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期刊图片复现|Python绘制地理探测器单因子及交互作用组合图

  • 2026-08-19 15:38:50
期刊图片复现|Python绘制地理探测器单因子及交互作用组合图

代码绘制成果展示

论文:An integrated analysis for spatio-temporal evolution of food water-carbon-energy footprint and its sustainable production in northwest China
论文原图
仿图
该图展示了基于地理探测器模型分析得出的单因子及其交互作用结果。左上角和右下角的三角形热图分别揭示了双因子交互作用强度,右侧颜色条颜色由粉色向深绿色过渡代表交互作用q值,深绿色区域意味着对应的两个因子共同作用时的解释力最强。中间的双层圆环图则直观展示了单因子探测(Factor Detector)结果,各扇区颜色对应左下角图例中的不同因子,扇区大小及标注的百分比数值量化了各独立因子对目标空间分异的解释程度。但是这张图也有一点为题就是看不出交互作用的数值以及影响的类型这些信息,之后会改进一下,还有就是我感觉这张图可以用于展示其他的分析结果,所以数值都是手动填入的,而不是用地理探测器分析后来进行绘图,我觉得这样做可以更具有适用性、可移植性。
多种配色

代码解释

第一部分

库的导入以及字体设置
# =========================================================================================# ====================================== 1. 库的导入 =========================================# =========================================================================================import matplotlib.pyplot as pltimport matplotlib.patches as patchesimport matplotlib.colors as mcolorsimport numpy as npimport pandas as pdimport osimport mathimport matplotlibmatplotlib.rcParams['pdf.fonttype'] = 42matplotlib.rcParams['ps.fonttype'] = 42plt.rcParams['font.family'] = 'Times New Roman'plt.rcParams['axes.unicode_minus'] = False

第二部分

颜色库的设置以及配色方案的选择
# =========================================================================================# ====================================== 2. 颜色库==========================# =========================================================================================COLOR_SCHEMES = {    1: {        'palette': ['#D32F2F''#FF5722''#FF9800''#FDD835''#CDDC39''#4CAF50''#009688''#1976D2''#673AB7''#E91E63''#795548''#607D8B'],        'cmap_colors': ["#F8BBD0""#FFF176""#66BB6A""#1B5E20"]    },}SELECTED_SCHEME= 2  # 选择配色方案CURRENT_SCHEME = COLOR_SCHEMES.get(SELECTED_SCHEME, COLOR_SCHEMES[1])  #获取当前选择的配色方案PALETTE = CURRENT_SCHEME['palette']  #单因子颜色CMAP_COLORS = CURRENT_SCHEME['cmap_colors']  #交互作用热力图渐变色

第三部分

数据预处理,创建自定义颜色映射,并初始化画布
# =========================================================================================# ====================================== 3. 绘图函数 =========================================# =========================================================================================def draw_geodetector_chart(wheat_matrix,#左上角热图数据                           maize_matrix,#右下角数据                           wheat_single,#内圈数据                           maize_single,#外圈数据                           labels,#特征                           save_dir):#保存路径    custom_cmap = mcolors.LinearSegmentedColormap.from_list(f"custom_scheme_{SELECTED_SCHEME}", CMAP_COLORS, N=256)    # vmin = min(np.nanmin(wheat_matrix), np.nanmin(maize_matrix))    # vmax = max(np.nanmax(wheat_matrix), np.nanmax(maize_matrix))    norm = mcolors.Normalize(vmin=0, vmax=1)  #创建颜色归一化对象    # 创建画布    fig = plt.figure(figsize=(1610), facecolor='white')

