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Showcase example: (kind of mandatory) Mandelbrot set #7447

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74 changes: 74 additions & 0 deletions examples/showcase/mandelbrot.py
Original file line number Diff line number Diff line change
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"""
===================================
Shaded & power normalized rendering
===================================

The Mandelbrot set rendering can be improved by using a normalized recount
associated with a power normalized colormap (gamma=0.3). Rendering can be
further enhanced thanks to shading.

The `maxiter` gives the precision of the computation. `maxiter=200` should
take a few seconds on most modern laptops.
"""
import numpy as np


def mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon=2.0):
X = np.linspace(xmin, xmax, xn, dtype=np.float32)
Y = np.linspace(ymin, ymax, yn, dtype=np.float32)
C = X + Y[:, None]*1j
N = np.zeros(C.shape, dtype=int)
Z = np.zeros(C.shape, np.complex64)
for n in range(maxiter):
I = np.less(abs(Z), horizon)
N[I] = n
Z[I] = Z[I]**2 + C[I]
N[N == maxiter-1] = 0
return Z, N


if __name__ == '__main__':
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I don't think this conditional block is necessary.

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It doesn't hurt and allow to import mandelbrot if you want to experience with it.

import time
import matplotlib
from matplotlib import colors
import matplotlib.pyplot as plt

xmin, xmax, xn = -2.25, +0.75, 3000/2
ymin, ymax, yn = -1.25, +1.25, 2500/2
maxiter = 200
horizon = 2.0 ** 40
log_horizon = np.log(np.log(horizon))/np.log(2)
Z, N = mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon)

# Normalized recount as explained in:
# https://linas.org/art-gallery/escape/smooth.html
# https://www.ibm.com/developerworks/community/blogs/jfp/entry/My_Christmas_Gift

# This line will generate warnings for null values but it is faster to
# process them afterwards using the nan_to_num
with np.errstate(invalid='ignore'):
M = np.nan_to_num(N + 1 - np.log(np.log(abs(Z)))/np.log(2) + log_horizon)

dpi = 72
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Is changing dpi important?

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More or less. I wanted to be sure of the approximate pixel resolution.

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I'm not sure I understand. The dataset is 3000 samples wide (xn) and the figure is 10 inches wide (width), giving an effective resolution of 300 dpi (if corresponding 1-to-1.) With dpi=72, the data needs to be resampled at a ratio of 25:6 = ~4.166667, which seems odd.

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Oops, just realized I missed the /2, so it's really 1500 samples wide with effective resolution of 150 dpi, and resampling ratio of 25:12 = ~2.083333. But in that case, how about making it exactly 2 by setting dpi=75?

width = 10
height = 10*yn/xn
fig = plt.figure(figsize=(width, height), dpi=dpi)
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0], frameon=False, aspect=1)

# Shaded rendering
light = colors.LightSource(azdeg=315, altdeg=10)
M = light.shade(M, cmap=plt.cm.hot, vert_exag=1.5,
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Should we use our fancy new colour maps (magma, inferno, or plasma)? I'm not sure how well they work with shading though.

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I tried but output was not as nice as with the hot colormap.

norm=colors.PowerNorm(0.3), blend_mode='hsv')
plt.imshow(M, extent=[xmin, xmax, ymin, ymax], interpolation="bicubic")
ax.set_xticks([])
ax.set_yticks([])

# Some advertisement for matplotlib
year = time.strftime("%Y")
major, minor, micro = matplotlib.__version__.split('.', maxsplit=2)
text = ("The Mandelbrot fractal set\n"
"Rendered with matplotlib %s.%s, %s — http://matplotlib.org"
% (major, minor, year))
ax.text(xmin+.025, ymin+.025, text, color="white", fontsize=12, alpha=0.5)

plt.show()