{ "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": 24, "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_MQ309A, 'CH4': MQ309A_CH4}\n", "dataCO = {'RsRo': x_MQ309A, 'CO': MQ309A_CO}\n", "dataH2 = {'RsRo': x_MQ309A, 'H2': MQ309A_H2}\n", "dataAlcohol = {'RsRo': x_MQ309A, '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(dataAlcohol)\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": 25, "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": 26, "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": 27, "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": 28, "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": 29, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\r\n", "\r\n", "\r\n", "\r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", 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" ] }, "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 }