第四部分

绘制左上角的三角形热图
    # ------------------------------------------------------------------    #左上角热图    # -------------------------------------------------------------------    # 在画布上添加一个新的坐标轴用于绘制交互作用热图,[左, 下, 宽, 高]    ax_wheat = fig.add_axes([0.150.450.350.5])    ax_wheat.set_aspect('equal')  #设置坐标轴的纵横比为相等    ax_wheat.axis('off')  #不显示边框和刻度    ax_wheat.set_xlim(0, n_factors)  # x 轴范围    ax_wheat.set_ylim(0, n_factors)  #y 轴范围    for y in range(n_factors):  # 遍历行        for x in range(n_factors):  # 遍历列            if y >= x:                val = wheat_matrix[y, x]  #获取对应位置的值                if pd.isna(val): continue  # 如果值为空,跳过                color = custom_cmap(norm(val))  #根据数值获取对应的颜色                # 创建一个矩形块                rect = patches.Rectangle((x + 0.02, y + 0.02),  #坐标                                         0.96,  #宽度                                         0.96,  #高度                                         linewidth=0,  #边框线宽                                         facecolor=color)  #填充颜色                ax_wheat.add_patch(rect)  #将矩形块添加到坐标轴中    for y in range(n_factors):  # 遍历 y 轴方向的因子        # 在左侧添加行标签        ax_wheat.text(-0.2#x 坐标                      y + 0.5,#y 坐标                      labels[y],#文本内容                      ha='right',#水平对齐                      va='center'#垂直对齐                      fontsize=12)#字体大小    #子标题位置    ax_wheat.text(n_factors * 0.8,  #x 坐标                  n_factors * 0.7,  #y 坐标                  "Wheat",  #文本内容                  fontsize=20,  #字体大小为                  bbox=dict(  # 文本边框的样式                      facecolor='white',  #填充背景色                      edgecolor='gray',  #边框线的颜色                      pad=6  #内边距                  ))

第五部分

绘制右下角的三角形热图
    # -------------------------------------------------------------------    # 右下角热图    # -------------------------------------------------------------------    # 添加坐标轴,[左, 下, 宽, 高]    ax_maize = fig.add_axes([0.450.050.350.5])    ax_maize.set_aspect('equal')  #设置纵横比    ax_maize.axis('off')  #关闭坐标轴    ax_maize.set_xlim(0, n_factors)  #x轴范围    ax_maize.set_ylim(0, n_factors)  #y轴范围    for y in range(n_factors):  # 遍历行        for x in range(n_factors):  # 遍历列            if x >= y:                val = maize_matrix[y, x]  #获取对应位置的值                if pd.isna(val): continue  # 如果值为空,跳过                color = custom_cmap(norm(val))  #获取颜色                # 创建矩形块                rect = patches.Rectangle((x + 0.02, y + 0.02),#坐标                                         0.96,#宽                                         0.96,#高                                         linewidth=0,#边框线粗细                                         facecolor=color)#颜色                ax_maize.add_patch(rect)  #添加矩形块    # 添加子图标题 (Maize)    ax_maize.text(n_factors * 0.6,  # x 坐标                  n_factors * 0.8,  #y 坐标                  "Maize",  #文本内容                  fontsize=20,  # 字体大小                  bbox=dict(  # 设置文本背景框                      facecolor='white',  #背景框填充色                      edgecolor='gray',  #边框线颜色                      pad=6  # 内边距                  ))    # 添加主标题    ax_maize.text(n_factors * 0.5,  #x 坐标                  n_factors * 1.3,  #y 坐标                  "Double Factors\nInteraction",  #内容                  fontsize=24,  #大小                  color='#5C6BC0',  #字体颜色                  ha='center',  #水平对齐方式                  fontweight='bold')  #字体粗细

