matplotlib & pandas

Python/matplotlab/gallery/showcase/mandelbrot.md

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+# 阴影和增强标准化渲染
+
+通过使用与幂归一化色图（gamma = 0.3）相关联的归一化重新计数，可以改善Mandelbrot集渲染。 由于阴影，渲染可以进一步增强。
+
+maxiter给出了计算的精度。 在大多数现代笔记本电脑上，maxiter = 200应该需要几秒钟。
+
+![阴影和增强标准化渲染示例](https://matplotlib.org/_images/sphx_glr_mandelbrot_001.png)
+
+```python
+import numpy as np
+
+
+def mandelbrot_set(xmin, xmax, ymin, ymax, xn, yn, maxiter, horizon=2.0):
+    X = np.linspace(xmin, xmax, xn).astype(np.float32)
+    Y = np.linspace(ymin, ymax, yn).astype(np.float32)
+    C = X + Y[:, None] * 1j
+    N = np.zeros_like(C, dtype=int)
+    Z = np.zeros_like(C)
+    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__':
+    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
+    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,
+                    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")
+    text = ("The Mandelbrot fractal set\n"
+            "Rendered with matplotlib %s, %s - http://matplotlib.org"
+            % (matplotlib.__version__, year))
+    ax.text(xmin+.025, ymin+.025, text, color="white", fontsize=12, alpha=0.5)
+
+    plt.show()
+```
+
+Total running time of the script: ( 0 minutes 4.800 seconds)
+
+## 下载这个示例
+            
+- [下载python源码: mandelbrot.py](https://matplotlib.org/_downloads/mandelbrot.py)
+- [下载Jupyter notebook: mandelbrot.ipynb](https://matplotlib.org/_downloads/mandelbrot.ipynb)