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MISOfinal.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Nov 29 13:55:01 2017
@author: davidkumar
"""
import numpy as np
import scipy.signal as sig
import matplotlib.pyplot as plt
from scipy.interpolate import Rbf, InterpolatedUnivariateSpline
from scipy import interpolate
from utility import *
from tkinter import *
cc = 343 # speed of sound [m/s]
fs = 8000 # sampling frequency [Hz]
# Parameters
N = 150 # length of the impulse response
K = 90 # desired number of impulse responses
Lf = 13 # length of the fractional delay filter
R = 0.5 # radius
# Source position
num_methods = 4
num_source = 2
xs = [[0, 2], [0, -2]]
top = Tk()
var1 = IntVar()
c = Checkbutton(top, text="All interpolation methods", variable=var1)
c.pack()
L1 = Label(top, text="Omega")
L1.pack(side=TOP)
textBox = Text(top, height=2, width=10)
textBox.pack()
MODES = [
("linear", 'linear'),
("NearestNeighbour", 'nearestNeighbour'),
("spline", 'spline'),
("sinc", 'sinc'),
]
v = StringVar()
v.set("L") # initialize
for text, mode in MODES:
b = Radiobutton(top, text=text,
variable=v, value=mode)
b.pack(anchor=W)
def initialize(R, cc, p, Q, fs, Lf, xs):
Omega = 2 * np.pi / Q # angular speed of the microphone [rad/s]
L = int(2 * np.pi / Omega * fs)
t = (1 / fs) * np.arange(L)
phi = Omega * t
distance = np.sqrt((R * np.cos(phi) - xs[0]) ** 2 + (R * np.sin(phi) - xs[1]) ** 2)
delay = distance / cc
weight = 1 / distance
type = 'lagrange' # FD filters
waveform, shift, offset = fractional_delay(delay, Lf, fs=fs, type=type) # getting impulse_respones
waveform = waveform * weight[:, np.newaxis]
# h, _, _ = construct_ir_matrix(waveform*weight[:, np.newaxis], shift, N)
# getting captured signal for each microphone
s = captured_signal(waveform, shift, p)
return s, phi
def calc_impulse_response(K, N, s, phi, Phi, interp_method, h1, h2, p):
# calculating of impulse_response
impulse_response = np.zeros((N, K))
for k in range(K):
y = np.zeros(N)
for i in range(N):
s_i_linear = s[i::N]
phi_i_linear = phi[i::N] # Decompose the captured signal into N sub-signals
y[i] = spatial_interpolation(s_i_linear, phi_i_linear, Phi[k], interp_method) # interpolation
impulse_response[:, k] = cxcorr(y, p)
DD = np.zeros(K)
for psi in range(K):
nummer = numerator(impulse_response[:, psi], h1[:, psi], h2[:, psi]) # numerator of formula
denom = denominator(h1[:, psi], h2[:, psi])
DD[psi] = nummer / denom
return DD, impulse_response
##########################################################################################
def callback(Q, mode):
D = np.zeros((num_source, K))
D1 = np.zeros((num_source, K))
D2 = np.zeros((num_source, K))
Avg_D = np.zeros((num_source, K))
Phi = np.linspace(0, 2 * np.pi, num=K, endpoint=False)
