semvec/pypi-packages huggingface dataset:
dsnd-probability-tl2024 0.5
Gaussian Binomial distributions

# dsnd-probability-tl2024 package

Package includes a Gaussian and Binomial class for calculating and visualizing a Gaussian and Binomial distributions respectively. 

# Files
    1. Generaldistribution.py 
        a.  Parent class
    2. Binomialdistribution.py
        a. Contains Binomial class
    3. Gaussiandistribution.py
        a. Contains Gassian class

# installation
### pip install dsnd-probabilitytl2024

# import the distribution
### from dsnd-probability-tl2024 import Gaussian, Binomail

# Instantiate Guassian Object
    1. g_instance = Gaussian(10,7)
    2. g_instance.data = [1, 3, 99, 100, 120, 32, 330, 23, 76, 44, 31]
    3. g_instance.calculate_mean()
    4. g_instance.calculate_stdev()
    5. g_instance.pdf(25)
    6. g_instance.plot_histogram()
    7. g_instance.plot_histogram_pdf()

# Instantiate Binomial Object
    1. b_instance1 = Binomial(.4,25)
    2. b_instance1.data = [0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0]
    3. b_instance1.calculate_mean()
    4. b_instance1.calculate_stdev()
    5. b_instance1.pdf(5)
    6. b_instance1.plot_bar_pdf()
    7. b_instance2_ = Binomial(.4, 60)
    8. binomial_sum = b_instance1 + b_instance2
    9. print(binomial_sum)
CellPLM-wendeppkdc 0.1.0
Package of CellPLM: A pretrain-ed cell language model beyond single cells. Paper link: https://www.biorxiv.org/content/10.1101/2023.10.03.560734
Requires Python: >=3.9

# CellPLM
This is the official codebase for [CellPLM: Pre-training of Cell Language Model Beyond Single Cells](https://www.biorxiv.org/content/10.1101/2023.10.03.560734).

[![Preprint](https://img.shields.io/badge/Preprint-bioRxiv-brightgreen)](https://www.biorxiv.org/content/10.1101/2023.10.03.560734)
[![License](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)

***CellPLM*** is the first single-***Cell*** ***P***re-trained ***L***anguage ***M***odel that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x higher inference speed compared to existing pre-trained models.

## Installation
We plan to release our project on PyPI soon. For now, please follow the instruction below to set up the environment for CellPLM:
### Quick Installaton
```
git clone git@github.com:OmicsML/cellplm.git && cd cellplm
pip install -r requirements.txt
```

### Complete Installation (recommended for HPC users)
```
conda create -n cellplm python=3.9 -y && conda activate cellplm
conda install cudatoolkit=11.7 -c pytorch -c nvidia
pip install torch==1.13.0+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
pip install einops ipdb pydance torchmetrics wandb hdf5plugin dgl mygene
```
We recommend using `python 3.9` and `cuda 11.7` but they are adjustable.

## Tutorials
We offer several [notebooks](https://github.com/OmicsML/CellPLM/tree/main/tutorials) for various downstream tasks as introductory tutorials. 

We are also working on developing more streamlined protocols for supported tasks and a comprehensive documentation. We aim to release these by the end of the year.

## Pretrained CellPLM Model Checkpoints
The checkpoint can be acquired from our [dropbox](https://www.dropbox.com/scl/fo/i5rmxgtqzg7iykt2e9uqm/h?rlkey=o8hi0xads9ol07o48jdityzv1&dl=0). We might update our checkpoints from time to time.

[10/10/2023] The latest version is `20230926_85M`.

## Citation
```
@article{wen2023cellplm,
  title={CellPLM: Pre-training of Cell Language Model Beyond Single Cells},
  author={Wen, Hongzhi and Tang, Wenzhuo and Dai, Xinnan and Ding, Jiayuan and Jin, Wei and Xie, Yuying and Tang, Jiliang},
  journal={bioRxiv},
  pages={2023--10},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}
```