5. Charts

# Import essential libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.patches as mpatches

# Set style and font sizes for better-looking plots
plt.style.use('seaborn-v0_8-darkgrid')
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['font.size'] = 11
plt.rcParams['lines.linewidth'] = 2

# Configure Seaborn for improved aesthetics
sns.set_palette("husl")

# For better display in Jupyter
%matplotlib inline

# Create a sample geospatial dataset
np.random.seed(42)
years = np.arange(2015, 2023)

# Create more illustrative data with clear trends
# The story: As protected areas increase, total forest area is better preserved
data = {
    'Year': years,
    'Forest_Area_km2': 45000 + (years - 2015) * 400 + np.random.normal(0, 300, len(years)),
    'Deforestation_Rate_%': 2.8 - (years - 2015) * 0.12 + np.random.normal(0, 0.15, len(years)),
    'Protected_Area_km2': 12000 + (years - 2015) * 450 + np.random.normal(0, 200, len(years)),
    'Population': 1000000 + (years - 2015) * 50000 + np.random.normal(0, 10000, len(years))
}

df = pd.DataFrame(data)
print("Sample Dataset:")
print(df)

# Simple line plot
plt.figure(figsize=(10, 6))
df.plot(x='Year', y='Forest_Area_km2', kind='line', marker='o', color='green', linewidth=2)

plt.title('Forest Area Over Time', fontsize=14, fontweight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Area (km²)', fontsize=12)
plt.tight_layout()
plt.show()

# Bar plot - subset of data
subset_years = df[df['Year'].isin([2016, 2018, 2020, 2022])]

plt.figure(figsize=(10, 6))
subset_years.plot(x='Year', y='Deforestation_Rate_%', kind='bar', color='orangered')

plt.title('Deforestation Rate by Year', fontsize=14, fontweight='bold')
plt.xlabel('Year', fontsize=12)
plt.ylabel('Deforestation Rate (%)', fontsize=12)
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()

# Scatter plot showing relationship between two variables
plt.figure(figsize=(10, 6))
plt.scatter(df['Protected_Area_km2'], df['Forest_Area_km2'], 
            s=100, alpha=0.6, color='#2E7D32', edgecolors='black', linewidth=1)

plt.title('Forest Area vs Protected Area', fontsize=14, fontweight='bold')
plt.xlabel('Protected Area (km²)', fontsize=12)
plt.ylabel('Forest Area (km²)', fontsize=12)

plt.tight_layout()
plt.show()

# Create a 2x2 subplot grid
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Plot 1: Forest Area (top-left)
axes[0, 0].plot(df['Year'], df['Forest_Area_km2'], marker='o', color='green', linewidth=2)
axes[0, 0].set_title('Forest Area Trend', fontweight='bold')
axes[0, 0].set_ylabel('Area (km²)')

# Plot 2: Deforestation Rate (top-right)
axes[0, 1].plot(df['Year'], df['Deforestation_Rate_%'], marker='s', color='red', linewidth=2)
axes[0, 1].set_title('Deforestation Rate', fontweight='bold')
axes[0, 1].set_ylabel('Rate (%)')

# Plot 3: Protected Area (bottom-left)
axes[1, 0].bar(df['Year'], df['Protected_Area_km2'], color='blue', alpha=0.7)
axes[1, 0].set_title('Protected Area', fontweight='bold')
axes[1, 0].set_ylabel('Area (km²)')
axes[1, 0].set_xlabel('Year')

# Plot 4: Population (bottom-right)
axes[1, 1].plot(df['Year'], df['Population']/1000, marker='^', color='purple', linewidth=2)
axes[1, 1].set_title('Population', fontweight='bold')
axes[1, 1].set_ylabel('Population (thousands)')
axes[1, 1].set_xlabel('Year')

# Add overall title
fig.suptitle('Environmental and Demographic Metrics', fontsize=16, fontweight='bold', y=0.995)

# Adjust spacing
plt.tight_layout()
plt.show()

# Professional-looking plot with custom styling
fig, ax = plt.subplots(figsize=(12, 7))

# Plot multiple lines
ax.plot(df['Year'], df['Forest_Area_km2']/1000, 
        marker='o', linewidth=2.5, label='Forest Area', color='#2E7D32')
ax.plot(df['Year'], df['Protected_Area_km2']/1000, 
        marker='s', linewidth=2.5, label='Protected Area', color='#1976D2')

