Frequenz Core Library

Introduction

Core utilities to complement Python's standard library. This library provides essential building blocks for Python applications, including mathematical utilities, datetime constants, typing helpers, strongly-typed identifiers, and module introspection tools.

The frequenz-core library is designed to be lightweight, type-safe, and follow modern Python best practices. It fills common gaps in the standard library with utilities that are frequently needed across different projects.

Supported Platforms

The following platforms are officially supported (tested):

• Python: 3.11
• Operating System: Ubuntu Linux 20.04
• Architectures: amd64, arm64

Installation

You can install the library from PyPI using pip:

python -m pip install frequenz-core

Or add it to your project's dependencies in pyproject.toml:

[project]
dependencies = [
    "frequenz-core >= 1.0.2, < 2",
]

Note

We recommend pinning the dependency to the latest version for programs, like "frequenz-core == 1.0.2", and specifying a version range spanning one major version for libraries, like "frequenz-core >= 1.0.2, < 2". We follow semver.

Quick Start

Here's a quick overview of the main functionality:

from frequenz.core.math import is_close_to_zero, Interval
from frequenz.core.datetime import UNIX_EPOCH
from frequenz.core.module import get_public_module_name

# Math utilities
print(is_close_to_zero(1e-10))  # True - check if float is close to zero
interval = Interval(1, 10)
print(5 in interval)  # True - check if value is in range

# Datetime utilities
print(UNIX_EPOCH)  # 1970-01-01 00:00:00+00:00

# Module utilities
public_name = get_public_module_name("my.package._private.module")
print(public_name)  # "my.package"

Code Examples

Math Utilities

The math module provides utilities for floating-point comparisons and interval checking:

from frequenz.core.math import is_close_to_zero, Interval

# Robust floating-point zero comparison
assert is_close_to_zero(1e-10)  # True
assert not is_close_to_zero(0.1)  # False

# Interval checking with inclusive bounds
numbers = Interval(0, 100)
assert 50 in numbers  # True
assert not (150 in numbers)  # False - 150 is outside the interval

# Unbounded intervals
positive = Interval(0, None)  # [0, ∞]
assert 1000 in positive  # True

Typing Utilities

Disable class constructors to enforce factory pattern usage:

from frequenz.core.typing import disable_init

@disable_init
class ApiClient:
    @classmethod
    def create(cls, api_key: str) -> "ApiClient":
        # Factory method with validation
        instance = cls.__new__(cls)
        # Custom initialization logic here
        return instance

# This will raise TypeError:
# client = ApiClient()  # ❌ TypeError

# Use factory method instead:
client = ApiClient.create("my-api-key")  # ✅ Works

Strongly-Typed IDs

Create type-safe identifiers for different entities:

from frequenz.core.id import BaseId

class UserId(BaseId, str_prefix="USR"):
    pass

class OrderId(BaseId, str_prefix="ORD"):
    pass

user_id = UserId(123)
order_id = OrderId(456)

print(f"User: {user_id}")  # User: USR123
print(f"Order: {order_id}")  # Order: ORD456

# Type safety prevents mixing different ID types
def process_user(user_id: UserId) -> None:
    print(f"Processing user: {user_id}")

process_user(user_id)  # ✅ Works
# process_user(order_id)  # ❌ Type error

Documentation

For information on how to use this library, please refer to the documentation.

Contributing

If you want to know how to build this project and contribute to it, please check out the Contributing Guide.