# -*- coding: utf-8 -*- """ Created on Tue Mar 21 10:44:01 2023 @author: BorowyP """ import pandas as pd import hvplot.pandas # Adds .hvplot and .interactive methods to Pandas dataframes 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", ] ACCENT_BASE_COLOR = PALETTE[0] #import time #start = time.time() #print('load data') import numpy as np from math import pi from bokeh.palettes import Category20c, Category20 from bokeh.plotting import figure from bokeh.transform import cumsum from bokeh.layouts import row from bokeh.models.formatters import DatetimeTickFormatter formatter = DatetimeTickFormatter(months='%b %Y') # wird in .hvplot benötigt für x-achse! air_temp = pd.read_csv(r'df_air_temp.csv', sep=',', header=0, #skiprows=[1], decimal=',', #dtype={'temp': np.float64}, na_values=('#-INF', '#NAN')) air_temp.index = pd.to_datetime(air_temp['Date'], format='%Y-%m-%d %H:%M:%S') air_temp = air_temp.drop(['Date'], axis=1) air_temp['temp'] = air_temp['temp'].astype(np.float64, copy=True, errors='ignore') air_temp = air_temp.round(1) air_date_slider = pn.widgets.DateRangeSlider(name='Date', start=air_temp.index.min(), end=air_temp.index.max()) Stndrt = pn.widgets.RadioButtonGroup(name='Standort', options=['Glienig', 'Groß Liebitz', 'Krausnick', 'Halbe', 'Spreeau', 'Hangelsberg'],button_type='success') air_temp_inter = air_temp.interactive() air_temp_inter = ( air_temp_inter[ (air_temp_inter.Standort == Stndrt) & (air_temp_inter.index >= air_date_slider.param.value_start) & (air_temp_inter.index <= air_date_slider.param.value_end) ]) def lin_reg(dfx,dfy, date): # Formel für regres-gerade: y= alpha + b * x # https://www.crashkurs-statistik.de/einfache-lineare-regression/ lin_df = pd.DataFrame({'Date' : date, 'Temperatur' : dfy, 'x-x.mean' : dfx-dfx.mean(), 'y-y.mean' : dfy-dfy.mean(), '(x-x.mean) * (y-y.mean)': (dfx-dfx.mean()) * (dfy-dfy.mean()), '(x-x.mean)²' : (dfx-dfx.mean()) * (dfx-dfx.mean()),}) b = lin_df['(x-x.mean) * (y-y.mean)'].sum()/ lin_df['(x-x.mean)²'].sum() alpha = dfy.mean() - b * dfx.mean() lin_df['Lineare Regression'] = round(alpha + b * dfx,2) lin_plot = lin_df.hvplot(x='Date', xlabel='Datum', title=Stndrt, y=['Temperatur', 'Lineare Regression'], ylabel='Lufttemperatur [°C]', color=PALETTE, line_width=0.5, xformatter=formatter) SQE = ((lin_df['Lineare Regression']-dfy.mean())*(lin_df['Lineare Regression']-dfy.mean())).sum() SQT = (lin_df['y-y.mean'] * lin_df['y-y.mean']).sum() R_Wert = round(SQE/SQT,2) mean = round(dfy.mean(),2) median = dfy.median() maxm = dfy.max() minm = dfy.min() anz = dfy.count() monitor_df = pd.DataFrame({'Standort' : [Stndrt.value], 'von' : [air_date_slider.value[0]], 'bis' : [air_date_slider.value[1]], 'Mittelwert' : [mean], 'Median' : [median], 'Maximum' : [maxm], 'Minimum' : [minm], 'Anzahl' : [anz], 'R²' : [R_Wert] }) return pn.Column(lin_plot, monitor_df) def callback(air_temp_inter): y = air_temp_inter.temp x = air_temp_inter['Unnamed: 0'] return pn.Column(lin_reg(x,y, air_temp_inter.index)) airtempplot = air_temp_inter.pipe(callback) temp_glienig = pd.DataFrame({'Glienig': air_temp.loc[air_temp['Standort'] == 'Glienig']['temp']}, index = air_temp.loc[air_temp['Standort'] == 'Glienig'].index) temp_grlieb = pd.DataFrame({'Groß Liebitz': air_temp.loc[air_temp['Standort'] == 'Groß Liebitz']['temp']}, index = air_temp.loc[air_temp['Standort'] == 'Groß Liebitz'].index) temp_halbe = pd.DataFrame({'Halbe' : air_temp.loc[air_temp['Standort'] == 'Halbe']['temp']}, index= air_temp.loc[air_temp['Standort'] == 'Halbe'].index) temp_hberg = pd.DataFrame({'Hangelsberg' : air_temp.loc[air_temp['Standort'] == 'Hangelsberg']['temp']}, index= air_temp.loc[air_temp['Standort'] == 'Hangelsberg'].index) temp_krausnick = pd.DataFrame({'Krausnick' : air_temp.loc[air_temp['Standort'] == 'Krausnick']['temp']}, index= air_temp.loc[air_temp['Standort'] == 'Krausnick'].index) temp_spreeau = pd.DataFrame({'Spreeau' : air_temp.loc[air_temp['Standort'] == 'Spreeau']['temp']}, index= air_temp.loc[air_temp['Standort'] == 'Spreeau'].index) air_temp_hist = pd.concat([temp_glienig,temp_grlieb,temp_halbe,temp_hberg,temp_krausnick,temp_spreeau]) dfi_temp = air_temp_hist.interactive() filtered = dfi_temp[ (dfi_temp.index >= air_date_slider.param.value_start) & (dfi_temp.index <= air_date_slider.param.value_end)] plot_air_temphist = filtered.hvplot(y=['Glienig', 'Groß Liebitz', 'Halbe', 'Hangelsberg', 'Krausnick', 'Spreeau'], kind='hist', responsive=True, min_height=300, xlabel='Lufttemperatur [°C]', alpha=0.5) standort_label = pn.pane.Markdown('### Standort') standort_col = pn.Column(standort_label, Stndrt) import os 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) sidebar_link_list = pn.pane.HTML( '''

Startseite

Lufttemperatur

Luftfeuchte

Niederschlag

Bodentemperatur

Bodenfeuchte

Hemisfere

Streufall

Masseverlust

''') sidebar_menu = pn.Column(hd_logo, pn.pane.Markdown("## Menu"), sidebar_link_list, lfe_logo, fnr_logo ) template = pn.template.FastListTemplate( title='Holzdeko Lufttemperatur', sidebar=[sidebar_menu ], main=[pn.pane.Markdown("## Luft Temperaturdaten"), standort_col, air_date_slider, airtempplot.panel(), plot_air_temphist, ], accent_base_color="#00613a", header_background="#00613a" ) 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 20230224_air_temp_docker.ipynb --autoreload