alexey.zholtikov/MQSensorsLib
1
0
Fork 0
mirror of https://github.com/miguel5612/MQSensorsLib.git synced 2025-03-15 05:17:30 +03:00
Code Issues Packages Projects Releases Wiki Activity

Fixed MQ309A

Browse Source
This commit is contained in:
miguel5612 2019-07-03 20:23:49 -05:00
parent 57cb7bf3f9
commit 30130db55e
6 changed files with 4846 additions and 1346 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2077
Experiments/.ipynb_checkpoints/MQ303_Regression-checkpoint.ipynb Normal file
View File

File diff suppressed because it is too large Load Diff

2077
Experiments/MQ303_Regression.ipynb Normal file
View File

File diff suppressed because it is too large Load Diff

1048
Experiments/MQ309A.eps
View File

File diff suppressed because it is too large Load Diff

BIN
Experiments/MQ309A.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 136 KiB

After

Width:  |  Height:  |  Size: 126 KiB

938
Experiments/MQ309A.svg
View File

File diff suppressed because it is too large Load Diff
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 59 KiB

After

Width:  |  Height:  |  Size: 51 KiB

52
Experiments/MQ309_Regression.ipynb
View File

@ -98,20 +98,20 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"x_MQ309A = sheetMQ3.col_values(0)[2:]\n",
"MQ5_CH4 = sheetMQ3.col_values(1)[2:]\n",
"MQ5_CO = sheetMQ3.col_values(2)[2:]\n",
"MQ5_H2 = sheetMQ3.col_values(3)[2:]\n",
"MQ5_Alcohol = sheetMQ3.col_values(4)[2:]"
"x_MQ309A = sheetMQ309A.col_values(0)[2:]\n",
"MQ309A_CH4 = sheetMQ309A.col_values(1)[2:]\n",
"MQ309A_CO = sheetMQ309A.col_values(2)[2:]\n",
"MQ309A_H2 = sheetMQ309A.col_values(3)[2:]\n",
"MQ309A_Alcohol = sheetMQ309A.col_values(4)[2:]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@ -122,23 +122,39 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"MQ5_CH4 =zero_to_nan(MQ5_CH4)\n",
"MQ5_CO =zero_to_nan(MQ5_CO)\n",
"MQ5_H2 =zero_to_nan(MQ5_H2)\n",
"MQ5_Alcohol =zero_to_nan(MQ5_Alcohol)"
"MQ309A_CH4 =zero_to_nan(MQ309A_CH4)\n",
"MQ309A_CO =zero_to_nan(MQ309A_CO)\n",
"MQ309A_H2 =zero_to_nan(MQ309A_H2)\n",
"MQ309A_Alcohol =zero_to_nan(MQ309A_Alcohol)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [],
"outputs": [
{
"ename": "ValueError",
"evalue": "arrays must all be same length",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-11fdc20bbac0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mdataLPG\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'RsRo'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx_MQ309\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Alcohol'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mMQ309A_Alcohol\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mdfMQ309A_CH4\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataCH4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 15\u001b[0m \u001b[0mdfMQ309A_CO\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataCO\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mdfMQ309A_H2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataH2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 390\u001b[0m dtype=dtype, copy=copy)\n\u001b[0;32m 391\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 392\u001b[1;33m \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 393\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 394\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m 210\u001b[0m \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 52\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 315\u001b[0m \u001b[0mlengths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 316\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'arrays must all be same length'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 318\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 319\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: arrays must all be same length"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
@ -148,10 +164,10 @@
"from sklearn import datasets\n",
"from sklearn import linear_model\n",
"\n",
"dataCH4 = {'RsRo': x_MQ309, 'CH4': MQ5_CH4}\n",
"dataCO = {'RsRo': x_MQ309, 'CO': MQ5_CO}\n",
"dataH2 = {'RsRo': x_MQ309, 'H2': MQ5_H2}\n",
"dataLPG = {'RsRo': x_MQ309, 'Alcohol': MQ5_Alcohol}\n",
"dataCH4 = {'RsRo': x_MQ309, 'CH4': MQ309A_CH4}\n",
"dataCO = {'RsRo': x_MQ309, 'CO': MQ309A_CO}\n",
"dataH2 = {'RsRo': x_MQ309, 'H2': MQ309A_H2}\n",
"dataLPG = {'RsRo': x_MQ309, 'Alcohol': MQ309A_Alcohol}\n",
"\n",
"dfMQ309A_CH4 = pd.DataFrame(dataCH4)\n",
"dfMQ309A_CO = pd.DataFrame(dataCO)\n",