semvec/pypi-packages huggingface dataset:
fastmlcore 1.1.0
A high-performance, extensible machine learning library for Python. fastmlcore provides a rich suite of algorithms, preprocessing tools, metrics, and utilities for rapid prototyping and production-grade ML workflows. Designed for clarity, speed, and flexibility, it empowers researchers, engineers, and data scientists to build, evaluate, and deploy models with ease.
Requires Python: >=3.7

# mlcore

**mlcore** is a simple, extensible machine learning library for Python.

## Features
- LinearRegression, LogisticRegression, DecisionTree, KMeans, SVM, RandomForest, NaiveBayes, NeuralNetwork
- Preprocessing: StandardScaler, MinMaxScaler, OneHotEncoder, Imputer, PCA
- Metrics: mean_squared_error, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, log_loss, roc_auc_score
- Utilities: train_test_split, cross_val_score, grid_search

## Installation

```bash
pip install mlcore
```

## Usage Example

```python
from mlcore import LinearRegression, mean_squared_error
import numpy as np

X = np.array([[2, 600], [3, 900], [6, 1700], [5, 1300], [4, 1050], [7, 1900]])
y = np.array([120000, 180000, 400000, 320000, 250000, 450000])

model = LinearRegression()
model.fit(X, y)
preds = model.predict(X)
print(mean_squared_error(y, preds))
```

## License

MIT
orch_serv 0.2.0
Library for fast build service and interaction management
Requires Python: >=3.10

# orch_serv
[![Coverage Status](https://img.shields.io/badge/%20Python%20Versions-%3E%3D3.10-informational)](https://pypi.org/project/orch-serv/)
[![Coverage Status](https://coveralls.io/repos/github/Shchusia/orchestrator_service/badge.svg)](https://coveralls.io/github/Shchusia/orchestrator_service)

[![Coverage Status](https://img.shields.io/badge/Version-0.2.0-informational)](https://pypi.org/project/orch-serv/)
[![Coverage Status](https://img.shields.io/badge/Docs-passed-green)](https://github.com/Shchusia/orchestrator_service/tree/master/docs)
> A library for microservice architecture, for interconnected services with different execution sequences, for orchestration services designed for such kind of services relationships.


### Installation

```bash
pip install orch_serv
```

### Problem

We have a microservice architecture

Your architecture has many services. Each individual service performs its own task. You need to set up the sequence of interactions between services, taking into account that one service can be connected to others and fired in a different sequence.

<u>Example:</u>

Task | Service execution sequence 
--- | --- 
*Task1* | service1 -> service2 -> service3
*Task2* | service2 -> service4 -> service1
*...* | ...
*TaskN* | service3 -> service2 -> ... -> serviceM

For centralized management of the services interactions, we offer an **orchestrator**: service containing valid block connections.

![file not found](http://raw.githubusercontent.com/Shchusia/orchestrator_service/refs/heads/master/materials/solution_application_diagram.png "solution_application_diagram")

What you need to do in the orchestrator service:
- create blocks with known interaction logic with the services they belong to,
- create accessible flows from these blocks (for different tasks there can be your specific flows, it is important to use unique flow names),
- define a message for the interaction of services, such that they have a single standard and override key methods,
- initialize the orchestrator and use,
- [More details](./orяch_serv/orchestrator/README.md).

What you need to use the service:
- define a message for the interaction of services, such that they have a single standard and override key methods,
- define the commands in the service which are going to be executed and what to do after the main task is completed (this is divided to separate the logic).
- [More details](./orch_serv/service/README.md)

#### Bonus

Added functionality for the formation of a sequence of steps - [details here](./orch_serv/stepper/README.MD)
nrl-test-data-generator 0.3.1
A utility for generating test data for the NRL (Nasjonalt Register over Luftfartshindre) project.
Requires Python: >=3.13

# NRL Test Data Generator

A utility for generating test data for the NRL (Nasjonalt Register over Luftfartshindre) project.

## Installation
Using pip:

```bash
pip install nrl-test-data-generator
```

or using pipx:

```bash
pipx install nrl-test-data-generator
```

## Usage

Generate Excel and GeoJSON files with NRL-specific data:

```bash
nrl-test-data-generator --num-elements 5 --status eksisterende
```

Options:
- `--num-elements`, `-n`: Number of elements of each type to generate (default: 2)
- `--total-elements`, `-t`: Total number of elements to generate (divided equally)
- `--output-prefix`, `-o`: Prefix for output filenames (default: testdata)
- `--status`, `-s`: Status value for elements (choices: eksisterende, fjernet, planlagtFjernet, planlagtOppført; default: planlagtOppført)

### Help

To see all available options:

```bash
nrl-test-data-generator --help
```

## Output Files

The tool generates two files:
1. An Excel file (.xlsx) with detailed information about mast points and trase elements
2. A GeoJSON file (.geojson) with geographic features corresponding to the same elements

Both files include:
- Mast points (with properties like height, installation year, material type)
- Trase lines (connections between mast points)

The filenames include the number of elements and a timestamp to ensure uniqueness.

### Note on Coordinates and Heights

To prevent validation errors with the Norwegian National Detailed Height Model (NDH), this tool uses predefined coordinates near Larvik with height=0. This approach ensures the generated data will be accepted by the NRL API, which validates that object heights are consistent with the terrain elevation data.

The fixed coordinates limit geographic variation but allow for creating valid test data that can be used with the NRL system.

## Setting Up a Development Environment

This project uses `uv` for Python package management. If you haven't installed `uv` yet, you can find instructions at [the uv GitHub repository](https://github.com/astral-sh/uv).

```bash
# Clone the repository
# Navigate to the project directory

# Install dependencies
uv sync
```

Run pipeline:

```bash
uv run poe release
```

## Dependencies

- click: For command-line interface
- xlsxwriter: For generating Excel files