semvec/pypi-packages huggingface dataset:
timc-vector-toolkit 20250919
Ensemble package for all Tutte Institute librairies involved in data vectorization, dimension reduction, clustering and exploratory visualization
Requires Python: >=3.10

This package brings together the various libraries that the Tutte Institute
has built towards exploratory analysis, unsupervised learning and interactive
visualization for unstructured data. It includes the following individual
packages.

Learn more at <https://github.com/TutteInstitute>

***IMPORTANT***: this package includes the libraries described below as
dependencies without upper-bounding their versions.
As such, newer versions of the package are mainly produced when editing
the toolkit roster, and not necessarily when new versions of
its dependencies are released. Thus, do not mistake the age of this
package for dereliction.


----------------------
Vector space embedding
----------------------

**vectorizers**

Embeds various types of data into large-dimension vector spaces. This
includes data that consists in distributions on vector spaces,
which are embedded by the approximate resolution of optimal transport
problems.


-----------------------------------
Nearest neighbour network discovery
-----------------------------------

**pynndescent**

Builds the k-nearest neighbour graph of a set of high-dimension vectors
expressed as either dense or sparse arrays, under a large set of distances
and pseudo-metrics. Doubles as an in-memory index for querying neighbours
to arbitrary vectors.


-------------------
Dimension reduction
-------------------

**umap** (package name is umap-learn)

Uniform Manifold Approximation and Projection is a manifold learning
dimension reduction algorithm that preserves the local similarity
structure of a set of vectors. It works on both dense and sparse
vector arrays.


----------
Clustering
----------

**hdbscan**

Hierarchical Density-Based Spatial Clustering of Applications with Noise.
This clustering algorithm partitions a set of vectors into groups based on
mutual reachability distance, discarding outliers as noise.

**fast_hdbscan**

A new implementation of HDBSCAN optimized for runtime efficiency by
restricting computations to low-dimension vectors in Euclidean geometry.

**evoc**

Embedding Vector-Oriented Clustering is a new clustering algorithm that
streamlines and approximates the UMAP-HDBSCAN combo approach to clustering,
so as to compute high-quality clusterings of high-dimension vector sets
at a fraction of the computational cost.


-------------------------
Interactive visualization
-------------------------

**datamapplot**

Creates static plots and interactive views of 2D vectors and metadata,
with an emphasis on presentation aesthetics and interactive exploration
for insight discovery.

**toponymy**

Generates a multiresolution hierarchy of annotation labels for text
embeddings by querying a large language model with representative,
distinctive and contrastive characterizations of data clusters. These
labels are then useful for annotating data maps produced with
datamapplot.
EasyMCP2221 1.8.4
Python module to interface with MCP2221 focused on ease of use.
Requires Python: >=3.6

=====================================================
Easy MCP2221
=====================================================

Easy MCP2221 is a Python module to interface with Microchip MCP2221 and MCP2221A.

Connected to the USB port, this 14 pin chip part can provide a **normal computer** with the capabilities of a **basic microcontroller**.


MCP2221's peripherals:

- 4 GPIO
- 3 channel ADC
- DAC
- I2C
- UART
- Clock Output
- USB Wake-up via Interrupt Detection.

So you can practice the basics of digital electronics, microcontrollers, and robotics using nothing more than a regular computer, a breadboard, a few parts, and Python.


Quick start
-----------

Install:

.. code-block:: console

	$ pip install EasyMCP2221


First run:

.. code-block:: python

    >>> import EasyMCP2221
    >>> mcp = EasyMCP2221.Device()
    >>> print(mcp)
    {
        "Chip settings": {
            "Power management options": "enabled",
            "USB PID": "0x00DD",
            "USB VID": "0x04D8",
            "USB requested number of mA": 100
        },
        "Factory Serial": "01234567",
        "GP settings": {},
        "USB Manufacturer": "Microchip Technology Inc.",
        "USB Product": "MCP2221 USB-I2C/UART Combo",
        "USB Serial": "0000000000"
    }


GUI
---

*EasyMCP2221 Workbench* is a GUI to play with MCP2221 and MCP2221A chips based on EasyMCP2221 library.

https://github.com/electronicayciencia/EasyMCP2221-GUI


Documentation
-------------

Read the Install Guide, Examples and full API Reference here: https://easymcp2221.readthedocs.io/

Illustrative blog post with examples, pictures and schematics (spanish): `El integrado MCP2221(A) <https://www.electronicayciencia.com/2023/09/integrado-mcp2221.html>`_


Author
----------------------------------------------------

Reinoso Guzman (https://www.electronicayciencia.com).

Initially based on PyMCP2221A library by Yuta Kitagami (https://github.com/nonNoise/PyMCP2221A).


