[SOLVED] CS152 Project 3 Calculating Thermoclines Python

CS152

Project 3: Calculating Thermoclines

The first task of this project is to build a library of functions that can be reused by other programs. In particular, we are going to write a set of functions that can calculate statistics for us.

The second task will involve computing the depth of the thermocline on Great Pond and plotting it for the month of July. The thermocline is the depth at which there is the largest difference in water density, with a layer of denser water below and a layer of less dense water above.

Outline of Lab/Project 3 Program Development

Lab: Developing and working with libraries

A.  Write a module that contains common statistics named stats.py

B. Use the library functions in analyze.py using command line arguments

Project:  Calculating thermoclines

A. Write a function that converts water temperatures into water densities

B. Write a function that computes the depth of the maximum change in density

C. Write a top-level function that reads in the data file and guides the computation.

Setup your workspace for the project

1. Create a Project 3 folder in a conveniently accessible location

2. Download GoldieJuly2019.csv, testDensity.py and testThermocline.py

3. Copy stats.py and analyze.py from lab folder into the Project03 folder

Project Tasks (T1-T3)

T1. Write your library of useful statistical functions

In the stats.py file you started in the lab, add the following four (4) functions. Each function should have a single parameter, which should be a list of numbers. The functions should loop over the numbers in the list, compute the given statistic, and return it.

1.  mean(data) – computes the mean of the list of data.

2.  min(data) – computes the min of the list of data.

3.  max(data) – computes the max of the list of data.

4.  variance(data) – computes the variance of the list of data.

As you probably recall, to compute the mean you sum all the values in the list and divide by the number of items. Notice that stats.py already has a function to do the summing up. Instead of reinventing the wheel why not have the mean function call the sum function you already wrote? This is an example of code reuse.

The number of items in the list is the same as the length of the list. In this case, do use the Python built-in function len() to determine how many items are in the list. This is another example of code reuse.

Write functions to calculate the min and max of the items in the list. Do NOT use built-in functions to do this.

Finally, to calculate the variance you can use the following formula:

The algorithm for this formula says that you will sum up all of the squared differences between   each list item and the mean of the items and divide by the total number of items minus 1. There is a mathematical reason why you divide by N-1 and not N.

Run stats.py and make sure that it is working correctly.

Required Output 1: Include a description in your report how you determined your stats functions were working correctly. (e.g., take a screenshot of the output of calling functions with a simple list(s) of values and display the result)

T2. Write a program to compute statistics of a column of data

Using your analyze.py file from the lab, update it so that it computes the sum, mean, variance, max, and min statistics. Recall that we are using command line arguments to obtain the file name and column number from the user. Print the statistics to the terminal.

Calling analyze.py with the hurricanes.csv file and column 1 as command line arguments should produce the following statistics:

sum : 103.00

mean: 7.36

var : 12.55

min : 2.00

max : 15.00

Required Output 2: Demonstrate that your program works with a column from GoldieJuly2019.csv data file provided with this project. One way to do this is to insert a function in the cell at the bottom of a column to calculate a statistic. For example, placing =Average(B2:B2977) in the cell below any column will calculate the average of all the cells above it. For column 2 this would give a value of 10.795. Compare this value with your stats mean function. Include a screenshot of the printed output of your program and a screenshot of the data files calculation as evidence that your library function works.

T3. Calculate the thermocline depth in Great Pond for July 2019

The next task will be to write a program that computes the depth of the thermocline on Great Pond for each day in July 2019. The thermocline is the depth at which the water density changes most quickly, creating a layer of colder, denser water below a layer of warmer water that tends not to mix.

To do this you will write three functions:

1. a function that converts water temperatures into water densities

2. a function that computes the depth of the maximum change in density

3. a top-level function that reads in the data file and guides the computation.

T3a. Setup

Create a new file, thermocline.py. Put your name, date, and class at the top, along with a comment indicating what the program will do (compute the thermocline).

