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Problem In 1827, the Scottish botanist Robert Brown observed that pollen particl

Problem
In 1827, the Scottish botanist Robert Brown observed that pollen particles suspended in water seemed to float around at random. He had no plausible explanation for what came to be known as Brownian motion, and made no attempt to model it mathematically. Louis Bachelier presented a clear mathematical model in his doctoral thesis, The Theory of Speculation in 1900. His thesis was largely ignored by respectable academics because it dealt with the then disreputable field of understanding financial markets. In 1905, Albert Einstein used similar stochastic thinking in physics to describe how it could be used to confirm the existence of atoms. People seemed to think that understanding physics was more important than making money, and the world started paying attention.
Brownian motion is an example of a random walk. Today, random walks are widely used to model physical processes like diffusion, biological processes like the kinetics of displacement of RNA from heteroduplexes by DNA, and social processes like movements of the stock market.
We are interested in random walks because of their wide applications to many problems, and for learning more about how to structure simulations nicely in Python.
Farmer John has an old grandparent (“Pa”) that likes to wander off randomly when working in the barn. Pa starts from the barn and every second take one step in a random direction North, South, East or West. What is Pa’s expected distance away from the barn after 1000 steps? If Pa takes many steps, will Pa be likely to move ever further from the origin, or be more likely to wander back to the origin over and over, and end up not far from where she started? Let’s write a simulation to find out.
This particular barn is in the center of a large grassy field. One day Pa starts to wander off, and notices that the grass has been mysteriously cut (by John) to resemble graph paper. Notice that after one step Pa is always exactly one unit away from the start. Let’s assume that Pa wanders eastward from the initial location on the first step. How far away might Pa be from the initial location after the second step? John sees that with a probability of 0.25 Pa will be 0 units away, with a probability of 0.25 Pa will be 2 units away, and with a probability of 0.5 Pa will be √2 units away. So, on average Pa will be further away after two steps than after one step. What about the third step? If the second step is to the north or south, the third step will bring the farmer closer to origin half the time and further half the time. If the second step is to the west (back to the start), the third step will be away from the origin. If the second step is to the east, the third step will be closer to the origin a quarter of the time, and further away three quarters of the time.
It seems like the more steps Pa takes, the greater the expected distance from the origin. We could continue this exhaustive enumeration of possibilities and perhaps develop a pretty good intuition about how this distance grows with respect to the number of steps. However, it is getting pretty tedious, so it seems like a better idea to write a program to do it for us.
However, there are a couple more twists to the situation. Pa’s wife “Mi-Ma”, another grandparent of John’s, also likes to wander away randomly, but riding an old mule. The mule goes South twice as often as any other direction. Lastly, John’s favorite hog “Reg” has an odd habit of wandering off too, but only randomly going east or west at each step, never north or south. People think he’s a sun-follower, but nobody’s really sure. John figures your Python program ought to model these two as well, while you’re at it.
simulate()
Define a function called simulate() that takes three parameters:
A list of “walk lengths” to simulate,
The number of trials (how many times to do walks of the specified lengths), and
Which walker we are modeling: ‘Pa’, ‘Mi-Ma’, ‘Reg’, or ‘all’.
For your convenience, define a main() function (that runs with conditional execution, per usual). main() should read the following three arguments from the command line:
