From 30130db55e2bfeaab5452edd432d2444ef089ee6 Mon Sep 17 00:00:00 2001
From: miguel5612 <miguelangelcu@ufps.edu.co>
Date: Wed, 3 Jul 2019 20:23:49 -0500
Subject: [PATCH] Fixed MQ309A

---
 .../MQ303_Regression-checkpoint.ipynb         | 2077 +++++++++++++++++
 Experiments/MQ303_Regression.ipynb            | 2077 +++++++++++++++++
 Experiments/MQ309A.eps                        | 1048 +++------
 Experiments/MQ309A.png                        |  Bin 139340 -> 129047 bytes
 Experiments/MQ309A.svg                        |  938 +++-----
 Experiments/MQ309_Regression.ipynb            |   52 +-
 6 files changed, 4846 insertions(+), 1346 deletions(-)
 create mode 100644 Experiments/.ipynb_checkpoints/MQ303_Regression-checkpoint.ipynb
 create mode 100644 Experiments/MQ303_Regression.ipynb

diff --git a/Experiments/.ipynb_checkpoints/MQ303_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ303_Regression-checkpoint.ipynb
new file mode 100644
index 0000000..69d3517
--- /dev/null
+++ b/Experiments/.ipynb_checkpoints/MQ303_Regression-checkpoint.ipynb
@@ -0,0 +1,2077 @@
+{
+ "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": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01 \t  \t  \t \n",
+      "0.02 \t  \t  \t \n",
+      "0.03 \t  \t  \t \n",
+      "0.04 \t  \t  \t \n",
+      "0.05 \t  \t  \t \n",
+      "0.06 \t  \t  \t \n",
+      "0.07 \t  \t  \t \n",
+      "0.08 \t  \t  \t \n",
+      "0.09 \t  \t  \t \n",
+      "0.1 \t 3000.0 \t  \t 1000.0\n",
+      "0.2 \t  \t 300.0 \t \n",
+      "0.3 \t 300.0 \t 100.0 \t \n",
+      "0.4 \t 100.0 \t  \t 30.0\n",
+      "0.5 \t  \t  \t \n",
+      "0.6 \t  \t  \t 10.0\n",
+      "0.7 \t  \t  \t \n",
+      "0.8 \t  \t 10.0 \t \n",
+      "0.9 \t  \t  \t \n",
+      "1.0 \t  \t  \t \n"
+     ]
+    }
+   ],
+   "source": [
+    "for row_index in range(1,20): #reading first columns\n",
+    "    RsR0, Iso_butano, Hidrogeno , Alcohol = sheetMQ303A.row_values(row_index, start_colx=0, end_colx=4)\n",
+    "    print(RsR0, \"\t\", Iso_butano, \"\t\", Hidrogeno, \"\t\", Alcohol)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_MQ303A = sheetMQ3.col_values(0)[2:]\n",
+    "MQ5_CH4 = sheetMQ3.col_values(1)[2:]\n",
+    "MQ5_CO = sheetMQ3.col_values(2)[2:]\n",
+    "MQ5_H2 = sheetMQ3.col_values(3)[2:]\n",
+    "MQ5_Alcohol = sheetMQ3.col_values(4)[2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MQ5_CH4 =zero_to_nan(MQ5_CH4)\n",
+    "MQ5_CO =zero_to_nan(MQ5_CO)\n",
+    "MQ5_H2 =zero_to_nan(MQ5_H2)\n",
+    "MQ5_Alcohol =zero_to_nan(MQ5_Alcohol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "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': MQ5_CH4}\n",
+    "dataCO = {'RsRo': x_MQ309, 'CO': MQ5_CO}\n",
+    "dataH2 = {'RsRo': x_MQ309, 'H2': MQ5_H2}\n",
+    "dataLPG = {'RsRo': x_MQ309, 'Alcohol': MQ5_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": {},
+   "outputs": [
+    {
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+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
+       " <defs>\r\n",
+       "  <clipPath id=\"pe97a4f1778\">\r\n",
+       "   <rect height=\"298.98\" width=\"502.2\" x=\"45.478125\" y=\"46.8\"/>\r\n",
+       "  </clipPath>\r\n",
+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<Figure size 1800x1100 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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": []
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Experiments/MQ303_Regression.ipynb b/Experiments/MQ303_Regression.ipynb
new file mode 100644
index 0000000..69d3517
--- /dev/null
+++ b/Experiments/MQ303_Regression.ipynb
@@ -0,0 +1,2077 @@
+{