T3b. Convert temperatures to densities

Write a function called density that takes in one parameter, temps, that is a list of temperatures. The function should first create a new empty list to hold density values, typically designated with the Greek letter rho. Then, it should loop over the temps list and for each temperature value compute the density using the following equation:

rho = 1000 * (1 – (t + 288.9414) * (t – 3.9863)**2 / (508929.2*(t + 68.12963)))

It should then append each computed density to a list that will be returned to the caller.

Test your function using the included testDensity.py file. It should print out the following if your density function is working correctly.

24.47 -> 997.21

23.95 -> 997.34

24.41 -> 997.22

23.81 -> 997.37

19.92 -> 998.25

16.88 -> 998.82

14.06 -> 999.26

11.56 -> 999.57

9.82 -> 999.74

9.13 -> 999.80

8.82 -> 999.82

T3c. Compute the derivative of the densities

Note: If you have not taken Univariate Calculus yet a derivative is just a slope at a point. Slope is defined as the rise of the function over the run. So in this program we are going to calculate   the change in density as the depth increases and locate the depth where the maximum change occurs.

Add a function named thermocline_depth to thermocline.py that computes the derivative of density with respect to depth, or how fast the density is changing as you get deeper. The function will take in two lists: one is the set of temperatures, the other is the set of corresponding depths. The function will return one value: the depth of the maximum change in density. The  algorithm below gives the function.

def thermocline_depth( temps, depths ):

# assign to rhos the result of calling the density function with temps as the argument

# create an empty list named drho_dz

# loop for one less than the length of rhos

# append to drho_dz the quantity rhos[i+1] minus rhos[i] divided by the quantity depths[i+1] minus depths[i]

# sanity check (optional): print out temps[i], rhos[i], and drho_dz[i] for visual inspection

# assign to max_drho_dz the value -1.0

# assign the maxindex the value -1

# loop for the length of drho_dz (loop variable i)

# if drho_dz[i] is greater than max_drho_dz

# assign to max_drho_dz the value drho_dz[i]

# assign to maxindex the value i

# assign to thermoDepth the average of depths[maxindex] and depths[maxindex+1]

return thermoDepth

Test your thermocline_depth function using the included testThermocline.py file. It should output a depth of 6.0m (note that the maximum change of 0.44 at that depth — you do not need to report this, but if you run into problems, knowing what the maximum change is supposed to   be can help you debug your code).

T3d. Compute the thermocline for each day in July

The final step is to write the main function that reads in data from the included GoldieJuly2019 file, extracts all of the temperature fields in order, computes the thermocline_depth and prints  the day and thermocline_depth value.

The file includes all of the data fields for the month of July and a single header line.

Note: Pay particular attention to the indices. The field indices for the depths (in meters) [1, 3, 5, 7, 9, 11, 13, 15] are located at field numbers  [10, 11, 16, 17, 15, 14, 13, 12] in the

GoldieJuly2019 file. You may want to double-check the field numbers before starting by looking at the header line.

The algorithm given below is not strictly line by line. Each comment will correspond to one or more lines of Python.

def main():

# these are the fields corresponding to the temperatures in order by depth

# note they use 0-indexing

fields = [10, 11, 16, 17, 15, 14, 13, 12]

# these are the depth values for each temperature measurement

depths = [ 1, 3, 5, 7, 9, 11, 13, 15 ]

# open the data file and read past the header line

# assign to day the value 0

# for each line in the file

# split the line on commas and assign it to words

# if the time is about noon (12:03:00 PM)

# add one to the day variable

# assign to temps the empty list

# loop over the number of items in depths (loop variable i)

# append to temps the result of casting words[ fields[i] ] to a float

# assign to thermo_depth the result of calling thermocline_depth with temps and depths as arguments

# print (or save to a file) the day of the month and thermo_depth separated by a comma

if __name__ == “__main__”:

main()

Required Output 3: Run your program and create a plot of the results with day on the x-axis and thermocline depth on the y-axis. Include this plot in your report

Reflection Questions: Required Element  4: Follow-up Questions:

1.  Explain what is meant by the term “code reuse” and give an example?

2.   Explain what is meant by the term “modular design” and give an example?

3.  Perform. a Google search for a woman statistician and give a one sentence description of a contribution they made.

Extensions

Each assignment will have a set of suggested extensions. The required tasks constitute about 85% of the assignment, and if you do only the required tasks and do them well you will earn a B+. To earn a higher grade, you need to undertake one or more extensions. The difficulty and  quality of the extension or extensions will determine your final grade for the assignment. One complex extension, done well, or 2-3 smaller extensions are typical.

