Could you share MATLAB code for this python example:

import numpy as np

import matplotlib.pyplot as plt

from scipy.linalg import eigh

from numpy.linalg import svd

# Parameters

M $=8$ # number of sensors

d $=0\mathrm{.}5$ # inter$-$element spacing in wavelengths

thetas $=$ np$.$array$([-5,15,25])$ # DOAs in degrees

P $=$ np$.$array$([2,2,1])$ # signal powers

N $=100$ # number of snapshots

k$0,$ k$1,$ k$2=0,2,5$

sigma$\_$n$2=0\mathrm{.}1*(1+$ k$2)$ # noise variance

# Generate the steering matrix

def steering$\_$vector$($M$,$ d$,$ theta$)$:

angles $=$ np$.$radians$($theta$)$

return np$.$exp$(-1$j $*2*$ np$.$pi $*$ d $*$ np$.$arange$($M$)[$:$,$ np$.$newaxis$]*$ np$.$sin$($angles$))$

A $=$ steering$\_$vector$($M$,$ d$,$ thetas$)$

# Generate the signals and noise

S $=$ np$.$random.randn$($len$($thetas$),$ N$)+1$j $*$ np$.$random.randn$($len$($thetas$),$ N$)$

S $*=$ np$.$sqrt$($P$[$:$,$ np$.$newaxis$])$

noise $=$ np$.$sqrt$($sigma$\_$n$2/2)*($np$.$random.randn$($M$,$ N$)+1$j $*$ np$.$random.randn$($M$,$ N$))$

# Received signal

X $=$ A @ S $+$ noise

# Compute the covariance matrix

R $=$ X @ X$.$conj$().$T $/$ N

# Perform eigendecomposition

eigvals, eigvecs $=$ eigh$($R$)$

signal$\_$eigvecs $=$ eigvecs$[$:$,-$len$($thetas$)$:$]$ # last len$($thetas$)$ eigenvectors

noise$\_$eigvecs $=$ eigvecs$[$:$,$ :$-$len$($thetas$)]$ # remaining eigenvectors

# MUSIC pseudo$-$spectrum

def music$\_$spectrum$($theta$\_$scan, A$,$ noise$\_$eigvecs$)$:

a$\_$scan $=$ steering$\_$vector$($M$,$ d$,$ theta$\_$scan$)$

return $1/$ np$.$sum$($np$.$abs$($a$\_$scan.conj$().$T @ noise$\_$eigvecs$)**2,$ axis$=1)$

theta$\_$scan $=$ np$.$linspace$(-90,90,180)$

P$\_$music $=$ music$\_$spectrum$($theta$\_$scan, A$,$ noise$\_$eigvecs$)$

# Plot the pseudo$-$spectrum

plt$.$figure$($figsize$=(10,6))$

plt$.$plot$($theta$\_$scan, $10*$ np$.$log$10($P$\_$music$))$

plt$.$title$("$MUSIC Pseudo$-$Spectrum"$)$

plt$.$xlabel$("$Angle $($degrees$)")$

plt$.$ylabel$("$Pseudo$-$Spectrum $($dB$)")$

plt$.$grid$($True$)$

plt$.$show$()$