$2.$ A Python code is given for a simulation of a Markov chain.

import numpy as np

T $=1000$

S$[\text{'}$Cloud$\text{'},$ 'Rain', 'Snow', 'Clear'$]$

for t in range $($T$)$:

X$[$t$]=$ np$.$random.choice $($S$,$ p$=[0\mathrm{.}5,0\mathrm{.}5,0,0])$

if t $==0$:

if X$[$t$-1]==$ 'Cloud': X$[$t$]$ np$.$random.choice $($S$,$ p$=[0\mathrm{.}5,0\mathrm{.}3,0\mathrm{.}1,0\mathrm{.}1])$

elif X$[$t$-1]==$ 'Rain':

else:

X$[$t$]$ np$.$random.choice $($S$,$ p$[0\mathrm{.}4,0\mathrm{.}2,0\mathrm{.}1,0\mathrm{.}3])$ elif X$[$t$-1]==$ 'Snow':

X$[$t$]$ np$.$random.choice $($S$,$ p$=[0\mathrm{.}2,0\mathrm{.}1,0\mathrm{.}2,0\mathrm{.}5])$

elif X$[$t$-1]==$ 'Clear': X$[$t$]=$ np$.$random.choice $($S$,$ p$[0\mathrm{.}2,0\mathrm{.}1,0\mathrm{.}1,0\mathrm{.}6])$

$($a$)(1$ pt$)$ What are the states of this Markov chain?