{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOFN4F4GHhjRJjflYbPFHgw"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Welcome to the Phys 2208 labs!**\n","\n","A core practice in physics is developing quantitative models that can be used (or extended) to describe and predict complex systems. For example, physics models of how charges flow in circuits are often used to model blood flow in the human body. This semester, lab is all about developing these physics skills and using them to learn something new---meaning using them to learn something that we, as a scientific community, do not yet understand.\n","\n","***Throughout this semseter, our guiding question will be: \"Is this model sufficient to describe our observations?\"*** If it is, great! If it's not, you will have an opportunity to either:\n","1. Revise the model based on the data you collect OR\n","2. Revise the experiment to more accurately align with the model.\n","\n","The goal is for you to develop the scientific skills of using physics to learn about a complex, novel system. By the end of the semester you will be able to:\n","\n","*   Describe and represent complex, novel scenarios from physics or other sciences using simple physics models.\n","*   Evaluate whether a simple model is sufficient to represent a system, and make decisions about what constitutes a sufficient model.\n","*   Develop, execute, and revise an experimental procedure to build and/or assess a model using physics data analysis techniques.\n","*   Choose between multiple models and develop an argument to defend the choice based on data and data analysis.\n","\n","### **Course Overview**\n","\n","We'll spend the first three sessions evaluating how well physics models for charges moving in electric fields and current through a wire can be used to model hexbots---small, self-propelling toys that will serve as our novel, complex system. Each week, we will return to our experimental question: **\"Are these physics models good descriptors of HexBots, and if not, what changes should we make to the model or the system?\"** You will learn new methods of collecting and analyzing data to make decisions about whether the model aligns with your observations, and what changes to make.\n","\n","The second three sessions will be spent developing a research question, designing and conducting an experiment to answer your question, and presenting your results at a \"mini symposium\" of your peers. Your research question might be to adjust the physics model to better describe HexBots, revising a prior experiment to better align with the model (using HexBots or not!), or another question that is particularly interesting to your group.\n","\n","| Lab Session | Activity | Check-out |\n","|---|---|---|\n","|1 | Building and evaluating a model: HexBot Velocity | TA will check your conclusions are supported by data. |\n","|2 | Building and evaluating a model: Charge in an E-Field | TA will check your conclusions are supported by data. |\n","|3 (first half)| Building and evaluating a model: Charge in a wire | TA will check your conclusions are supported by data. |\n","|3 (second half)| Final Project Part 1: Designing your research question | TA will approve: (1) your question, (2) plan for data collection/analysis |\n","|4 | Final Project Part 2: Pilot experiment and revisions | TA will check the initial data you collect, and your plan for revision. |\n","|5 | Final Project Part 3: Conducting experiments; finalize results and start presentation | TA will check on progress. |\n","|6 (first half) | Writing presentation |  |\n","|6 (second half) | Mini Symposium| TA will grade your presentation and participation.\n","\n","## **The Role of Colab Notebooks**\n","We'll be doing all our work in Google Colab Notebooks. Notebooks are documents that can contain text, code, visualizations, and more. These documents include your lab instructions and will serve as running notes of your lab activities each week.\n","\n","* **During lab:** You will work together in one notebook. (This is important, because unlike Google docs you cannot edit this notebook collaboratively.)\n","\n","* **At the end of lab:** Each person in your group will submit a pdf of your group's notebook to Canvas.\n","\n","Some students may have used Colab before; for others, it may be new! Today's lab activity is designed to help you feel comfortable using colab and collecting data about hexbots.\n","\n","**Note for Physics Lab Exchange Instructors:** We will have a group conversation about using Colab before we get started. Feel free to ask questions as we go!"],"metadata":{"id":"Jew7nSe9xajP"}},{"cell_type":"markdown","source":["## **What is a hexbot? (Pick one up and play while one team member reads this outloud!)**\n","\n","A HexBot (formerly known as HexBug) is a small toy, also known as a bristle bot, with a small motor that causes the bot to vibrate and move around on a surface. These clever little devices can mimic the behavior of microscopic particles, modeling the motion of active matter ([Daniel, 2024](https://pubs.aip.org/aip/sci/article/2024/43/431101/3317856/Using-Hexbugs-to-model-active-matter)), particle collisions ([Horvath et. al, 2023](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.024603)), dynamics of a harmonic trap ([Dauchot and Démery, 2019](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.068002)), coherent motion ([Binysh and Souslov, 2022](https://www.nature.com/articles/s41567-022-01735-4)), and more!\n","\n","In this lab, we'll use these devices to learn about designing experiments and analyzing data in order to quantitatively model biological systems. Think of it as an intro to how quantitative reasoning in physics can apply to experiments in biology or other sciences. The aim is for you to develop the physics skills to assess what makes a good model, and what data you need to support that decision.