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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Example usage of the Weather Geo ERA5 Dataset
This script demonstrates how to use the weather_geo_utils module to load
and analyze weather data from the HuggingFace dataset.
Requirements:
pip install pandas huggingface-hub matplotlib seaborn
"""
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from weather_geo_utils import (
load_point_data,
load_cities_data,
convert_temperature_units,
get_dataset_info
)
def example_single_city():
"""Example: Load data for a single city (Paris)."""
print("=" * 60)
print("EXAMPLE 1: Single City Analysis (Paris)")
print("=" * 60)
# Load 2023 data for Paris
print("Loading Paris weather data for 2023...")
df = load_point_data(
lat=48.8566,
lon=2.3522,
start_date="2023-01-01",
end_date="2023-12-31"
)
# Convert temperatures to Celsius
df = convert_temperature_units(df, from_unit='K', to_unit='C')
print(f"Loaded {len(df)} records")
print(f"Date range: {df['time'].min()} to {df['time'].max()}")
print(f"Temperature range: {df['t2m'].min():.1f}°C to {df['t2m'].max():.1f}°C")
print(f"Average temperature: {df['t2m'].mean():.1f}°C")
# Plot temperature timeline
plt.figure(figsize=(12, 6))
plt.plot(df['time'], df['t2m'], linewidth=0.5, alpha=0.7)
plt.title('Paris Temperature 2023')
plt.xlabel('Date')
plt.ylabel('Temperature (°C)')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('paris_temperature_2023.png', dpi=300, bbox_inches='tight')
plt.show()
return df
def example_multiple_cities():
"""Example: Compare multiple European cities."""
print("\n" + "=" * 60)
print("EXAMPLE 2: Multiple Cities Comparison")
print("=" * 60)
# Define cities
cities = [
(48.8566, 2.3522, "Paris"),
(51.5074, -0.1278, "London"),
(52.5200, 13.4050, "Berlin"),
(41.9028, 12.4964, "Rome"),
(59.3293, 18.0686, "Stockholm"),
]
print(f"Loading data for {len(cities)} cities...")
# Load 2023 data for all cities
df = load_cities_data(cities, start_date="2023-01-01", end_date="2023-12-31")
# Convert temperatures to Celsius
df = convert_temperature_units(df, from_unit='K', to_unit='C')
print(f"Total records loaded: {len(df)}")
# Calculate monthly averages
df['month'] = df['time'].dt.month
monthly_temps = df.groupby(['city', 'month'])['t2m'].mean().reset_index()
monthly_temps['month_name'] = pd.to_datetime(monthly_temps['month'], format='%m').dt.strftime('%b')
# Plot comparison
plt.figure(figsize=(12, 8))
# Temperature comparison
plt.subplot(2, 1, 1)
for city in df['city'].unique():
city_data = monthly_temps[monthly_temps['city'] == city]
plt.plot(city_data['month'], city_data['t2m'], marker='o', label=city, linewidth=2)
plt.title('Monthly Average Temperature Comparison 2023')
plt.xlabel('Month')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.xticks(range(1, 13), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
# Annual statistics
plt.subplot(2, 1, 2)
annual_stats = df.groupby('city')['t2m'].agg(['mean', 'min', 'max']).reset_index()
x = range(len(annual_stats))
plt.bar(x, annual_stats['mean'], alpha=0.7, label='Average')
plt.errorbar(x, annual_stats['mean'],
yerr=[annual_stats['mean'] - annual_stats['min'],
annual_stats['max'] - annual_stats['mean']],
fmt='none', color='black', capsize=5, alpha=0.8)
plt.title('Annual Temperature Statistics 2023')
plt.xlabel('City')
plt.ylabel('Temperature (°C)')
plt.xticks(x, annual_stats['city'], rotation=45)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('cities_temperature_comparison.png', dpi=300, bbox_inches='tight')
plt.show()
# Print summary statistics
print("\nAnnual Temperature Summary (°C):")
print(annual_stats.round(1))
return df
def example_climate_analysis():
"""Example: Long-term climate analysis."""
print("\n" + "=" * 60)
print("EXAMPLE 3: Long-term Climate Analysis (Paris)")
print("=" * 60)
print("Loading historical data for Paris (1980-2023)...")
