semvec/pypi-packages huggingface dataset:
more-tk 0.1.3
A project made to replicate tkinter but with different commands.
Requires Python: >=3.12

# more_tk

## What is more_tk?

**more_tk** is a super lightweight Python package built as a wrapper around [tkinter](https://tkinter.com).  
It simplifies basic GUI setup and provides an alternate syntax that’s a little more challenging — perfect for fun projects, experiments, or testing yourself.

> ⚠️ **Disclaimer:** more_tk is not designed to replace tkinter — it's a minimalistic rework for learning, challenges, or simple GUIs.

---

## Getting Started

### Installation

```bash
pip install more-tk
```

---

### Setting Up the Window

To create a basic window:

```python
from more_tk import *

app = mtk()
mtk_mainloop()
```

You can customize it like this:

```python
app = mtk(geo="500x300", title="My App", bg="lightblue", full=False)
```

---

### `mtk()` Parameters

| Parameter | Description | Example |
|----------|-------------|---------|
| `geo`    | Sets window size (format: `"widthxheight"`) | `"600x400"` |
| `title`  | Window title (default: `"Wrapped by more_tk"`) | `"My App"` |
| `bg`     | Background color (supports names or hex) | `"lightblue"` or `"#33cc33"` |
| `full`   | Fullscreen toggle (`True` or `False`) | `True` |

---

## Widgets

### Labels

```python
label_mtk(text="Hello", bg_mtk="white", fg_mtk="black", x=0, y=0)
```

| Parameter | Description |
|-----------|-------------|
| `text`    | Text to display |
| `bg_mtk`  | Background color |
| `fg_mtk`  | Text color |
| `x`, `y`  | Position on screen |

---

### Buttons

```python
button_mtk(text="Click Me", bg_mtk="white", fg_mtk="black", x=100, y=100, command_=my_function)
```

All parameters are the same as `label_mtk`, with the addition of:

- `command_`: The function to call when clicked.

---

### Entry Fields

```python
entry = entry_mtk(bg_mtk="white", fg_mtk="black", x=50, y=50)
```

You can later use `entry.get()` to retrieve the input text.

---

## Binding Keys

```python
def my_keypress(event):
    print("Key pressed!")

bind_mtk("Return", my_keypress)
```

Note: You must call `mtk()` before using `bind_mtk()`.

---

## Example App

```python
from more_tk import *

def hello():
    print("Hello from more_tk!")

app = mtk(geo="400x300", title="My First App", bg="lightgray")
label_mtk(text="Welcome!", x=150, y=50)
button_mtk(text="Click", command_=hello, x=150, y=100)
entry = entry_mtk(x=150, y=150)

def print_text(event):
    print("Typed:", entry.get())

bind_mtk("Return", print_text)

mtk_mainloop()
```

---

## Why use more_tk?

- ✅ Tiny size — installs in seconds  
- ✅ No heavy dependencies  
- ✅ Great for challenges or teaching beginners  
- ✅ Makes simple GUIs easier to read and build
multivelovae 0.1.0
Velocity inference from multi-lineage, multi-omic, and multi-sample single-cell data
Requires Python: >=3.8
Keywords: bioinformatics, RNA velocity, single-cell, multi-omic, variational autoencoder, deep generative model, batch correction, bayesian inference, trajectory inference, ordinary differential equation

# MultiVeloVAE - Velocity inference from multi-lineage, multi-omic, and multi-sample single-cell data
## Package Installation
The package depends on several popular packages in computational biology and machine learning, including [scanpy](https://scanpy.readthedocs.io/en/stable/), [scVelo](https://scvelo.readthedocs.io/en/stable/), [PyTorch](https://pytorch.org/), and [scikit-learn](https://scikit-learn.org/stable/). We suggest using a GPU to accelerate the training process.

To install the MultiVeloVAE package through PyPI:
```
pip install multivelovae
```
And import the package inside python:
```python
import multivelovae as vv
```

## Package Usage
Please feel free to test this method on our previously published 10X Multiome datasets. See https://multivelo.readthedocs.io/en/latest/MultiVelo_Demo.html. The example of running the mouse brain dataset is located in [paper-notebooks](https://github.com/welch-lab/MultiVeloVAE/tree/main/paper-notebooks). Alternatively, you can apply the same training and analysis steps on our single-sample HSPC dataset for which we provide the AnnData objects directly in [figshare](https://multivelo.readthedocs.io/en/latest/MultiVelo_Fig5.html). Expected runtimes can be found inside each notebook.

[This file](https://github.com/welch-lab/MultiVeloVAE/blob/main/paper-notebooks/reproducible_package_versions.txt) lists the versions of packages used to generate manuscript figures.

## TODO
bioconda
readthedocs