Define a function, plot$\_$temperature$\_$histogram$($Ts$,$ bins, range$),$ which produces a histogram $($as a matplotlib figure$)$ showing the temperatures encountered at the end of the random walks grouped into counts based on bins of temperature ranges. The parameter Ts is a numpy array of temperatures from all individual walks; it is part of the output from a call to wos$\_$temperature$\_$estimate. The

parameter bins is an integer which indicates how many bins are used in the histogram, and range is a tuple with two floats of form $($T$\_$min, T$\_$max$)$ where Tmin is the lower temperature bound for the histogram and Tmax is the upper bound. As a hint, take a look at the matplotlib documentation for producing histograms.

sample

$>>>$ x $=0\mathrm{.}6$; y $=0\mathrm{.}3$

$>>>$ length $=1\mathrm{.}0$; epsilon $=0\mathrm{.}01$

$>>>$ cnr$\_$temps $=\{\text{'}$sw$\text{'}$:$600\mathrm{.}0,\text{'}$se$\text{'}$:$700\mathrm{.}0,\text{'}$ne$\text{'}$:$1000\mathrm{.}0,\text{'}$nw$\text{'}$:$850\mathrm{.}0\}$

$>>>$ number$\_$walks $=100$

$>>>$ random.seed$(1)$

$>>>($T$\_$avg, Ts$)=$ wos$\_$temperature$\_$estimate$($x$,$ y$,$ length, cnr$\_$temps, epsilon,

$,->$ number$\_$walks$)$

$>>>$ plot$\_$temperature$\_$histogram$($Ts$,8,(600\mathrm{.}0,1000\mathrm{.}0))$