# Navigate to project root
cd ../../../
# Run tests for your module
pytest src/codomyrmex/text_analysis/tests/ -v
# Run with coverage
pytest src/codomyrmex/text_analysis/tests/ --cov=src/codomyrmex/text_analysis --cov-report=term
# Add to main test suite
pytest src/codomyrmex/tests/unit/test_text_analysis.py -vCreate a test script to verify functionality:
# test_my_module.py
import sys
sys.path.insert(0, 'src')
from codomyrmex.text_analysis import analyze_text, TextAnalyzer
# Test basic functionality
text = """
This is a sample text for testing our new text analysis module.
It contains multiple sentences and should provide interesting metrics.
The readability should be reasonable for this type of content.
"""
print("=== Testing Text Analysis Module ===")
# Test convenience function
result = analyze_text(text.strip())
print(f"Words: {result.word_count}")
print(f"Characters: {result.character_count}")
print(f"Sentences: {result.sentence_count}")
print(f"Readability: {result.readability_score:.1f}")
print(f"Sentiment: {result.sentiment} ({result.sentiment_score:.2f})")
# Test class-based approach
analyzer = TextAnalyzer(enable_sentiment=False)
result2 = analyzer.analyze_text(text.strip())
print(f"
Without sentiment: {result2.sentiment}")
print("
✅ Module working correctly!")Your module should automatically be discovered by the system. Test this:
# Run system discovery
python -c "
import sys; sys.path.insert(0, 'src')
from codomyrmex.system_discovery import SystemDiscovery
discovery = SystemDiscovery()
modules = discovery.discover_modules()
print('text_analysis' in modules)
print(modules.get('text_analysis', 'Not found'))
"Create integration examples:
# integration_test.py
import sys
sys.path.insert(0, 'src')
from codomyrmex.text_analysis import analyze_text
from codomyrmex.data_visualization import create_bar_chart
# Analyze multiple texts
texts = [
"This is a simple text.",
"This text is more complex with sophisticated vocabulary and longer sentences.",
"Short. Quick. Fast."
]
readability_scores = []
for i, text in enumerate(texts):
result = analyze_text(text)
readability_scores.append(result.readability_score)
print(f"Text {i+1} readability: {result.readability_score:.1f}")
# Create visualization
create_bar_chart(
categories=[f"Text {i+1}" for i in range(len(texts))],
values=readability_scores,
title="Text Readability Comparison",
y_label="Readability Score",
output_path="text_readability.png",
show_plot=False
)
print("✅ Integration test completed! Check text_readability.png")Update USAGE_EXAMPLES.md:
# Text Analysis Usage Examples
## Basic Text Analysis
```python
from codomyrmex.text_analysis import analyze_text
text = """
Artificial intelligence is transforming how we work and live.
It offers tremendous opportunities for innovation and efficiency.
However, we must consider the ethical implications carefully.
"""
result = analyze_text(text)
print(f"Readability Score: {result.readability_score:.1f}")
print(f"Sentiment: {result.sentiment}")from codomyrmex.text_analysis import analyze_file
# Analyze a document
result = analyze_file("my_document.txt")
print(f"Document has {result.word_count} words")
print(f"Average words per sentence: {result.average_words_per_sentence:.1f}")from codomyrmex.text_analysis import analyze_file
from codomyrmex.data_visualization import create_line_plot
import os
# Analyze all text files in a directory
files = [f for f in os.listdir('.') if f.endswith('.txt')]
readability_scores = []
for file in files:
result = analyze_file(file)
readability_scores.append(result.readability_score)
# Plot readability trend
create_line_plot(
x_data=list(range(len(files))),
y_data=readability_scores,
title="Document Readability Analysis",
x_label="Document Index",
y_label="Readability Score",
output_path="readability_trend.png"
)from codomyrmex.text_analysis import TextAnalyzer
from pathlib import Path
import json
analyzer = TextAnalyzer(enable_sentiment=True)
results = {}
# Process all markdown files in a project
for md_file in Path("docs/").rglob("*.md"):
try:
result = analyzer.analyze_file(str(md_file))
results[str(md_file)] = {
'words': result.word_count,
'readability': result.readability_score,
'sentiment': result.sentiment
}
except Exception as e:
print(f"Error processing {md_file}: {e}")
# Save results
with open('text_analysis_results.json', 'w') as f:
json.dump(results, f, indent=2)
print(f"Analyzed {len(results)} files")
## 🚀 Step 9: Final Integration
### **Add to Main Test Suite**
Create `src/codomyrmex/tests/unit/test_text_analysis.py`:
```python
"""Integration tests for text_analysis module with main test suite"""
import pytest
from codomyrmex.text_analysis import analyze_text, TextAnalyzer
class TestTextAnalysisIntegration:
"""Integration tests for text analysis module"""
def test_module_import(self):
"""Test that module imports correctly"""
from codomyrmex.text_analysis import TextAnalyzer, analyze_text
assert TextAnalyzer is not None
assert analyze_text is not None
def test_basic_functionality(self):
"""Test basic module functionality"""
result = analyze_text("This is a test sentence.")
assert result.word_count == 5
assert result.sentence_count == 1
def test_logging_integration(self):
"""Test integration with Codomyrmex logging"""
# This test ensures the module uses Codomyrmex logging
analyzer = TextAnalyzer()
result = analyzer.analyze_text("Test logging integration.")
assert result is not None
Add your module to the main documentation in docs/modules/overview.md (add to the appropriate table).
# Run all tests to ensure no regressions
pytest src/codomyrmex/tests/ -v
# Run your module specifically
pytest src/codomyrmex/tests/unit/test_text_analysis.py -v
# Check system discovery
python -c "
import sys; sys.path.insert(0, 'src')
from codomyrmex.system_discovery import SystemDiscovery
discovery = SystemDiscovery()
discovery.run_full_discovery()
"- Module structure follows Codomyrmex conventions
- Core functionality implemented and working
- Comprehensive test suite with >90% coverage
- Integration with Codomyrmex logging system
- API documentation complete and accurate
- Usage examples work correctly
- Integration with other modules tested
- Error handling follows project conventions
- Security considerations documented
- Performance tested with reasonable limits
If contributing back to Codomyrmex:
-
Commit your changes:
git add . git commit -m "feat: add text_analysis module with comprehensive text analysis capabilities"
-
Push and create PR:
git push origin feature/text-analysis-module
-
Include in PR description:
- Overview of module functionality
- Testing approach and coverage
- Integration points with existing modules
- Performance characteristics
- Future enhancement possibilities
You've successfully created a complete Codomyrmex module! Your text_analysis module now provides:
- ✅ Comprehensive text analysis capabilities
- ✅ Full integration with the Codomyrmex ecosystem
- ✅ Professional documentation and examples
- ✅ Robust test coverage
- ✅ Integration with other modules
- ✅ Following all Codomyrmex conventions
- Add support for more languages
- Implement advanced sentiment analysis with ML models
- Add text classification capabilities
- Create visualization templates for common analyses
- Add export formats (CSV, JSON, XML)
- Submit a pull request to the main Codomyrmex repository
- Create blog posts about your module
- Present at community meetups
- Help others create their own modules
- Create workflows combining text analysis with AI code editing
- Build documentation analysis tools
- Add text analysis to build pipelines
- Create automated content quality checks
This tutorial showed you the complete process of creating a professional-grade Codomyrmex module. The patterns and practices you've learned can be applied to create any type of module for the Codomyrmex ecosystem!
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