semvec/pypi-packages huggingface dataset:
orjsonless_pickledb 1.3.2.post4
orjson-less fork of pickledb
Keywords: pickledb, pickle db, database

# orjson-less pickledb
A fork of pickledb that pretty much just replaces this line:
```python
import orjson
```
With the following:
```python
import json as orjson
```

 \
The `option` argument was also removed from the `save` method due to it being flags passed to orjson's `dumps` implementation that weren't being used by pickledb itself.

Why? \
Because orjson refuses to support PyPy with the reason being that it *"uses private undocumented functions in cpython's API"* (See [#90](https://github.com/ijl/orjson/issues/90#issuecomment-2380389948)).

Forked from commit [46ab99ffad71ea4fd8c29adcb3aea076e24a1865](https://github.com/patx/pickledb/commit/46ab99ffad71ea4fd8c29adcb3aea076e24a1865) (version 1.3.2).
codeatlas 0.1.0
A Python library for visualizing and understanding codebases.
Requires Python: >=3.6

# CodeAtlas

## 🚀 Overview
CodeAtlas is a groundbreaking Python library designed to help developers visualize and comprehend their codebases effortlessly. By generating interactive and intuitive diagrams, CodeAtlas provides insights into the structure and flow of your Python projects, facilitating better understanding, maintenance, and collaboration.

## 🎯 Key Features
- **Function and Class Diagrams**: Automatically generate diagrams showcasing the relationships between functions and classes in your code.
- **Module Dependency Graphs**: Visualize how different modules in your project depend on each other.
- **Interactive Web Interface**: Explore your codebase through an interactive web interface with zooming, panning, and filtering capabilities.
- **Command-Line Interface (CLI)**: Easily integrate CodeAtlas into your workflow using its powerful CLI.
- **Export Options**: Export diagrams in various formats such as PNG, SVG, and PDF for documentation or presentations.

## 🧠 Why CodeAtlas?
Understanding complex codebases can be challenging, especially when onboarding new team members or revisiting old projects. While tools like Graphviz and Doxygen exist, they often require extensive configuration and lack interactivity. CodeAtlas fills this gap by providing an easy-to-use, interactive, and visually appealing way to explore Python codebases.

## 📦 Installation
To install CodeAtlas, use the following command:

```bash
pip install codeatlas
```

## 🛠️ Usage
To generate a graph for your Python project, run the following command:

```bash
python -m codeatlas.main --path <project_path> --output <output_file>
```

Example:

```bash
python -m codeatlas.main --path examples/sample_project --output demo_graph.png
```

## 🤝 Contributing
We welcome contributions from the community! Please see the `CONTRIBUTING.md` file for guidelines on how to contribute.

## 🚀 Future Plans
- Add module dependency graphs.
- Develop an interactive web interface for exploring codebases.
- Support exporting diagrams in SVG and PDF formats.
- Enhance CLI features for better usability.

## 📜 License
CodeAtlas is licensed under the MIT License. See the `LICENSE` file for more details.

---

Happy coding with CodeAtlas! 🎉
rnbgrader 0.3.5
RNBGrader package
Requires Python: >=3.6

# RNBGrader - utilities for grading R Markdown notebooks

Utilities for grading notebooks in [R
Markdown](https://rmarkdown.rstudio.com).

Notebooks can be [R
notebooks](https://bookdown.org/yihui/rmarkdown/notebook.html) or
[Jupyter
notebooks](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html)
converted to R Markdown with
[Jupytext](https://github.com/mwouts/jupytext)

## Quickstart

See the tests for examples.

## Installation

```
pip install rnbgrader
```

## Code

See <https://github.com/matthew-brett/rnbgrader>

Released under the BSD two-clause license - see the file `LICENSE`
in the source distribution.

[travis-ci](https://travis-ci.org/matthew-brett/rnbgrader) kindly
tests the code automatically under Python versions 3.6 through
3.8.

The latest released version is at
<https://pypi.python.org/pypi/rnbgrader>

## Tests

### R requirements

You need the R kernel installed for the tests.

