渐变色哑铃图 Python版

"left_fill":"#F6A3C2",

"left_edge":"#C2185B",

"right_fill":"#FFE082",

"right_edge":"#F9A825",

"bg_left":"#FCE4EC",

"bg_mid":"#FFF7E6",

"bg_right":"#FFFDE7",

"line_left":"#EC407A",

"line_right":"#FFD54F",

"grid":"#DDDDDD"

}

# 3. 绘制渐变线函数 -----------------------------------------------

defdraw_gradient_line(ax,x1,x2,y,color1,color2,lw=7,alpha=0.85):

"""Draw one horizontal gradient line from x1 to x2."""

xs= np.linspace(x1,x2,120)

ys= np.full_like(xs,y)

points= np.array([xs,ys]).T.reshape(-1,1,2)

segments= np.concatenate([points[:-1],points[1:]],axis=1)

cmap=LinearSegmentedColormap.from_list("line_gradient",[color1,color2])

lc=LineCollection(

segments,

cmap=cmap,

norm=Normalize(0,1),

linewidth=lw,

alpha=alpha,

zorder=2

)

lc.set_array(np.linspace(0,1,len(segments)))

ax.add_collection(lc)

# 4. 主绘图函数 ----------------------------------------------------

defdraw_dumbbell_plot(data):

data=data.copy()

data=data.iloc[::-1].reset_index(drop=True)

y_pos= np.arange(len(data))

x_min=0

x_max=max(data["After_Treatment"].max(),data["Baseline"].max())+8

y_min=-0.8

y_max=len(data)-0.2

mean_left=data["Baseline"].mean()

mean_right=data["After_Treatment"].mean()

fig,ax= plt.subplots(figsize=(11,8))

# 渐变背景

bg= np.linspace(0,1,1200).reshape(1,-1)

bg_cmap=LinearSegmentedColormap.from_list(

"soft_background",

[COLOR["bg_left"],COLOR["bg_mid"],COLOR["bg_right"]]

)

ax.imshow(

bg,

extent=[x_min,x_max,y_min,y_max],

aspect="auto",

cmap=bg_cmap,

alpha=0.95,

zorder=0

)

# 水平辅助线

foryiny_pos:

ax.hlines(

y,

x_min,

x_max,

color="white",

linewidth=1.1,

alpha=0.75,

zorder=1

)

# 渐变连接线

fori,rowindata.iterrows():

draw_gradient_line(

ax,

row["Baseline"],

row["After_Treatment"],

y_pos[i],

COLOR["line_left"],

COLOR["line_right"],

lw=7,

alpha=0.78

)

# 两组散点

ax.scatter(

data["Baseline"],

y_pos,

s=620,

facecolor=COLOR["left_fill"],

edgecolor=COLOR["left_edge"],

linewidth=2.6,

zorder=4,

label="Baseline"

)

ax.scatter(

data["After_Treatment"],

y_pos,

s=620,

facecolor=COLOR["right_fill"],

edgecolor=COLOR["right_edge"],

linewidth=2.6,

zorder=4,

label="After Treatment"

)

# 均值虚线

ax.axvline(

mean_left,

color=COLOR["left_edge"],

linestyle="--",

linewidth=1.7,

alpha=0.8,

zorder=2

)

ax.axvline(

mean_right,

color=COLOR["right_edge"],

linestyle="--",

linewidth=1.7,

alpha=0.8,

zorder=2

)

ax.text(

mean_left,

y_max-0.15,

f"Mean: {mean_left:.1f}",

ha="center",

va="top",

fontsize=12,

fontweight="bold",

color=COLOR["left_edge"]

)

ax.text(

mean_right,

y_max-0.15,

f"Mean: {mean_right:.1f}",

ha="center",

va="top",

fontsize=12,

fontweight="bold",

color=COLOR["right_edge"]

)

# 数值与差值标注

fori,rowindata.iterrows():

y=y_pos[i]

x1=row["Baseline"]

x2=row["After_Treatment"]

mid=(x1+x2)/2

ax.text(

x1,

y+0.24,

f"{x1:.0f}",

ha="center",

va="bottom",

fontsize=12,

fontweight="bold",

color=COLOR["left_edge"]

)

ax.text(

x2,

y+0.24,

f"{x2:.0f}",

ha="center",

va="bottom",

fontsize=12,

fontweight="bold",

color=COLOR["right_edge"]

)

ax.text(

mid,

y-0.25,

f"+{row['Diff']:.0f}",

ha="center",

va="top",

fontsize=11.5,

fontweight="bold",

color="#222222"

)

# 坐标轴设置

ax.set_yticks(y_pos)

ax.set_yticklabels(data["Feature"],fontsize=13,fontweight="bold")

ax.set_xlim(x_min,x_max)

ax.set_ylim(y_min,y_max)

ax.set_xlabel(

"Measurement Value",

fontsize=14,

fontweight="bold"

)

ax.set_ylabel(

"Clinical Features",

fontsize=14,

fontweight="bold"

)

ax.set_title(

"Gradient Dumbbell Plot of Clinical Indicators",

fontsize=19,

fontweight="bold",

pad=24

)

# 图例

leg=ax.legend(

loc="center right",

bbox_to_anchor=(0.98,0.50),

frameon=True,

fontsize=12,

borderpad=0.8

)

leg.get_frame().set_edgecolor("black")

leg.get_frame().set_linewidth(1.0)

leg.get_frame().set_alpha(0.92)

# 美化边框和网格

ax.grid(

axis="x",

linestyle=":",

linewidth=0.9,

color=COLOR["grid"],

alpha=0.85

)

forspineinax.spines.values():

spine.set_linewidth(1.8)

spine.set_color("black")

ax.tick_params(axis="x",labelsize=12,width=1.5)

ax.tick_params(axis="y",width=1.5)

    plt.tight_layout()

# 保存到桌面

desktop=os.path.join(os.path.expanduser("~"),"Desktop")

png_path=os.path.join(desktop,"gradient_dumbbell_plot.png")

    plt.savefig(png_path,dpi=300,bbox_inches="tight",facecolor="white")

draw_dumbbell_plot(df)