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我正在使用 Tkinter 和 ttk 构建一个 GUI，并使用 matplotlib 来创建交互式绘图 - 再次，就像其他数百万人一样。尽管到目前为止我遇到的大多数问题都有据可查，但这个问题似乎很少见：

当在 3d 中绘制并随后使用命令调整轴比例时set_lim()，绘制的线超出了看起来不好的坐标系。另外，我对看起来有点小的框架不满意。这是一个例子：

# Missmatch.py
"""Graphical User Interface for plotting the results
calculated in the script in Octave"""

# importing libraries
import matplotlib, ttk, threading
matplotlib.use('TkAgg')
import numpy as nm
import scipy as sc
import pylab as pl
import decimal as dc
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from matplotlib.figure import Figure
from mpl_toolkits.mplot3d import Axes3D
from oct2py import octave as oc
import Tkinter as tki

class CS:
    """CS - Controlset. This part creates the GUI with all important
    Elements. Major changes and calculations will be executed 
    in the Calculation-Class in a seperate thread. This prevents the 
    GUI from hanging"""

    def __init__(self,parent):
        """Building the main GUI"""
        self.ThisParent=parent
        ### Entire Window
        # Mainframe that contains everything.
        self.main=tki.Frame(parent) 
        # Pack manager to expand the mainframe as the windowsize changes.
        self.main.pack(fill=tki.BOTH, expand=tki.YES)
        # Configure the grid of the mainframe so that only the top left
        # cell grows if the users expands the window.
        self.main.grid_rowconfigure(0, weight=1)
        self.main.grid_rowconfigure(1, weight=1)


        ### Canvas for drawings
        # Creating a figure of desired size
        self.f = Figure(figsize=(6,6), dpi=100)
        # Creating a canvas that lives inside the figure
        self.Paper=FigureCanvasTkAgg(self.f, master=self.main)
        # Making the canvas's drawings visible (updating)
        self.Paper.show()
        # positioning the canvas
        self.Paper.get_tk_widget().grid(row=0,rowspan=3, column=0, sticky='NSWE')
        # creating a toolbarframe for options regarding the plots
        self.toolbarframe=tki.Frame(self.main)
        self.toolbarframe.grid(row=3, column=0, sticky='NWE')
        # Creating a toolbar for saving, zooming etc. (matplotlib standard)
        self.toolbar = NavigationToolbar2TkAgg(self.Paper, self.toolbarframe)
        self.toolbar.grid(row=0,column=0, sticky='NWE')
        # setting the standard option on zoom
        self.toolbar.zoom()



        ### Axis configuration toolbar
        # A frame containing the axis config-menu
        self.axisscaleframe=tki.Frame(self.main)
        self.axisscaleframe.grid(row=5, column=0, sticky='SNEW')
        # In that Frame, some Entry-boxes to specify scale
        self.xaxisscalef=ttk.Entry(self.axisscaleframe, width=10)
        self.xaxisscalef.insert(0,0)
        self.xaxisscalet=ttk.Entry(self.axisscaleframe, width=10)
        self.xaxisscalet.insert(0,15)
        self.yaxisscalef=ttk.Entry(self.axisscaleframe, width=10)
        self.yaxisscalef.insert(0,0)
        self.yaxisscalet=ttk.Entry(self.axisscaleframe, width=10)
        self.yaxisscalet.insert(0,15)
        self.zaxisscalef=ttk.Entry(self.axisscaleframe, width=10)
        self.zaxisscalef.insert(0,0)
        self.zaxisscalet=ttk.Entry(self.axisscaleframe, width=10)
        self.zaxisscalet.insert(0,15)
        # And some Labels so we know what the boxes are for
        self.xaxlab=ttk.Label(self.axisscaleframe, text='X-Axis', width=10)
        self.yaxlab=ttk.Label(self.axisscaleframe, text='Y-Axis', width=10)
        self.zaxlab=ttk.Label(self.axisscaleframe, text='Z-Axis', width=10)
        self.axinfolab=ttk.Label(self.axisscaleframe, text='Adjust axis scale:')
        # And a Button to validate the desired configuration
        self.scaleset=ttk.Button(self.axisscaleframe, text='Set', command=self.SetAxis2)
        self.scaleset.bind('<Return>', self.SetAxis)
        # Let's organize all this in the axisscaleframe-grid
        self.axinfolab.grid(row=0, column=0, sticky='W')
        self.xaxlab.grid(row=1, column=0, sticky='W')
        self.yaxlab.grid(row=2, column=0, sticky='W')
        self.zaxlab.grid(row=3, column=0, sticky='W')
        self.xaxisscalef.grid(row=1,column=1, sticky='W')
        self.yaxisscalef.grid(row=2,column=1, sticky='W')
        self.xaxisscalet.grid(row=1,column=2, sticky='W')
        self.yaxisscalet.grid(row=2,column=2, sticky='W')
        self.zaxisscalef.grid(row=3,column=1,sticky='W')
        self.zaxisscalet.grid(row=3,column=2,sticky='W')
        self.scaleset.grid(row=3,column=3,sticky='E')


