0️⃣ 前言

  又到了毕业季，学弟学妹们开始了毕设之旅，提到毕设想到了什么呢？对，没错，必备技巧就是绘制各种精美绝伦，举世无双的高清美图。这不，我刚炖了碗鲜美的极坐标热力图气象图汤。😢
  如下：

1️⃣ 数据准备

  数据可以是随机产生，或者放在csv文件中读。在csv中存储格式如下：
| pos | 0 | 30 | 60 | 90 |
|-----|--------------|--------------|--------------|--------------|
| 0 | 1.101447148 | 1.308827831 | 1.526038083 | 1.603848713 |
| 30 | 1.101447148 | 1.279591136 | 1.49432297 | 1.577829862 |
| 60 | 1.101447148 | 1.204513965 | 1.435064241 | 1.52576792 |
| 90 | 1.101447148 | 1.108569817 | 1.404547306 | 1.499676995 |
| 120 | 1.101447148 | 1.204513965 | 1.435064241 | 1.52576792 |
| 150 | 1.101447148 | 1.279591136 | 1.49432297 | 1.577829862 |
| 180 | 1.101447148 | 1.308827831 | 1.526038083 | 1.603848713 |
| 210 | 1.101447148 | 1.279591136 | 1.49432297 | 1.577829862 |
| 240 | 1.101447148 | 1.204513965 | 1.435064241 | 1.52576792 |
| 270 | 1.101447148 | 1.108569817 | 1.404547306 | 1.499676995 |
| 300 | 1.101447148 | 1.204513965 | 1.435064241 | 1.52576792 |
| 330 | 1.101447148 | 1.279591136 | 1.49432297 | 1.577829862 |
| 360 | 1.101447148 | 1.308827831 | 1.526038083 | 1.603848713 |

  因为要绘制的是极坐标图，所以列名代表的就是弧度，而行名代表的就是半径。
csv文件下载：data.csv，下载后复制成四份，分别命名为data1.csv，data2.csv，data3.csv，data4.csv。

2️⃣ 代码

2️⃣.1️⃣ 导入需要的包

import numpy as np
import pandas as pd
from scipy.interpolate import interp2d # 后面需要的插值库
from matplotlib import pyplot as plt 

2️⃣.2️⃣ 从csv文件中读取数据

data1 = pd.read_csv('data1.csv')
data2 = pd.read_csv('data2.csv')
data3 = pd.read_csv('data3.csv')
data4 = pd.read_csv('data4.csv')
data = [data1, data2, data3, data4]
pos = np.array(data['pos']/180*np.pi)
ind = np.array(data.columns[1:], dtype=np.int)
values = np.array(data[ind.astype('str')])

2️⃣.3️⃣ 随机产生数据

pos = np.radians(np.linspace(0, 360, 30))
ind = np.arange(0, 90, 10)
values = np.random.random((pos.size, ind.size))

2️⃣.4️⃣ 全部代码(方便大家直接复制运行)

import numpy as np
import pandas as pd
from scipy.interpolate import interp2d
from matplotlib import pyplot as plt

data1 = pd.read_csv('data1.csv')
data2 = pd.read_csv('data2.csv')
data3 = pd.read_csv('data3.csv')
data4 = pd.read_csv('data4.csv')
data = [data1, data2, data3, data4]


def plot_weather_heatmap(dataList, title):
    plt.figure(figsize=(25, 25))
    for i in range(len(dataList)):
        data = dataList[i]
        '''
        方法一：从csv文件中读取数据
        '''

        # pos = np.array(data['pos']/180*np.pi)
        # ind = np.array(data.columns[1:], dtype=np.int)
        # values = np.array(data[ind.astype('str')])
        
        '''
        方法二：随机产生数据
        '''
        pos = np.radians(np.linspace(0, 360, 30))
        ind = np.arange(0, 90, 10)
        values = np.random.random((pos.size, ind.size))

        #计算插值函数
        func = interp2d(pos, ind, values.T, kind='cubic')
        tnew = np.linspace(0, 2*np.pi, 200)  # theta
        #绘图数据点
        rnew = np.linspace(0, 90, 100)  # r
        vnew = func(tnew, rnew)
        tnew, rnew = np.meshgrid(tnew, rnew)
        ax = plt.subplot(2, 2, i+1, projection='polar')
        plt.pcolor(tnew, rnew, vnew, cmap='jet')
        plt.grid(c='black')
        plt.colorbar()
        ax.set_theta_zero_location("N")
        ax.set_theta_direction(-1)
        plt.title(title[i], fontsize=20)
        #设置坐标标签标注和字体大小
        plt.xlabel(' ', fontsize=15)
        plt.ylabel(' ', fontsize=15)
        
        #设置坐标刻度字体大小
        plt.xticks(fontsize=15, rotation=90)
        plt.yticks(fontsize=15)
        # cb.set_label("Pixel reflectance")


title = ['Spring', 'Summer', 'Autumn', 'Winter']
plot_weather_heatmap(data, title)
plt.savefig("pic.png", dpi=300)
plt.show()

3️⃣ cmap参数，为了更好看

  关于下面这句中的jet参数是指定图的色域，可以更换。

plt.pcolor(tnew, rnew, vnew, cmap='jet')

可选值如下