# -*- coding: utf-8 -*-
"""
Created on Fri Mar  3 12:12:35 2023

@author: BorowyP
"""

import pandas as pd
import numpy as np
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

pn.extension(sizing_mode="stretch_width")

PALETTE = ["#ff6f69", "#ffcc5c", "#88d8b0", ]
from bokeh.models.formatters import DatetimeTickFormatter
formatter = DatetimeTickFormatter(months='%b %Y') # wird in .hvplot benötigt für x-achse!

air_hum = pd.read_csv(r'df_air_hum.csv', sep=',',
                       header=0, 
                       #skiprows=[1], 
                       decimal=',', 
                       na_values=('#-INF', '#NAN'))

air_hum.index = pd.to_datetime(air_hum['Date'], format='%Y.%m.%d %H:%M:%S')
#air_hum.index.names = ['Date']
air_hum = air_hum.drop(['Date'], axis=1)
air_hum['hum'] = air_hum['hum'].astype(np.float64, copy=True, errors='ignore')

air_hum = air_hum.round(1)



air_date_slider = pn.widgets.DateRangeSlider(name='Date', start=air_hum.index.min(), end=air_hum.index.max())
Stndrt = pn.widgets.RadioButtonGroup(name='Standort', options=['Glienig', 'Groß Liebitz', 'Krausnick', 'Halbe', 'Spreeau', 'Hangelsberg'],button_type='success')
air_date_slider

air_hum_inter = air_hum.interactive()

air_hum_inter = (
    air_hum_inter[
        (air_hum_inter.Standort == Stndrt) &
        (air_hum_inter.index >= air_date_slider.param.value_start) &
        (air_hum_inter.index <= air_date_slider.param.value_end)
 
    ])

def lin_reg_hum(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,
                           'Luftfeuchte' : 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=['Luftfeuchte', 'Lineare Regression'],
                                    ylabel='rel Luftfeuchte [%]', 
                                    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_hum(air_hum_inter):
    y = air_hum_inter.hum
    x = air_hum_inter['Unnamed: 0']
    return pn.Column(lin_reg_hum(x,y, air_hum_inter.index))


airhumplot = air_hum_inter.pipe(callback_hum)

airhumplot


hum_glienig = pd.DataFrame({'Glienig': air_hum.loc[air_hum['Standort'] == 'Glienig']['hum']}, 
                           index = air_hum.loc[air_hum['Standort'] == 'Glienig'].index)
hum_grlieb = pd.DataFrame({'Groß Liebitz': air_hum.loc[air_hum['Standort'] == 'Groß Liebitz']['hum']}, 
                          index = air_hum.loc[air_hum['Standort'] == 'Groß Liebitz'].index)
hum_halbe = pd.DataFrame({'Halbe' : air_hum.loc[air_hum['Standort'] == 'Halbe']['hum']}, 
                         index= air_hum.loc[air_hum['Standort'] == 'Halbe'].index)
hum_hberg = pd.DataFrame({'Hangelsberg' : air_hum.loc[air_hum['Standort'] == 'Hangelsberg']['hum']}, 
                         index= air_hum.loc[air_hum['Standort'] == 'Hangelsberg'].index)
hum_kraunick = pd.DataFrame({'Krausnick' : air_hum.loc[air_hum['Standort'] == 'Krausnick']['hum']}, 
                            index= air_hum.loc[air_hum['Standort'] == 'Krausnick'].index)
hum_spreeau = pd.DataFrame({'Spreeau' : air_hum.loc[air_hum['Standort'] == 'Spreeau']['hum']}, 
                           index= air_hum.loc[air_hum['Standort'] == 'Spreeau'].index)
air_hum_hist = pd.concat([hum_glienig,hum_grlieb,hum_halbe,hum_hberg,hum_kraunick,hum_spreeau])




dfi_hum = air_hum_hist.interactive()

filtered = dfi_hum[
    (dfi_hum.index >= air_date_slider.param.value_start) &
    (dfi_hum.index <= air_date_slider.param.value_end)]

plot_air_humhist  = filtered.hvplot(y=['Glienig',
                                       'Groß Liebitz', 
                                       'Halbe', 
                                       'Hangelsberg',
                                       'Krausnick',
                                       'Spreeau'],   
                                    kind='hist', 
                                    responsive=True, 
                                    min_height=300, 
                                    xlabel='rel Luftfeuchte[%]',
                                    alpha=0.5)

plot_air_humhist

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
            ], 
    main=[pn.pane.Markdown("## Relative Luftfeuchte"),         
          air_date_slider,         
          airhumplot.panel(),
          plot_air_humhist # LUFT         
    #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 20230303_air_hum_docker.ipynb --autoreload