semvec/pypi-packages huggingface dataset:
nomoji 0.0.5
Silly Python Package to print digits using emojis
Requires Python: >=3.6

# NoMoJi

`NoMoJi` (Number-Emoji) is a simple and silly Python package to print integer numbers using Emoji digits.

Plus there is also a fancy `GAME_DIE` to use for apps like [`dnd-roller`](https://github.com/leriomaggio/dnd-roller).

This project is used as part of the PyConUS tutorial **Publishing your Python project, the conda way** 
presented at [PyConUS](https://us.pycon.org/2023/schedule/presentation/94/) by:

* [Dave Clements](https://us.pycon.org/2023/speaker/profile/108/)
* [Bianca Henderson](https://us.pycon.org/2023/speaker/profile/75/)
* [Mahe Iram Khan](https://us.pycon.org/2023/speaker/profile/166/)
* [Valerio Maggio](https://us.pycon.org/2023/speaker/profile/109/)

This package will be published on PyPi and it will be used solely
to demonstrate how to add `pip` dependecies to a `conda` package.

### Usage

```python
from nomoji import emojize

emojize(2)
```
This would produce 2️⃣ (the number as keypad emoji).

Similarly:
```Python
emojize(10)
```
would generate the 🔟 emoji unicode character, while:
```Python
emojize(24)
```
would result in 2️⃣4️⃣.
registryspy 1.1.0
Cross-platform Windows Registry browser
Requires Python: >=3.8
Keywords: registry,forensics,windows forensics,forensics tools

![Registry Spy](https://github.com/andyjsmith/Registry-Spy/raw/master/registryspy/img/wordmark.png)

# Registry Spy: Cross-Platform Windows Registry Browser

Registry Spy is a free, open-source cross-platform Windows Registry viewer. It is a fast, modern, and versatile explorer for raw registry files.

Features include:

- Fast, on-the-fly parsing means no upfront overhead
- Open multiple hives at a time
- Searching
- Hex viewer
- Modification timestamps

## Requirements

- Python 3.8+

## Installation

Download the latest version from the [releases page](https://github.com/andyjsmith/Registry-Spy/releases). Alternatively, use one of the following methods.

### pip (recommended)

1. `pip install registryspy`
2. `registryspy`

### Manual

1. `pip install -r requirements.txt`
2. `python setup.py install`
3. `registryspy`

### Standalone

1. `pip install -r requirements.txt`
2. `python registryspy.py`

## Screenshots

#### Main Window

![Main Window](https://github.com/andyjsmith/Registry-Spy/raw/master/screenshots/main.png)

#### Find Dialog

![Find Dialog](https://github.com/andyjsmith/Registry-Spy/raw/master/screenshots/find.png)

## Building

Dependencies:

- PyInstaller 5.10+

Delete any existing venv, dist, and build directories.

Create and activate a new venv and install the requirements.txt and pyinstaller.

Regular building:
`pyinstaller registryspy_install.spec`

Creating a single file: `pyinstaller registryspy_onefile.spec`

Create the EXE installer with Inno Setup.

## License

Registry Spy

Copyright (C) 2023 Andy Smith

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
dr-buster 1.1.0
Simple, yet effective web path finder implemented with multiprocessing in Python
Requires Python: >=3

# dr.buster
Simple, yet effective web path finder implemented with multiprocessing in Python

### Install
```
pip3 install dr_buster
```

### Module usage
```py
from dr_buster.core import start_scan

start_scan(url, wordlist_path)
```
You will have a report generated after program finishes and you can parse it easy cause url and code is in `url [code]` format

### CLI usage
```
$ python3 -m dr_buster https://example.com /home/user/wordlist.txt

starts scan on example.com with wordlist wordlist.txt
and generates report dr.buster.report.$datetime$
```

##### Example output when started with words.txt on testserver
![alt text](res/usage.png)

##### Sample report
```
user@hostname:~/dr.buster$ cat dr.buster.report.09-08-2020_10-54-28 
http://localhost:5000/900 [200]
http://localhost:5000/623 [200]
http://localhost:5000/500 [200]
http://localhost:5000/130 [200]
http://localhost:5000/904 [200]
http://localhost:5000/100 [200]
http://localhost:5000/504 [200]
http://localhost:5000/102 [200]
http://localhost:5000/103 [200]
http://localhost:5000/104 [200]
http://localhost:5000/600 [200]
http://localhost:5000/105 [200]
http://localhost:5000/200 [200]
http://localhost:5000/604 [200]
http://localhost:5000/700 [200]
http://localhost:5000/300 [200]
http://localhost:5000/703 [200]
http://localhost:5000/303 [200]
http://localhost:5000/800 [200]
http://localhost:5000/120 [200]
http://localhost:5000/770 [200]
http://localhost:5000/400 [200]
```

### Test it out on testserver
```
$ pip3 install flask
$ export FLASK_APP=testserver.py
$ python3 -m flask run

and run dr.buster in another terminal
```

## TODO:
* [x] - add argsparse
* [x] - add support for dirbusting on specific path
* [x] - create C++ version of dr.buster -> https://github.com/kelj0/dr.faster

##### DISCLAMER: I AM NOT RESPONSIBLE FOR ANY ACTIONS DONE WITH THIS SCRIPT, USE IT ONLY IF YOU HAVE PERMISSION
pyroundtrip 2.0.1
Roundtrip: density estimation with deep generative neural networks
Requires Python: >=3.7

Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks. Roundtrip is freely available at https://github.com/kimmo1019/Roundtrip.