To begin, click 'Cell' on the toolbar then select 'Run All'. __Only do this once__.
\n",
"-
__All work must be done in Markdown cells.__ To __type a response__ in a Markdown cell, __double click the Markdown cell then begin writing__. When __finished, press _shift_ and _enter___ and your text will render.
\n",
"-
To insert a Markdown cell, click the small '+' icon located just below the 'file' button on the toolbar. Then change the cell type from Code to Markdown by clicking on the pull down menu located just below the 'Widgets button on the toolbar.
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Refer to the links at the bottom of the page for ways to create symbols, tables, and lists in Markdown."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Date: \n",
" \n",
"Group Number:\n",
"\n",
"Author:\n",
" \n",
"Partner 1:\n",
" \n",
"Partner 2:\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Abstract"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your abstract. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Procedure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Measure the diameter of the first cylinder using the Digital caliper. Remember to include units and an error estimate. Do this several times (Why?) -- you decide how many.\n",
"\n",
"![Image of Digital Calipers](http://www.physics.smu.edu/rguarino/apparatus/caliper/dcaliper.jpeg)\n",
"\n",
"- Instructions on caliper use:\n",
"> The digital Caliper is an extremely precise measuring instrument; the reading error is 1/100 mm = 0.01 mm.\n",
"> Loosen the lock screw, open the jaws and wipe clean using your finger. Close the jaws and verify the reading is zero, if not, press the red zero button.\n",
"> Close the jaws __lightly__ on the object to be measured.\n",
"> If you are measuring something with a round cross section, make sure that the axis of the object is perpendicular to the caliper. This is necessary to ensure that you are measuring the full diameter and not merely a chord.\n",
"> Insure the readout is displaying milimeters not inches.\n",
"\n",
"- Think of a method to measure the circumference of the first cylinder precisely using the tools provided. (This was part of the prelab assignment.) Perform this measurement several times. (Why?)\n",
"\n",
"- Repeat for the other three cylinders."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
"A neat table of all measurments with units and with estimated measurment errors. Record the __raw__ data, before reduction (that is, before performing any mathematical operations on them)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for recording any measurements. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to use plotting feature in this section\n",
"\n",
"### THIS IS JUST AN EXAMPLE. DO NOT INPUT YOUR VALUES HERE\n",
"\n",
"### To use the plot below: Press Toggle Display Code\n",
" Look for something that looks like,\n",
"\n",
" #-------Student Input-------------------------\n",
" \n",
" Student_input_x_values=[0.5,1.3,2.1,2.9,3.3] \n",
" Student_input_y_values=[3.3,3.9,4.8,5.5,6.9]\n",
" \n",
" #---------------------------------------------\n",
"\n",
" Replace the values inside the brackets with the numbers from your experiment. For example, if I have x measurements of 10, 20, 20.5, 21.1, and 22, the result will look like,\n",
"\n",
" Student_input_x_values=[10, 20, 20.5, 21.1, 22]\n",
"\n",
" Similarly, if I have y measurements of 5, 7, 10, 3, and 15, the result will look like,\n",
"\n",
" Student_input_y_values=[5, 7, 10, 3, 15]\n",
"\n",
"### Once you are done inputting values, press _Shift_ and _Enter_ simultaneously and wait for new plot to appear.\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 1__:\n",
"\n",
"Record the average diameter of each cylinder as $d_{best} \\pm \\Delta d_{best}$, where $\\Delta d_{best}$ is the \"error on the mean\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your response to question 1. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 2__: \n",
"\n",
"Record the average circumference of each cylinder as $ C_{best} \\pm \\Delta C_{best}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your response to question 2. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 3__: \n",
"\n",
"For each cylinder, determine the best value of the ratio = circumference/diameter with propagated error: $ R_{best} \\pm R_{best}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your response to question 3. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 4__: \n",
"\n",
"Graph the best straight line through the circumference (vertical axis) vs. diameter (horizontal axis) data and record the slope and the y-intercept. What numerical value should the slope approximate? "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"\n",
"\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Y-intercept: 0.808078770413\n",
"Slope: 6.5088376561\n",
"y = 6.5088376561x + 0.808078770413\n"
]
},
{
"data": {
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1vDzB+HlRhuUdfNXhew2wU1X3qWouMBOwvQCNMcUq2KF74MABZs2aZR26HuSr5P8zcJmI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\nWfI3xpgw9P8Bvv9rGoHCHmYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#-------Student Input-----------------------------------------------------------------------------------------\n",
"\n",
"Student_input_x_values = [0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00, 1.30] # Enter values here.\n",
"Student_input_y_values = [1.79, 2.5, 3.4, 4.1, 4.9, 5.5, 6.3, 6.9, 7.8, 8.5] # Enter values here.\n",
"\n",
"#-------------------------------------------------------------------------------------------------------------\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#---------------------Do not touch anything below this line---------------------------------------------------\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Best fit line (First degree polynomial)\n",
"(m,b) = np.polyfit(Student_input_x_values,Student_input_y_values,1)\n",
"print('Y-intercept:', b)\n",
"print('Slope:', m)\n",
"print('y = ',m,'x + ', b, sep='')\n",
"\n",
"# Defining best fit y values.\n",
"best_y_values = np.polyval([m,b],Student_input_x_values)\n",
"\n",
"# Plotting both original and best fit line\n",
"plt.plot(Student_input_x_values,best_y_values, color=\"black\", label=\"Best Fit\")\n",
"plt.scatter(Student_input_x_values,Student_input_y_values, label=\"Collected Data\")\n",
"plt.grid(True)\n",
"plt.xlabel('Diameter (m)', fontsize = 12)\n",
"plt.ylabel('Circumference (m)', fontsize = 12)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your response to question 4. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 5__: \n",
"\n",
"Identify at least two sources of statistical error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your response to question 5. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 6__: \n",
"\n",
"Identify at least two sources of systematic error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your response to question 6. Erase this message when done."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Click on this Markdown cell and use it for your conclusion. Erase this message when done.\n",
"\n",
"Summarize what you __learned__ today (not what you __did__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Useful Links\n",
"\n",
"[Markdown Cheat Sheet](https://guides.github.com/pdfs/markdown-cheatsheet-online.pdf)\n",
"\n",
"[Online Markdown Table Generator](https://www.tablesgenerator.com/markdown_tables#)\n",
"\n",
"[Latex Mathematical Cheat Sheet](http://reu.dimacs.rutgers.edu/Symbols.pdf)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}