semvec/pypi-packages huggingface dataset:
teste-bem-vindo 0.0.1
Desafio Dio criando um pacote
Requires Python: >=3.8

# package_name

Description. 
The package bem_vindo is used to:
	- Print Bem Vindo 
	-

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install bem_vindo
```bash
pip install bem_vindo
```

## Usage

```python
from package_name import file1_name
file1_name.bem_vindo()
```

## Author
Felipe

## License
[MIT](https://choosealicense.com/licenses/mit/)
plateypus 0.6.11
Processes and plots high throughput cytometry experiments through a GUI

![PLATEypusLogo](https://user-images.githubusercontent.com/49458891/116960335-185c1500-ac6e-11eb-92e0-369334eb7882.png)

A GUI to process and plot high throughput bulk cytokine, surface marker, and single cell data exported from Flowjo  
  
Main powerpoint tutorial for GUI is hosted on google drive:   
https://drive.google.com/file/d/1jt8ZlOiArbiVK-RyF1_rrS8swEYsqWae/view?usp=sharing

To use this package:
1. If anaconda is not installed, follow installation instructions here: https://docs.anaconda.com/anaconda/install/
2. Start terminal, type in and enter:
```
conda activate
```
3. Now install plateypus via PyPi by typing the following line in the terminal:
```
pip install plateypus
```
4. In this anaconda environment, type: 
```
python3 -m plateypus
```
to start the GUI
autoslurm 0.1.0
custom decorator to turn any function slurm-deployable. Can submit multiple jobs and multiple job_arrays
Requires Python: >=3.12

# autoslurm

A lightweight Python decorator to conditionally submit functions as SLURM jobs (or job arrays), falling back to local execution when SLURM is not available.

## 🚀 Key Features

- 📦 **Drop-in simple**: Decorate any function with `@autoslurm(...)`.
- 🔍 **Auto-detects SLURM**: Will submit jobs via SLURM if available, otherwise runs locally.
- ⚙️ **Unified interface**: Same code works on your laptop or cluster — no changes needed.
- 🧠 **Smart job control**: Supports both individual job submission and SLURM job arrays.

## 🔧 Requirements

- Python 3.8+
- [`submitit`](https://github.com/facebookincubator/submitit)

---

## 🧠 Usage

### Step 1: Import

```python
from autoslurm import autoslurm, batch
```
### Step 2: Decorate your function
Each decorated function must accept a use_slurm: bool argument.

--- 

# ✅ Example 1: Submitting a SLURM Job Array

```python
from autoslurm import autoslurm

@autoslurm(slurm_array_parallelism=True, timeout_min=20)
def train(a: int, b: int, use_slurm: bool = False):
    print(f"Training with a={a}, b={b}")

# Run job array of 5 parallel job_arrays
train([1, 2, 3, 4, 5], [10, 20, 30, 40, 50], use_slurm=True)
```

---

# ✅ Example 2: Submitting Multiple Individual Jobs

```python
from autoslurm import autoslurm

@autoslurm(timeout_min=10)
def run_experiment(seed: int, use_slurm: bool = False):
    print(f"Running experiment with seed={seed}")

for seed in range(5):
    run_experiment(seed, use_slurm=True)
```
Each call submits its own SLURM job (or runs locally).

---

# ✅ Example 3: Submitting Multiple Batches with Job Arrays
```python
from autoslurm import autoslurm, batch

@autoslurm(slurm_array_parallelism=10, timeout_min=30)
def evaluate(x: int, y: int, use_slurm: bool = False):
    print(f"Evaluating with x={x}, y={y}")
    # Prepare large input lists

xs = list(range(1000))
ys = [1] * 1000

# Submit in batches of 200 using job arrays
for x_batch, y_batch in batch(200, xs, ys):
    evaluate(x_batch, y_batch, use_slurm=True)
```
This submits 5 SLURM job arrays, each with 200 jobs.

---

# 📦 @autoslurm(...) Parameters
You can pass any SLURM submitit parameters directly to the decorator:
```python
@autoslurm(timeout_min=30, cpus_per_task=4, gpus_per_node=1, partition="gpu")
```

Special key:

slurm_array_parallelism=10 → Triggers job array mode. 

---

# 🧰 batch(batch_size: int, *args)
Utility to chunk long input lists into mini-batches.
```python
from autoslurm import batch

for a_batch, b_batch in batch(100, list_a, list_b):
    train(a_batch, b_batch, use_slurm=True)
```
---

# 🛡️ Notes
✅ If SLURM is not available (sinfo not found or no job ID in environment), the jobs run locally using submitit.LocalExecutor.
---
# 📜 License
MIT
columnator 0.1.1
Una librería simple para formatear listas en tablas.
Requires Python: >=3.6

# Columnator

`Columnator` es una librería simple para formatear listas en tablas con múltiples columnas en Python. Es útil para mostrar datos de forma ordenada en la terminal, similar al comando `column` de Bash.

## Instalación

Después de subir el paquete a PyPI, puedes instalarlo con:

```bash
pip install columnator
```

Si estás probando localmente antes de subirlo, instálalo con:

```bash
pip install .
```

## Uso

```python
from columnator import format_table

data = ["file1.txt", "file2.txt", "file3.txt", "file4.txt", "file5.txt", "file6.txt"]
print(format_table(data, columns=3))
```

### Salida esperada:
```
file1.txt      file3.txt      file5.txt      
file2.txt      file4.txt      file6.txt      
```

## Parámetros de `format_table`

- `data` (list): Lista de elementos a mostrar en la tabla.
- `columns` (int, opcional): Número de columnas. Por defecto es 3.
- `col_width` (int, opcional): Ancho de cada columna. Por defecto es 15.

## Contribuir

Si deseas mejorar esta librería, ¡tus contribuciones son bienvenidas! Puedes clonar el repositorio y hacer un pull request.

```bash
git clone https://github.com/PanecilloPY/Columnator/
cd Columnator
```

## Licencia

Este proyecto está bajo la licencia MIT.