pip install pandas lightningchart

file_path = '/bev-share-new-ev.csv'
data = pd.read_csv(file_path)

# Extracting the 'Year' and the global electric car stock
years = data.iloc[:, 0].astype(int).tolist()
global_stock = data.iloc[:, 1:].sum(axis=1).astype(float).tolist()
# Create the XY chart with a real-time simulation
chart = lc.ChartXY(theme=Themes.White, title='Global Electric Car Stock (2013-2023)')
series = chart.add_line_series()
# Initialize an empty data list for simulating real-time updates
x_values = []
y_values = []
# Set axis titles
x_axis = chart.get_default_x_axis()
x_axis.set_title("Year")
x_axis.set_decimal_precision(0)
y_axis = chart.get_default_y_axis()
y_axis.set_title("Number of Electric Cars (in millions)")
# Open the chart in real-time mode
chart.open(live=True)
# Simulate real-time data update
for i in range(len(years)):
    x_values.append(int(years[i]))
    y_values.append(float(global_stock[i]))
    series.set_samples(x_values, y_values)
    time.sleep(1)

# Specify the regions of interest
regions_of_interest = ['World', 'Norway', 'United Kingdom', 'European Union (27)', 'China', 'United States']

# Filter the dataset for the specified regions and sort by year
filtered_data = data[data['Entity'].isin(regions_of_interest)].sort_values(by='Year')

# Function to create live line chart for a region
def create_live_chart(region_name):
    # Filter data for the specific region
    region_data = filtered_data[filtered_data['Entity'] == region_name]
    years = region_data['Year'].astype(int).tolist()
    sales_share = region_data['Share of new cars that are electric'].astype(float).tolist()
    
    # Create a chart for each region
    chart = lc.ChartXY(theme=Themes.White)
    chart.set_title(f"{region_name} Electric Car Sales Share")
    series = chart.add_line_series()

    # Initialize empty lists for live simulation
    x_values = []
    y_values = []

    # Set axis titles
    chart.get_default_x_axis().set_title("Year")
    chart.get_default_y_axis().set_title("Sales Share (%)")

    # Simulate live updates
    for i in range(len(years)):
        # Append the new data point
        x_values.append(int(years[i]))  # Ensure values are Python integers
        y_values.append(float(sales_share[i]))  # Ensure values are Python floats

        # Update the chart with the new data
        series.set_samples(x_values, y_values)

        # Pause for a moment to simulate real-time update
        time.sleep(0)  # Update every second (adjust time interval as needed)

    # Open the chart in the browser
    chart.open(live=True)

# Create live charts for each region in separate browser windows/tabs
create_live_chart('World')
create_live_chart('Norway')
create_live_chart('United Kingdom')
create_live_chart('European Union (27)')
create_live_chart('China')
create_live_chart('United States')

# Create the chart
chart = lc.ChartXY(
    theme=lc.Themes.White,
    title='Share of new cars that are electric'
)

# Define colors for each entity
colors = [
    lc.Color(0, 0, 255),
    lc.Color(255, 165, 0), 
    lc.Color(255, 255, 0),
    lc.Color(0, 255, 0),
    lc.Color(255, 0, 0),
    lc.Color(0, 0, 0)
]

# Create a line series for each entity
line_series_dict = {}
for i, entity in enumerate(entities):
    line_series = chart.add_line_series()
    line_series.set_name(entity)
    line_series.set_line_color(colors[i])
    line_series.set_line_thickness(2)
    line_series_dict[entity] = line_series

# Customize x-axis
x_axis = chart.get_default_x_axis()
x_axis.set_title('Year')
x_axis.set_decimal_precision(0)

# Customize y-axis
y_axis = chart.get_default_y_axis()
y_axis.set_title('Battery-electric as a share of electric cars sold (percentage)')

# Add a legend to the chart
chart.add_legend(
    horizontal=False,
    title='Regions',
    x=12 , y=42,
    data=chart
)

# Open the chart
chart.open(live=True)

# Add data points with a delay to simulate real-time drawing
for index, row in data.iterrows():
    if row['Entity'] in entities:
        year = row['Year']
        emission = row['Battery-electric as a share of electric cars sold']
        if pd.notna(year) and pd.notna(emission):
            emission_million = emission / 1e6  # Convert to million metric tons
            print(f"Adding data for {row['Entity']}: Year={year}, Emission={emission_million}")
            line_series_dict[row['Entity']].add(year, emission_million)
            time.sleep(0.1)  # 0.1 second delay between each data point

chart.close()

years = data['Year'].astype(str)
bev_share = data['Battery-electric as a share of electric cars sold']
chart = lc.BarChart(theme=Themes.White, title='BEV Share of New EVs')
chart.set_sorting('alphabetical')
bar_data = [{'category': year, 'value': bev_share_value} for year, bev_share_value in zip(years, bev_share)]
chart.set_data(bar_data)
chart.add_textbox('Year', x=50, y=10) 
chart.add_textbox('BEV Share (%)', x=5, y=50) 
chart.open()

# Create a chart
chart = lc.BarChart(
    vertical=True,
    theme=lc.Themes.White,
    title='BEV and PHEV sales by Region (2013-2023)'
)
chart.set_sorting('disabled')
# Set stacked bar chart data
chart.set_data_stacked(
    years,
    chart_data
)

# Function to create a world map chart
def create_world_map_chart(data, year):
    # Filter data for the given year
    data_year = data[data['Year'] == year]
    data_year = data_year[['Code', 'Share of new cars that are electric']].rename(columns={'Code': 'ISO_A3', 'Share of new cars that are electric': 'value'})

    # Create the MapChart for World
    chart = lc.MapChart(map_type='World', theme=lc.Themes.White)

    # Set sales share data
    chart.invalidate_region_values(data_year.to_dict(orient='records'))
    # Set title with year
    chart.set_title(f"Electric Car Sales Share - Year {year} - World")
    chart.set_palette_colors(
        steps=[
            {'value': 0, 'color': Color('#f7fbff')},
            {'value': 0.2, 'color': Color('#FFFF99')},
            {'value': 0.5, 'color': Color('#FFD700')},
            {'value': 0.7, 'color': Color('#FFCC99')},
            {'value': 0.9, 'color': Color('#FF8C00')},
            {'value': 1, 'color': Color('#FF9999')},
            {'value': 1.5, 'color': Color('#FF0000')},
            {'value': 2, 'color': Color('#238b45')},
            {'value': 25, 'color': Color('#41ae76')},
            {'value': 30, 'color': Color('#66c2a4')}
        ],
        look_up_property='value',
        percentage_values=True
    )
    # Enable hover highlighting
    chart.set_highlight_on_hover(enabled=True)
    legend = chart.add_legend(horizontal=True, title=f"Electric Car Sales Share - Year: {year}", data=chart)
    legend.set_font_size(10)
    chart.open(live=True)
    return chart

create_world_map_chart(data, 2023)

# Preparing data for the pie chart
data_grouped = data.groupby('Entity')['Share of new cars that are electric'].mean().reset_index()

regions = data_grouped['Entity']
sales_share = data_grouped['Share of new cars that are electric']

# Create the pie chart
chart = lc.PieChart(theme=Themes.White, title='Average Electric Car Sales Share by Region')

# Add slices to the pie chart
for region, share in zip(regions, sales_share):
    chart.add_slice(name=region, value=share)

# Open the chart
chart.open()