semvec/pypi-packages huggingface dataset:
clientry 2.0.0
A collection of code for clientry.
Requires Python: >=3.12

# Clientry

[![CI](https://github.com/gao-hongnan/clientry/actions/workflows/ci.yml/badge.svg)](https://github.com/gao-hongnan/clientry/actions/workflows/ci.yml)
[![Python](https://img.shields.io/badge/python-3.12%20%7C%203.13-blue.svg)](https://www.python.org/downloads/)
[![PyPI](https://img.shields.io/badge/pypi-v22.0.0-blue.svg)](https://github.com/gao-hongnan/clientry)
[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Mypy](https://img.shields.io/badge/typed-mypy-blue?style=flat-square&logo=python)](http://mypy-lang.org/)
[![Pyright](https://img.shields.io/badge/typed-pyright-blue?style=flat-square&logo=python)](https://github.com/microsoft/pyright)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
jotun 0.1.0
A petabyte scale data processing framework for AI models using Ray.
Requires Python: >=3.10

# teraflopai-data

A petabyte scale data processing framework for AI models using Daft + Ray.

## Installation
```python
uv pip install teraflopai-data
```
Install specific multimodal components
```python
# Image
uv pip install teraflopai-data[image]

# Text
uv pip install teraflopai-data[text]

# Everything
uv pip install teraflopai-data[all]
```

## Community
[Join our Discord community](https://discord.gg/Fh4DfwQGhd)

## Examples

### Pipeline
```python
import daft

from teraflopai_data.components.text.embedding import SentenceTransformersEmbed
from teraflopai_data.components.text.fineweb_edu import FinewebEduClassifier
from teraflopai_data.pipeline import Pipeline

df = daft.from_pydict(
    {
        "text": [
            "My mother told me",
            "Someday I will buy",
            "Galleys with good oars",
            "Sail to distant shores",
        ],
    }
)

classifier = FinewebEduClassifier(
    input_column="text",
    batch_size=4,
    concurrency=1,
    num_cpus=6,
    num_gpus=1,
)

embedder = SentenceTransformersEmbed(
    input_column="text",
    model_name="all-MiniLM-L6-v2",
    batch_size=4,
    concurrency=1,
    num_cpus=6,
    num_gpus=1,
)

pipeline = Pipeline(
    ops=[classifier, embedder],
)

df = pipeline(df)
df.show()
```

### Text
```python
import daft

from teraflopai_data.components.text.fineweb_edu import FinewebEduClassifier

df = daft.from_pydict(
    {
        "text": [
            "My mother told me",
            "Someday I will buy",
            "Galleys with good oars",
            "Sail to distant shores",
        ],
    }
)

classifier = FinewebEduClassifier(
    input_column="text",
    batch_size=4,
    concurrency=1,
    num_cpus=6,
    num_gpus=1,
)
df = classifier(df)
df.show()
```

### Image
```python
import daft
from daft import col

from teraflopai_data.components.image.image_hashing import ImageHasher

df = daft.from_pydict(
    {
        "urls": [
            "https://live.staticflickr.com/65535/53671838774_03ba68d203_o.jpg",
            "https://live.staticflickr.com/65535/53671700073_2c9441422e_o.jpg",
            "https://live.staticflickr.com/65535/53670606332_1ea5f2ce68_o.jpg",
            "https://live.staticflickr.com/65535/53671838039_b97411a441_o.jpg",
            "https://live.staticflickr.com/65535/53671698613_0230f8af3c_o.jpg",
        ],
    }
)

hasher = ImageHasher(
    input_column="image",
    hashing_algorithm="wavelet",
    concurrency=1,
    num_cpus=6,
)

df = df.with_column("image_bytes", col("urls").url.download(on_error="null"))
df = df.with_column("image", col("image_bytes").image.decode())
df = hasher(df)
df = df.drop_duplicates("image_hash")
df.show()
```

## Citation
```bibtex
@misc{shippole2025petabyte,
    title   = {Distributed},
    author  = {Enrico Shippole},
    year    = {2025},
}
```
hackaclime 0.1.9
CLI utility to read Hack Club's Hackatime time tracking API
Requires Python: >=3.12

## HackaCLIme
A CLI terminal app to read time tracked on Hack Club's Wakatime modification, known as Hackatime!

## Installation
`pip install hackaclime`

## Usage
Make sure you've set up your Hackatime, and have a .wakatime.cfg file. Run `hackaclime` in your chosen terminal emulator. If you'd like to change your theme, do so - you may also create a custom theme from there, as long as you have the RGB or hex values. HackaCLIme comes prepackaged with about 15 commonly used themes (or at least my interpretation of their colors - some aren't too clear with the docs), and your current theme is saved. To open a user, open the https://hackclub.slack.com, and select any user, then open their profile and select Copy Member ID from the kebab menu. It should look something like `U091JJ2JF8E`.

## Themes
A utility to create custom themes is included, or you can make yourself in `hackaclime.cfg`!
rebhelp 0.2.0
Helper package for REBOUND: units, simulation generators, and orbital hierarchy utilities
Requires Python: >=3.8

# rebhelp

Helper package for REBOUND:

## Features

- **units**: Mass and velocity converters (Earth, Moon, Sun)
- **simgen**: One-line REBOUND simulation generators
- **get_orbit**: Hierarchical orbit calculations for S-type planetary systems, all methods also globally available

Rest should have self explanatory docstrings

## Installation

```bash
pip install rebhelp
jsonschema-specifications 2025.9.1
The JSON Schema meta-schemas and vocabularies, exposed as a Registry
Requires Python: >=3.9
Keywords: data validation, json, json schema, jsonschema, validation

=============================
``jsonschema-specifications``
=============================

|PyPI| |Pythons| |CI| |ReadTheDocs|

JSON support files from the `JSON Schema Specifications <https://json-schema.org/specification.html>`_ (metaschemas, vocabularies, etc.), packaged for runtime access from Python as a `referencing-based Schema Registry <https://referencing.readthedocs.io/en/stable/api/#referencing.Registry>`_.

.. |PyPI| image:: https://img.shields.io/pypi/v/jsonschema-specifications.svg
  :alt: PyPI version
  :target: https://pypi.org/project/jsonschema-specifications/

.. |Pythons| image:: https://img.shields.io/pypi/pyversions/jsonschema-specifications.svg
  :alt: Supported Python versions
  :target: https://pypi.org/project/jsonschema-specifications/

.. |CI| image:: https://github.com/python-jsonschema/jsonschema-specifications/workflows/CI/badge.svg
  :alt: Build status
  :target: https://github.com/python-jsonschema/jsonschema-specifications/actions?query=workflow%3ACI

.. |ReadTheDocs| image:: https://readthedocs.org/projects/jsonschema-specifications/badge/?version=stable&style=flat
  :alt: ReadTheDocs status
  :target: https://jsonschema-specifications.readthedocs.io/en/stable/