semvec/pypi-packages huggingface dataset:
enea-outages 0.3.1
Python library to get information about power outages from Enea Operator.
Requires Python: >=3.9
Keywords: api, enea, outages, power

# Enea Outages Python Library

A simple Python library to get information about power outages from the Enea Operator website.

## Installation

```bash
pip install enea-outages
```

## Usage (Python)

```python
from enea_outages.client import EneaOutagesClient
from enea_outages.models import OutageType

# Initialize the synchronous client
client = EneaOutagesClient()

# Get a list of available regions
regions = client.get_available_regions()
print(f"Available regions: {regions}")

# Get all planned outages for the "Poznań" region
planned_outages = client.get_outages_for_region("Poznań", outage_type=OutageType.PLANNED)
print(f"Found {len(planned_outages)} planned outages in Poznań.")

# Get all unplanned outages for a specific address in "Szczecin"
unplanned_outages = client.get_outages_for_address(
    address="Wojska Polskiego",
    region="Szczecin",
    outage_type=OutageType.UNPLANNED
)
print(f"Found {len(unplanned_outages)} unplanned outages for the address.")

if unplanned_outages:
    outage = unplanned_outages[0]
    print(
        f"Example -> Area: {outage.region}, "
        f"Description: {outage.description}, "
        f"End Time: {outage.end_time}"
    )
```

## Usage (CLI)

The library also provides a command-line interface (CLI) for quick checks.

```bash
# List all available regions
enea-outages --list-regions

# Get unplanned outages for a specific region
enea-outages --region "Poznań" --type unplanned

# Get planned outages for a specific address in a region
enea-outages --region "Szczecin" --address "Wojska Polskiego" --type planned
```

---

*This project was developed with the assistance of AI tools (Google Gemini). While the code has been reviewed, please use it with standard caution.*
csa-prediction-engine 2.3.0
The csa_prediction_engine library provides a suite of tools and functions for performing relevance-based predictions using the Cambridge Sports Analytics Prediction Engine API. The package is designed to facilitate single and multi-task predictions, allowing for flexible model evaluation and experimentation.

# Cambridge Sports Analytics Prediction Engine

The **CSA Prediction Engine** is the official Python client for interacting with the Cambridge Sports Analytics (CSA) API. It enables relevance-based predictions using flexible configurations, including support for batch jobs, grid evaluations, and multi-task prediction workflows.

📦 **Source code**: [github.com/CambridgeSportsAnalytics/csa_prediction_engine](https://github.com/CambridgeSportsAnalytics/csa_prediction_engine)


## 🔍 Key Features

- **Single Task Predictions**: Support for predictions with one dependent variable and one set of circumstances.
- **Multi-y Predictions**: Perform predictions with multiple dependent variables and a single set of circumstances.
- **Multi-theta Predictions**: Perform predictions with one dependent variable and multiple sets of circumstances.
- **Relevance-Based Grid Predictions**: Generate optimal predictions by evaluating all thresholds and variable combinations.
- **Grid Singularity Predictions**: Analyze grid predictions to find the singular optimal solution.
- **MaxFit Predictions**: Find the best-fit model based on adjusted relevance.

## 🚀 Installation

Install via PyPI:

```bash
pip install csa-prediction-engine
```
Requires Python 3.11

## 📘 Documentation & Examples

For example scripts, OpenAPI specs, and quickstart usage, visit the companion repo:
👉 [CSA Prediction Engine Quickstart](https://github.com/)

## 🤝 Contributing

We welcome feedback, feature suggestions, and bug reports. Reach out to our team 📧 support@csanalytics.io

## ⚖️ License

Copyright c) 2023 - 2025 Cambridge Prediction Analytics, LLC. All rights reserved.
csa-common-lib 2.3.0
csa_common_lib is a shared library designed to provide utility modules, class definitions, enumerations, and helper functions for the CSA Prediction Engine Python client. It standardizes and simplifies complex operations across different parts of the CSA Prediction Engine.

# Cambridge Sports Analytics Common Library

`csa_common_lib` is a shared library designed to provide utility modules, class definitions, enumerations, and helper functions for the CSA Prediction Engine Python client. It standardizes and simplifies complex operations across different parts of the system.

## Folder Structure

- **`classes/`**: Contains class definitions for prediction-related data structures.
  - `prediction_options.py`: Defines options and configurations for predictions.
  - `prediction_receipt.py`: Structures data related to prediction receipts.
  - `prediction_results.py`: Contains classes for handling prediction results.
  
- **`enum_types/`**: Contains enumerations that categorize various elements of the system.
  - `exit_flags.py`: Defines exit flag states used during execution.
  - `functions.py`: Enumerates available functions in the system.
  - `job_types.py`: Enumerates different job types for concurrency.

- **`helpers/`**: Provides helper functions for common low-level tasks.
  - `_arrays.py`: Array manipulation and utility functions.
  - `_conversions.py`: Data conversion utilities.
  - `_os.py`: Functions for interacting with the operating system.

- **`toolbox/`**: A general-purpose set of utilities.
  - **`classes/utilities.py`**: Utilities for class objects.
  - **`concurrency/`**: Tools for concurrent execution.
  - **`database/`**: Database related tools and utilities.
  - **`npz/`**: NPZ utility functions.

## Usage

Here is an example of how to use the prediction class options from the common library:

```python
from csa_common_lib.classes.prediction_options import {
        PredictionOptions,
        MaxFitOptions,
        GridOptions
    }
```


## 🤝 Contributing

We welcome feedback, feature suggestions, and bug reports. Reach out to our team 📧 support@csanalytics.io

## ⚖️ License

Copyright c) 2023 - 2025 Cambridge Prediction Analytics, LLC. All rights reserved.