License
----------------------------------------------------

The MIT License (MIT).
craft-grammar 2.3.0
Provide python interfaces for using advanced grammar in craft-parts
Requires Python: >=3.10

# Craft Grammar

This project aims to provide python interfaces for using advanced
grammar in [Craft Parts](https://craft-grammar.readthedocs.io)

# License

Free software: GNU Lesser General Public License v3

# Documentation:

https://craft-grammar.readthedocs.io.

# Contributing

A `Makefile` is provided for easy interaction with the project. To see
all available options run:

    make help

## Running tests

To run all tests in the suite run:

    make tests

## Adding new requirements

If a new dependency is added to the project run:

    make freeze-requirements

## Verifying documentation changes

To locally verify documentation changes run:

    make docs

After running, newly generated documentation shall be available at
`./docs/_build/html/`.

## Committing code

Please follow these guidelines when committing code for this project:

- Use a topic with a colon to start the subject
- Separate subject from body with a blank line
- Limit the subject line to 50 characters
- Do not capitalize the subject line
- Do not end the subject line with a period
- Use the imperative mood in the subject line
- Wrap the body at 72 characters
- Use the body to explain what and why (instead of how)

As an example:

    endpoints: support package attenuations

    Required in order to obtain credentials that apply only to a given package;
    be it charm, snap or bundle.
hexdoc-ioticblocks 1.0.2.1.20.1.1.0
Python web book docgen and hexdoc plugin for IoticBlocks.
Requires Python: >=3.11
Keywords: hexdoc

# hexdoc-ioticblocks

Python web book docgen and [hexdoc](https://pypi.org/project/hexdoc) plugin for IoticBlocks.

## Version scheme

We use [hatch-gradle-version](https://pypi.org/project/hatch-gradle-version) to generate the version number based on whichever mod version the docgen was built with.

The version is in this format: `mod-version.python-version.mod-pre.python-dev.python-post`

For example:
* Mod version: `0.11.1-7`
* Python package version: `1.0.dev0`
* Full version: `0.11.1.1.0rc7.dev0`

## Setup

Install Python 3.11.

```sh
python3.11 -m venv venv

.\venv\Scripts\activate   # Windows
. venv/bin/activate.fish  # fish
source venv/bin/activate  # everything else

# run from the repo root, not doc/
pip install -e .[dev]
```

## Usage

For local testing, create a file called `.env` in the repo root following this template:
```sh
GITHUB_REPOSITORY=object-Object/IoticBlocks
GITHUB_SHA=main
GITHUB_PAGES_URL=https://object-object.github.io/IoticBlocks
```

Useful commands:
```sh
# show help
hexdoc -h

# render and serve the web book in watch mode
nodemon --config doc/nodemon.json

# render and serve the web book
hexdoc serve

# export, render, and merge the web book
hexdoc build
hexdoc merge

# start the Python interpreter with some extra local variables
hexdoc repl
```
m1-cis 0.2.0
Context Image Search Utility
Requires Python: >=3.9

## Context Image Search
This utility provides you with advanced context to image search. 

## Requiered variablas
1. `GOOGLE_API_KEY` - google custom search api key
2. `GOOGLE_CX` programmable search engine id

As `GOOGLE_CX` you can use `37d934a53d9a8443e` which i set up to work with those domains.
```
www.flickr.com/*
www.freepik.com/*
*.openverse.org/*
*.nappy.co/*
www.wikipedia.org/*
commons.wikimedia.org/*
*.unsplash.com/*
www.pexels.com/*
*.pixabay.com/*
```
Or create your own [google custom search engine](https://programmablesearchengine.google.com)

Please enable those API's for your key:
- [Generative Language API (Gemini API)](https://console.cloud.google.com/apis/api/generativelanguage.googleapis.com)
- [Custom Search API](https://console.cloud.google.com/marketplace/product/google/customsearch.googleapis.com)

## Demo
[See github page](https://github.com/M1chol/m1-cis?tab=readme-ov-file#demo)

## Example
context: `Rheinmetall to Acquire German Naval Shipbuilder NVL`

| Result 1 | Result 2 |
| -------- | -------- | 
|![image](https://upload.wikimedia.org/wikipedia/commons/e/e1/USS_Dale_%28DLG-19%29_ready_for_launching_at_the_New_York_Shipbuilding_Company_on_27_July_1962_%28NH_98150%29.jpg?20100708150818)| ![image](https://images.pexels.com/photos/31389737/pexels-photo-31389737/free-photo-of-modern-naval-warship-docked-in-rotterdam-harbor.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500)|
bsit-lofty 0.1.1
BACnet Semantic Interoperability Toolkit - Lofty - Real Estate Core
Requires Python: >=3.13

# BSIT Lofty

This project builds a module `lofty.py` which is a part of the BACnet Semantic
Interoperability Toolkit (BSIT) for constructing ASHRAE 223 models of HVAC
systems.