Matplotlib-关闭窗口，无需显式单击鼠标

Question

以下代码显示以下窗口：

using System;
using System.Diagnostics;
using System.Text;
using System.Windows.Forms;

namespace ConsoleOutput_test
{
public partial class Form1 : Form
{
    Process sortProcess;
    private static StringBuilder sortOutput = null;

    public Form1()
    {
        InitializeComponent();
    }

    private void button1_Click(object sender, EventArgs e)
    {
        sortProcess = new Process();
        sortProcess.StartInfo.FileName = "C:\\Windows\\System32\\cmd.exe";
        // Set UseShellExecute to false for redirection.
        sortProcess.StartInfo.CreateNoWindow = true;
        sortProcess.StartInfo.UseShellExecute = false;

        // Redirect the standard output of the sort command.  
        // This stream is read asynchronously using an event handler.
        sortProcess.StartInfo.RedirectStandardOutput = true;
        sortProcess.StartInfo.RedirectStandardInput = true;
        sortProcess.StartInfo.RedirectStandardError = true;
        sortOutput = new StringBuilder("");

        // Set our event handler to asynchronously read the sort output.
        sortProcess.OutputDataReceived += new DataReceivedEventHandler(SortOutputHandler);
        sortProcess.ErrorDataReceived += new DataReceivedEventHandler(SortErrorHandler);

        // Redirect standard input as well.  This stream
        // is used synchronously.
        sortProcess.StartInfo.RedirectStandardInput = true;

        // Start the process.
        sortProcess.Start();

        // Start the asynchronous read of the sort output stream.
        sortProcess.BeginOutputReadLine();
        while (!sortProcess.HasExited)
        {
            Application.DoEvents(); // This keeps your form responsive by processing events
        }
    }

    private void SortOutputHandler(object sendingProcess, DataReceivedEventArgs outLine)
    {
        if (txtConsole.InvokeRequired) { txtConsole.BeginInvoke(new DataReceivedEventHandler(SortOutputHandler), new[] { sendingProcess, outLine }); }
        else
        {
            txtConsole.AppendText(Environment.NewLine + outLine.Data);
        }
    }
    private void SortErrorHandler(object sendingProcess, DataReceivedEventArgs outLine)
    {
        if (txtConsole.InvokeRequired) { txtConsole.BeginInvoke(new DataReceivedEventHandler(SortErrorHandler), new[] { sendingProcess, outLine }); }
        else
        {
            txtConsole.AppendText(Environment.NewLine + outLine.Data);
        }
    }


    private void button2_Click(object sender, EventArgs e)
    {
        sortProcess.StandardInput.WriteLine(txtOutput.Text);
        txtOutput.Text = "";
    }
}
}

如何在不显式单击鼠标的情况下关闭窗口？

我需要可视化很多数据，因此我正在寻找一种自动执行打开和关闭窗口过程的方法。

我知道import numpy as np import matplotlib.pylab as pl import matplotlib.gridspec as gridspec from matplotlib import pyplot as plt def plot_stuff(x,y,z): gs = gridspec.GridSpec(3, 1) plt.style.use('dark_background') pl.figure("1D Analysis") ax = pl.subplot(gs[0, 0]) ax.set_ylabel('X VALUE') pl.plot(x, color="red") ax = pl.subplot(gs[1, 0]) ax.set_ylabel('Y VALUE') pl.plot(y, color="green") ax = pl.subplot(gs[2, :]) ax.set_ylabel('Z VALUE') pl.plot(z, color="blue") plt.show() 是一个阻止操作，并且我尝试使用plt.show()方法（如相关问题中所述），但是该窗口仍然存在，不会关闭我必须手动关闭它

我需要一个简单的代码来自动执行打开窗口，可视化数据，在一定时间间隔后关闭窗口的过程；然后以for循环的方式重复该过程。

Answer 1

这是另一种解决方案，使用显式close语句关闭并在每次迭代时重新创建图形

from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np


def plot_stuff(x, y, z):
    gs = gridspec.GridSpec(3, 1)
    plt.style.use('dark_background')
    fig = plt.figure("1D Analysis")
    ax = plt.subplot(gs[0, 0])
    ax.set_ylabel('X VALUE')
    plt.plot(x, color="red")
    ax = plt.subplot(gs[1, 0])
    ax.set_ylabel('Y VALUE')
    plt.plot(y, color="green")
    ax = plt.subplot(gs[2, :])
    ax.set_ylabel('Z VALUE')
    plt.plot(z, color="blue")
    return fig


things_to_plot = [np.random.random(size=(100, 3)),
                  np.ones((100, 3)),
                  np.random.random(size=(100, 3))]
delay = 5

if __name__ == "__main__":
    plt.ion()
    for things in things_to_plot:
        fig = plot_stuff(x=things[:, 0], y=things[:, 1], z=things[:, 2])
        plt.show()
        plt.pause(delay)
        plt.close()