+ "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": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01 \t  \t  \t \n",
+      "0.02 \t  \t  \t \n",
+      "0.03 \t  \t  \t \n",
+      "0.04 \t  \t  \t \n",
+      "0.05 \t  \t  \t \n",
+      "0.06 \t  \t  \t \n",
+      "0.07 \t  \t  \t \n",
+      "0.08 \t  \t  \t \n",
+      "0.09 \t  \t  \t \n",
+      "0.1 \t 3000.0 \t  \t 1000.0\n",
+      "0.2 \t  \t 300.0 \t \n",
+      "0.3 \t 300.0 \t 100.0 \t \n",
+      "0.4 \t 100.0 \t  \t 30.0\n",
+      "0.5 \t  \t  \t \n",
+      "0.6 \t  \t  \t 10.0\n",
+      "0.7 \t  \t  \t \n",
+      "0.8 \t  \t 10.0 \t \n",
+      "0.9 \t  \t  \t \n",
+      "1.0 \t  \t  \t \n"
+     ]
+    }
+   ],
+   "source": [
+    "for row_index in range(1,20): #reading first columns\n",
+    "    RsR0, Iso_butano, Hidrogeno , Alcohol = sheetMQ303A.row_values(row_index, start_colx=0, end_colx=4)\n",
+    "    print(RsR0, \"\t\", Iso_butano, \"\t\", Hidrogeno, \"\t\", Alcohol)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_MQ303A = sheetMQ3.col_values(0)[2:]\n",
+    "MQ5_CH4 = sheetMQ3.col_values(1)[2:]\n",
+    "MQ5_CO = sheetMQ3.col_values(2)[2:]\n",
+    "MQ5_H2 = sheetMQ3.col_values(3)[2:]\n",
+    "MQ5_Alcohol = sheetMQ3.col_values(4)[2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MQ5_CH4 =zero_to_nan(MQ5_CH4)\n",
+    "MQ5_CO =zero_to_nan(MQ5_CO)\n",
+    "MQ5_H2 =zero_to_nan(MQ5_H2)\n",
+    "MQ5_Alcohol =zero_to_nan(MQ5_Alcohol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "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': MQ5_CH4}\n",
+    "dataCO = {'RsRo': x_MQ309, 'CO': MQ5_CO}\n",
+    "dataH2 = {'RsRo': x_MQ309, 'H2': MQ5_H2}\n",
+    "dataLPG = {'RsRo': x_MQ309, 'Alcohol': MQ5_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": {},
+   "outputs": [
+    {
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+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<Figure size 1800x1100 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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": []
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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-    <g id="text_9">
+    <g id="text_8">
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diff --git a/Experiments/MQ309_Regression.ipynb b/Experiments/MQ309_Regression.ipynb
index ca28036..519f5ff 100644
--- a/Experiments/MQ309_Regression.ipynb
+++ b/Experiments/MQ309_Regression.ipynb
@@ -98,20 +98,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x_MQ309A = sheetMQ3.col_values(0)[2:]\n",
-    "MQ5_CH4 = sheetMQ3.col_values(1)[2:]\n",
-    "MQ5_CO = sheetMQ3.col_values(2)[2:]\n",
-    "MQ5_H2 = sheetMQ3.col_values(3)[2:]\n",
-    "MQ5_Alcohol = sheetMQ3.col_values(4)[2:]"
+    "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": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,23 +122,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "MQ5_CH4 =zero_to_nan(MQ5_CH4)\n",
-    "MQ5_CO =zero_to_nan(MQ5_CO)\n",
-    "MQ5_H2 =zero_to_nan(MQ5_H2)\n",
-    "MQ5_Alcohol =zero_to_nan(MQ5_Alcohol)"
+    "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": 6,
+   "execution_count": 20,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "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",
@@ -148,10 +164,10 @@
     "from sklearn import datasets\n",
     "from sklearn import linear_model\n",
     "\n",
-    "dataCH4 = {'RsRo': x_MQ309, 'CH4': MQ5_CH4}\n",
-    "dataCO = {'RsRo': x_MQ309, 'CO': MQ5_CO}\n",
-    "dataH2 = {'RsRo': x_MQ309, 'H2': MQ5_H2}\n",
-    "dataLPG = {'RsRo': x_MQ309, 'Alcohol': MQ5_Alcohol}\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",