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So I wanted to see if I could make fractal flames using matplotlib and figured a good test would be the sierpinski triangle. I modified a working version I had that simply performed the chaos game by normalizing the x range from -2, 2 to 0, 400 and the y range from 0, 2 to 0, 200. I also truncated the x and y coordinates to 2 decimal places and multiplied by 100 so that the coordinates could be put in to a matrix that I could apply a color map to. Here's the code I'm working on right now (please forgive the messiness): 

import numpy as np
import matplotlib.pyplot as plt
import math
import random

def f(x, y, n):
    N = np.array([[x, y]])

    M = np.array([[1/2.0, 0], [0, 1/2.0]])

    b = np.array([[.5], [0]])
    b2 = np.array([[0], [.5]])

    if n == 0:
        return np.dot(M, N.T)

    elif n == 1:
       return np.dot(M, N.T) + 2*b

    elif n == 2:
        return np.dot(M, N.T) + 2*b2

    elif n == 3:
        return np.dot(M, N.T) - 2*b

def norm_x(n, minX_1, maxX_1, minX_2, maxX_2):
    rng = maxX_1 - minX_1
    n = (n - minX_1) / rng

    rng_2 = maxX_2 - minX_2
    n = (n * rng_2) + minX_2

    return n

def norm_y(n, minY_1, maxY_1, minY_2, maxY_2):
    rng = maxY_1 - minY_1
    n = (n - minY_1) / rng

    rng_2 = maxY_2 - minY_2
    n = (n * rng_2) + minY_2

    return n    

# Plot ranges
x_min, x_max = -2.0, 2.0
y_min, y_max = 0, 2.0

# Even intervals for points to compute orbits of
x_range = np.arange(x_min, x_max, (x_max - x_min) / 400.0)
y_range = np.arange(y_min, y_max, (y_max - y_min) / 200.0)

mat = np.zeros((len(x_range) + 1, len(y_range) + 1))   

random.seed()

x = 1
y = 1

for i in range(0, 100000):
    n = random.randint(0, 3)
    V = f(x, y, n)

    x = V.item(0)
    y = V.item(1)

    mat[norm_x(x, -2, 2, 0, 400), norm_y(y, 0, 2, 0, 200)] += 50

plt.xlabel('x0')
plt.ylabel('y')
fig = plt.figure(figsize=(10,10))
plt.imshow(mat, cmap="spectral", extent=[-2,2, 0, 2])
plt.show()

The mathematics seem solid here so I suspect something weird is going on with how I'm handling where things should go into the 'mat' matrix and how the values in there correspond to the colormap.

enter image description here

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1 回答 1

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如果我正确理解了您的问题，您需要使用方法转置矩阵.T。所以只需更换

fig = plt.figure(figsize=(10,10))
plt.imshow(mat, cmap="spectral", extent=[-2,2, 0, 2])
plt.show()

经过

fig = plt.figure(figsize=(10,10))
ax = gca()
ax.imshow(mat.T, cmap="spectral", extent=[-2,2, 0, 2], origin="bottom")
plt.show()

在此处输入图像描述

该参数origin=bottom告诉imshow您矩阵的原点位于图的底部。

希望能帮助到你。

于 2015-01-22T03:33:01.477 回答