{
"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 \n",
"0.2 \t \t \t \n",
"0.3 \t \t \t \n",
"0.4 \t \t \t \n",
"0.5 \t \t \t 100.0\n",
"0.6 \t \t \t \n",
"0.7 \t \t \t \n",
"0.8 \t \t \t \n",
"0.9 \t \t \t \n",
"1.0 \t \t 50.0 \t \n",
"2.0 \t \t \t 20.0\n",
"3.0 \t 100.0 \t \t \n",
"4.0 \t \t \t \n",
"5.0 \t \t \t \n",
"6.0 \t \t 10.0 \t 5.0\n",
"7.0 \t 10.0 \t \t \n",
"8.0 \t \t 5.0 \t \n",
"9.0 \t 5.0 \t \t \n",
"10.0 \t \t \t \n"
]
}
],
"source": [
"for row_index in range(1,20): #reading first columns\n",
" RsR0, Nox, CL2, O3 = sheetMQ131.row_values(row_index, start_colx=0, end_colx=4)\n",
" print(RsR0, \"\t\", Nox, \"\t\", CL2, \"\t\", O3)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"x_MQ131 = sheetMQ131.col_values(0)[2:]\n",
"MQ131_Nox = sheetMQ131.col_values(1)[2:]\n",
"MQ131_CL2 = sheetMQ131.col_values(2)[2:]\n",
"MQ131_O3 = sheetMQ131.col_values(3)[2:]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def zero_to_nan(values):\n",
" \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n",
" return [float('nan') if x==0 else x for x in values]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"MQ131_Nox =zero_to_nan(MQ131_Nox)\n",
"MQ131_CL2 =zero_to_nan(MQ131_CL2)\n",
"MQ131_O3 =zero_to_nan(MQ131_O3)"
]
},
{
"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",
"dataNox = {'RsRo': x_MQ131, 'Nox': MQ131_Nox}\n",
"dataCL2 = {'RsRo': x_MQ131, 'CL2': MQ131_CL2}\n",
"dataO3 = {'RsRo': x_MQ131, 'O3': MQ131_O3}\n",
"\n",
"dfMQ131_Nox = pd.DataFrame(dataNox)\n",
"dfMQ131_CL2 = pd.DataFrame(dataCL2)\n",
"dfMQ131_O3 = pd.DataFrame(dataO3)\n",
"\n",
"dfMQ131_Nox['Nox'] = pd.to_numeric(dfMQ131_Nox['Nox'])\n",
"dfMQ131_CL2['CL2'] = pd.to_numeric(dfMQ131_CL2['CL2'])\n",
"dfMQ131_O3['O3'] = pd.to_numeric(dfMQ131_O3['O3'])\n",
"\n",
"dfMQ131_Nox['Nox'] = dfMQ131_Nox['Nox'].replace('',None, regex=True)\n",
"dfMQ131_CL2['CL2'] = dfMQ131_CL2['CL2'].replace('',None, regex=True)\n",
"dfMQ131_O3['O3'] = dfMQ131_O3['O3'].replace('',None, regex=True)\n",
"\n",
"#Global X_Predict variable\n",
"X_Predict = dfMQ131_Nox.RsRo.apply(lambda x: [x]).tolist()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#Model and train Nox\n",
"dataset2TrainNox = dfMQ131_Nox.copy()\n",
"dataset2TrainNox.dropna(inplace=True)\n",
"X_trainNox = dataset2TrainNox.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainNox = dataset2TrainNox['Nox'].tolist()\n",
"model = linear_model.Lasso(alpha=0.1)\n",
"model.fit(X_trainNox, y_trainNox)\n",
"#Predict\n",
"Nox_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
"MQ131_Nox = Nox_Predicted"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Model and train CL2\n",
"dataset2TrainCL2 = dfMQ131_CL2.copy()\n",
"dataset2TrainCL2.dropna(inplace=True)\n",
"X_trainCL2 = dataset2TrainCL2.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainCL2 = dataset2TrainCL2['CL2'].tolist()\n",
"model = linear_model.Lasso(alpha=0.1)\n",
"model.fit(X_trainCL2, y_trainCL2)\n",
"#Predict\n",
"CL2_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
"MQ131_CL2 = CL2_Predicted"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#Model and train O3\n",
"dataset2TrainO3 = dfMQ131_O3.copy()\n",
"dataset2TrainO3.dropna(inplace=True)\n",
"X_trainO3 = dataset2TrainO3.RsRo.apply(lambda x: [x]).tolist()\n",
"y_trainO3 = dataset2TrainO3['O3'].tolist()\n",
"model = linear_model.Lasso(alpha=0.1)\n",
"model.fit(X_trainO3, y_trainO3)\n",
"#Predict\n",
"O3_Predicted = model.predict(X_Predict)\n",
"#save into MQ2\n",
"MQ131_O3 = O3_Predicted"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\r\n",
"\r\n",
"\r\n",
"\r\n"
],
"text/plain": [
""
]
},
"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(MQ131_Nox, x_MQ131, marker='o', linewidth=1, label='Nox')\n",
"plt.plot(MQ131_CL2, x_MQ131, marker='o', linewidth=1, label='CL2')\n",
"plt.plot(MQ131_O3, x_MQ131, marker='o', linewidth=1, label='O3')\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-131 data')\n",
"ax.set_xlabel('PPM Concentration')\n",
"ax.set_ylabel('Rs/Ro')\n",
"\n",
"\n",
"#Save image\n",
"plt.savefig('MQ131.svg', format = 'svg', dpi = 1200)\n",
"plt.savefig('MQ131.png')\n",
"plt.savefig('MQ131.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
}