Source code for spharpy.indexing.channel_indexing
"""
Commonly used channel indexing functions used in Ambisonics
"""
import numpy as np
import spharpy
[docs]
def sid(n_max):
"""Calculates the SID indices up to spherical harmonic order n_max.
TODO: Add citation Daniel
Parameters
----------
n_max : int
Returns
-------
sid : numpy array
"""
n_sh = (n_max+1)**2
sid_n = sph_identity_matrix(n_max, 'n-nm').T @ np.arange(0, n_max+1)
sid_m = np.zeros(n_sh, dtype=int)
idx_n = 0
for n in range(1, n_max+1):
for m in range(1, n+1):
sid_m[idx_n + 2*m-1] = n-m+1
sid_m[idx_n + 2*m] = -(n-m+1)
sid_m[idx_n + 2*n + 1] = 0
idx_n += 2*n+1
return sid_n, sid_m
[docs]
def sid2acn(n_max):
"""Convert from SID channel indexing as proposed by Daniel in
TODO: add citation
Returns the indices to achieve a corresponding linear acn indexing.
Parameters
----------
sid : numpy array
Returns
-------
acn : numpy array
"""
sid_n, sid_m = spharpy.indexing.sid(n_max)
linear_sid = spharpy.spherical.nm2acn(sid_n, sid_m)
return np.argsort(linear_sid)
[docs]
def sph_identity_matrix(n_max, type='n-nm'):
"""Calculate a spherical harmonic identity matrix.
TODO: Implement the other identity matrices
Parameters
----------
n_max : TODO
type : TODO, optional
Returns
-------
identity_matrix : numpy array
"""
n_sh = (n_max+1)**2
if type != 'n-nm':
raise NotImplementedError
identity_matrix = np.zeros((n_max+1, n_sh), dtype=int)
# linear_n0 = np.cumsum(np.arange(0, 2*(n_max+1), 2))
for n in range(n_max+1):
m = np.arange(-n, n+1)
linear_nm = spharpy.spherical.nm2acn(np.tile(n, m.shape), m)
identity_matrix[n, linear_nm] = 1
return identity_matrix
