# Importing the useful libraries and modules
import pandas as pd
import numpy as np
import panel as pn
import hvplot.pandas

pn.extension("tabulator")

# -------------- Load the data -----------------------------
data = pd.read_csv("clean_data.csv")

# -------------- Make DataFrame Pipeline Interactive -----------
idf = data.interactive()

# ------------- CO2 emission over time by continent Plot ------------------

# Year Slider
year_slider = pn.widgets.IntSlider(name="Year slider", start=1750, end=2020, step=5, value=1850)

# Radio Buttons for CO2 Measures
yaxis_co2 = pn.widgets.RadioButtonGroup(
    name="Y axis",
    options=["co2", "co2_per_capita",],
    button_type="success"
)

# Y-axis Options
yaxis_co2_source = pn.widgets.RadioButtonGroup(
    name="Y axis",
    options=["coal_co2", "oil_co2", "gas_co2"],
    button_type="success"
)

# Continent Options
continents = ["World", "Asia", "Oceania", "Europe", "Africa", "North America", "South America", "Atarctica"]

# Defining the Pipeline
co2_pipeline = (
    idf[
        (idf.year <= year_slider) &
        (idf.country.isin(continents))
    ]
    .groupby(["country", "year"])[yaxis_co2].mean()
    .to_frame()
    .reset_index()
    .sort_values(by="year")
    .reset_index(drop=True)
)

# Creating the Plot
co2_plot = co2_pipeline.hvplot(
    x = "year", 
    by = "country", 
    y=yaxis_co2, 
    line_width = 2, 
    title = "CO2 emission by continent")


# ------------- Table-CO2 emission over time by continent -----------------

# Creating the Table    
co2_table = co2_pipeline.pipe(pn.widgets.Tabulator, pagination="remote", page_size = 10, sizing_mode = "stretch_width")

# ------------- CO2 vs GDP scatterplot -----------------------------------

# Creating the Pipeline
co2_vs_gdp_scatterplot_pipeline = (
    idf[
        (idf.year == year_slider) &
        (~ (idf.country.isin(continents)))
    ].groupby(["country", "year", "gdp_per_capita"])["co2"].mean()
    .to_frame().reset_index().sort_values(by="year").reset_index(drop=True)
)

# Creating the Plot
co2_vs_gdp_scatterplot = co2_vs_gdp_scatterplot_pipeline.hvplot(x="gdp_per_capita", 
                                                                y="co2", 
                                                                by="country", 
                                                                size=80, 
                                                                kind="scatter", 
                                                                alpha=0.7, 
                                                                legend=False, 
                                                                height=500, 
                                                                width=500)

# ------------- Bar chart with CO2 sources by continent ------------------

# Y-axis Content
yaxis_co2_source = pn.widgets.RadioButtonGroup(
    name="Y axis",
    options=["coal_co2", "oil_co2", "gas_co2"],
    button_type="success"
)

# Continent Options
continents_excl_world = ["Asia", "Oceania", "Europe", "Africa", "North America", "South America", "Antarctica"]

# Creating the Pipeline
co2_source_bar_pipeline = (
    idf[
        (idf.year == year_slider) & 
        (idf.country.isin(continents_excl_world))
    ].groupby(["year", "country"])[yaxis_co2_source].sum()
    .to_frame()
    .reset_index()
    .sort_values(by="year")
    .reset_index(drop=True)
)

# Create the Plot
co2_source_bar_plot = co2_source_bar_pipeline.hvplot(kind="bar", 
                                                     x="country", 
                                                     y=yaxis_co2_source, 
                                                     title="CO2 source by continent")

# ------------------------------ Creating the Dashboard --------------------------------

# Layout using Template
template = pn.template.FastListTemplate(
    title='World CO2 emission dashboard', 
    sidebar=[pn.pane.Markdown("# CO2 Emissions and Climate Change"), 
             pn.pane.Markdown("#### Carbon dioxide emissions are the primary driver of global climate change. It’s widely recognised that to avoid the worst impacts of climate change, the world needs to urgently reduce emissions. But, how this responsibility is shared between regions, countries, and individuals has been an endless point of contention in international discussions."), 
             pn.pane.PNG('climate_day.png', sizing_mode='scale_both'),
             pn.pane.Markdown("## Settings"),   
             year_slider],
    main=[pn.Row(pn.Column(yaxis_co2, 
                           co2_plot.panel(width=700), margin=(0,25)), 
                 co2_table.panel(width=500)), 
          pn.Row(pn.Column(co2_vs_gdp_scatterplot.panel(width=600), margin=(0,25)), 
                 pn.Column(yaxis_co2_source, co2_source_bar_plot.panel(width=600)))],
    accent_base_color="#88d8b0",
    header_background="#88d8b0",
)

# ----------------- Run the Dashboard ------------------------

if __name__ == "__main__":
    template.show()