diff --git a/LESO_EIB_KNX_v3.ipynb b/LESO_EIB_KNX_v3.ipynb new file mode 100644 index 0000000..11bbdaa --- /dev/null +++ b/LESO_EIB_KNX_v3.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import time\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as dates\n", + "%matplotlib inline \n", + "from datetime import datetime, timedelta" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#defining time format\n", + "formata = '%H:%M:%S'\n", + "formatb = '%Y-%m-%d %H:%M:%S.%f'\n", + "formatc = '%d-%m-%Y %H:%M:%S.%f'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ghi=pd.read_csv(\"AR496/global_irr_11042019.csv\", comment=',', header=\"infer\", parse_dates=[['Date','Time(hh:mm:ss)']], dayfirst=True, error_bad_lines=False, sep=';',keep_default_na=False,na_values=[' '],encoding = 'utf8')\n", + "#dhi=pd.read_csv(\"leso_data_25102019-08112019/diffuse_horizontal_irradiance.csv\", comment=',', header=\"infer\", parse_dates=[['Date','Time(hh:mm:ss)']], dayfirst=True, error_bad_lines=False, sep=';',keep_default_na=False,na_values=[' '],encoding = 'utf8')\n", + "temp=pd.read_csv(\"AR496/LESO_temperature_11_12_09_2018.csv\", comment=',', header=\"infer\", parse_dates=[['Date','Time(hh:mm:ss)']], dayfirst=True, error_bad_lines=False, sep=';',keep_default_na=False,na_values=[' '],encoding = 'utf8')\n", + "#wind=pd.read_csv(\"leso_data_25102019-08112019/wind_speed.csv\", comment=',', header=\"infer\", parse_dates=[['Date','Time(hh:mm:ss)']], dayfirst=True, error_bad_lines=False, sep=';',keep_default_na=False,na_values=[' '],encoding = 'utf8')" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#ghi=pd.read_csv(\"leso_data_25102019-08112019/global_horizontal_irradiance.csv\", comment=',', header=\"infer\", parse_dates=[['Date','Time(hh:mm:ss)']], dayfirst=True, error_bad_lines=False, sep=';',keep_default_na=False,na_values=[' '],encoding = 'utf8')" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#pd.to_datetime(ghi['Date'] + ' ' + ghi['Time(hh:mm:ss)'],format=formatc)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ghi['Date_Time(hh:mm:ss)']=pd.to_datetime(ghi['Date_Time(hh:mm:ss)'], format=formatb)\n", + "#dhi['Date_Time(hh:mm:ss)']=pd.to_datetime(dhi['Date_Time(hh:mm:ss)'], format=formatb)\n", + "temp['Date_Time(hh:mm:ss)']=pd.to_datetime(temp['Date_Time(hh:mm:ss)'], format=formatb)\n", + "#wind['Date_Time(hh:mm:ss)']=pd.to_datetime(wind['Date_Time(hh:mm:ss)'], format=formatb)\n", + "\n", + "ghi=ghi.set_index('Date_Time(hh:mm:ss)')#['2019-10-25 ': '2019-11-08 23:59:59']\n", + "#dhi=dhi.set_index('Date_Time(hh:mm:ss)')#['2019-10-25 00:00:00': '2019-11-08 23:59:59']\n", + "temp=temp.set_index('Date_Time(hh:mm:ss)')#['2019-10-25 00:00:00': '2019-11-08 23:59:59']\n", + "#wind=wind.set_index('Date_Time(hh:mm:ss)')#['2019-10-25 00:00:00': '2019-11-08 23:59:59']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ghi_resam=ghi['Decimal value'].resample('5min').mean()\n", + "#dhi_resam=dhi['Decimal value'].resample('5min').mean()\n", + "temp_resam=temp['Decimal value'].resample('5min').mean()\n", + "#wind_resam=wind['Decimal value'].resample('5min').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "temp_resam.to_csv('resampled_temp_11_12092018.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df=pd.DataFrame()\n", + "df['GHI']=ghi_resam\n", + "df['DHI']=dhi_resam\n", + "df['TEMP']=temp_resam\n", + "df['WIND']=wind_resam" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns (2,3,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + } + ], + "source": [ + "#Reading file from MoTUS for wind\n", + "wind_motus=pd.read_csv(\"leso_data_25102019-08112019/anem3.txt\", comment=',',header=None, error_bad_lines=False, sep=',',keep_default_na=False,na_values=[' '],names = [\"Q\", \"Dir\", \"U\", \"w\", \"M\", \"sonicsp\", \"sonictemp\",\"Time\",\"COM\"])\n", + "wind_motus=wind_motus[wind_motus.COM == 'COM14'] #removing bad lines\n", + "\n", + "formatmotus = '%d.%m.%Y %H:%M:%S'\n", + "\n", + "#Setting index and datetimes\n", + "wind_motus['Time']=pd.to_datetime(wind_motus['Time'], format=formatmotus)\n", + "wind_motus=wind_motus.set_index('Time')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use Series.infer_objects()\n", + "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], + "source": [ + "wind_motus['U']=wind_motus['U'].convert_objects(convert_numeric=True)\n", + "wind_motus_resam=wind_motus['U'].resample('5min').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plotting graph for temperatures from Station and from MoTUS\n", + "aaa=df['WIND']['2019-10-25': '2019-11-08']\n", + "#bbb=meas_list[9]['Wind temp. [°C]']\n", + "ccc=wind_motus_resam['2019-11-01': '2019-11-01']\n", + "\n", + "x=aaa.plot(label='toit')\n", + "#z=temp_motus['Temp.'].plot(label='motus')\n", + "#sonc=bbb.plot(label='mumimm-temp')\n", + "tag=ccc.plot(label='motus')\n", + "\n", + "plt.ylabel('Wind speed (ms-1)')\n", + "plt.xlabel('Time (Days)')\n", + "plt.legend(loc='best', bbox_to_anchor=(0.85, 0.25, 0.5, 0.5))\n", + "plt.tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='minor', # both major and minor ticks are affected\n", + "# bottom='True', # ticks along the bottom edge are off\n", + "# top='True', # ticks along the top edge are off\n", + " labelright='True') # labels along the bottom edge are off\n", + "\n", + "#Saving figures\n", + "\n", + "#plt.savefig('mumimm_tag_10.jpg', bbox_inches='tight',dpi=900)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df.to_csv('resampled_25102019_08112019.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/read.me b/read.me new file mode 100644 index 0000000..08c96ed --- /dev/null +++ b/read.me @@ -0,0 +1,2 @@ +This is a script for reading the data from the LESO-PB KNX network. +Please contact the LESO-PB / EPFL team for the data. \ No newline at end of file