第六部分

在画布中心绘制一个单因子双层圆环图
    # -------------------------------------------------------------------    # 中间圆环图    # -------------------------------------------------------------------    # 添加中间圆环图的坐标轴    ax_pie = fig.add_axes([0.33,  #左                           0.35,  # 下边界                           0.3,  #占画布总宽度                           0.3])  # 占画布总高度    ax_pie.set_aspect('equal')  # 设置纵横比    ax_pie.set_xlim(-1.71.7)  # x 轴范围    ax_pie.set_ylim(-1.71.7)  # y 轴范围    ax_pie.axis('off')  #关闭坐标    # =========================================================================================    # 绘制外层圆环    # =========================================================================================    wedges_out, _ = ax_pie.pie(        maize_single,  # 输入数据        radius=r_outer,  # 半径        colors=color_list,  # 颜色        startangle=90,  # 起始角度        counterclock=False,  #顺时针绘制        wedgeprops=dict(            width=w_outer,  #环的宽度            edgecolor='white',  #扇形之间的分割线颜色            linewidth=1.5  #分割线的宽度        )    )    # =========================================================================================    # 绘制内层圆环    # =========================================================================================    wedges_in, _ = ax_pie.pie(        wheat_single,  # 输入数据        radius=r_inner,  #半径        colors=color_list,  #颜色        startangle=90,  # 起始角度        counterclock=False,  #方向        wedgeprops=dict(            width=w_inner,  #内环的宽度            edgecolor='white',  # 分割线颜色            linewidth=1.5  #分割线宽度        )    )

第七部分

绘制中间圆环图的虚线圆圈以及文本标注
     # =========================================================================================    # 绘制装饰性虚线圆    # =========================================================================================    # 创建外层虚线圆    circle_dash_out = patches.Circle((00),  # 圆心坐标                                     r_outer + 0.15,  #半径                                     transform=ax_pie.transData,  #坐标系转换                                     fill=False,  # 不填充颜色                                     edgecolor='black',  #边框颜色                                     linestyle='-.',  #点划线                                     linewidth=1,  #线条粗细                                     clip_on=False)  #允许图形超出坐标轴边界显示    ax_pie.add_patch(circle_dash_out)  # 将创建好的外层虚线圆添加到坐标轴上    # =========================================================================================    # 添加圆环标签文字    # =========================================================================================    # 外环标签    ax_pie.text(0, r_outer + 0.15,  #文本坐标                "maize",  # 文本内容                ha='center',  # 水平居中对齐                va='center',  # 垂直居中对齐                fontsize=12,  # 字体大小                backgroundcolor='white',  # 设置背景色                fontweight='bold')  # 字体加粗

第八部分

绘制图例
    # =========================================================================================    #图例    # =========================================================================================    # 添加图例区域的坐标轴    ax_legend = fig.add_axes([0.15,                              0.1,                              0.25,                              0.25])    legend_cols = 3  #图例列数    legend_rows = math.ceil(n_factors / legend_cols)  #行数    ax_legend.set_xlim(0, legend_cols)  # x 范围    ax_legend.set_ylim(0, legend_rows)  # y 范围    ax_legend.axis('off')  #关闭坐标轴显示    #遍历所有因子生成图例    for idx, label in enumerate(labels):        row_idx = legend_rows - 1 - (idx // legend_cols)  # 行索引        col_idx = idx % legend_cols  # 列索引        color = factor_colors[label]  #当前因子的颜色

第九部分

绘制颜色条以及绘图结果的保存
    # =========================================================================================    #颜色条    # =========================================================================================    # 添加颜色条的坐标轴,位置在最右侧    ax_cbar = fig.add_axes([0.85,                            0.15,                            0.02,                            0.6])    # 创建垂直颜色条    cb = matplotlib.colorbar.ColorbarBase(ax_cbar,                                          cmap=custom_cmap,                                          norm=norm,                                          orientation='vertical')    cb.ax.tick_params(labelsize=12)  #设置刻度标签字体大小    # 保存    png_path = os.path.join(save_dir, f"Scheme_{SELECTED_SCHEME}.png")    pdf_path = os.path.join(save_dir, f"Scheme_{SELECTED_SCHEME}.pdf")    plt.savefig(png_path, dpi=300, bbox_inches='tight')    plt.savefig(pdf_path, bbox_inches='tight')