    def SetAxis(self,event):
        self.SetAxis2()

    def SetAxis2(self):
        self.x1=float(self.xaxisscalef.get())
        self.x2=float(self.xaxisscalet.get())
        self.y1=float(self.yaxisscalef.get())
        self.y2=float(self.yaxisscalet.get())
        self.z1=float(self.zaxisscalef.get())
        self.z2=float(self.zaxisscalet.get())
        self.a.set_xlim(self.x1, self.x2)
        self.a.set_ylim(self.y1, self.y2)
        self.a.set_zlim(self.z1, self.z2)
        self.Paper.show()
        print "Set axis"



class Calculate3D(threading.Thread):
    def __init__(self):
        threading.Thread.__init__(self)

    def run(self):
        self.x=range(100)
        self.y=range(100)
        self.z=range(100)
        print 'Done!'
        controlset.a = controlset.f.add_subplot(111, projection='3d')
        controlset.a.clear()
        controlset.a.plot(self.x,self.y,self.z)
        controlset.a.mouse_init()
        controlset.a.set_xlabel('X')
        controlset.a.set_ylabel('Y')
        controlset.a.set_zlabel('Z')
        controlset.a.set_title('Title')
        controlset.Paper.show()
        return


mainw=tki.Tk()
mainw.title("Example")
mainw.geometry('+10+10')
controlset=CS(mainw) 
#for this example code, we run our Calculate3D class automatically
CL=Calculate3D()
CL.run()

mainw.mainloop()

只需运行代码，然后点击“SET”按钮。有我的问题。

编辑：添加截图： 在此处输入图像描述

4

2 回答 2

3

这里的问题是，mplot3d 没有 OpenGL 后端。因此，用于显示数据的计算基于 2d。我在这里发现了同样的问题和解决方法。尽管我认为解决方法不是最好的，因为它取决于数据的分辨率。

无论如何，我遵循了第二个链接。所以，我现在正在做的是复制数组并将所有高于和低于我想要的比例的值设置为 NaN。绘制这些时，数据点超过所需限制的线将被切断。

def SetAxis2(self):
    self.dummyx=CL.x*1
    self.dummyy=CL.y*1
    self.dummyz=CL.z*1
    #clipping manually
    for i in nm.arange(len(self.dummyx)):
        if self.dummyx[i] < self.x1:
            self.dummyx[i] = nm.NaN
        else:
            pass

    for i in nm.arange(len(self.dummyy)):
        if self.dummyy[i] < self.y1:
            self.dummyy[i] = nm.NaN
        else:
            pass

    for i in nm.arange(len(self.dummyz)):
        if self.dummyz[i] < self.z1:
            self.dummyz[i] = nm.NaN
        else:
            pass     

    controlset.a.plot(self.dummyx,\
    self.dummyy,\
    self.dummyz)

    self.a.set_xlim3d(self.x1, self.x2)
    self.a.set_ylim3d(self.y1, self.y2)
    self.a.set_zlim3d(self.z1, self.z2)

如果现在您的比例设置为从 0 到 10，并且您有六个数据点：[-1, 3 4 12 5 1]该行将从 3 变为 4，从 5 变为 1，因为 -1 和 12 将设置为 NaN。关于这个问题的改进会很好。 Mayavi可能会更好，但我没有尝试过，因为我想坚持使用 matplotlib。

于 2013-04-23T11:11:59.783 回答
1

以下代码甚至适用于网格数据表示：

@ numpy.vectorize
def clip_z_data(z):
  return z if Z_MIN <= z <= Z_MAX else n.nan

z = clip_z_data(z)

Z_MIN 和 Z_MAX 是全局的，因为 vectorize 无法处理额外的属性。

于 2013-12-13T00:15:47.767 回答