科学计算及可视化

 1 import numpy as np
 2 from PIL import Image
 3 
 4 class picture:
 5     
 6     def __init__ (self,position):
 7         self.position = position
 8 
 9     def Hand_drawn_style(self):
10         vec_el = np.pi/2   # 光源的俯视角度，弧度值
11         vec_az = np.pi/3    # 光源的方位角度，弧度值
12         depth = 6          # 深度权值，值越小背景区域越接近白色，值越大背景区域越接近黑色
13         im = Image.open(self.position).convert(‘L‘)     # 打开图像并转变为灰度模式
14         a = np.asarray(im).astype(‘float‘)
15         grad = np.gradient(a)              # 取图像灰度的梯度值
16         grad_x, grad_y = grad              # 分别取图像的横纵梯度值
17         grad_x = grad_x * depth / 100.
18         grad_y = grad_y * depth / 100.
19         dx = np.cos(vec_el) * np.cos(vec_az) # 光源对x轴的影响
20         dy = np.cos(vec_el) * np.sin(vec_az) # 光源对y轴的影响
21         dz = np.sin(vec_el)                  # 光源对z轴的影响
22         A = np.sqrt(grad_x**2 + grad_y**2 + 1.)
23         uni_x = grad_x/A
24         uni_y = grad_y/A
25         uni_z = 1./A
26         a2 = 255*(dx * uni_x + dy * uni_y + dz * uni_z) # 光源归一化
27         a2 = a2.clip(0, 255)                 # 预防溢出
28         im2 = Image.fromarray(a2.astype(‘uint8‘))       # 重构图像
29         im2.save(‘猫和老鼠_.jpg‘) # 保存图像
30         im2.show()                # 显示图像
31 
32 if __name__ ==‘__main__‘:
33     position = ‘猫和老鼠1.jpg‘
34     picture(position).Hand_drawn_style()

 1 import numpy as np
 2 import matplotlib.pyplot as plt
 3 import matplotlib
 4 matplotlib.rcParams[‘font.family‘]=‘SimHei‘
 5 matplotlib.rcParams[‘font.sans-serif‘]=[‘SimHei‘]
 6 labels=np.array([‘高代‘,‘数分‘,‘python‘,‘解几‘,‘数据结构‘])
 7 nAttr=5
 8 Python=np.array([71,72,80,84,75])
 9 angles=np.linspace(0,2*np.pi,nAttr,endpoint=False)
10 Python=np.concatenate((Python,[Python[0]]))
11 angles=np.concatenate((angles,[angles[0]]))
12 fig=plt.figure(facecolor="white")
13 plt.subplot(111,polar=True)
14 plt.plot(angles,Python,‘bo-‘,color=‘g‘,linewidth=2)
15 plt.fill(angles,Python,facecolor=‘g‘,alpha=0.2)
16 plt.thetagrids(angles*180/np.pi,labels)
17 plt.figtext(0.52,0.95,‘2019310143025 星星的成绩表‘,ha=‘center‘)
18 plt.grid(True)
19 plt.savefig(‘dota_radar.JPG‘)
20 plt.show()

 1 import numpy as np
 2 import matplotlib
 3 import scipy.stats
 4 matplotlib.use(‘TkAgg‘)
 5 import matplotlib.pyplot as plt
 6 import matplotlib.mlab as mlab
 7 
 8 if __name__ == "__main__":
 9     # 期望0，标准差1，条数50
10     mu, sigma, num_bins = 0, 1, 500
11     # 1M个随机数
12     x = mu + sigma * np.random.randn(10000)
13 
14     # 正态分布的数据, 颜色的透明度0.5
15     n, bins, patches = plt.hist(x, num_bins, density=True, facecolor=‘blue‘, alpha=0.5)
16 # 直方图函数，x为x轴的值，normed=1表示为概率密度，
17 #即和为一，绿色方块，色深参数0.5.返回n个概率，直方块左边线的x值，及各个方块对象
18     # 拟合曲线
19     y = scipy.stats.norm.pdf(bins, mu, sigma)
20     plt.plot(bins, y, ‘r--‘)
21     plt.xlabel(‘Expectation‘)
22     plt.ylabel(‘Probability‘)
23     plt.title(‘histogram of normal distribution: $\mu = 0$, $\sigma=1$‘)
24 
25     plt.subplots_adjust(left=0.15) #左边距
26     plt.grid(True)  #打开网格线
27     plt.show()