semvec/pypi-packages huggingface dataset:
passgent 0.3.0
Password generator
Requires Python: >=3.6

# passgent

**`passgent`** is a lightweight Python module to generate a password with custom arguments and password amounts.

---

## 🔧 Features

- Generate many password with suffixies and symbol.
- Doesn't require a module.

---

## 📦 Installation

If using locally, just save `passgent.py` in your project directory.

```bash
pip install passgent
```

---

## Code example:
```python
import passgent as g

keywords = ["admin", "dashboard"]
years = ["2024"]
symbols = ["@"]
suffixes = ["123"]

generate=g.setup(keywords, years, symbols, suffixes)
generate=generate.generate(10)
print(generate)
```

---

## Without args:
```python
import passgent as g

generate=g.setup()
generate=generate.generate(10)
print(generate)
```
---

```

## Output:
['Adminmanager#2021', 'Adm1n15tr4t0r%4dm1n4pp5', 'ADMINLOGIN%2023', 'ADMIN@ADMINISTRATORl0g1n', 'ADMIN%Adm1n15tr4t0rp455', 'MANAGER@4dm1n15tr4t0rl0g1n', 'loginADMIN#2024', 'L0g1nDASHBOARDp455', 'adminDASHBOARD123', 'Managerdashboardapps']
```
3designs 1.0.1
A FastMCP server for 3designs SwiftUI development assistance.
Requires Python: >=3.10

# 3designs MCP Server

**Create awesome iOS experiences with SwiftUI** by juggling  UIX components, not coding.

# About

It is a SwiftUI-focused MCP that provides production-ready SwiftUI components and code snippets to help you create awesome iOS experiences.

Browse experiences in the gallery, select a design / animation / haptic and bring it into your app. Or create a new custom UIX from scratch.

Think 21st-dev tuned for iOS. Plus there is smth else completely awesome in wip.

When I started using it - i stopped writting SwiftUI code. Im just juggling components (whether that's good or bad).
My workflow with this MCP made me feel something like what @matt described in his blog post - [on symbolic to semantic leap in code understanding](https://nshipster.com/as-we-may-code/#future-applications-the-promise-of-what-lies-ahead)

Okay this page will be updated later. maybe.


## Installation and Usage

Use via uvx or docker. uvx is like so:
```bash
uvx --python ">=3.10" --force-reinstall --from 3designs 3designs-stdio-server
```

For installation issues see instructions at [DevBrain MCP](https://github.com/mimeCam/mcp-devbrain-stdio).


## License
This project is released under the MIT License and is developed by mimeCam as an open-source initiative.
e3tools 0.1.3
A repository of building blocks in PyTorch for E(3)/SE(3)-equivariant neural networks
Requires Python: >=3.10

# e3tools

A repository of building blocks in PyTorch 2.0 for E(3)/SE(3)-equivariant neural networks, built on top of [e3nn](https://github.com/e3nn/e3nn):
- Equivariant Convolution: [`e3tools.nn.Conv`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_conv.py#L16) and [`e3tools.nn.SeparableConv`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_conv.py#L124)
- Equivariant Multi-Layer Perceptrons (MLPs): [`e3tools.nn.EquivariantMLP`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_mlp.py#L86)
- Equivariant Layer Norm: [`e3tools.nn.LayerNorm`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_layer_norm.py#L9)
- Equivariant Activations: [`e3tools.nn.Gate`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_gate.py#L10), [`e3tools.nn.GateWrapper`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_gate.py#L117) and [`e3tools.nn.Gated`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_gate.py#L68)
- Separable Equivariant Tensor Products: [`e3tools.nn.SeparableTensorProduct`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_tensor_product.py#L8)
- Extracting Irreps: [`e3tools.nn.ExtractIrreps`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_extract_irreps.py#L5)
- Self-Interactions: [`e3tools.nn.LinearSelfInteraction`](https://github.com/prescient-design/e3tools/blob/main/src/e3tools/nn/_interaction.py#L5)

All modules are compatible with `torch.compile` for JIT compilation.

## Installation

Install from PyPI:

```bash
pip install e3tools
```

or get the latest development version from GitHub:
```bash
pip install git+https://github.com/prescient-design/e3tools.git
```

## Examples

We provide examples of a [convolution-based](https://github.com/prescient-design/e3tools/blob/main/examples/models/conv.py) and [attention-based](https://github.com/prescient-design/e3tools/blob/main/examples/models/transformer.py) E(3)-equivariant message passing networks built with `e3tools`. We also provide an [example training script on QM9](https://github.com/prescient-design/e3tools/blob/main/examples/train_qm9.py):
```bash
python examples/train_qm9.py --model conv
```

We see an approximate 2.5x improvement in training speed with `torch.compile`.
functiontrace 0.5.1
A profiling tool that traces your application's execution
Requires Python: >=3.9
Keywords: profiler, debugger

Py-Functiontrace
================
The Python module for Functiontrace (https://functiontrace.com).

Dependencies
------------

The FunctionTrace server must be installed for this package to work.
See installation instructions at https://functiontrace.com#installation.

Usage
-----

To use, add `-m functiontrace` to the Python script you want to profile.  For example:

.. code:: sh

    $ python foo.py

should be run as

.. code:: sh

    $ python -m functiontrace foo.py

To see the various arguments, run

.. code:: sh

    $ python -m functiontrace --help
ytm-fetcher 0.1.2
Small wrapper package to interface with YouTube Music

# YTM Fetcher

A Python-based tool for interacting with YouTube Music, allowing users to fetch, download, and play music tracks.

## Features

- Fetch song and playlist information from YouTube Music (YTM)
- Save songs locally in WAV or MP3 formats
- Playback full songs or samples
- Models to create and modify playlists, then push them to YTM

## Requirements

- yt-dlp - Used for saving music locally
- ytmusicapi - Allows interaction with YTM API
- playsound - Allows local playback within Python
- ffmpeg executable - For downloads and sampling
- Cookie file and browser headers (see Authentication)

## Installation
- Either install from PyPI or place the ytm_fetcher folder in your project directory.
- Follow the steps under Authentication to unlock most of the package's functionality.

## Authentication
- Folder
  - Create a folder 'auth' in your project directory. 
- Cookies
  - Export browser cookies for YTM to 'cookies.txt' and place the file in the auth folder.
  - If you are unsure of how to do this, search your browser webstore for extensions designed to export cookies, or search 'export browser cookies' in your engine of choice.
- Browser Headers
  - Export raw headers for YTM to 'headers.txt' and place the file in the auth folder.
  - Step-by-step:
    1. Sign in to YouTube Music on your desired account
    2. Navigate to music.youtube.com & open developer tools (F12)
    3. Navigate to the 'Network' tab
    4. Reload the page to generate new requests
    5. Find a POST request beginning with browse? and type json
    6. Under 'Headers', find 'Request Headers'
    7. Copy the raw headers to a file headers.txt and place this in your auth folder



## Usage
```python
# TODO: Usage examples
print("Hello world")
```