Fast Mandelbrot set explorer
Features
- Accelerated on GPU and CPU using numba CUDA JIT
- Interactive exploration using Matplotlib
- Use mousewheel or left/right click to zoom in/out
- Use sliders to change the rendering parameters
- Save still and animated images (GIF)
- Smooth iteration coloring, anti-aliasing by oversampling
- Shading: Blinn-Phong & Lambert lighting, stripe average coloring, steps shading
- Customizable color palette
- Precision: zoom down to float64 precision (approx. 16 significants digits)
- 100% Python
🐍
Quick start
from mandelbrot import Mandelbrot
mand = Mandelbrot() # set `gpu = False` if no GPU is availableExplore the set
# Explore the set using interactive Matplotlib GUI
mand.explore()Draw an image
# Draw an image and save it to file
mand.draw('mandelbrot.png')
Make a zoom animation
# We decrease the image size to avoid overloading git and the readme page
# We also increase the number of iterations, and add stripe coloring
mand = Mandelbrot(maxiter = 5000, xpixels = 426, stripe_s = 5)
# Point to zoom at
x_real = -1.749705768080503
x_imag = -6.13369029080495e-05
mand.animate(x_real, x_imag, 'mandelbrot.gif')
Run from command line
Use the following command from terminal to launch a GUI with default parameters:
python ./mandelbrot.pyGallery
Some examples of HD images, and corresponding code:
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.11, .02, .92], stripe_s = 2,
coord = [-0.5503295086752807,
-0.5503293049351449,
-0.6259346555912755,
-0.625934541001796])
mand.draw('crown.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.29, .02, 0.9], ncycle = 8,
step_s = 10,
coord = [-1.9854527029227764,
-1.9854527027615938,
0.00019009159314173224,
0.00019009168379912058])
mand.draw('pow.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.83, .01, .99], stripe_s = 5,
coord = [-1.749289287806423,
-1.7492892878054118,
-1.8709586016347623e-06,
-1.8709580332005737e-06])
mand.draw('octogone.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.87, .83, .77],
coord = [-1.9415524417847085,
-1.9415524394561112,
0.00013385928801614168,
0.00013386059768851223])
mand.draw('julia.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.54, .38, .35], stripe_s = 8,
coord = [-0.19569582393630502,
-0.19569331188751315,
1.1000276413181806,
1.10002905416902])
mand.draw('lightning.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.47, .51, .63], step_s = 20,
coord = [-1.7497082019887222,
-1.749708201971718,
-1.3693697170765535e-07,
-1.369274301311596e-07])
mand.draw('web.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.6, .57, .45], stripe_s = 12,
coord = [-1.8605721473418524,
-1.860572147340747,
-3.1800170324714687e-06,
-3.180016406837821e-06])
mand.draw('wave.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.63, .83, .98],
coord = [-0.7545217835886875,
-0.7544770820676441,
0.05716740181493137,
0.05719254327783547])
mand.draw('tiles.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.29, .52, .59], stripe_s = 5,
coord = [-1.6241199193994318,
-1.624119919281773,
-0.00013088931048083944,
-0.0001308892443058033])
mand.draw('velvet.png')
Runtime 🚀
Computing a sequence of 100 frames of HD pictures (1280*720 pixels), with 2000 iterations takes approximately 1 second on a Tesla K80 GPU.
Requirements
- NumPy
- Matplotlib
- Numba
- (optional, for much faster rendering) A CUDA compatible GPU & CUDA Toolkit