prostata 0.2.0

A Python library for tracking statistics with timers, counters, ratios, and attributes

prostata

A Python library for PROcessing STATistics with timers, counters, ratios, and attributes.

Installation

pip install prostata

Usage

from prostata import Stats

# Create a stats instance
stats = Stats()

# Create a timer
stats.set_timer("response_time")

# Create counters
stats.set_counter("requests", 0, "count")
stats.set_counter("errors", 0, "count")

# Create an attribute
stats.set_attribute("version", "1.0.0")

# Start timing
stats.start_response_time()  
# Equivalent to stats.start_timer("response_time")

# Simulate some work
import time
time.sleep(0.1)

stats.incr_requests()        
# Equivalent to stats.incr("requests")

# Add 10 to errors
stats.incr_errors(10)
# Equivalent to stats.incr("errors", 10)

# Stop timing
stats.stop_response_time()
#stats.stop_timer("response_time")

# Get values
print(f"Response time: {stats.get_response_time()):.2f} seconds")
print(f"Requests: {stats.get_requests()}")
print(f"Errors: {stats.get_errors()}")
print(f"Version: {stats.get_version()}")

# Equivalent:
#print(f"Response time: {stats.get_timer('response_time'):.2f} seconds")
#print(f"Requests: {stats.get_counter('requests')}")
#print(f"Errors: {stats.get_counter('errors')}")
#print(f"Version: {stats.get_attribute('version')}")

# Create a ratio
stats.set_ratio("error_rate", "errors", "requests")
print(f"Error rate: {stats.get_error_rate():.2%}")
# print(f"Error rate: {stats.get_ratio('error_rate'):.2%}")

Features

Timers

Track elapsed time with start/stop functionality:

stats.set_timer("operation_time")
stats.start_timer("operation_time")
# ... do work ...
stats.stop_timer("operation_time")
elapsed = stats.get_timer("operation_time")

Counters

Count events with units:

stats.set_counter("bytes_processed", 0, "bytes")
stats.incr("bytes_processed", 1024)
stats.decr("bytes_processed", 100)
current = stats.get_counter("bytes_processed")

Ratios

Calculate ratios between counters:

stats.set_counter("success", 95)
stats.set_counter("total", 100)
stats.set_ratio("success_rate", "success", "total")
rate = stats.get_ratio("success_rate")  # 0.95

Attributes

Store arbitrary values:

stats.set_attribute("config", {"debug": True})
stats.set_attribute("timeout", 30)
value = stats.get_attribute("config")

Dynamic Methods

Each stat creates dynamic methods for easy access:

stats.set_timer("load_time")
stats.set_counter("items")
stats.set_attribute("status")

# Dynamic methods are automatically created
stats.start_load_time()
stats.incr_items()
stats.set_status("running")

Labels

Each stat can have a descriptive label (defaults to the stat name):

# Custom labels
stats.set_timer("db_query", "Database Query Time")
stats.set_counter("requests", label="HTTP Requests")
stats.set_attribute("version", "1.2.0", "App Version")

# Default labels (same as name)
stats.set_timer("cpu_time")  # label = "cpu_time"

# Update labels
stats.set_label("db_query", "Database Query Execution Time")

# Get labels
all_labels = stats.get_labels()
timer_labels = stats.get_labels_for_timers()
counter_labels = stats.get_labels_for_counters()
ratio_labels = stats.get_labels_for_ratios()
attr_labels = stats.get_labels_for_attributes()

Labels can be repeated across different stat types but names must be unique.

Name Validation

• Names must be unique across all stat types
• Names can only contain lowercase letters, digits and underscore (i.e [a-z0-9_])
• Names cannot use reserved words: "timer" "timers", "counter", "counters" "ratio", "ratios", "attribute" and "attributes"
• Raises NameExists if name is already used
• Raises NameNotAllowed for reserved words or invalid names

Exceptions

• NameNotAllowed: When using reserved or incorrect format names
• NameExists: When name is already in use
• NameNotExists: When accessing non-existent stats

Development

To install in development mode:

pip install -e .[dev]

To run tests:

python -m pytest

To run tests with coverage:

python -m pytest --cov=prostata --cov-report term-missing

Publishing

GitHub Actions

To publish a new release to PyPI:

1. Create a new release on GitHub with a version tag (e.g., v0.1.0)
2. The GitHub Action will automatically build and publish the package to PyPI

Add PYPI_API_TOKEN as a repository secret.

Manual (Command Line)

Alternatively, you can publish manually using the provided script. Make sure you're in an activated virtual environment with the necessary tools installed:

# Activate virtual environment (if not already active)
source .venv/bin/activate

# For production PyPI
PYPI_API_TOKEN=your_token_here ./bin/release.sh

# For Test PyPI (recommended first)
TEST_PYPI_API_TOKEN=your_test_token_here ./bin/release.sh --test

# Skip confirmations (useful for automation)
TEST_PYPI_API_TOKEN=your_test_token_here ./bin/release.sh --test -y

Documentation

The documentation is built using MkDocs with the Material theme.

Building Documentation

To build the documentation locally:

# Install development dependencies (includes MkDocs)
pip install -e .[dev]

# Build the documentation
mkdocs build

# The built site will be in the `site/` directory

Serving Documentation Locally

To serve the documentation with live reload during development:

# Start the development server
mkdocs serve

# Open http://localhost:8000 in your browser

Documentation Structure

The documentation is versioned using mike:

To work with versioned documentation locally:

# Install mike
pip install mike

# Deploy a new version (replace X.Y.Z with actual version)
mike deploy X.Y.Z latest

# List all versions
mike list

# Set default version
mike set-default latest

# Delete a version
mike delete X.Y.Z

The documentation is automatically deployed to GitHub Pages on the main branch and on new releases (.github/docs.yml)