mirror of
https://github.com/miguel5612/MQSensorsLib.git
synced 2025-03-15 05:17:30 +03:00
1795 lines
81 KiB
Plaintext
1795 lines
81 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n",
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"Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n",
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"Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n",
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"Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n",
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"Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
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"Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n",
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"Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
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"Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.21.2)\n",
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"Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n",
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"Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n",
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"Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n",
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"Requirement already satisfied: imblearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n",
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"Requirement already satisfied: imbalanced-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from imblearn) (0.5.0)\n",
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"Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n",
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"Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n",
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"Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n",
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"Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\n"
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]
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}
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],
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"source": [
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"!pip install pandas\n",
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"!pip install xlrd\n",
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"!pip install sklearn\n",
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"!pip install imblearn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import xlrd\n",
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"book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n",
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"sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n",
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"sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n",
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"sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n",
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"sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n",
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"sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n",
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"sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n",
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"sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n",
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"sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n",
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"sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n",
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"sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n",
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"sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n",
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"sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.1 \t \t \t \t \n",
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"0.2 \t \t 5000.0 \t \t \n",
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"0.3 \t \t \t \t \n",
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"0.4 \t \t 800.0 \t 2000.0 \t \n",
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"0.5 \t \t \t 1000.0 \t \n",
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"0.6 \t \t \t \t \n",
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"0.7 \t 5000.0 \t 200.0 \t 500.0 \t \n",
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"0.8 \t 2000.0 \t \t \t \n",
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"0.9 \t \t \t \t \n",
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"1.0 \t 1000.0 \t \t \t \n",
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"2.0 \t \t \t \t \n",
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"3.0 \t \t \t \t 800.0\n",
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"4.0 \t \t \t \t 200.0\n",
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"5.0 \t \t \t \t \n",
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"6.0 \t \t \t \t \n",
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"7.0 \t \t \t \t \n",
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"8.0 \t \t \t \t \n",
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"9.0 \t \t \t \t \n",
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"10.0 \t \t \t \t \n"
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]
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}
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],
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"source": [
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"for row_index in range(1,20): #reading first columns\n",
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" RsR0, H2, LPG , CH4, CO = sheetMQ5.row_values(row_index, start_colx=0, end_colx=5)\n",
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" print(RsR0, \"\t\", H2, \"\t\", LPG, \"\t\", CH4, \"\t\", CO)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_MQ5 = sheetMQ5.col_values(0)[2:]\n",
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"MQ5_H2 = sheetMQ5.col_values(1)[2:]\n",
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"MQ5_LPG = sheetMQ5.col_values(2)[2:]\n",
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"MQ5_CH4 = sheetMQ5.col_values(3)[2:]\n",
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"MQ5_CO = sheetMQ5.col_values(4)[2:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def zero_to_nan(values):\n",
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" \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n",
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" return [float('nan') if x==0 else x for x in values]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"MQ5_H2 =zero_to_nan(MQ5_H2)\n",
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"MQ5_LPG =zero_to_nan(MQ5_LPG)\n",
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"MQ5_CH4 =zero_to_nan(MQ5_CH4)\n",
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"MQ5_CO =zero_to_nan(MQ5_CO)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.datasets import load_iris\n",
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"#from sklearn.cross_validation import train_test_split\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn import datasets\n",
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"from sklearn import linear_model\n",
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"\n",
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"dataH2 = {'RsRo': x_MQ5, 'H2': MQ5_H2}\n",
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"dataLPG = {'RsRo': x_MQ5, 'LPG': MQ5_LPG}\n",
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"dataCH4 = {'RsRo': x_MQ5, 'CH4': MQ5_CH4}\n",
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"dataCO = {'RsRo': x_MQ5, 'CO': MQ5_CO}\n",
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"\n",
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"dfMQ5_H2 = pd.DataFrame(dataH2)\n",
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"dfMQ5_LPG = pd.DataFrame(dataLPG)\n",