● Write functions in your stats.py file to compute more types of statistics.

● Use your code to compute statistics on a data set of your own choosing.

● Compare different times or time periods in the Goldie data.

●   Add more command-line control options, such as specifying what time of day to compute the thermocline.

●   Explore how the thermocline changes and why? What are the min and max thermocline values for July? What if you graph wind direction and thermocline together, is there a relationship?

● Automate the process of making a graph from data( Hint: Matplotlib)

Submit your code

Turn in your code (all files ending with .py) by zipping the file and uploading it to Google Classroom.

When submitting your code, double check the following.

1.   Is your name at the top of each Python file?

2.   Does every function have a docstring (‘’’ ‘’’) specifying what it does?

3.   Is your Lab 04 folder in your Project 04 folder?

4.   Have you checked to make sure you have included all required elements and outputs in your project report?

5.   If you have done an Extension, have you included this information in your report under the Extension heading? Even if you have not done any extensions, include a section in your report where you state this.

6.   Have you acknowledged any help you may have received from classmates, your

instructor, the TAs, or outside sources (internet, books, videos, etc.)? If you received no help at all, have you indicated that under the Sources heading of the report?

Write your project report

Reports are not included in the compressed file! Please don’t make the graders hunt for your report.

You can write your report in any word processor you like and submit a PDF document in the Google Classroom assignment folder. Or just use a Google Document format.

Review the Writeup Guidelines document.

Your intended audience for your report is your peers who are not taking CS classes.

From week to week, you can assume your audience has read your prior reports. Your goal should be to explain to peers what you accomplished in the project and to give them a sense of how you did it. The following is a list and description of the mandatory sections you must include in your report. Do not include the descriptions in your report, but use them as a guide in writing your report.

● Abstract

A summary of the project, in your own words. This should be no more than a few

sentences. Give the reader context and identify the key purpose of the assignment. An abstract should define the project’s key lecture concepts in your own words for a general, non-CS audience. It should also describe the program’s context and output, highlighting a couple of important algorithmic and/or scientific details. Writing an effective abstract is an important skill. Consider the following questions while writing it.

○   Does it describe the CS concepts of the project (e.g. writing well-organized and efficient code)?

○   Does it describe the specific project application (e.g. generating data)?

○   Does it describe your solution and how it was developed (e.g. what code did you write)?

○   Does it describe the results or outputs (e.g. did your code work as expected and what did the results tell you)?

○ Is it concise?

○   Are all of the terms well-defined?

○   Does it read logically and in the proper order?

● Methods

The method section should describe in clear sentences (without pasting any code) at least one example of your own computational thinking that helped you complete your project. This could involve illustrating how a key lecture concept was applied to creating an image, how you solved a challenging problem, or explaining an algorithmic feature that is essential to your program as well as why it is so essential. The explanation should be suitable for a general audience who  does not know Python.

● Results

Present your results in a clear manner using human-friendly images or graphs labeled with captions and interpreted for a general audience such as your peers not in the course. Explain, for a general, non-CS audience, what your output means and whether it makes sense.

● Reflection and Follow-up questions

Draw connections between lecture concepts utilized in this project and real-world problems that interest you. How else could these concepts apply to our everyday lives? What are some specific things you had to learn or discover in order to complete the project? Look for a set of short answer questions in this section of the report template.

● Extensions (Required even if you did not do any)

A description of any extensions you undertook, including text output or images demonstrating those extensions. If you added any modules, functions, or other design components, note their structure and the algorithms you used.

● References/Acknowledgements (Required even if there are none)

Identify your collaborators, including TAs and professors. Include in that list anyone whose code you may have seen, such as those of friends who have taken the course in a previous semester. Cite any other sources, imported libraries, or tutorials you used to complete the project.