{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
      "Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n",
      "Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.21.2)\n",
      "Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n",
      "Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n",
      "Requirement already satisfied: imblearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from imblearn) (0.5.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n",
      "Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n",
      "Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas\n",
    "!pip install xlrd\n",
    "!pip install sklearn\n",
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd\n",
    "book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n",
    "sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n",
    "sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n",
    "sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n",
    "sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n",
    "sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n",
    "sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n",
    "sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n",
    "sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n",
    "sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n",
    "sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n",
    "sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n",
    "sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 \t  \t  \t  \t \n",
      "0.2 \t  \t  \t  \t \n",
      "0.3 \t 7000.0 \t  \t  \t \n",
      "0.4 \t  \t  \t  \t \n",
      "0.5 \t  \t  \t  \t \n",
      "0.6 \t  \t  \t  \t \n",
      "0.7 \t  \t  \t 3000.0 \t \n",
      "0.8 \t  \t  \t  \t \n",
      "0.9 \t  \t  \t  \t \n",
      "1.0 \t 1000.0 \t  \t 1000.0 \t \n",
      "2.0 \t 300.0 \t  \t 300.0 \t \n",
      "3.0 \t  \t  \t  \t 7000.0\n",
      "4.0 \t  \t 7000.0 \t  \t 3000.0\n",
      "5.0 \t  \t 3000.0 \t  \t 1000.0\n",
      "6.0 \t  \t 1000.0 \t  \t \n",
      "7.0 \t  \t  \t  \t \n",
      "8.0 \t  \t  \t  \t \n",
      "9.0 \t  \t  \t  \t \n",
      "10.0 \t  \t  \t  \t \n"
     ]
    }
   ],
   "source": [
    "for row_index in range(1,20): #reading first columns\n",
    "    RsR0, CH4, CO , H2, Alcohol = sheetMQ309A.row_values(row_index, start_colx=0, end_colx=5)\n",
    "    print(RsR0, \"\t\", CH4, \"\t\", CO, \"\t\", H2, \"\t\", Alcohol)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_MQ309A = sheetMQ309A.col_values(0)[2:]\n",
    "MQ309A_CH4 = sheetMQ309A.col_values(1)[2:]\n",
    "MQ309A_CO = sheetMQ309A.col_values(2)[2:]\n",
    "MQ309A_H2 = sheetMQ309A.col_values(3)[2:]\n",
    "MQ309A_Alcohol = sheetMQ309A.col_values(4)[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_to_nan(values):\n",
    "    \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n",
    "    return [float('nan') if x==0 else x for x in values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "MQ309A_CH4 =zero_to_nan(MQ309A_CH4)\n",
    "MQ309A_CO =zero_to_nan(MQ309A_CO)\n",
    "MQ309A_H2 =zero_to_nan(MQ309A_H2)\n",
    "MQ309A_Alcohol =zero_to_nan(MQ309A_Alcohol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "arrays must all be same length",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-11fdc20bbac0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mdataLPG\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'RsRo'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx_MQ309\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Alcohol'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mMQ309A_Alcohol\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mdfMQ309A_CH4\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataCH4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[0mdfMQ309A_CO\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataCO\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mdfMQ309A_H2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataH2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    390\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[0;32m    391\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 392\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    315\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'arrays must all be same length'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    319\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: arrays must all be same length"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "\n",
    "dataCH4 = {'RsRo': x_MQ309, 'CH4': MQ309A_CH4}\n",
    "dataCO = {'RsRo': x_MQ309, 'CO': MQ309A_CO}\n",
