canoa-data-validate 0.7.65

The software data_validate is a robust multilingual spreadsheet validator and processor developed specifically to automate integrity and structure validation of data files for the AdaptaBrasil climate adaptation platform.

Data Validate

pt_BR: Sistema de validação e processamento de planilhas para a plataforma AdaptaBrasil

en_US: Spreadsheet validation and processing system for the AdaptaBrasil platform

Testing Linux
Testing Windows
Coverage
Package
Meta

Built with the tools and technologies:

Data Validate is a robust multilingual spreadsheet validator and processor developed specifically to automate integrity and structure validation of data files for the AdaptaBrasil climate adaptation platform. It is especially useful for projects requiring standardization and rigorous validation of tabular data, such as scientific research, environmental databases, and indicator systems.

📋 Index

• Features
• Architecture
• Installation
• Usage
• Implemented Validations
• Project Structure
• Testing
• Development
• Documentation
• Contributing
• License

🚀 Features and Validation Protocol

Validation Protocol

Data Validate implements the detailed specification defined in the validation protocol version 1.13, which establishes clear rules for the structure and content of spreadsheets used in the AdaptaBrasil platform.

Key Features

• Structural Validation: Verifies spreadsheet structure, column names, and organization
• Content Validation: Applies specific business rules for each spreadsheet type
• Spell Checking: Multilingual spell correction system with custom dictionaries
• Hierarchical Validation: Validates indicator relationships and tree structures
• Detailed Reports: Generates detailed HTML, PDF, and validation logs
• Multilingual Support: Internationalization support in Portuguese and English
• Logging System: Detailed logging for auditing and debugging

Technologies

• Python 3.12+: Main language
• Pandas: Data manipulation and analysis
• PyEnchant: Spell checking
• Calamine: Excel file reading
• Babel: Internationalization
• PDFKit: PDF report generation
• Poetry: Dependency management

🏗️ Architecture

The project follows a modular architecture based on clean design patterns:

📁 data_validate/
├── 🎛️ controllers/     # Orchestration and flow control
├── 📊 models/          # Data models for spreadsheets
├── ✅ validators/      # Validation logic
├── 🛠️ helpers/        # Utilities and helper functions
├── ⚙️ config/         # Global configurations
├── 🔧 middleware/     # Initialization layer
└── 📄 static/         # Static resources (templates, dictionaries, i18n)

Processing Flow

1. Initialization: Bootstrap configures environment and dependencies
2. Loading: Reading and preprocessing spreadsheets
3. Validation: Sequential execution of specialized validators
4. Aggregation: Collection and organization of errors and warnings
5. Reporting: Generation of detailed output reports

📦 Installation

Prerequisites

• Python 3.12 or higher
• Poetry for dependency management
• Wkhtmltopdf (for PDF generation)

System Dependencies

GNU/LINUX

Ensure python-dev and wkhtmltopdf are installed:

# Install dependencies
sudo apt install python3-dev wkhtmltopdf

Windows

To install wkhtmltopdf, download the installer from the official website: https://wkhtmltopdf.org/downloads.html Or using chocolatey:

choco install -y wkhtmltopdf

Installation via PyPI

Create a virtual environment (optional but recommended)

# 1.0 Create and activate a virtual environment
python -m venv .venv

# Activate the virtual environment
source .venv/bin/activate # On Linux/MacOS
.venv\Scripts\activate # On Windows

Install the package via pip

pip install canoa-data-validate

Usage example after PyPI installation

canoa-data-validate --input_folder data/input --output_folder data/output --locale pt_BR --debug

Installation via GitHub Repository

# 1.1 Clone the repository
git clone https://github.com/AdaptaBrasil/data_validate.git
cd data_validate

# 1.2 Create and activate a virtual environment (optional but recommended)
python -m venv .venv

# 1.3 Activate the virtual environment
source .venv/bin/activate # On Linux/MacOS
.venv\Scripts\activate # On Windows

# 2. Install Poetry (if needed)
pip install poetry

# 3. Install dependencies
poetry install

# 4. Activate the virtual environment
eval $(poetry env activate)

💻 Usage

Basic Command

Full command

python -m data_validate.main \
    --input_folder data/input \
    --output_folder data/output \
    --locale pt_BR \
    --debug

