Graphics.PLine和Graphics.Venn的示例

Author: 埃博拉酱

+MATLAB/+Graphics/PLine.m

@@ -4,7 +4,26 @@
 %[text] [Lines,Texts]=MATLAB.Graphics.PLine(Descriptors);
 %[text] ```
 %[text] ## 示例
-%[text] 
+%[text] ```matlabCodeExample
+%[text] load('+MATLAB\+Graphics\PLine.mat');
+%[text] figure;
+%[text] Bars=bar(ActiveLightRising{["MOp2/3","MOp5"],["NaiveLight","Transfer"],"Mean"});
+%[text] colororder([1,0,1;0,0.5,0]);
+%[text] MATLAB.Graphics.FigureAspectRatio(8,5,1/3);
+%[text] Ax=gca;
+%[text] Ax.Units='points';
+%[text] hold on;
+%[text] EBs=errorbar(vertcat(Bars.XEndPoints).',ActiveLightRising{["MOp2/3","MOp5"],["NaiveLight","Transfer"],"Mean"},[],ActiveLightRising{["MOp2/3","MOp5"],["NaiveLight","Transfer"],"Sem"},CapSize=Ax.Position(3)*Bars(1).GroupWidth*Bars(1).BarWidth/(numel(Bars)*diff(xlim)*2),Color='k',LineStyle='none');
+%[text] xticks(1:2);
+%[text] xticklabels(["Layer 2/3","Layer 5"]);
+%[text] box off;
+%[text] MATLAB.Graphics.PLine(table(EBs(1),EBs(end),1,2,"n.s.",'VariableNames',["ObjectA","ObjectB","IndexA","IndexB","Text"]));
+%[text] xlabel(Layout,'Time from 💡(:)💧(|)');
+%[text] ylim([0,diff(ylim)]);
+%[text] ylabel('Rising time');
+%[text] legend(Bars,["Naive","Transfer"],Location=MATLAB.Graphics.OptimizedLegendLocation(Bars));
+%[text] ```
+%[text] ![](text:image:59f8)
 %[text] ## 输入参数
 %[text] Descriptors tabular，一行一个标识，包含以下列：
 %[text] - ObjectA(:,1)matlab.graphics.chart.primitive.Bar|matlab.graphics.chart.primitive.ErrorBar|matlab.graphics.primitive.Line|matlab.graphics.chart.primitive.Scatter，必需，参与显著性比较的其中一个图形对象。所有图形对象必须隶属于同一个坐标区，且坐标区的YAxis标尺数据类型必须支持算术运算（如数值类型、duration等）。
@@ -27,6 +46,7 @@
 %[text] ## 返回值
 %[text] Lines(:,1)matlab.graphics.primitive.Line，标识线
 %[text] Texts(:,1)matlab.graphics.primitive.Text，标识文本对象
