From 83cd43acc1c13e5d7ce2506b41bc70ef801b3a60 Mon Sep 17 00:00:00 2001
From: "Ryan C. Cooper" <ryan.c.cooper@uconn.edu>
Date: Thu, 10 Sep 2020 16:05:43 -0400
Subject: [PATCH] added google.csv

---
 ME3263_Lab-01.ipynb |   55 +-
 google.csv          | 2041 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 2094 insertions(+), 2 deletions(-)
 create mode 100644 google.csv

diff --git a/ME3263_Lab-01.ipynb b/ME3263_Lab-01.ipynb
index f220bae..02345bc 100644
--- a/ME3263_Lab-01.ipynb
+++ b/ME3263_Lab-01.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,6 +344,57 @@
     "3. Perform t-test using equal-sized groups from the two data sets. Are the 2 phones  stastistically equivalent measurements of g? _If there are differences are these __systematic__ or __random__?_ What is the resulting error?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing your data to Jupyter\n",
+    "\n",
+    "1. While you're in the Lab_01 folder, click the \"Upload\" button in the top right.\n",
+    "2. Choose your csv file from your cell phone data collection of your accelerometer\n",
+    "3. Click the blue \"Upload\" button next to the file. \n",
+    "4. In this folder, there is an example file called `google.csv`\n",
+    "5. In the next code cell, I load the `google.csv` file into a variable called `data`.\n",
+    "6. The `google.csv` file has a column for each x-, y-, and z-directions, so I'll load just the z-direction to measure $$g$$\n",
+    "7. Finally, I create a histogram of the accelerometer values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('google.csv')\n",
+    "zacc = data['Acceleration z (m/s^2)'].values\n",
+    "\n",
+    "plt.hist(zacc,30)\n",
+    "plt.ylabel('number of measurements')\n",
+    "plt.xlabel('acceleration (m/s/s)');"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/google.csv b/google.csv
new file mode 100644
index 0000000..a2ad531
--- /dev/null
+++ b/google.csv
@@ -0,0 +1,2041 @@
+"Time (s)","Acceleration x (m/s^2)","Acceleration y (m/s^2)","Acceleration z (m/s^2)","Absolute acceleration (m/s^2)"
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