# %%
# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""



from numpy import arange
import xarray as xr
import highspy
from linopy import Model, EQUAL
import pandas as pd
import plotly.express as px
import streamlit as st
import sourced as src
import numpy as np
import tempfile

## Setting
write_pickle_from_standard_excel = True


st.set_page_config(layout="wide")
# you can create columns to better manage the flow of your page
# this command makes 3 columns of equal width
col1, col2, col3, col4 = st.columns(4)
col1.header("Data Input")
col4.header("Download Results")

# Color dictionary for figures
color_dict = {'Biomasse': 'lightgreen',
              'Braunkohle': 'red',
              'Erdgas': 'orange',
              'Steinkohle': 'darkgrey',
              'Erdöl': 'brown',
              'Laufwasser': 'aquamarine',
              'Kernenergie': 'cyan',
              'PV': 'yellow',
              'WindOff': 'darkblue',
              'WindOn': 'blue',
              'Batteriespeicher': 'purple'}

# %%
with col1:
    with open('Input_Jahr_2021.xlsx', 'rb') as f:
        st.download_button('Download Excel Vorlage', f, file_name='Input_Jahr_2021.xlsx')  # Defaults to 'application/octet-stream'

#url_excel = r'Input_Jahr_2021.xlsx'
    url_excel = st.file_uploader(label = 'Excel Datei hochladen')


if url_excel == None:
    if write_pickle_from_standard_excel:
        url_excel = r'Input_Jahr_2021.xlsx'
        sets_dict, params_dict= src.load_data_from_excel(url_excel, write_to_pickle_flag= True)
    sets_dict, params_dict = src.load_from_pickle()
    with col4:
        st.write('Lauf mit Standarddaten')
else:
    # sets_dict, params_dict= src.load_data_from_excel(url_excel, load_from_pickle_flag = False)
    sets_dict, params_dict= src.load_data_from_excel(url_excel, write_to_pickle_flag = True)

    with col4:
        st.write('Lauf mit Nutzerdaten')

# Debugging output to verify that sets_dict is defined
# st.write(f"sets_dict: {sets_dict}")
# st.write(f"params_dict: {params_dict}")

# # %%

def timstep_aggregate(time_steps_aggregate, xr ):
    return xr.rolling( t = time_steps_aggregate).mean().sel(t = t[0::time_steps_aggregate])

#s_t_r_iRes = timstep_aggregate(6,s_t_r_iRes)



# %%
#sets_dict, params_dict= src.load_data_from_excel(url_excel,write_to_pickle_flag=True)

# %%



#sets_dict, params_dict= load_data_from_excel(url_excel, load_from_pickle_flag = False)


# Unpack sets_dict into the workspace
t = sets_dict['t']
t_original = sets_dict['t']
i = sets_dict['i']
iSto = sets_dict['iSto']
iConv = sets_dict['iConv']
# iPtG = sets_dict['iPtG']
iRes = sets_dict['iRes']
# iHyRes = sets_dict['iHyRes']

# Unpack params_dict into the workspace
l_co2 = params_dict['l_co2']
p_co2 = params_dict['p_co2']

eff_i = params_dict['eff_i']
life_i = params_dict['life_i']
c_fuel_i = params_dict['c_fuel_i']
c_other_i = params_dict['c_other_i']
c_inv_i = params_dict['c_inv_i']
co2_factor_i = params_dict['co2_factor_i']
c_var_i = params_dict['c_var_i']
K_0_i = params_dict['K_0_i']
e2p_iSto = params_dict['e2p_iSto']

# Sliders and input boxes for parameters
with col2:
    # Slider for CO2 limit [mio. t]
    l_co2 = st.slider(value=int(params_dict['l_co2']), min_value=0, max_value=750, label="CO2 Limit [Mio. t]", step=10)

    # # Slider for H2 price / usevalue [€/MWH_th]
    # price_h2 = st.slider(value=100, min_value=0, max_value=300, label="Wasserstoffpreis [€/MWh]", step=10)

    for i_idx in c_fuel_i.get_index('i'):
            if i_idx in ['Braunkohle']:
                c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Preis [€/MWh]' , step=10)

    dt = st.number_input(label="Zeitliche Auflösung [h]", min_value=1, max_value=len(t), value=6, help="Geben Sie nur ganze Zahlen zwischen 1 und 8760 (oder 8784 für Schaltjahre) ein.")

