Matplotlib quiver绘制Fluent插值速度数据时箭头全黑问题排查

问题原因 #

你的箭头始终显示为黑色，核心原因有两个：

1. Matplotlib的quiver函数默认会给箭头添加黑色边缘线，当你将矢量归一化后箭头尺寸很小，边缘线会完全覆盖填充色，导致看起来全黑。
2. 未添加颜色条，无法直观验证颜色映射是否生效。

解决步骤 #

1. 给quiver添加edgecolor='face'参数，让箭头边缘颜色与填充色一致；或者设置linewidth=0直接移除边缘线。
2. 使用已插值得到的v_mag_interpol作为颜色数据（无需重复计算r，避免冗余）。
3. 添加颜色条，关联当前子图的quiver对象，展示颜色与速度幅值的对应关系。

修改后的关键代码 #

# Plot interpolated data with normalized vectors and normalized colormap
# 直接使用已插值的速度幅值v_mag_interpol作为颜色数据
u_norm = v_x_interpol / v_mag_interpol
v_norm = v_y_interpol / v_mag_interpol

# 创建quiver对象，设置边缘颜色与填充色一致
q = axs[3].quiver(xx_filtered, yy_filtered, u_norm, v_norm, v_mag_interpol,
                  norm=matplotlib.colors.Normalize(vmin=np.min(v_mag_interpol), vmax=np.max(v_mag_interpol)),
                  cmap='bwr',
                  edgecolor='face')  # 关键：让边缘颜色和填充色一致

# 添加颜色条，关联quiver对象
fig.colorbar(q, ax=axs[3], label='速度幅值')

完整修改后代码 #

import pandas as pd
import numpy as np
import scipy
import matplotlib.pyplot as plt
import matplotlib

input_filename = 'period_rot_shadow_vel-mag-vel_comps.txt'

df_results = pd.read_csv(input_filename,
                         sep=',', skiprows=1,
                         names=['nodenumber', 'X', 'Y', 'Z', 'vel_mag', 'v_x', 'v_y', 'v_z',
                                'v_ax', 'v_rad', 'v_tang'])

x_data_min = df_results['X'].min()
x_data_max = df_results['X'].max()
y_data_min = df_results['Y'].min()
y_data_max = df_results['Y'].max()

x_size = np.abs(x_data_max) + np.abs(x_data_min)
y_size = np.abs(y_data_max) + np.abs(y_data_min)

rect_mesh_spacing = 0.001

N_x_steps = int(x_size/rect_mesh_spacing) + 1
N_y_steps = int(y_size/rect_mesh_spacing) + 1

rect_mesh_x_axis = np.array([])
rect_mesh_y_axis = np.array([])

for i in range(0, N_x_steps):
    x = x_data_min + i * rect_mesh_spacing
    rect_mesh_x_axis = np.append(rect_mesh_x_axis, x)

for i in range(0, N_y_steps):
    y = y_data_min + i * rect_mesh_spacing
    rect_mesh_y_axis = np.append(rect_mesh_y_axis, y)

# 使用meshgrid创建矩形网格
xx, yy = np.meshgrid(rect_mesh_x_axis, rect_mesh_y_axis)

# 生成网格点数组
xy_mesh = np.array((xx,yy)).T

# 定义输入数据坐标
input_coords = np.array(list(zip(df_results['X'].to_numpy(), df_results['Y'].to_numpy())))

# KDTree查找最近邻点
tree = scipy.spatial.KDTree(input_coords)
dist, idx = tree.query(xy_mesh)

# 过滤距离符合要求的网格点
radius = (rect_mesh_spacing + rect_mesh_spacing) / 2
distance_filter = dist <= radius
xx_filtered, yy_filtered = xy_mesh[distance_filter].T

# 插值速度分量和幅值
v_x_interpol= scipy.interpolate.griddata((df_results['X'], df_results['Y']), df_results['v_x'],
                                               (xx_filtered, yy_filtered), method = 'nearest')
v_y_interpol= scipy.interpolate.griddata((df_results['X'], df_results['Y']), df_results['v_y'],
                                               (xx_filtered, yy_filtered), method = 'nearest')
v_mag_interpol = scipy.interpolate.griddata((df_results['X'], df_results['Y']), df_results['vel_mag'],
                                               (xx_filtered, yy_filtered), method = 'nearest')

# 创建子图
fig, axs = plt.subplots(1,4)
fig.set_size_inches(13, 4)

# 绘制原始数据
axs[0].quiver(df_results['X'], df_results['Y'], df_results['v_x'], df_results['v_y'])
axs[0].set_title('原始数据')

# 绘制插值后数据
axs[1].quiver(xx_filtered, yy_filtered, v_x_interpol, v_y_interpol)
axs[1].set_title('插值后数据')

# 绘制归一化矢量尺寸的插值数据
r = (v_x_interpol**2 + v_y_interpol**2)**0.5
axs[2].quiver(xx_filtered, yy_filtered, v_x_interpol/r, v_y_interpol/r)
axs[2].set_title('归一化矢量尺寸')
 
# 绘制带颜色映射的归一化矢量数据
u_norm = v_x_interpol / v_mag_interpol
v_norm = v_y_interpol / v_mag_interpol

q = axs[3].quiver(xx_filtered, yy_filtered, u_norm, v_norm, v_mag_interpol,
                  norm=matplotlib.colors.Normalize(vmin=np.min(v_mag_interpol), vmax=np.max(v_mag_interpol)),
                  cmap='bwr',
                  edgecolor='face')

fig.colorbar(q, ax=axs[3], label='速度幅值')
axs[3].set_title('归一化矢量+颜色映射')

plt.tight_layout()
plt.show()

内容的提问来源于stack exchange，提问作者user2882635