semvec/pypi-packages huggingface dataset:
mdzip 0.1.1
Compress MD trajectories using deep convolutional autoencoder
Requires Python: >=3.6

# MDZip
This Git repository contains MDZip, a program that leverages the power of autoencoders with residual connections to compress molecular dynamics trajectories while reconstructing global and local structural properties with minimal information loss.
___
#### _Author_: [_Namindu De Silva_](https://github.com/nami-rangana)
![molzip](molzip.jpg)
[Add Description Here]

## Dependencies

- wheel
- mdtraj
- torch
- torchvision
- torchaudio
- pytorch-lightning
- scikit-learn
- numpy
- tqdm

## Installation
### Linux/Windows with CUDA
Create conda environment
```
conda create -n <my-env> python=3.10
conda activate <my-env>
```
Install dependencies (recomended for CUDA build)
```
conda install -c conda-forge mdtraj
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
pip install lightning
```
Install MolZip
```
git clone https://github.com/PDNALab/MDZip.git
cd MolZip
python setup.py sdist bdist_wheel
cd dist
pip insall molzip-0.1.0-py3-none-any.whl
```
### Linux/Windows/OSX without CUDA
```
conda create -n <my-env> python=3.10
conda activate <my-env>
conda install -c confa-forge mdtraj

git clone https://github.com/nami-rangana/MolZip.git
cd MolZip
python setup.py sdist bdist_wheel
cd dist
pip insall molzip-0.1.0-py3-none-any.whl
```

## Help
```
compress(traj: str, top: str, stride: int = 1, out: str = '/blue/alberto.perezant/t.desilva/MolZip/testing', fname: str = '', epochs: int = 100, batchSize: int = 128, lat: int = 20, w: float = 1.0, memmap: bool = False)

compressing trajectory
----------------------
traj (str) : Path to the trajectory file
top (str) : Path to the topology file
stride (int) : Read every strid-th frame [Default=1]
out (str) : Path to save compressed files [Default=current directory]
fname (str) : Prefix for all generated files [Default=None]
epochs (int) : Number of epochs to train AE model [Default=100]
batchSize (int) : Batch size to train AE model [Default=128]
lat (int) : Latent vector length [Default=20]
w (float) : Non-negative weight for loss function [Default=1.0]
memmap (bool) : Use memory-map to read trajectory [Default=False]
```
```
decompress(top: str, model: str, compressed: str, out: str)

decompress compressed-trajectory
--------------------------------
top (str) : Path to the topology file (parm7|pdb)
model (str) : Path to the saved model file
compressed (str) : Path to the compressed trajectory file
out (str) : Output trajectory file path with name. Use extention to define file type (*.nc|*.xtc)
```

## Cite

doi : <https://doi.org/10.1101/2025.07.31.667955>
MDZip 0.1.0
Compress MD trajectories using deep convolutional autoencoder
Requires Python: >=3.6

# MDZip
This Git repository contains MDZip, a program that leverages the power of autoencoders with residual connections to compress molecular dynamics trajectories while reconstructing global and local structural properties with minimal information loss.
___
#### _Author_: [_Namindu De Silva_](https://github.com/nami-rangana)
![molzip](molzip.jpg)
[Add Description Here]

## Dependencies

- wheel
- mdtraj
- torch
- torchvision
- torchaudio
- pytorch-lightning
- scikit-learn
- numpy
- tqdm

## Installation
### Linux/Windows with CUDA
Create conda environment
```
conda create -n <my-env> python=3.10
conda activate <my-env>
```
Install dependencies (recomended for CUDA build)
```
conda install -c conda-forge mdtraj
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
pip install lightning
```
Install MolZip
```
git clone https://github.com/PDNALab/MDZip.git
cd MolZip
python setup.py sdist bdist_wheel
cd dist
pip insall molzip-0.1.0-py3-none-any.whl
```
### Linux/Windows/OSX without CUDA
```
conda create -n <my-env> python=3.10
conda activate <my-env>
conda install -c confa-forge mdtraj

git clone https://github.com/nami-rangana/MolZip.git
cd MolZip
python setup.py sdist bdist_wheel
cd dist
pip insall molzip-0.1.0-py3-none-any.whl
```

