# -*- coding: utf-8 -*-
"""
Created on Fri Mar  3 12:57:34 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_temp = pd.read_csv('df_soil_temp.csv',header=0,sep=',')

soil_temp.index = pd.to_datetime(soil_temp['Date'], format='%Y.%m.%d %H:%M:%S')
soil_temp.index.names = ['Date']
soil_temp = soil_temp.drop(['Date'], axis=1)
soil_temp = soil_temp.drop(['Unnamed: 0'], axis=1)
soil_temp[soil_temp.columns[list(range(6))]] = soil_temp[soil_temp.columns[list(range(6))]].round(1) #RUNDEN auf 1 Kommastelle fuer bessere Perfomance!!!

soil_temp = soil_temp.rename(columns={'Temp05' : '50', 
                             'Temp10' : '100', 
                             'Temp20' : '200', 
                             'Temp30' : '300', 
                             'Temp50' : '500' , 
                             'Temp100' : '1000', 
                             'Standort' : 'Standort'})



Stndrt = pn.widgets.RadioButtonGroup(name='Standort', options=['Glienig', 'Groß Liebitz', 'Krausnick', 'Halbe', 'Spreeau', 'Hangelsberg'],button_type='success')

soiltempbtn = pn.widgets.ToggleGroup(
    name='soiltemperature',
    options=['50', '100', '200', '300', '500', '1000'], 
    value=['50'],
    button_type='success')

soil_date_slider = pn.widgets.DateRangeSlider(name='Date', start=soil_temp.index.min(), end=soil_temp.index.max())



soil_temp_i= soil_temp.interactive()

soil_temp_i = ( 
    soil_temp_i[
        (soil_temp_i.Standort == Stndrt) &
    (soil_temp_i.index >= soil_date_slider.param.value_start) &
    (soil_temp_i.index <= soil_date_slider.param.value_end)
                      ]
           )

soiltempplot = soil_temp_i.hvplot(x='Date',xlabel='Datum',title=Stndrt, y=soiltempbtn, color=PALETTE, line_width=1, xformatter=formatter, ylabel='Bodentemperatur [°C]')



def callback_temp(soil_temp_i):
    
    df = soil_temp_i
    
    monitor = pd.DataFrame({#'Standort' :  [Stndrt.value, Stndrt.value],
                           #'von' : soil_temp_i.index.min(),
                           #'bis' : soil_temp_i.index.max(),
                            'Mittelwert' : [df['50'].mean(), 
                                            df['100'].mean(), 
                                            df['200'].mean(),
                                            df['300'].mean(),
                                            df['500'].mean(),
                                            df['1000'].mean(),
                                           ] ,
                            'Median' : [df['50'].median(), 
                                            df['100'].median(), 
                                            df['200'].median(),
                                            df['300'].median(),
                                            df['500'].median(),
                                            df['1000'].median(),
                                       ] , 
        'Maximum' : [df['50'].max(), 
                                            df['100'].max(), 
                                            df['200'].max(),
                                            df['300'].max(),
                                            df['500'].max(),
                                            df['1000'].max()], 
        'Minimum' : [df['50'].min(), 
                                            df['100'].min(), 
                                            df['200'].min(),
                                            df['300'].min(),
                                            df['500'].min(),
                                            df['1000'].min()], 
        'Anzahl' : [df['50'].count(), 
                                            df['100'].count(), 
                                            df['200'].count(),
                                            df['300'].count(),
                                            df['500'].count(),
                                            df['1000'].count()]
                           
                           }, index=['50', 
                                     '100', 
                                     '200', 
                                     '300', 
                                     '500', 
                                     '1000', 
                                     
                                    ])
    monitor.index.name='Profiltiefe'
    
    #soiltemp_scatter = scatter_matrix(soil_temp, alpha=0.2) #Scattermatrix zum Vergleich der einzelnen Profiltiefen???
    
    soiltemp_scatter_df = soil_temp[soil_temp['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 **{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_temp_i.pipe(callback_temp)

callback

hd_logo = pn.pane.PNG('HD_Logo.png', width=100)
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
             
             
            ], 
    main=[pn.pane.Markdown("## Bodentemperatur"),
          soiltempbtn,
          soil_date_slider,
          soiltempplot.panel(),
          callback.panel(),
          
          
          
          
                
          
    #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_temp_docker.ipynb --autoreload