\n","\n","This semester, our data will mostly be collected by video. Some of you may have participated in HexBot labs in a prior physics course. If you have not, don't worry! Today is about making sure that everyone is on the same page and learning some new ways to analyze HexBot data."],"metadata":{"id":"w4HReDEqyhOF"}},{"cell_type":"markdown","source":[],"metadata":{"id":"mumyYRYbytjp"}},{"cell_type":"markdown","source":["**Disclaimer:** Do Not Panic. Learning how to code is NOT an explicit learning goal of this course. And, being familiar with (or at least not fearful of) programming will be very useful for anyone living in the 21st century. We're going to introduce a few quick ideas to working in Google Colab before we get into the Hexbots. We'll do as much of the coding for you as we can and you'll never be expected to create code from scratch or be evaluated on your code.\n","\n","Do not panic.\n","\n","## **Using Google Colaboratory: Interactive Coding Notebooks (PLE Instructors: skip if you are comfortable)**\n","\n","A notebook is composed of rectangular sections called **cells**. There are 2 kinds of cells: markdown cells and code cells."],"metadata":{"id":"WwFn1M2MTAGA"}},{"cell_type":"markdown","source":["### **Markdown cells**\n","\n","A **markdown cell**, such as this one, contains text in *natural* language (e.g. English). Text in Markdown cells is written in **Markdown**, a formatting syntax for plain text, so you may see some funky symbols when you edit a text cell. Don't worry too much about them (unless you want to! You can learn more about Markdown [here.](https://daringfireball.net/projects/markdown/))\n","\n","You can edit a Markdown cell by clicking it twice and editing the text on the left. You can see what your changes will look like on the right.\n","\n","After you've made your changes, you can exit text editing mode by clicking out of the cell or pressing Shift-Enter on your keyboard.\n","\n","\n","**Test it out:** Edit the next markdown cell to enter a group name."],"metadata":{"id":"V698kWqzTUQS"}},{"cell_type":"markdown","source":["**Our group name is:** ...\n","\n","---"],"metadata":{"id":"lVuUribFTU1w"}},{"cell_type":"markdown","source":["### **Code cells**\n","\n","A **code cell** contains code in Python, a *programming* language that we will be using to analyze our data and perform calculations in lab.\n","\n","**Test it out:** You can edit a code cell by clicking on it and then typing in the cell. Try editing the next code cell to print your group's name by replacing the word ``Awesome Possums`` in the last line."],"metadata":{"id":"tPj0yrSzTYcX"}},{"cell_type":"code","source":["# edit the code to print your group name\n","print(\"Hello: our group name is Awesome Possums\")"],"metadata":{"id":"H_z-Rs0rTaDH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["\n","\n","**To run/process a code cell either:**\n","- press Shift-Enter with the code cell selected, or\n","- click the Run button (small triangle) on the left side of the code cell.\n","\n","A cell is running when you see the Run button replaced with a progress wheel. A cell is finished running when a green check appears beside the Run button and any output from the code appears under the cell."],"metadata":{"id":"cdmX6lc4lNRf"}},{"cell_type":"markdown","source":["The code cell where you typed in your group name contains a line of green text that starts with a `#`. This is a **comment**. Comments often contain helpful information about what the code does or what you are supposed to do in the cell. The leading `#` tells the computer to ignore everything that comes after the `#` on that line. In other words, comments are *not* code and you can write whatever you want in a comment.\n","\n","**Test it out:** Read and then run the following code cell to show how comments work.\n"],"metadata":{"id":"kd7z6t93TN1y"}},{"cell_type":"code","source":["# here is a comment and below is some code\n","# you can write whatever you want in a comment\n","# it won't change the output of the code\n","print(\"this will print no matter what's commented\")"],"metadata":{"id":"CBPMbrZsTPQe"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### **Saving and Loading**\n","\n","Your notebook can record all of your text and code edits, as well as any graphs you generate or calculations you make.\n","\n","**To save your notebook:** Press Control-S, or go to the File menu and select \"Save\" (just like a google doc!).\n","\n","**What is saved:** All of your cells, and any outputs that you've produced (graphs, computations, etc.)\n","\n","**What needs to be run again next time:** Any functions or variables that you define in a code cell. (More on what exactly we mean by a function or a variable in a bit.) You will need to re-run these cells when you open the file to use these again. The easiest way is to highlight the cell where you left off work, then go to the Runtime menu at the top of the screen and click \"Run before\". You can also use this menu to run all cells in the notebook by clicking \"Run all\".\n","\n","**What happens if two people edit the same notebook:** You can and should share a notebook with your group mates. Unlike Google Docs, however, more than one person cannot edit the notebook at the same time. This means that, with two people working on the same notebook simultaneously, one person's work will get overwritten by the other person's. We wish there was a way around this. Until there is, please just have one person work in the notebook at a time."],"metadata":{"id":"-Z1x7UYqTfvm"}},{"cell_type":"markdown","source":["### **Filling in the Notebooks**\n","\n","As you navigate the notebooks, you'll see cells with markdown instructions and empty cells where you can add your own text or code.