#######################End of Static response######################
# Excitation by perfet sequences.
# p = perfect_sequence_randomphase(N)
p = perfect_sweep(N)
p1 = np.roll(p, int(N / 2))
distance = np.sqrt((R * np.cos(Phi) - xs[0][0]) ** 2 + (R * np.sin(Phi) - xs[0][1]) ** 2)
delay = distance / cc
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h1, _, _ = construct_ir_matrix(waveform * weight[:, np.newaxis], shift, N)
h1 = h1.T
distance = np.sqrt((R * np.cos(Phi) - xs[1][0]) ** 2 + (R * np.sin(Phi) - xs[1][1]) ** 2)
delay = distance / cc
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h2, _, _ = construct_ir_matrix(waveform * weight[:, np.newaxis], shift, N)
h2 = h2.T
s_0, phi = initialize(R, cc, p, Q, fs, Lf, xs[0])
s_1, _ = initialize(R, cc, p1, Q, fs, Lf, xs[1])
s = s_0 + s_1
#####################################Interpolation method is linear#####################################################
interp_method = mode
D[0, :], impulse_response1 = calc_impulse_response(K, N, s, phi, Phi, interp_method, h1, h2, p)
D1[0, :], impulse_response1 = calc_impulse_response(K, N, s_0, phi, Phi, interp_method, h1, h2, p)
D2[0, :], impulse_response2 = calc_impulse_response(K, N, s_1, phi, Phi, interp_method, h1, h2, p)
# Avg_D[0, ii] = 20*np.log10(average_fwai(D[ii, 0, :], np.linspace(90, 270, num=K)))
Omega = 2 * np.pi / Q
# Omega = np.rad2deg(2 * np.pi / Q)
min_o = np.amin(Avg_D)
max_o = np.amax(Avg_D)
# Descret line for Omege_C
y_val = np.linspace(min_o, max_o, num=50)
Qmega_o = 2 * np.pi / 1.375
Omega_seq = np.ones((1, 50)) * Qmega_o
# Plot
# impulse_response += impulse_response1
# if ii==1:
plt.figure()
plt.xlabel(r'$\phi$ / ⁰')
plt.ylabel(r'$Impulse$ $Response$ / dB')
plt.imshow(impulse_response1, extent=[0, 360, 0, 150], aspect="auto")
plt.title("IR1")
plt.figure()
plt.imshow(impulse_response1 + h1, extent=[0, 360, 0, 150], aspect="auto")
plt.title("h1:IR1")
plt.figure()
plt.xlabel(r'$\phi$ / ⁰')
plt.ylabel(r'$Impulse$ $Response$ / dB')
plt.imshow(impulse_response2, extent=[0, 360, 0, 150], aspect="auto")
plt.title("IR2")
plt.figure()
plt.imshow(impulse_response2 + h2, extent=[0, 360, 0, 150], aspect="auto")
plt.title("h2:IR2")
plt.figure()
xx = np.linspace(0, 2 * np.pi, num=90, endpoint=False)
plt.plot(xx, db(D[0]))
# plt.plot(Omega_seq[0, :], y_val, label="Omega_C(Q=1.375):{}".format(Qmega_o)+"rad/s")
plt.legend()
plt.grid()
# plt.xlim(0, 360)
# plt.ylim(max_o)
plt.xlabel(r'$\phi$/ radian')
plt.ylabel(r'$System$ $distance$ / dB')
plt.title('System distance')
plt.show()
def callback_all(Q):
D = np.zeros((num_methods, K))
D1 = np.zeros((num_methods, K))
D2 = np.zeros((num_methods, K))
Avg_D = np.zeros((num_source, K))
# Receiver positions on a circle
Phi = np.linspace(0, 2 * np.pi, num=K, endpoint=False)
#######################End of Static response######################
# Excitation by perfet sequences.
# p = perfect_sequence_randomphase(N)
p = perfect_sweep(N)
impulse_response = np.zeros((num_methods, N, K))
impulse_response1 = np.zeros((num_methods, N, K))
impulse_response2 = np.zeros((num_methods, N, K))
# for ii in range(num_source):
p1 = np.roll(p, int(N / 2))
distance = np.sqrt((R * np.cos(Phi) - xs[0][0]) ** 2 + (R * np.sin(Phi) - xs[0][1]) ** 2)
delay = distance / cc
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h1, _, _ = construct_ir_matrix(waveform * weight[:, np.newaxis], shift, N)
h1 = h1.T
distance = np.sqrt((R * np.cos(Phi) - xs[1][0]) ** 2 + (R * np.sin(Phi) - xs[1][1]) ** 2)
delay = distance / cc
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h2, _, _ = construct_ir_matrix(waveform * weight[:, np.newaxis], shift, N)
h2 = h2.T
s_0, phi = initialize(R, cc, p, Q, fs, Lf, xs[0])
s_1, _ = initialize(R, cc, p1, Q, fs, Lf, xs[1])
s = (s_0 + s_1)
#####################################Interpolation method is linear#####################################################
interp_method = mode
D[0, :], impulse_response[0, :] = calc_impulse_response(K, N, s, phi, Phi, 'linear', h1, h2, p)
D[1, :], impulse_response[1, :] = calc_impulse_response(K, N, s, phi, Phi, 'spline', h1, h2, p)
D[2, :], impulse_response[2, :] = calc_impulse_response(K, N, s, phi, Phi, 'nearestNeighbour', h1, h2, p)
D[3, :], impulse_response[3, :] = calc_impulse_response(K, N, s, phi, Phi, 'sinc', h1, h2, p)
