semvec/pypi-packages huggingface dataset:
funcy-bear 0.0.28
Bear's functional programming playground: currying, dispatching, type magic, and chaos - all with bearly any dependencies
Requires Python: >=3.12
Keywords: bear, currying, dispatcher, functional-programming, type-introspection, utilities


# Funcy Bear

[![pypi version](https://img.shields.io/pypi/v/funcy-bear.svg)](https://pypi.org/project/funcy-bear/)

A FP Based Repo

## Installation

```bash
pip install funcy-bear
```

With [`uv`](https://docs.astral.sh/uv/):

```bash
uv tool install funcy-bear
```
dcjdict 0.2.1
Create/convert dataclasses from/to jsonable dict
Requires Python: >=3.12

# Dataclass Jsonable Dict parsing

Dataclass in Python is a excelent way to implement Data Transfer Objects, but lacks of a good integrated encoding system to transfer between applications.

One of the most used encoding is json. The standard library `json` only need a jsonable dictionary to dump to bytes or be loaded from them.

This library contains two easy functions to load/convert to dataclases from jsonable dictionaries:

1. `to_jdict(value: DataclassInstance, ommit_defaults: bool = True, enum_as_string: bool = True) -> JsonableDict`:
    1. `ommit_defaults` sets if values with default will be generated as fields in the dict
    2. `enum_as_string`: sets the enum fields as its name instead its value.
2. `from_jdict(value: dict , cls: DataclassType, validate_types: bool = True )-> DataclassInstance`
   1. `validate`: checks that the json fields has correct or compatible type for constructing the dataclass.
slvrov-tools 0.1.48
Tools for math, networking, joysticks, I2C devices, and more for use in SLVROV.
Requires Python: >=3.7

# slvrov-tools

A utility library for SLVROV that includes tools for:

- Non-trivial math operations
- Network communication
- Netowkr setup
- Exit handling
- Joysticks
- I2C control
- PCA9685 utils
- Misc tools
- CLI for gstreamer installation, udp video streaming

## Github
[slrov-tools github](https://github.com/LegionaryOfLogic/slvrov-tools)

## Installation

```bash
pip install slvrov-tools
usss-mysqlhandler 0.3.0
MySQL convenience package 
Requires Python: >=3.14

# Simplify using MySQL
## Class and methods to simplify using MySQL

MysqlHandler class is a context manager, use thus:


  
This 
1. opens connection to MySQL
2. Provides methods simplify executing queries (delete, insert, select, update) and getting results.
3. Closes the connection reliably on exit from scope

Simple: insert ... on duplicate key update

https://guides.github.com/features/mastering-markdown/
cgkmc 0.2.0
Crystal Growth Kinetic Monte Carlo
Requires Python: >=3.9
Keywords: crystal, graph, growth, kmc, morphology, nanocrystal, surface

# cgkmc

[![Custom shields.io](https://img.shields.io/badge/docs-brightgreen?logo=github&logoColor=green&label=gh-pages)](https://jwjeffr.github.io/cgkmc/)

[![PyPI version shields.io](https://img.shields.io/pypi/v/cgkmc.svg)](https://pypi.python.org/pypi/cgkmc/)
[![PyPI pyversions shields.io](https://img.shields.io/pypi/pyversions/cgkmc.svg)](https://pypi.python.org/pypi/cgkmc/)

## 💎 What is cgkmc?

`cgkmc` is a package for performing crystal growth simulations using the 
[Kinetic Monte Carlo](https://en.wikipedia.org/wiki/Kinetic_Monte_Carlo) method. `cgkmc`'s namesake is shorthand for
Crystal Growth Kinetic Monte Carlo.

## 📥 Installation

