.float64)
else:
g = np.asarray(gains, dtype=np.float64)
g = np.broadcast_to(g, az.shape)
Y = sh_sn3d(az, el, degrees=degrees, max_order=max_order) # (N, C)
# a = sum_i g_i Y_i
return np.einsum("n,nc->c", g.reshape(-1), Y.reshape(-1, Y.shape[-1]))
def encode_plane_waves(
azimuths: Sequence[float] | np.ndarray,
elevations: Sequence[float] | np.ndarray,
signals: np.ndarray,
*,
degrees: bool = True,
max_order: int = MAX_ORDER,
) -> np.ndarray:
"""Encode multi-channel time signals as plane waves.
signals: (n_sources, n_samples) or (n_sources,)
Returns: (n_channels, n_samples) or (n_channels,)
"""
az = np.atleast_1d(np.asarray(azimuths, dtype=np.float64))
el = np.atleast_1d(np.asarray(elevations, dtype=np.float64))
sig = np.asarray(signals, dtype=np.float64)
if sig.ndim == 1:
if sig.shape[0] == az.shape[0]:
# (n_sources,) static frame
return encode_points(az, el, sig, degrees=degrees, max_order=max_order)
raise ValueError("1-D signals length must equal n_sources")
if sig.ndim != 2 or sig.shape[0] != az.shape[0]:
raise ValueError("signals must be (n_sources, n_samples)")
Y = sh_sn3d(az, el, degrees=degrees, max_order=max_order) # (S, C)
# a[c,t] = sum_s Y[s,c] * sig[s,t]
return np.einsum("sc,st->ct", Y, sig)
def mix(*fields: np.ndarray) -> np.ndarray:
"""Superpose HOA fields (same channel count)."""
if not fields:
return np.zeros(N_CHANNELS, dtype=np.float64)
out = np.zeros_like(np.asarray(fields[0], dtype=np.float64))
for f in fields:
out = out + np.asarray(f, dtype=np.float64)
return out
def encode_xyz(
directions_xyz: np.ndarray,
gains: np.ndarray | None = None,
*,
max_order: int = MAX_ORDER,
) -> np.ndarray:
"""Encode sources given as unit (or non-unit) Cartesian directions."""
d = np.atleast_2d(np.asarray(directions_xyz, dtype=np.float64))
Y = sh_sn3d_batch(d, max_order=max_order)
if gains is None:
g = np.ones(d.shape[0], dtype=np.float64)
else:
g = np.asarray(gains, dtype=np.float64).reshape(-1)
return np.einsum("n,nc->c", g, Y)
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