导入所需支持库

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.colors as mcolors
import numpy as np

先画单行热力图表示风速

f, ax = plt.subplots(figsize=(3, 3))
plt.rcParams['font.sans-serif'] = ['SimHei']#解决中文乱码
uniform_data = []
wind_speed =  [2,4,4,3,4,2,1,1,1,2,1,2,1,2,1,1,1,1,0,0,0,1,0,1]
uniform_data.append(wind_speed)
colors = ['lightskyblue','darkturquoise','lime','greenyellow','orangered','red']
clrmap = mcolors.LinearSegmentedColormap.from_list("mycmap",colors)
x_time = ['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23']
sns.heatmap(uniform_data,square=True,annot=True, fmt="d",linewidths=.5,cmap=clrmap,yticklabels=['风速'],xticklabels=x_time,cbar=False,vmin=0,vmax=8,ax = ax)
ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)

设置画布

f, ax = plt.subplots(figsize=(3, 3))
plt.rcParams['font.sans-serif'] = ['SimHei']#解决中文乱码

设置风速数据

uniform_data = []
wind_speed =  [2,4,4,3,4,2,1,1,1,2,1,2,1,2,1,1,1,1,0,0,0,1,0,1]
uniform_data.append(wind_speed)

这一步时风速列表变成一列数据，用于后续绘制单行热力图使用

设置色板

colors = ['lightskyblue','darkturquoise','lime','greenyellow','orangered','red']
clrmap = mcolors.LinearSegmentedColormap.from_list("mycmap",colors)

这里作者用的是自定义的色板，也可以不做这一步，使用matplotlib自带色板

利用seaborn绘制热力图

x_time = ['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23']
sns.heatmap(uniform_data,square=True,annot=True, fmt="d",linewidths=.5,cmap=clrmap,yticklabels=['风速'],xticklabels=x_time,cbar=False,vmin=0,vmax=8,ax = ax)
ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)

sns.heatmap()函数中各参数含义可以自行去理解，这里作者就不赘述了，其中uniform_data是数据，yticklabels是纵坐标标签，vmin,vmax是数据最大最小值，配合色板显示颜色用。

画风向箭头

换箭头函数

def drawArrow(A, B ,ax_1):ax = ax_1.twinx()if A[0] ==B[0] and A[1] ==B[1]:ax.plot(A[0],A[1],'ko')else:ax.annotate("", xy=(B[0], B[1]), xytext=(A[0], A[1]),arrowprops=dict(arrowstyle="->"))ax.spines['left'].set_visible(False)ax.spines['right'].set_visible(False)ax.spines['top'].set_visible(False)ax.spines['bottom'].set_visible(False)ax.set_xlim(0,24)ax.set_ylim(0,5)ax.get_yaxis().set_visible(False)ax.set_aspect('equal') #x轴y轴等比例plt.tight_layout()

上述是画箭头函数，读者可不必细究，后面作者会封装好给大家使用，其实原理就是在热力图的上面利用 ax.annotate()画箭头A, B分别为起始和终点坐标。

导入风向数据

wind_direction  =  [340,110,90,110,180,130,350,30,230,260,240,240,230,210,200,250,250,230,'c','c','c',100,'c',250]

导入计算坐标

for i in range(24):if wind_direction[i] != 'c':a = np.array([0.5+0.5*np.sin(wind_direction[i]/ 180 * np.pi)+i,3.5+0.5*np.cos(wind_direction[i]/ 180 * np.pi)])b = np.array([0.5-0.5*np.sin(wind_direction[i]/ 180 * np.pi)+i,3.5-0.5*np.cos(wind_direction[i]/ 180 * np.pi)])drawArrow(a,b,ax)else:a = np.array([0.5+i,3.5])drawArrow(a,a,ax)

这里作者画的是24小时整点风向风速，所以循环中为24，大家可以根据需要修改，'C’代表静风

移动坐标轴

ax.spines['bottom'].set_position(('data',1))

可以发现，画完风向以后时间轴被移动到下方，通过上述代码移动回热力图下

完整代码

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.colors as mcolors
import numpy as npdef drawArrow(A, B ,ax_1):#画箭头ax = ax_1.twinx()if A[0] ==B[0] and A[1] ==B[1]:#静风画点ax.plot(A[0],A[1],'ko')else:ax.annotate("", xy=(B[0], B[1]), xytext=(A[0], A[1]),arrowprops=dict(arrowstyle="->"))ax.spines['left'].set_visible(False)ax.spines['right'].set_visible(False)ax.spines['top'].set_visible(False)ax.spines['bottom'].set_visible(False)ax.set_xlim(0,24)ax.set_ylim(0,5)ax.get_yaxis().set_visible(False)ax.set_aspect('equal') #x轴y轴等比例plt.tight_layout()def drow_wind_heatmap(wd,ws,xticklabels):f, ax = plt.subplots(figsize=(12, 12))plt.rcParams['font.sans-serif'] = ['SimHei']#解决中文乱码uniform_data = []uniform_data.append(ws)colors = ['lightskyblue','darkturquoise','lime','greenyellow','orangered','red']clrmap = mcolors.LinearSegmentedColormap.from_list("mycmap",colors)#自定义色标sns.heatmap(uniform_data,square=True,annot=True, fmt="d",linewidths=.5,cmap=clrmap,yticklabels=['风速'],xticklabels=xticklabels,cbar=False,vmin=0,vmax=8,ax = ax)ax.set_xticklabels(ax.get_xticklabels(), rotation=0)#坐标轴标签旋转ax.set_yticklabels(ax.get_yticklabels(), rotation=0)#坐标轴标签旋转ax.spines['bottom'].set_position(('data',1))#移动x轴for i in range(24):#画24个箭头if wd[i] != 'c':a = np.array([0.5+0.5*np.sin(wd[i]/ 180 * np.pi)+i,3.5+0.5*np.cos(wd[i]/ 180 * np.pi)])b = np.array([0.5-0.5*np.sin(wd[i]/ 180 * np.pi)+i,3.5-0.5*np.cos(wd[i]/ 180 * np.pi)])drawArrow(a,b,ax)else:#静风a = np.array([0.5+i,3.5])drawArrow(a,a,ax)plt.show()if __name__ == "__main__":x_time = ['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23']wind_speed =  [2,4,4,3,4,2,1,1,1,2,1,2,1,2,1,1,1,1,0,0,0,1,0,1]wind_direction  =  [340,110,90,110,180,130,350,30,230,260,240,240,230,210,200,250,250,230,'c','c','c',100,'c',250]drow_wind_heatmap(wind_direction,wind_speed,x_time)

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