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118 changes: 117 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,123 @@

## Introduction

Core utilities to complement Python's standard library.
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.

## Installation

You can install the library from PyPI using pip:

```bash
pip install frequenz-core
```

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

```toml
[project]
dependencies = [
"frequenz-core",
]
```

## Quick Start

Here's a quick overview of the main functionality:

```python
from frequenz.core.math import is_close_to_zero, Interval
from frequenz.core.datetime import UNIX_EPOCH
from frequenz.core.typing import disable_init
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This is imported in this example but not used, same with BaseId

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Fixed by removing the unused imports from the Quick Start section (dd17d89). The section now only imports what it actually demonstrates: math utilities, datetime constants, and module introspection.

from frequenz.core.id import BaseId
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:

```python
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:

```python
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:

```python
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

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