df = load_point_data(
lat=48.8566,
lon=2.3522,
start_date="1980-01-01",
end_date="2023-12-31"
)
# Convert temperatures to Celsius
df = convert_temperature_units(df, from_unit='K', to_unit='C')
print(f"Loaded {len(df)} records over {df['time'].dt.year.nunique()} years")
# Calculate annual averages
df['year'] = df['time'].dt.year
annual_temps = df.groupby('year')['t2m'].mean().reset_index()
# Plot long-term trend
plt.figure(figsize=(14, 10))
# Annual temperature trend
plt.subplot(2, 2, 1)
plt.plot(annual_temps['year'], annual_temps['t2m'], linewidth=1, alpha=0.7)
# Add trend line
from numpy.polynomial import Polynomial
p = Polynomial.fit(annual_temps['year'], annual_temps['t2m'], 1)
plt.plot(annual_temps['year'], p(annual_temps['year']), 'r--', linewidth=2, label='Trend')
plt.title('Annual Average Temperature (Paris)')
plt.xlabel('Year')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.grid(True, alpha=0.3)
# Monthly climatology
plt.subplot(2, 2, 2)
df['month'] = df['time'].dt.month
monthly_climate = df.groupby('month')['t2m'].agg(['mean', 'std']).reset_index()
plt.plot(monthly_climate['month'], monthly_climate['mean'], 'b-', linewidth=2, label='Average')
plt.fill_between(monthly_climate['month'],
monthly_climate['mean'] - monthly_climate['std'],
monthly_climate['mean'] + monthly_climate['std'],
alpha=0.3, label='±1 std dev')
plt.title('Monthly Temperature Climatology')
plt.xlabel('Month')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.xticks(range(1, 13), ['J', 'F', 'M', 'A', 'M', 'J',
'J', 'A', 'S', 'O', 'N', 'D'])
# Temperature distribution
plt.subplot(2, 2, 3)
plt.hist(df['t2m'], bins=50, alpha=0.7, density=True)
plt.axvline(df['t2m'].mean(), color='red', linestyle='--', linewidth=2, label=f'Mean: {df["t2m"].mean():.1f}°C')
plt.title('Temperature Distribution')
plt.xlabel('Temperature (°C)')
plt.ylabel('Density')
plt.legend()
plt.grid(True, alpha=0.3)
# Decade comparison
plt.subplot(2, 2, 4)
df['decade'] = (df['year'] // 10) * 10
decade_temps = df.groupby('decade')['t2m'].mean().reset_index()
plt.bar(decade_temps['decade'], decade_temps['t2m'], width=8, alpha=0.7)
plt.title('Average Temperature by Decade')
plt.xlabel('Decade')
plt.ylabel('Temperature (°C)')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('paris_climate_analysis.png', dpi=300, bbox_inches='tight')
plt.show()
# Calculate warming trend
warming_rate = p.coef[1] # Slope of trend line
total_warming = warming_rate * (annual_temps['year'].max() - annual_temps['year'].min())
print(f"\nClimate Analysis Results:")
print(f"Period: {annual_temps['year'].min()}-{annual_temps['year'].max()}")
print(f"Average temperature: {df['t2m'].mean():.1f}°C")
print(f"Warming rate: {warming_rate:.3f}°C/year")
print(f"Total warming: {total_warming:.1f}°C over {annual_temps['year'].max() - annual_temps['year'].min()} years")
return df
def main():
"""Run all examples."""
print("Weather Geo ERA5 Dataset - Example Usage")
print("=" * 60)
# Display dataset info
info = get_dataset_info()
print("Dataset Information:")
for key, value in info.items():
print(f" {key}: {value}")
try:
# Example 1: Single city
df1 = example_single_city()
# Example 2: Multiple cities
df2 = example_multiple_cities()
# Example 3: Climate analysis
df3 = example_climate_analysis()
print("\n" + "=" * 60)
print("All examples completed successfully!")
print("Generated plots:")
print(" - paris_temperature_2023.png")
print(" - cities_temperature_comparison.png")
print(" - paris_climate_analysis.png")
print("=" * 60)
except Exception as e:
print(f"\nError running examples: {e}")
print("Make sure you have installed the required packages:")
print(" pip install pandas huggingface-hub matplotlib seaborn")
if __name__ == "__main__":
main()
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