RepLikCompare
RepLikCompare is a Python package for working with Molecular Dynamics data, including dataset loading, clustering, dimensionality reduction, plotting, and ensemble comparison workflows.
Installation
Conda environment (recommended)
An environment.yml file is provided to create an environment with all
dependencies, including hdbscan and umap-learn:
conda env create -f environment.yml
conda activate RepLikCompare
Installing the package
Once your environment is ready, you have two options:
Option A — without cloning (once published on PyPI)
pip install RepLikCompare
Option B — from source (works today)
git clone https://github.com/regueialaa/RepLikCompare.git
cd RepLikCompare
pip install .
Either way installs RepLikCompare together with all of its dependencies so it is available in both the terminal and Jupyter notebooks.
Quick start
You have two options for creating a dataset:
- Use an existing pandas DataFrame.
- Load the data directly from a CSV file.
The input dataset must contain two required columns:
- System: the system or condition being analyzed (e.g.,
WTorMutant). - Replica: the replicate identifier for each system (e.g.,
WT1,WT2,WT3,Mut1,Mut2,Mut3).
import RepLikCompare as rlc
# Create a dataset from an existing pandas DataFrame
dataset = rlc.Dataset.from_dataframe(df)
# Or load a dataset directly from a CSV file
dataset = rlc.Dataset.from_csv("df.csv")
Modules overview
| Module | Key functions | What it does |
|---|---|---|
| Plotting | plot_scatter, plot_free_energy, plot_lineplot_avg, plot_distri_norm, plot_vonmises, plot_rmsd, plot_rmsf, plot_contact_map, plot_cluster_timeline, compute_secondary_structure_timeline |
Scatter/line/distribution plots, free-energy landscapes, RMSD/RMSF and contact-map plots, and cluster/secondary-structure timelines — most with a faceted (per-system) variant. |
| Clustering | compute_cluster_kmean, compute_cluster_GMM, compute_cluster_dbscan, compute_cluster_hdbscan, hierarchical_clustering, assign_cluster_representative |
Cluster conformations with K-means, GMM, DBSCAN, HDBSCAN, or hierarchical clustering, and pick a representative frame per cluster. |
| Dimensionality reduction | compute_pca, compute_umap, compute_tsne, compute_kpca |
Reduce a set of numeric features to a low-dimensional embedding (PCA, UMAP, t-SNE, kernel PCA), with optional support for circular/angular features. |
| Ensemble comparison | compute_jsd, compute_wasserstein, compute_convergence |
Statistically compare feature distributions across systems and replicas (Jensen-Shannon divergence, Wasserstein distance) and check trajectory convergence. |
See the Documentation for the full parameter reference of every function.
Documentation
For more details on the package, including installation, usage, and examples, check out the online documentation:
Authors
- Alaa REGUEI, PhD Student - Université Paris Cité, BFA.
- Samuel Murail, Associate Professor - Université Paris Cité, BFA.
License
Distributed under the MIT license -- see LICENSE for details.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file replikcompare-0.1.0.tar.gz.
File metadata
- Download URL: replikcompare-0.1.0.tar.gz
- Upload date:
- Size: 87.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.13.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0364a626a9b6c44c97378445f6e02b7b89b0bc02294a33228a12502f57775d36
|
|
| MD5 |
ee5e653ee8f337ee4e19ecc87e36015f
|
|
| BLAKE2b-256 |
4258d8f57c7d225af05e08d88d24d29fe4e7cc74e5d152e7e8da6e14258703a9
|
File details
Details for the file replikcompare-0.1.0-py3-none-any.whl.
File metadata
- Download URL: replikcompare-0.1.0-py3-none-any.whl
- Upload date:
- Size: 109.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.13.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1baafce27977be584ab4603b9e516b234fdee1e28748472b8efb7ba004ad736a
|
|
| MD5 |
2ffa12e7277bd073cab6ef71c06b6c0b
|
|
| BLAKE2b-256 |
9b0af6dd049f84b0a4c241d6f87bc1a0acb6b9ced56ce3d40c8c856d8095df9d
|