semvec/pypi-packages huggingface dataset:
dynamic-moops 0.0.1.dev0
A mathematical implementation of dynamic MOO problems for a practical problem typically involving conflicting objectives.
Requires Python: >=3.8
Keywords: dynamic, multi-objective, optimization, data science, data analysis, data scientist, data analyst, statistics

<h1 align = "center">dynMOOPs</h1>

<div align = "center"></div>

<div align = "justify">

A mathematical implementation of the classical **Dynamic Multi-Objective Optimization Problems (`dynMOOPs`)** for practical problems
that typically involves a set of conflicting objectives. In addition, the method allows penalty on premiumization (typically for costing) and
appreciations (typically for objectives involving maximization) methods for control.

</div>
almgroad0aa 2.0
Is a dhsh FREE asynchronous library from reverse engineered Shazam API written in Python 3.6+jdjs siisd 76x  with asyncio and aiohttp. Includes all the methods that Shazam has, including searching for a song by file.

Python is a high-level, interpreted programming language known for its simplicity and readability. Created by Guido van Rossum and first released in 1991, Python emphasizes code readability with its clear and concise syntax, making it an ideal language for beginners and experienced programmers alike.Python supports multiple programming paradigms, including procedural, object-ask are widely used for building web applications, while libraries like NumPy and Pandas are essential for data manipulation and analysis.One of Pythons key strengths is its strong community support and active development, with a large number of contributors continuously improving the language and its ecosystem.
test-metta-motto 0.0.2
Integration of MeTTa and LLMs for prompt templates, guidance, and chaining as well as composition with other agents.
Requires Python: >=3.8

# Metta Motto

This package provides integration of MeTTa and LLMs for prompt tamplates, guidance, and chaining as well as composition with other agents.

## Installation

The main requirement is [MeTTa](https://github.com/trueagi-io/hyperon-experimental/).

The project itself can be installed via
```bash
git clone git@github.com:zarqa-ai/metta-motto.git
cd metta-motto
```

## Usage

The package can be used both as a Python package
```python
import motto
```

and MeTTa extention
```
!(import! &self motto)
```

Please, refer to the [tutorial](tutorial) and [examples](examples). [Tests](tests) can also be considered in addition.

## Tests

The unit tests can be executed via

```bash
cd tests
pytest
```
func-parallelizer 0.4.1
Func_Parallelizer is a simple Python module for parallel execution of functions using multiprocessing. Ideal for parallel execution of heavy cpu operations
Requires Python: >=3.6
Keywords: multiprocessing

# func_parallelizer

Func_Parallelizer is a simple Python module for parallel execution of functions using multiprocessing.

Ideal for parallel execution of heavy cpu operations like processing huge no of files in a directory or reading files for pandas.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install func_parallelizer.

```bash
pip install func-parallelizer
```

## Usage

Import the required packages.

```python
from func_parallelizer import parallel_runner
```

Create a class that has your function and arguments.

If your function doesn't require any arguments you can skip this and directly add them in tasks.

```python
class task:
    def __init__(self, arg1, arg2):
        self.arg1 = arg1
        self.arg2 = arg2

    def this(self):
        consequences = do_this_with(self.arg1, self.arg2)
        return consequences

    def that(self):
        consequences = do_that_with(self.arg1, self.arg2)
        return consequences
```

Create objects using the above class.

```python
def do_nothing():
    print("Eat 5 Star")
    print("⭐⭐⭐⭐⭐")

do = task(arg1, arg2)
```

Make a list of tasks you have to do.

Note: Don't use parentheses for functions otherwise function will execute in this stage also.

```python
tasks = [
    do.this,
    do.that,
    do_nothing
]
```

Pass the tasks to the parallel_runner function

By default all cores are used but if you have a life and want to do some other things, I would recommend to use 50% to 80% of your CPU cores.
```python
total_mayhem = parallel_runner(tasks, cpu_cores=2)
```
## 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.

Feel free to ask any help(If it's about this package, dealing a lot IRL can't help yours too.)

## License

[MIT](https://choosealicense.com/licenses/mit/)

## Code of Conduct

Everyone interacting in the func-parallelizer project's codebases, issue trackers,
chat rooms, and mailing lists is expected to follow the [PSF Code of Conduct](https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md).

Copyright © 2014, [The Python Packaging Authority].
almgroad0770a 1.0
Is a FREE asynchronous library from reverse engineered Shazam API written in Python 3.6+ with asyncio and aiohttp. Includes all the methods that Shazam has, including searching for a song by file.

Python is a high-level, interpreted programming language known for its simplicity and readability. Created by Guido van Rossum and first released in 1991, Python emphasizes code readability with its clear and concise syntax, making it an ideal language for beginners and experienced programmers alike.Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It offers dynamic typing and automatic memory management, which simplifies development by reducing the amount of code needed to perform tasks compared to lower-level languages.Python has a vast ecosystem of libraries and frameworks, making it suitable for a wide range of applications, from web development and data analysis to artificial intelligence and scientific computing. Popular frameworks such as Django and Flask are widely used for building web applications, while libraries like NumPy and Pandas are essential for data manipulation and analysis.One of Pythons key strengths is its strong community support and active development, with a large number of contributors continuously improving the language and its ecosystem.