semvec/pypi-packages huggingface dataset:
deep-doors-2-labelled 1.0.1
Deep doors 2 labelled dataset
License: MIT

# DeepDoors2 Labeled

[![pypi](https://img.shields.io/pypi/v/deep-doors-2-labelled.svg)](https://pypi.org/project/deep-doors-2-labelled/)

To download the new version of DeepDoors2, click [here](https://drive.google.com/file/d/1wSmFUHF9aSJkomwFdOmepMevBvkRpf3D/view?usp=sharing).

This repository contains the necessary code to manage the relabeled version of the [DeepDoors2](https://github.com/gasparramoa/DeepDoors2) dataset. The new dataset version we provide is built to perform object detection over 2D and 3D doors data. Each examples is composed by an 480x640 RGB image, the depth data, and the bounding boxes coordinates with their label. Each doors can assume 3 different statuses: open, semi-open, and closed. The dataset provided in this repo is managed using the [generic-dataset](https://github.com/micheleantonazzi/generic-dataset) package.
Exaroton 0.0.2
Exaroton API Wrapper
License: MIT
Requires Python: >=3.6
Keywords: exaroton minecraft api wrapper

# exaroton

A Python Wrapper for the [exaroton API](https://developers.exaroton.com/)

Simply get an API Token from [your Account](https://exaroton.com/account/) and you're good to go.

[![Python: 3.6+](https://img.shields.io/badge/Python-3.6%2B-blue)](https://www.python.org/downloads)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-red)](https://gitlab.com/ColinShark/exaroton/-/blob/master/LICENSE)
<!-- [![Gitmoji: 💻🔥](https://img.shields.io/badge/Gitmoji-%F0%9F%92%BB%F0%9F%94%A5-yellow)](https://github.com/carloscuesta/gitmoji#readme) -->

## Installation

exaroton requires Python 3.6 or newer.

```sh
python3 -m pip install -U exaroton
```

A Virtual Environment is recommended to not mess with system installs.
This module has minimal requirements (`requests`), but you can never be safe enough.

```sh
python3 -m venv venv
source ./venv/bin/activate
pip install exaroton
```

## Example Usage

Currently all methods are (in my opinion) well documented and properly typehinted.
If you see something wrong, don't hestitate to [create an Issue](https://github.com/ColinShark/exaroton/issues/new).

I may create a full list of all available methods, or even utilize readthedocs.org

```python
# Import exaroton and set our token
>>> from exaroton import Exaroton
>>> exa = Exaroton("API_TOKEN")

# Get information about the authenticated account
>>> exa.get_account()
{
    "_": "Account",
    "name": "Username",
    "email": "email@example.org",
    "verified": true,
    "credits": 420.69
}

# Get a list of our servers
>>> exa.get_servers()
[
    {
        "_": "Server",
        "id": "7ZxuNK5RX879BFaH",  # Thanks, random.org!
        ...
    },
    {
        "_": "Server",
        "id": "Kf48Td5iVlr8Xu24",  # Thanks, random.org!
        ...
    }
]

# Upload logs to https://mclo.gs
>>> exa.upload_logs("7ZxuNK5RX879BFaH")
{
    "_": "Logs",
    "id": "N5FR4K2",  # Thanks, random.org!
    "url": "https://mclo.gs/N5FR4K2",
    "raw": "https://api.mclo.gs/1/raw/N5FR4K2"
}

# Print logs (this'll most likely spam your output lol)
>>> exa.get_server_logs("7ZxuNK5RX879BFaH")
'one extremely long string with lines seperated by the newline escape character \n'
# It'll print each line seperately when used with `print()`!
```

All you need to make calls to the API is the Authentication Token you can get
from your account page. If you make server-specific calls, you'll need that
servers ID, too.


## The boring stuff

Licensed under [MIT](https://github.com/ColinShark/exaroton/blob/master/LICENSE)
ads2gephi 0.3.8
A command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi
License: MIT
Requires Python: >=3.5
Keywords: citation network,network analysis,astrophysical data system

[![PyPI version](https://badge.fury.io/py/ads2gephi.svg)](https://badge.fury.io/py/ads2gephi)
![CI](https://github.com/03b8/ads2gephi/actions/workflows/github-ci.yml/badge.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

# ads2gephi

is a command line tool for querying and modeling citation networks from the Astrophysical Data System (ADS) in a format compatible with Gephi, a popular network visualization tool. ads2gephi has been developed at the history of science department of TU Berlin as part of [a research project on the history of extragalactic astronomy](https://gepris.dfg.de/gepris/projekt/289438140?language=en) financed by the German Research Foundation DFG (PI Karin Pelte).

You can install `ads2gephi` from PyPI:
```
pip install ads2gephi
```

### Usage

When using the tool for the first time to model a network, you will be prompted to enter your ADS API key. Your key will then be stored in a configuration file under ~/.ads2gephi.

In order to sample an initial citation network, you need to provide ads2gephi with a plain text file with bibcodes (ADS unique identifiers), one per line, as input. The queried network will be output in a SQLite database stored in the current directory:

```
ads2gephi -c bibcodes_example.txt -d my_fancy_netzwerk.db
```

Afterwards you can extend the queried network by providing the existing database file and using the additional sampling options. For example, you can extend the network by querying all the items cited in every publication previously queried:

```
ads2gephi -s ref -d my_fancy_netzwerk.db
```

Finally you might want to also generate the edges of the network. There are several options for generating edges. For example you can use a semantic similarity measure like bibliographic coupling or co-citation:
```
ads2gephi -e bibcp -d my_fancy_netzwerk.db
```

You can also do everything at once:
```
ads2gephi -c bibcodes_example.txt -s ref -e bibcp -d my_fancy_netzwerk.db
```

All other querying and modelling options are described in the help page:
```
ads2gephi --help
```

Once you've finished querying and modeling, the database file can be directly imported in Gephi for network visualization and analysis.

## Special thanks to

* Edwin Henneken
tpyfilestructure 0.0.1
Python package to read and display the file/folder names present in a directory
License: MIT
Requires Python: >=3.7

# tpyfilestructure

Python package to read and display segregated file names present in a directory based on type of the file. 

# Installation

You can install tpyfilestructure from [PyPI](https://pypi.org/project/tpyfilestructure/)
```python
pip install tpyfilestructure
```

# How to use
Navigate to the directory from where the file structure should be displayed. Open Command Prompt(cmd)/powershell in the same path. Once the Command Prompt/powershell pops up just type 'tpyfilestructure' and hit enter. 

    $ tpyfilestructure
    --displays categorized file names based on type--