    "dataH2 = {'RsRo': x_MQ309, 'H2': MQ309A_H2}\n",
    "dataLPG = {'RsRo': x_MQ309, 'Alcohol': MQ309A_Alcohol}\n",
    "\n",
    "dfMQ309A_CH4 = pd.DataFrame(dataCH4)\n",
    "dfMQ309A_CO = pd.DataFrame(dataCO)\n",
    "dfMQ309A_H2 = pd.DataFrame(dataH2)\n",
    "dfMQ309A_Alcohol = pd.DataFrame(dataLPG)\n",
    "\n",
    "dfMQ309A_CH4['CH4'] = pd.to_numeric(dfMQ309A_CH4['CH4'])\n",
    "dfMQ309A_CO['CO'] = pd.to_numeric(dfMQ309A_CO['CO'])\n",
    "dfMQ309A_H2['H2'] = pd.to_numeric(dfMQ309A_H2['H2'])\n",
    "dfMQ309A_Alcohol['Alcohol'] = pd.to_numeric(dfMQ309A_Alcohol['Alcohol'])\n",
    "\n",
    "dfMQ309A_CH4['CH4'] = dfMQ309A_CH4['CH4'].replace('',None, regex=True)\n",
    "dfMQ309A_CO['CO'] = dfMQ309A_CO['CO'].replace('',None, regex=True)\n",
    "dfMQ309A_H2['H2'] = dfMQ309A_H2['H2'].replace('',None, regex=True)\n",
    "dfMQ309A_Alcohol['Alcohol'] = dfMQ309A_Alcohol['Alcohol'].replace('',None, regex=True)\n",
    "\n",
    "#Global X_Predict variable\n",
    "X_Predict = dfMQ309A_CH4.RsRo.apply(lambda x: [x]).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model and train CH4\n",
    "dataset2TrainCH4 = dfMQ309A_CH4.copy()\n",
    "dataset2TrainCH4.dropna(inplace=True)\n",
    "X_trainCH4 = dataset2TrainCH4.RsRo.apply(lambda x: [x]).tolist()\n",
    "y_trainCH4 = dataset2TrainCH4['CH4'].tolist()\n",
    "model = linear_model.Lasso(alpha=0.1)\n",
    "model.fit(X_trainCH4, y_trainCH4)\n",
    "#Predict\n",
    "CH4_Predicted = model.predict(X_Predict)\n",
    "#save into MQ2\n",
    "MQ309A_CH4 = CH4_Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model and train CO\n",
    "dataset2TrainCO = dfMQ309A_CO.copy()\n",
    "dataset2TrainCO.dropna(inplace=True)\n",
    "X_trainCO = dataset2TrainCO.RsRo.apply(lambda x: [x]).tolist()\n",
    "y_trainCO = dataset2TrainCO['CO'].tolist()\n",
    "model = linear_model.Lasso(alpha=0.1)\n",
    "model.fit(X_trainCO, y_trainCO)\n",
    "#Predict\n",
    "CO_Predicted = model.predict(X_Predict)\n",
    "#save into MQ2\n",
    "MQ309A_CO = CO_Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model and train H2\n",
    "dataset2TrainH2 = dfMQ309A_H2.copy()\n",
    "dataset2TrainH2.dropna(inplace=True)\n",
    "X_trainH2 = dataset2TrainH2.RsRo.apply(lambda x: [x]).tolist()\n",
    "y_trainH2 = dataset2TrainH2['H2'].tolist()\n",
    "model = linear_model.Lasso(alpha=0.1)\n",
    "model.fit(X_trainH2, y_trainH2)\n",
    "#Predict\n",
    "H2_Predicted = model.predict(X_Predict)\n",
    "#save into MQ2\n",
    "MQ309A_H2 = H2_Predicted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model and train Alcohol\n",
    "dataset2TrainAlcohol = dfMQ309A_Alcohol.copy()\n",
    "dataset2TrainAlcohol.dropna(inplace=True)\n",
    "X_trainAlcohol = dataset2TrainAlcohol.RsRo.apply(lambda x: [x]).tolist()\n",
    "y_trainAlcohol = dataset2TrainAlcohol['Alcohol'].tolist()\n",
    "model = linear_model.Lasso(alpha=0.1)\n",
    "model.fit(X_trainAlcohol, y_trainAlcohol)\n",
    "#Predict\n",
    "Alcohol_Predicted = model.predict(X_Predict)\n",
    "#save into MQ2\n",
    "MQ309A_Alcohol = Alcohol_Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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      "text/plain": [
       "<Figure size 1800x1100 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_formats = ['svg']\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.transforms as mtransforms\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.set_size_inches(9, 5.5, forward=True)\n",
    "fig.set_dpi(200)\n",
    "\n",
    "# only these two lines are calibration curves\n",
    "plt.plot(MQ309A_CH4, x_MQ309A, marker='o', linewidth=1, label='CH4')\n",
    "plt.plot(MQ309A_CO, x_MQ309A, marker='o', linewidth=1, label='CO')\n",
    "plt.plot(MQ309A_H2, x_MQ309A, marker='o', linewidth=1, label='H2')\n",
    "plt.plot(MQ309A_Alcohol, x_MQ309A, marker='o', linewidth=1, label='Alcohol')\n",
    "\n",
    "\n",
    "\n",
    "# reference line, legends, and axis labels\n",
    "#line = mlines.Line2D([0, 1], [0, 1], color='black')\n",
    "#transform = ax.transAxes\n",
    "#line.set_transform(transform)\n",
    "#ax.add_line(line)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.legend()\n",
    "\n",
    "plt.grid(b=True, which='minor', color='lightgrey', linestyle='--')\n",
    "\n",
    "fig.suptitle('Calibration plot for MQ-309A data')\n",
    "ax.set_xlabel('PPM Concentration')\n",
    "ax.set_ylabel('Rs/Ro')\n",
    "\n",
    "\n",
    "#Save image\n",
    "plt.savefig('MQ309A.svg', format = 'svg', dpi = 1200)\n",
    "plt.savefig('MQ309A.png')\n",
    "plt.savefig('MQ309A.eps', format = 'eps', dpi = 1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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