\n","\n","**To add new cells:** Click the \"+ Code\" or \"+ Text\" buttons at the top. (These also appear if you hover between cells.)\n","\n","**To delete a cell:** Select the cell, then click the trash can icon on the upper right side of the cell.\n","\n"],"metadata":{"id":"fQagzwXHTkY3"}},{"cell_type":"markdown","source":["Those are the basic skills you should be familiar with to start. As we go along, we will teach you any other more sophisticated skills that you might need. Again, DO NOT PANIC."],"metadata":{"id":"yxEwJW-tVfgB"}},{"cell_type":"markdown","source":[],"metadata":{"id":"ukbNehZ7y1Gs"}},{"cell_type":"markdown","source":["## **Group organization (PLE Instructors: skim and skip ahead)**\n","\n","In this course you'll be working in groups on lab activities and related assignments. Negotiating group dynamics successfully is therefore an integral component of the course. To help you get to know each other, and to ensure effective collaboration and fairness, group members should discuss and write down the answers to the following questions in order to establish a “Partner Agreement.”\n","\n","**Q.** Edit the next Markdown cell to list each of your group members and how they prefer to be referred (nicknames, pronouns, etc.)?\n","\n","\n"],"metadata":{"id":"uhFnctd4n0iJ"}},{"cell_type":"markdown","source":["**[Your group members info here -- throughout this notebook keep your responses in boldface so we can easily distinguish your work from the main text of the lab manual]**\n","\n","---"],"metadata":{"id":"3hJwEHsqqzL4"}},{"cell_type":"markdown","source":["**Q.** Take turns having each group member share one thing about themselves (where you’re from, what you like to do outside of class, etc.). Keep going around until you’ve found one thing you all have in common that you expect no other group will have in common and record it below."],"metadata":{"id":"FE20Wnt-q0KP"}},{"cell_type":"markdown","source":["\n","\n","**[Your unique commonality]**\n","\n","---"],"metadata":{"id":"R3Csi6itq2wJ"}},{"cell_type":"markdown","source":["**Q.** Discuss with your partners your reasons for taking this course and what you’re personally hoping to get out of it. How do your goals compare? Discuss how you can support each other’s goals. Can you agree on a collective goal for the group (e.g. to ensure everyone gets an A, to have fun, to provide a physics study group)?  Establish your collective goal and record it below."],"metadata":{"id":"AW-c89mmq4fA"}},{"cell_type":"markdown","source":["**[Your collective goal]**\n","\n","---"],"metadata":{"id":"awX9KnjNq6qA"}},{"cell_type":"markdown","source":["**Q.** To complete each lab a number of distinct tasks will need to be completed by individual group members or collaboratively by the group as a whole. These tasks include, but are not limited to, setting up equipment, collecting data, taking notes, performing analysis, and managing the group. The tasks each of you spend the most time on will also likely be the ones you learn the most about and grow the most in your ability to perform. How will you ensure each member is able to be involved in these tasks in the ways they want to? For example, will you divide tasks and stick with them, rotate the task assignments partway through lab, rotate task assignments each week, all work together on each task simultaneously, something else? If some form of dividing, how will you decide who takes on which task?"],"metadata":{"id":"6QenesWQq8OR"}},{"cell_type":"markdown","source":["**[Your group's plan here]**\n","\n","---"],"metadata":{"id":"x4jR9AxYq_Wg"}},{"cell_type":"code","source":[],"metadata":{"id":"zMfGX9smyhqc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Measuring Hexbot Motion**"],"metadata":{"id":"9IpOgTxby_Hn"}},{"cell_type":"markdown","source":["## **Getting comfortable with HexBots: Measuring the average velocity of 1 bot**\n","\n","This task is typically given to students at the start of their first lab; we'll run through it quickly to get comfortable with the measurement tools and what the Tracker software does.\n","\n","**1.** Build an arena for the HexBots using the white cloud pieces. Your instructor can help demonstrate how to do this if you need help.\n","\n","**2.** Collect data (see instructions below) for 1 bot by itself for several trials.\n","\n","**3.** Determine the average velocity for 1 bot across many trials. (Analysis instructions are after the Data Collection section below.)\n","\n"],"metadata":{"id":"XJmOz7iaSoJ1"}},{"cell_type":"markdown","source":["## **Data Collection: 1 Bot**\n","**1.** Use the Camera app on the lab computer to record one Hexbot scuttling around for about 30 seconds. Some helpful hints:\n","\n","* You can find the camera app in the taskbar of the lab computer.\n","* Don't forget to turn on the camera to use it!\n","* You'll need to have any hexbots you want to record in the arena before starting the video for the tracking software to work.\n","\n","**2.** After you record a video, find the video file from the camera under \"Pictures\" -> \"Camera Roll\". Rename the video with your group name, number of hexbots, and today's date. For example: ``AwesomePossums_1bot_Jan19.mp4``\n","\n","**3.** Use the HexbugTracker app to track the motion of the hexbot. The app will record the position of the Hexbot at regular intervals (fractions of a second). Some helpful hints:\n","\n","* You can find the HexbugTracker app on the desktop of the lab computer.\n","* You can find the video file from the camera under \"Pictures\" -> \"Camera Roll\".\n","* Make sure to change the file type to \"All Files\" in the bottom right of the pop up window.