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
[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
Metadata
Release files for mdzip 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| mdzip-0.1.1.tar.gz | 16.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mdzip-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 34.8 kB
Release files / mdzip-0.1.1.tar.gz
| Download URL | mdzip-0.1.1.tar.gz |
|---|---|
| Size | 16.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.10.19
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Release files / mdzip-0.1.1-py3-none-any.whl
| Download URL | mdzip-0.1.1-py3-none-any.whl |
|---|---|
| Size | 18.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f1d989d165c884e0f12403b3a67287154338de97f8ddec1ab3b8bf6c8a360d9b
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.10.19
|
Security for mdzip 0.1.1
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