with col3:
    # Slider for CO2 limit [mio. t]
    for i_idx in c_fuel_i.get_index('i'):
            if i_idx in ['Steinkohle', 'Erdöl','Erdgas']:
                c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Preis [€/MWh]' , step=10)

    # technologies_invest = st.multiselect(label='Technologien für Investitionen', options=i, default=['Biomasse','Laufwasser','Kernenergie','Braunkohle','Steinkohle','Erdöl','Erdgas','WindOff','WindOn','PV','Batteriespeicher'])
    technologies_invest = st.multiselect(label='Technologien für Investitionen', options=i, default=['Kernenergie','Braunkohle','Steinkohle','Erdgas', 'Erdöl','Biomasse','Laufwasser','WindOn','WindOff','PV','Batteriespeicher'])
    technologies_no_invest = [x for x in i if x not in technologies_invest]

# Aggregate time series
D_t = timstep_aggregate(dt,params_dict['D_t'])
s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
# h_t = timstep_aggregate(dt,params_dict['h_t'])
t = D_t.get_index('t')
partial_year_factor = (8760/len(t))/dt



#time_steps_aggregate = 6
#= xr_profiles.rolling( time_step = time_steps_aggregate).mean().sel(time_step = time[0::time_steps_aggregate])
price_co2 = 0

# Aggregate time series
#D_t = timstep_aggregate(dt,params_dict['D_t'])
#s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
#h_t = timstep_aggregate(dt,params_dict['h_t'])
#t = D_t.get_index('t')
#partial_year_factor = (8760/len(t))/dt

#technologies_no_invest = st.multiselect(label='Technology invest', options=i)
#technologies_no_invest = ['Electrolyzer','Biomass','RoR','Hydro Water Reservoir','Nuclear']
# %%
### Variables
m = Model()

C_tot = m.add_variables(name = 'C_tot')     # Total costs
C_op = m.add_variables(name = 'C_op', lower = 0)       # Operational costs
C_inv = m.add_variables(name = 'C_inv', lower = 0)     # Investment costs

K = m.add_variables(coords = [i], name = 'K', lower = 0)          # Endogenous capacity
y = m.add_variables(coords = [t,i], name = 'y', lower = 0)          # Electricity production --> für Elektrolyseure ausschließen
y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen
l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level
w = m.add_variables(coords = [t], name = 'w', lower = 0)          
y_curt = m.add_variables(coords = [t,i], name = 'y_curt', lower = 0) # RES curtailment
# y_h2 = m.add_variables(coords = [t,i], name = 'y_h2', lower = 0)

## Objective function
C_tot = C_op + C_inv
m.add_objective(C_tot)

## Costs terms for objective function
# Operational costs (minus revenue for produced hydrogen)
# C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt - (y_h2.sel(i = iPtG) * price_h2).sum() * dt  == C_op, name = 'C_op_sum')
C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt  == C_op, name = 'C_op_sum')

# Investment costs
C_inv_sum = m.add_constraints((K * c_inv_i).sum() == C_inv, name = 'C_inv_sum')

## Load serving
loadserve_t = m.add_constraints((((y ).sum(dims = 'i') - y_ch.sum(dims = 'i')) * dt  == D_t.sel(t = t) * dt), name =  'load')
# loadserve_t = m.add_constraints((((y ).sum(dims = 'i') ) * dt  == D_t.sel(t = t) * dt), name =  'load')

## Maximum capacity limit
maxcap_i_t = m.add_constraints((y - K <= K_0_i), name = 'max_cap')

## Maximum capacity limit
maxcap_invest_i = m.add_constraints((K.sel(i = technologies_no_invest) <= 0), name = 'max_cap_invest')

## Prevent power production by PtG
# no_power_prod_iPtG_t = m.add_constraints((y.sel(i = iPtG) <= 0), name = 'prevent_ptg_prod')

## Maximum storage charging and discharging
maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) - y_ch.sel(i = iSto) - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')

## Maximum electrolyzer capacity
# ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG) <= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')

## PtG H2 production
# h2_prod_iPtG_t = m.add_constraints(y_ch.sel(i = iPtG) * eff_i.sel(i = iPtG) == y_h2.sel(i = iPtG), name = 'ptg_h2_prod')

