写在前面，最近需要在java中调用matplotlib，其他一些画图包都没这个好，毕竟python在科学计算有优势。找到了matplotlib4j，大概看了下github上的https://github.com/sh0nk/matplotlib4j，maven repository：

<dependency>
   <groupId>com.github.sh0nk</groupId>
       <artifactId>matplotlib4j</artifactId>
       <version>0.5.0</version>
</dependency>

简单贴个测试类，更多的用法在test报下有个MainTest.class。

 @Test
    public void testPlot() throws IOException, PythonExecutionException, ClassNotFoundException, NoSuchMethodException, illegalaccessexception, InvocationTargetException, InstantiationException {
        Plot plot = Plot.create(PythonConfig.pythonBinPathConfig("D:\\python3.6\\python.exe"));
        plt.plot()
                .add(Arrays.asList(1.3, 2))
                .label("label")
                .linestyle("--");
        plt.xlabel("xlabel");
        plt.ylabel("ylabel");
        plt.text(0.5, 0.2, "text");
        plt.title("Title!");
        plt.legend();
        plt.show();

    }

下面问题来了，这个对matplotlib的封装不是很全面，源码里也有很多todo，有很多函数简单用用还行，很多重载用不了，比如plt.plot(xdata,ydata)可以，但是无法在其中指定字体plt.plot(xdata,ydata，fontsize=30);

所以想要更全面的用法还得自己动手，几种办法：

1. 大部分还是用matplotlib4j中的，个别的自己需要的但里头没有的函数，实现他的builder接口，重写build方法，返回一个py文件中命令行的字符串形式，然后反射取到PlotImpl中的成员变量registeredBuilders，往后追加命令行，感觉适用于只有极个别命令找不到的情况，挺麻烦的，而且要是传nd.array(…)这种参数还得额外拼字符串。

//拿到plotImpl中用于组装python脚本语句的的registeredBuilders，需要加什么直接添加新的builder就行了
        Field registeredBuildersField = plt.getClass().getDeclaredField("registeredBuilders");
        registeredBuildersField.setAccessible(true);
        List<Builder> registeredBuilders = (List<Builder>) registeredBuildersField.get(plt);

        TicksBuilder ticksBuilder = new TicksBuilder(yList, "yticks", fontSize);
        registeredBuilders.add(ticksBuilder);

2. 这种比较直接，参照matplotlib4j底层，直接写py.exe文件，执行命令行，比较推荐这种，一行一行脚本自己写，数据拼装方便，看起来直观。比如写如下的脚本并执行，搞两组数据，画个散点图

import numpy as np
import matplotlib.pyplot as plt
plt.plot(np.array(自己的x数据), np.array(自己的y数据), 'k.', markersize=4)
plt.xlim(0,6000)
plt.ylim(0,24)
plt.yticks(np.arange(0, 25, 1), fontsize=10)
plt.title("waterfall")
plt.show()

像下面这么写就行了

		//1. 准备自己的数据 不用管
        List<Float> y_secondList_formatByHours = y_secondList.stream().map(second -> (float)second / 3600).collect(Collectors.toList());

        //2.准备命令行list，逐行命令添加
        List<String> scriptLines = new ArrayList<>();
        scriptLines.add("import numpy as np");
        scriptLines.add("import matplotlib.pyplot as plt");
        scriptLines.add("plt.plot("+"np.array("+x_positionList+"),"+"np.array(" +y_secondList_formatByHours+"),\"k.\",label=\"waterfall\",lw=1.0,markersize=4)");
        scriptLines.add("plt.xlim(0,6000)");
        scriptLines.add("plt.ylim(0,24)");
        scriptLines.add("plt.yticks(np.arange(0, 25, 1), fontsize=10)");
        scriptLines.add("plt.title(\"waterfall\")");
        scriptLines.add("plt.show()");
        //3. 调用matplotlib4j 里面的pycommond对象，传入自己电脑的python路径
        pycommand command = new pycommand(PythonConfig.pythonBinPathConfig("D:\\python3.6\\python.exe"));
        //4. 执行，每次执行会生成临时文件 如C:\Users\ADMINI~1\AppData\Local\Temp\1623292234356-0\exec.py，这个带的日志输出能看到，搞定
        command.execute(Joiner.on('\n').join(scriptLines));

matplotlib ( python) - 在点击事件中更改全局变量

def menu_event(event):
    global state
    state +=1
    print(state)

def main():
    ...
    b = Button(button_ax,name,color=color,hovercolor=font_color)
    b.on_clicked(menu_event)
    ...
    while True:
        print(state)
    

state = 0
if __name__ == "__main__":
    main()

0
0
0
1 # A push of a button happened
0
0
0
2 # A push of a button happened
0
0
0
3 # A push of a button happened
0
0

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button

freqs = np.arange(2,20,3)

fig,ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
t = np.arange(0.0,1.0,0.001)
s = np.sin(2*np.pi*freqs[0]*t)
l,= plt.plot(t,s,lw=2)

def next(event):
    global ind
    ind += 1
    i = ind % len(freqs)
    ydata = np.sin(2*np.pi*freqs[i]*t)
    l.set_ydata(ydata)
    plt.draw()

def prev(event):
    global ind
    ind -= 1
    i = ind % len(freqs)
    ydata = np.sin(2*np.pi*freqs[i]*t)
    l.set_ydata(ydata)
    plt.draw()


ind = 0

def main():
    axprev = plt.axes([0.7,0.05,0.1,0.075])
    axnext = plt.axes([0.81,0.075])
    bnext = Button(axnext,''Next'')
    bnext.on_clicked(next)
    bprev = Button(axprev,''Previous'')
    bprev.on_clicked(prev)
    plt.show()

if __name__ == "__main__":
    main()

class State(object):

    def __init__(self):
        self._state = 0
        ...

    def click_event(self,event):
        self._state += 1
        ...
        plt.draw()

state = State()
b = Button(button_ax,name,color=color,hovercolor=font_color)
b.on_clicked(state.click_event)

dic = {''red'': ((0., 1, 1),                (0.00000000001, 0, 0),                (0.66, 1, 1),                (0.89,1, 1),                (1, 0.5, 0.5)),        ''green'': ((0., 1, 1),                 (0.00000000001, 0, 0),                 (0.375,1, 1),                 (0.64,1, 1),                 (0.91,0,0),                 (1, 0, 0)),        ''blue'': ((0., 1, 1),                (0.00000000001, 1, 1),                (0.34, 1, 1),                (0.65,0, 0),                (1, 0, 0))}

matrix[0][0]=0 matrix[0][1]=0.002

from matplotlib.colors import LinearSegmentedColormapimport matplotlib.pyplot as pltimport numpy as npdic = {''red'': ((0., 1, 0),                (0.66, 1, 1),                (0.89,1, 1),                (1, 0.5, 0.5)),        ''green'': ((0., 1, 0),                 (0.375,1, 1),                 (0.64,1, 1),                 (0.91,0,0),                 (1, 0, 0)),        ''blue'': ((0., 1, 1),                (0.34, 1, 1),                (0.65,0, 0),                (1, 0, 0))}a = np.random.rand(10,10)a[0,:2] = 0a[0,2:4] = 0.0001fig, ax = plt.subplots(1,1, figsize=(6,6))cmap = LinearSegmentedColormap(''custom_cmap'', dic)cmap.set_bad(''white'')ax.imshow(np.ma.masked_values(a, 0), interpolation=''none'', cmap=cmap)