https://github.com/la-niche/lick

A high level Line Integral Convolution (LIC) library for Python, including post-processing and visualization
https://github.com/la-niche/lick

fluid-dynamics line-integral-convolution magnetic-fields matplotlib post-processing python visualization

Last synced: 3 days ago
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A high level Line Integral Convolution (LIC) library for Python, including post-processing and visualization

• Host: GitHub
• URL: https://github.com/la-niche/lick
• Owner: la-niche
• License: gpl-3.0
• Created: 2021-11-12T21:28:59.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2026-02-14T12:56:12.000Z (29 days ago)
• Last Synced: 2026-02-14T21:40:07.585Z (28 days ago)
• Topics: fluid-dynamics, line-integral-convolution, magnetic-fields, matplotlib, post-processing, python, visualization
• Language: Python
• Homepage:
• Size: 12.1 MB
• Stars: 5
• Watchers: 2
• Forks: 2
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

          
# lick

[![PyPI](https://img.shields.io/pypi/v/lick.svg?logo=pypi&logoColor=white&label=PyPI)](https://pypi.org/project/lick/)

[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/la-niche/lick/main.svg)](https://results.pre-commit.ci/latest/github/la-niche/lick/main)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/charliermarsh/ruff)

Line Integral Convolution Knit : clothe a 2D field (ex: density field) with a LIC texture,

given two vector fields (ex: velocity (vx, vy)).

This package builds on top of [rLIC](https://pypi.org/project/rlic), adding

post-processing and visualization functionalities.

Authors: Gaylor Wafflard-Fernandez, Clément Robert

Author-email: gaylor.wafflard@univ-grenoble-alpes.fr



    



## Installation

Install with `pip`

```

pip install lick

```

To import lick:

```python

import lick as lk

```

The important functions are `lick_box` and `lick_box_plot`. While `lick_box` interpolates the data and perform a line integral convolution, `lick_box_plot` directly plots the final image. Use `lick_box` if you want to have more control of the plots you want to do with the lic. Use `lick_box_plot` if you want to take advantage of the fine-tuning of the pcolormesh parameters.

## Example

```python

import numpy as np

import matplotlib.pyplot as plt

from lick import lick_box_plot

fig, ax = plt.subplots()

x = np.geomspace(0.1, 10, 128)

y = np.geomspace(0.1, 5, 128)

a, b = np.meshgrid(x, y)

v1 = np.cos(a)

v2 = np.sin(b)

field = v1 ** 2 + v2 ** 2

lick_box_plot(

    fig,

    ax,

    x,

    y,

    v1,

    v2,

    field,

    size_interpolated=256,

    xmin=1,

    xmax=9,

    ymin=1,

    ymax=4,

    kernel=np.sin(np.linspace(0, np.pi, 64)),

    niter_lic=5,

    post_lic="north-west-light-source",

    cmap="inferno",

    stream_density=0.5,

)

plt.show()

```