使用 Matplotlib 绘制圆形温度热图：插值与优化

| x | y | temp|
| -140 | 0 | 397.32 |
| -100 | 90 | 396.76 |
| -100 | -90 | 396.34 |
| -70 | 0 | 396 |
| -50 | 44 | 395.34 |
| -50 | -44 | 395.57 |
| 0 | 140 | 396.37 |
| 0 | 70 | 395.82 |
| 0 | 0 | 393.52 |
| 0 | -70 | 393.52 |
| 0 | -140 | 395.61|
| 50 | 44 | 395.82 |
| 50 | -44 | 394.08 |
| 50 | -44 | 394.08 |
| 70 | 0 | 394.62 |
| 100 | 90 | 395.79 |
| 100 | -90 | 395.25 |
| 140 | 0 | 396.12 |

| x | y | temp|
| -150 | -150 | 398 |
| 150 | 150 | 398 |
| 150 | -150 | 398 |
| -150 | 150 | 398 |

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
from matplotlib.colors import LinearSegmentedColormap

# Load temperature data from CSV file
file_path = 'tcdata.csv'
data = pd.read_csv(file_path)

# Extract coordinates and temperatures from the data
x = data['x']
y = data['y']
temperatures = data['temp']

# Set the radius for the circle
radius = 150

# Create a grid for interpolation
grid_x, grid_y = np.mgrid[-radius:radius:300j, -radius:radius:300j]

# Interpolate the temperature data over the grid
grid_temperatures = griddata((x, y), temperatures, (grid_x, grid_y), method='cubic')

# Create a circular mask to limit the heatmap within the circle
mask = np.sqrt(grid_x**2 + grid_y**2) > radius
grid_temperatures = np.ma.masked_where(mask, grid_temperatures)

# Create a custom color map: blue for the lowest, red for the highest, and green for intermediate temperatures
cmap = LinearSegmentedColormap.from_list('custom_heatmap', ['blue', 'green', 'red'], N=256)

# Plot the heatmap
plt.figure(figsize=(8, 6))
plt.imshow(grid_temperatures.T, extent=(-radius, radius, -radius, radius), origin='lower', cmap=cmap)
plt.colorbar(label='Temperature (°C)')

# Set the title
plt.title('Circular Temperature Distribution Heatmap')

# Disable the grid
plt.grid(False)

# Display the plot
plt.show()