\n","* For now, you do not need to worry about the frame rate.\n","* **Where to find the data:** The app will store the position data (in .csv form) in the lab computer folder named ***Data For Tracker***. The data file will have the same name as your video.\n","\n","**4.** Upload the .csv file to your Google Drive where this Colab notebook is located (which should be a folder called \"*P2208LabMaterials*\" in your Google Drive Colab Notebooks folder with NO SPACES).\n","\n","**5.** Make sure your folder \"*P2208LabMaterials*\" has both this file and the utilities file from Canvas in it.\n","\n","**6.** Once your data file is uploaded to Google drive, you need to import it into this notebook to analyze the data. The next code cell is written to do this.\n","\n","*   To use the code below, replace the \"YourFileName.csv\" in the last line  (which starts with ``file_paths = ``) with the name of the data file.\n","*   For example, for the file called ``AwesomePossums_1bot__Jan19.csv``, the last line would read:\n","\n"," ``file_paths = [\"/content/drive/My Drive/Colab Notebooks/P2207LabMaterials/AwesomePossums_1bot__Jan19.csv\", ]``\n","\n","**7.** When you've changed the last line of the code below, run the code cell. A new popup will appear to ask you access to you Drive. Grant access and continue."],"metadata":{"id":"DEKQuwoKUKsa"}},{"cell_type":"code","source":["#The next two lines will mount your Google Drive folders\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)\n","\n","#The next line will load our utilities file,\n","# which has some prewritten functions we'll use later in this lab\n","%run \"/content/drive/My Drive/Colab Notebooks/P2208LabMaterials/utilities_Lab1.ipynb\"\n","\n","#THIS IS THE LINE WHERE YOU NEED TO ADD YOUR FILE NAME: it will import the data into this notebook.\n","file_path = \"/content/drive/My Drive/Colab Notebooks/P2208LabMaterials/YourFileName.csv\""],"metadata":{"id":"kiQJ_J4uSrhE"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Analysis for Experiment 1: 1 Bot**\n","\n","### **Step 1: Making sense of the raw data**\n","Before we start to analyze the data, run the cell below to see the data you collected and make a simple plot for your bot's position at each time the Tracker collected data."],"metadata":{"id":"3IH2lOQi-ZCh"}},{"cell_type":"code","source":["# This line of code imports your data file and saves the bot positions and times.\n","bot_positions = get_bot_position(file_path)\n","\n","# These lines separate the x and y position data so you can look at the individually.\n","bot_x_positions = get_bot_position(file_path)[0]\n","bot_y_positions = get_bot_position(file_path)[1]"],"metadata":{"id":"92Qdzv00DC7H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# This line prints out the x-positions of the bots\n","print(bot_x_positions)"],"metadata":{"id":"jfwqSLFG-yny"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# This line prints out the y-positions of the bots\n","print(bot_y_positions)"],"metadata":{"id":"tFRMGW4sDG7U"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# This line creates graphs of position vs time for both x and y.\n","scatter_over_time(bot_positions, bots=None, variable=\"position\")"],"metadata":{"id":"FjN4K1FaAszf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Q.** Based on your data, which direction on your table is the  +x-direction? Which direction is the +y-direction? Write a description in the box below, including how you figured it out, and use a piece of masking tape on your table to label the directions."],"metadata":{"id":"3ACjI-wUE5lZ"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"U-5brBiWFN81"}},{"cell_type":"markdown","source":["**Q.** Are there times where the Tracker lost track of your bot? What are they? How do you know?"],"metadata":{"id":"605g88zEFbqn"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"bwhBjSdwFcqr"}},{"cell_type":"markdown","source":["**Q.** Choose a time interval in your data set that you want to analyze. You might choose to use the entire time interval, a particular interval where all the bots are tracked well, or one as short as a few seconds. Write a justification for your choice below."],"metadata":{"id":"_ayEnesaHE40"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"JbmOvqb7HS5o"}},{"cell_type":"markdown","source":["Let's trim our position data down to the interval you have decided to analyze. The code cell below defines a new dataset called `bot_position_subset` that is only for the time interval you indicate.\n","\n","To change the time interval, change the `start_time` and `end_time` in the line of code below and run it."],"metadata":{"id":"kTHiVIeAMKgn"}},{"cell_type":"code","source":["# Define the a subset of the data to analyze.\n","bot_position_subset = trim_data_tuple(bot_positions, start_time=0, end_time=30)"],"metadata":{"id":"EOAjcCs_MaAD"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["The next line of code will generate a position vs time plot for the subset you've chosen, so that you can check it looks right.\n","\n","Right now you have two datasets: `bot_positions` and `bot_position_subset`. The code is written to use `bot_position_subset`. If you want to instead use your entire dataset, replace the variable `bot_positions` with `bot_position_subset`."],"metadata":{"id":"70_JcMTGItiZ"}},{"cell_type":"code","source":["# Produce a scatter plot of bot positions over your specified time interval:\n","scatter_over_time(bot_position_subset, bots=None, variable=\"position\")\n"],"metadata":{"id":"qj6DERovIlV9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### **Step 2: Finding 1 bot's velocity for 1 trial**\n","\n","You may remember from a previous physics class that we can find the average velocity of an object by determining the slope of its position vs time graph.