Can someone help me with this code:

import numpy as np

import matplotlib.pyplot as plt

from scipy.stats import norm

# Example of a symmetric Gaussian proposal distribution

def gaussian$\_$proposal$($current$\_$value, sigma$=1)$:

return np$.$random.normal$($current$\_$value, sigma$)$

def metropolis$\_$exposure$\_$limit$($initial$\_$value, num$\_$samples, proposal$\_$distribution, data$)$:

samples $=[$initial$\_$value$]$

current$\_$value $=$ initial$\_$value

for $\_$ in range$($num$\_$samples$)$:

candidate $=$ proposal$\_$distribution$($current$\_$value$)$

acceptance$\_$ratio $=$ exposure$\_$target$($candidate$,$ data$)/$ exposure$\_$target$($current$\_$value, data$)$

acceptance$\_$ratio $=$ min$($acceptance$\_$ratio, $1)$ # Ensure acceptance ratio is $<=1$

if np$.$random.uniform$(0,1)<=$ acceptance$\_$ratio:

current$\_$value $=$ candidate

samples.append$($current$\_$value$)$

return samples

# Fix the code by correcting the data type mismatch in the exposure$\_$target function

def exposure$\_$target$($x$,$ data$)$:

# Convert data to a NumPy array to enable broadcasting

data $=$ np$.$array$($data$)$

# Calculate the target distribution using the kernel density estimation formula

return np$.$exp$(-(($x $-$ data$)**2).$sum$()/(2*$ len$($data$)))/$ np$.$sqrt$(2*$ np$.$pi$)$

# Re$-$run the code with the corrected function

samples $=$ metropolis$\_$exposure$\_$limit$($initial$\_$value, num$\_$samples, gaussian$\_$proposal, data$)$

# Prompt the user to input the data

print$("$Enter exposure data $($comma$-$separated values$)$: $")$

data$\_$input $=$ input$().$split$(\text{'},\text{'})$

data $=[$float$($x$)$ for x in data$\_$input$]$ # Convert input string to a list of floats

# Example usage

initial$\_$value $=$ np$.$mean$($data$)$ # Initial exposure level $($mean of input data$)$

num$\_$samples $=10000$ # Number of samples to generate

samples $=$ metropolis$\_$exposure$\_$limit$($initial$\_$value, num$\_$samples, gaussian$\_$proposal, data$)$

# Calculate the $95$th percentile exposure limit

exposure$\_$limit $=$ np$.$percentile$($samples$,95)$

# Calculate the mean and standard deviation for the Bayesian analysis

mean$\_$estimate $=$ np$.$mean$($samples$)$

std$\_$estimate $=$ np$.$std$($samples$)$

# Calculate the probability of overexposure $($risk$)$ using a normal distribution

risk$\_$probability $=1-$ norm.cdf$($exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)$

# Calculate probabilities for each risk band

percentile$\_1=$ norm.cdf$(0\mathrm{.}01*$ exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)$

percentile$\_10=$ norm.cdf$(0\mathrm{.}1*$ exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)-$ percentile$\_1$

percentile$\_50=$ norm.cdf$(0\mathrm{.}5*$ exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)-$ norm.cdf$(0\mathrm{.}1*$ exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)$

percentile$\_100=$ norm.cdf$($exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)-$ norm.cdf$(0\mathrm{.}5*$ exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)$

overexposure$\_$risk $=1-$ norm.cdf$($exposure$\_$limit$,$ loc$=$mean$\_$estimate, scale$=$std$\_$estimate$)$

# Print risk analysis results

print$("$

Risk analysis based on $95$th percentile:"$)$

print$("95$th Percentile:", exposure$\_$limit$)$

print$("$Estimated Mean:", mean$\_$estimate$)$

print$("$Estimated Standard Deviation:", std$\_$estimate$)$

print$("$Risk Probability $($Risk of overexposure$)$: $\{$:$\mathrm{.}1$f$\}\%".$format$($risk$\_$probability $*100))$

print$("$Probability that true $95$th percentile $<1\%$ of ELV:", percentile$\_1)$

print$("$Probability that true $95$th percentile is between $1\%$ and $10\%$ of ELV:", percentile$\_10)$

print$("$Probability that true $95$th percentile is between $10\%$ and $50\%$ of ELV:", percentile$\_50)$

print$("$Probability that true $95$th percentile is between $50\%$ and $100\%$ of ELV:", percentile$\_100)$

# Decision based on risk probability

if risk$\_$probability $*100<30$:

decision $=$ "Well controlled"

else:

decision $=$ "Poorly controlled"

print$("$Accordingly$,$ the situation is declared:", decision$)$

# Plot probability distribution by risk band

risk$\_$bands $=[\text{'}<1\%$ of ELV', $\text{'}1-10\%$ of ELV', $\text{'}10-50\%$ of ELV', $\text{'}50-100\%$ of ELV', $\text{'}>$ VLE'$]$

probabilities $=[$percentile$\_1,$ percentile$\_10,$ percentile$\_50,$ percentile$\_100,$ overexposure$\_$risk$]$

plt$.$bar$($risk$\_$bands, probabilities, color$=\text{'}$b$\text{'})$

plt$.$xlabel$(\text{'}$Risk Band'$)$

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

plt$.$title$(\text{'}$Probability Distribution by Risk Band'$)$

plt$.$axhline$($y$=$overexposure$\_$risk, color$=\text{'}$r$\text{'},$ linestyle$=\text{'}--\text{'},$ label$=$'Overexposure Risk'$)$

plt$.$legend$()$

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

plt$.$show$()$

# Plot histogram of exposure samples

plt$.$hist$($samples$,$ bins$=50,$ density$=$True, alpha$=0\mathrm{.}6,$ color$=\text{'}$g$\text{'},$ label$=$'Exposure Samples'$)$

plt$.$axvline$($x$=$exposure$\_$limit$,$ color$=\text{'}$r$\text{'},$ linestyle$=\text{'}--\text{'},$ label$=$'Exposure Limit $(95\%)\text{'})$

plt$.$xlabel$(\text{'}$Exposure Level'$)$

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

plt$.$title$(\text{'}$Histogram of Exposure Levels'$)$

plt$.$legend$()$

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

plt$.$show$()$