静态单图
import numpy as np
import matplotlib.pyplot as plt
# 号码热力图
pre = 49
a = np.random.randint(49, size=pre) + 1 # 模拟前期数据(这里不妨取49)
import collections
c = collections.Counter(a).most_common() # 统计次数
d = np.zeros(49)
for i, x in c:
d[i-1] = x
image = d.reshape(7,7) # 构造成一个图像
plt.imshow(image, cmap=plt.cm.hot) # 画热力图
plt.colorbar()
#plt.imshow(image, cmap=plt.cm.hot, interpolation="nearest")
#plt.colorbar()
# 为了方便,把号码也对应显示
xx, yy = np.meshgrid(np.arange(7), np.arange(7))
for i, (x, y) in enumerate(zip(xx.flatten(), yy.flatten())):
c = str(i+1)
plt.text(x, y, c, va='center', ha='center')
plt.show()
另一个动态的热力图
import numpy as np
import matplotlib.pyplot as plt
import collections
'''动态号码热力图'''
#plt.imshow(image, cmap=plt.cm.hot, interpolation="nearest")
#plt.colorbar()
# 为了方便,把号码也对应显示
xx, yy = np.meshgrid(np.arange(7), np.arange(7))
for i, (x, y) in enumerate(zip(xx.flatten(), yy.flatten())):
c = str(i+1)
plt.text(x, y, c, va='center', ha='center')
# 根据前面历史数据,构造成一个图像
def build_image(a_list):
c = collections.Counter(a_list).most_common() # 统计次数
d = np.zeros(49)
for i, x in c:
d[i-1] = x
image = d.reshape(7,7) # 构造成一个图像
return image
for i in range(100):
if i == 0:
# 号码热力图
pre = 49
a_list = np.random.randint(49, size=pre) + 1 # 模拟前期数据(这里不妨取49)
image = build_image(a_list)
im = plt.imshow(image, cmap=plt.cm.hot) # 画热力图
plt.colorbar()
else:
a_list = np.hstack((a_list[1:], np.random.randint(49)+1))
image = build_image(a_list)
im.set_data(image)
plt.pause(0.1)