🌧️ RainData - Historical Precipitation Data Explorer

Web application for accessing and downloading historical precipitation data in Brazil.

🚀 Key Features

• 🗺️ Interactive Map:

  • Geospatial visualization of monitoring stations across Brazil using Folium.
  • Intuitive navigation: click on a map point to view station details.

• 📊 Detailed Dashboard:

  • Dynamic Filters: Filter by year, month, date range, and operational status.
  • Interactive Charts: Time-series precipitation analysis.
  • Metadata Display: Station code, coordinates, and status.

• 💧 Hydrological & Statistical Analysis:

  • Distributions & Tests: PDF, CDF, and Kolmogorov-Smirnov test to find the best statistical fit (GEV, Gumbel, Normal, Weibull, etc.).
  • IDF Curves & HMax: Intensity-Duration-Frequency curves and maximum precipitation by return period.
  • SPI-1 Index: Standardized Precipitation Index calculations modeling drought and wet cycles.

• 🌾 Commodities vs SPI:

  • Dual-axis time-series visualizations correlating the State's average SPI-1 with local commodity prices (Soybean, Corn, Coffee, Sugarcane) using normalized scales.

• ⚡ High Performance:

  • Uses Parquet format for ultra-fast data loading.
  • Optimized data pipeline.

• 🌍 Bilingual Support: English and Portuguese (PT-BR) localizations.

📡 Data Source

The meteorological data used in this project is extracted from BDMEP (Banco de Dados Meteorológicos para Ensino e Pesquisa), provided by INMET (National Institute of Meteorology - Brazil). Commodity data relies on historical price series for Brazilian states.

🛠️ Tech Stack

• Language: Python 3.12
• Framework: Streamlit
• Data Processing: Pandas, NumPy, SciPy (Scientific & Hydrological computing)
• Visualization: Plotly Express, Plotly Graph Objects, Matplotlib, Folium

📂 Project Structure

raindata/
├── app.py                # Application entry point (Navigation)
├── src/                  
│   ├── functions/        # Analytical functions (charts, data prep, hydrology, statistics)
│   └── utils/            # Utilities (i18n, Streamlit wakeup script)
├── pages/
│   ├── home.py           # Home Page (Folium Map)
│   ├── explorer_page.py  # Dataset Explorer (Station filters and raw metrics)
│   └── data_analysis_page.py # Hydrological Analysis & Commodities vs SPI
├── data/
│   ├── metadata_estacoes.parquet # Generated metadata file
└── requirements.txt      # Project dependencies

⚙️ Installation & Usage

1. Clone the repository:

   git clone https://github.com/your-username/raindata.git
   cd raindata

2. Create a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # Linux/Mac
   # or
   .venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Prepare Data (ETL):

   • Place your raw .csv files from BDMEP in the rain_datasets folder.
   • Run the convert.ipynb notebook to generate metadata_estacoes.parquet and convert data to Parquet.

5. Run the App:

   streamlit run app.py

🎨 Theme

The application uses a custom dark theme with blue accents for better data visualization. Configuration is located in .streamlit/config.toml.