semvec/pypi-packages huggingface dataset:
PromptFileLoader 1.0.2
A simple prompt loader supporting YAML and TXT files with caching
Requires Python: >=3.6

# PromptFileLoader

A simple and efficient Python package to load prompt files in YAML or TXT format with built-in caching.

---

## Overview

`PromptFileLoader` helps you easy to load prompt files for your applications, supporting both:

- YAML files (`.yaml`, `.yml`) parsed into Python dictionaries or lists, and  
- Plain text files (`.txt`) loaded as strings.

It caches loaded files internally for faster repeated access without redundant disk reads.

---

## Features

- Supports YAML and TXT prompt files  
- Caching to optimize file loading performance  
- Easy to initialize with a custom prompts directory or default folder  
- Minimal dependencies (only requires PyYAML)  
- Clear error handling for missing files or unsupported extensions

---

## Installation

Install via pip:

```bash
pip install promptfileloader
ntypo 2.1.0
A terminal typing speed test built with ncurses
Requires Python: >=3.8

# Ntypo - Yet Another Typing Test

**Ntypo** is a minimal terminal-based typing speed test built with Python and `ncurses`.

It lets you challenge yourself to type a custom number of randomly selected words from a list, no distractions, no GUI fluff. Just you, your keyboard, and the clock.

## Features

- Choose how many words you want to type (e.g. 30, 50, 100…)
- Loads words from an external file (`words.txt`)
- Measures WPM (Words Per Minute)
- Tracks accuracy, correct and incorrect keystrokes
- ESC to quit anytime (with confirmation)
- Saves session logs to CSV (`ntypo.csv`)
- Adaptive layout with top/bottom bars
- Fully keyboard-driven and terminal-native

## Installation

Install via pip:

```bash
pip install ntypo
```

Then run the app:
```bash
ntypo
```

To install from source:

```bash
git clone https://github.com/bearenbey/ntypo.git
cd ntypo
pip install .
```

## Dependencies:

- Python 3.8 or higher
- A terminal that supports ncurses

## Usage

1. Launch the app in your terminal
2. Enter how many words you want to practice
3. Type the displayed text — your speed and stats will be tracked
4. Press ESC anytime to quit (confirmation popup included)

Keybindings:

- Type normally: your input is tracked in real time
- Backspace: delete last character
- ESC: open quit confirmation
- Any key: dismiss the end-of-test message

The app adjusts to terminal size and wraps text dynamically. All results are saved to ntypo.csv.

## Words File

The app uses a simple plaintext file (words.txt) where each line is a word:

```bash
apple
orange
keyboard
syntax
...
```
You can modify this list or swap it out for your own vocabulary.

## License

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see https://www.gnu.org/licenses/.

© 2025 Eren Öğrül — termapp@pm.me
uk-tides-mcp 1.0.0
Model Context Protocol (MCP) server for UK low & high beach tides
Requires Python: >=3.11

# UK tide times MCP

Signup for $UKHO_API_KEY here: https://admiraltyapi.developer.azure-api.net/

UK Tidal API - Discovery is free for a year

    claude mcp add uktides -s project -e UKHO_API_KEY=$UKHO_API_KEY -- uvx uk-tides-mcp
Olivattam 0.0.3
A package for extraction of major colors from the Images
Requires Python: >=3.6

# 🎨 Olivattam

**Olivattam** is a Python module for extracting dominant colors from images and converting them into multiple formats like RGB, HEX, HSL, and RGBA. Built using [Pillow](https://python-pillow.org), it's designed to be lightweight, fast, and easy to integrate into any image-processing workflow.

---

## 🚀 Features

- Extract top N dominant colors from any image
- Convert RGB colors to:
  - HEX (`#rrggbb`)
  - HSL (`hue, saturation, lightness`)
  - RGBA (`r, g, b, alpha`)
- Simple API for quick analysis
- Modular design for easy extension

---

## 📦 Installation

Clone the repo and install in editable mode:

```bash
git clone https://github.com/Dheenack/olivattam.git
cd olivattam
pip install -e .
nix-shell 0.0.3
Invoke reproducible Nix shells from Python.
Requires Python: >=3.10

# py-nix-shell

Invoke commands sourced from Nix. This is mainly intended for those
who vend cli tools inside environments that often support Nix and need
to be able to reproducibly invoke those tools.

If folks don't have Nix, it will attempt to run the command anyways,
and then provide an information install-error style message (either
packages to install or a suggestion to install Nix).

This is a drop-in replacement for `subprocess` -- so you can
substitute your calls from `subprocess.<method>` to
`nix_shell.<method>`.

If Nix is not installed, this (by default) falls back to a non-Nix shell.

