如何按关联Slot分组设置条形宽度绘制广播频谱图

我们有一个包含广播介质数据的DataFrame：

字段说明：

• MHz：载波频率
• Slot：业务名称
• dB：单载波测量值
• dB_median：单业务中位数值

一个业务可对应多个载波（即一个Slot对应多个MHz），需要绘制同一Slot的多个连续载波合并为完整色块的频谱图，但当前代码只能按Slot给单个条形染色，每个载波都是独立色块，不符合需求。

最小可复现代码 #

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df_test = pd.DataFrame({
    "MHz":[10,20,30,40,50,60,70,80,90,100],
    "Slot":["Slot1","Slot1","Slot2","Slot3","Slot4",
            "Slot4","Slot4","Slot5","Slot6","Slot6"],
    "dB":[20,21,19,18,21,17,20,22,19,19],
    "dB_median":[20.5,20.5,19,18,19,19,19,22,19,19]
})

plt.figure(figsize=(8,4))
g = sns.barplot(
    data=df_test,
    x="MHz",
    y="dB_median",
    hue="Slot"
)
sns.move_legend(g, "lower center", ncol=6, bbox_to_anchor=(0.5, 1))
plt.show()

核心思路 #

放弃seaborn的条形图，直接用matplotlib绘制矩形色块：

1. 按Slot分组，计算每个业务的频率区间（最小/最大MHz）和对应的中位值
2. 用matplotlib.patches.Rectangle绘制覆盖整个频率区间的连续色块
3. 可选叠加单载波的实际测量值作为标记

修改后的代码示例 #

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D

df_test = pd.DataFrame({
    "MHz":[10,20,30,40,50,60,70,80,90,100],
    "Slot":["Slot1","Slot1","Slot2","Slot3","Slot4",
            "Slot4","Slot4","Slot5","Slot6","Slot6"],
    "dB":[20,21,19,18,21,17,20,22,19,19],
    "dB_median":[20.5,20.5,19,18,19,19,19,22,19,19]
})

# 分组计算每个Slot的频率范围、中位值和对应颜色
slot_groups = df_test.groupby("Slot").agg(
    min_mhz=("MHz", "min"),
    max_mhz=("MHz", "max"),
    median_db=("dB_median", "first"),
    color=("Slot", lambda x: plt.cm.tab10(df_test["Slot"].unique().tolist().index(x.iloc[0])))
).reset_index()

plt.figure(figsize=(8,4))
ax = plt.gca()

freq_width = df_test["MHz"].diff().dropna().unique()[0]  # 获取载波间隔（此处为10MHz）

# 绘制每个Slot的连续色块
for _, row in slot_groups.iterrows():
    # 调整矩形位置和宽度，确保覆盖所有对应载波
    rect = Rectangle(
        (row["min_mhz"] - freq_width/2, 0),
        row["max_mhz"] - row["min_mhz"] + freq_width,
        row["median_db"],
        color=row["color"],
        alpha=0.7
    )
    ax.add_patch(rect)

# 叠加单载波测量值（可选）
ax.scatter(df_test["MHz"], df_test["dB"], color="black", zorder=5)

# 设置坐标轴参数
ax.set_xlim(df_test["MHz"].min() - freq_width/2, df_test["MHz"].max() + freq_width/2)
ax.set_ylim(0, df_test["dB"].max() + 2)
ax.set_xlabel("MHz")
ax.set_ylabel("dB")

# 自定义图例
slot_legends = [Line2D([0], [0], color=row["color"], lw=4, label=row["Slot"]) for _, row in slot_groups.iterrows()]
measure_legend = Line2D([0], [0], marker='o', color='w', markerfacecolor='black', label='单载波测量值')
ax.legend(handles=slot_legends + [measure_legend], loc="lower center", ncol=6, bbox_to_anchor=(0.5, 1))

plt.tight_layout()
plt.show()

关键说明 #

• 计算频率区间时，需结合载波间隔调整矩形位置，避免色块间出现间隙
• 使用内置配色tab10自动分配Slot颜色，也可自定义颜色映射
• 散点标记可按需添加，用于展示单载波的实际测量值

内容的提问来源于stack exchange，提问作者Marco_CH