## Infeed of renewables
infeed_iRes_t = m.add_constraints((y.sel(i = iRes) - s_t_r_iRes.sel(i = iRes).sel(t = t) * K.sel(i = iRes) + y_curt.sel(i = iRes) == s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')

## Maximum filling level restriction storage power plant
maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')

## Filling level restriction hydro reservoir
# filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')

## Filling level restriction other storages
filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) + (y.sel(i = iSto) / eff_i.sel(i = iSto)) * dt - y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')

## CO2 limit
# l_co2 = 50
CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000 , name = 'CO2_limit')

## set run-of-river power plants capacity limit to 5 GW
RoR_cap = m.add_constraints(K.sel(i = 'Laufwasser') <= 5000, name = 'RoR_cap')
Biomass_cap = m.add_constraints(K.sel(i = 'Biomasse') <= 9000, name = 'Biomass_cap')
# Nuclear_cap = m.add_constraints(K.sel(i = 'Kernenergie') <= 3000, name = 'Kernenergie_cap')
# nuclear_production_constraint = m.add_constraints(y.sel(i='Kernenergie') == K.sel(i='Kernenergie'), name='Nuclear_Production_Capacity')

# %%
m.solve(solver_name = 'highs')

st.markdown("---")

colb1, colb2 = st.columns(2)

# %%
#c_var_i.to_dataframe(name='VarCosts')
# %%
# Installed Cap
# Assuming df_excel has columns 'All' and 'Capacities'

fig = px.bar((m.solution['K']+K_0_i).to_dataframe(name='K').reset_index(), \
                y='i', x='K', orientation='h', title='Installierte Kapazitäten insgesamt [MW]', color='i')

#fig

# %%
total_costs = float(m.solution['C_inv'].values) + float(m.solution['C_op'].values)
total_costs_rounded = round(total_costs/1e9, 2)
df_total_costs = pd.DataFrame({'Total costs':[total_costs]})

with colb1:
    st.write('Gesamtkosten: ' + str(total_costs_rounded) + ' Mrd. €')

# %%
#df_Co2_price = pd.DataFrame({'CO2_Price: ':[float(m.constraints['CO2_limit'].dual.values) * (-1)]})
CO2_price = float(m.constraints['CO2_limit'].dual.values) * (-1)
CO2_price_rounded = round(CO2_price, 2)
df_CO2_price = pd.DataFrame({'CO2 price':[CO2_price]})

with colb2:
    #st.write(str(df_Co2_price))
    st.write('CO2 Preis: ' + str(CO2_price_rounded) + ' €/t')

# %%
df_new_capacities = m.solution['K'].to_dataframe().reset_index()
fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='Neu installierte Kapazitäten [MW]', color='i', color_discrete_map=color_dict)

with colb1:
    fig

# %%
D_t_sorted = D_t.sortby(D_t, ascending = False).to_dataframe().reset_index()
# NaN entries to the end
D_t_sorted = D_t_sorted.sort_values(by='Nachfrage', ascending=False).reset_index(drop=True)
# expand df_price to the size of t
D_t_sorted = D_t_sorted.loc[D_t_sorted.index.repeat(dt)].reset_index(drop=True)
x_loadcurve = np.arange(1, D_t_sorted['Nachfrage'].size + 1)

# residual load curve
df_production_res = m.solution['y'].sel(i = iRes).to_dataframe().reset_index()
# sum up over t
df_production_res_sum = df_production_res.groupby('t')['y'].sum().reset_index()
# D_t into dateframe
D_t_df = D_t.to_dataframe().reset_index()
df_residual = D_t_df['Nachfrage'] - df_production_res_sum['y']
# sort
df_residual = df_residual.sort_values(ascending=False).reset_index(drop=True)
df_residual = df_residual.loc[df_residual.index.repeat(dt)].reset_index(drop=True)

df_combined = pd.DataFrame({
    'x': np.concatenate([x_loadcurve, x_loadcurve]),
    'y': np.concatenate([D_t_sorted['Nachfrage'], df_residual]),
    'label': ['Nachfrage'] * len(x_loadcurve) + ['Residual Load'] * len(x_loadcurve)
})

# Create the integrated plot using Plotly Express
fig = px.line(df_combined, x='x', y='y', color='label', title='Lastdauerlinie [MW]',
              labels={"x": "Stunden im Jahr", "y": "Leistung [MW]"})

