semvec/pypi-packages huggingface dataset:
bibo 0.1.9
Command line reference manager with single source of truth: the .bib file. Inspired by beets
Keywords: bib latex reference interface

.. image:: https://raw.githubusercontent.com/Nagasaki45/bibo/master/logo/logo.svg
    :alt: bibo logo

Command line reference manager with a single source of truth: the ``.bib`` file. Inspired by `beets`_.

.. image:: https://github.com/Nagasaki45/bibo/actions/workflows/ci_pipeline.yml/badge.svg
  :target: https://github.com/Nagasaki45/bibo/actions/workflows/ci_pipeline.yml
  :alt: CI status

.. image:: https://codecov.io/gh/Nagasaki45/bibo/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/Nagasaki45/bibo

.. image:: https://readthedocs.org/projects/bibo/badge/?version=latest
  :target: https://bibo.readthedocs.io/en/latest/?badge=latest
  :alt: Documentation Status

.. image:: https://asciinema.org/a/313768.svg
  :target: https://asciinema.org/a/313768

.. _beets: https://github.com/beetbox/beets


Links
-----

* Documentation: https://bibo.readthedocs.io/en/latest
* Source code: https://github.com/Nagasaki45/bibo
* PyPI releases: https://pypi.org/project/bibo
* License: `MIT`_

.. _MIT: https://github.com/Nagasaki45/bibo/blob/master/LICENSE
printannotate 0.0.1
A package to annotate print statements in provided script.
Requires Python: >=3.6

# printannotate

A Python package to annotate print statements with encoded information about the calling script and line number.

![CI](https://github.com/saikotek/printannotate/actions/workflows/ci.yml/badge.svg)
![Latest Release](https://img.shields.io/github/v/release/saikotek/printannotate)

## Example
### Input
```py
print(f"i: {i:_}")
print(f"f: {f:.2f}")
print(f"f: {f:.0f}")
print(f"f: {f:_.2f}")
```
### Output
```py
# number formatting
print(f"i: {i:_}") # i: 1_000_000_000
print(f"f: {f:.2f}") # f: 10123.14
print(f"f: {f:.0f}") # f: 10123
print(f"f: {f:_.2f}") # f: 10_123.14
```

## Installation

You can install the package using pip:

pip install printannotate

## Usage

`py -m printannotate <script_path>`<br>
or<br>
`printannotate <script_path>`
zcryptpy 1.0.1
zCrypt - Simple encryption algorithm
Requires Python: >=3.8

# zCrypt.py
It's just a simple encryption algorithm, y'know, not much  
(it converts every letter to uppercase upon encryption because of an issue with encrypted values above 256, sorry about that)

```py
zencrypt(string)
# Encrypts an ASCII string

zencrypt_narrated(string)
# Narrates the encryption of an ASCII string

zdecrypt(string)
# Decrypts an encrypted string

zdecrypt_narrated(string)
# Narrates the decryption of an encrypted string
```
lili-activation 0.1.3
A custom non-monotonic activation function
Requires Python: >=3.6

# lili_activation

## Description
`lili_activation` is a custom activation function designed for TensorFlow and Keras frameworks. This package introduces `fnm3`, a sine-based transformation activation function that provides an alternative to traditional activation functions like ReLU or sigmoid. The `fnm3` function is particularly useful in scenarios where traditional activation functions might not capture complex patterns effectively.

## Installation
Install `lili_activation` directly from PyPI using pip:

```bash
pip install lili_activation
```
Ensure that you have pip updated and tensorflow installed in your environment, as lili_activation_keras depends on TensorFlow.

## Usage
To use fnm3 in your Keras model, follow these steps:

import tensorflow as tf

from lili_activation import fnm3

## Simple model example using `fnm3` as the activation function
model = tf.keras.Sequentia([
    tf.keras.layers.Dense(10, input_shape=(10,), activation=fnm3),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy')
model.summary()

## Features
Non-monotonic: Introduces a controlled non-monotonic that may be more suitable in scenarios where the relationships among input data are complex.

