EGR 103/Fall 2022/Lab 2

Introduction

Among other things, you will learn how to use Spyder as well as how to upload assignments for EGR 103.

Errors / Additions

• None yet!

2.1 Introduction

The main purpose of this lab is to go through the process of writing a complete program to load a data set, manipulate the values, perform some analysis, and create a graph. The following is a companion piece to the lab handout itself. The sections in this guide will match those in the handout.

2.2 Resources

You will want to have a browser open with at least the Python:Script and Python:Plotting pages available to you. You will also want to go through all the steps in EGR 103/Fall 2022/Beginning of Lab right when you get into the lab room. If you have any issues with those steps, let a TA know as soon as possible to get help. These steps will get the files you need in the places you need them and then will get Spyder ready to go.

2.3 Preview

Read and work through this section, taking notes as you go. Type in the commands at the lettered sections to see how Python responds.

2.4 .py Files

Read and work through this section, taking notes as you go. Note that the first time you run a script, Spyder will bring up a dialog box about how you would like to run scripts. The defaults are all good and you do not need to see that dialog box again, so just click the Run button.

2.5 Cantilever Beam Analysis

Read and work through this section, taking notes as you go. Note especially the process of rearranging the equation to get displacement as a function of force based on the fact that - for this experiment - the independent variable is the force.

To see the data set, make sure Spyder is looking at the correct directory (it should be by now), then click the File tab at the bottom of the information panel (the one above the console window). Double-click the Cantilever.dat file and Spyder will open it in the text editor. You will see an 8x2 array of data. The first column of numbers is the amount of mass in kg placed on the end of the beam - for this experiment, it ranged from 0 to about 0.8 kg (i.e. about 8 N). The second column of numbers is the displacement measured in inches, because that is what the device I used measured in. You should always take data in the original units of the device; you can convert it later.

Typically, data sets should have descriptions such as the items in the data set, units, perhaps the equipment used, and the person who took the measurements. For this week, however, I wanted to give you an array of data that Python can easily load. Later in the course, we will see more sophisticated ways of loading annotated data sets.

2.6 Creating the Script

Note when you open a second script that there are tabs in the editing window; you can choose which one to make active by clicking its tab. Be careful not to accidentally click the part of the tab that closes that tab (the X).

2.6.1 Lab Manual Syntax

For this lab, there will be several times you will simply type some items into the command window to see what they do before actually adding codes to the script. Command-line only items are surrounded by a box whereas code that goes in the script has a shadowbox.

2.6.2 Comments

While you are not explicitly required to include comments in your labs -- other than the header and community standard information at the top -- comments are a great way to tell yourself and your TAs what you meant to do with particular chunks of code. Also, note that a line starting with # %% will set off a section in the editor window. It will have no effect on the way the program runs, just what it looks like in your editing window. By creating cells that way, you can also run your code a cell at a time.

2.6.3 Importing Modules

Read and work through this section, taking notes as you go. These lines will import two modules with nicknames. If you want to see what a module has in it, once imported, type help(NAME) - for instance,

help(np)

will show everything in np. If you want help on a particular command, put the command in the argument - for instance,

help(plt.plot)

2.6.4 Loading and Manipulating the Data

Read and work through this section, taking notes as you go. Also, take a look at the information on the page on Pandas regarding how to load different types of text files. Note that when you slice arrays, you need to append the .copy() to duplicate the data rather than have two different variables pointing at the same data.

2.6.5 Plotting

Read and work through this section, taking notes as you go. You may also want to check out Python:Plotting and specifically the simp[le examples at the top of the page.

Note that the code for this will end up several lines under what you've written so far; you will be filling in the blank space later. Also - as will be described in the lab, there are a few different ways to make plots. During the lab, you will change the preferences in Spyder to create graphics using the "Automatic" backend instead of "Inline". This generally means that figures will be in their own window instead of in the console window. To make this change in Spyder:

• Open the preferences window
  • On Windows, go to the Tools menu near the top right of the window and select Preferences
  • On MACs, go to the python menu near the top left of the window and select Preferences
• In the Preferences window at the left select IPython console
• In the right half of the Preferences window, select the Graphics tab
• In the Backend pulldown, select Automatic
• Click OK in the Preferences window

This change will take effect the next time you start Spyder. To make it take effect now, you need to restart the kernel. Go to the settings icon at the top right of the console (the gear or three horizontal lines), click it, and Restart kernel. Note that this will clear all variables and loaded modules as well. Re-run your script to see the figure in its own window.