# Specific updates for each trace
fig.for_each_trace(
    lambda trace: trace.update(line=dict(color='blue')) if trace.name == 'Nachfrage' else trace.update(line=dict(color='red', dash='dash'))
)

with colb2:
    fig.update_layout(
    legend_title='Legende'
    )
    fig

# fig.show()
# %%
# calculate full load hours
i_with_capacity = m.solution['K'].where( m.solution['K'] > 0).dropna(dim = 'i').get_index('i')
df_production = m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index()
df_capacity = m.solution['K'].sel(i = i_with_capacity).to_dataframe().reset_index()
df_production_sum = (df_production.groupby('i')['y'].sum() * dt).round(0).reset_index()
# reorder rows according to i_with_capacity
df_production_sum = df_production_sum.set_index('i').loc[i_with_capacity].reset_index()
# df_production_sum['i'] = pd.Categorical(df_production_sum['i'], categories=desired_order, ordered=True)

df_fullload = df_production_sum['y']/df_capacity['K']
# to dataframe
df_fullload = df_fullload.to_frame()
# rename column
df_fullload.columns = ['fullload']
df_fullload['i'] = df_production_sum['i']
# change order of columns
df_fullload = df_fullload[['i', 'fullload']]
fig = px.bar(df_fullload, y='i', x=df_fullload['fullload'], orientation='h', title='Volllaststunden [h]', color='i', color_discrete_map=color_dict)
with colb1:
    fig
# fig.show()


# %%
fig = px.area(m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Stromproduktion Lastgang [MW]', color='i', color_discrete_map=color_dict)
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))

with colb1:
    fig
# fig.show()


# %%
df_price = m.constraints['load'].dual.to_dataframe().reset_index()
# expand df_price to the size of t
df_price = df_price.loc[df_price.index.repeat(dt)].reset_index(drop=True)
# sort prices descending
df_sorted_price = df_price["dual"].sort_values(ascending=False).reset_index(drop=True)
# generate x-axis for price duration curve
x_price = np.arange(1, df_sorted_price.size + 1)

fig = px.line(y=df_sorted_price, x=x_price,  title='Preisdauerlinie [€/MWh]', labels={"x": "Stunden im Jahr"},range_y=[0,350])
with colb2:
    fig


# %%

fig = px.line(df_price, y='dual', x='t',  title='Strompreis [€/MWh]', range_y=[0,350])
with colb2:
    fig

# %%
    
# curtailment
df_curtailment = m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index()
fig = px.area(m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index(), y='y_curt', x='t',  title='Abregelung [MWh]', color='i', color_discrete_map=color_dict)
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))

with colb1:
    fig    


# %%
df_charging = m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index()
fig = px.area(m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index(), y='y_ch', x='t',  title='Speicherbeladung [MWh]', color='i', color_discrete_map=color_dict)
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))

with colb2:
    fig

# %% 

# df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index() 
# df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1)

# fig = px.line(df_contr_marg, y='dual', x='t',title='Deckungsbeitrag [€]', color='i', range_y=[0,350], color_discrete_map=color_dict)
# with colb2:
#     fig

# %%
# generate dataframe steplength = 1 same size as t
# x = np.arange(1, t.size + 1)
x = np.arange(1,t.size)
df_production_pivot = df_production.pivot(index='t', columns='i', values='y')
# sort columns according to i_with_capacity
df_production_pivot = df_production_pivot[i_with_capacity]
df_efficiency = eff_i.sel(i = i_with_capacity)
co2_factor_i_with_capacity = co2_factor_i.sel(i = i_with_capacity)
# colour_dict = {i: color_dict[i] for i in i_with_capacity}
color_dict_with_capacity = {i: color_dict[i] for i in i_with_capacity}
desired_order = i_with_capacity.tolist()
# multiply df_production with co2 factor
df_production_emissions = df_production_pivot/df_efficiency * co2_factor_i_with_capacity*dt
# unpivot df_production_emissions, sorting by datetime
df_production_emissions_unpivot = df_production_emissions.reset_index().melt(id_vars='t', var_name='i', value_name='y')
df_production_emissions_unpivot['i'] = pd.Categorical(df_production_emissions_unpivot['i'], categories=desired_order, ordered=True)
df_production_emissions_unpivot = df_production_emissions_unpivot.sort_values(by=['t', 'i'])
# rearrange rows according to i_with_capacity


