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Initial commit
2024-03-11 20:54:28 +01:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"data_file = \"/home/martin/code/ansible/roles/bluetooth-monitor/other/logfile_2024-03-07 20:23:13.359108.csv\"\n",
"df = pd.read_csv(data_file)\n",
"\n",
"# Cleanup\n",
"df = df.rename(columns=lambda x: x.strip())\n",
"df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
"rooms = set(df['real_room']).union(set(df['room']))\n",
"room_dtype = pd.CategoricalDtype(categories=list(rooms), ordered=True)\n",
"df['room'] = df['room'].astype(room_dtype)\n",
"df['real_room'] = df['real_room'].astype(room_dtype)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_449868/2929942538.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'# device'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"martins_apple_watch\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tx_power'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tx_power'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1537\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 1538\u001b[0m \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1539\u001b[0m \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
]
}
],
"source": [
"df[df['# device'] == \"martins_apple_watch\" and df['tx_power'] > 0]['tx_power'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_449868/10187827.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
" ax.set_xticklabels(labels, rotation=90) # set new labels\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 2880x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 4102.1x360 with 11 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams[\"xtick.labelsize\"] = 5\n",
"sns.set_theme()\n",
"plt.figure(figsize=(40, 6))\n",
"g = sns.relplot(data=df, x=\"room\", y=\"rssi\", style=\"room\", hue=\"room\", col=\"real_room\")\n",
"for ax in g.axes.flat:\n",
" labels = ax.get_xticklabels() # get x labels\n",
" ax.set_xticklabels(labels, rotation=90) # set new labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SVM"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import svm\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CategoricalDtype(categories=[' arbeitszimmer', ' küche', ' wohnzimmer', ' kinderzimmer',\n",
" ' kueche', ' wohnzimmeroben', ' schlafzimmer', ' garten',\n",
" ' az_oben', ' esszimmer', ' sofa'],\n",
", ordered=True)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th># device</th>\n",
" <th>room</th>\n",
" <th>rssi</th>\n",
" <th>tx_power</th>\n",
" <th>real_room</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>martins_iphone</td>\n",
" <td>wohnzimmeroben</td>\n",
" <td>-90</td>\n",
" <td>12</td>\n",
" <td>arbeitszimmer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>martins_apple_watch</td>\n",
" <td>wohnzimmer</td>\n",
" <td>-97</td>\n",
" <td>2</td>\n",
" <td>arbeitszimmer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>martins_iphone</td>\n",
" <td>garten</td>\n",
" <td>-89</td>\n",
" <td>-1</td>\n",
" <td>arbeitszimmer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>martins_iphone</td>\n",
" <td>wohnzimmeroben</td>\n",
" <td>-89</td>\n",
" <td>12</td>\n",
" <td>arbeitszimmer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>martins_iphone</td>\n",
" <td>garten</td>\n",
" <td>-89</td>\n",
" <td>-1</td>\n",
" <td>arbeitszimmer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49258</th>\n",
" <td>martins_apple_watch</td>\n",
" <td>kinderzimmer</td>\n",
" <td>-91</td>\n",
" <td>8</td>\n",
" <td>sofa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49259</th>\n",
" <td>martins_iphone</td>\n",
" <td>kueche</td>\n",
" <td>-96</td>\n",
" <td>12</td>\n",
" <td>sofa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49260</th>\n",
" <td>martins_iphone</td>\n",
" <td>wohnzimmer</td>\n",
" <td>-69</td>\n",
" <td>2</td>\n",
" <td>sofa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49261</th>\n",
" <td>martins_iphone</td>\n",
" <td>garten</td>\n",
" <td>-86</td>\n",
" <td>2</td>\n",
" <td>sofa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49262</th>\n",
" <td>martins_apple_watch</td>\n",
" <td>wohnzimmeroben</td>\n",
" <td>-86</td>\n",
" <td>8</td>\n",
" <td>sofa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>49263 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" # device room rssi tx_power real_room\n",
"0 martins_iphone wohnzimmeroben -90 12 arbeitszimmer\n",
"1 martins_apple_watch wohnzimmer -97 2 arbeitszimmer\n",
"2 martins_iphone garten -89 -1 arbeitszimmer\n",
"3 martins_iphone wohnzimmeroben -89 12 arbeitszimmer\n",
"4 martins_iphone garten -89 -1 arbeitszimmer\n",
"... ... ... ... ... ...\n",
"49258 martins_apple_watch kinderzimmer -91 8 sofa\n",
"49259 martins_iphone kueche -96 12 sofa\n",
"49260 martins_iphone wohnzimmer -69 2 sofa\n",
"49261 martins_iphone garten -86 2 sofa\n",
"49262 martins_apple_watch wohnzimmeroben -86 8 sofa\n",
"\n",
"[49263 rows x 5 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def cleanup_data(df):\n",
" df['real_room']\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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",
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"nbformat": 4,
"nbformat_minor": 2
}