```bash
pip install cgkmc
```

## 📃 License

`cgkmc` is released under the MIT License.

## 🔖 Citing

If you found this package useful, please cite [our work in Crystal Growth & Design](https://doi.org/10.1021/acs.cgd.5c01428).

## ❤️ Acknowledgements

This work was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory 
under project no. 20220431ER. This research used resources provided by the Los Alamos National Laboratory Institutional 
Computing Program. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear 
Security Administration of U.S. Department of Energy (contract no. 89233218CNA000001).

Additionally, this material is based on work supported by the National Science Foundation under Grant Nos. 
MRI# 2024205, MRI# 1725573, and CRI# 2010270 for allotment of compute time on the Clemson University Palmetto Cluster.
fit-plot 0.1.14
Tool for fitting lines to data
Requires Python: >=3.7

 # fit_plot

- Fit data with a straight line chosen by clicking points on a plot.

Requires: ipympl

To use:

      import fit_plot
      %matplotlib widget
      
      fit_plots.line(unique_name, xdata, ydata, yerr, chi2=False, input_boxes=True, render_to_html=False)

      or

      fit_plots.with_background(unique_name, xdata, ydata, yerr,chi2=False, input_boxes=True, render_to_html=False)

where xdata, ydata and yerr must be numpy arrays of at least two points.

Click in the data portion of the figure to set a point for the fit line.
Whichever point you click closer to will move to the new position.

For with_background(), a background value can be set: choose the
Floor radio button, then click at the desired level of background.

If chi2 == True, a chi-squared value will be calulated and displayed.

If input_boxes == True (default) boxes are displayed so that the slope,
intercept, and "floor" values may be entered manually.

If render_to_html == True, then a static page is rendered.

Caveats:

1) if you change the unique name, the fit parameters will be lost.

2) fit parameters are stored in hidden files and won't travel if the
   notebook is renamed or moved to another directory.

3) any plots made after using one of these functions should start with
   plt.figure()
anoy 0.4.0
This is a library that provides simple type checking for YAML.
Requires Python: >=3.9
Keywords: text, YAML, CLI, type, check

# Index

- **English Document**

  English Document <- Here

- **Japanese Document**

  [Japanese Document](docs/README_JP.md)

- **Annotation YAML(ANOY)**

  A YAML file that allows type checking for Map types.

  More about [ANOY](about_anoy.md)

- **Configuration YAML(Config YAML)**

  A YAML file that defines the data types usable in Annotation YAML.

  More about [Config YAML](about_config.md)

- **ANOY CLI**
  
  A CLI application that verifies whether Annotation YAML adheres to the data type defined in Configuration YAML.

  More about [ANOY CLI](about_anoycli.md)

# Introduction

YAML maps often become nested and complex.

And these requests have come up.

- Detecting typos in map keys.
- Verifying the data type of map values.
- Ensuring the map's nested structure is appropriate.

This library satisfies these requests.

# Installing

`pip install anoy`

## Package Dependencies

The following packages may not work properly if they are not installed:

- [PyYAML](https://pypi.org/project/PyYAML/): Most popular YAML parser for Python.

# Usage

library_config.yaml

```
"@Books":
  "@Summary": List the book titles.
  "!Child": "!FreeMap"
"@Author":
  "@Summary": Author of the book.
  "!Child": "!Str"
"@PublishYear":
  "@Summary": The year of the publishment.
  "!Child": "!Int"
"@Country":
  "@Summary": The author's native language.
  "!Child": "!Str"
```

valid_library.yaml:

```
"@Books":
  "Alice's Adventures in Wonderland":
    "@Author": Lewis Carroll
    "@PublishYear": 1865
    "@Country": UK
  "The Little Prince":
    "@Author": Antonie de Saint-Exupéry
    "@PublishYear": 1945
    "@Country": France
  "Harry Potter":
    "@Author": J.K.Rowling
    "@PublishYear": 1997
    "@Country": UK
```

Use the `anoy` command to verify data types.

If data type issues are not found, it outputs nothing.

```
>>> anoy library_config.yaml library.yaml
>>> 
```

If you mistyped `@Auther` as `@Auther`.

invalid_library.yaml:

```
"@Books":
  "Alice's Adventures in Wonderland":
    "@Auther": Lewis Carroll
    "@PublishYear": 1865
    "@Country": UK
  "The Little Prince":
    "@Auther": Antonie de Saint-Exupéry
    "@PublishYear": 1945
    "@Country": France
  "Harry Potter":
    "@Auther": J.K.Rowling
    "@PublishYear": 1997
    "@Country": UK
```

When there is an issue with the annotation YAML, `anoy` outputs as follows.

