Skip to main content

A brief description of your package

Project description

VMMC

README for VMMC Model Package Overview The VMMC (Virtual Move Monte Carlo) Model package is a Python tool designed for simulating the dynamics of particles with specific interactions in a confined space. It is particularly suited for modeling the movement and interaction of biological macromolecules or any system where particles are subject to specific interaction rules. The package leverages NumPy for efficient numerical computations and Matplotlib for visualization, including 3D animations to observe the system's evolution over time.

Features Customizable Interactions: Define specific interaction strengths between particle types. 3D Visualization: Utilize Matplotlib to create dynamic 3D animations of particle movements within a confined space. Periodic Boundary Conditions: Apply periodic boundary conditions to simulate an infinite system effect within a finite space. Energy-Based Movement: Implement a Metropolis criterion for particle movement based on the change in system energy, allowing for a realistic simulation of physical processes.

Installation

This package requires Python 3.6 or later, along with the NumPy and Matplotlib libraries. You can install these dependencies using pip:

pip install numpy matplotlib

Usage

To use the VMMC Model, first import the necessary libraries:

import numpy as np import matplotlib.pyplot as plt from vmmc_model import VMMC_model

Then, define your system's parameters, including the types of particles, interaction strengths, and colors for visualization:

base_types_strands = [np.array(['A', 'T', 'C', 'G', ...]), ...] interaction_strengths = {('A', 'T'): 1.0, ('G', 'C'): 1.5, ...} base_colors_dict = {'A': 'red', 'T': 'blue', 'C': 'yellow', 'G': 'green'} base_colors = np.array([base_colors_dict[base] for strand in base_types_strands for base in strand])

Instantiate the model with your defined parameters:

vmmc_model = VMMC_model(base_types_strands, interaction_strengths, base_colors)

Finally, run the simulation and visualize the results:

vmmc_model.animate(frames=100, interval=50) vmmc_model.save_animation('your_animation_file.mp4')

Contributing

Contributions to the VMMC Model package are welcome. Please fork the repository, make your changes, and submit a pull request for review.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or support, please contact [james.broster@st-annes.ox.ac.uk].

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Virtual-MMC-0.1.2.tar.gz (5.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

Virtual_MMC-0.1.2-py3-none-any.whl (5.6 kB view details)

Uploaded Python 3

File details

Details for the file Virtual-MMC-0.1.2.tar.gz.

File metadata

  • Download URL: Virtual-MMC-0.1.2.tar.gz
  • Upload date:
  • Size: 5.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.1

File hashes

Hashes for Virtual-MMC-0.1.2.tar.gz
Algorithm Hash digest
SHA256 78a00d861a004a8b351fe00c23336fc7ddc4dc441065ab2b7f0154f483c75928
MD5 b53eb74cadcb952407ab33e021feeb4a
BLAKE2b-256 e746e4e38cbcd8b7177b2c330d57b8174eaab2b0adf29a5ee9e88d85fb77ccdd

See more details on using hashes here.

File details

Details for the file Virtual_MMC-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: Virtual_MMC-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 5.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.1

File hashes

Hashes for Virtual_MMC-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 fa5ffb009ea258d2c0b0fa3a2f3fcac17d9fc99852add6da8ddd16a0867f25b6
MD5 1ddee90775c3c0e6a5091ba457f8d62a
BLAKE2b-256 989788e24df3b8f38dac3d277101205d0471c27c653a7073c50aaf693173d466

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page