semvec/pypi-packages huggingface dataset:
dolphin-memory-engine-mpn 0.0.0
Hooks into the memory of a running Dolphin processes, allowing access to the game memory.
Requires Python: >=3.8

# Python Dolphin Memory Engine

A python library designed to read and write the emulated memory of [the Dolphin emulator](https://github.com/dolphin-emu/dolphin) during runtime. 

Binary wheels are available on pypi for Python 3.8 to 3.12. Use `python -m pip install dolphin-memory-engine`.


## System requirements
Any x86_64 based system should work, however, Mac OS is _not_ supported. Additionally, 32-bit x86 based systems are unsupported since Dolphin dropped their support.

You need to have Dolphin running ***and*** _have the emulation started_ for this program to be useful. As such, the system must meet Dolphin's [system requirements](https://github.com/dolphin-emu/dolphin#system-requirements). Additionally, at least 250 MB of free memory is required.


If it doesn't work, verify that you do not have the `nosuid` mount flag on your `/etc/fstab` as it can cause this command to silently fail.

## License
This program is licensed under the MIT license which grants you the permission to do  anything you wish to with the software, as long as you preserve all copyright notices. (See the file LICENSE for the legal text.)


## Development Help

To compile the extension in debug:
```bash
python3 -m venv venv
```
```bash
source venv/bin/activate
```
```bash
python -m pip install Cython pytest
```
```bash
python setup.py build_ext -g --inplace --force
```
```bash
python -m pytest
```
dogma-data 0.2.19
Data processing for Dogma
Requires Python: >=3.10
Keywords: single-cell, RNA-seq, embedding, pytorch, uce

# Dogma Data

**Dogma Data** is a Python package built for fast and efficient parsing of FASTA files, optimized for high-performance computing. It leverages multi-threading to fully utilize all available system threads, enabling parallel processing. Additionally, the package supports exporting parsed data to the HDF5 file format for easy storage and access.

## Installation

To install Dogma Data, you can use **pip**:

```bash
pip install dogma-data
```

## Usage
```python
import dogma_data

vocab = {
    'a': 0,
    'g': 1,
    'c': 2,
    't': 3,
    ...
}

mapping = dogma_data.FastaMapping(vocab, vocab['a'])
(tokens, sequences, (taxons)) = dogma_data.parse_fasta('input_path.fa', dogma_data.HeaderType.TaxonId, mapping)

header_info = {"taxons": taxons}

dogma_data.export_hdf5(
    'output_path.h5',
    dogma_data.Splitter(
        train_prop=0.95,
        val_prop=0.025,
        test_prop=0.025,
        length=len(sequences) - 1,
    ),
    tokens,
    sequences,
    header_info,
    mapping
)
```

## Requirements
- Python 3.10
  
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact
For any questions, feel free to reach out:
- **Marcel Rød** - roed@stanford.edu
- **Miha Krajnc** - miha.krajnc@cs.stanford.edu
message-store 0.3.0
Message Store is an event sourcing implementation on top of NATS JetStream
Requires Python: >=3.10

![Test Coverage](test/coverage.svg)

# Zencastr Message Store for Python

Message store implementation in Python

This package implements an event sourcing model of storing application data. It is similar to [Eventide](http://docs.eventide-project.org/core-concepts/event-sourcing.html#pub-sub).


## Usage

```python
# python3 -m asyncio
client = await nats.connect("nats://localhost:4222")
message_store = MessageStore(client, "env", should_create_missing_streams=True)

# Create stream
await message_store.ensure_stream("stream-name")

# Prepare subscription with handlers
subscription = message_store.create_subscription(
    "stream-name.>",
    "durable-consumer-name",
    handlers={
        "Command": lambda msg: print(msg.seq, msg.subject, msg.data, sep="\t"),
        "FailingCommand": lambda msg: 1 / 0,
    },
)
subscription.start()

# Publish messages and await processing

await message_store.publish_message("stream-name.unique-id1", Message("Command", {"key": "value"}))
# `1   stream-name.unique-id   {'key': 'value'}`
await message_store.publish_message("stream-name.unique-id2", Message("FailingCommand", {"key": "badvalue"}))
# NOTE: Default behavior is to retry 3 times, on the 4th attempt it will TERM the message
# Failed to handle message with subject env.stream-name.unique-id2, seq: 2, data: b'{"type": "FailingCommand", "data": {"key": "badvalue"}}', exception: ZeroDivisionError division by zero
# Failed to handle message with subject env.stream-name.unique-id2, seq: 2, data: b'{"type": "FailingCommand", "data": {"key": "badvalue"}}', exception: ZeroDivisionError division by zero
# Failed to handle message with subject env.stream-name.unique-id2, seq: 2, data: b'{"type": "FailingCommand", "data": {"key": "badvalue"}}', exception: ZeroDivisionError division by zero
# Giving up on processing message #2, subject env.stream-name.unique-id2 from stream env-stream-name. This attempt (#4) exceeds max
await asyncio.sleep(1)  # Leave time for the messages to be processed
```

