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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Tue Mar 7 12:04:33 2023 | |
| @author: BorowyP | |
| """ | |
| import pandas as pd | |
| import hvplot.pandas # Adds .hvplot and .interactive methods to Pandas dataframes | |
| from hvplot.plotting import scatter_matrix | |
| import panel as pn # Panel is a simple, flexible and enterprise-ready data app framework | |
| import holoviews as hv | |
| from holoviews.operation.timeseries import rolling, rolling_outlier_std | |
| hv.extension('bokeh') | |
| #pn.extension('tabulator') | |
| pn.extension(sizing_mode="stretch_width") | |
| #pd.set_option("precision", 0) | |
| PALETTE = ["#ff6f69", "#ffcc5c", "#88d8b0", ] | |
| import numpy as np | |
| from bokeh.models.formatters import DatetimeTickFormatter | |
| formatter = DatetimeTickFormatter(months='%b %Y') # wird in .hvplot benötigt für x-achse! | |
| soil_hum = pd.read_csv('df_soil_hum.csv',header=0,sep=',') | |
| soil_hum.index = pd.to_datetime(soil_hum['Date'], format='%Y.%m.%d %H:%M:%S') | |
| soil_hum.index.names = ['Date'] | |
| soil_hum = soil_hum.drop(['Date'], axis=1) | |
| soil_hum = soil_hum.drop(['Unnamed: 0'], axis=1) | |
| soil_hum[soil_hum.columns[list(range(6))]] = soil_hum[soil_hum.columns[list(range(6))]].round(1) | |
| soil_hum = soil_hum.rename(columns={'Prof100' : '100', | |
| 'Prof200' : '200', | |
| 'Prof300' : '300', | |
| 'Prof400' : '400', | |
| 'Prof600' : '600' , | |
| 'Prof1000' : '1000', | |
| 'Standort' : 'Standort'}) | |
| Stndrt = pn.widgets.RadioButtonGroup(name='Standort', options=['Glienig', 'Groß Liebitz', 'Krausnick', 'Halbe', 'Spreeau', 'Hangelsberg'],button_type='success') | |
| soilhumbtn = pn.widgets.ToggleGroup( | |
| name='soilhumidity', | |
| options=['100', '200', '300', '400', '600', '1000'], | |
| value=['100'], | |
| button_type='success') | |
| soil_date_slider = pn.widgets.DateRangeSlider(name='Date', start=soil_hum.index.min(), end=soil_hum.index.max()) | |
| soil_hum_i = soil_hum.interactive() | |
| soil_hum_i = ( soil_hum_i[ | |
| (soil_hum_i.Standort == Stndrt) & | |
| (soil_hum_i.index >= soil_date_slider.param.value_start) & | |
| (soil_hum_i.index <= soil_date_slider.param.value_end) ]) | |
| soilhumplot = soil_hum_i.hvplot(x='Date',xlabel='Datum',title=Stndrt, y=soilhumbtn, color=PALETTE, line_width=1, xformatter=formatter, ylabel='Bodenfeuchte[%]') | |
| def callback_hum(soil_hum_i): | |
| df = soil_hum_i | |
| monitor = pd.DataFrame({#'Standort' : [Stndrt.value, Stndrt.value], | |
| #'von' : soil_temp_i.index.min(), | |
| #'bis' : soil_temp_i.index.max(), | |
| 'Mittelwert' : [df['100'].mean(), | |
| df['200'].mean(), | |
| df['300'].mean(), | |
| df['400'].mean(), | |
| df['600'].mean(), | |
| df['1000'].mean(), | |
| ] , | |
| 'Median' : [df['100'].median(), | |
| df['200'].median(), | |
| df['300'].median(), | |
| df['400'].median(), | |
| df['600'].median(), | |
| df['1000'].median(), | |
| ] , | |
| 'Maximum' : [df['100'].max(), | |
| df['200'].max(), | |
| df['300'].max(), | |
| df['400'].max(), | |
| df['600'].max(), | |
| df['1000'].max()], | |
| 'Minimum' : [df['100'].min(), | |
| df['200'].min(), | |
| df['300'].min(), | |
| df['400'].min(), | |
| df['600'].min(), | |
| df['1000'].min()], | |
| 'Anzahl' : [df['100'].count(), | |
| df['200'].count(), | |
| df['300'].count(), | |
| df['400'].count(), | |
| df['600'].count(), | |
| df['1000'].count()] | |
| }, index=['100', | |
| '200', | |
| '300', | |
| '400', | |
| '600', | |
| '1000', | |
| ]) | |
| monitor.index.name='Profiltiefe' | |
| #soiltemp_scatter = scatter_matrix(soil_temp, alpha=0.2) #Scattermatrix zum Vergleich der einzelnen Profiltiefen??? | |
| soiltemp_scatter_df = soil_hum[soil_hum['Standort'] == Stndrt.value]#.iloc[df.index.min(): df.index.max()] | |
| soiltemp_scatter = scatter_matrix(soiltemp_scatter_df, alpha=0.2) | |
| scatter_headline = pn.pane.Markdown(f"## Scattermatrix Bodenfeuchte **{Stndrt.value}**") | |
| scatter_discription = pn.pane.Markdown('scatter_matrix shows all the pairwise relationships between the columns of your data. Each non-diagonal entry plots the corresponding columns against another, while the diagonal plot shows the distribution of the data within each individual column.' | |
| ) | |
| monitor_gesamt = pd.DataFrame({'Standort' : [Stndrt.value], | |
| 'von' : df.index.min(), | |
| 'bis' : df.index.max(), | |
| 'Mittelwert' : df.mean(numeric_only=True).mean(), | |
| 'Median' : df.median(numeric_only=True).median(), | |
| 'Maximum' : df.max(numeric_only=True).max(), | |
| 'Minimum' : df.min(numeric_only=True).min() | |
| }, index=['Gesamt']) | |
| return pn.Column(monitor_gesamt, monitor,scatter_headline, soiltemp_scatter, scatter_discription) | |
| callback = soil_hum_i.pipe(callback_hum) | |
| callback | |
| hd_logo = pn.pane.PNG('HD_Logo.png', width=100) | |
| hd_logo | |
| lfe_logo = pn.pane.PNG('LFE_Logo.png', width=100) | |
| fnr_logo = pn.pane.PNG('fnr_logo.png', width=100) | |
| template = pn.template.FastListTemplate( | |
| title='Holzdeko Dashboard', | |
| sidebar=[hd_logo, | |
| pn.pane.Markdown("## Einstellungen"), | |
| 'Standort',Stndrt, | |
| lfe_logo, | |
| fnr_logo | |
| #'Boden', | |
| #'Gekalkt/ungekalkt', yaxis, | |
| ], | |
| main=[pn.pane.Markdown("## Bodenfeuchte"), | |
| soilhumbtn, | |
| soil_date_slider, | |
| soilhumplot.panel(), | |
| callback | |
| #accent_base_color="#88d8b0", | |
| #header_background="#88d8b0", | |
| ]) | |
| template.servable(); | |
| #print('fertig!') | |
| # To launch this dashboard as a web server, we can simply run | |
| # cd C:\Users\BorowyP\Desktop\Dashboard-Preasi\soil_air\ | |
| # panel serve 20230307_soil_hum_docker.ipynb --autoreload |