D1[0, :], impulse_response1[0, :] = calc_impulse_response(K, N, s_0, phi, Phi, 'linear', h1, h2, p)
D1[1, :], impulse_response1[1, :] = calc_impulse_response(K, N, s_0, phi, Phi, 'spline', h1, h2, p)
D1[2, :], impulse_response1[2, :] = calc_impulse_response(K, N, s_0, phi, Phi, 'nearestNeighbour', h1, h2, p)
D1[3, :], impulse_response1[3, :] = calc_impulse_response(K, N, s_0, phi, Phi, 'sinc', h1, h2, p)
D2[0, :], impulse_response2[0, :] = calc_impulse_response(K, N, s_1, phi, Phi, 'linear', h1, h2, p)
D2[1, :], impulse_response2[1, :] = calc_impulse_response(K, N, s_1, phi, Phi, 'spline', h1, h2, p)
D2[2, :], impulse_response2[2, :] = calc_impulse_response(K, N, s_1, phi, Phi, 'nearestNeighbour', h1, h2, p)
D2[3, :], impulse_response2[3, :] = calc_impulse_response(K, N, s_1, phi, Phi, 'sinc', h1, h2, p)
# Avg_D[0, ii] = 20*np.log10(average_fwai(D[ii, 0, :], np.linspace(90, 270, num=K)))
Omega = 2 * np.pi / Q
# Omega = np.rad2deg(2 * np.pi / Q)
min_o = np.amin(Avg_D)
max_o = np.amax(Avg_D)
# Descret line for Omege_C
y_val = np.linspace(min_o, max_o, num=50)
Qmega_o = 2 * np.pi / 1.375
Omega_seq = np.ones((1, 50)) * Qmega_o
# Plot
plt.figure()
plt.title('Impulse_Response1(linear)')
plt.imshow(impulse_response1[0, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1:h1(linear)')
plt.imshow(impulse_response1[0, :] + h1, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2(linear)')
plt.imshow(impulse_response2[0, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2:h2(linear)')
plt.imshow(impulse_response2[0, :] + h2, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1(NearestNeighbour)')
plt.imshow(impulse_response1[1, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1:h1(NearestNeighbour)')
plt.imshow(impulse_response1[1, :] + h1, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2(NearestNeighbour)')
plt.imshow(impulse_response2[1, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2:h2(NearestNeighbour)')
plt.imshow(impulse_response2[1, :] + h2, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1(spline)')
plt.imshow(impulse_response1[2, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1:h1(spline)')
plt.imshow(impulse_response1[2, :]+h1, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2(spline)')
plt.imshow(impulse_response2[2, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2:h2(spline)')
plt.imshow(impulse_response2[2, :] + h2, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1(sinc)')
plt.imshow(impulse_response1[3, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response1:h1(sinc)')
plt.imshow(impulse_response1[3, :]+h1, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2(sinc)')
plt.imshow(impulse_response2[3, :], extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
plt.title('Impulse_Response2:h2(sinc)')
plt.imshow(impulse_response2[3, :] + h2, extent=[0, 360, 0, 150], aspect="auto")
plt.figure()
xx = np.linspace(0, 2 * np.pi, num=90, endpoint=False)
plt.xlabel(r'$\phi$/ radian')
plt.ylabel(r'$System$ $distance$ / dB')
plt.title('System distance')
plt.plot(xx, db(D[0, :]), label='linear')
plt.plot(xx, db(D[1, :]), label='spline')
plt.plot(xx, db(D[2, :]), label='nearestNeighbour')
plt.plot(xx, db(D[3, :]), label='sinc')
# plt.plot(Omega_seq[0, :], y_val, label="Omega_C(Q=1.375):{}".format(Qmega_o)+"rad/s")
plt.legend()
plt.grid()
# plt.xlim(0, 360)
# plt.ylim(max_o)
plt.show()
def callback_avg_D():
m_omega = 3
Q = np.linspace(1, 10, num=m_omega, endpoint=False) # 12
D = np.zeros((m_omega, num_methods, K))
D1 = np.zeros((m_omega, num_methods, K))
D2 = np.zeros((m_omega, num_methods, K))
Avg_D = np.zeros((num_methods, m_omega))
Phi = np.linspace(0, 2 * np.pi, num=K, endpoint=False)
distance = np.sqrt((R * np.cos(Phi) - xs[0][0]) ** 2 + (R * np.sin(Phi) - xs[0][1]) ** 2)
delay = distance / cc
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h1, _, _ = construct_ir_matrix(waveform * weight[:, np.newaxis], shift, N)
h1 = h1.T
distance = np.sqrt((R * np.cos(Phi) - xs[1][0]) ** 2 + (R * np.sin(Phi) - xs[1][1]) ** 2)
delay = distance / cc
weight = 1 / distance
#######################Static impulse respones########################
waveform, shift, _ = fractional_delay(delay, Lf, fs=fs, type='lagrange')
h2, _, _ = construct_ir_matrix(waveform * weight[:, np.newaxis], shift, N)
