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Getting Started

Welcome to PyDvlp Debug! This guide will help you start using our Python development utilities in minutes.

Quick Install

pip install pydvlp-debug

First Example

from pydvlp.debug import debugkit

# Debug any value
x = 42
debugkit.ice(x)  # Shows: x = 42

# Debug multiple values
name = "Alice"
debugkit.ice(name, x)  # Shows: name = 'Alice', x = 42

Core Features

1. Smart Debugging

# Debug complex objects
data = {"users": [{"name": "Alice", "age": 30}]}
debugkit.ice(data)

# Debug with context
debugkit.ice("Processing user", id=123, status="active")

2. Performance Profiling

# Profile any function
@debugkit.instrument(profile=True)
def slow_function():
    time.sleep(0.1)
    return "done"

result = slow_function()
# Shows: slow_function took 0.1s

3. Code Analysis

# Analyze code quality
def messy_function(x, y, z=None):
    if x > 0:
        if y > 0:
            return x + y
    return 0

report = debugkit.analyze_code(messy_function)
print(f"Code quality: {report.combined_score}/100")

Common Patterns

Development Workflow

# Configure for development
debugkit.configure(
    debug_enabled=True,
    profile_enabled=True,
    log_level="DEBUG"
)

# Use context for operations
with debugkit.context("user_signup") as ctx:
    ctx.debug("Validating email")
    # ... your code ...
    ctx.success("User created")

Production Mode

# Set production environment
import os
os.environ["PYDVLP_ENVIRONMENT"] = "production"

from pydvlp.debug import debugkit
# Automatically optimized - only errors logged

Installation Options

Basic Install

pip install pydvlp-debug

With Extra Features

# Rich console output
pip install "pydvlp-debug[rich]"

# All features
pip install "pydvlp-debug[all]"

Next Steps

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