A list of comma-separated “walk lengths” to simulate (there should be no spaces in this list),
the number of trials, or times to try each walk length, and
which type of walk we are modeling: ‘Pa’, ‘Mi-Ma’ or ‘Reg’ or ‘all’.
If invoked with python3 main.py 100,1000 50 all, your main() function should call simulate([100,1000], 50, ‘all’).
Assume the wanderer always starts each walk at (0,0) in an infinite grid. Your output values should be close to the examples given below, though they won’t be exact because we are using random numbers. So much depends on the order in which random numbers are consumed, and exactly how they are used, but the mean distance for final location should be pretty close no matter how movement is modeled. The format of the output should be the same.
Pa random walk of 100 steps
Mean = 8.5 CV = 0.6
Max = 19.8 Min = 1.4
Pa random walk of 1000 steps
Mean = 31.4 CV = 0.5
Max = 57.0 Min = 1.4
Mi-Ma random walk of 100 steps
Mean = 26.7 CV = 0.4
Max = 52.6 Min = 7.6
Mi-Ma random walk of 1000 steps
Mean = 243.4 CV = 0.2
Max = 318.2 Min = 156.2
Reg random walk of 100 steps
Mean = 7.6 CV = 0.8
Max = 22.0 Min = 0.0
Reg random walk of 1000 steps
Mean = 33.2 CV = 0.7
Max = 86.0 Min = 0.0
In the output above:
mean is the average distance over all walks of that length as the crow flies,
max is the longest distance as the crow flies,
min is the shortest distance as the crow flies, and
CV is the Coefficient of Variance, defined as the standard deviation divided by the mean.
plot()
Define a function called plot() that plots a sample of final locations in a turtle window in order to visualize their behaviors. Your output should be similar to the picture below. Do only one plot: plot all three characters for 50 trials on walk length 100. Don’t plot the 1000 walk length; it takes too long.
Hint: The fastest programs calculate all final locations first, separate from plotting, and only move turtles to plot final locations.
.guides/img/random_walk
Details of plot()
The autograder will compare your output to expected output. In order to ensure that your output matches the expected output, do the following:
Use turtle.shape() to change the turtle’s shape (the three shapes are ‘circle’, ‘square’, and ‘triangle’).
Use turtle.color() to change the turtle’s color (‘black’, ‘green’, and ‘red’).
Use turtle.stamp() to plot individual points.
Use turtle.shapesize() to scale the shapes to half of their original size in both dimensions.
Use turtle.speed() and set it to the fastest speed if you have trouble with timing out.
Use a scale of five pixels (for the turtle) per step (for Pa, Mi-Ma, or Reg).
Set the size of the screen to 300×400.
When you are finished, call the provided save_to_image() function to save the results to a file.
Logistics
Testing
To test your program, run it from the terminal. Specify different walk lengths, numbers of trials, and walkers.
Submission
Once you have tested your program, check it with the autograder (which will be included in a future version of this assignment). After you are satisfied with the grade you have earned, mark the assignment as complete.
Grading
0-20 points: design doc is correct and complete
21-100: all automated and manual tests pass
Rubric
(5 points) simulate() produces correct results for Pa for 100 steps for 50 trials.
(5 points) simulate() produces correct results for Pa for 1000 steps for 50 trials.
(5 points) simulate() produces correct results for Mi-Ma for 100 steps for 50 trials.
(5 points) simulate() produces correct results for Mi-Ma for 1000 steps for 50 trials.
(5 points) simulate() produces correct results for Reg for 100 steps for 50 trials.
(5 points) simulate() produces correct results for Reg for 1000 steps for 50 trials.
(20 points ) simulate() produces correct results for undisclosed inputs.
(10 points) plot() produces an image as shown.
(4 points) Code has a main function with conditional execution.
(4 points) File has a module docstring with required information in it.
(4 points) Every function has a proper function docstring.
(4 points) Variables use snake case.
(4 points) The style checker emits no warnings.

