def unit_test_iem():
import example_speaker_arrays as esa
s = esa.stage2017()
# json dump, read
s.to_iem_file("test_iem_stage.json")
lsl = from_iem_file("test_iem_stage.json")
return lsl
def unit_test():
"""Run tests for this module."""
from matplotlib import pyplot as plt
import example_speaker_arrays as esa
s = esa.stage2017()
plt.scatter(s.x, s.y, c=s.z, marker='o')
plt.grid()
plt.colorbar()
plt.show()
plt.figure(figsize=(12, 6))
plt.scatter(s.az*180/π, s.el*180/π, c='white', marker='o')
for x, y, r, t in zip(s.az*180/π, s.el*180/π, s.r, s.ids):
plt.text(x, y, t,
bbox=dict(facecolor='lightblue', alpha=0.4),
horizontalalignment='center',
verticalalignment='center')
plt.grid()
# plt.colorbar()
plt.show()
lsl = unit_test_iem()
return s, lsl
def stage_test(ambisonic_order=3, **kwargs):
S = esa.stage2017()
return optimize_dome(S, ambisonic_order, **kwargs)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 9 15:55:28 2021
@author: heller
"""
from numpy import pi as π
import optimize_dome as od
import example_speaker_arrays as esa
import localization_models as lm
import program_channels as pc
import basic_decoders as bd
# %%
S_stage = esa.stage2017(add_imaginary=True)
C31 = pc.ChannelsAmbiX(3, 1)
C33 = pc.ChannelsAmbiX(3, 3)
# %%
# good directionality result
res_31 = od.optimize_dome(
S_stage,
ambisonic_order=C31,
eval_order=C31,
sparseness_penalty=1.0,
el_lim=-π / 4,
)
# compare with
res_33 = od.optimize_dome(
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 16 21:38:12 2021
@author: heller
"""
from numpy import pi as π
import optimize_dome as od
import example_speaker_arrays as esa
import localization_models as lm
import program_channels as pc
import basic_decoders as bd
# %%
S = esa.stage2017(add_imaginary=True)
C = pc.ChannelsAmbiX(5, 5)
order_h, order_v, sh_l, sh_m, id_string = pc.ambisonic_channels(C)
title = f"{S.name}: AllRAD {C.id_string()}"
M = bd.allrad(sh_l, sh_m, S.az, S.el, speaker_is_real=S.is_real)
lm.plot_performance(M, S.u[S.is_real].T, sh_l, sh_m, title=title)
lm.plot_matrix(M, title=title)
df_gain_spk, df_gain_tot = lm.diffuse_field_gain(M)
# %%