Innovative: Explores new avenues in activation functions that could prove beneficial in certain types of neural networks.

## Contributions
Contributions are always welcome. If you have ideas for improvements or extensions, please feel free to create an issue or pull request.

## License
This project is licensed under the MIT License - see the LICENSE file for details.

## Contact
Lili Chen - lilichen577@gmail.com

## Acknowledgements
Special thanks to the TensorFlow and Keras community for providing an excellent platform for the experimentation and development of new ideas in machine learning.
pysfpubsub 0.1.2
Python gRPC client for the Salesforce PubSub API.
Requires Python: >=3.8

# pysfpubsub

Python gRPC client for the Salesforce Pub/Sub API.

https://developer.salesforce.com/docs/platform/pub-sub-api/overview

### Installation

```
pip install pysfpubsub
```

### Usage

```
from datetime import datetime
from pysfpubsub import Client

def process_event(event, client):
    """
    This is a callback that gets passed to the `Client.subscribe()` method.
    When no events are received within a certain time period, the API's subscribe
    method sends keepalive messages and the latest replay ID through this callback.
    """
    if event.events:
        print("Number of events received in FetchResponse: ", len(event.events))
        # If all requested events are delivered, release the semaphore
        # so that a new FetchRequest gets sent by `Client.fetch_req_stream()`.
        if event.pending_num_requested == 0:
            client.release_subscription_semaphore()

        for evt in event.events:
            # Get the event payload and schema, then decode the payload
            payload_bytes = evt.event.payload
            json_schema = client.get_schema_json(evt.event.schema_id)
            decoded_event = client.decode(json_schema, payload_bytes)
            print(decoded_event)
    else:
        print(f"[{datetime.now():%Y-%m-%d %H:%M:%S}] The subscription is active.")

config = {
    "url": "https://login.salesforce.com",
    "username": "myusername",
    "password": "mypassword",
    "grpc_host": "api.pubsub.salesforce.com",
    "grpc_port": 7443,
    "api_version": "57.0"
}
sfdc_handler = Client(**config)
sfdc_handler.auth()
sfdc_handler.subscribe("/events/Example_Event__c", "LATEST", "", 1, process_event)
```
hack4u-69 0.6.9.1
Una biblioteca para consultar cursos de hack4u.

# Hack4U Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando `pip3`:
```python3
pip3 install hack4u
```

## Uso básico

### Listar todos los cursos

```python3
from hack4u import list_courses

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

### Obtener un curso por nombre

```python3
from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)
```

### Calcular duración total de los cursos

```python3
from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")
```
mrtommy 0.1.1
Multi-Robot Trajectory Organization and Motion Modeling Yard
Requires Python: >=3.8

<h1 align="center">
    🤖 Mr. Tommy
    <br />
    <a href="https://rye.astral.sh">
        <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/rye/main/artwork/badge.json" alt="Rye">
    </a>
    <a href="https://github.com/astral-sh/ruff">
        <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json" alt="Ruff">
    </a>
</h1>
<p align="center">
    <b>Multi-Robot Trajectory Organization and Motion Modeling Yard</b>
</p>

Tommy is a Python library that aims to speed up the development and experimentation of motion planning.
Among the goals of Tommy are:

-   Easy to work with (typing) and extend.
-   Easy to experiment with (matplotlib and notebook workflow).
-   Comparable performance.

Tommy was developed as part of the [Algorithmic Robotics and Motion Planning](https://www.cgl.cs.tau.ac.il/courses/algorithmic-robotics-and-motion-planning-fall-2023-2024/) course given in Tel Aviv University by Prof. Dan Halperin.

## Getting Started

Install the dependencies with `pip install numpy networkx matplotlib tqdm pandas`, or use Rye.

Uncomment pieces of code inside the [main](src/mrtommy/__main__.py), add the necessary imports, then run `python3 -m mrtommy` from inside `src`.

## Acknowledgements

Mr. Tommy is **heavily** inspired by [discopygal](https://www.cs.tau.ac.il/~cgl/discopygal/docs/index.html), although it aims to provide a different experience.