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"dfMQ5_CH4 = pd.DataFrame(dataCH4)\n",
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"dfMQ5_CO = pd.DataFrame(dataCO)\n",
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"\n",
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"dfMQ5_H2['H2'] = pd.to_numeric(dfMQ5_H2['H2'])\n",
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"dfMQ5_LPG['LPG'] = pd.to_numeric(dfMQ5_LPG['LPG'])\n",
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"dfMQ5_CH4['CH4'] = pd.to_numeric(dfMQ5_CH4['CH4'])\n",
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"dfMQ5_CO['CO'] = pd.to_numeric(dfMQ5_CO['CO'])\n",
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"\n",
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"dfMQ5_H2['H2'] = dfMQ5_H2['H2'].replace('',None, regex=True)\n",
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"dfMQ5_LPG['LPG'] = dfMQ5_LPG['LPG'].replace('',None, regex=True)\n",
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"dfMQ5_CH4['CH4'] = dfMQ5_CH4['CH4'].replace('',None, regex=True)\n",
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"dfMQ5_CO['CO'] = dfMQ5_CO['CO'].replace('',None, regex=True)\n",
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"\n",
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"#Global X_Predict variable\n",
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"X_Predict = dfMQ5_LPG.RsRo.apply(lambda x: [x]).tolist()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Model and train H2\n",
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"dataset2TrainH2 = dfMQ5_H2.copy()\n",
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"dataset2TrainH2.dropna(inplace=True)\n",
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"X_trainH2 = dataset2TrainH2.RsRo.apply(lambda x: [x]).tolist()\n",
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"y_trainH2 = dataset2TrainH2['H2'].tolist()\n",
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"model = linear_model.Lasso(alpha=0.1)\n",
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"model.fit(X_trainH2, y_trainH2)\n",
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"#Predict\n",
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"H2_Predicted = model.predict(X_Predict)\n",
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"#save into MQ2\n",
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"MQ5_H2 = H2_Predicted\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Model and train LPG\n",
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"dataset2TrainLPG = dfMQ5_LPG.copy()\n",
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"dataset2TrainLPG.dropna(inplace=True)\n",
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"X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n",
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"y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n",
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"model = linear_model.Lasso(alpha=0.1)\n",
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"model.fit(X_trainLPG, y_trainLPG)\n",
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"#Predict\n",
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"LPG_Predicted = model.predict(X_Predict)\n",
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"#save into MQ2\n",
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"MQ5_LPG = LPG_Predicted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Model and train CH4\n",
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"dataset2TrainCH4 = dfMQ5_CH4.copy()\n",
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"dataset2TrainCH4.dropna(inplace=True)\n",
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"X_trainCH4 = dataset2TrainCH4.RsRo.apply(lambda x: [x]).tolist()\n",
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"y_trainCH4 = dataset2TrainCH4['CH4'].tolist()\n",
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"model = linear_model.Lasso(alpha=0.1)\n",
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"model.fit(X_trainCH4, y_trainCH4)\n",
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"#Predict\n",
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"CH4_Predicted = model.predict(X_Predict)\n",
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"#save into MQ2\n",
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"MQ5_CH4 = CH4_Predicted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Model and train CO\n",
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"dataset2TrainCO = dfMQ5_CO.copy()\n",
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"dataset2TrainCO.dropna(inplace=True)\n",
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"X_trainCO = dataset2TrainCO.RsRo.apply(lambda x: [x]).tolist()\n",
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"y_trainCO = dataset2TrainCO['CO'].tolist()\n",
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"model = linear_model.Lasso(alpha=0.1)\n",
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"model.fit(X_trainCO, y_trainCO)\n",
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"#Predict\n",
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"CO_Predicted = model.predict(X_Predict)\n",
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"#save into MQ2\n",
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"MQ5_CO = CO_Predicted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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"source": [
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"%config InlineBackend.figure_formats = ['svg']\n",
|
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"%matplotlib inline\n",
|
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"import matplotlib.pyplot as plt\n",
|
|
"import matplotlib.lines as mlines\n",
|
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"import matplotlib.transforms as mtransforms\n",
|
|
"\n",
|
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"fig, ax = plt.subplots()\n",
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"\n",
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"fig.set_size_inches(9, 5.5, forward=True)\n",
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"\n",
|
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"# only these two lines are calibration curves\n",
|
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"plt.plot(MQ5_H2, x_MQ5, marker='o', linewidth=1, label='H2')\n",
|
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"plt.plot(MQ5_LPG, x_MQ5, marker='o', linewidth=1, label='LPG')\n",
|
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"plt.plot(MQ5_CH4, x_MQ5, marker='o', linewidth=1, label='CH4')\n",
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"plt.plot(MQ5_CO, x_MQ5, marker='o', linewidth=1, label='CO')\n",
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"\n",
|
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"\n",
|
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"# reference line, legends, and axis labels\n",
|
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|
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"#line.set_transform(transform)\n",
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"plt.legend()\n",
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"\n",
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"plt.grid(b=True, which='minor', color='lightgrey', linestyle='--')\n",
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"\n",
|
|
"fig.suptitle('Calibration plot for MQ-5 data')\n",
|
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"ax.set_xlabel('PPM Concentration')\n",
|
|
"ax.set_ylabel('Rs/Ro')\n",
|
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"\n",
|
|
"\n",
|
|
"#Save image\n",
|
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"plt.savefig('MQ5.svg', format = 'svg', dpi = 1200)\n",
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"plt.savefig('MQ5.png')\n",
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"plt.savefig('MQ5.eps', format = 'eps', dpi = 1200)"
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]
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},
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