semvec/pypi-packages huggingface dataset:
macos-keychain 0.2.1
A Simple MacOS Keychain Store
License: MIT
Requires Python: >=3.6,<4.0

# macos_keychain

MacOS keychain password manager

## Adding a password to keychain

```python
import macos_keychain

macos_keychain.add(name='API token', value='<TOKEN>')
```

## Removing passwords in keychain

```python
import macos_keychain

macos_keychain.rm(name='API token')
```

## Listing passwords in keychain

```python
import macos_keychain

macos_keychain.ls()
```

## Getting password in keychain
```python
import macos_keychain

macos_keychain.get(name='API token')
```
pySpacell 0.1.5
A Python package for single cell spatial image analysis
License: MIT
Keywords: spatial analysis microscopy cells statistics

# pySpacell
A Python package for single-cell spatial image analysis.

## Availability
* It runs with python3.6
* It is available through `pip install pySpacell` or downloadable via `git clone https://github.com/biocompibens/pySpacell.git`
* NB: the git depot contains large data files. To download them correctly, `git lfs clone https://github.com/biocompibens/pySpacell.git`. The rest of the files are not impacted by a standard git clone.

## Usage examples
Example data are located in *data/* along with exemple scripts in *example/*
FNE-todo 0.1.1
Simple command-line Todo app.
License: MIT
Keywords: todo task

![Python](https://img.shields.io/badge/python-3.7.2-green.svg)
![Version](https://img.shields.io/badge/version-0.1.1-yellow.svg)

# Todo1

Commandline `todo` application for internship assignment.

## Requirements

This project requires `Python 3.7.2` or newer run-time.

## Install


```sh
$ cd $home  
$ git clone  https://github.com/FatmanurEraslan/Todo1.git
$ cd Todo1
$ python 3 setup.py install
```

or

```sh

$ pip install FNEtodo  

```



## License

This project is licensed under MIT

## Usage
```sh

$ todo add 'Buy a ticket'

```

   Buy a ticket is added .

   0001 : Buy a ticket

```sh

$  todo add 'Clean the house'

```

  Clean the house is added .

  0001 : Buy a ticket
  0002 : Clean the house


```sh

$  todo complete 1

```

  Buy a ticket is completed!

  0001 : Clean the house


```sh

$  todo list

```
  0001 : Clean the house


```sh

$  todo completed

```

  0001 : Buy a ticket


```sh

$  todo add 'Visit grandpa' 'Go to shopping mall'

```
  Visit grandpa is added .

  Go to shopping mall is added .

  0001 : Clean the house
  0002 : Visit grandpa
  0003 : Go to shopping mall

## Contributer(s)

- [Fatmanur Eraslan](https://github.com/FatmanurEraslan) - Creator, maintainer
- [Uğur "vigo" Özyılmazel](https://github.com/vigo) - Mentor

## Contribute

All PR’s are welcome!

1. `fork` (https://github.com/FatmanurEraslan/Todo1/fork)
1. Create your `branch` (`git checkout -b my-features`)
1. `commit` yours (`git commit -am 'added killer options'`)
1. `push` your `branch` (`git push origin my-features`)
1. Than create a new **Pull Request**!

## Change Log

**2019-09-03**

- Removed irrelevant files
- Added `.gitignore`
- Updated `README.md`

**2019-08-28**

- Initial start
aunnl 3.0.3
Another Unnecessary Neural Network Library
License: MIT
Keywords: Deep Learning,Machine Learning,Artificial Neural Networks

![](https://i.ibb.co/ypC5ZVv/aunnl.png)
___

AUNNL is another unnecessary neural network library for Python 3.x. It is intended to help create and train basic neural networks very easily.

## Getting Started

### Installation

It is recommended you install via pip for Python 3:

```
pip install aunnl
```

After this, you can import it into your python program with:

```
import aunnl
```

### Basic Example

The following example trains a neural network to classify handwritten digits from the MNIST dataset. The dataset is loaded using the `mnist_web` module, which is not packaged with AUNNL. Download and install it with the command `pip install mnist_web`.

```
import aunnl

from mnist_web import mnist
data, labels, _, _ = mnist(path="dataset")

model = aunnl.NeuralNetwork([784, 256, 10], ["relu", "sigmoid"])

epochs, lr, batch_size = 1, 0.1, 64

model.fit(data, labels, epochs, batch_size, lr, aunnl.losses.MSE)
model.save("mnist.aunn")
```

In the above example, a neural network with a hidden layer of 256 neurons is trained - its activation being ReLU and the output layer activation being sigmoid. The model, which is an `aunnl.NeuralNetwork` object, is then saved to the file `mnist.aunn`. The model can be loaded from the file with `aunnl.loadModel('mnist.aunn')`.

To use the model, simply pass the image in the form of a flat numpy array (denoted here as `img_arr`) to the model with `model.feedForward(img_arr)`. The `feedForward` function returns a list of the values outputted by the model.