import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

# The code below fetches the data from the "Our World in Data" Github page. 
# This is the data behind this graph/table: https://ourworldindata.org/grapher/cumulative-co-emissions
# If the url does not work you can also use the data provided, by uncommenting the line below.
df = pd.read_csv('owid-co2-data.csv', header =0)
# df = pd.read_csv('https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.csv', header=0)

# Select only data for World
df = df[df['country'] == 'World']

# Select year and Cumulative CO2
df = df[['year','cumulative_co2']]

# Turn year to integer
df['year'] = df['year'].astype(int)

# Subtract from every cumulative value what has been emitted until 1750
df['cumulative_co2']=df['cumulative_co2']-df['cumulative_co2'].iloc[0]

# For every row:
# 1. Calculate difference between "cumulative CO2 in year i" and "cumulative CO2 in last year" 
# 2. Divide this difference with "cumulative CO2 in last year" 

sums = []

for i in range(len(df)):
    share_gen = (df['cumulative_co2'].iloc[-1]-df['cumulative_co2'].iloc[i])/df['cumulative_co2'].iloc[-1]*100
    sums.append(share_gen)

# Add the new column of sums to the original DataFrame
df['share_gen'] = sums

# Select every fifth row
df = df.iloc[::5]

# Select data for people who were born in the last 100 years
scope = int(100/5)
df = df.tail(scope).reset_index(drop=True)

min_year = df['year'].iloc[0].item()
max_year = df['year'].iloc[-1].item()

# Create a Dash application
app = dash.Dash(__name__)


app.layout = html.Div([
    html.P('1. Works best on desktop 2. Use the slider to adjust the graph 3. Hoover mouse over graph to see (i.a.) the download button', style={'textAlign': 'left'}),
    dcc.Slider(
        id='year-slider',
        min=min_year,
        max=max_year,
        value=1985,
        marks={str(year): str(year) for year in range(min_year,max_year+1, 5)},
        step=None
    ),

    dcc.Graph(
        id="sine-graph",
        style={'height': '90vh', 'width': '100vw'}
    ),
])

@app.callback(
    Output("sine-graph", "figure"),
    [Input("year-slider", "value")]
)



def update_graph(year_born):



    # Round the year born to intervals of 5
    year_born_round = int(5 * round(float(year_born)/5))

    # Find index in df of year born
    year_born_index = df[df['year'] == year_born_round].index.tolist()[0]

    # data frame to plotly graph
    fig = go.Figure(data=[
    go.Bar(y=df['year'], x=df['share_gen'], orientation='h',
        showlegend=False,
        marker_color=['silver' if i != year_born_index else 'lightcoral' for i in range(df.shape[0])],
        name = '')
    ])

    subtitle= "See:  <a href=\"https://git.pub.solar/misha/CO2-generation\">https://git.pub.solar/misha/CO2-generation</a> (original from <a href=\"https://x.com/neilrkaye/status/1365247133507604486\">https://x.com/neilrkaye/status/1365247133507604486</a>)"
    explainer = "If you were born in %s, %s%% of<br>the total amount of CO2 that has <br>been emitted since 1750, has been<br>emitted in your lifetime." % (int(df['year'].iloc[year_born_index].item()), int(df['share_gen'].iloc[year_born_index].item()))

    (int(df['year'].iloc[year_born_index].item()), int(df['share_gen'].iloc[year_born_index].item()))


    # Create subplot for vertical line
    fig_vline = go.Figure(data=[go.Scatter(x = [df['share_gen'].iloc[year_born_index]]*2, 
                                            y=[df['year'].min()-2.5, df['year'].iloc[year_born_index]], mode='lines',
                                            showlegend=False,
                                            line=dict(color='lightcoral'))])

    # Merge figures
    fig.add_trace(fig_vline.data[0])


    fig.update_layout(title_text='Percentage of global CO2 emissions since 1750 occurring in your lifetime (last updated: %s)<br><sub>%s</sub>' % (df['year'].iloc[-1],subtitle),
                    xaxis_title="percentage", yaxis_title="year of birth")


    fig.update_yaxes(range=[df['year'].min()-2.5, df['year'].max()+2.5],
                    tickmode = 'array',
                    tickvals = list(range(df['year'].min(), df['year'].max(), 5)))

    fig.add_annotation(
        text=explainer,
        xref="paper", yref="paper",
        x=0.99, y=0.98, showarrow=False,
        align="left",
        font=dict(size=16),
        bgcolor="lightcoral",
        borderwidth=2
    )

    return fig


if __name__ == '__main__':
    app.run_server(debug=True,host='0.0.0.0')