add notebook

vscode/notebooks/data.ipynb

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DeviceScript Data\n",
+    "\n",
+    "### Setup\n",
+    "\n",
+    "Run `pip install pandas matplotlib` to load the necessary python libs.\n",
+    "\n",
+    "### Loading data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# what file are you analyzing today?\n",
+    "file = '2023-04-03-15-16.csv'"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This snippet loads the CSV in a panda dataframe, fills the empty cells and displays basic statistical values about the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(file, index_col='time')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "\n",
+    "Because of the unstructured way `console.data` collects data points, there may be empty cells. One strategy is to fill empty cells with the previous known values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.fillna(method='ffill', inplace=True) # fill missing values with previous known values\n",
+    "df.fillna(method='backfill', inplace=True) # fill missing values with next known values\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting data\n",
+    "\n",
+    "We're now ready to plot this data using matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6)) # set figsize to (width, height)\n",
+    "# plot all columns, expect index\n",
+    "for i, col in enumerate(df.columns):\n",
+    "    ax.plot(df.index, df[col], label=col)\n",
+    "ax.set_xlabel('time')\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  }
+ ],
+ "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": "3.11.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}