\n","\n","That means can find the average bot velocity by fitting our position plot to a linear function and calculating the slope of the best fit line."],"metadata":{"id":"obmwi83aGe3q"}},{"cell_type":"markdown","source":["**Run the next line to fit your data.**"],"metadata":{"id":"UVh7fx19QpSH"}},{"cell_type":"code","source":["bot_x_positions_subset = bot_position_subset[0]\n","bot_y_positions_subset = bot_position_subset[1]\n","\n","bot_x_position_fit = autoFit(x = bot_x_positions_subset['timestamp'], y = bot_x_positions_subset['Bot 1'], dy=0.001,\n","        title = \"Bot X-Position Fit\", xaxis = \"timestamp\", yaxis=\"position\", showPlot=True)\n","bot_y_position_fit = autoFit(x = bot_y_positions_subset['timestamp'], y = bot_y_positions_subset['Bot 1'], dy=0.001,\n","        title = \"Bot Y-Position Fit\", xaxis = \"timestamp\", yaxis=\"position\", showPlot=True)"],"metadata":{"id":"NmQBty5mQ_9I"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Q.** Based on the directions you assigned for positive x and positive y, use the values from the best fit line in the plots to describe, in words, your bot's average motion. *[Hint: what are the units of the numbers?]*"],"metadata":{"id":"-lVtgbJVSZTC"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"Xb-0t44oSfO9"}},{"cell_type":"markdown","source":["# **Pause Here:**\n","\n","**Group Discussion:** How many bots do we need for reliable measurements?\n","\n","Below are two plots from a day's worth of student data, where each group collected velocity data on different numbers of bots. In Lab 1, students use this collective data to decide how many bots they need to use in future experiments.\n","\n","![BotsVsVelocityPlots.png](data:image/png;base64,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)"],"metadata":{"id":"J81HvbNd0UFx"}},{"cell_type":"markdown","source":["# **Lab 2: Charge in an E-Field**\n","\n","In our first lab session, we explored measuring the velocity of a group of HexBots and sought to understand whether it was more reliable to measure a single bot multiple times or many bots at once.\n","\n","Our guiding question this semester is: **\"Are these physics models good descriptors of HexBots, and if not, what changes should we make to the model or the system?\"** This week we'll apply the model of a charge released in a uniform electric field to HexBots and rubber balls released on a ramp. At the end of the lab you will be asked to make a claim about whether a charge released in a uniform E-field is a good simple model to describe HexBots moving on a ramp or not.\n","\n","**If you decide it is a sufficient model:** you'll be asked to justify your choice using experimental evidence.\n","\n","**If you decide its not sufficient:** you'll be asked to explain why not and explore possible changes to either the physics model (i.e. including another force, adjusting the field expression, etc.) or to the system (i.e. adjust the properties of the arena, the properties of the objects examined, etc.) to improve the agreement.\n","\n","You might notice that we haven't told you what specific evidence tells you whether the model is sufficient -- that is part of what you are investigating today. While you collect and analyze data, ensure you are asking one another whether you're convinced by the data and why."],"metadata":{"id":"Y6JsKxB2BAG2"}},{"cell_type":"markdown","source":["## **Learning Goals for Today**\n","\n","By the end of this activity you should be able to:\n","* Describe what evidence indicates that a model sufficiently describes experimental data in this experiment.\n","* Develop a plan to collect and analyze data that would help you decide if the model is sufficient.\n","* Justify experimental design decisions with evidence and revisit decisions as new evidence is generated, while maintaining transparency and scientific integrity.\n","* Generate and explain the meaning of a data plot fitted with a simple physics model.\n","* Compare two datasets and draw a conclusion about which (if either) is aligned with a simple physics model."],"metadata":{"id":"ts0WEIZsDBb0"}},{"cell_type":"markdown","source":["## **Focus of Today's Activities: Scientific Transparency**"],"metadata":{"id":"Mx_BIEydbQFy"}},{"cell_type":"markdown","source":["In an experiment, you need to make decisions. Those decisions influence your results. For example, you may choose to eliminate outliers in a dataset (and have good reasons for doing that!) but doing so changes the average value of your measurements. What we're working on today is transparency --- how do we, as a scientific community, make experimental decisions that are as transparent as possible? In today's lab, you'll make experimental design decisions and justify them towards building a practice of being transparent about that process.\n","\n","One of our learning goals for these labs is for you to \"Develop a plan to collect and analyze data that would help you decide if a model is sufficient\". Part of that process is figuring out what sufficient means. How do you decide what you can and what you can't conclude from the evidence you collect from a particular experimental design?\n","\n","In addition, given that we want to make precise arguments, we may need to change our strategy once we see the data. That leads to another important question: how do we decide what is reasonable flexiblity and what is manipulating the data?\n","\n","**Q.** How will you know as a group when it is time to change your strategy in today's experiment? How will you make sure those changes are within the realm of scientific integrity and ensure they are transparent? Work with your team to come up with a plan and describe it below."],"metadata":{"id":"q476u485MmBo"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"pLaPL4rflkdC"}},{"cell_type":"markdown","source":["## **Designing an experiment**\n","\n","In a few weeks, you will write a research question with your group, design and execute an experiment to answer that question, and present your results to your section.