diff --git a/Experiments/.ipynb_checkpoints/MQ3_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ3_Regression-checkpoint.ipynb index 94b706c..3375f49 100644 --- a/Experiments/.ipynb_checkpoints/MQ3_Regression-checkpoint.ipynb +++ b/Experiments/.ipynb_checkpoints/MQ3_Regression-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -10,29 +10,22 @@ "output_type": "stream", "text": [ "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n", - "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\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) (0.20.3)\n", - "Requirement already satisfied: scipy>=0.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n", - "Requirement already satisfied: numpy>=1.8.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n", - "Collecting imblearn\n", - " Using cached 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Successfully uninstalled scikit-learn-0.20.3\n", - "Successfully installed imbalanced-learn-0.5.0 imblearn-0.0 scikit-learn-0.21.2\n" + "Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\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: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n" ] } ], @@ -45,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -105,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 76, "metadata": { "scrolled": false }, @@ -193,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -347,10 +340,10 @@ " \r\n", " \r\n", + "\" id=\"md0e3683457\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -408,7 +401,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -423,7 +416,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -437,7 +430,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", @@ -445,262 +438,262 @@ " \r\n", " \r\n", + "\" id=\"md63d1a0a53\" style=\"stroke:#000000;stroke-width:0.6;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " 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+ " \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", " \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", - " \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", " \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", " \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", @@ -955,10 +948,10 @@ " \r\n", " \r\n", + "\" id=\"mc1bcec9881\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -973,7 +966,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -988,7 +981,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1028,7 +1021,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", @@ -1036,286 +1029,286 @@ " \r\n", " \r\n", + "\" id=\"mc49a650fd5\" style=\"stroke:#000000;stroke-width:0.6;\"/>\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", - " \r\n", + " 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" \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", @@ -1397,7 +1390,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"meebf1fc7a9\" style=\"stroke:#1f77b4;\"/>\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", " \r\n", " \r\n", - " \r\n", + "\" id=\"m4f70cb6133\" style=\"stroke:#ff7f0e;\"/>\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", + " \r\n", + " \r\n", + " \r\n", + 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- " \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", + " \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", @@ -1769,7 +1762,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1832,7 +1825,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1897,7 +1890,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1948,7 +1941,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1985,7 +1978,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -2038,7 +2031,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -2273,7 +2266,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", diff --git a/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb new file mode 100644 index 0000000..5fcf942 --- /dev/null +++ b/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb @@ -0,0 +1,2010 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 24, + "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: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\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: 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: 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: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\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: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\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" + ] + } + ], + "source": [ + "!pip install pandas\n", + "!pip install xlrd\n", + "!pip install sklearn\n", + "!pip install imblearn" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "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": 26, + "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 \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 \t \n", + "0.8 \t \t \t \t \n", + "0.9 \t 5000.0 \t \t \t \n", + "1.0 \t \t 1000.0 \t \t \n", + "2.0 \t 1000.0 \t 200.0 \t \t \n", + "3.0 \t 200.0 \t \t 1000.0 \t 5000.0\n", + "4.0 \t \t \t 200.0 \t 200.0\n", + "5.0 \t \t 4000.0 \t \t \n", + "6.0 \t \t \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 = sheetMQ4.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": 27, + "metadata": {}, + "outputs": [], + "source": [ + "x_MQ4 = sheetMQ3.col_values(0)[2:]\n", + "MQ4_CH4 = sheetMQ3.col_values(1)[2:]\n", + "MQ4_CO = sheetMQ3.col_values(2)[2:]\n", + "MQ4_H2 = sheetMQ3.col_values(3)[2:]\n", + "MQ4_Alcohol = sheetMQ3.col_values(4)[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "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": 29, + "metadata": {}, + "outputs": [], + "source": [ + "MQ4_CH4 =zero_to_nan(MQ4_CH4)\n", + "MQ4_CO =zero_to_nan(MQ4_CO)\n", + "MQ4_H2 =zero_to_nan(MQ4_H2)\n", + "MQ4_Alcohol =zero_to_nan(MQ4_Alcohol)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "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_MQ4, 'CH4': MQ4_CH4}\n", + "dataCO = {'RsRo': x_MQ4, 'CO': MQ4_CO}\n", + "dataH2 = {'RsRo': x_MQ4, 'H2': MQ4_H2}\n", + "dataAlcohol = {'RsRo': x_MQ4, 'Alcohol': MQ4_Alcohol}\n", + "\n", + "dfMQ4_CH4 = pd.DataFrame(dataCH4)\n", + "dfMQ4_CO = pd.DataFrame(dataCO)\n", + "dfMQ4_H2 = pd.DataFrame(dataH2)\n", + "dfMQ4_Alcohol = pd.DataFrame(dataAlcohol)\n", + "\n", + "dfMQ4_CH4['CH4'] = pd.to_numeric(dfMQ4_CH4['CH4'])\n", + "dfMQ4_CO['CO'] = pd.to_numeric(dfMQ4_CO['CO'])\n", + "dfMQ4_H2['H2'] = pd.to_numeric(dfMQ4_H2['H2'])\n", + "dfMQ4_Alcohol['Alcohol'] = pd.to_numeric(dfMQ4_Alcohol['Alcohol'])\n", + "\n", + "dfMQ4_CH4['CH4'] = dfMQ4_CH4['CH4'].replace('',None, regex=True)\n", + "dfMQ4_CO['CO'] = dfMQ4_CO['CO'].replace('',None, regex=True)\n", + "dfMQ4_H2['H2'] = dfMQ4_H2['H2'].replace('',None, regex=True)\n", + "dfMQ4_Alcohol['Alcohol'] = dfMQ4_Alcohol['Alcohol'].replace('',None, regex=True)\n", + "\n", + "#Global X_Predict variable\n", + "X_Predict = dfMQ4_Alcohol.RsRo.apply(lambda x: [x]).tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CH4\n", + "dataset2TrainCH4 = dfMQ4_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", + "MQ4_CH4 = CH4_Predicted\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CO\n", + "dataset2TrainCO = dfMQ4_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", + "MQ4_CO = CO_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train H2\n", + "dataset2TrainH2 = dfMQ4_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", + "MQ4_H2 = H2_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train