Abbreviated command

python -m data_validate.main --i data/input --o data/output --l pt_BR --d

Pipeline Script

# Full pipeline execution
bash scripts/run_main_pipeline.sh

Execution Modes

Development Mode (Recommended)

# With debug active and detailed logs
python -m data_validate.main --input_folder data/input --debug

Production Mode

# Without logs, time, or version in report
python -m data_validate.main \
    --input_folder data/input \
    --output_folder data/output \
    --no-time \
    --no-version

Fast Mode (without spell checking and title length warnings)

# For quick executions, skipping spell check and title length warnings
python -m data_validate.main \
    --input_folder data/input \
    --no-spellchecker \
    --no-warning-titles-length

Command Line Parameters

Main Arguments

Parameter	Abbreviation	Type	Description	Default	Required
--input_folder	--i	str	Path to input folder with spreadsheets	-	✅
--output_folder	--o	str	Path to output folder for reports	output_data/	❌
--locale	-l	str	Interface language (pt_BR or en_US)	pt_BR	❌

Action Arguments

Parameter	Abbreviation	Type	Description	Default
--debug	--d	flag	Activates debug mode with detailed logs	False
--no-time		flag	Hides execution time information	False
--no-version		flag	Hides script version in final report	False
--no-spellchecker		flag	Disables spell checking	False
--no-warning-titles-length		flag	Disables title length warnings	False

Report Arguments (Optional)

Parameter	Type	Description	Default
--sector	str	Strategic sector name for report	None
--protocol	str	Protocol name for report	None
--user	str	User name for report	None
--file	str	Specific file name to analyze	None

Data Structure

Input (data/input/)

Place your Excel spreadsheets (.xlsx) in the input folder. The system processes:

• descricao.xlsx: Indicator descriptions and metadata
• valores.xlsx: Indicator values
• cenarios.xlsx: Analysis scenarios
• referencia_temporal.xlsx: Temporal references
• composicao.xlsx: Hierarchical compositions
• proporcionalidades.xlsx: Proportions and relationships
• legenda.xlsx: Legends and categories
• dicionario.xlsx: Dictionaries and vocabularies

Output (data/output/)

The system generates:

• HTML Reports: Interactive visualization of results
• PDF Reports: Report generation in PDF format
• Detailed Logs: Execution and error logs

✅ Implemented Validations

Structural Validation

• ✅ Verification of required file existence
• ✅ Validation of column names and order
• ✅ Checking expected data types

Content Validation

• ✅ Sequential codes: Verification of numeric sequence (1, 2, 3...)
• ✅ Unique values: Detection of duplicates in key fields
• ✅ Relationships: Referential integrity validation between spreadsheets
• ✅ Hierarchical levels: Verification of tree structures
• ✅ Scenarios and temporality: Validation of valid combinations

Format Validation

• ✅ Capitalization: Text standardization maintaining acronyms
• ✅ Punctuation: Verification of specific punctuation rules
• ✅ Special characters: Detection of CR/LF and invalid characters
• ✅ Text length: Validation of character limits
• ✅ HTML: Detection of non-permitted HTML tags

Spell Checking

• ✅ Multiple languages: Support for pt_BR and en_US
• ✅ Custom dictionaries: Technical and domain-specific terms
• ✅ Correction suggestions: Automatic recommendations

Data Validation

• ✅ Numeric values: Type and range verification
• ✅ Decimal places: Numeric precision validation
• ✅ Required data: Verification of non-empty fields
• ✅ Valid combinations: Validation of data relationships