\n","\n","Today, we will provide the research questions and you will practice building an experimental design.\n","\n","**Questions for today:**\n","\n","1. In what ways does the model of a charge in a uniform E-field describe the motion of HexBots well?\n","2. In what ways does the model fall short, and how could it---or the hexbot system---be revised?\n","3. **If you have extra time:** Does the model better describe rubber balls or HexBots, if we assume the charge is released from rest?\n","4. **If you have extra time:** Does the model better describe rubber balls or HexBots, if we change the physics model to a uniform E-field applied across a solid piece of metal?"],"metadata":{"id":"TzDUJnC1EP6q"}},{"cell_type":"markdown","source":["### **Step 1: The Big Picture**\n","\n","When designing an experiment, it is helpful to have a clear idea of:\n","* What your experimental set up will be.\n","* What variable you will change.\n","* What variable you will measure.\n","* What variables you need to control.\n","* How you will clean the data (i.e choose a time interval, managing outliers,etc.).\n","* What graph(s) you will generate.\n","* How you will use your graph(s) to answer your experimental question.\n","* How you will know your answer is reliable.\n","\n","The answers to these questions often change as you begin collecting data and that's okay! Coming up with a preliminary plan, and being flexible with that plan, is an essential part of experimental science. Today we will provide you with a preliminary plan and you will fill in the details based on what you've already learned about HexBot motion.\n","\n","**Preliminary Plan:**\n","\n","1. Create a ramp of varying steepness by making a rectangular HexBot arena on the table, and tilting the table by adding risers under one side. The arena should have straight sides and an opening at the low end for the bots to escape through (so you know when they've reached the end of the ramp).\n","2. For each ramp angle, release the bots at once from the top of the ramp and measure their motion using Tracker. Catch the bots when they fall off the end of the ramp (so we don't lose them under the tables).\n","4. Use the position vs time data from Tracker to figure out the average velocity of the center of mass of the bots for each release height (same analysis as in Lab 1).\n","5. Plot average velocity of the center of mass of the bots vs release height, and use this graph to determine if the physics model of a charge in a uniform electric field is a good model for HexBot motion on a ramp."],"metadata":{"id":"Q7q11jf3Hqnf"}},{"cell_type":"markdown","source":["### **Step 2: The details**"],"metadata":{"id":"8gSFUGAlob58"}},{"cell_type":"markdown","source":["Now you need to work with your team to fill in the details, clearly justifying each decision.\n","\n","**Q. How many trials will you conduct at each release height?**\n","Go back to your data from lab 1 and make a decision informed by the variance between your trials when the table top was flat. Do you need to re-test this variable, now that we're changing the ramp steepness? Why or why not?"],"metadata":{"id":"9c062hCWfl-q"}},{"cell_type":"markdown","source":["*[type your decision and justification here]*"],"metadata":{"id":"ZI3q_DOlOqhV"}},{"cell_type":"markdown","source":["**Q. How many bots will you use in each trial?** Go back to the class-wide lab 1 data and make a decision informed by the reliablility of your data. Do you need to re-test this variable now that we're changing the ramp steepness? Why or why not?"],"metadata":{"id":"uAB_AX0PgGDN"}},{"cell_type":"markdown","source":["*[type your decision and justification here]*"],"metadata":{"id":"QMt9qhDngYnu"}},{"cell_type":"markdown","source":["**Q. How many different release heights should you test?** We don't have data from a previous lab to rely on to answer this question. How will you know that you've tested enough angles?"],"metadata":{"id":"0_1-N1IegbAU"}},{"cell_type":"markdown","source":["*[type your decision and plan here]*"],"metadata":{"id":"b5oyQGggghsb"}},{"cell_type":"markdown","source":["**Q. How will you decide on what section of the position vs time data to analyze so that you can find the average velocity of the HexBots?** Consider that some bots may move down the ramp before others. Make an initial plan, and explain how you will know if the plan needs to be revised."],"metadata":{"id":"O2OKUgZ4gjro"}},{"cell_type":"markdown","source":["*[type your decision and plan here]*"],"metadata":{"id":"g563GHWjgve4"}},{"cell_type":"markdown","source":["## **Data Collection and Analysis**\n","\n","Now that we have a plan, let's put it into action!\n","\n","Before you get started, run the code cell below to import the utilities file you'll need to run code."],"metadata":{"id":"hFtApZaUXpS3"}},{"cell_type":"code","source":["#The next two lines will mount your Google Drive folders\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)\n","\n","#The next line will load our utilities file,\n","# which has some prewritten functions we'll use later in this lab\n","%run \"/content/drive/My Drive/Colab Notebooks/P2208LabMaterials/utilities_Lab1.ipynb\""],"metadata":{"id":"Oeyms6r9gILv"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### **Data Collection**"],"metadata":{"id":"WDUkIa4sYNt2"}},{"cell_type":"markdown","source":["**Reminder instructions for using Tracker (repeated from lab 1):**\n","\n","**1.