h2 = h2.T
# denom = denominator(h, Phi)# denominator of formula
#######################End of Static response######################
# Excitation by perfet sequences.
# p = perfect_sequence_randomphase(N)
p = perfect_sweep(N)
impulse_response = np.zeros((N, K))
for ii in range(len(Q)):
s_0, phi = initialize(R, cc, p, Q[ii], fs, Lf, xs[0])
s_1, _ = initialize(R, cc, np.roll(p, int(N / 2)), Q[ii], fs, Lf, xs[1])
s = (s_0 + s_1)
#####################################Interpolation method is linear#####################################################
interp_method = 'linear'
D[ii, 0, :], hl = calc_impulse_response(K, N, s, phi, Phi, interp_method, h1, h2, p)
D1[ii, 0, :], hl1 = calc_impulse_response(K, N, s_0, phi, Phi, interp_method, h1, h2, p)
D2[ii, 0, :], hl2 = calc_impulse_response(K, N, s_1, phi, Phi, interp_method, h1, h2, p)
# print(D[ii, 0, :])
Avg_D[0, ii] = db(np.mean(D[ii, 0, :]))
#####################################Interpolation method is nearestNeighbour#####################################################
interp_method = 'nearestNeighbour'
D[ii, 1, :], hn = calc_impulse_response(K, N, s, phi, Phi, interp_method, h1, h2, p)
D1[ii, 1, :], hn1 = calc_impulse_response(K, N, s_0, phi, Phi, interp_method, h1, h2, p)
D2[ii, 1, :], hn2 = calc_impulse_response(K, N, s_1, phi, Phi, interp_method, h1, h2, p)
Avg_D[1, ii] = db(np.mean(D[ii, 1, :]))
#####################################Interpolation method is sinc#####################################################
interp_method = 'sinc'
D[ii, 2, :], hs = calc_impulse_response(K, N, s, phi, Phi, interp_method, h1, h2, p)
D1[ii, 2, :], hs1 = calc_impulse_response(K, N, s_0, phi, Phi, interp_method, h1, h2, p)
D2[ii, 2, :], hs2 = calc_impulse_response(K, N, s_1, phi, Phi, interp_method, h1, h2, p)
Avg_D[2, ii] = db(np.mean(D[ii, 2, :]))
#####################################Interpolation method is spline#####################################################
interp_method = 'spline'
D[ii, 3, :], hsp = calc_impulse_response(K, N, s, phi, Phi, interp_method, h1, h2, p)
D1[ii, 3, :], hsp1 = calc_impulse_response(K, N, s_0, phi, Phi, interp_method, h1, h2, p)
D2[ii, 3, :], hsp2 = calc_impulse_response(K, N, s_1, phi, Phi, interp_method, h1, h2, p)
Avg_D[3, ii] = db(np.mean(D[ii, 3, :]))
plt.imshow(impulse_response)
Omega = 2 * np.pi / Q
# Omega = np.rad2deg(2 * np.pi / Q)
min_o = np.amin(Avg_D)
max_o = np.amax(Avg_D)
# Descret line for Omege_C
y_val = np.linspace(min_o, max_o, num=50)
Qmega_o = 2 * np.pi / 1.375
Omega_seq = np.ones((1, 50)) * Qmega_o
# Plot
plt.figure()
plt.plot(Omega, Avg_D[0, :], label="Interp_Method is linear")
plt.plot(Omega, Avg_D[1, :], label="Interp_Method is nearestNeighbour")
plt.plot(Omega, Avg_D[2, :], label="Interp_Method is sinc")
plt.plot(Omega, Avg_D[3, :], label="Interp_Method is spline")
plt.plot(Omega_seq[0, :], y_val, label="Omega_C(Q=1.375):{}".format(Qmega_o) + "rad/s")
plt.ylim(-50, 0)
plt.legend()
plt.grid()
# plt.xlim(0, 360)
# plt.ylim(max_o)
plt.xlabel('Omega : rad/s')
plt.ylabel(r'$Average$ $System$ $distance$ / dB')
plt.title('Average System distance')
plt.show()
def retrieve_input():
if var1.get() == True:
inputValue = textBox.get("1.0", "end-1c")
callback_all(float(inputValue))
else:
inputValue = textBox.get("1.0", "end-1c")
mode = v.get()
callback(float(inputValue), mode)
BB = Button(top, height=1, width=10, text="IR&D", command=lambda: retrieve_input())
AA = Button(top, height=1, width=10, text="Avg_D", command=lambda: callback_avg_D())
BB.pack()
AA.pack()
top.mainloop()