## Help
```
compress(traj: str, top: str, stride: int = 1, out: str = '/blue/alberto.perezant/t.desilva/MolZip/testing', fname: str = '', epochs: int = 100, batchSize: int = 128, lat: int = 20, w: float = 1.0, memmap: bool = False)

compressing trajectory
----------------------
traj (str) : Path to the trajectory file
top (str) : Path to the topology file
stride (int) : Read every strid-th frame [Default=1]
out (str) : Path to save compressed files [Default=current directory]
fname (str) : Prefix for all generated files [Default=None]
epochs (int) : Number of epochs to train AE model [Default=100]
batchSize (int) : Batch size to train AE model [Default=128]
lat (int) : Latent vector length [Default=20]
w (float) : Non-negative weight for loss function [Default=1.0]
memmap (bool) : Use memory-map to read trajectory [Default=False]
```
```
decompress(top: str, model: str, compressed: str, out: str)

decompress compressed-trajectory
--------------------------------
top (str) : Path to the topology file (parm7|pdb)
model (str) : Path to the saved model file
compressed (str) : Path to the compressed trajectory file
out (str) : Output trajectory file path with name. Use extention to define file type (*.nc|*.xtc)
```

## Cite

doi : <https://doi.org/10.1101/2025.07.31.667955>
yaqd-pmc 2025.11.0
yaq daemons for Precision MicroControl Corp. hardware
Requires Python: >=3.7

# yaqd-pmc

[![PyPI](https://img.shields.io/pypi/v/yaqd-pmc)](https://pypi.org/project/yaqd-pmc)
[![Conda](https://img.shields.io/conda/vn/conda-forge/yaqd-pmc)](https://anaconda.org/conda-forge/yaqd-pmc)
[![yaq](https://img.shields.io/badge/framework-yaq-orange)](https://yaq.fyi/)
[![black](https://img.shields.io/badge/code--style-black-black)](https://black.readthedocs.io/)
[![ver](https://img.shields.io/badge/calver-YYYY.0M.MICRO-blue)](https://calver.org/)
[![log](https://img.shields.io/badge/change-log-informational)](https://github.com/yaq-project/yaqd-pmc/blob/main/CHANGELOG.md)

yaq daemons for Precision MicroControl Corp. hardware

This package contains the following daemon(s):

- https://yaq.fyi/daemons/pmc

## maintainers

- [Kent Meyer](https://github.com/kameyer226)
litebee 0.8.3
A Python3 module to compile LiteBee Client Case files.
Requires Python: >=3.10

# PyBeeClient

##### A Python module designed to take Python code and compile it into LiteBeeClient-compatible bytecode.

This project is not designed to be a full replacement to LiteBeeClient, nor is it intended to have implementations for every single feature of LiteBeeClient. The following components are currently working for LBC v1.3.9 - v1.3.11:

All commands (except for Curve4) and basic usage have been implemented, this includes:

- Calibrate
- Takeoff
- Land
- Move3D
- Around
  - Around(H)
  - Around(D)
- RGB
- RGBGradient
- Curve3

Basic usage:

```python
from litebee.commands import *
from litebee.core import Case

show = Case(
    "Python Light Show", 5, 5
)

show.add_drone().add_commands(
    Calibrate(),
    Takeoff().add_rgb((255, 0, 0)),
    Move3D((250, 250, 250), 2.0),
    Land().add_rgb((0, 0, 255))
)

"""
Commands can also be added individually;
drone_show.add_command(Land())
"""

# Leaving the save() function empty will use the Case.name instead.
drone_show.save()


```