** Use the Camera app on the lab computer to record the motion you are interested in studying. Some helpful hints:\n","\n","* You can find the camera app in the taskbar of the lab computer.\n","* Don't forget to turn on the camera to use it!\n","* You'll need to have any hexbots you want to record in the arena before starting the video for the tracking software to work.\n","\n","**2.** After you record a video, find the video file from the camera under \"Pictures\" -> \"Camera Roll\". Rename the video with your group name, number of hexbots, and today's date. For example: ``AwesomePossums_1bot_Jan19.mp4``\n","\n","**3.** Use the HexbugTracker app to track the motion of the hexbot. The app will record the position of the Hexbot at regular intervals (fractions of a second). Some helpful hints:\n","\n","* You can find the HexbugTracker app on the desktop of the lab computer.\n","* You can find the video file from the camera under \"Pictures\" -> \"Camera Roll\".\n","* Make sure to change the file type to \"All Files\" in the bottom right of the pop up window.\n","* For now, you do not need to worry about the frame rate.\n","* **Where to find the data:** The app will store the position data (in .csv form) in the lab computer folder named ***Data For Tracker***. The data file will have the same name as your video.\n","\n","**4.** Upload the .csv file to your Google Drive where this Colab notebook is located (which should be a folder called \"*P2208LabMaterials*\" in your Google Drive Colab Notebooks folder with NO SPACES).\n","\n","**5.** Make sure your folder \"*P2208LabMaterials*\" has both this file and the utilities file from Canvas in it.\n","\n","**6.** Once your data file is uploaded to Google drive, you need to import it into this notebook to analyze the data. The next code cell is written to do this.\n","\n","*   To use the code below, replace the \"YourFileName.csv\" in the last line  (which starts with ``file_paths = ``) with the name of the data file.\n","*   For example, for the file called ``AwesomePossums_1bot__Jan19.csv``, the last line would read:\n","\n"," ``file_paths = [\"/content/drive/My Drive/Colab Notebooks/P2207LabMaterials/AwesomePossums_1bot__Jan19.csv\", ]``\n","\n","**7.** When you've changed the last line of the code below, run the code cell. A new popup will appear to ask you access to you Drive. Grant access and continue."],"metadata":{"id":"pVN_cz6mYZwf"}},{"cell_type":"markdown","source":["**Include any notes you need to take during data collection here:**"],"metadata":{"id":"NPrPbrGBg44P"}},{"cell_type":"markdown","source":["*[replace this text with your notes, as needed]*"],"metadata":{"id":"UGMpDfjOg8Qd"}},{"cell_type":"markdown","source":["### **Data Analysis**"],"metadata":{"id":"uMf49brbYCem"}},{"cell_type":"markdown","source":["For each trial and each release height, you'll need to fit your position vs time graph to get an average velocity. This is the same procedure as we did last session! The code has been replicated for you below, and there is a table for you to record your average velocities just like before.\n","\n"],"metadata":{"id":"wsqRLPprala_"}},{"cell_type":"markdown","source":["**1. Import the data you want to analyze and check the scatter plot**"],"metadata":{"id":"9jPGUUpAjJdn"}},{"cell_type":"code","source":["#THIS IS THE LINE WHERE YOU NEED TO ADD YOUR FILE NAME: it will import the data into this notebook.\n","new_file_path = \"/content/drive/MyDrive/Colab Notebooks/P2208LabMaterials/filename.csv\"\n","\n","# This line of code imports your data file and saves the bot positions and times.\n","bot_positions = get_bot_position(new_file_path)\n","\n","# This line creates graphs of position vs time for both x and y, for all the bots in your video.\n","scatter_over_time(bot_positions, bots=None, variable=\"position\")"],"metadata":{"id":"TALH_TKnagzX"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**2. Define the subset, trim the data, and verify it looks right.**\n","\n","Previously, you answered the question \"How will you decide on what section of the position vs time data to analyze so that you can find the average velocity of the HexBots?\" and made an initial plan. Now that you're able to look at the data, do you need to revise your plan?\n","\n","**If so,** write your revisions below, along with a justification for why the plan needed to be revised.\n","\n","**If not,** justify why your initial plan will work based on the data."],"metadata":{"id":"IzfY0lcUjQtf"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"Rx8WCtjSIvL3"}},{"cell_type":"markdown","source":["Change the ``start_time``  and ``end_time`` values below and then run this code cell to trim your data."],"metadata":{"id":"za5F-yDSI1Lf"}},{"cell_type":"code","source":["# Define the subset of the data for which you want to find the speeds and velocities of each bot.\n","bot_position_subset = trim_data_tuple(bot_positions, start_time=0, end_time=30) #update start_time and end_time values"],"metadata":{"id":"5JtbHbA7jIKI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**3. Calculate the center of mass of the HexBot system, and check the scatter plot to make sure it looks right.**\n","\n","It's up to you whether you'd like to use this! You may also copy the code from the last lab to analyze one bug at a time, if you prefer."],"metadata":{"id":"h8jM71FUjySw"}},{"cell_type":"code","source":["# This line calculates the center of mass for the bot system at each time stamp.\n","bot_CM_positions = get_bot_CM(bot_position_subset)\n","\n","# This line shows a scatter plot of the center of mass positions with respect to time for the bot system.\n","scatter_over_time(bot_CM_positions, bots=\"CM\", variable=\"position\")"],"metadata":{"id":"46oeVWGIj9Y_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**4. Fit the center of mass data to a linear function and get the fit parameters.