added streamlit app for renawables energies

Author: steven-ja

README.md

@@ -34,4 +34,8 @@ Moreover, I will use this website as a blogging platform. I will talk about seve
          - [ ] Music Generation
          - [ ] Text-to-Speech
    - Graph NN
-   - Reinforcement Learing: 
+   - Reinforcement Learing: 
+4. Renewables: 
+   - [ ] World Energy Production
+   - [ ] World Energy Potential 
+   - [ ] Solar Panel Simulations

content/posts/renewables/world-energy/analysis.ipynb

@@ -0,0 +1,118 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import geopandas as gpd
+import plotly.express as px
+
+energy_columns = ['renewables_electricity', 'coal_electricity', 'gas_electricity', 'oil_electricity', 'nuclear_electricity', 'wind_electricity', 'solar_electricity', "hydro_electricity"]
+# Load and prepare data
+@st.cache_data
+def load_data():
+    url = "https://raw.githubusercontent.com/owid/energy-data/master/owid-energy-data.csv"
+    df = pd.read_csv(url)
+    df['year'] = pd.to_datetime(df['year'], format='%Y')
+    df.rename(columns={
+        "renewables_electricity": "Renewables",
+        "hydro_electricity": "Hydro",
+        "solar_electricity": "Solar",
+        "wind_electricity": "Wind",
+        "coal_electricity": 'Coal',
+        'gas_electricity': "Gas",
+        'oil_electricity': "Oil",
+        'nuclear_electricity' : "Nuclear"
+    }, inplace=True)
+
+    df.drop(columns=[])
+    
+    return df.sort_values('year')
+
+df = load_data()
+
+
+# Streamlit app
+st.title('Global Energy Production Analysis')
+# st.write(df.columns)
+# st.write([col.split("_")[0].capitalize() for col in energy_columns])
+# Sidebar for user input
+st.sidebar.header('Filter Data')
+start_year = st.sidebar.slider('Start Year', 1900, 2023, 1980)
+end_year = st.sidebar.slider('End Year', 1900, 2022, 2023)
+
+# Filter data based on selected years
+filtered_df = df[(df['year'].dt.year >= start_year) & (df['year'].dt.year <= end_year)]
+# st.write(filtered_df)
+# Global trend plot
+st.header('Global Energy Production Trends')
+energy_types = st.multiselect(
+    'Select Energy Types',
+    [col.split("_")[0].capitalize() for col in energy_columns], 
+    default=["Solar", "Wind", "Hydro"]
+)
+
+global_trend = filtered_df.groupby('year')[energy_types].mean()
+
+fig = px.line(global_trend.reset_index(), x='year', y=energy_types,
+              title='Global Energy Production Trends')
+fig.update_yaxes(title="Electricity Production (TWh)")
+st.plotly_chart(fig)
+
+# Map of energy production
+st.header('Energy Production Map')
+energy_type_map = st.selectbox('Select Energy Type for Map', energy_types)
+year_map = st.slider('Select Year for Map', start_year, end_year, end_year)
+
+map_data = filtered_df[filtered_df['year'].dt.year == year_map]
+world = gpd.read_file("ne_110m_admin_0_countries/ne_110m_admin_0_countries.dbf")
+# data processing
+world.rename(columns={"ADMIN": "country"}, inplace=True)
+# replace country name to fit the other dataset
+world.country = world.country.replace(
+    ["United States of America", "Democratic Republic of the Congo", "Republic of the Congo", "United Republic of Tanzania", "The Bahamas", "Czechia", "eSwatini", "Republic of Serbia"], 
+    ["United States", "Democratic Republic of Congo", "Congo", "Tanzania", "Bahamas", "Czechoslovakia", "Eswatini", "Serbia"]
+    )
+
+# world.SOV_A3 = world.SOV_A3.replace(["US1", "CH1", "FR1", "KA1", "GB1", "NZ1", "AU1"], ["USA", "CHN", "FRA", "KAZ", "GBR", "NZL", "AUS"])
+# latest_year = recent_data['year'].max() - pd.Timedelta(days=365*2)
+# latest_data = recent_data[recent_data['year'] == latest_year]
+world = world.merge(map_data, on=['country'])
+
+fig = px.choropleth(world, locations='ADM0_A3', color=energy_type_map, 
+                    hover_name='country', projection='natural earth2', color_continuous_scale='Viridis',
+                    title=f'{energy_type_map.replace("_", " ").title()} Production in {year_map}')
+# fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
+st.plotly_chart(fig)
+
+# Top countries comparison
+st.header('Top Countries Comparison')
+n_countries = st.slider('Number of top countries to compare', 1, 10, 5)
+top_countries = filtered_df.groupby('country')[energy_types[0]].mean().nlargest(n_countries).index.tolist()
+
+top_countries_data = filtered_df[filtered_df['country'].isin(top_countries)]
+
+fig = px.line(top_countries_data, x='year', y=energy_types[0], color='country',
+              title=f'Top {n_countries} Countries in {energy_types[0].replace("_", " ").title()} Production')
+st.plotly_chart(fig)
+
+# Energy mix comparison
+st.header('Energy Mix Comparison')
+selected_country = st.selectbox('Select a Country', df['country'].unique(), index=None,
+   placeholder="Select a country ...",)
+country_data = filtered_df[filtered_df['country'] == selected_country]
+st.write(country_data)
+
+
+energy_mix = country_data[energy_types]
+# energy_mix = energy_mix.div(energy_mix.sum(axis=1), axis=0) * 100
+energy_mix['year'] = country_data.year
+
+fig = px.area(energy_mix.dropna(), x='year', y=energy_types,
+              title=f'Energy Mix for {selected_country}')
+fig.update_yaxes(title="Electricity Production (TWh)")
+st.plotly_chart(fig)
+st.write(energy_mix)
+
+st.write("""
+This Streamlit app provides an interactive analysis of global energy production trends. 
+You can use the sidebar and various selectors to customize the visualizations and explore different aspects of energy production data.
+""")