Skip to main content

Visualization tool designed to analyze and illustrate the Lorenz Energy Cycle for atmospheric science.

Project description

PyPI CircleCI

Lorenz Phase Space Visualization

Overview

The Lorenz Phase Space (LPS) visualization tool is designed to analyze and illustrate the dynamics of the Lorenz Energy Cycle in atmospheric science.

This tool offers a unique perspective for studying the intricate processes governing atmospheric energetics and instability mechanisms. It visualizes the transformation and exchange of energy within the atmosphere, specifically focusing on the interactions between kinetic and potential energy forms as conceptualized by Edward Lorenz.

Key features of the tool include:

  • Mixed Mode Visualization: Offers insights into both baroclinic and barotropic instabilities, which are fundamental in understanding large-scale atmospheric dynamics. This mode is particularly useful for comprehensively analyzing scenarios where both instabilities are at play.

  • Baroclinic Mode: Focuses on the baroclinic processes, highlighting the role of temperature gradients and their impact on atmospheric energy transformations. This mode is vital for studying weather systems and jet stream dynamics.

  • Barotropic Mode: Concentrates on barotropic processes, where the redistribution of kinetic energy is predominant. This mode is essential for understanding the horizontal movement of air and its implications on weather patterns.

By utilizing the LPS tool, researchers and meteorologists can delve into the complexities of atmospheric energy cycles, gaining insights into how different energy forms interact and influence weather systems and climate patterns. The tool's ability to switch between different modes (mixed, baroclinic, and barotropic) allows for a multifaceted analysis of atmospheric dynamics, making it an invaluable resource in the field of meteorology and climate science.

Features

  • Visualization of data in Lorenz Phase Space.
  • Support for different types of Lorenz Phase Spaces: mixed, baroclinic, and barotropic.
  • Dynamic adjustment of visualization parameters based on data scale.
  • Customizable plotting options for detailed analysis.

Installation

To use this tool, ensure you have Python installed along with the required libraries: pandas, matplotlib, numpy, and cmocean. You can install these packages using pip:

pip install pandas matplotlib numpy cmocean

Usage

Import the LorenzPhaseSpace class from LPS.py and initialize it with your data. Here's a basic example:

from LPS import LorenzPhaseSpace
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Initialize the Lorenz Phase Space plotter
lps = LorenzPhaseSpace(
    x_axis=data['Ck'],
    y_axis=data['Ca'],
    marker_color=data['Ge'],
    marker_size=data['Ke'],
    LPS_type='mixed'  # Choose from 'mixed', 'baroclinic', 'barotropic'
)

# Plot and save the visualization
fig, ax = lps.plot()
plt.savefig('LPS_visualization.png', dpi=300)

Contributing

Contributions to the LPS project are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

License

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

Contact

For any queries or further assistance with the Lorenz Phase Space project, please reach out to danilo.oceano@gmail.com.

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

lorenz_phase_space-0.0.8.tar.gz (8.4 kB view details)

Uploaded Source

Built Distribution

lorenz_phase_space-0.0.8-py3-none-any.whl (9.2 kB view details)

Uploaded Python 3

File details

Details for the file lorenz_phase_space-0.0.8.tar.gz.

File metadata

  • Download URL: lorenz_phase_space-0.0.8.tar.gz
  • Upload date:
  • Size: 8.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for lorenz_phase_space-0.0.8.tar.gz
Algorithm Hash digest
SHA256 cbe5703d7491bb433fe935860c0b87d8538965db5d033e970c8575e47fa01778
MD5 f6be59ca93168dfc45cb39e3d537e2b6
BLAKE2b-256 52b71d1185f3a09b273a02afe76c0869c6298344f2088536fddcc25d4b179c16

See more details on using hashes here.

File details

Details for the file lorenz_phase_space-0.0.8-py3-none-any.whl.

File metadata

File hashes

Hashes for lorenz_phase_space-0.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 1c81a527706e444d8ed0ff9399bc24cf8c6d506951d592039f2cf1c96463adf9
MD5 e18e7b19c02e96c5a6f9bc8f4ddb02d1
BLAKE2b-256 200fc5c57a8fc1ecd9cc93303dbd1dd6b117b92e1c53ac991e32e4b36e7de47e

See more details on using hashes here.

Supported by

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