semvec/pypi-packages huggingface dataset:
ciderpress 0.4.0
CiderPress: DFT Calculations with Machine-Learned XC Functionals

<div align="left">
  <img src="https://github.com/mir-group/CiderPress/blob/main/docs/logos/cider_logo_and_name.png" height="80px"/>
</div>

CiderPress: Machine Learned Exchange-Correlation Functionals
------------------------------------------------------------
![Build Status](https://github.com/mir-group/CiderPress/actions/workflows/basic_tests.yml/badge.svg)

CiderPress provides tools for training and evaluating CIDER functionals for use in Density Functional Theory calculations. Interfaces to the GPAW and PySCF codes are included.

Please see the [CiderPress website](https://mir-group.github.io/CiderPress/) for installation instructions and documentation.

## Questions and Comments

Find a bug? Areas of code unclearly documented? Other questions? Feel free to contact
Kyle Bystrom at kylebystrom@gmail.com AND/OR create an issue on the [Github page](https://github.com/mir-group/CiderPress/).

## Citing

If you find CiderPress or CIDER functionals useful in your research, please cite the following article
```
@article{PhysRevB.110.075130,
  title = {Nonlocal machine-learned exchange functional for molecules and solids},
  author = {Bystrom, Kyle and Kozinsky, Boris},
  journal = {Phys. Rev. B},
  volume = {110},
  issue = {7},
  pages = {075130},
  numpages = {30},
  year = {2024},
  month = {Aug},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevB.110.075130},
  url = {https://link.aps.org/doi/10.1103/PhysRevB.110.075130}
}
```
The above article introduces the CIDER23X functionals and much of the algorithms in CiderPress. If you use the CIDER24X functionals, please also cite
```
@article{doi:10.1021/acs.jctc.4c00999,
  author = {Bystrom, Kyle and Falletta, Stefano and Kozinsky, Boris},
  title = {Training Machine-Learned Density Functionals on Band Gaps},
  journal = {Journal of Chemical Theory and Computation},
  volume = {20},
  number = {17},
  pages = {7516-7532},
  year = {2024},
  doi = {10.1021/acs.jctc.4c00999},
  note ={PMID: 39178337},
  URL = {https://doi.org/10.1021/acs.jctc.4c00999},
  eprint = {https://doi.org/10.1021/acs.jctc.4c00999}
}
```
tensorsat 0.1.0
A SAT solver based on relational network contraction.
Requires Python: >=3.13

# TensorSat

[![Generic badge](https://img.shields.io/badge/python-3.13+-green.svg)](https://docs.python.org/3.13/)
[![Checked with Mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](https://github.com/python/mypy)
[![PyPI version shields.io](https://img.shields.io/pypi/v/tensorsat.svg)](https://pypi.python.org/pypi/tensorsat/)
[![PyPI status](https://img.shields.io/pypi/status/tensorsat.svg)](https://pypi.python.org/pypi/tensorsat/)

A SAT/SMT Solver based on hyper-optimised tensor network contraction.

- The [tensorsat](./tensorsat) folder contains code for the future `tensorsat` Python package which was deemed to be relatively stable.
- The [notebooks](./notebooks) folder contains sub-folders with Jupyter notebooks containing feature demonstrations and experiments.
- The [notebooks/prototypes](./notebooks/prototypes) folder contains sub-folder with notebooks for feature prototypes and draft experiments.
- The [paper](./paper) folder contains the working draft of the companion paper for the library.

Contents and structure are subject to change during development.


## Install

You can install/upgrade this package from [PyPI](https://pypi.org/project/tensorsat) using pip:

```
pip install -U tensorsat
```

## Contributing

All contributions, big and small, are very appreciated!
[File an issue](./CONTRIBUTING.md/#file-an-issue) for bug reports, suggestions and questions, or [make a pull request](./CONTRIBUTING.md/#make-a-pull-request) to actively contribute to the code or documentation.

However you decide to help, please refer to the [Contributor Covenant](http://contributor-covenant.org/version/1/3/0/) Code of Conduct for what we expect from our community.

For more information, please see [CONTRIBUTING](./CONTRIBUTING.md).


