Skip to main content

LIC plotting algorithm.

Project description

Line Integral Convolution

Demo

The Line Integral Convolution (LIC) is an algorithm used to image a vector field. Its main advantage is to show in intricate detail the fine structure of the vector field. It does not display the direction or magnitude of the vectors, although this information can be color coded in a postprocessing step.

The result of course depends on the shape of the kernel and the length
of the streamline. If it is too small, the texture is not sufficiently filtered and the motion is not clear. If it is too large, the image is smoothed and details of the motion are lost. For an image of size (256, 256), a value of 20 provides acceptable results.

Install

If you want to install LIC you can clone the repo and run.

    pip install -e .

or install from pypi

    pip install licplot

Usage

The basic usage is shown in and a runnable example can be found under examples/lic_demo.py

    from lic import lic_internal
    import numpy as np
    import matplotlib.pyplot as plt
    # create vector field and kernel
    size = 500
    u = np.zeros((size, size), dtype=np.float32)
    v = np.zeros((size, size), dtype=np.float32)
    texture = np.random.rand(size, size).astype(np.float32)

    # create a kernel
    kernel_length = 31
    kernel = np.sin(np.arange(kernel_length) * np.pi / kernel_length).astype(np.float32)

    # compute the lic
    image = lic_internal.line_integral_convolution(u, v, texture, kernel)

    plt.imshow(image, cmap="hot")
    plt.show()

Forked from https://github.com/aarchiba/scikits-vectorplot

by Anne Archibald

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

licplot-1.0.3.tar.gz (47.6 kB view details)

Uploaded Source

File details

Details for the file licplot-1.0.3.tar.gz.

File metadata

  • Download URL: licplot-1.0.3.tar.gz
  • Upload date:
  • Size: 47.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.55.0 CPython/3.8.5

File hashes

Hashes for licplot-1.0.3.tar.gz
Algorithm Hash digest
SHA256 d0743fb3da4bfddf93569844ff1435f7e8860acf66216921868a2b0eddacbb3f
MD5 59db642cd53c798b659400059632d268
BLAKE2b-256 b062cc8d0954530e577ffa24185ff19f4802f96547091c7399e0a26d0134a9d9

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