From d91597cd67ff80cc051b34aeecf95a7264786b29 Mon Sep 17 00:00:00 2001
From: miguel5612 <miguelangelcu@ufps.edu.co>
Date: Wed, 3 Jul 2019 21:04:15 -0500
Subject: [PATCH] Added MQ-8 graphs

---
 .../MQ8_Regression-checkpoint.ipynb           | 2605 +++++++++++
 Experiments/MQ8.eps                           | 3999 +++++++++++++++++
 Experiments/MQ8.png                           |  Bin 0 -> 174892 bytes
 Experiments/MQ8.svg                           | 2239 +++++++++
 Experiments/MQ8_Regression.ipynb              | 2605 +++++++++++
 5 files changed, 11448 insertions(+)
 create mode 100644 Experiments/.ipynb_checkpoints/MQ8_Regression-checkpoint.ipynb
 create mode 100644 Experiments/MQ8.eps
 create mode 100644 Experiments/MQ8.png
 create mode 100644 Experiments/MQ8.svg
 create mode 100644 Experiments/MQ8_Regression.ipynb

diff --git a/Experiments/.ipynb_checkpoints/MQ8_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ8_Regression-checkpoint.ipynb
new file mode 100644
index 0000000..03be25a
--- /dev/null
+++ b/Experiments/.ipynb_checkpoints/MQ8_Regression-checkpoint.ipynb
@@ -0,0 +1,2605 @@
+{
+ "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": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01 \t  \t  \t  \t  \t \n",
+      "0.02 \t  \t  \t  \t  \t \n",
+      "0.03 \t  \t  \t  \t  \t \n",
+      "0.04 \t  \t  \t  \t  \t \n",
+      "0.05 \t  \t  \t  \t  \t \n",
+      "0.06 \t  \t  \t  \t  \t \n",
+      "0.07 \t  \t  \t  \t  \t \n",
+      "0.08 \t  \t  \t  \t  \t \n",
+      "0.09 \t  \t  \t  \t  \t \n",
+      "0.1 \t  \t 5000.0 \t  \t  \t \n",
+      "0.2 \t  \t  \t  \t  \t \n",
+      "0.3 \t  \t  \t  \t  \t \n",
+      "0.4 \t  \t  \t  \t  \t \n",
+      "0.5 \t  \t  \t  \t  \t \n",
+      "0.6 \t  \t  \t  \t  \t \n",
+      "0.7 \t  \t  \t  \t  \t \n",
+      "0.8 \t  \t  \t  \t  \t \n",
+      "0.9 \t  \t  \t  \t  \t \n",
+      "1.0 \t  \t 1000.0 \t  \t  \t \n"
+     ]
+    }
+   ],
+   "source": [
+    "for row_index in range(1,20): #reading first columns\n",
+    "    RsR0, H2, LPG, CH4, CO, Alcohol = sheetMQ8.row_values(row_index, start_colx=0, end_colx=6)\n",
+    "    print(RsR0, \"\t\", LPG, \"\t\", H2, \"\t\", CH4, \"\t\", CO, \"\t\", Alcohol)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_MQ8 = sheetMQ8.col_values(0)[2:]\n",
+    "MQ8_H2 = sheetMQ8.col_values(1)[2:]\n",
+    "MQ8_LPG = sheetMQ8.col_values(2)[2:]\n",
+    "MQ8_CH4 = sheetMQ8.col_values(3)[2:]\n",
+    "MQ8_CO = sheetMQ8.col_values(4)[2:]\n",
+    "MQ8_Alcohol = sheetMQ8.col_values(5)[2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MQ8_H2 =zero_to_nan(MQ8_H2)\n",
+    "MQ8_LPG =zero_to_nan(MQ8_LPG)\n",
+    "MQ8_CH4 =zero_to_nan(MQ8_CH4)\n",
+    "MQ8_CO =zero_to_nan(MQ8_CO)\n",
+    "MQ8_Alcohol =zero_to_nan(MQ8_Alcohol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+    "dataH2 = {'RsRo': x_MQ8, 'H2': MQ8_H2}\n",
+    "dataLPG = {'RsRo': x_MQ8, 'LPG': MQ8_LPG}\n",
+    "dataCH4 = {'RsRo': x_MQ8, 'CH4': MQ8_CH4}\n",
+    "dataCO = {'RsRo': x_MQ8, 'CO': MQ8_CO}\n",