📁 Project Structure

data_validate/
├── assets/                       # Badges and visual resources
├── data/                         # Input and output data
│   ├── input/                    # Spreadsheets for validation
│   └── output/                   # Generated reports and logs
├── data_validate/                # Main source code
│   ├── config/                   # Global configurations
│   ├── controllers/              # Orchestration and control
│   │   ├── context/              # Data contexts
│   │   └── report/               # Report generation
│   ├── helpers/                  # Utilities and helper functions
│   │   ├── base/                 # Base classes
│   │   ├── common/               # Common functions
│   │   └── tools/                # Specialized tools
│   ├── middleware/               # Initialization and bootstrap
│   ├── models/                   # Spreadsheet data models
│   ├── static/                   # Static resources
│   │   ├── dictionaries/         # Spell check dictionaries
│   │   ├── locales/              # Translation files
│   │   └── report/               # Report templates
│   └── validators/               # Specialized validators
│       ├── spell/                # Spell checking
│       ├── spreadsheets/         # Spreadsheet validation
│       └── structure/            # Structural validation
├── docs/                         # Generated documentation
├── tests/                        # Unit tests
├── scripts/                      # Automation scripts
└── Configuration Files           # Config files
    ├── pyproject.toml
    ├── Makefile
    └── TESTING.md

🧪 Testing

The project uses pytest for unit testing with complete coverage.

Test Commands

# Run all tests
make test

# Tests with coverage
make test

# Fast tests (stops on error)
make test-fast

# Generate coverage HTML report
make test-short

# Clean test artifacts
make test-clean

# See all available commands
make help

Coverage Metrics

• Current coverage: 45%
• Minimum threshold: 4%
• Modules with 100% coverage: Text and number formatting

Run Specific Tests

# Test specific modules
pytest tests/unit/helpers/common/formatting/ -v
pytest tests/unit/helpers/base/ -v

🛠️ Development

Development Environment Setup

# Install development dependencies
poetry install

# Configure pre-commit hooks
pre-commit install

# Format code with black
make black

# Lint with ruff
make ruff

# Run all linting
make lint

Available Make Commands

Command	Description
make test	Run all tests with coverage
make test-fast	Fast tests (stops on first error)
make test-short	Tests with short output
make test-clean	Remove test artifacts
make badges	Generate coverage and test badges
make clean	Remove temporary files
make black	Format code with Black
make ruff	Lint code with Ruff
make lint	Run all linting tools
make docs	Generate documentation
make help	Show all commands

Test Structure

tests/
└── unit/
    └── helpers/
        ├── base/                 # Base utilities tests
        ├── common/               # Common utilities tests
        │   ├── formatting/       # Formatting tests
        │   ├── generation/       # Generation tests
        │   ├── processing/       # Processing tests
        │   └── validation/       # Validation tests
        └── tools/                # Tools tests

📚 Documentation

Generate Documentation

# Generate documentation with pdoc
make docs

Available Documents

• HOW_IT_WORKS.md: Detailed system architecture
• TESTING.md: Complete testing and coverage guide
• CODE_OF_CONDUCT.md: Development guidelines
• CHANGELOG.md: Version history

🔧 Main Dependencies

Production

• pandas (>=2.2.3): Data manipulation
• chardet (>=5.2.0): Encoding detection
• calamine (>=0.5.3): Excel file reading
• pyenchant (>=3.2.2): Spell checking
• pdfkit (>=1.0.0): PDF generation
• babel (>=2.17.0): Internationalization

Development

• pytest (^8.4.1): Testing framework
• pytest-cov (^6.2.1): Code coverage
• pytest-mock (^3.15.0): Mocking support
• ruff (^0.12.11): Fast linting
• black (^25.1.0): Code formatting
• pre-commit (^4.3.0): Pre-commit hooks

💡 Usage Examples

Basic Validation

# Minimal validation (only input folder is required)
python -m data_validate.main --input_folder data/input

# Validation with specific folder and debug
python -m data_validate.main \
    --input_folder /path/to/spreadsheets \
    --output_folder /path/to/reports \
    --debug

Validation with Different Languages

# Interface in Portuguese (default)
python -m data_validate.main --input_folder data/input --locale pt_BR

# Interface in English
python -m data_validate.main --input_folder data/input --locale en_US

Validation with Advanced Arguments

# Full execution with all arguments
python -m data_validate.main \
    --input_folder data/input \
    --output_folder data/output \
    --locale pt_BR \
    --debug \
    --sector "Biodiversidade" \
    --protocol "Protocolo v2.1" \
    --user "Pesquisador"