## License

Multiple licensing terms apply to different parts of this repository:

- The [`tensorsat`](./tensorsat) library is licensed under [LGPL v3](./LICENSE).
- All code in the [`notebooks`](./notebooks) folder and subfolders is licensed under [GPL v3](./LICENSE).
- All rights to the companion [`paper`](./paper/) are reserved.
AIUT-RoboticsToolbox 0.0.0.6
A simple Robotics package
Requires Python: >=3.8

# A Robotics Toolbox from Maziar Palhang

This is a simple Robotics Toolbox package.

To install this package:\
on windows:\
py -m pip install AIUT_RoboticsToolbox\
\
on Linux and Mac:\
python3 -m pip install AIUT_RoboticsToolbox\
\
To upgrade:\
on Windows:\
py -m pip install AIUT_RoboticsToolbox --upgrade\
\
on Linux and Mac:\
python3 -m pip install AIUT_RoboticsToolbox --upgrade\
To use this package in your program, add the following line:\
from AIUT_RoboticsToolbox.Toolbox import *

## Routines:
version()             #prints the version of the toolbox
rotx(ang, mode='rad') # mode='rad' or 'deg', default is 'rad'\
roty(ang, mode='rad') # mode='rad' or 'deg', default is 'rad'\
rotz(ang, mode='rad') # mode='rad' or 'deg', default is 'rad'\
\
hrotx(ang, mode='rad') # homogeneous rotation about x, mode='rad' or 'deg', default is 'rad'\
hroty(ang, mode='rad') # homogeneous rotation about y, mode='rad' or 'deg', default is 'rad'\
hrotz(ang, mode='rad') # homogeneous rotation about z, mode='rad' or 'deg', default is 'rad'\
htransl(x,y,z)         # homogeneous translation \
\
rpy2r(gamma,beta,alpha, mode='rad')  # XYZ rotation\
r2rpy(r, mode='rad')\
euler2r(alpha,beta,gamma, mode='rad') # ZYZ rotation\
r2euler(r, mode='rad')\
angvec2r(theta,v, mode='rad')\
r2angvec(r, mode='rad')\
\
vec2skew(k)         #make a skew matrix from a 3 element vector\
skew2vec(S)         #make a 3 element vector from a skew matrix \
vec2skewa(k)        #make an augmented skew matrix from a 6 element vector\
skewa2vec(S)        #make a 6 element vector from an augmented skew matrix \
r2skew(r)           #finds the corresponding skew matrix of a rotation matrix \
\
mlog(r)             #finds the matrix logarithm of r \
mexp(S)             #finds matrix exponentiation of skew matrix S\
\
plot(r)             #plot a rotation matrix\
hplot(T)            #plot a frame \

## Classes:

SerialLink(name,links)\
Puma560(name)\
SCARA(name,l1,l2)\
shufflemates 0.1.2
A Python package for randomly grouping students or teams.
Requires Python: >=3.6

# Shufflemates 🎲✨
> A simple Python package for randomly grouping members while supporting fixed pairs and locked teams.

Shufflemates is a Python package that helps you shuffle a list of members into balanced groups while ensuring that some members stay together (fixed pairs) and others remain in exclusive groups (locked teams).

## 🚀 Installation
You can install **Shufflemates** via `pip`:
```sh
pip install shufflemates
```

## 🎯 Features
- Randomly shuffle members into groups of a specified size.
- Ensure fixed pairs or teams are always grouped together.
- Lock specific groups so that no extra members are added.
- Ensure balanced team sizes even when the total number of members is not a perfect multiple of the group size.

## 🛠️ Usage
Here's a quick example of how to use Shufflemates:

```python
from shufflemates import shuffle_groups
members = list(range(17))  # Members 0 to 16

group_size = 3
fixed_pairs = [(3, 4), (7, 8, 9)]
locked_teams = [(11, 12, 13)]

# case 1
groups = shuffle_groups(members, group_size, fixed_pairs=fixed_pairs, locked_teams=locked_teams)

for idx, group in enumerate(groups, 1):
    print(f"Group {idx}: {group}")

# or 
# case 2
print_shuffle_groups(
    members,
    group_size,
    fixed_pairs=fixed_pairs,
    locked_teams=locked_teams,
    num_iterations=12,
    iterations_time=0.5)
```

## ✅ Example Output
```sql
Group 1: [3, 4, 1]
Group 2: [7, 8, 9]
Group 3: [11, 12, 13]  <-- Locked team (No extra members)
Group 4: [2, 10, 14]
Group 5: [15, 0, 6]
Group 6: [5, 16]
```
 
## 📜 License
This project is licensed under the MIT License.