+    "dataALcohol = {'RsRo': x_MQ8, 'Alcohol': MQ8_Alcohol}\n",
+    "\n",
+    "dfMQ8_H2 = pd.DataFrame(dataH2)\n",
+    "dfMQ8_LPG = pd.DataFrame(dataLPG)\n",
+    "dfMQ8_CH4 = pd.DataFrame(dataCH4)\n",
+    "dfMQ8_CO = pd.DataFrame(dataCO)\n",
+    "dfMQ8_Alcohol = pd.DataFrame(dataALcohol)\n",
+    "\n",
+    "dfMQ8_H2['H2'] = pd.to_numeric(dfMQ8_H2['H2'])\n",
+    "dfMQ8_LPG['LPG'] = pd.to_numeric(dfMQ8_LPG['LPG'])\n",
+    "dfMQ8_CH4['CH4'] = pd.to_numeric(dfMQ8_CH4['CH4'])\n",
+    "dfMQ8_CO['CO'] = pd.to_numeric(dfMQ8_CO['CO'])\n",
+    "dfMQ8_Alcohol['Alcohol'] = pd.to_numeric(dfMQ8_Alcohol['Alcohol'])\n",
+    "\n",
+    "dfMQ8_H2['H2'] = dfMQ8_H2['H2'].replace('',None, regex=True)\n",
+    "dfMQ8_LPG['LPG'] = dfMQ8_LPG['LPG'].replace('',None, regex=True)\n",
+    "dfMQ8_CH4['CH4'] = dfMQ8_CH4['CH4'].replace('',None, regex=True)\n",
+    "dfMQ8_CO['CO'] = dfMQ8_CO['CO'].replace('',None, regex=True)\n",
+    "dfMQ8_Alcohol['Alcohol'] = dfMQ8_Alcohol['Alcohol'].replace('',None, regex=True)\n",
+    "\n",
+    "#Global X_Predict variable\n",
+    "X_Predict = dfMQ8_LPG.RsRo.apply(lambda x: [x]).tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train H2\n",
+    "dataset2TrainH2 = dfMQ8_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",
+    "MQ8_H2 = H2_Predicted\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train LPG\n",
+    "dataset2TrainLPG = dfMQ8_LPG.copy()\n",
+    "dataset2TrainLPG.dropna(inplace=True)\n",
+    "X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n",
+    "y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n",
+    "model = linear_model.Lasso(alpha=0.1)\n",
+    "model.fit(X_trainLPG, y_trainLPG)\n",
+    "#Predict\n",
+    "LPG_Predicted = model.predict(X_Predict)\n",
+    "#save into MQ2\n",
+    "MQ8_LPG = LPG_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train CH4\n",
+    "dataset2TrainCH4 = dfMQ8_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",
+    "MQ8_CH4 = CH4_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train CO\n",
+    "dataset2TrainCO = dfMQ8_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",
+    "MQ8_CO = CO_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train Alcohol\n",
+    "dataset2TrainAlcohol = dfMQ8_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",
+    "MQ8_Alcohol = Alcohol_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
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+   ],
+   "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(MQ8_H2, x_MQ8, marker='o', linewidth=1, label='H2')\n",
+    "plt.plot(MQ8_LPG, x_MQ8, marker='o', linewidth=1, label='LPG')\n",
+    "plt.plot(MQ8_CH4, x_MQ8, marker='o', linewidth=1, label='CH4')\n",
+    "plt.plot(MQ8_CO, x_MQ8, marker='o', linewidth=1, label='CO')\n",
+    "plt.plot(MQ8_Alcohol, x_MQ8, marker='o', linewidth=1, label='Alcohol')\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-8 data')\n",
+    "ax.set_xlabel('PPM Concentration')\n",
+    "ax.set_ylabel('Rs/Ro')\n",
+    "\n",
+    "\n",
+    "#Save image\n",
