merida_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read HOBO Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "hobo_path = \"/Users/diego/Desktop/Projects_Code/sonora_cooling_code/inputs/hobo/21742077_2023-06-02_14_53_28_CST.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel(hobo_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fecha/hora (UTC Standard Time-6)', 'Ch: 1 - CorrienteCA   (amp)'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11cbc9340>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(data['Fecha/hora (UTC Standard Time-6)'],data['Ch: 1 - CorrienteCA   (amp)'],color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geocene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/Users/diego/Desktop/Projects_Code/sonora_cooling_code/inputs/geocene/may_2023/metrics/\"\n",
    "#output_path = \"/Users/diego/Desktop/Projects_Code/sonora_cooling_code/data_dumps/october/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missions\n",
    "# ALWAYS\n",
    "# missions['house_id'] = (\"ac_temp_sensor\",\"ambient_sensor\",\"energy_sensor\")\n",
    "# missions['house_id'] = (\"ac_temp_sensor\",\"ac_temp_sensor\",\"ambient_sensor\",\"energy_sensor\",\"energy_sensor\")\n",
    "# Make sure you create the dictionary \n",
    "\n",
    "missions={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HOUSE ID 1 FCD0\n",
    "# Three Sensors\n",
    "missions['id_1']=(\"casa1-_fcd0-6478bb83-0000-0000-0000-cc78ab77fcd0.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_limit = pd.to_datetime('2023-05-30',utc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Household ID: id_1\n"
     ]
    }
   ],
   "source": [
    "for mission in missions:\n",
    "    print(\"Household ID: \"+ str(mission))\n",
    "    \n",
    "    mission_list_dfs=[]\n",
    "    if len(missions[mission])==1:\n",
    "        print(\"1 SENSORS TOTAL\")\n",
    "        \n",
    "        for num in range(0,1):\n",
    "            print(\"a\")\n",
    "            print(num)\n",
    "        \n",
    "#             data_sensor = pd.read_csv(path+missions[mission][num])\n",
    "#             data_sensor['timestamp'] = pd.to_datetime(data_sensor['timestamp'])\n",
    "#             name_sensor = data_sensor['mission_id'][0][-4:]\n",
    "#             length_days = len(data_sensor['timestamp'].dt.date.unique())\n",
    "            \n",
    "#             first_date = data_sensor['timestamp'].sort_values(ascending=True).reset_index()['timestamp'][0].date()\n",
    "#             recent_date = data_sensor['timestamp'].sort_values(ascending=False).reset_index()['timestamp'][0].date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missions['id_1'])"
   ]
  },
  {
   "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",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}