You are going to create a deck of cards and deal a five (5) card poker hand. You

You are going to create a deck of cards and deal a five (5) card poker hand.
Your program should create a deck of cards, shuffle it, and then put the first five cards in the deck into a 5 element list which represents a hand. Use the functions we created together. You can (and should) import the card program we created in lecture directly. You do not need to copy and paste the code into a new program. Simply add an import statement and use the functions.
You are going to write a series of functions which take a hand (a list of 5 cards) as an input and return Boolean values that can be used to analyze this hand.
– is_high_card: should return true if there are no cards with matching faces, and the cards do not increase in value (no straight) and do not have a matching suit (no flush). This hand is also known as nothing.
– is_pair: should return true if there are exactly 2 cards of the same value
– is_2_pair: should return true if there is one set of 2 cards with a common value and a second set of 2 cards with a different common value
– is_3_of_a_kind: returns true if there are exactly 3 cards with a common value
– is_4_of_a_kind: returns true if there are exactly 4 cards with a common value
– is_full_house: returns true if there are 3 cards with a common value and the other cards share a different common value
– is_flush: if the five cards all have the same suit
– is_straight: returns true if the value of the five cards form a sequence which increases by 1 in each case. For instance (2,”hearts”). (3,”spades”), (4, “diamonds”), (5,”hearts”), (6,”clubs”)
– is_straight_flush: returns true if the value of the five cards form a sequence which increases by 1 in each case and each card has the same suit. For instance (2,”hearts”). (3,”hearts”), (4, “hearts”), (5,”hearts”), (6,”hearts”)
Your program should create a deck, shuffle it, draw a hand from the first five cards, then call each of the above functions and print out which of them return True. Do not print out information about functions that return False. Finally, the program should print the cards from the hand.
Rules and Requirements
– Your program MUST BE broken into two files.
– One file MUST BE a module that contains all the card hand assessment functions.
– The module containing the test functions file MUST BE named “card_tests.py”. If the module is named anything else, your program will instantly lose 50% credit.
– Your program that creates the deck and deals the hand and tests the hand MUST BE named “deal_and_test.py” this program should consolidate all code into functions and should run a main function as per our normal class standard. This file can have in import statement to use the functions we created in class.
– The functions used to test the cards MUST BE named as described above. If the functions are named anything else, your program will instantly lose 50% credit.
– Your functions MUST accept a list of cards (dictionaries) as input. Any function that accepts anything else will receive a zero score.
– Your functions MUST return Boolean values. Any function that returns anything else will receive a zero score.
– You MUST use the playing card structure that we produced in class. Do not change the names of the dictionary keys.
– The order of functions in the module does not matter.
– You may not use list comprehension or classes of your own creation. Any check that uses these techniques will earn zero points.
Your final submission will include three (3) files. The playing card program we created in class, your deal_and_test.py file and your card_test.py file.
Note: It is possible for multiple conditions to be met in a single hand. For instance a straight-flush is both a straight and a flush. But while three of a kind also technically contains a single pair, it should not be listed as a pair because a pair is EXACTLY two matching cards. I will be testing your program by feeding it different hands. Your functions must accurately categorize valid and invalid hands. That is to say, “is_pair” or “is_3_of_a_kind” should return True when given a pair or three of a kind respectively but False when given a full house. As a result, “is_pair” is probably the hardest function to get right.
For Extra Credit, make a third file in your program that creates two hands by alternating cards from the shuffled deck into each hand. Determine which hand wins based on the rankings of poker hands (https://en.wikipedia.org/wiki/List_of_poker_hands#Hand-ranking_categories (Links to an external site.)). You may add functions to your module of card tests but make sure the original functions still work properly and return the proper results. If you do the extra credit, your submission will include four (4) files.

Requirements: 1. In the Grader class of the grade_view module, complete the met

Requirements:
1. In the Grader class of the grade_view module, complete the method read_source. This method shall minimally:
a. Check if the filepath argument exists. Raise a FileNotFoundError with the given file path if it does not exist. Otherwise:
i. Read from csv file
ii. Return a collection object with the stored data (i.e. first names, last names and scores) from the csv file (NOTE: remember to convert score to a number type)
2. In the Grader class of the grade_view module, complete the method write_results. This method shall minimally:
a. Create a new csv file (use the filepath parameter as file name) to store the data in the grade_book dictionary of the course’s GradeBook object. (HINT: access from the Grader object’s course attribute i.e. self.course.grade_book.grade_book)
b. The header for the new csv file must be as follows header: [‘G-number’, ‘First_name’, ‘Surname’, ‘Score’, ‘Grade’]
3. In the Grader class of the grade_view module, complete the method initialize_course. This method shall minimally:
a. Initialize the instance attribute course, by assigning it to a new Course object using the parameters: crse_num, crs_code and dept_code,
b. Invoke the read_source method with the filepath parameter and use the returned collection object to:
i. Create a list containing Student objects (See columns 2 and 3 from student_data.csv)
ii. Invoke the enroll method of the now initialized course instance attribute to enroll all Student objects previously created.
iii. Invoke the update_gradebook method of the course attribute to update scores for all Student objects previously created (See column 4 from student_data.csv)

