雷达系列：两种基于雷达基数据绘制雷达CAPPI图的方式

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import matplotlib.pyplot as plt
import pyart
from pycwr.io import read_auto
PATH='/home/mw/input/pycwr5461/Z_RADR_I_Z9898_20190828181529_O_DOR_SAD_CAP_FMT (1).bin.bz2'
PRD = read_auto(PATH)
radar = PRD.ToPyartRadar()
# Create CAPPI at 2,000 meters for the 'reflectivity' field
cappi = pyart.retrieve.create_cappi(
    radar, fields=["reflectivity"], height=3000
)

# Create RadarMapDisplay objects for both PPI and CAPPI
radar_display = pyart.graph.RadarMapDisplay(radar)
cappi_display = pyart.graph.RadarMapDisplay(cappi)

# Plotting the PPI and CAPPI for comparison
fig, ax = plt.subplots(1, 2, figsize=(13, 5))

# Plot PPI for 'reflectivity' field
radar_display.plot_ppi("reflectivity",cmap="CN_ref", vmin=-5, vmax=75, ax=ax[0])
ax[0].set_title("PPI Reflectivity")

# Plot CAPPI for 'reflectivity' field
cappi_display.plot_ppi("reflectivity",cmap="CN_ref", vmin=-5, vmax=75, ax=ax[1])
ax[1].set_title("CAPPI Reflectivity at 3000 meters")

# Show the plots
plt.show()

import matplotlib.pyplot as plt
import numpy as np
from pycwr.draw.RadarPlot import Graph, plot_xy

# 创建一个新的图形，并设置为1行2列的子图布局
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

# 绘制PPI图像
graph = Graph(PRD)
graph.plot_ppi(ax1, 0, "dBZ", cmap="CN_ref")  # 0代表第一层, dBZ代表反射率产品
ax1.set_title("PPI Plot", fontsize=16)
ax1.set_xlabel("Distance From Radar In East (km)", fontsize=14)
ax1.set_ylabel("Distance From Radar In North (km)", fontsize=14)

# 准备CAPPI数据
x1d = np.arange(-230000, 230001, 1000)  # x方向1km等间距， -150km～150km范围
y1d = np.arange(-230000, 230001, 1000)  # y方向1km等间距， -150km～150km范围
PRD.add_product_CAPPI_xy(XRange=x1d, YRange=y1d, level_height=3000)  # level height units:meters
grid_x, grid_y = np.meshgrid(x1d, y1d, indexing="ij")

# 绘制CAPPI图像
plot_xy(ax2, grid_x, grid_y, PRD.product.CAPPI_3000)  # 画图显示
ax2.set_title("CAPPI Plot at 2000m", fontsize=16)
ax2.set_xlabel("Distance From Radar In East (km)", fontsize=14)
ax2.set_ylabel("Distance From Radar In North (km)", fontsize=14)

# 调整布局以防止标签重叠
plt.tight_layout()
# 显示图像
plt.show()