```
>>> anoy library_config.yaml library.yaml
>>> Traceback (most recent call last):
    ... (omission) ...
    src.anoyModule.anoyErrors.ConfigYamlError: `@Auther` is not defined.
```
# For Developers

## Testing

This project uses `pytest`.

If you want to test, put in following command.

`pytest tests\unit\test_dictTraversal.py`

## Next To Do

- [x] READMEの詳細documentの更新。

## Ideas

```
- key01
- key02
- key03
```
と
```
- {key01:value01}
- {key02:value02}
- {key03:value03}
```
を等価に扱うsystemが欲しい。
trainsum 0.0.1
A Python package for dealing with quantics tensor trains
Requires Python: >=3.14
Keywords: quantics, tensor train, tensor network, quantum inspired

trainsum
========

Welcome to trainsum, a Python package designed for working with quantics tensor trains. The development was done by the ZAQC-team at the Fraunhofer Institute for Graphical Data Analysis (IGD). trainsum is licensed under EUPL 1.2 (similar to GPL).

The main features are:

- easy definition of N-dimensional tensor trains
- quantization of dimensions independent of their size
- einsum-operations equivalent to NumPy’s einsum function
- generic backends for NumPy, Torch and CuPy
- tensorized solver for eigenvalue equations and linear equation systems

Installation
------------
You can install trainsum using pip:

:code:`pip install trainsum`

The dependencies are:

- numpy
- array_api_compat
- opt_einsum
- hdf5

Documentation
-------------
The documentation for trainsum can be found at https://trainsum.readthedocs.io.
genie-flow-invoker 0.8.1
Core classes for Genie Flow Invokers
Requires Python: >=3.11.0

# Genie Flow Invoker framework
[![PyPI version](https://badge.fury.io/py/genie-flow-invoker.svg?icon=si%3Apython)](https://badge.fury.io/py/genie-flow-invoker)
![PyPI - Downloads](https://img.shields.io/pypi/dm/genie-flow-invoker)

This package contains the base classes for Genie Flow Invokers.

## What are Invokers
An Invoker is a class that gets used when an external service needs to be called. That
external service may be a known GenAI API, or a Vector Database or anything else. Calling
that external service should follow the pattern of input -> process -> output.
featureform-enterprise 0.20.0
Package for the Featureform Enterprise Feature Store
Requires Python: >=3.9

# Featureform Python Client

## Overview

Featureform’s Python client is a SDK for defining, managing and serving resources (e.g. infrastructure providers, data sources, transformations, etc.). At a high level, the API is divided into two parts:

* Registration: register data stores (e.g. PostgreSQL), data sources (e.g. tables or CSV files) as resources or get and/or list previously registered resources
* Serving: retrieve training sets and features for offline training or online inference

## Requirements

* Python 3.9-3.12

## Setting Up Your Local Development Environment

### Step 1: Install gRPC and Protocol Buffer Tooling

See grpc.io for instructions on installing the [protocol buffer compiler](https://grpc.io/docs/protoc-installation/) for your OS and language-specific plugins for [Golang](https://grpc.io/docs/languages/go/quickstart/#prerequisites) (**NOTE**: the Golang dependencies can also be installed via [Homebrew](https://brew.sh/).)

### Step 2: Install uv and Sync Dependencies

Install [uv](https://docs.astral.sh/uv/getting-started/installation/) if you haven't already. Then sync the project dependencies:

```shell
> uv sync --group dev
```

This creates a virtual environment and installs all required dependencies, including the `featureform` client in editable mode.

### Step 3: Compile API Protocol Buffers and Python Stub

The shell script `gen_grpc.sh` has been provided for convenience. Change the file access permissions to make it executable and run it:

```shell
> chmod +x gen_grpc.sh
> ./gen_grpc.sh
```

### Step 4: Optionally Run Client Test Suite

To ensure your changes haven’t broken the client, run the test suite with the following make target:

```shell
> make pytest
```

## Outcome

With the steps above successfully completed, you should have the `featureform` CLI command accessible via `uv run`:

```shell
> uv run featureform -h
```

To further verify that your setup is complete and correct, you may optionally walk through the [Quickstart](https://docs.featureform.com/quickstart-local) tutorial. You may put the `definitions.py` file at the root of the project, which won’t be ignored by Git, or use a URL to a file (e.g. hosted on GitHub).