# generate area plot of df_production_emissions_unpivot over t 
fig = px.area(df_production_emissions_unpivot, y='y', x='t',  title='Co2-Emissionen [t]', color='i', color_discrete_map=color_dict_with_capacity)
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))

with colb1:
    fig  

# %%
# Sum up second row of df_production_emissions
df_production_emissions_sum = df_production_emissions.copy()
df_production_emissions_sum['total'] = df_production_emissions_sum.sum(axis=1)
# sort by total generation
df_production_emissions_sum = df_production_emissions_sum.sort_values(by='total', ascending=True)
# generate new dataframe where all columns but dateTime are cumulated
df_production_emissions_sum_cumsum = df_production_emissions_sum.cumsum(axis=0)
# remove columns which are completely zero
df_production_emissions_sum_cumsum = df_production_emissions_sum_cumsum.loc[:, (df_production_emissions_sum_cumsum != 0).any(axis=0)]
# unpivot df_production_emissions_sum_cumsum	
df_production_emissions_sum_unpivot = df_production_emissions_sum_cumsum.reset_index().melt(id_vars='t', var_name='i', value_name='y')
# keep i = i_with_capacity
df_production_emissions_sum_unpivot = df_production_emissions_sum_unpivot[df_production_emissions_sum_unpivot['i'].isin(i_with_capacity)].reset_index(drop=True)
# set values 0= NaN
df_production_emissions_sum_unpivot['y'] = df_production_emissions_sum_unpivot['y'].replace(0, np.nan)

# generate layered area plot of unpivoted_df_sorted_cap over num
fig = px.area(df_production_emissions_sum_unpivot, y='y', x='t',  title='Kumulierte Co2-Emissionen [t]', color='i', color_discrete_map=color_dict_with_capacity)

# fig = px.area(unpivoted_df_sorted_cap, y='cumsum', x='t',  title='Kumulierte Co2-Emissionen [t]', color='i', color_discrete_map=color_dict_with_capacity)

# Update traces
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color))

# fig.show()
with colb2:
    fig  

# %% 
# plot investment costs
# c-inv_i to dataframe
if c_inv_i.name is None:
    c_inv_i.name = 'c_inv_i'
c_inv_i_df = c_inv_i.to_dataframe().reset_index()
# multiply c_inv_i_df with K
df_invest_costs = df_new_capacities['K']* c_inv_i
df_invest_costs = df_invest_costs.to_frame()
df_invest_costs.columns = ['K']
df_invest_costs['i'] = df_new_capacities['i']
fig = px.bar(df_invest_costs, y='i', x='K', orientation='h', title='Investitionskosten [Mrd. €]', color='i', color_discrete_map=color_dict)
# fig.show()
with colb1:
    fig  


# %% 
df_production_all = m.solution['y'].sel(i = i).to_dataframe().reset_index()
# Deckungsbeitrag = Erlöse - Kosten
df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index()
# # contr_margin for i_with_capacity
# df_contr_marg = df_contr_marg[df_contr_marg['i'].isin(i_with_capacity)]. reset_index(drop=True)
# # multiply
df_merged = pd.merge(df_production_all, df_contr_marg, on=['t', 'i'])
# Perform the multiplication
df_merged['y_new'] = df_merged['y'] * df_merged['dual']
df_merged = df_merged[['t', 'i', 'y_new']]
df_contr_marg_sum = df_merged.groupby('i')['y_new'].sum().reset_index()

df_production_res = m.solution['y'].sel(i = iRes).to_dataframe().reset_index()
df_price_res = m.constraints['load'].dual.to_dataframe().reset_index()
# multiply with df_price_res
df_merged_res = pd.merge(df_production_res, df_price_res, on='t')
df_merged_res['multiplied_value'] = df_merged_res['y'] * df_merged_res['dual']
df_merged_res = df_merged_res[['t', 'i', 'multiplied_value']]
df_contr_marg_res = df_merged_res.groupby('i')['multiplied_value'].sum().reset_index()
df_contr_marg_res['multiplied_value'] = df_contr_marg_res['multiplied_value'] * -dt

df_contr_marg_sum = pd.merge(df_contr_marg_sum, df_contr_marg_res, on='i', how='left')
df_contr_marg_sum['y_new'] = df_contr_marg_sum['multiplied_value'].combine_first(df_contr_marg_sum['y_new'])
df_contr_marg_sum = df_contr_marg_sum.drop(columns=['multiplied_value'])
df_contr_marg_sum['y'] = df_contr_marg_sum['y_new']*(-1)
# rearrange rows according to i
df_contr_marg_sum = df_contr_marg_sum.set_index('i').loc[i].reset_index()
# # # barplot
fig = px.bar(df_contr_marg_sum, y='i', x='y', orientation='h', title='Deckungsbeitrag [Mrd. €]', color='i', color_discrete_map=color_dict)
# fig.show()
with colb2:
    fig  