Answer 2

这是使用<fragment>的另一种方法：

animation

您甚至可以从中提取.gif输出：

但是，我已经做了很多这样的视觉数据分析，而且很多时候您想在浏览结果时来回走动，花一些时间来绘制一些图，而另一些图则不那么有趣，您只需快速跳过它们即可。

我知道这不是您要的，但是将地物保存到import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation interval = 100 # in ms rescale_axis = False # Properties of the data num_batches = 10 num_obs = [20, 30, 10] feature_names = ['X VALUE', 'Y VALUE', 'Z VALUE'] feature_colors = ['red', 'green', 'blue'] num_features = len(feature_names) data_to_plot = [np.random.rand(num_batches, num_obs[f]) for f in range(num_features)] # Create the figure plt.style.use('dark_background') fig, axes = plt.subplots(num_features, 1) fig.canvas.set_window_title('1D Analysis') # Initial plot lines = [] for f in range(num_features): line, = axes[f].plot(data_to_plot[f][0, :], c=feature_colors[f]) lines.append(line) axes[f].set_ylabel(feature_names[f]) if not rescale_axis: axes[f].set_ylim(0, 1) def plot_stuff(xyz): x, y, z = xyz for f, data in enumerate([x, y, z]): lines[f].set_data([ix for ix in range(len(data))], data) if rescale_axis: axes[f].relim() axes[f].autoscale_view() return lines def data_gen(): for x, y, z in zip(*data_to_plot): yield x, y, z ani = animation.FuncAnimation(fig, plot_stuff, data_gen, interval=interval) ani.save('results.gif', dpi=80, writer='imagemagick') plt.show() 可能会有所帮助，每个地物都放在不同的页面中：

.pdf

Answer 3

您可以将matplotlib图嵌入到使用python的GUI库创建的窗口中，并使用GUI库API处理该窗口。

matplotlib user_interfaces Examples提供了使用各种GUI库的许多示例。

我将使用PySide2（请参阅embedding_in_qt5）选择Qt5，使用PySide（请参阅embedding_in_qt4或embedding_in_qt4_wtoolbar）或Tkinter选择Qt4。 （请参见embedding_in_tk或embedding_in_tk_canvas）。

Answer 4

我已经测试了以下解决方案，并且效果很好。我只用了pylab模块。

import numpy as np 
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec

def plot_stuff(x,y,z):
    pl.ion() # interactive mode on
    gs = gridspec.GridSpec(3, 1) 
    pl.style.use('dark_background')
    pl.figure("1D Analysis")
    ax = pl.subplot(gs[0, 0]) 
    ax.set_ylabel('X VALUE')
    pl.plot(x, color="red")
    ax = pl.subplot(gs[1, 0]) 
    ax.set_ylabel('Y VALUE')
    pl.plot(y, color="green")    
    ax = pl.subplot(gs[2, :])
    ax.set_ylabel('Z VALUE')
    pl.plot(z, color="blue")
    pl.show()
    pl.pause(3) # pause for 3 sec
    pl.close()  # close the window

items = [np.random.rand(100, 3),
            np.random.randint(10, size=(100, 3)),
            np.random.rand(100, 3)]


for item in items:
    plot_stuff(x=item[:, 0], y=item[:, 1], z=item[:, 2])

Answer 5

我将以不同的方式解决该问题，只创建一个图形，并在每次迭代时更新内容。

import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.axes import Axes
import numpy as np
from matplotlib.figure import Figure


def plot_stuff(x, y, z, fig: Figure = None):
    print(f"plotting x[{x.shape}],y[{y.shape}],z[{z.shape}] in fig[{fig.__repr__()}]")
    if fig is None:
        fig = plt.gcf()
    fig.clf()
    gs = gridspec.GridSpec(3, 1)
    fig.canvas.set_window_title("1D Analysis")
    ax1: Axes = plt.subplot(gs[0, 0])
    ax1.set_ylabel('X VALUE')
    ax1.plot(x, color="red")
    ax2: Axes = plt.subplot(gs[1, 0])
    ax2.set_ylabel('Y VALUE')
    ax2.plot(y, color="green")
    ax3: Axes = plt.subplot(gs[2, :])
    ax3.set_ylabel('Z VALUE')
    ax3.plot(z, color="blue")
    fig.canvas.draw_idle()


things_to_plot = [np.random.random(size=(100, 3)),
                  np.ones((100, 3)),
                  np.random.random(size=(100, 3))]
delay = 5

if __name__ == "__main__":
    plt.ion()
    plt.show()
    fig = plt.figure()
    for things in things_to_plot:
        plot_stuff(x=things[:, 0], y=things[:, 1], z=things[:, 2], fig=fig)
        plt.draw()
        plt.pause(delay)