semvec/pypi-packages huggingface dataset:
scdiffeq 1.1.0
scDiffEq: modeling single-cell dynamics using neural differential equations.
License: MIT
Requires Python: >=3.11

# <a href=""><img src=/docs/images/scdiffeq_logo.png alt="scdiffeq_logo" width="320" />

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/scdiffeq.svg)](https://pypi.python.org/pypi/scdiffeq/)
[![PyPI version](https://badge.fury.io/py/scdiffeq.svg)](https://badge.fury.io/py/scdiffeq)
<a href="https://doi.org/10.5281/zenodo.17238593"><img src="https://zenodo.org/badge/616276336.svg" alt="DOI"></a>

An analysis framework for modeling dynamical single-cell data with **neural differential equations**, most notably ***stochastic*** differential equations  allow us to build **generative models** of single-cell dynamics.

## Quickstart

Please see the [**scDiffEq website**](https://scdiffeq.com) for a quickstart notebook: [link](https://www.scdiffeq.com/_tutorials/quickstart.html)

## Install the development package

Install generally only takes a few seconds.

### Using uv (recommended)

```BASH
git clone https://github.com/scDiffEq/scDiffEq.git; cd ./scDiffEq;

# Install uv if you haven't already: curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
```

### Using pip

```BASH
git clone https://github.com/scDiffEq/scDiffEq.git; cd ./scDiffEq;

pip install -e .
```

### With documentation dependencies

```BASH
# Using uv
uv sync --extra docs

# Using pip
pip install -e ".[docs]"
```

## Main API
  
```python
import scdiffeq as sdq

model = sdq.scDiffEq(adata=adata)

model.fit(train_epochs = 1500)
```

## Built on

<img width="50" hspace="20" alt="pytorch_logo" href="https://pytorch.org/" src="https://user-images.githubusercontent.com/47393421/187940001-61655a05-5393-419a-be96-75d11f233d6e.png"><img width="50" href="https://www.pytorchlightning.ai/" hspace="20" alt="pytorch_lightning_logo" src="https://user-images.githubusercontent.com/47393421/187939281-19139d2c-84fe-47b8-a77c-b87e04feca36.png">
<img width="110" href="https://github.com/mvinyard/neural-diffeqs" alt="neural_diffeqs_logo" src="https://github.com/user-attachments/assets/e9cc2860-bfa4-43a1-bc15-85d074b44fc7" />

## System requirements

- Developed on linux20.04 and MacOS (with Apple Silicon), using Python3.11.
- Software dependencies are listed in [pyproject.toml](./pyproject.toml).
- Tested with NVIDIA GPUs (A100, T4) and Apple Silicon. Most datasets likely only require an NVIDIA Tesla T4 (free in Google Colab).

## Reproducibility

- All results described in the [manuscript](https://rdcu.be/eVhnL) detailing scDiffEq can be reproduced using notebooks in the companion repository: [scdiffeq-analyses](https://github.com/scDiffEq/scdiffeq-analyses)
sparkback 0.3.0
Sparklines Library
License: MIT
Requires Python: >=3.8.1

sparkback
=========

A Python library for generating sparklines.

## Installation

```
$ uv sync --all-extras
$ uv run spark --help
```

## Usage
You can use sparkback from the command line or in your Python scripts.

## Command Line

Run sparkback with a series of numbers:

```
spark --ticks default 10 20 30 40 50
```


You can also use different styles of ticks:

```
spark --ticks block 10 20 30 40 50
spark --ticks ascii 10 20 30 40 50
spark --ticks numeric 10 20 30 40 50
spark --ticks braille 10 20 30 40 50
spark --ticks arrows 10 20 30 40 50
spark --ticks line 10 20 30 40 50
spark --ticks multiline 10 20 30 40 50
```

Use the --stats option to display statistics about your data:

```
spark --ticks default 10 20 30 40 50 --stats
```

## Python API

You can also use sparkback in your Python scripts:

```
import sparkback

data = [10, 20, 30, 40, 50]
ticks = sparkback.scale_data(data, sparkback.TICKS_OPTIONS["default"])
sparkback.print_ansi_spark(ticks)
```

## See also:

* https://github.com/ajacksified/Clark/
* https://github.com/holman/spark