**\n","\n","Note: for this code, you can set `showPlot` to either `True` or `False`. `True` will show the plot, `False` will not."],"metadata":{"id":"LbUvUIbjjZdn"}},{"cell_type":"code","source":["# Fit the subset data to a linear function for both x- and y-positions.\n","bot_x_CM_pos_tofit = bot_CM_positions[0]\n","bot_y_CM_pos_tofit = bot_CM_positions[1]\n","\n","# Show the plots of the data with fits on them, and print the parameters of the fit.\n","bot_x_position_fit = autoFit(x = bot_x_CM_pos_tofit['timestamp'], y = bot_x_CM_pos_tofit['CM'], dy=0.001,\n","        title = \"Bot X-Position Fit\", xaxis = \"timestamp\", yaxis=\"position\", showPlot=True)\n","bot_y_position_fit = autoFit(x = bot_y_CM_pos_tofit['timestamp'], y = bot_y_CM_pos_tofit['CM'], dy=0.001,\n","        title = \"Bot Y-Position Fit\", xaxis = \"timestamp\", yaxis=\"position\", showPlot=True)"],"metadata":{"id":"L6IXCUYsjY43"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**5. Record the results of your analysis for each height and trial.**\n","\n","When you add data, replace the placeholder text `\"(your value here)\"` with a number. Make sure to get rid of the parentheses and the quotation marks when you do!\n","\n","*[Hint: which velocity do you want to store? Why not both?]*\n","\n","You can edit the table to make more rows or columns, if you'd like. As an example, say we ran two trials each for two different heights and measured average speeds of 1.1 and 1.3 for the height of 1 riser and average speeds of 0.9 and 1.4 for the height of 2 risers, the code cell below would read:\n","\n","```\n","bot_vel_by_height = pd.DataFrame({\n","    \"Height\":  [ 1 ,  2],\n","    \"Trial 1\": [ 1.1 ,  0.9 ],\n","    \"Trial 2\": [ 1.3 ,  1.4]\n","})\n","```\n","\n","Update and run the cell to print the table and verify it looks how you want it to.\n","\n"],"metadata":{"id":"pRYBU48MkMeQ"}},{"cell_type":"code","source":["# This is a blank table for you to fill in. Run the cell to see it printed.\n","bot_vel_by_height = pd.DataFrame({\n","    \"Height\":  [ \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" , \"(your value here)\" ],\n","    \"Trial 1\": [ \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" , \"(your value here)\" ],\n","    \"Trial 2\": [ \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" , \"(your value here)\" ],\n","    \"Trial 3\": [ \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" , \"(your value here)\" ],\n","    \"Trial 4\": [ \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" ,  \"(your value here)\" , \"(your value here)\" ]\n","})\n","\n","print(bot_vel_by_height)"],"metadata":{"id":"GFKxgpkbkVwA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**6. Repeat the steps above with new heights.**\n","\n","Rather than copying new code cells, update the filename at the top of the analysis section, re-run the analysis, and add the new value to the table above."],"metadata":{"id":"y-5WgVVDnoD9"}},{"cell_type":"markdown","source":["**7. Find the average of the trials, and plot them against the ramp height.**"],"metadata":{"id":"wXeYTC-Hlv-w"}},{"cell_type":"code","source":["# These lines calculate the average and adds it to the table.\n","bot_vel_avg = np.mean(bot_vel_by_height.filter(like='Trial'), axis=1)\n","bot_vel_by_height = bot_vel_by_height.assign(Average=bot_vel_avg.astype(float))\n","\n","# These lines calculate the standard deviation and adds it to the table.\n","bot_vel_std = np.std(bot_vel_by_height.filter(like='Trial'), axis=1)\n","bot_vel_std = bot_vel_std.apply(lambda x: f'{x:.1g}')\n","bot_vel_by_height = bot_vel_by_height.assign(StDev=bot_vel_std.astype(float))\n","\n","# This line makes sure all the values are numbers that we can plot.\n","bot_vel_by_height[\"Height\"] = bot_vel_by_height[\"Height\"].astype(float)\n","\n","print(bot_vel_by_height)\n"],"metadata":{"id":"nQJ6fCqM2Gph"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# This line plots the height versus the average velocity values, using the standard deviation for error bars, and fits it to a linear function.\n","autoFit(x = bot_vel_by_height['Height'], y=bot_vel_by_height['Average'], dy=bot_vel_by_height['StDev'],\n","        title=\"Average Velocity of HexBots vs Height\", xaxis=\"Ramp Height\", yaxis=\"Average Velocity\", showPlot=True)"],"metadata":{"id":"AhaWoZOyl6No"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Check Out: Is a charge in a uniform E-Field a good model for bots released on a ramp?**\n","\n","The goal of the lab today is to collect data that helps us decide if the physics model of a charge released in a uniform electric field is a good representation of HexBots on a ramp. Our experimental questions were:\n","\n","1. In what ways does the model of a charge in a uniform E-field describe the motion of HexBots well?\n","2. In what ways does the model fall short, and how could it---or the hexbot system---be revised?"],"metadata":{"id":"-5pCqlwB-vuI"}},{"cell_type":"markdown","source":["**Q.** Based on the data you collected and analyzed, reflect on whether you have sufficient evidence to decide if the model is appropriate to describe HexBot motion.\n","\n","**If so:** do you think the model is appropriate? Be sure to state your evidence clearly. If you don't think the model is appropriate, suggest one change that should be made *either* to the physics model or to the system.\n","\n","**If not:** why not? What should you do next to get data that you can make a decision about?"],"metadata":{"id":"Ap6CCcXv_Bd3"}},{"cell_type":"markdown","source":["*[type your response here]*"],"metadata":{"id":"A4nG-f8u_N1Z"}},{"cell_type":"code","source":[],"metadata":{"id":"l9rudMaE0Y2Y"},"execution_count":null,"outputs":[]}]}