Validation with Optimization Flags

# Fast execution without spell checking and length warnings
python -m data_validate.main \
    --input_folder data/input \
    --no-spellchecker \
    --no-warning-titles-length \
    --no-time \
    --no-version

Using Abbreviations (for fast development)

# More concise command using abbreviations
python -m data_validate.main --i data/input --o data/output --l pt_BR --d

Full Pipeline

# Execute full pipeline with logs
bash scripts/run_main_pipeline.sh

📊 Supported Spreadsheet Types

Spreadsheet	Description	Main Validations
sp_description	Indicator descriptions	Sequential codes, hierarchical levels, formatting
sp_value	Indicator values	Referential integrity, numeric types, decimal places
sp_scenario	Analysis scenarios	Unique values, punctuation, relationships
sp_temporal_reference	Temporal references	Temporal sequence, unique symbols
sp_composition	Hierarchical compositions	Tree structure, parent-child relationships
sp_proportionality	Proportions	Mathematical validation, consistency
sp_legend	Legends and categories	Categorical consistency, valid values
sp_dictionary	Dictionaries	Vocabulary integrity

⚡ Performance and Optimization

• Efficient processing: Optimized use of pandas for large datasets
• Parallel validation: Simultaneous execution of independent validations
• Smart caching: Reuse of loaded data
• Structured logging: Optimized logging system for performance

🔍 Monitoring and Quality

Status Badges

• Test Coverage: Automatically generated with genbadge
• Test Status: Updated with each execution
• Version: Synchronized with pyproject.toml

Quality Metrics

• Minimum code coverage: 4%
• Automated tests with pytest
• Linting with ruff
• Automatic formatting with black

🤝 Contributing

Development Process

1. Fork the repository
2. Clone your fork locally
3. Create a branch for your feature (git checkout -b feature/new-feature)
4. Implement your changes with tests
5. Run tests (make test)
6. Commit following the guidelines
7. Push to your branch (git push origin feature/new-feature)
8. Open a Pull Request

Code Guidelines

• Follow PEP 8 standard
• Maintain test coverage >= 50%
• Use type hints
• Document public functions
• Run make black before commit

📋 Roadmap

Version 0.7.X (Planned)

• Complete code refactoring to improve modularity
• Creation of detailed documentation for each module and function in PDOC style
• Full deployment flow via PyPI to facilitate installation and use
• Improvements in the CI/CD Pipeline to include integration tests
• Implementation of integration tests to validate the complete system flow
• Optimization of the logging system for better performance and readability
• Addition of more specific validations for each type of spreadsheet
• Creation of a detailed contribution guide for new collaborators

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Authors

• Pedro Andrade - Coordinator - MAIL and GitHub
• Mário de Araújo Carvalho - Contributor and Developer - GitHub
• Mauro Assis - Contributor - GitHub
• Miguel Gastelumendi - Contributor - GitHub

🔗 Useful Links

• Homepage: AdaptaBrasil GitHub
• Documentation: Docs
• Issues: Bug Tracker
• Changelog: Version History

🐛 Troubleshooting

Uninstalling canoa-data-validate installed via PyPI

pip uninstall canoa-data-validate

Required Arguments

# Error: "argument --input_folder is required"
# Solution: Always specify the input folder
python -m data_validate.main --input_folder data/input

Slow Performance

# For faster execution, disable slow validations
python -m data_validate.main \
    --input_folder data/input \
    --no-spellchecker \
    --no-warning-titles-length

Excessive Logs

# To reduce console output
python -m data_validate.main \
    --input_folder data/input \
    --no-time \
    --no-version

Encoding Problems

# The system automatically detects encoding with chardet
# For problematic files, verify they are in UTF-8

Missing Dependencies

# Install complete dependencies
poetry install

# For pdfkit issues on Linux
sudo apt-get install wkhtmltopdf

# For pyenchant issues
sudo apt-get install libenchant-2-2

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Developed with ❤️ by the AdaptaBrasil team