semvec/pypi-packages huggingface dataset:
pocket_dimension 0.2.0
Memory-efficient, dense, random projection of sparse vectors.
Requires Python: >=3.11
Keywords: numba, random projection, term-frequency, tfidf, dimension reduction

.. image:: ../../images/logo.png
    :align: center
    :alt: A small spiral galaxy inside a small glass sphere

==================================

Pocket Dimension provides a memory-efficient, dense, random projection of sparse vectors. This
random projection is the used to be able to take records {"id": str, "features": List[bytes],
"counts": List[int]}, convert them into sparse random vectors using scikit-learn's FeatureHasher,
and then project them down to lower dimensional dense vectors.

When the very large sparse universe becomes too inhospitable, escape into a cozy pocket dimension.

Documentation
=============
Documentation for the API and theoretical foundations of the algorithms can be
found at https://mhendrey.github.io/pocket_dimension

Installation
============
Pocket Dimension may be install using pip::

    pip install pocket_dimension

I'm working on a conda-forge version, but this uses pybloomfiltermmap3 which is currently only on PyPi.
strox 0.1.5
Find strings that matches approximately the given query
Requires Python: >=3.10

# Strox - String Approximate

Find strings that matches approximately the given query.

## Running as tool

You can run `strox` from the terminal if installed, usually through `pipx install` or `uv tool install`.

To enable colors, install with the `cli-color` extra:

```bash
pipx install strox[cli-extra]
# or
uv tool install strox[cli-extra]
```

Example of querying for the closest matching option, with _weight_ parameter:

```bash
strox "an" "Apple pie" "Indigo" "Banana" --insertion-cost 0.1
# Result: "Banana"
```

**Tip**: Run `strox --help` for more options.

## License

MIT
hexdoc-hyperhexcast 0.1.1.1.0
Another HexCasting addon I guess
Requires Python: >=3.12
Keywords: hexdoc

# hexdoc-hyperhexcast

Python web book docgen and [hexdoc](https://pypi.org/project/hexdoc) plugin for Hyper's Hexcast addon.

Another HexCasting addon I guess

## 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 [uv](https://docs.astral.sh/uv/getting-started/installation/), then:

```sh
uv sync

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

## Usage

For local testing, create a file called `.env` in the repo root following this template:

```sh
GITHUB_REPOSITORY=janIpen/hyper_hexcast
GITHUB_SHA=main
GITHUB_PAGES_URL=https://janipen.github.io/hyper_hexcast
```

Useful commands:

```sh
# update your Python environment and lockfile if you added new dependencies
uv sync

# 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

# build and merge the web book
hexdoc build
hexdoc merge

# start the Python interpreter with some extra local variables
hexdoc repl
```
davidkhala.gcp 0.4.2
Core gcp components
Requires Python: >=3.11

[python sdk index](https://cloud.google.com/python/docs/reference)
# Dependencies graph
- `google-auth`
- `google-cloud-core`: optional in extra `davidkhala.gcp[core]`
    - It includes `google-api-core`
- `google-api-core`