Alcohol\n", + "dataset2TrainAlcohol = dfMQ4_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", + "MQ4_Alcohol = Alcohol_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "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", + " \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(MQ4_CH4, x_MQ4, marker='o', linewidth=1, label='CH4')\n", + "plt.plot(MQ4_CO, x_MQ4, marker='o', linewidth=1, label='CO')\n", + "plt.plot(MQ4_H2, x_MQ4, marker='o', linewidth=1, label='H2')\n", + "plt.plot(MQ4_Alcohol, x_MQ4, marker='o', linewidth=1, label='Alcohol')\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-4 data')\n", + "ax.set_xlabel('PPM Concentration')\n", + "ax.set_ylabel('Rs/Ro')\n", + "\n", + "\n", + "#Save image\n", + "plt.savefig('MQ4.svg', format = 'svg', dpi = 1200)\n", + "plt.savefig('MQ4.png')\n", + "plt.savefig('MQ4.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/MQ3.eps b/Experiments/MQ3.eps index 83f9428..f691d65 100644 --- a/Experiments/MQ3.eps +++ b/Experiments/MQ3.eps @@ -1,7 +1,7 @@ %!PS-Adobe-3.0 EPSF-3.0 %%Title: MQ3.eps %%Creator: matplotlib version 3.0.3, http://matplotlib.org/ -%%CreationDate: Mon Jul 1 21:44:55 2019 +%%CreationDate: Mon Jul 1 22:07:09 2019 %%Orientation: portrait %%BoundingBox: -18 198 630 594 %%EndComments diff --git a/Experiments/MQ3.svg b/Experiments/MQ3.svg index a7673af..97681d7 100644 --- a/Experiments/MQ3.svg +++ b/Experiments/MQ3.svg @@ -32,10 +32,10 @@ z +" id="ma8165da9da" style="stroke:#000000;stroke-width:0.8;"/> - 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"execution_count": 1, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -10,29 +10,22 @@ "output_type": "stream", "text": [ "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n", - "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\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", - 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"Collecting scikit-learn>=0.21 (from imbalanced-learn->imblearn)\n", - " Using cached https://files.pythonhosted.org/packages/75/14/fde90de4fc6722303ddbc183ec253c288dae8c7e4eb5a069ac49f14c9a0f/scikit_learn-0.21.2-cp37-cp37m-win_amd64.whl\n", + "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.21.2)\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: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\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: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n", - "Installing collected packages: scikit-learn, imbalanced-learn, imblearn\n", - " Found existing installation: scikit-learn 0.20.3\n", - " Uninstalling scikit-learn-0.20.3:\n", - " Successfully uninstalled scikit-learn-0.20.3\n", - "Successfully installed imbalanced-learn-0.5.0 imblearn-0.0 scikit-learn-0.21.2\n" + "Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\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: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n" ] } ], @@ -45,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -105,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 76, "metadata": { "scrolled": false }, @@ -193,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -347,10 +340,10 @@ " \r\n", " \r\n", + "\" id=\"md0e3683457\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -408,7 +401,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -423,7 +416,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -437,7 +430,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", @@ -445,262 +438,262 @@ " \r\n", " \r\n", + "\" id=\"md63d1a0a53\" style=\"stroke:#000000;stroke-width:0.6;\"/>\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", - " \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", " \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", " \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", " \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", + " \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", " \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", - " \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", " \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", " \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", @@ -955,10 +948,10 @@ " \r\n", " \r\n", + "\" id=\"mc1bcec9881\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -973,7 +966,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -988,7 +981,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1028,7 +1021,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", @@ -1036,286 +1029,286 @@ " \r\n", " \r\n", + "\" id=\"mc49a650fd5\" style=\"stroke:#000000;stroke-width:0.6;\"/>\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", - " \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", " \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", " \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", " \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", + " \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", " \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", - " \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", " \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", " \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", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1397,7 +1390,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"meebf1fc7a9\" style=\"stroke:#1f77b4;\"/>\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", " \r\n", " \r\n", - " \r\n", + "\" id=\"m4f70cb6133\" style=\"stroke:#ff7f0e;\"/>\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", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"m33cbaacdb3\" style=\"stroke:#2ca02c;\"/>\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", - " \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", " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"mf17cc4d4af\" style=\"stroke:#d62728;\"/>\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", + " \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", " \r\n", - " \r\n", + "\" id=\"me809331671\" style=\"stroke:#9467bd;\"/>\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", + " \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", " \r\n", - " \r\n", + "\" id=\"m55067d34b2\" style=\"stroke:#8c564b;\"/>\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", + " \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", " \r\n", @@ -1769,7 +1762,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1832,7 +1825,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1897,7 +1890,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1948,7 +1941,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1985,7 +1978,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -2038,7 +2031,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -2273,7 +2266,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", diff --git a/Experiments/MQ4.eps b/Experiments/MQ4.eps new file mode 100644 index 0000000..9c19ddf --- /dev/null +++ b/Experiments/MQ4.eps @@ -0,0 +1,2967 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Title: MQ4.eps +%%Creator: matplotlib version 3.0.3, http://matplotlib.org/ +%%CreationDate: Mon Jul 1 22:07:15 2019 +%%Orientation: portrait +%%BoundingBox: -18 198 630 594 +%%EndComments +%%BeginProlog +/mpldict 8 dict def +mpldict begin +/m { moveto } bind def +/l { lineto } bind def +/r { rlineto } bind def +/c { curveto } bind def +/cl { closepath } bind def +/box { +m +1 index 0 r +0 exch r +neg 0 r +cl +} bind def +/clipbox { +box +clip +newpath +} bind def +%!PS-Adobe-3.0 Resource-Font +%%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 +%%Creator: Converted from TrueType to type 3 by PPR +25 dict begin +/_d{bind def}bind def +/_m{moveto}_d +/_l{lineto}_d +/_cl{closepath eofill}_d +/_c{curveto}_d +/_sc{7 -1 roll{setcachedevice}{pop pop pop pop pop pop}ifelse}_d +/_e{exec}_d +/FontName /DejaVuSans def +/PaintType 0 def +/FontMatrix[.001 0 0 .001 0 0]def +/FontBBox[-1021 -463 1793 1232]def +/FontType 3 def +/Encoding [ /space /hyphen /slash /zero /one /two /four /A /C /H /M /O /P /Q /R /a /b /c /d /e /f /h /i /l /n /o /p /r /s /t /minus ] def +/FontInfo 10 dict dup begin +/FamilyName (DejaVu Sans) def +/FullName (DejaVu Sans) def +/Notice (Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. DejaVu changes are in public domain ) def +/Weight (Book) def +/Version (Version 2.35) def +/ItalicAngle 0.0 def +/isFixedPitch false def +/UnderlinePosition -130 def +/UnderlineThickness 90 def +end readonly def +/CharStrings 32 dict dup begin +/.notdef 0 def +/space{318 0 0 0 0 0 _sc +}_d +/hyphen{361 0 49 234 312 314 _sc +49 314 _m +312 314 _l +312 234 _l +49 234 _l +49 314 _l +_cl}_d +/slash{337 0 0 -92 337 729 _sc +254 729 _m +337 729 _l +83 -92 _l +0 -92 _l +254 729 _l +_cl}_d +/zero{636 0 66 -13 570 742 _sc +318 664 _m +267 664 229 639 203 589 _c +177 539 165 464 165 364 _c +165 264 177 189 203 139 _c +229 89 267 64 318 64 _c +369 64 407 89 433 139 _c +458 189 471 264 471 364 _c +471 464 458 539 433 589 _c +407 639 369 664 318 664 _c +318 742 _m +399 742 461 709 505 645 _c +548 580 570 486 570 364 _c +570 241 548 147 505 83 _c +461 19 399 -13 318 -13 _c +236 -13 173 19 130 83 _c +87 147 66 241 66 364 _c +66 486 87 580 130 645 _c +173 709 236 742 318 742 _c +_cl}_d +/one{636 0 110 0 544 729 _sc +124 83 _m +285 83 _l +285 639 _l +110 604 _l +110 694 _l +284 729 _l +383 729 _l +383 83 _l +544 83 _l +544 0 _l +124 0 _l +124 83 _l +_cl}_d +/two{{636 0 73 0 536 742 _sc +192 83 _m +536 83 _l +536 0 _l +73 0 _l +73 83 _l +110 121 161 173 226 239 _c +290 304 331 346 348 365 _c +380 400 402 430 414 455 _c +426 479 433 504 433 528 _c +433 566 419 598 392 622 _c +365 646 330 659 286 659 _c +255 659 222 653 188 643 _c +154 632 117 616 78 594 _c +78 694 _l +118 710 155 722 189 730 _c +223 738 255 742 284 742 _c +}_e{359 742 419 723 464 685 _c +509 647 532 597 532 534 _c +532 504 526 475 515 449 _c +504 422 484 390 454 354 _c +446 344 420 317 376 272 _c +332 227 271 164 192 83 _c +_cl}_e}_d +/four{636 0 49 0 580 729 _sc +378 643 _m +129 254 _l +378 254 _l +378 643 _l +352 729 _m +476 729 _l +476 254 _l +580 254 _l +580 172 _l +476 172 _l +476 0 _l +378 0 _l +378 172 _l +49 172 _l +49 267 _l +352 729 _l +_cl}_d +/A{684 0 8 0 676 729 _sc +342 632 _m +208 269 _l +476 269 _l 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+/O{787 0 56 -13 731 742 _sc +394 662 _m +322 662 265 635 223 582 _c +181 528 160 456 160 364 _c +160 272 181 199 223 146 _c +265 92 322 66 394 66 _c +465 66 522 92 564 146 _c +606 199 627 272 627 364 _c +627 456 606 528 564 582 _c +522 635 465 662 394 662 _c +394 742 _m +496 742 577 707 639 639 _c +700 571 731 479 731 364 _c +731 248 700 157 639 89 _c +577 21 496 -13 394 -13 _c +291 -13 209 21 148 89 _c +86 157 56 248 56 364 _c +56 479 86 571 148 639 _c +209 707 291 742 394 742 _c +_cl}_d +/P{603 0 98 0 569 729 _sc +197 648 _m +197 374 _l +321 374 _l +367 374 402 385 427 409 _c +452 433 465 467 465 511 _c +465 555 452 588 427 612 _c +402 636 367 648 321 648 _c +197 648 _l +98 729 _m +321 729 _l +402 729 464 710 506 673 _c +548 636 569 582 569 511 _c +569 439 548 384 506 348 _c +464 311 402 293 321 293 _c +197 293 _l +197 0 _l +98 0 _l +98 729 _l +_cl}_d +/Q{{787 0 56 -128 731 742 _sc +394 662 _m +322 662 265 635 223 582 _c +181 528 160 456 160 364 _c +160 272 181 199 223 146 _c +265 92 322 66 394 66 _c +465 66 522 92 564 146 _c +606 199 627 