## Authors

- Rui Figueiredo (@ruidfigueiredo)
- Alex Cannan (@alexcannan)
hack4upolsiv 0.1.2
Library for hack4u courses

# Hack4U Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introccion a Linux [15 Horas]
- Personalizacion de Linux [3 Horas]
- Introduccion al Hacking [53 Horas]
- Python Ofensivo [35 Horas]

## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u-silv
```

## Uso basico

### Listar todos los cursos

```python
from hack4u_silv import list_courses

for course in list_courses:
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

print(get_course_by_name("Python Ofensivo"))
```

### Calcular duracion total de los cursos

```python3
from hack4u_silv.utils import total_duration
print(f"Total: {total_duration()} horas")
```
hack4u-Silv 0.1.1
Library for hack4u courses

# Hack4U Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introccion a Linux [15 Horas]
- Personalizacion de Linux [3 Horas]
- Introduccion al Hacking [53 Horas]
- Python Ofensivo [35 Horas]

## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u-silv
```

## Uso basico

### Listar todos los cursos

```python
from hack4u_silv import list_courses

for course in list_courses:
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

print(get_course_by_name("Python Ofensivo"))
```

### Calcular duracion total de los cursos

```python3
from hack4u_silv.utils import total_duration
print(f"Total: {total_duration()} horas")
```
hack4u-silv 0.1.0
Library for hack4u courses

# Hack4U Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introccion a Linux [15 Horas]
- Personalizacion de Linux [3 Horas]
- Introduccion al Hacking [53 Horas]
- Python Ofensivo [35 Horas]

## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u-silv
```

## Uso basico

### Listar todos los cursos

```python
from hack4u_silv import list_courses

for course in list_courses:
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

print(get_course_by_name("Python Ofensivo"))
```

### Calcular duracion total de los cursos

```python3
from hack4u_silv.utils import total_duration
print(f"Total: {total_duration()} horas")
```
gobnb 0.0.12
Airbnb scraper in Python
Keywords: airbnb, scraper, crawler, requests

# Airbnb scraper in Python

## Overview
This project is an open-source tool developed in Python for extracting product information from Airbnb. It's designed to be easy to use, making it an ideal solution for developers looking for Airbnb product data.

# Notice
**This project has moved!** Please visit the new repository at [https://github.com/johnbalvin/pyairbnb](https://github.com/johnbalvin/pyairbnb) for the latest updates and releases.
supMath 0.5.1.2
Provides large amount of unique mathematical functions and operators

#supMath Library#

##What is this##
This module contains plenty of different mathematical functions and operators
which can be used in loads of operations.

##Quick Guide##
Library consists of 3 modules: supOperators, supGeometry and supConverters.
Here are some funcs from supOperators module:

	sigma(start,end,rule):
		Returns the sum of rules with i in range[start,end]

		sigma(1,4,"i+2") => 

		You should be careful with these type funcs, as to transform string formula to an operating one,
		eval() func is used - You have been warned!

		There is also an advanced version where powers are introduced the way they
		are in int_sum(power,n)

	div_sigma(num,power,aliquocy):
		Returns sum of all num's divs raising each div to the power.
		aliquocy is 1 ==> the num**power is subtracted from result.

		It can be a powerful tool when working with nums and their properties.
		With help of this, you can check if num is prime or perfect.
	
	int_sum(power,n):
		Returns the int sum from 1 to n (power = 1):
				
			int_sum(1,4) => 1+2+3+4 => 10
		
		Increasing the power, returns sum of sums of ints from 1 to n.

			int_sum(2,4) => int_sum(1,1) + int_sum(1,2) + int_sum(1,3) + int_sum(1,4) => 20

		You can increase the power indefinitely.
		This tool gives an opportunity to work with Pascal triangle and its sequences.
		Func actually returns n-th unit of (power+2)-th sequence in Pascal triangle.

supGeometry module contains various geometrical formulas, more of them are coming in further library updates:
		
	pythagor(a,b):
		returns the hypotenuse value => sqrt(a**2+b**2)

	circle_area(r):	
		returns circle's area with radiues given => pi*r**2

supConverters module has a range of measure-to measure and numerical converting functions:

	convert(num,scale_base):
		Converts decimal to any notation scale:
			
			convert(2,2) => 10
			convert(10,16) => A
			
		As you can see, letters are supported.

	lb_to_kg(lb):
		Converts pounds to kilograms.
		kg_to_lb(kg) is also supported