第十部分

主程序,负责读取 Excel 数据,提取标签和数值,然后调用绘图函数。
# =========================================================================================# ====================================== 4. 主程序执行 ========================================# =========================================================================================if __name__ == "__main__":    base_dir = r"组合图"  #保存路径    #读取数据    df_wheat_mat = pd.read_excel(r"组合图\geodetector_data.xlsx" , sheet_name='Wheat_Interaction_Matrix', index_col=0)    df_maize_mat = pd.read_excel(r"组合图\geodetector_data.xlsx" , sheet_name='Maize_Interaction_Matrix', index_col=0)    df_single = pd.read_excel(r"组合图\geodetector_data.xlsx" , sheet_name='Single_Factor_q', index_col=0)    #提取特征名称    dynamic_labels = df_wheat_mat.index.tolist()    print(f"检测到的因子 ({len(dynamic_labels)}个): {dynamic_labels}")    wheat_matrix_val = df_wheat_mat.values  #转换为 numpy 数组    maize_matrix_val = df_maize_mat.values  #转换为 numpy 数组    wheat_single_list = df_single['Wheat_Single_q'].tolist()  #转换为列表    maize_single_list = df_single['Maize_Single_q'].tolist()  #转换为列表    #调用绘图函数    draw_geodetector_chart(        wheat_matrix_val,  #交互矩阵数据        maize_matrix_val,  # 交互矩阵数据        wheat_single_list,  #单因子        maize_single_list,  #单因子        dynamic_labels,  #标签列表        base_dir  #保存路径    )

如何应用到你自己的数据

1.设置颜色方案:

SELECTED_SCHEME40  # 选择配色方案

2.设置绘图结果的保存路径:

base_dir = r"组合图"  #保存路径

3.读取数据:

#读取数据df_wheat_mat = pd.read_excel(r"detector_data.xlsx" , sheet_name='Wheat_Interaction_Matrix', index_col=0)

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获取方式

需要的后台私信我,注意只会分享练习数据和代码文件,不会提供答疑服务,代码文件中已经包含了每行代码的完整注释!!!

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  1. CONNECT:[ UseTime:0.001210s ] mysql:host=127.0.0.1;port=3306;dbname=f_mffb;charset=utf8mb4
  2. SHOW FULL COLUMNS FROM `fenlei` [ RunTime:0.001585s ]
  3. SELECT * FROM `fenlei` WHERE `fid` = 0 [ RunTime:0.000710s ]
  4. SELECT * FROM `fenlei` WHERE `fid` = 63 [ RunTime:0.000687s ]
  5. SHOW FULL COLUMNS FROM `set` [ RunTime:0.001243s ]
  6. SELECT * FROM `set` [ RunTime:0.000537s ]
  7. SHOW FULL COLUMNS FROM `article` [ RunTime:0.001367s ]
  8. SELECT * FROM `article` WHERE `id` = 509229 LIMIT 1 [ RunTime:0.001827s ]
  9. UPDATE `article` SET `lasttime` = 1787309700 WHERE `id` = 509229 [ RunTime:0.012069s ]
  10. SELECT * FROM `fenlei` WHERE `id` = 66 LIMIT 1 [ RunTime:0.000607s ]
  11. SELECT * FROM `article` WHERE `id` < 509229 ORDER BY `id` DESC LIMIT 1 [ RunTime:0.001228s ]
  12. SELECT * FROM `article` WHERE `id` > 509229 ORDER BY `id` ASC LIMIT 1 [ RunTime:0.001124s ]
  13. SELECT * FROM `article` WHERE `id` < 509229 ORDER BY `id` DESC LIMIT 10 [ RunTime:0.002647s ]
  14. SELECT * FROM `article` WHERE `id` < 509229 ORDER BY `id` DESC LIMIT 10,10 [ RunTime:0.001803s ]
  15. SELECT * FROM `article` WHERE `id` < 509229 ORDER BY `id` DESC LIMIT 20,10 [ RunTime:0.004679s ]
0.216386s