Problem In 1827, the Scottish botanist Robert Brown observed that pollen particl

Problem
In 1827, the Scottish botanist Robert Brown observed that pollen particles suspended in water seemed to float around at random. He had no plausible explanation for what came to be known as Brownian motion, and made no attempt to model it mathematically. Louis Bachelier presented a clear mathematical model in his doctoral thesis, The Theory of Speculation in 1900. His thesis was largely ignored by respectable academics because it dealt with the then disreputable field of understanding financial markets. In 1905, Albert Einstein used similar stochastic thinking in physics to describe how it could be used to confirm the existence of atoms. People seemed to think that understanding physics was more important than making money, and the world started paying attention.
Brownian motion is an example of a random walk. Today, random walks are widely used to model physical processes like diffusion, biological processes like the kinetics of displacement of RNA from heteroduplexes by DNA, and social processes like movements of the stock market.
We are interested in random walks because of their wide applications to many problems, and for learning more about how to structure simulations nicely in Python.
Farmer John has an old grandparent (“Pa”) that likes to wander off randomly when working in the barn. Pa starts from the barn and every second take one step in a random direction North, South, East or West. What is Pa’s expected distance away from the barn after 1000 steps? If Pa takes many steps, will Pa be likely to move ever further from the origin, or be more likely to wander back to the origin over and over, and end up not far from where she started? Let’s write a simulation to find out.
This particular barn is in the center of a large grassy field. One day Pa starts to wander off, and notices that the grass has been mysteriously cut (by John) to resemble graph paper. Notice that after one step Pa is always exactly one unit away from the start. Let’s assume that Pa wanders eastward from the initial location on the first step. How far away might Pa be from the initial location after the second step? John sees that with a probability of 0.25 Pa will be 0 units away, with a probability of 0.25 Pa will be 2 units away, and with a probability of 0.5 Pa will be √2 units away. So, on average Pa will be further away after two steps than after one step. What about the third step? If the second step is to the north or south, the third step will bring the farmer closer to origin half the time and further half the time. If the second step is to the west (back to the start), the third step will be away from the origin. If the second step is to the east, the third step will be closer to the origin a quarter of the time, and further away three quarters of the time.
It seems like the more steps Pa takes, the greater the expected distance from the origin. We could continue this exhaustive enumeration of possibilities and perhaps develop a pretty good intuition about how this distance grows with respect to the number of steps. However, it is getting pretty tedious, so it seems like a better idea to write a program to do it for us.
However, there are a couple more twists to the situation. Pa’s wife “Mi-Ma”, another grandparent of John’s, also likes to wander away randomly, but riding an old mule. The mule goes South twice as often as any other direction. Lastly, John’s favorite hog “Reg” has an odd habit of wandering off too, but only randomly going east or west at each step, never north or south. People think he’s a sun-follower, but nobody’s really sure. John figures your Python program ought to model these two as well, while you’re at it.
simulate()
Define a function called simulate() that takes three parameters:
A list of “walk lengths” to simulate,
The number of trials (how many times to do walks of the specified lengths), and
Which walker we are modeling: ‘Pa’, ‘Mi-Ma’, ‘Reg’, or ‘all’.
For your convenience, define a main() function (that runs with conditional execution, per usual). main() should read the following three arguments from the command line:
A list of comma-separated “walk lengths” to simulate (there should be no spaces in this list),
the number of trials, or times to try each walk length, and
which type of walk we are modeling: ‘Pa’, ‘Mi-Ma’ or ‘Reg’ or ‘all’.
If invoked with python3 main.py 100,1000 50 all, your main() function should call simulate([100,1000], 50, ‘all’).
Assume the wanderer always starts each walk at (0,0) in an infinite grid. Your output values should be close to the examples given below, though they won’t be exact because we are using random numbers. So much depends on the order in which random numbers are consumed, and exactly how they are used, but the mean distance for final location should be pretty close no matter how movement is modeled. The format of the output should be the same.
Pa random walk of 100 steps
Mean = 8.5 CV = 0.6
Max = 19.8 Min = 1.4
Pa random walk of 1000 steps
Mean = 31.4 CV = 0.5
Max = 57.0 Min = 1.4
Mi-Ma random walk of 100 steps
Mean = 26.7 CV = 0.4
Max = 52.6 Min = 7.6
Mi-Ma random walk of 1000 steps
Mean = 243.4 CV = 0.2
Max = 318.2 Min = 156.2
Reg random walk of 100 steps
Mean = 7.6 CV = 0.8
Max = 22.0 Min = 0.0
Reg random walk of 1000 steps
Mean = 33.2 CV = 0.7
Max = 86.0 Min = 0.0
In the output above:
mean is the average distance over all walks of that length as the crow flies,
max is the longest distance as the crow flies,
min is the shortest distance as the crow flies, and
CV is the Coefficient of Variance, defined as the standard deviation divided by the mean.
plot()
Define a function called plot() that plots a sample of final locations in a turtle window in order to visualize their behaviors. Your output should be similar to the picture below. Do only one plot: plot all three characters for 50 trials on walk length 100. Don’t plot the 1000 walk length; it takes too long.
Hint: The fastest programs calculate all final locations first, separate from plotting, and only move turtles to plot final locations.
.guides/img/random_walk
Details of plot()
The autograder will compare your output to expected output. In order to ensure that your output matches the expected output, do the following:
Use turtle.shape() to change the turtle’s shape (the three shapes are ‘circle’, ‘square’, and ‘triangle’).
Use turtle.color() to change the turtle’s color (‘black’, ‘green’, and ‘red’).
Use turtle.stamp() to plot individual points.
Use turtle.shapesize() to scale the shapes to half of their original size in both dimensions.
Use turtle.speed() and set it to the fastest speed if you have trouble with timing out.
Use a scale of five pixels (for the turtle) per step (for Pa, Mi-Ma, or Reg).
Set the size of the screen to 300×400.
When you are finished, call the provided save_to_image() function to save the results to a file.
Logistics
Testing
To test your program, run it from the terminal. Specify different walk lengths, numbers of trials, and walkers.
Submission
Once you have tested your program, check it with the autograder (which will be included in a future version of this assignment). After you are satisfied with the grade you have earned, mark the assignment as complete.
Grading
0-20 points: design doc is correct and complete
21-100: all automated and manual tests pass
Rubric
(5 points) simulate() produces correct results for Pa for 100 steps for 50 trials.
(5 points) simulate() produces correct results for Pa for 1000 steps for 50 trials.
(5 points) simulate() produces correct results for Mi-Ma for 100 steps for 50 trials.
(5 points) simulate() produces correct results for Mi-Ma for 1000 steps for 50 trials.
(5 points) simulate() produces correct results for Reg for 100 steps for 50 trials.
(5 points) simulate() produces correct results for Reg for 1000 steps for 50 trials.
(20 points ) simulate() produces correct results for undisclosed inputs.
(10 points) plot() produces an image as shown.
(4 points) Code has a main function with conditional execution.
(4 points) File has a module docstring with required information in it.
(4 points) Every function has a proper function docstring.
(4 points) Variables use snake case.
(4 points) The style checker emits no warnings.