# Customize appearance
ax.set_xlabel('Year', fontsize=12, fontweight='bold')
ax.set_ylabel('Area (thousands km²)', fontsize=12, fontweight='bold')
ax.set_title('Forest Conservation Status', fontsize=14, fontweight='bold', pad=20)

# Customize legend
ax.legend(loc='best', fontsize=11, frameon=True, shadow=True)

# Add subtle background color
ax.set_facecolor('#F5F5F5')
fig.patch.set_facecolor('white')

# Customize spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_linewidth(1.5)
ax.spines['bottom'].set_linewidth(1.5)

plt.tight_layout()
plt.show()

# Colorblind-friendly color palette
import warnings
warnings.filterwarnings('ignore')

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Data for comparison - use values on comparable scales
categories = df['Year'].astype(str).values
# Scale forest and protected areas to comparable units (in thousands km²)
values1 = (df['Forest_Area_km2']/1000).values
values2 = (df['Protected_Area_km2']/1000).values

# Plot 1: NOT colorblind-friendly (red-green)
width = 0.35
x = np.arange(len(categories[::2]))
axes[0].bar(x - width/2, values1[::2], width, color='red', label='Forest', alpha=0.7)
axes[0].bar(x + width/2, values2[::2], width, color='green', label='Protected', alpha=0.7)
axes[0].set_xticks(x)
axes[0].set_xticklabels(categories[::2])
axes[0].set_ylabel('Area (1000s km²)')
axes[0].set_title('Not Colorblind-Friendly (Red-Green)', fontweight='bold')
axes[0].set_ylim(0, 52)  # Add space at top for legend
axes[0].legend(loc='upper left')

# Plot 2: Colorblind-friendly (blue-orange)
axes[1].bar(x - width/2, values1[::2], width, color='#0173B2', label='Forest', alpha=0.8)
axes[1].bar(x + width/2, values2[::2], width, color='#DE8F05', label='Protected', alpha=0.8)
axes[1].set_xticks(x)
axes[1].set_xticklabels(categories[::2])
axes[1].set_ylabel('Area (1000s km²)')
axes[1].set_title('Colorblind-Friendly (Blue-Orange)', fontweight='bold')
axes[1].set_ylim(0, 52)  # Add space at top for legend
axes[1].legend(loc='upper left')

plt.tight_layout()
plt.show()

# Create a simple plot for demonstration
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(df['Year'], df['Forest_Area_km2'], marker='o', linewidth=2, color='green')
ax.set_title('Forest Area Over Time', fontsize=14, fontweight='bold')
ax.set_xlabel('Year', fontsize=12)
ax.set_ylabel('Area (km²)', fontsize=12)

# Save in different formats
# PNG (good for screen, presentations)
fig.savefig('forest_trend.png', dpi=300, bbox_inches='tight', facecolor='white')
print("✓ Saved: forest_trend.png (PNG - 300 DPI)")

# PDF (vector format, good for publishing)
fig.savefig('forest_trend.pdf', format='pdf', bbox_inches='tight', facecolor='white')
print("✓ Saved: forest_trend.pdf (PDF - Vector)")

# SVG (editable vector format)
fig.savefig('forest_trend.svg', format='svg', bbox_inches='tight', facecolor='white')
print("✓ Saved: forest_trend.svg (SVG - Editable)")

plt.show()

# Create a comprehensive publication-ready dashboard
fig = plt.figure(figsize=(16, 10))
gs = fig.add_gridspec(3, 2, hspace=0.3, wspace=0.3)

# Title
fig.suptitle('Environmental Monitoring Dashboard', fontsize=18, fontweight='bold', y=0.98)

# Plot 1: Main trend
ax1 = fig.add_subplot(gs[0, :])
ax1.plot(df['Year'], df['Forest_Area_km2']/1000, marker='o', linewidth=3, 
         label='Forest Area', color='#2E7D32', markersize=8)
ax1.fill_between(df['Year'], df['Forest_Area_km2']/1000 - 0.5, df['Forest_Area_km2']/1000 + 0.5,
                 alpha=0.2, color='#2E7D32')
ax1.set_ylabel('Forest Area (1000s km²)', fontweight='bold')
ax1.legend(fontsize=11)
ax1.set_title('Forest Area Increasing Over Time (Due to Conservation)', fontweight='bold', fontsize=11)

