Generate historical animation for Nuclear and Renewable Energy Share of Energy Production

get_lcoe_table.py

import pandas as pd
import matplotlib.pyplot as plt

cost_type = 'LCOE ($/MWh)'

plt.style.use('dark_background')

dataframe = pd.read_csv('Net Zero by 2050 - A Roadmap for the Global Energy Sector_p202_transformed.csv')
print(dataframe.head())

regions = dataframe['Region'].unique()
dataframe = dataframe[dataframe['Costtype'] == 'LCOE ($/MWh)']
dataframe = dataframe[['EnergyType', 'Region', '2020', '2030', '2050']]
num_types = len(regions)
fig, axes = plt.subplots(1, num_types, figsize=(num_types * 5, 10))  # Adjust the size as needed

for i, region in enumerate(regions):
    region_data = dataframe[dataframe['Region'] == region]
    region_data = region_data[['EnergyType', '2020', '2030', '2050']]
    region_data = region_data.T
    region_data.columns = region_data.iloc[0].values.tolist()
    region_data = region_data[1:]

    region_data.plot(ax=axes[i], marker="o")
    axes[i].set_title(region)
    axes[i].set_xlabel('Year')  # Replace with your actual label
    axes[i].set_ylabel(cost_type)  # Replace with your actual label

plt.suptitle("Levelized cost of electricity by region\nhttps://www.iea.org/events/net-zero-by-2050-a-roadmap-for-the-global-energy-system")
plt.tight_layout()
plt.savefig("lcoe_costs_by_region.png", dpi=150)
plt.close()

main_generation.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.ticker import MaxNLocator

plt.style.use('dark_background')
filebase = "electricity_share_animation_smooth_60fps_highlight"
smoothness = 30
MIN_YEAR = 2000
highlighted_regions = ["World", "France", "South Africa"]

def create_ax(ax: plt.Axes, e_type: str, csv_path: str):
    """create chart"""
    # Load your data
    data = pd.read_csv(csv_path)
    data = data[data['Year'] >= MIN_YEAR]

    generation_column = f"{e_type} - % electricity"

    # Assuming you've identified the top 10 economies or have a way to filter them, for example:
    region_selection = ['China', 'World', 'Japan', 'Germany', "South Africa", "Brazil", 'India', 'France', 'Australia', "United States"]
    data = data[data['Entity'].isin(region_selection)]

    ax.set_xlim(data['Year'].min(), data['Year'].max())
    ax.set_ylim(0, data[generation_column].max() + 5)
    ax.set_xlabel('Year')
    ax.set_ylabel(generation_column)
    ax.set_title(f'Dynamic Share of {e_type} Electricity Production')
    ax.xaxis.set_major_locator(MaxNLocator(integer=True))

    lines = {entity: ax.plot([], [], label=entity, alpha=0.6)[0] for entity in region_selection}
    parts = {}

    for reg in highlighted_regions:
        lines[reg].set_alpha(1)  # Make the highlighted country fully opaque
    for reg in region_selection:
        marker, = ax.plot([], [], 'o', color=lines[reg].get_color())  # Circle marker for the highlighted country
        text = ax.text(0, 0, '', verticalalignment='bottom', horizontalalignment='right', color=lines[reg].get_color())
        parts[reg] = [marker, text]

    # Prepare interpolation
    year_range = np.arange(data['Year'].min(), data['Year'].max() + 1, 1. / (smoothness))
    interpolated = {entity: np.interp(year_range, data[data['Entity'] == entity]['Year'],
                                      data[data['Entity'] == entity][generation_column]) for entity in region_selection}

    def init():
        for line in lines.values():
            line.set_data([], [])
        for reg in highlighted_regions:
            parts[reg][0].set_data([], [])
            parts[reg][1].set_text('')
        return tuple(lines.values()) + (marker, text)

    # Animation update function
    def update(i):
        for entity in region_selection:
            lines[entity].set_data(year_range[:i + 1], interpolated[entity][:i + 1])
            parts[entity][0].set_data(year_range[i], interpolated[entity][i])
            if entity in highlighted_regions:
                parts[entity][1].set_position((year_range[i], interpolated[entity][i]))
                parts[entity][1].set_text(f'{entity}: {interpolated[entity][i]:.2f}')
        return tuple(lines.values()) + (marker, text)

    return ax, init, update, year_range


def main():
    # Set up the figure
    fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(13, 17))
    axes[0], nuclear_init, nuclear_update, year_range = create_ax(axes[0], "Nuclear", 'share-electricity-nuclear.csv')
    axes[1], renewable_init, renewable_update, year_range = create_ax(axes[1], "Renewables", 'share-electricity-renewables.csv')
    axes[2], fossils_init, fossils_update, year_range = create_ax(axes[2], "Fossil fuels", 'share-electricity-fossil-fuels.csv')

    def init():
        nuclear_init()
        renewable_init()
        fossils_init()

    def update(i):
        nuclear_update(i)
        renewable_update(i)
        fossils_update(i)

    # Create the animation
    ani = animation.FuncAnimation(fig, update, init_func=init, frames=len(year_range), blit=False, repeat=True)
    axes[0].legend(title='Selected Economies', bbox_to_anchor=(1.05, 1), loc='upper left')

    # Add a legend
    # Save the animation with higher frame rate
    plt.tight_layout()

    ani.save(f'{filebase}.mp4', writer='ffmpeg', fps=smoothness)
    ani.save(f'{filebase}.gif', writer='pillow', fps=smoothness)
    plt.show()


if __name__ == '__main__':
    main()

Net Zero by 2050 - A Roadmap for the Global Energy Sector_p202_transformed.csv

share-electricity-fossil-fuels.csv

share-electricity-nuclear.csv

share-electricity-renewables.csv