On Mac:

```bash
brew install libgit2 harfbuzz fribidi
```

On Debian / Ubuntu:

```bash
sudo apt install -y libgit2-dev libharfbuzz-dev libfribidi-dev
```

On any platform:

```bash
Rscript -e "install.packages(c('repr', 'IRdisplay', 'crayon', 'pbdZMQ', 'devtools'))"
Rscript -e "devtools::install_github('IRkernel/IRkernel')"
Rscript -e "IRkernel::installspec()"
```

### The rest

*   Install `rnbgrader`
*   Install the test requirements:

    ```bash
    pip install -r test-requirements
    ```

*   Run the tests with:

    ```bash
    pytest rnbgrader
    ```

## Support

Please put up issues on the [rnbgrader issue
tracker](https://github.com/matthew-brett/rnbgrader/issues).
explainbench 0.1.1
A toolkit for interpretable machine learning and fairness auditing
Requires Python: >=3.8

# Explainbench: An Open-Source Toolkit for Interpretable Machine Learning

**Explainbench** is a Python toolkit that makes powerful ML interpretability techniques like SHAP, LIME, counterfactuals, and global surrogate models accessible and usable — especially for high-stakes, public-sector applications.

## Features

- **Unified Interface** for SHAP, LIME, and DiCE
- **Fairness & Explainability Metrics** (Disparate Impact, Fidelity, Consistency)
- **Preloaded Datasets** (COMPAS, Adult Income, etc.)
- **Interactive Visualizations** with Streamlit and Plotly
- **Notebook Examples** for quick understanding and classroom use

## Why It Matters

As ML systems are increasingly used in criminal justice, healthcare, and finance, it's crucial that we can explain, audit, and challenge their decisions. `Explainbench` provides transparent tools for evaluating black-box models in real-world, socially relevant contexts.

## Installation

```bash
pip install explainbench
neured 1.0
NeuRED: Framework for Processing Time-series Neutron Radiography of Electrochemical Devices
Requires Python: >=3.10

NeuRED
NeuRED: Framework for Processing Time-series Neutron Radiography of Electrochemical Devices
Developed by the Applied Materials Group (AMG), Paul Scherrer Institut (PSI)

Overview
NeuRED provides tools and example frameworks for processing time-series neutron imaging datasets, particularly for energy storage and conversion devices.
It has been primarily developed for PSI internal research, but is freely available to external users under the MIT license.

Installation
pip install neured
Make sure you have Python 3.8 or newer and pip installed.
We recommend using Anaconda as the base Python distribution.

Dependencies
NeuRED automatically installs the following core dependencies:

numpy
scipy
matplotlib
opencv-python
astropy
jupyter
ipython
pyqt5
pyqtgraph

Optional (legacy only): jupyter_contrib_nbextensions
This package is no longer included by default due to its incompatibility with modern PEP 517 builds.
If you wish to activate the optional legacy Magic Selector buttons inside Jupyter notebooks, install manually:

pip install jupyter_contrib_nbextensions 

There has been a known conflict with nbextensions install with updated versions of pip. In such case, use the following command on conda prompt (as an admin):

conda install -c conda-forge jupyter_contrib_nbextensions

Then run:

jupyter contrib nbextension install --user

Please note that jupyter_contrib_nbextensions is considered deprecated.

Example notebooks and demo data
Example notebooks and demonstration data are installed with NeuRED under the folder:

<python site-packages>/neured/demo_notebooks/

You can also access the folder programmatically after installation:

import neured
from pathlib import Path
demo_path = Path(neured.__file__).parent / "demo_notebooks"
print(demo_path)

The demo_notebooks folder contains example Jupyter notebooks and demonstration datasets to get started.

Usage example
After installation, verify that the package works by opening Python and running:

go
Copy
Edit
import neured
print(neured.__version__)
For further examples, refer to the Jupyter notebooks provided in the demo_notebooks folder.

Developer information (internal PSI setup)
Internal PSI users may optionally install Anaconda3 and TortoiseGit via the software kiosk.
A recommended local working directory is:

makefile
Copy
Edit
C:\Software\NeuRED
To clone and work with the source code:

bash
Copy
Edit
git clone https://gitlab.psi.ch/your-repository-url.git
cd NeuRED
pip install -e .
This allows live development without needing repeated installation after code changes.

License
NeuRED is distributed under the MIT License.
Copyright 2025 Paul Scherrer Institute (PSI) and NeuRED contributors.

Contact
For support, bug reports, or contributions, please contact:
Dr. Pierre Boillat (pierre.boillat@psi.ch) or Dr. Jongmin Lee (jongmin.lee@psi.ch)