Write a python program that demonstrates your knowledge of the following topics.

How to plot histograms using matplotlib

How to populate a figure using matplotlib

How to decorate a figure using matplotlib

How to find the mean and standard deviation of a dataset

How to create a normal distribution curve for a given mean and standard deviation

How to overlay a normal distribution curve on a histogram

Write and submit a Python module named Proj$04$Runner.py that works in conjunction with the driver source code contained in the file named Proj$04.$py

The program must terminate and return control to the operating system when the user dismisses the image by clicking the X$-$button on the image window.

You may not modify the code in the file named Proj$04.$py for the purpose of meeting this specification. The normed parameter has been removed from the hist method in later versions of pyplot. Don't use "normed$=$True". Use "density$=$True" instead.

Here is Proj$04.$py:

from Proj$04$Runner import Runner

import sys

import random

#Define a utility function that will be used later to create

#several datasets.

def normalRandomGenerator$($seed$=1,$dataLength$=10000,$numberSamples$=50,\backslash$

lowLim$=0,$highLim$=100)$:

$\text{'}\text{'}\text{'}$

Create a new dataset of dataLength values consisting of the

average of numberSamples samples taken from a population of

uniformly distributed values between lowLim and highLim

generated with a seed of seed.

Input keyword parameters and their default values:

seed $=1$ seed used to generate the uniformly distributed values

dataLength $=10000$ number of samples in the returned list of values

numberSamples $=50$ number of samples taken from the uniformly

distributed population and averaged into the output

lowLim $=0$ lower limit value of the uniformly distributed population

highLim $=100$ high limit value of the uniformly distributed population

returns: a list containing the dataset

$\text{'}\text{'}\text{'}$

data $=[]$

random.seed$($seed$)$

for cnt in range$($dataLength$)$:

theSum $=0$

for cnt$1$ in range$($numberSamples$)$:

theSum $+=$ random.uniform$($lowLim$,$highLim$)$

data.append$($theSum$/$numberSamples$)$

return data

#$=========================================================================$#

#Populate variables that will be used to create four datasets.

xSeed $=1$

xDataLength $=5000$

xNumberSamples $=1$

xLowLim $=600$

xHighLim $=1000$

print$("$

xSeed $=",$xSeed$)$

print$("$xDataLength $=",$xDataLength$)$

print$(\text{'}$xNumberSamples $=\text{'},$xNumberSamples$)$

print$(\text{'}$xLowLim $=\text{'},$xLowLim$)$

print$(\text{'}$xHighLim $=\text{'},$xHighLim$)$

print$()$

#Create four datasets

data$01=$ normalRandomGenerator$($seed$=$xSeed,dataLength$=$xDataLength,$\backslash$

numberSamples$=$xNumberSamples,$\backslash$

lowLim$=$xLowLim,$\backslash$

highLim$=$xHighLim$\backslash$

$)$

data$02=$ normalRandomGenerator$($seed$=$xSeed,dataLength$=$xDataLength,$\backslash$

numberSamples $=2*$xNumberSamples$,\backslash$

lowLim$=$xLowLim,$\backslash$

highLim$=$xHighLim$\backslash$

$)$

data$03=$ normalRandomGenerator$($seed$=$xSeed,dataLength$=$xDataLength,$\backslash$

numberSamples $=4*$xNumberSamples$,\backslash$

lowLim$=$xLowLim,$\backslash$

highLim$=$xHighLim$\backslash$

$)$

data$04=$ normalRandomGenerator$($seed$=$xSeed,dataLength$=$xDataLength,$\backslash$

numberSamples $=8*$xNumberSamples$,\backslash$

lowLim$=$xLowLim,$\backslash$

highLim$=$xHighLim$\backslash$

$)$

print$("$Output from student code begins here."$)$

#Call the student's run method in the class named Runner in the

#file named Proj$04$Runner.py$,$ passing four datasets as parameters.

Runner.run$($data$01,$data$02,$data$03,$data$04)$

#$-----$end of proj$04$ program$----$

My questions is$,$ is my runner file okay?

Here is my Proj$04$Runner.py file:

import matplotlib.pyplot as plt

import numpy as np

class Runner:

@staticmethod

def run$($data$01,$ data$02,$ data$03,$ data$04)$:

# Create histograms for each dataset

plt$.$figure$($figsize$=(10,6))$

plt$.$hist$($data$01,$ bins$=50,$ density$=$True, alpha$=0\mathrm{.}5,$ label$=$'Dataset $01\text{'})$

plt$.$hist$($data$02,$ bins$=50,$ density$=$True, alpha$=0\mathrm{.}5,$ label$=$'Dataset $02\text{'})$

plt$.$hist$($data$03,$ bins$=50,$ density$=$True, alpha$=0\mathrm{.}5,$ label$=$'Dataset $03\text{'})$

plt$.$hist$($data$04,$ bins$=50,$ density$=$True, alpha$=0\mathrm{.}5,$ label$=$'Dataset $04\text{'})$

# Calculate mean and standard deviation for each dataset

mean$01,$ std$01=$ np$.$mean$($data$01),$ np$.$std$($data$01)$

mean$02,$ std$02=$ np$.$mean$($data$02),$ np$.$std$($data$02)$

mean$03,$ std$03=$ np$.$mean$($data$03),$ np$.$std$($data$03)$

mean$04,$ std$04=$ np$.$mean$($data$04),$ np$.$std$($data$04)$

# Plot normal distribution curves

x $=$ np$.$linspace$($min$($data$01+$ data$02+$ data$03+$ data$04),$ max$($data$01+$ data$02+$ data$03+$ data$04),100)$

plt$.$plot$($x$,(1/($std$01*$ np$.$sqrt$(2*$ np$.$pi$)))*$ np$.$exp$(-0\mathrm{.}5*(($x $-$ mean$01)/$ std$01)**2),$ label$=$'Normal Dist. $01\text{'})$

plt$.$plot$($x$,(1/($std$02*$ np$.$sqrt$(2*$ np$.$pi$)))*$ np$.$exp$(-0\mathrm{.}5*(($x $-$ mean$02)/$ std$02)**2),$ label$=$'Normal Dist. $02\text{'})$

plt$.$plot$($x$,(1/($std$03*$ np$.$sqrt$(2*$ np$.$pi$)))*$ np$.$exp$(-$