# %%
# #Add pie chart of total production per technology type in GWh(divide by 1000)
# df_production_sum = (df_production.groupby('i')['y'].sum() * dt / 1000 ).round(0).sort_values(ascending=False).reset_index()

# fig = px.pie(df_production_sum, names="i", values='y', title='Gesamtproduktion [GWh] als Kuchendiagramm',
#              color='i', color_discrete_map=color_dict)

# with colb2:
#     fig

# %%
# # %%
# df_h2_prod = m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index()
# fig = px.area(m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index(), y='y_h2', x='t',  title='Produktion Wasserstoff [MWh_th]', color='i', color_discrete_map=color_dict)
# fig.update_traces(line=dict(width=0))
# fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))

# with colb2:
#     fig


# %%
# #add pie chart which shows new capacities
# #round number of new capacities
# df_new_capacities_rounded = m.solution['K'].round(0).to_dataframe()
# #drop all technologies with K<= 0
# df_new_capacities_rounded = df_new_capacities_rounded[df_new_capacities_rounded["K"] > 0].reset_index() 

# total_k_sum = df_new_capacities_rounded["K"].sum()

# #df_new_capacities_rounded["percentage"] = df_new_capacities_rounded["K"].apply(lambda x: (x/total_k_sum)*100).abs().round(2)

# fig = px.pie(df_new_capacities_rounded, names='i', values='K', title='Neu installierte Kapazitäten [MW] als Kuchendiagramm',
#              color='i', color_discrete_map=color_dict)

# with colb1:
#     fig


# %%
((m.solution['y'] / eff_i) * co2_factor_i * dt).sum()
# %%

import pandas as pd
from io import BytesIO
#from pyxlsb import open_workbook as open_xlsb
import streamlit as st
import xlsxwriter
# %%
output = BytesIO()


# ## 


def disaggregate_df(df):
    

    if not "t" in list(df.columns):
        return df

    #df_repeated = df.iloc[idx_repeat,:].reset_index(drop = True).drop('t', axis = 1)
    df_t_all = pd.DataFrame({"t_all": t_original.to_series(), 't': t.repeat(dt)}).reset_index(drop=True)

    ## %%
    df_output = df.merge(df_t_all,on = 't').drop('t',axis = 1).rename({'t_all':'t'}, axis = 1)
    # last column to first column
    cols = list(df_output.columns)
    cols = [cols[-1]] + cols[:-1]
    df_output = df_output[cols]
    return df_output.sort_values('t')




# Create a Pandas Excel writer using XlsxWriter as the engine
with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
    # Write each DataFrame to a different sheet
    disaggregate_df(df_total_costs).to_excel(writer, sheet_name='Gesamtkosten', index=False)
    disaggregate_df(df_CO2_price).to_excel(writer, sheet_name='CO2 Preis', index=False)
    disaggregate_df(df_price).to_excel(writer, sheet_name='Preise', index=False)
    # disaggregate_df(df_contr_marg).to_excel(writer, sheet_name='Deckungsbeiträge', index=False)
    disaggregate_df(df_new_capacities).to_excel(writer, sheet_name='Kapazitäten', index=False)
    disaggregate_df(df_production).to_excel(writer, sheet_name='Produktion', index=False)
    disaggregate_df(df_charging).to_excel(writer, sheet_name='Ladevorgänge', index=False)
    disaggregate_df(D_t.to_dataframe().reset_index()).to_excel(writer, sheet_name='Nachfrage', index=False)
    disaggregate_df(df_curtailment).to_excel(writer, sheet_name='Abregelung', index=False)
    # disaggregate_df(df_h2_prod).to_excel(writer, sheet_name='H2 produktion', index=False)

with col4:
    st.download_button(
        label="Download Excel Arbeitsmappe Ergebnisse",
        data=output.getvalue(),
        file_name="Arbeitsmappe_Ergebnisse.xlsx",
        mime="application/vnd.ms-excel"
    )

# %%