272 627 364 _c +627 456 606 528 564 582 _c +522 635 465 662 394 662 _c +532 13 _m +662 -128 _l +543 -128 _l +435 -11 _l +424 -11 416 -12 410 -12 _c +404 -12 399 -13 394 -13 _c +291 -13 209 21 148 89 _c +}_e{86 157 56 248 56 364 _c +56 479 86 571 148 639 _c +209 707 291 742 394 742 _c +496 742 577 707 639 639 _c +700 571 731 479 731 364 _c +731 279 714 206 680 146 _c +646 86 596 41 532 13 _c +_cl}_e}_d +/R{{695 0 98 0 666 729 _sc +444 342 _m +465 334 486 319 506 296 _c +526 272 546 240 566 199 _c +666 0 _l +560 0 _l +467 187 _l +443 235 419 268 397 284 _c +374 300 343 308 304 308 _c +197 308 _l +197 0 _l +98 0 _l +98 729 _l +321 729 _l +404 729 466 711 507 677 _c +548 642 569 589 569 519 _c +569 473 558 434 537 404 _c +515 374 484 353 444 342 _c +197 648 _m +197 389 _l +321 389 _l +}_e{368 389 404 400 428 422 _c +452 444 465 476 465 519 _c +465 561 452 593 428 615 _c +404 637 368 648 321 648 _c +197 648 _l +_cl}_e}_d +/a{{613 0 60 -13 522 560 _sc +343 275 _m +270 275 220 266 192 250 _c +164 233 150 205 150 165 _c +150 133 160 107 181 89 _c +202 70 231 61 267 61 _c +317 61 357 78 387 114 _c +417 149 432 196 432 255 _c +432 275 _l +343 275 _l +522 312 _m +522 0 _l +432 0 _l +432 83 _l +411 49 385 25 355 10 _c +325 -5 287 -13 243 -13 _c +187 -13 142 2 109 33 _c +76 64 60 106 60 159 _c +}_e{60 220 80 266 122 298 _c +163 329 224 345 306 345 _c +432 345 _l +432 354 _l +432 395 418 427 391 450 _c +364 472 326 484 277 484 _c +245 484 215 480 185 472 _c +155 464 127 453 100 439 _c +100 522 _l +132 534 164 544 195 550 _c +226 556 256 560 286 560 _c +365 560 424 539 463 498 _c +502 457 522 395 522 312 _c +_cl}_e}_d +/b{{635 0 91 -13 580 760 _sc +487 273 _m +487 339 473 390 446 428 _c +418 466 381 485 334 485 _c +286 485 249 466 222 428 _c +194 390 181 339 181 273 _c +181 207 194 155 222 117 _c +249 79 286 61 334 61 _c +381 61 418 79 446 117 _c +473 155 487 207 487 273 _c +181 464 _m +199 496 223 520 252 536 _c +281 552 316 560 356 560 _c +422 560 476 533 518 481 _c +559 428 580 359 580 273 _c +}_e{580 187 559 117 518 65 _c +476 13 422 -13 356 -13 _c +316 -13 281 -5 252 10 _c +223 25 199 49 181 82 _c +181 0 _l +91 0 _l +91 760 _l +181 760 _l +181 464 _l +_cl}_e}_d +/c{{550 0 55 -13 488 560 _sc +488 526 _m +488 442 _l +462 456 437 466 411 473 _c +385 480 360 484 334 484 _c +276 484 230 465 198 428 _c +166 391 150 339 150 273 _c +150 206 166 154 198 117 _c +230 80 276 62 334 62 _c +360 62 385 65 411 72 _c +437 79 462 90 488 104 _c +488 21 _l +462 9 436 0 410 -5 _c +383 -10 354 -13 324 -13 _c +242 -13 176 12 128 64 _c +}_e{79 115 55 185 55 273 _c +55 362 79 432 128 483 _c +177 534 244 560 330 560 _c +358 560 385 557 411 551 _c +437 545 463 537 488 526 _c +_cl}_e}_d +/d{{635 0 55 -13 544 760 _sc +454 464 _m +454 760 _l +544 760 _l +544 0 _l +454 0 _l +454 82 _l +435 49 411 25 382 10 _c +353 -5 319 -13 279 -13 _c +213 -13 159 13 117 65 _c +75 117 55 187 55 273 _c +55 359 75 428 117 481 _c +159 533 213 560 279 560 _c +319 560 353 552 382 536 _c +411 520 435 496 454 464 _c +148 273 _m +148 207 161 155 188 117 _c +215 79 253 61 301 61 _c +}_e{348 61 385 79 413 117 _c +440 155 454 207 454 273 _c +454 339 440 390 413 428 _c +385 466 348 485 301 485 _c +253 485 215 466 188 428 _c +161 390 148 339 148 273 _c +_cl}_e}_d +/e{{615 0 55 -13 562 560 _sc +562 296 _m +562 252 _l +149 252 _l +153 190 171 142 205 110 _c +238 78 284 62 344 62 _c +378 62 412 66 444 74 _c +476 82 509 95 541 113 _c +541 28 _l +509 14 476 3 442 -3 _c +408 -9 373 -13 339 -13 _c +251 -13 182 12 131 62 _c +80 112 55 181 55 268 _c +55 357 79 428 127 481 _c +175 533 241 560 323 560 _c +397 560 455 536 498 489 _c +}_e{540 441 562 377 562 296 _c +472 322 _m +471 371 457 410 431 440 _c +404 469 368 484 324 484 _c +274 484 234 469 204 441 _c +174 413 156 373 152 322 _c +472 322 _l +_cl}_e}_d +/f{352 0 23 0 371 760 _sc +371 760 _m +371 685 _l +285 685 _l +253 685 230 678 218 665 _c +205 652 199 629 199 595 _c +199 547 _l +347 547 _l 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462 _l +202 494 227 519 257 535 _c +286 551 320 560 358 560 _c +420 560 468 540 500 501 _c +532 462 549 405 549 330 _c +_cl}_d +/o{612 0 55 -13 557 560 _sc +306 484 _m +258 484 220 465 192 427 _c +164 389 150 338 150 273 _c +150 207 163 156 191 118 _c +219 80 257 62 306 62 _c +354 62 392 80 420 118 _c +448 156 462 207 462 273 _c +462 337 448 389 420 427 _c +392 465 354 484 306 484 _c +306 560 _m +384 560 445 534 490 484 _c +534 433 557 363 557 273 _c +557 183 534 113 490 63 _c +445 12 384 -13 306 -13 _c +227 -13 165 12 121 63 _c +77 113 55 183 55 273 _c +55 363 77 433 121 484 _c +165 534 227 560 306 560 _c +_cl}_d +/p{{635 0 91 -207 580 560 _sc +181 82 _m +181 -207 _l +91 -207 _l +91 547 _l +181 547 _l +181 464 _l +199 496 223 520 252 536 _c +281 552 316 560 356 560 _c +422 560 476 533 518 481 _c +559 428 580 359 580 273 _c +580 187 559 117 518 65 _c +476 13 422 -13 356 -13 _c +316 -13 281 -5 252 10 _c +223 25 199 49 181 82 _c +487 273 _m +487 339 473 390 446 428 _c +418 466 381 485 334 485 _c +}_e{286 485 249 466 222 428 _c +194 390 181 339 181 273 _c +181 207 194 155 222 117 _c +249 79 286 61 334 61 _c +381 61 418 79 446 117 _c +473 155 487 207 487 273 _c +_cl}_e}_d +/r{411 0 91 0 411 560 _sc +411 463 _m +401 469 390 473 378 476 _c +366 478 353 480 339 480 _c +288 480 249 463 222 430 _c +194 397 181 350 181 288 _c +181 0 _l +91 0 _l +91 547 _l +181 547 _l +181 462 _l +199 495 224 520 254 536 _c +284 552 321 560 365 560 _c +371 560 378 559 386 559 _c +393 558 401 557 411 555 _c +411 463 _l +_cl}_d +/s{{521 0 54 -13 472 560 _sc +443 531 _m +443 446 _l +417 458 391 468 364 475 _c +336 481 308 485 279 485 _c +234 485 200 478 178 464 _c +156 450 145 430 145 403 _c +145 382 153 366 169 354 _c +185 342 217 330 265 320 _c +296 313 _l +360 299 405 279 432 255 _c +458 230 472 195 472 151 _c +472 100 452 60 412 31 _c +372 1 316 -13 246 -13 _c +216 -13 186 -10 154 -5 _c +}_e{122 0 89 8 54 20 _c +54 113 _l +87 95 120 82 152 74 _c +184 65 216 61 248 61 _c +290 61 323 68 346 82 _c +368 96 380 117 380 144 _c +380 168 371 187 355 200 _c +339 213 303 226 247 238 _c +216 245 _l +160 257 119 275 95 299 _c +70 323 58 356 58 399 _c +58 450 76 490 112 518 _c +148 546 200 560 268 560 _c +301 560 332 557 362 552 _c +391 547 418 540 443 531 _c +}_e{_cl}_e}_d +/t{392 0 27 0 368 702 _sc +183 702 _m +183 547 _l +368 547 _l +368 477 _l +183 477 _l +183 180 _l +183 135 189 106 201 94 _c +213 81 238 75 276 75 _c +368 75 _l +368 0 _l +276 0 _l +206 0 158 13 132 39 _c +106 65 93 112 93 180 _c +93 477 _l +27 477 _l +27 547 _l +93 547 _l +93 702 _l +183 702 _l +_cl}_d +/minus{838 0 106 272 732 355 _sc +106 355 _m +732 355 _l +732 272 _l +106 272 _l +106 355 _l +_cl}_d +end readonly def + +/BuildGlyph + {exch begin + CharStrings exch + 2 copy known not{pop /.notdef}if + true 3 1 roll get exec + end}_d + +/BuildChar { + 1 index /Encoding get exch get + 1 index /BuildGlyph get exec +}_d + +FontName currentdict end definefont pop +end +%%EndProlog +mpldict begin +-18 198 translate 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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: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\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: 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: 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: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\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: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\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" + ] + } + ], + "source": [ + "!pip install pandas\n", + "!pip install xlrd\n", + "!pip install sklearn\n", + "!pip install imblearn" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "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": 26, + "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 \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 \t \n", + "0.8 \t \t \t \t \n", + "0.9 \t 5000.0 \t \t \t \n", + "1.0 \t \t 1000.0 \t \t \n", + "2.0 \t 1000.0 \t 200.0 \t \t \n", + "3.0 \t 200.0 \t \t 1000.0 \t 5000.0\n", + "4.0 \t \t \t 200.0 \t 200.0\n", + "5.0 \t \t 4000.0 \t \t \n", + "6.0 \t \t \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 = sheetMQ4.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": 27, + "metadata": {}, + "outputs": [], + "source": [ + "x_MQ4 = sheetMQ3.col_values(0)[2:]\n", + "MQ4_CH4 = sheetMQ3.col_values(1)[2:]\n", + "MQ4_CO = sheetMQ3.col_values(2)[2:]\n", + "MQ4_H2 = sheetMQ3.col_values(3)[2:]\n", + "MQ4_Alcohol = sheetMQ3.col_values(4)[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "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": 29, + "metadata": {}, + "outputs": [], + "source": [ + "MQ4_CH4 =zero_to_nan(MQ4_CH4)\n", + "MQ4_CO =zero_to_nan(MQ4_CO)\n", + "MQ4_H2 =zero_to_nan(MQ4_H2)\n", + "MQ4_Alcohol =zero_to_nan(MQ4_Alcohol)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "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_MQ4, 'CH4': MQ4_CH4}\n", + "dataCO = {'RsRo': x_MQ4, 'CO': MQ4_CO}\n", + "dataH2 = {'RsRo': x_MQ4, 'H2': MQ4_H2}\n", + "dataAlcohol = {'RsRo': x_MQ4, 'Alcohol': MQ4_Alcohol}\n", + "\n", + "dfMQ4_CH4 = pd.DataFrame(dataCH4)\n", + "dfMQ4_CO = pd.DataFrame(dataCO)\n", + "dfMQ4_H2 = pd.DataFrame(dataH2)\n", + "dfMQ4_Alcohol = pd.DataFrame(dataAlcohol)\n", + "\n", + "dfMQ4_CH4['CH4'] = pd.to_numeric(dfMQ4_CH4['CH4'])\n", + "dfMQ4_CO['CO'] = pd.to_numeric(dfMQ4_CO['CO'])\n", + "dfMQ4_H2['H2'] = pd.to_numeric(dfMQ4_H2['H2'])\n", + "dfMQ4_Alcohol['Alcohol'] = pd.to_numeric(dfMQ4_Alcohol['Alcohol'])\n", + "\n", + "dfMQ4_CH4['CH4'] = dfMQ4_CH4['CH4'].replace('',None, regex=True)\n", + "dfMQ4_CO['CO'] = dfMQ4_CO['CO'].replace('',None, regex=True)\n", + "dfMQ4_H2['H2'] = dfMQ4_H2['H2'].replace('',None, regex=True)\n", + "dfMQ4_Alcohol['Alcohol'] = dfMQ4_Alcohol['Alcohol'].replace('',None, regex=True)\n", + "\n", + "#Global X_Predict variable\n", + "X_Predict = dfMQ4_Alcohol.RsRo.apply(lambda x: [x]).tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CH4\n", + "dataset2TrainCH4 = dfMQ4_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", + "MQ4_CH4 = CH4_Predicted\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CO\n", + "dataset2TrainCO = dfMQ4_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", + "MQ4_CO = CO_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train H2\n", + "dataset2TrainH2 = dfMQ4_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", + "MQ4_H2 = H2_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train Alcohol\n", + "dataset2TrainAlcohol = dfMQ4_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", + "MQ4_Alcohol = Alcohol_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\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(MQ4_CH4, x_MQ4, marker='o', linewidth=1, label='CH4')\n", + "plt.plot(MQ4_CO, x_MQ4, marker='o', linewidth=1, label='CO')\n", + "plt.plot(MQ4_H2, x_MQ4, marker='o', linewidth=1, label='H2')\n", + "plt.plot(MQ4_Alcohol, x_MQ4, marker='o', linewidth=1, label='Alcohol')\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-4 data')\n", + "ax.set_xlabel('PPM Concentration')\n", + "ax.set_ylabel('Rs/Ro')\n", + "\n", + "\n", + "#Save image\n", + "plt.savefig('MQ4.svg', format = 'svg', dpi = 1200)\n", + "plt.savefig('MQ4.png')\n", + "plt.savefig('MQ4.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 +}