ax.set_xticks([]) ax.set_yticks([])

这是一段代码，它用于隐藏图形中的 x 轴和 y 轴刻度。通过调用 `ax.set_xticks([])` 和 `ax.set_yticks([])`，图形的 x 轴和 y 轴的刻度将被清空，从而隐藏刻度线和刻度标签。这在某些情况下可以用于改善图形的可视化效果，例如当你只关注数据的趋势而不需要具体的刻度信息时。

相关问题

#使用前面定义的函数进行画图 X0,X1=X_train[:,0],X_train[:,1] xx,yy=make_meshgrid(X0,X1) for clf,title,ax in zip(models,titles,sub.flatten()): plot_contours(ax,clf,xx,yy,cmap=plt.cm.plasma,alpha=0.8) ax.scatter(X0,X1,c=y,cmap=plt.cm.plasma,s=20,edgecolors='k') ax.set_xlim(xx.min(),xx.max()) ax.set_ylim(yy.min(),yy.max()) ax.set_xlabel('Feature 0') ax.set_ylabel('Feature 1') ax.set_xticks(()) ax.set_yticks(()) ax.set_title(title) plt.show()

这段代码是用来画出分类器的决策边界和数据点的散点图的。其中，`make_meshgrid`函数用来生成网格点，`plot_contours`函数用来画出分类器的决策边界，`ax.scatter`用来画出数据点的散点图。`models`是一个分类器列表，`titles`是对应分类器的标题列表，`sub.flatten()`是用来展平子图的二维数组。最后，通过`plt.show()`来显示图像。

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6): self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') self.ax2 = plt.subplot(self.gs[1, 0]) self.ax2.plot(vals1) self.ax2.set_xlim(self.xlim) self.ax2.set_xticks(locs) self.ax2.yaxis.set_major_formatter(FuncFormatter(format_bps)) self.ax2.use_sticky_edges = False self.ax2.set_title('Total Bandwidth Usage') self.ax3 = plt.subplot(self.gs[2, 0]) self.ax3.plot(vals2) self.ax3.set_xlim(self.xlim) self.ax3.set_xticks(locs) self.ax3.use_sticky_edges = False self.ax3.set_title('Bandwidth Usage Ratio in Slices (Averaged)') self.ax4 = plt.subplot(self.gs[3, 0]) self.ax4.plot(vals3) self.ax4.set_xlim(self.xlim) self.ax4.set_xticks(locs) self.ax4.use_sticky_edges = False self.ax4.set_title('Client Count Ratio per Slice') self.ax5 = plt.subplot(self.gs[0, 1]) self.ax5.plot(vals4) self.ax5.set_xlim(self.xlim) self.ax5.set_xticks(locs) self.ax5.use_sticky_edges = False self.ax5.set_title('Coverage Ratio') self.ax6 = plt.subplot(self.gs[1, 1]) self.ax6.plot(vals5) self.ax6.set_xlim(self.xlim) self.ax6.set_xticks(locs) self.ax6.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax6.use_sticky_edges = False self.ax6.set_title('Block ratio') self.ax7 = plt.subplot(self.gs[2, 1]) self.ax7.plot(vals6) self.ax7.set_xlim(self.xlim) self.ax7.set_xticks(locs) self.ax7.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax7.use_sticky_edges = False self.ax7.set_title('Handover ratio')修改为一张张输出图片

好的，你可以将这段代码修改为：

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6, save_path):
        fig, axs = plt.subplots(4, 2, figsize=(16, 16))
        axs[0, 0].plot(vals)
        axs[0, 0].set_xlim(self.xlim)
        locs = axs[0, 0].get_xticks()
        locs[0] = self.xlim[0]
        locs[-1] = self.xlim[1]
        axs[0, 0].set_xticks(locs)
        axs[0, 0].use_sticky_edges = False
        axs[0, 0].set_title(f'Connected Clients Ratio')

        axs[1, 0].plot(vals1)
        axs[1, 0].set_xlim(self.xlim)
        axs[1, 0].set_xticks(locs)
        axs[1, 0].yaxis.set_major_formatter(FuncFormatter(format_bps))
        axs[1, 0].use_sticky_edges = False
        axs[1, 0].set_title('Total Bandwidth Usage')

        axs[2, 0].plot(vals2)
        axs[2, 0].set_xlim(self.xlim)
        axs[2, 0].set_xticks(locs)
        axs[2, 0].use_sticky_edges = False
        axs[2, 0].set_title('Bandwidth Usage Ratio in Slices (Averaged)')

        axs[3, 0].plot(vals3)
        axs[3, 0].set_xlim(self.xlim)
        axs[3, 0].set_xticks(locs)
        axs[3, 0].use_sticky_edges = False
        axs[3, 0].set_title('Client Count Ratio per Slice')

        axs[0, 1].plot(vals4)
        axs[0, 1].set_xlim(self.xlim)
        axs[0, 1].set_xticks(locs)
        axs[0, 1].use_sticky_edges = False
        axs[0, 1].set_title('Coverage Ratio')

        axs[1, 1].plot(vals5)
        axs[1, 1].set_xlim(self.xlim)
        axs[1, 1].set_xticks(locs)
        axs[1, 1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
        axs[1, 1].use_sticky_edges = False
        axs[1, 1].set_title('Block ratio')

        axs[2, 1].plot(vals6)
        axs[2, 1].set_xlim(self.xlim)
        axs[2, 1].set_xticks(locs)
        axs[2, 1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
        axs[2, 1].use_sticky_edges = False
        axs[2, 1].set_title('Handover ratio')

        plt.tight_layout()
        plt.savefig(save_path)
        plt.show()

这样就可以一张张输出图片了，你只需要传入一个保存路径参数 `save_path` 即可。