+    "plt.savefig('MQ8.svg', format = 'svg', dpi = 1200)\n",
+    "plt.savefig('MQ8.png')\n",
+    "plt.savefig('MQ8.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/MQ8.eps b/Experiments/MQ8.eps
new file mode 100644
index 0000000..55430d9
--- /dev/null
+++ b/Experiments/MQ8.eps
@@ -0,0 +1,3999 @@
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+%%Title: MQ8.eps
+%%Creator: matplotlib version 3.0.3, http://matplotlib.org/
+%%CreationDate: Wed Jul  3 21:04:01 2019
+%%Orientation: portrait
+%%BoundingBox: -18 198 630 594
+%%EndComments
+%%BeginProlog
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+%%Title: DejaVu Sans
+%%Copyright: Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. DejaVu changes are in public domain 
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diff --git a/Experiments/MQ8_Regression.ipynb b/Experiments/MQ8_Regression.ipynb
new file mode 100644
index 0000000..03be25a
--- /dev/null
+++ b/Experiments/MQ8_Regression.ipynb
@@ -0,0 +1,2605 @@
+{
+ "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": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01 \t  \t  \t  \t  \t \n",
+      "0.02 \t  \t  \t  \t  \t \n",
+      "0.03 \t  \t  \t  \t  \t \n",
+      "0.04 \t  \t  \t  \t  \t \n",
+      "0.05 \t  \t  \t  \t  \t \n",
+      "0.06 \t  \t  \t  \t  \t \n",
+      "0.07 \t  \t  \t  \t  \t \n",
+      "0.08 \t  \t  \t  \t  \t \n",
+      "0.09 \t  \t  \t  \t  \t \n",
+      "0.1 \t  \t 5000.0 \t  \t  \t \n",
+      "0.2 \t  \t  \t  \t  \t \n",
+      "0.3 \t  \t  \t  \t  \t \n",
+      "0.4 \t  \t  \t  \t  \t \n",
+      "0.5 \t  \t  \t  \t  \t \n",
+      "0.6 \t  \t  \t  \t  \t \n",
+      "0.7 \t  \t  \t  \t  \t \n",
+      "0.8 \t  \t  \t  \t  \t \n",
+      "0.9 \t  \t  \t  \t  \t \n",
+      "1.0 \t  \t 1000.0 \t  \t  \t \n"
+     ]
+    }
+   ],
+   "source": [
+    "for row_index in range(1,20): #reading first columns\n",
+    "    RsR0, H2, LPG, CH4, CO, Alcohol = sheetMQ8.row_values(row_index, start_colx=0, end_colx=6)\n",
+    "    print(RsR0, \"\t\", LPG, \"\t\", H2, \"\t\", CH4, \"\t\", CO, \"\t\", Alcohol)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_MQ8 = sheetMQ8.col_values(0)[2:]\n",
+    "MQ8_H2 = sheetMQ8.col_values(1)[2:]\n",
+    "MQ8_LPG = sheetMQ8.col_values(2)[2:]\n",
+    "MQ8_CH4 = sheetMQ8.col_values(3)[2:]\n",
+    "MQ8_CO = sheetMQ8.col_values(4)[2:]\n",
+    "MQ8_Alcohol = sheetMQ8.col_values(5)[2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MQ8_H2 =zero_to_nan(MQ8_H2)\n",
+    "MQ8_LPG =zero_to_nan(MQ8_LPG)\n",
+    "MQ8_CH4 =zero_to_nan(MQ8_CH4)\n",
+    "MQ8_CO =zero_to_nan(MQ8_CO)\n",
+    "MQ8_Alcohol =zero_to_nan(MQ8_Alcohol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
+    "dataH2 = {'RsRo': x_MQ8, 'H2': MQ8_H2}\n",
+    "dataLPG = {'RsRo': x_MQ8, 'LPG': MQ8_LPG}\n",
+    "dataCH4 = {'RsRo': x_MQ8, 'CH4': MQ8_CH4}\n",
+    "dataCO = {'RsRo': x_MQ8, 'CO': MQ8_CO}\n",
+    "dataALcohol = {'RsRo': x_MQ8, 'Alcohol': MQ8_Alcohol}\n",
+    "\n",
+    "dfMQ8_H2 = pd.DataFrame(dataH2)\n",
