import matplotlib.pyplot as pltimport numpy as npfrom numpy.polynomial import Polynomialdef plot$\_$web$\_$traffic$($x$,$ y$,$ models$=$None$)$: $\text{'}\text{'}\text{'}$ Plot the web traffic $($y$)$ over time $($x$).$ If models is given, it is expected to be a list of fitted models, which will be plotted as well $($used later in this chapter$).\text{'}\text{'}\text{'}$ plt$.$figure$($figsize$=(12,6))$ # width and height of the plot in inches plt$.$scatter$($x$,$ y$,$ s$=10)$ plt$.$title$("$Web traffic over the last month"$)$ plt$.$xlabel$("$Time$")$ plt$.$ylabel$("$Hits$/$hour$")$ plt$.$xticks$([$w$*7*24$ for w in range$(5)],[\text{'}$week $\%$i$\text{'}\%($w$+1)$ for w in range$(5)])$ if models: colors $=$ plt$.$cm$.$viridis$($np$.$linspace$(0,1,$ len$($models$)))$ linestyles $=[\text{'}-\text{'},\text{'}-.\text{'},\text{'}--\text{'},\text{'}$:$\text{'},\text{'}-\text{'}]$ mx $=$ np$.$linspace$(0,$ x$[-1],1000)$ for model, color in zip$($models$,$ colors$)$: plt$.$plot$($mx$,$ model$($mx$),$ linestyle$=\text{'}-\text{'},$ linewidth$=2,$ c$=$color$)$ # Create legend based on model orders plt$.$legend$(["$d $=\%$i$"\%$ m$.$degree$()$ for m in models$],$ loc$=$"upper left"$)$ plt$.$autoscale$($tight$=$True$)$ plt$.$show$()$def calculate$\_$total$\_$error$($x$,$ y$,$ model$)$: $\text{'}\text{'}\text{'}$ Calculate the total error for a given model. $\text{'}\text{'}\text{'}$ return np$.$sum$(($y $-$ model$($x$))**2)$def predict$\_$time$\_$to$\_$reach$\_$hits$($model$,$ target$\_$hits$)$: $\text{'}\text{'}\text{'}$ Predict the time to reach a certain number of hits$/$hour using the model. $\text{'}\text{'}\text{'}$ # Create a range of time values to predict time$\_$values $=$ np$.$linspace$(0,4*7*24,10000)$ predicted$\_$hits $=$ model$($time$\_$values$)$ # Find the time where predicted hits reach the target try: time$\_$to$\_$reach $=$ time$\_$values$[$predicted$\_$hits $>=$ target$\_$hits$][0]$ except IndexError: time$\_$to$\_$reach $=$ np$.$nan # If the target is never reached, return NaN return time$\_$to$\_$reach# Generate some example datanp.random.seed$(0)$x $=$ np$.$arange$(0,4*7*24,1)$ # every hour for four weeksy $=2\mathrm{.}5*$ np$.$sin$($x $/24*2*$ np$.$pi$)+$ np$.$random.normal$(0,0\mathrm{.}5,$ len$($x$))$# Create $40$ polynomial modelsmodels $=[$Polynomial$.$fit$($x$,$ y$,$ deg$)$ for deg in range$(1,41)]$# Calculate total errors for each modeltotal$\_$errors $=[$calculate$\_$total$\_$error$($x$,$ y$,$ model$)$ for model in models$]$# Predict the time to reach $100,000$ hits$/$hour for each modeltarget$\_$hits $=100000$predicted$\_$times $=[$predict$\_$time$\_$to$\_$reach$\_$hits$($model$,$ target$\_$hits$)$ for model in models$]$# Plot the total error against polynomial degreeplt.figure$($figsize$=(12,6))$plt$.$plot$($range$(1,41),$ total$\_$errors, marker$=\text{'}$o$\text{'})$plt$.$title$("$Total Error vs$.$ Polynomial Degree"$)$plt$.$xlabel$("$Polynomial Degree"$)$plt$.$ylabel$("$Total Error"$)$plt$.$grid$($True$)$plt$.$show$()$# Plot the predicted time to reach $100,000$ hits$/$hour against polynomial degreeplt.figure$($figsize$=(12,6))$plt$.$plot$($range$(1,41),$ predicted$\_$times, marker$=\text{'}$o$\text{'})$plt$.$title$("$Predicted Time to Reach $100,000$ Hits$/$Hour vs$.$ Polynomial Degree"$)$plt$.$xlabel$("$Polynomial Degree"$)$plt$.$ylabel$("$Predicted Time $($hours$)")$plt$.$grid$($True$)$plt$.$show$()$# Plot the web traffic with the polynomial modelsplot$\_$web$\_$traffic$($x$,$ y$,$ models$=$models$)$

My last graph isn't showing any data. Can you please tell me what im doing wrong?