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# Original Python:
# from pyx import *
#
# # Mandelbrot calculation contributed by Stephen Phillips
#
# # Mandelbrot parameters
# re_min = -2
# re_max = 0.5
# im_min = -1.25
# im_max = 1.25
# gridx = 100
# gridy = 100
# max_iter = 10
#
# # Set-up
# re_step = (re_max - re_min) / gridx
# im_step = (im_max - im_min) / gridy
# d = []
#
# # Compute fractal
# for re_index in range(gridx):
# re = re_min + re_step * (re_index + 0.5)
# for im_index in range(gridy):
# im = im_min + im_step * (im_index + 0.5)
# c = complex(re, im)
# n = 0
# z = complex(0, 0)
# while n < max_iter and abs(z) < 2:
# z = (z * z) + c
# n += 1
# d.append([re, im, n])
#
# # Plot graph
# g = graph.graphxy(height=8, width=8,
# x=graph.axis.linear(min=re_min, max=re_max, title=r"$\Re(c)$"),
# y=graph.axis.linear(min=im_min, max=im_max, title=r'$\Im(c)$'))
# g.plot(graph.data.points(d, x=1, y=2, color=3, title="iterations"),
# [graph.style.density(gradient=color.rgbgradient.Rainbow)])
# g.writeEPSfile()
# g.writePDFfile()
# g.writeSVGfile()
using PyX
using LaTeXStrings
# Mandelbrot parameters
re_min = -2
re_max = 0.5
im_min = -1.25
im_max = 1.25
gridx = 100
gridy = 100
max_iter = 10
# Set-up
re_step = (re_max - re_min) / gridx
im_step = (im_max - im_min) / gridy
# Julia note: indexing is opposite of what would be idiomatic Julia
d = zeros(gridx*gridy, 3)
# Compute fractal
for re_index in 0:(gridx-1)
re = re_min + re_step * (re_index + 0.5)
for im_index in 0:(gridy-1)
im = im_min + im_step * (im_index + 0.5)
c = complex(re, im)
n = 0
z = complex(0, 0)
while n < max_iter && abs(z) < 2
z = (z * z) + c
n += 1
end
d[re_index*gridy + im_index + 1, :] = [re, im, n]
end
end
# Plot graph
g = graph.graphxy(height=8, width=8,
x=graph_axis.linear(min=re_min, max=re_max, title=L"$\Re(c)$"),
y=graph_axis.linear(min=im_min, max=im_max, title=L"$\Im(c)$"))
plot(g, graph_data.points(d, x=1, y=2, color=3, title="iterations"),
[graph_style.density(gradient=color_rgbgradient.Rainbow)])
writeEPSfile(g, "density")
writePDFfile(g, "density")
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