+%[text] **See also** [matlab.graphics.chart.primitive.Bar](<matlab:doc matlab.graphics.chart.primitive.Bar>) [matlab.graphics.chart.primitive.ErrorBar](<matlab:doc matlab.graphics.chart.primitive.ErrorBar>) [matlab.graphics.primitive.Line](<matlab:doc matlab.graphics.primitive.Line>) [matlab.graphics.chart.primitive.Scatter](<matlab:doc matlab.graphics.chart.primitive.Scatter>) [matlab.graphics.primitive.Text](<matlab:doc matlab.graphics.primitive.Text>)
 function [Lines,Texts]=PLine(Descriptors)
 HasColumns=ismember(["ObjectB", "IndexA", "IndexB"], Descriptors.Properties.VariableNames);
 if HasColumns(1)
@@ -310,3 +330,6 @@
 
 %[appendix]{"version":"1.0"}
 %---
+%[text:image:59f8]
+%   data: 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I+fx48eXLl0KCQlZv349LmUAAKAzurX6AIfDQd4AAEAn6b\/6gKenZ01NTZvrmwEAALSmZ+QwmcyIiIjff\/\/9wIEDSiXu2AAAgI7pP2Nt+PDh0dHR8fHxn332WetnqRlr9vb23agNAADMiv6Rk5OTs3LlSu0imwAAAO3TM3IUCsXRo0fHjh371Vdf8Xg8w+5TAAAAZknPyGlqaqqurhaJRCNGjDBsQQAAYK70nD5gYWHh4ODQ5hZtAAAAbdLzKofNZgcFBSUlJYWEhLi7uxu2JgDoOYba\/UgmkxlwL6UOt42XyWTh4eEGea\/GxsaioqI9e\/YY5Gxyudwg5+kj9Iyc5uZmHx+fKVOmhIaGBgcHi0QiNput2wDLegL0ThKJJPZRB3\/fO2MqmTrVcippY75ql0kGS0QikVAobL9Zo2Nj5dRKA7yf4by852Vjl2Bi9IycR48eRUVFFRQUEEL27t27d+\/eFg2wrCdArxVbZ4DIMSCZpczYJQBN9Iwca2vrVatWtbP0AIvFsra21rcqAAAwQ3pGDovF8vHxMWwpAABg3vScsQYAANBVXbjKqa2tjYyMJISkp6cTQiIjI6mxnDZhwRsAAGihC5HDYDCcnJyoB4QQ6vGLODk5YUkCAADQ1YXIsbOzS05O1v6o+xgAAKBDeo7lqFSq\/Pz8oqKiNp+9evXqnj17FApFNwoDAABzo2fkNDQ0fP755zk5OW0+e\/z48ZycnKampm4UBgAA5qYLHWsqlerw4cOVlZWEEKVSWVFRcfHixc2bN7du9uOPP\/J4PCZT\/50RAADA\/HQhFVgslqura0JCwpMnT6gj1dXVV69ebeOkTGZsbKyNjY1hagQAALPQtQuR8ePHnzlzRqPR1NfXL1u2zNvbOyYmpnUzCwuLFkuuAQAAdC1y+vXrZ2dnRwixsLAIDg7mcrm48wYAADpJ\/80LFixYYNhSAADAvGHBGwAAoAkiBwAAaILIAQAAmiByAACAJogcAACgCSIHAABoouckaWrvnHb2yyGEcDicoKCgqKgoBwcH\/d4FAADMiZ6RY2FhERgYOGjQoLNnz3I4HIFA4OHhUVRUdPny5ZqaGg6H4+Tk9PDhw\/T09N9\/\/33Lli0vvfSSYesGAACTo\/+toF5eXmlpaR9\/\/PH8+fNZLBZ1XKVSZWVl7dmzJzExceTIkcePH4+Li8vJyYmKijJczQAAYJL0HMtpamrKysry9PScM2eONm8IISwWKywsTCAQZGRkqNXqSZMmTZ069dKlS9g7BwAA9IwchUJRVFT08ssvt16+k8VicTicoqIihUJBPa6ursbeOQAAoGfkMBgMJpP59OlTtVrd4im1Wv306VMmk8lgMLpdHgAAmA89I8fGxsbT0zMnJ0cqlbZ4SiqV5uTkeHp62tjYKBSKO3fu8Hg8a2vrbpcKAACmTc\/pA0wmMzQ09NSpU5GRkYGBgQEBAWw2W6FQnDx5Mjc318nJKTQ0tLm5WSKRnDp1avXq1brjPQAA0Dfpv1e0u7v79u3bP\/300+zs7OzsbO1xHx+f9evXu7u7P378uKioaNasWbNmzTJEqQAAYNr0jxxCiLu7+\/79+8vLy69fv15cXOzq6urp6Tls2LB+\/foRQths9vbt29GlBgAAlG5FDiGkX79+zs7Ozs7O06ZNa\/0U8gYAALS6FTlqtVoul1dVVbV+isVivfrqqxjCAQAALf0jp6qq6sMPP8zLy2vzWT6fn56e3kORo1Ao1q5dO378+LCwsJ44PwAA9AQ9I0ej0WRlZeXl5YnF4tDQUDs7uxYNWCxWD\/Wq1dXVbdiw4fDhw+PHj++J8wMAQA\/RM3KePHmSn58fEBCQlJRkY2Nj2JpeRKVS\/fLLLwkJCXfv3qXnHQEAwID0vBVUrVY\/efJkxIgRtOUNISQnJ+e9995TKBSxsbFDhw6l7X0BAMAg9IwcKysrFxeXNhe86TkDBgz44IMPjh07NmPGDKymAwBgcvSPnKlTp545c+b27duGLagds2bNWrp0qb29PW3vCAAABqTnWE5zc7OPj09ISMicOXP+8z\/\/c\/z48RYWFroNMEkaAABa0DNyHj16FBUVRW1EnZaWlpaW1qJBj06SBgAAU6Rn5FhbW69atUqlUr2oQc9Nku4kiUQikUioxyKRKDMz04jFAAAA0TtyWCyWj4+PYUsxrNjY2NjYWGNXAQAA\/6bn9AEAAICu6sJVTm1tbWRkJCEkPT2dEBIZGUmN5bSJGsvB7DIAANDqQuQwGAwnJyfqASGEevwiTk5OuHUGAAB0dSFy7OzskpOTtT\/qPgYAAOhQd\/fLMQo3N7cXrWANAAC9VremD5SWlv7www9NTU2EkKqqqoULF\/J4vNdff\/3IkSMajcZAFQIAgJnQP3KuXbsWGhr6448\/NjU1qdXqnTt3njp1atiwYU1NTatWrZLJZAasEgAAzID+K0n\/4x\/\/GDBgQGRk5MCBA6urq8+dOzd58uQjR47k5ua6u7v\/z\/\/8D50rfgIAQO+nZ+Q8ffq0qKjI399fIBD069evsLDwX\/\/6l6en56BBgxwdHb29vW\/cuPHkyRPD1goAACatW\/vlDBw4kMlkEkJu3LhBCPH09NRtgOEcAADQpeeMNSaTaWNj8\/z5c41G09DQ8Ntvv\/F4vOHDhxNCVCpVTU2Ng4NDi7WlAQCgj9PzKmfgwIEeHh4\/\/\/zzb7\/9JpVKL1y44O3tPWTIELVa\/dNPPx07dmzcuHHGXdYTAAB6G\/2vckJDQ0+dOhUcHEwIsbGxCQoKUqlUS5cuPXv2rJeX16xZs7D6AAAA6NL\/VlB3d\/d9+\/YdOXJEpVK9+eab7u7uKpXKyclpyZIlc+fOdXBwMGCVAABgBrq1+gCXy120aJH2RxaLtXHjxm6XBAAA5qkLkdPc3FxfX08IsbW1JYTU19e3MyeNwWDY2tr264fNEQAA4P90IXIePXqEzQsAAEBvXYgcCwuLwMBA6gEhJDAw0NfX90WNbW1tMUkaAAB0dSFy2Gz2ggULtD\/qPgYAAOhQj4y1qFSqnJycurq6njg5AACYqC7PWGtubi4oKDh37tyzZ89efvnlKVOmtLjl8+bNm5988klTU1M73W4AANAHdS1yFArFhg0bvvvuO+2RkSNHpqSkuLu7E0JUKtWePXuSkpKePXsWEBAwYMAAAxcLAACmrGsda9nZ2d99993w4cOTkpJ2794dHh5eUlKydetWpVJZXV0dExOzadMmQsgHH3ywefNmGxubnqkZAABMUheuclQqVUFBwejRo9PS0oYNG0YI8fX1dXd3T0lJkUqlO3fulEqlPj4+q1evHjduXI8VDAAApqoLVzkNDQ1yufz111\/ncrnUEQaDMXHiRGrRgUuXLkVHR2dkZCBvAACgTV2ePtC\/f3\/d9To5HI6Dg8P169fXr18fGhqK5QYAAOBFDJMQQUFBQUFByBsAAGiHYUKCw+GwWCyDnAoAAMwVrksAAIAmiBwAAKBJl6cPXLx4cfPmzdoflUplRUVFi4OEEFtb27fffpvNZhugRgAAMAtdjpyrV69evXq1xcHq6uoWB\/l8flBQULdKAwAA89KFyLG2tl61apVKpepMYxaL1WLtNQAA6OO6EDksFsvHx6fnSgEAAPOG6QMAAEATRA4AANAEkQMAADRB5AAAAE0QOQAAQBNEDgAA0ASRAwAANEHkAAAATRA5AABAE0QOAADQBJEDAAA0QeQAAABNEDkAAEATRA4AANAEkQMAADRB5AAAAE0QOQAAQBNEDgAA0ASRAwAANDFy5NTV1SUlJfH5fHd397lz5xYUFGg0mhc1Li4uFovFvD9KTEyks2AAANAb04jvrVAoNmzY8Ouvv8bGxnK53IMHD86dO3fr1q1isbjN9g8ePKivr58zZ46dnZ324OjRo+mqFwAAusWYkXP69Onjx4+npKT4+fkRQsRicXx8fFpa2tixY3VDRausrGzIkCGLFi1ydnamvVgAAOguo3WsKZXKkydPTpgwQSAQUEfYbHZwcPDVq1dLSkpat9doNHK5nMvl2tra0looAAAYiNEiR6FQlJSUjBgxgs1maw86OTkNGjTo5s2brds\/ffq0srKSy+VaWlrSWCYAABiM0SLn0aNHNTU1w4cP1z3IYDAYDEZZWVnr9k+fPi0tLR08eHB6erqPj4+Hh8fixYtv3LhBV70AANBdRp6xxmAwdH\/kcDgODg5qtbp1y7q6uocPH2ZkZNy+fTsxMXHNmjWFhYXh4eF5eXl0FQsAAN1izOkDXdLQ0KBWqzdu3BgSEkIFlb+\/f0xMzLZt29qcbiCTySIiIqjHQqEwNjaW7ooBAOCPelfk1NTUVFdXM5ltVOXl5SWTyXSPODo6Tp8+ffPmzSUlJW3OcEPMAAD0KkaLnMGDB3M4nBaT0zQajUajcXJyavMlDQ0NVlZW\/fr9uzPQ2tq6oaFBpVK1biwUCoVCoUFLBgCAbjHaWA6bzXZ1db19+7ZCodAeLCsre\/z4sbu7e+v2SUlJb7755q1bt7RHNBpNVVUVl8vlcDh0VAwAAN1jtKscKyurgICA1atX\/\/Of\/6SWG1AoFIcPH\/by8nJ1dW3d3tvbOzU19ciRIzwej+p5Ky4uzsnJmThxoouLS3cqkclkUqm0O2foPImdhJ436jyJRNL+5aBMJrO8b2lH2ui6NJZGx0ZjlwAA+jDmWI5YLPbx8Vm+fPm8efNGjhx58ODBS5cubd26lRqYqa2tjYyMJISkp6fb29uLRKIFCxZs3769vLx8xowZd+7c2b9\/\/4ABAxYuXMhisbpThlQqlclkNPTC9cKxJSHp+FP3ti5KmUx29P5RY1cBAPowZuTY2dl9+eWXGRkZO3fuVCqVAoFg165d3t7ebTZmsVjLly8fOXJkRkZGdnY2h8MJCgqKiopycHDofiWY0mZCJBLJ0V8QOQAmycgz1uzs7Fas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