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mandelbrot.py
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mandelbrot.py
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from decimal import *
import random
from tqdm import tqdm
class Mandelbrot:
def __init__(self, w = Decimal(4), h = Decimal(2), x = Decimal(-1), y = Decimal(0)):
self.w = w
self.h = h
self.x = x
self.y = y
self.rendered = None
self.rendered_res_x = 0
self.rendered_res_y = 0
def mandel_point(self, C_x, C_y, iter):
Z_x = C_x
Z_y = C_y
for i in range(0, iter):
Z_x_old = Z_x
Z_x = Z_x * Z_x - Z_y * Z_y + C_x
Z_y = 2 * Z_x_old * Z_y + C_y
if (Z_x ** 2 + Z_y ** 2) > 4:
return 1
return 0
def render(self, res_x, res_y):
# Approximation for number of iterations
iter = int(50 + max(0,-Decimal.log10(self.w)) * 100)
# Updates precision
getcontext().prec = int(max(0,-Decimal.log10(self.w))+8)
columns = []
for y_offset_i in tqdm(range(res_y, 0, -1)):
row = []
for x_offset_i in range(0, res_x):
p_x = self.x - self.w / Decimal(2) + Decimal(x_offset_i) / Decimal(res_x) * self.w
p_y = self.y - self.h / Decimal(2) + Decimal(y_offset_i) / Decimal(res_y) * self.h
row += [self.mandel_point(p_x, p_y, iter)]
columns += [row]
self.rendered_res_x = res_x
self.rendered_res_y = res_y
self.rendered = columns
def get_render(self):
return self.rendered
def is_area_uniform(self, x_offset, y_offset, w, h, w_div, h_div, w_start, h_start):
first_point = self.rendered[int(y_offset) + int(h / h_div) * h_start][int(x_offset) + int(w / w_div) * w_start]
for x in range(0, int(w / w_div)):
for y in range(0, int(h / h_div)):
if first_point != self.rendered[int(y_offset) + int(h / h_div) * h_start + y][
int(x_offset) + int(w / w_div) * w_start + x]:
return False
return True
def get_uniformness_of_area(self, w, h, x_offset, y_offset, w_div, h_div):
uniformness = 0
for w_start in range(w_div):
for h_start in range(h_div):
if self.is_area_uniform(x_offset, y_offset, w, h, w_div, h_div, w_start, h_start):
uniformness += 1
return uniformness
def zoom_on_interesting_area(self):
choices = []
# Upper left
uniformness = self.get_uniformness_of_area(self.rendered_res_x / 2, self.rendered_res_y / 2, 0, 0, 2, 2)
choices += [(self.x-self.w/4, self.y+self.h/4, uniformness)]
# Upper right
uniformness = self.get_uniformness_of_area(self.rendered_res_x / 2, self.rendered_res_y / 2, self.rendered_res_x / 2, 0, 2, 2)
choices += [(self.x+self.w/4, self.y+self.h/4, uniformness)]
# Lower left
uniformness = self.get_uniformness_of_area(self.rendered_res_x / 2, self.rendered_res_y / 2, 0, self.rendered_res_y / 2, 2, 2)
choices += [(self.x-self.w/4, self.y-self.h/4, uniformness)]
# Lower right
uniformness = self.get_uniformness_of_area(self.rendered_res_x / 2, self.rendered_res_y / 2, self.rendered_res_x / 2, self.rendered_res_y / 2, 2, 2)
choices += [(self.x + self.w / 4, self.y - self.h / 4, uniformness)]
self.w = self.w / 2
self.h = self.h / 2
# Filter out completely uniform squares
choices = [x for x in choices if x[2]<4]
# Filter out squares that have 2 or more uniform squares
less_uniform_choices = [x for x in choices if x[2]<3]
if len(less_uniform_choices) != 0:
self.x, self.y, u = random.choice(less_uniform_choices)
else:
self.x, self.y, u = random.choice(choices)