# Plot 2: Bar chart
ax2 = fig.add_subplot(gs[1, 0])
subset_years = df[df['Year'].isin([2016, 2018, 2020, 2022])]
bars = ax2.bar(subset_years['Year'].astype(str), subset_years['Deforestation_Rate_%'], 
               color=['#2E7D32' if x < 2.3 else '#FFA726' for x in subset_years['Deforestation_Rate_%']])
ax2.set_ylabel('Deforestation Rate (%)', fontweight='bold')
ax2.set_title('Annual Deforestation Rate (Declining)', fontweight='bold')

# Plot 3: Scatter
ax3 = fig.add_subplot(gs[1, 1])
ax3.scatter(df['Protected_Area_km2']/1000, df['Forest_Area_km2']/1000, 
            s=100, alpha=0.6, color='#2E7D32', edgecolors='black', linewidth=1.5)
ax3.set_xlabel('Protected Area (1000s km²)', fontweight='bold')
ax3.set_ylabel('Forest Area (1000s km²)', fontweight='bold')
ax3.set_title('Protected vs Total Forest', fontweight='bold')

# Plot 4: Area chart
ax4 = fig.add_subplot(gs[2, :])
# Show protected area at the bottom, and remaining unprotected forest on top
protected_scaled = df['Protected_Area_km2']/1000
unprotected_forest = (df['Forest_Area_km2'] - df['Protected_Area_km2'])/1000

ax4.fill_between(df['Year'], 0, protected_scaled, 
                 alpha=0.6, label='Protected Forest', color='#1976D2')
ax4.fill_between(df['Year'], protected_scaled, protected_scaled + unprotected_forest, 
                 alpha=0.6, label='Unprotected Forest', color='#FFA726')
ax4.set_xlabel('Year', fontweight='bold')
ax4.set_ylabel('Area (1000s km²)', fontweight='bold')
ax4.set_title('Stacked Area Chart: Forest Protection Status', fontweight='bold')
ax4.legend(loc='upper right', fontsize=10)

plt.savefig('environmental_dashboard.png', dpi=300, bbox_inches='tight', facecolor='white')
plt.show()

# Calculate correlation matrix
corr = df.corr(numeric_only=True)

# Create heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', center=0,
            square=True, linewidths=1, cbar_kws={"shrink": 0.8})
plt.title('Correlation Matrix of Environmental Variables', fontsize=13, fontweight='bold', pad=15)
plt.tight_layout()
plt.show()

# Regression plot with confidence interval
plt.figure(figsize=(10, 6))
sns.regplot(x='Protected_Area_km2', y='Forest_Area_km2', data=df, 
            scatter_kws={'s': 100, 'alpha': 0.6}, 
            line_kws={'linewidth': 2, 'color': '#2E7D32'})

plt.title('Relationship: Protected Area vs Forest Area', fontsize=13, fontweight='bold')
plt.xlabel('Protected Area (km²)', fontsize=11)
plt.ylabel('Forest Area (km²)', fontsize=11)
plt.tight_layout()
plt.show()

# Pair plot - shows all relationships
selected_cols = ['Year', 'Forest_Area_km2', 'Protected_Area_km2', 'Deforestation_Rate_%']
df_subset = df[selected_cols].copy()

# Scale for better visualization
df_subset['Forest_Area_km2'] = df_subset['Forest_Area_km2'] / 1000  # Convert to thousands
df_subset['Protected_Area_km2'] = df_subset['Protected_Area_km2'] / 1000
df_subset = df_subset.rename(columns={
    'Forest_Area_km2': 'Forest (1000s km²)',
    'Protected_Area_km2': 'Protected (1000s km²)',
    'Deforestation_Rate_%': 'Deforestation (%)'
})

pair_plot = sns.pairplot(df_subset, diag_kind='hist', plot_kws={'alpha': 0.6, 's': 80},
                         diag_kws={'bins': 5, 'edgecolor': 'black'})

pair_plot.fig.suptitle('Pairwise Relationships: All Variables', fontsize=14, fontweight='bold', y=0.995)
plt.tight_layout()
plt.show()