## Installation

```bash
pip install nix-shell
```

## Usage

```python
import nix_shell

# nix_shell supports existing subprocess commands

# if the command name matches the nixpkgs name, use that
# see https://search.nixos.org/packages for a list of packages
nix_shell.run(["curl", "https://google.com"])

# you can also specify the nix packages to install
nix_shell.run(["curl", "https://google.com"], packages=["curlMinimal"])

# you can also use a dev environment from a flake
nix_shell.run(["curl", "https://google.com"], flake="github:chadac/py-nix-shell#sample-curl-env")

# this supports
```

It is also possible to specify a common environment to run commands under:

```python
import nix_shell

nix = nix_shell.from_flake("/path/to/my/flake.nix")

nix.run(["curl", "https://google.com"])
```

```python
nix_shell.mk_shell(packages=["git"])
```
Olivattam-Dheenack 0.0.1
A package for extraction of major colors from the Images
Requires Python: >=3.8

# 🎨 Olivattam

**Olivattam** is a Python module for extracting dominant colors from images and converting them into multiple formats like RGB, HEX, HSL, and RGBA. Built using [Pillow](https://python-pillow.org), it's designed to be lightweight, fast, and easy to integrate into any image-processing workflow.

---

## 🚀 Features

- Extract top N dominant colors from any image
- Convert RGB colors to:
  - HEX (`#rrggbb`)
  - HSL (`hue, saturation, lightness`)
  - RGBA (`r, g, b, alpha`)
- Simple API for quick analysis
- Modular design for easy extension

---

## 📦 Installation

Clone the repo and install in editable mode:

```bash
git clone https://github.com/Dheenack/olivattam.git
cd olivattam
pip install -e .
AdaptiveFoldTS 1.0.1
Validação Cruzada Adaptativa para Séries Temporais com Priorização Inteligente de Folds
Requires Python: >=3.7

# AdaptiveFoldTS

**Validação Cruzada Adaptativa para Séries Temporais com Priorização Inteligente de Folds**

AdaptiveFoldTS é uma biblioteca Python inovadora projetada para realizar validação cruzada em séries temporais utilizando uma abordagem adaptativa e priorizada. Utilizando estruturas de heap, a biblioteca organiza janelas de treino e teste com base em critérios dinâmicos — como variância, erro preditivo ou outras métricas customizáveis — permitindo que o processo de validação foque nos segmentos mais críticos ou desafiadores da série temporal.

Durante o processo, as prioridades dos folds são atualizadas iterativamente conforme as métricas dos modelos são calculadas. Isso significa que folds com maiores erros ou maior variabilidade recebem maior atenção, sendo reavaliados, o que possibilita uma melhor identificação do modelo que entrega o melhor desempenho global.

---

## Principais características

- Priorização adaptativa das janelas de validação via heap para maximizar eficiência e insight.  
- Atualização dinâmica das prioridades baseada nas métricas de erro dos modelos, para foco em folds mais relevantes.  
- Suporte a múltiplas estratégias de janelamento para treino e teste (rolling window, expanding window, etc).  
- Integração simples com modelos sklearn-like ou customizados.  
- Avaliação simultânea de múltiplos modelos com métricas configuráveis e ranking automático.  
- Ferramentas para análise detalhada do desempenho por fold e geração de relatórios.

---

## Instalação

```bash
pip install adaptivefoldts
```

## Exemplo básico de uso
```python
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.dummy import DummyRegressor

from adaptivefoldts import AdaptiveFoldTS

np.random.seed(42)
series = pd.Series(np.sin(np.linspace(0, 20, 100)) + np.random.normal(0, 0.5, 100))

models = {
    "linear_regression": LinearRegression(),
    "dummy_mean": DummyRegressor(strategy="mean"),
}

af = AdaptiveFoldTS(
    series=series,
    window_size=30,
    test_size=0.2,
    step_size=5,
    strategy="rolling",
    metric="mse",
    verbose=True,
    max_folds=None,
)

results = af.evaluate_models(models=models, max_iterations_per_fold=1)

print("Resultados agregados por modelo:")
for model_name, score in results.items():
    print(f"{model_name}: {score:.4f}")
```

## Funcionalidades Futuras

- Implementação de grid search e otimização automatizada de hiperparâmetros integrada ao processo adaptativo.
- Suporte a mais métricas customizáveis, incluindo métricas específicas para séries temporais (ex: MASE, SMAPE).
- Inclusão de outras estratégias de janelamento adaptativo, como janelas expansivas e segmentações baseadas em eventos.
- Visualização interativa dos folds, métricas e prioridades ao longo do processo de validação.
- Paralelização do processo de avaliação para acelerar experimentos em grandes conjuntos de dados e múltiplos modelos.