+    "dfMQ8_LPG = pd.DataFrame(dataLPG)\n",
+    "dfMQ8_CH4 = pd.DataFrame(dataCH4)\n",
+    "dfMQ8_CO = pd.DataFrame(dataCO)\n",
+    "dfMQ8_Alcohol = pd.DataFrame(dataALcohol)\n",
+    "\n",
+    "dfMQ8_H2['H2'] = pd.to_numeric(dfMQ8_H2['H2'])\n",
+    "dfMQ8_LPG['LPG'] = pd.to_numeric(dfMQ8_LPG['LPG'])\n",
+    "dfMQ8_CH4['CH4'] = pd.to_numeric(dfMQ8_CH4['CH4'])\n",
+    "dfMQ8_CO['CO'] = pd.to_numeric(dfMQ8_CO['CO'])\n",
+    "dfMQ8_Alcohol['Alcohol'] = pd.to_numeric(dfMQ8_Alcohol['Alcohol'])\n",
+    "\n",
+    "dfMQ8_H2['H2'] = dfMQ8_H2['H2'].replace('',None, regex=True)\n",
+    "dfMQ8_LPG['LPG'] = dfMQ8_LPG['LPG'].replace('',None, regex=True)\n",
+    "dfMQ8_CH4['CH4'] = dfMQ8_CH4['CH4'].replace('',None, regex=True)\n",
+    "dfMQ8_CO['CO'] = dfMQ8_CO['CO'].replace('',None, regex=True)\n",
+    "dfMQ8_Alcohol['Alcohol'] = dfMQ8_Alcohol['Alcohol'].replace('',None, regex=True)\n",
+    "\n",
+    "#Global X_Predict variable\n",
+    "X_Predict = dfMQ8_LPG.RsRo.apply(lambda x: [x]).tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train H2\n",
+    "dataset2TrainH2 = dfMQ8_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",
+    "MQ8_H2 = H2_Predicted\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train LPG\n",
+    "dataset2TrainLPG = dfMQ8_LPG.copy()\n",
+    "dataset2TrainLPG.dropna(inplace=True)\n",
+    "X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n",
+    "y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n",
+    "model = linear_model.Lasso(alpha=0.1)\n",
+    "model.fit(X_trainLPG, y_trainLPG)\n",
+    "#Predict\n",
+    "LPG_Predicted = model.predict(X_Predict)\n",
+    "#save into MQ2\n",
+    "MQ8_LPG = LPG_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train CH4\n",
+    "dataset2TrainCH4 = dfMQ8_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",
+    "MQ8_CH4 = CH4_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train CO\n",
+    "dataset2TrainCO = dfMQ8_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",
+    "MQ8_CO = CO_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Model and train Alcohol\n",
+    "dataset2TrainAlcohol = dfMQ8_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",
+    "MQ8_Alcohol = Alcohol_Predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "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(MQ8_H2, x_MQ8, marker='o', linewidth=1, label='H2')\n",
+    "plt.plot(MQ8_LPG, x_MQ8, marker='o', linewidth=1, label='LPG')\n",
+    "plt.plot(MQ8_CH4, x_MQ8, marker='o', linewidth=1, label='CH4')\n",
+    "plt.plot(MQ8_CO, x_MQ8, marker='o', linewidth=1, label='CO')\n",
+    "plt.plot(MQ8_Alcohol, x_MQ8, marker='o', linewidth=1, label='Alcohol')\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-8 data')\n",
+    "ax.set_xlabel('PPM Concentration')\n",
+    "ax.set_ylabel('Rs/Ro')\n",
+    "\n",
+    "\n",
+    "#Save image\n",
+    "plt.savefig('MQ8.svg', format = 'svg', dpi = 1200)\n",
+    "plt.savefig('MQ8.png')\n",
+    "plt.savefig('MQ8.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
+}