You are going to create a deck of cards and deal a five (5) card poker hand. You

You are going to create a deck of cards and deal a five (5) card poker hand.
Your program should create a deck of cards, shuffle it, and then put the first five cards in the deck into a 5 element list which represents a hand. Use the functions we created together. You can (and should) import the card program we created in lecture directly. You do not need to copy and paste the code into a new program. Simply add an import statement and use the functions.
You are going to write a series of functions which take a hand (a list of 5 cards) as an input and return Boolean values that can be used to analyze this hand.
– is_high_card: should return true if there are no cards with matching faces, and the cards do not increase in value (no straight) and do not have a matching suit (no flush). This hand is also known as nothing.
– is_pair: should return true if there are exactly 2 cards of the same value
– is_2_pair: should return true if there is one set of 2 cards with a common value and a second set of 2 cards with a different common value
– is_3_of_a_kind: returns true if there are exactly 3 cards with a common value
– is_4_of_a_kind: returns true if there are exactly 4 cards with a common value
– is_full_house: returns true if there are 3 cards with a common value and the other cards share a different common value
– is_flush: if the five cards all have the same suit
– is_straight: returns true if the value of the five cards form a sequence which increases by 1 in each case. For instance (2,”hearts”). (3,”spades”), (4, “diamonds”), (5,”hearts”), (6,”clubs”)
– is_straight_flush: returns true if the value of the five cards form a sequence which increases by 1 in each case and each card has the same suit. For instance (2,”hearts”). (3,”hearts”), (4, “hearts”), (5,”hearts”), (6,”hearts”)
Your program should create a deck, shuffle it, draw a hand from the first five cards, then call each of the above functions and print out which of them return True. Do not print out information about functions that return False. Finally, the program should print the cards from the hand.
Rules and Requirements
– Your program MUST BE broken into two files.
– One file MUST BE a module that contains all the card hand assessment functions.
– The module containing the test functions file MUST BE named “card_tests.py”. If the module is named anything else, your program will instantly lose 50% credit.
– Your program that creates the deck and deals the hand and tests the hand MUST BE named “deal_and_test.py” this program should consolidate all code into functions and should run a main function as per our normal class standard. This file can have in import statement to use the functions we created in class.
– The functions used to test the cards MUST BE named as described above. If the functions are named anything else, your program will instantly lose 50% credit.
– Your functions MUST accept a list of cards (dictionaries) as input. Any function that accepts anything else will receive a zero score.
– Your functions MUST return Boolean values. Any function that returns anything else will receive a zero score.
– You MUST use the playing card structure that we produced in class. Do not change the names of the dictionary keys.
– The order of functions in the module does not matter.
– You may not use list comprehension or classes of your own creation. Any check that uses these techniques will earn zero points.
Your final submission will include three (3) files. The playing card program we created in class, your deal_and_test.py file and your card_test.py file.
Note: It is possible for multiple conditions to be met in a single hand. For instance a straight-flush is both a straight and a flush. But while three of a kind also technically contains a single pair, it should not be listed as a pair because a pair is EXACTLY two matching cards. I will be testing your program by feeding it different hands. Your functions must accurately categorize valid and invalid hands. That is to say, “is_pair” or “is_3_of_a_kind” should return True when given a pair or three of a kind respectively but False when given a full house. As a result, “is_pair” is probably the hardest function to get right.
For Extra Credit, make a third file in your program that creates two hands by alternating cards from the shuffled deck into each hand. Determine which hand wins based on the rankings of poker hands (https://en.wikipedia.org/wiki/List_of_poker_hands#Hand-ranking_categories (Links to an external site.)). You may add functions to your module of card tests but make sure the original functions still work properly and return the proper results. If you do the extra credit, your submission will include four (4) files.