2.6.6 Polynomials in NumPy

Read and work through this section, taking notes as you go. Be sure you understand how Python - using NumPy - interprets polynomials and which commands use them.

2.6.7 Calculate Model Values

Read and work through this section, taking notes as you go. This part creates a new set of data points based on your equation so that you can actually plot it. You will thus have your original eight data points in force and disp. You will also have 100 linearly spaced values between the minimum and maximum force value in force_model and the 100 calculated estimates for the displacement, based on your best straight line, in disp_model.

2.6.8 Generating Plots (revisited)

Read and work through this section, taking notes as you go. This part adds the new line to your old graph.

2.6.9 Saving Plots

CRITICALLY IMPORTANT PART - never put .py in a savefig argument! Your graph might override your program!

2.6.10 Uploading Assignments

During the lab, you will upload the PDF of your graph only. You do not need to upload the files to the Drop Box nor will you need to upload an updated graph for this assignment ever - the one from the lab (missing the grid and title and such) is fine for the Cantilever assignment. Have a TA check to make sure your assignment was correctly uploaded.

2.6.11 Generating Plots (revisited*2)

Read and work through this section, taking notes as you go. This part adds useful parts to your graph and makes it take up space more efficiently.

2.7 The Assignment

Basically, once you get this script running perfectly, you are going to replicate it three times and change it to use three different data sets. Note: these three data sets are "cooked" - that is, I produced them; they did not come from an experiment.

2.7.1 What the .py-files Should Do

Read and work through this section, taking notes as you go.

2.7.2 The Lab Report

Read and work through this section, taking notes as you go. Note that the data sets have different numbers of points so the bottoms of the data tables will not line up. Also, note that you should use the original data (masses and displacements in inches) in the tables -- do not put in the forces or the displacements in meters. You are using the latter in your calculations but you are presenting the original data sets here.

Here is the code for getting the beam_data numbers to show up on the screen in a way that is easy to copy to your tex file:

for a in range(beam_data.size // 2):
    print(
        "{:0.4e} & {:0.4e} \\\\".format(
            beam_data.iloc[a, 0], beam_data.iloc[a, 1]
        )
    )

If you choose to add this to your script - you are certainly welcome to leave it in there when you turn your code it.

2.7.3 Processing the Lab Report

Seriously - check spelling!

2.8 Code From Lab

Chrome seems to work well for copying and pasting from line-numbered codes on Pundit. Some other browsers? Not so much...

 1 # %% Import modules
 2 import numpy as np
 3 import pandas as pd
 4 import matplotlib.pyplot as plt
 5 
 6 # %% Load and manipulate the data
 7 # Load data from Cantilever.dat
 8 beam_data = pd.read_table("Cantilever.dat", header=None)
 9 # Copy data from each column into new variables
10 mass = beam_data.iloc[:, 0].copy()
11 disp = beam_data.iloc[:, 1].copy()
12 # Convert mass to a force measurement
13 force = mass * 9.81
14 # Convert displacement in inches to meters
15 disp = (disp * 2.54) / 100.0
16 
17 # %% Perform calculations
18 # Use polyfit to find first-order fit polynomials
19 p = np.polyfit(force, disp, 1)
20 print(p)
21 
22 # %% Generate predictions
23 # Create 100 representational force values
24 force_model = np.linspace(force.min(), force.max(), 100)
25 # Calculate displacement predictions
26 disp_model = np.polyval(p, force_model)
27 
28 # %% Generate and save plots
29 # Create a Figure and Axes
30 fig = plt.figure(num=1, clear=True)
31 ax = fig.add_subplot(1, 1, 1)
32 # Plot Displacement as a function of Force
33 ax.plot(force, disp, "ko")
34 # Plot the model values
35 ax.plot(force_model, disp_model, "b-")
36 # Turn the grid on
37 ax.grid(True)
38 # Label and title the graph
39 ax.set(
40     xlabel="Force (Newtons)",
41     ylabel="Displacement (meters)",
42     title="Displacement vs. Force for Cantilever.dat (NetID)",
43 )
44 # Use tight layout
45 fig.tight_layout()
46 # Save the graph to Encapsulated PostScript, PDF, and PNG
47 fig.savefig("RunCanPlot.eps")
48 fig.savefig("RunCanPlot.pdf")
49 fig.savefig("RunCanPlot.png")