semvec/pypi-packages huggingface dataset:
darwin-pytorch 0.1.2
Small package for interfacing V7Lab's Darwin with Pytorch
Requires Python: >=3.6

# darwin-pytorch

This is a small package for interfacing [V7Lab](https://www.v7labs.com/)'s annotation tool 
[Darwin](https://darwin.v7labs.com) with [Pytorch](https://pytorch.org/). 

## Quick start

### Install Pytorch

First [install your preferred version of Pytorch](https://pytorch.org/get-started/locally/) including `torchvision`.

### Install darwin-pytorch
Then use pip to install `darwin-pytorch`:

```
pip install darwin_pytorch
```

## Examples

### Loading annotations

`darwin-pytorch` provides a high-level wrapper around 
[`darwin-cli`](https://github.com/v7labs/darwin-cli) for 
loading datasets from Darwin. 

Example of authenticating and downloading an annotated dataset:

```
import darwin_pytorch
# Checks for DARWIN_PASSWORD environment variable, if this is not found the user
# will be prompted for password.
client = darwin_pytorch.DarwinClient('me@email.com')
data_path = client.download_dataset('dataset-name')  # Downloads data and returns root-path.
```

### Dataset

`darwin-pytorch` also provides a Pytorch `Dataset` wrapping a dataset downloaded from Darwin. Example use:

```
ds = darwin_pytorch.DarwinDataset(data_path)
ds[0]       # --> (PIL.Image, target-int)
ds[:2]      # --> [(PIL.Image, target-int), (PIL.Image, target-int))]
ds[[0, 3]]  # --> [(PIL.Image, target-int), (PIL.Image, target-int)]
```

**Please note**, so far only binary classification datasets are supported.

## License

`darwin-pytorch` is MIT-style license as found in the LICENSE file.
clinput 1.0.2
Useful command line input functions with error checking.
Requires Python: >=3.5
Keywords: command line input

# Clinput

Clinput is an importable python package that contains tools to create command line user inputs with data type conversion, syntax checking and customizable error messages. Syntactically, the functions in this package are similar to Python default input function, with a variety of optional arguments to provide customizable performance.

## Contents

1. [Installation](#installation)
2. [Usage](#usage)
3. [Documentation](#documentation)
4. [Contributing](#contributing)
5. [Testing](#testing)
6. [License](#license)

## Installation

Install using `pip`:

```bash
pip install clinput
```

## Usage

It is recommended to import each of `single` and `multi` separately (to avoid confusion) as follows:

```python
import clinput.single as clis

# example use:
input_var = clis.positive("Enter a positive number: ")
```
or:

```python
import clinput.single as clim

# example use:
input_vars = clim.positive("Enter a list of positive number inputs: ")
```

## Documentation

Documentation can be found at [Read the Docs](http://clinput.readthedocs.io/).

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## Testing

Tests are run using `pytest`.

## License

[MIT](https://choosealicense.com/licenses/mit/)
publicServerAutomator 1.0.1
An SSH Python automation tool for quickly getting things done

# Public Server Automator
## V1.0.1

[PublicServerAutomator Github](https://github.com/gnubyte/publicServerAutomator)

## Purpose

This is a package intended to offer a much simpler, no hickups, no learning curve package + software alternative to Ansible/chef/puppet

## Installation

Install via pip
```
pip install publicServerAutomator
```


##  Usage



### When using a private key

```
from publicServerAutomator import Server

dockerInstructions = [
    "apt-get update -y",
    "apt-get install apt-transport-https -y",
    "apt-get install software-properties-common -y",
    "apt-get install curl -y",
    "apt-get install gnupg2 -y",
    "apt-get install git -y",
    "apt-get install acl -y",
    "apt-get install fail2ban -y"
    '''add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/debian $(lsb_release -cs) stable"''',
    "apt-get update -y",
    "apt-get install docker-ce -y",
    "docker run hello-world"
]

newDocker = Server(inputKeyPath="publickey.pem", inputKeyPassword='PASS', inputServerIP="0.0.0.0" )
newDocker.set_commands(commandList=dockerInstructions)
newDocker.connect()
newDocker.run_commands()
```

### Without using a private key


```
from publicServerAutomator import Server

dockerInstructions = [
    "apt-get update -y",
    "apt-get install apt-transport-https -y",
    "apt-get install software-properties-common -y",
    "apt-get install curl -y",
    "apt-get install gnupg2 -y",
    "apt-get install git -y",
    "apt-get install acl -y",
    "apt-get install fail2ban -y"
    '''add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/debian $(lsb_release -cs) stable"''',
    "apt-get update -y",
    "apt-get install docker-ce -y",
    "docker run hello-world"
]

newDocker = Server(inputServerIP='8.8.8.8',inputUserName='root', inputPassword='123lookatme', inputKeyPath='')
newDocker.set_commands(commandList=dockerInstructions)
newDocker.connect()
newDocker.run_commands()
```
nitk 0.1
A package for inferring sparse partial correlation networks

Network Inference Toolkit

A bunch of scripts written to infer correlation/partial correlation networks from data. The goal is to have them like sklearn models. Currently very much
a work in progress

Implemented:
SPACE - Partial Correlation Estimation by Joint Sparse Regression Models  by Peng, Wang and Zhu - https://doi.org/10.1198/jasa.2009.0126
SCIO - Fast and adaptive sparse precision matrix estimation in high dimensions - Liu and Luo - https://doi.org/10.1016/j.jmva.2014.11.005
CLIME - A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation - Cai, Liu and Luo - https://doi.org/10.1198/jasa.2011.tm10155
DTrace - Sparse precision matrix estimation via lasso penalized D-trace loss - Zou and Zhang - https://doi.org/10.1093/biomet/ast059
Correlation Permutation - Estimates a sparse correlation matrix by permuting the dataset repeatedly to get a p-value to see if
the correlation between two variables is just as likely to occur through noise 
Scaled Lasso - "Sparse Matrix Inversion with Scaled Lasso" by Sun and Zhang - http://www.jmlr.org/papers/volume14/sun13a/sun13a.pdf