Requirements: 1. In the Grader class of the grade_view module, complete the met

Requirements:
1. In the Grader class of the grade_view module, complete the method read_source. This method shall minimally:
a. Check if the filepath argument exists. Raise a FileNotFoundError with the given file path if it does not exist. Otherwise:
i. Read from csv file
ii. Return a collection object with the stored data (i.e. first names, last names and scores) from the csv file (NOTE: remember to convert score to a number type)
2. In the Grader class of the grade_view module, complete the method write_results. This method shall minimally:
a. Create a new csv file (use the filepath parameter as file name) to store the data in the grade_book dictionary of the course’s GradeBook object. (HINT: access from the Grader object’s course attribute i.e. self.course.grade_book.grade_book)
b. The header for the new csv file must be as follows header: [‘G-number’, ‘First_name’, ‘Surname’, ‘Score’, ‘Grade’]
3. In the Grader class of the grade_view module, complete the method initialize_course. This method shall minimally:
a. Initialize the instance attribute course, by assigning it to a new Course object using the parameters: crse_num, crs_code and dept_code,
b. Invoke the read_source method with the filepath parameter and use the returned collection object to:
i. Create a list containing Student objects (See columns 2 and 3 from student_data.csv)
ii. Invoke the enroll method of the now initialized course instance attribute to enroll all Student objects previously created.
iii. Invoke the update_gradebook method of the course attribute to update scores for all Student objects previously created (See column 4 from student_data.csv)

Create an application that a user can add and retain information about a Film(Ti

Create an application that a user can add and retain information about a Film(Title, Director, Brief Description, Year, Type, Actors)
You will start by constructing a Hierarch Chart (by using Flowgorithm) that sketches out the main parts of your application (no details, just major functions).
Once designed the Application will be written in Python that is capable of doing the following:
Adding a new Film with its information
Modifying the File entry (perhaps adding additional actors, or correcting a misspelling)
Deleting a Film Entry
List Movie in the data base (a list at first, later from a file)
Search Movie by all or any part of the fields(Title, Director, Description, Year, Type, or Actors)
Ability to sort the list by each of its individual fields.
The Film information should persistent (That is, information should be saved to disk and retrieved when the Application is started.)

In this assignment you will have to deal with files and functions. •You are aske

In this assignment you will have to deal with files and functions.
•You are asked to read 1000 floats from a file, which is provided to you (floats.txt). The floats have to be read into a list.
•Within your program you need to create a function insertion_sort(float_list) that takes as argument a list of floating point numbers and sorts it in ascending order (smallest first) using the Insertion Sort algorithm (see Nov 23rd lecture for an explanation of the algorithm).
•After reading the floats from the file into a list, your main program should call the insertion_sort function, then it should: 1) print the now sorted list onto the screen; 2) write the sorted list onto a file (sorted_floats.txt).