semvec/pypi-packages huggingface dataset:
introspection 1.12.1
New and improved introspection functions
Requires Python: >=3.10

## Introduction

This module is a collection of introspection functions.

Some functions are improved versions of the functions found in the [inspect module](https://docs.python.org/3/library/inspect.html), others are completely new.

## Documentation

The documentation can be found [here](https://Aran-Fey.github.io/introspection/build/html/index.html).

## Installation

```bash
python3 -m pip install introspection
```
gvar 13.1.9
Utilities for manipulating correlated Gaussian random variables.
Requires Python: >=3.11

gvar
------
This package facilitates the creation and manipulation of arbitrarily
complicated (correlated) multi-dimensional Gaussian random variables.
The random variables are represented by a new data type (``gvar.GVar``)
that can be used in arithmetic expressions and pure Python functions. Such
expressions/functions create new Gaussian random variables
while automatically tracking statistical correlations between the new
and old variables. This data type is useful for simple error propagation,
but also is heavily used by the Bayesian least-squares fitting module
``lsqfit.py`` to define priors and specify fit results, while accounting
for correlations between all variables. Documentation can is in the
``doc/`` subdirectory: see ``doc/html/index.html``
or look online at <https://gvar.readthedocs.io>.

These packages use ``numpy`` for efficient array arithmetic, and ``cython``
to compile efficient code. ``gvar`` uses automatic differentiation to
track covariances through arbitrary arithmetic.

Information on how to install the components is in the ``INSTALLATION`` file.

To test the libraries try ``make tests``. Some
examples are give in the ``examples/`` subdirectory.

``gvar`` version numbers have the form ``major.minor.patch`` where:
incompatible changes are signaled by incrementing the ``major`` version
number, the ``minor`` number signals new features, and the ``patch`` number
signals bug fixes.

| Created by G. Peter Lepage (Cornell University) 2008
| Copyright (c) 2008-2020 G. Peter Lepage

.. image:: https://zenodo.org/badge/37556070.svg
   :target: https://zenodo.org/badge/latestdoi/37556070
v-pvmismatch 0.2.4
Vectorized PVMismatch (v_PVMismatch) Package.
Keywords: pvmismatch, PV, shading, mismatch, IV

# v_PVMismatch
 Vectorized version of SunPower's PVMismatch using dynamic programming. PVMismatch is an explicit IV & PV curve trace calculator for PV system circuits.

 ![image](https://github.com/user-attachments/assets/35ae7565-d3ab-4123-9100-d77006e95215)


## Install & Setup
In a fresh Python virtual environment, simply run:

```
pip install v_pvmismatch
```

### Install using .whl file (Recommended)
Download files from Github and run the latest version of the .whl file using the command below from the parent folder.

```
pip install .\dist\v_pvmismatch-<version>-py3-none-any.whl
```

## Requirements

v_PVMismatch requires Matplotlib, Plotly, Future, NumPy, and SciPy.

## Other Projects that use PVMismatch

[PVShadeSim](https://github.com/Maxeon-RnD/PVShadeSim) includes a physical model for the PV system and utilizes v_PVMismatch for the electrical modeling. It allows for modeling physical representations of shading on a PV module. Various shade scenarios can be generated with an in-buit database. There are also structured databases for the PV cell and module classes in PVMismatch.

## Current Maintainer at Maxeon Solar

@k-bsub
jabs-core 0.1.0a1
Add your description here
Requires Python: <3.15,>=3.10

# JABS Core (`jabs-core`)

The infrastructure and shared utility layer for the JABS.

## Overview

`jabs-core` provides low-level, domain-agnostic utilities used across all JABS packages.
It is designed to be lightweight and free of heavy scientific dependencies (like
`scikit-learn` or `pandas`), making it safe to import at any level of the hierarchy.

## Responsibilities

- **Shared Constants**: Global constants used for file compression and configuration.
- **Exceptions**: Centralized exception hierarchy (`JabsError`, `PoseHashException`,
  etc.).
- **Infrastructure**: Base classes for registries and plugin discovery systems.
- **Abstract Bases**: High-level interface definitions (e.g., the `PoseEstimation`
  abstract base).
- **Utility Functions**: Generic helpers for file hashing, logging configuration, and
  basic string/path manipulation.

## Package Structure

- `jabs.core.constants`: Global constants.
- `jabs.core.exceptions`: Shared exception classes.
- `jabs.core.abstract`: Abstract base classes for the system.
- `jabs.core.utils`: Generic utility functions.
- `jabs.core.enums`: Shared enumerations (e.g., `ClassifierType`).