semvec/pypi-packages huggingface dataset:
jcell 0.0.4
Software package for training and testing deep learning based method applied to image segmentation
Requires Python: >=3.9

[![Python package](https://github.com/fagp/caltus/actions/workflows/python-package.yml/badge.svg)](https://github.com/fagp/caltus/actions/workflows/python-package.yml)
# JCELL

See our Colab examples for 2D[![2D segmentation example](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1iPJH3U4EX4sT4oglJF-Xa042PcvTiLxg?usp=sharing)
and 3D data[![3D segmentation example](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ns5AVr8idU0gOeZSi1JobVJ49Xxvxqpv?usp=sharing)

``jcell`` is an image segmentation software primarily dedicated to biological cells. The software is an implementation of our latest developments in cell segmentation using deep learning approaches specially aimed at segmenting clustered cells. We pioneered the use of multiclass segmentation to improve segmenting tightly packed cells (see ICIP2018) and recently introduced a combination of Youden's J Statistics and cross entropy as a robust loss working in tandem to promote a sharp classification of pixels/voxels (see ISBI2020). Due to *J*, highly imbalanced data can be directly trained bypassing the need of complicated data balancing strategies.  This is the software used for winning DIC-C2DH-Hela and PhC-C2DH-U373 Cell segmentation challenges (http://celltrackingchallenge.net/latest-csb-results/).

For an in depth understading how the methods work check our publications [ICIP2018], [MICCAIW2019], [ISBI2020].

See documentation for more details on how to use this software.

[ISBI2020] **J Regularization Improves Imbalanced Multiclass Segmentation**. *Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Paul T. Tarr, Tsang Ing Ren, Elliot M. Meyerowitz, Alexandre Cunha*. IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020. https://ieeexplore.ieee.org/abstract/document/9098550. Arxiv https://arxiv.org/abs/1910.09783.

[MICCAIW2019] **A Weakly Supervised Method for Instance Segmentation of Biological Cells**. *Fidel A. Guerrero Pena, Pedro D. Marrero Fernandez, Tsang Ing Ren, Alexandre Cunha*. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Springer. 2019. https://link.springer.com/chapter/10.1007/978-3-030-33391-1_25. Arxiv https://arxiv.org/abs/1908.09891.

[ICIP2018] **Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells**. *Fidel A. Guerrero Pena, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha*. IEEE International Conference on Image Processing (ICIP). 2018. https://ieeexplore.ieee.org/abstract/document/8451187/. Arxiv https://arxiv.org/abs/1802.07465.
AIscientists 0.1.2
AIscientists package for automated data science workflow.

# AIscientists

AIscientists is a powerful tool that automates complex workflows across multiple industries, making it easier to process large datasets, generate insights, and streamline decision-making. Designed to simplify tasks that traditionally require extensive manual effort, AIscientists is here to bring efficiency, precision, and scalability to your organization's operations.

## Features

- **Automated Workflow Management**: AIscientists can handle the end-to-end management of data processing workflows, allowing teams to focus on high-level analysis.
- **Scalable Data Processing**: Efficiently processes large volumes of data, whether structured or unstructured, without compromising speed or accuracy.
- **Customizable Industry Solutions**: Tailored modules for various industries, including healthcare, bio-engineering, finance, healthcare, retail, and manufacturing.
- **Integration-Ready**: Easily integrates with existing data sources, APIs, and software, providing seamless operation within your organization's ecosystem.

## Getting Started

To get started with AIscientists, first install the package via pip:

```bash
pip install AIscientists
RareGO 0.0.33
A link prediction biomedical discovery package
Requires Python: >=3.9
Keywords: snekpack, cookiecutter, Knowledge Graph Embeddings, Machine Learning, Data Mining, Linked Data

"RareGO Package" 
>The latest stable version of RareGO requires Python 3.8+. It can be downloaded and installed from PyPI with:

```
pip install RareGO
backtesting-data 0.0.6
Libreria para obtener datos para usar en backtesting.py
Keywords: python, backtesting.py, backtesting, exchange

# Backtesting data

Accesde a datos historicos rapidamente :D para usar en backtesting.py

## Installation

    $ pip install backtesting backtesting-data

## Usage

```python
from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from backtesting.test import SMA
from backtesting_data import historySymbol
import datetime


class SmaCross(Strategy):
    def init(self):
        price = self.data.Close
        self.ma1 = self.I(SMA, price, 10)
        self.ma2 = self.I(SMA, price, 20)

    def next(self):
        if crossover(self.ma1, self.ma2):
            self.buy()
        elif crossover(self.ma2, self.ma1):
            self.sell()


BTCUSDT = historyCoin('binanceusdm', 'BTCUSDT', '5m', 200, end_time=datetime.datetime(2024, 10, 25, 20, 30))

bt = Backtest(BTCUSDT, SmaCross, commission=.002,
              exclusive_orders=True)
stats = bt.run()
bt.plot()
```
password-magic 0.1.0
A package for generating, validating, and checking the strength of passwords
Requires Python: >=3.6

# Password Magic

Password Magic is a Python package for generating, validating, and checking the strength of passwords.

## Features

- Generate secure passwords with options for length, uppercase letters, numbers, and special characters.
- Validate passwords based on security criteria.
- Check password strength with a rating system.

## Installation

```bash
pip install password-magic