Could you share MATLAB code for this python example:

import numpy as np

# Function to compute MSE of DOA estimates

def compute$\_$mse$($true$\_$doa, estimated$\_$doa$)$:

return np$.$mean$(($true$\_$doa $-$ estimated$\_$doa$)**2)$

# Part $($b$)$: Accuracy for Different Noise Variances

noise$\_$variances $=$ np$.$linspace$(0\mathrm{.}1*$ sigma$\_$n$2,10*$ sigma$\_$n$2,10)$

mse$\_$noise $=[]$

for var in noise$\_$variances:

mse$\_$values $=[]$

for $\_$ in range$(100)$: # Monte Carlo simulations

noise $=$ np$.$sqrt$($var $/2)*($np$.$random.randn$($M$,$ N$)+1$j $*$ np$.$random.randn$($M$,$ N$))$

X $=$ A @ S $+$ noise

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

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

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

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

estimated$\_$doa $=$ theta$\_$scan$[$np$.$argmax$($P$\_$music$)]$

mse$\_$values.append$($compute$\_$mse$($thetas$,$ estimated$\_$doa$))$

mse$\_$noise.append$($np$.$mean$($mse$\_$values$))$

# Part $($c$)$: Accuracy for Different Number of Sensors

sensor$\_$counts $=$ np$.$arange$($M$,10*$ M $+1,$ M$)$

mse$\_$sensors $=[]$

for m in sensor$\_$counts:

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

mse$\_$values $=[]$

for $\_$ in range$(100)$: # Monte Carlo simulations

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

X $=$ A @ S$[$:m$,$ :$]+$ noise

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

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

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

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

estimated$\_$doa $=$ theta$\_$scan$[$np$.$argmax$($P$\_$music$)]$

mse$\_$values.append$($compute$\_$mse$($thetas$,$ estimated$\_$doa$))$

mse$\_$sensors$.$append$($np$.$mean$($mse$\_$values$))$

# Part $($d$)$: Accuracy for Different Number of Snapshots

snapshot$\_$counts $=$ np$.$arange$($int$(0\mathrm{.}1*$ N$),$ int$(10*$ N$)+1,$ int$(0\mathrm{.}1*$ N$))$

mse$\_$snapshots $=[]$

for n in snapshot$\_$counts:

mse$\_$values $=[]$

for $\_$ in range$(100)$: # Monte Carlo simulations

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

X $=$ A @ S$[$:$,$ :n$]+$ noise

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

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

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

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

estimated$\_$doa $=$ theta$\_$scan$[$np$.$argmax$($P$\_$music$)]$

mse$\_$values.append$($compute$\_$mse$($thetas$,$ estimated$\_$doa$))$

mse$\_$snapshots.append$($np$.$mean$($mse$\_$values$))$

# Plotting MSE results

plt$.$figure$($figsize$=(12,8))$

# Noise Variance

plt$.$subplot$(3,1,1)$

plt$.$plot$($noise$\_$variances, mse$\_$noise, marker$=\text{'}$o$\text{'})$

plt$.$title$(\text{'}$MSE vs$.$ Noise Variance'$)$

plt$.$xlabel$(\text{'}$Noise Variance'$)$

plt$.$ylabel$(\text{'}$MSE$\text{'})$

# Number of Sensors

plt$.$subplot$(3,1,2)$

plt$.$plot$($sensor$\_$counts, mse$\_$sensors$,$ marker$=\text{'}$o$\text{'})$

plt$.$title$(\text{'}$MSE vs$.$ Number of Sensors'$)$

plt$.$xlabel$(\text{'}$Number of Sensors'$)$

plt$.$ylabel$(\text{'}$MSE$\text{'})$

# Number of Snapshots

plt$.$subplot$(3,1,3)$

plt$.$plot$($snapshot$\_$counts, mse$\_$snapshots, marker$=\text{'}$o$\text{'})$

plt$.$title$(\text{'}$MSE vs$.$ Number of Snapshots'$)$

plt$.$xlabel$(\text{'}$Number of Snapshots'$)$

plt$.$ylabel$(\text{'}$MSE$\text{'})$

plt$.$tight$\_$layout$()$

plt$.$show$()$