如何在拟合函数时更新 matplotlib 图? [英] How do I update a matplotlib figure while fitting a function?

问题描述

我编写了一个 python 脚本，它打开 UV-Vis 光谱并尝试用大量函数拟合它们.但是，我希望在图中找到拟合步骤，因为找到了最小残差.实际上，Stackoverflow上有一些实例可以解决这个问题(http://stackoverflow.com/questions/4098131/matplotlib-update-a-plot)，但是由于某种原因，这种方法对我来说效果不佳.我的意思是效果不佳"是绘图窗口无法响应脚本中发生的更新.我试图将我的代码缩减为更易于理解、仍然可以编译的内容，但也比示例更接近我拥有的代码，如下所示.

I have written a python script which opens UV-Vis spectra and attempts to fit them with a large sum of functions. However, I would like the fitting steps to be shown in a plot as the minimum residual is being found. Stackoverflow actually has some examples that touch on this idea (http://stackoverflow.com/questions/4098131/matplotlib-update-a-plot), but for some reason this approach isn't working for me very well. What I mean by "isn't working very well" is that the plot window does not respond to the updates that are occurring in the script. I have attempted to cut down my code to something that is more understandable, still compiles, but is also closer to the code I have than the example and it is shown below.

重新表达我的问题:是否有更好的方法通过拟合过程来刷新这种类型的屏幕，以使窗口不会变为(无响应)"?

To re-phrase my question: Is there a better way of doing this type of screen refreshing through a fitting process so that the window does not become "(Not Responding)"?

这是我的简化代码:

# import modules that I'm using
import matplotlib
matplotlib.use('GTKAgg')
import Tkinter
from Tkinter import *
import numpy as np
import scipy as sc
import matplotlib.pyplot as pltlib
# lmfit is imported becuase parameters are allowed to depend on each other along with bounds, etc.
from lmfit import minimize, Parameters, Minimizer

#If button is pressed on the window, open a file and get the data
def open_File():
    # file is opened here and some data is taken
    # I've just set some arrays here so it will compile alone
    x=[]
    y=[]
    for num in range(0,1000):x.append(num*.001+1)
    # just some random function is given here, the real data is a UV-Vis spectrum
    for num2 in range(0,1000):y.append(sc.math.sin(num2*.06)+sc.math.e**(num2*.001))
    X = np.array(x)
    Y = np.array(y)

    # plot the initial data in one figure
    pltlib.ion()
    pltlib.interactive(True)
    pltlib.figure(1)
    pltlib.plot(X,Y, "r-")
    pltlib.show()

    #deconvolute this initial data into deveral lorentzian profiles
    deconvolute(X,Y)

#lorentz line for constructing the more complex function
def lorentz(x, amp, center, width):
    return amp*1/sc.math.pi*(width/((x-center)**2+width**2))

def deconvolute(X,Y):
    #make 2nd figure for the refreshing screen
    deconvFig = pltlib.figure(2)
    ax = deconvFig.add_subplot(111)
    line1,line2=ax.plot(X,Y,'r-',X,Y,'r-')

    # setup parameters for several (here is 30, I potentially hae many more in the real program)
    params = Parameters()
    for p in range(0,30):
        params.add('amp' + str(p), value=1)
        params.add('center' + str(p), value=1)
        params.add('width' + str(p), value=1)

    #get residual function for minimizing
    def residual(params, X, data=None):
        model = 0
        # get values for each lorentz and sum them up
        for p in range(0,30):
            amp = params['amp' + str(p)].value
            center = params['center' + str(p)].value
            width = params['width' + str(p)].value
            tmpLorentz = lorentz(X, amp, center, width)
            model = model + tmpLorentz

        # This is where the main problem is.
        # This 2nd figure stops responding after a very small (1?) number of iterations
        ########################################
        # I want redraw the figure at every step through the fitting process
        line2.set_ydata(model)
        deconvFig.canvas.draw()
        print 'screen should be refreshed'
        ########################################

        return (data - model)

    #fit the function to the data
    result = minimize(residual, params, args=(X, Y))
    print 'done fitting the program'

#create a window with a button
MainWindow = Tk()
Button(text='Open a File', command=open_File).pack(side=BOTTOM)
MainWindow.mainloop()

推荐答案

有趣，我尝试运行一个简单的测试.

interesting, I tried running a simple test.

import time
from matplotlib import pyplot as pltlib
deconvFig = pltlib.figure(2)
ax = deconvFig.add_subplot(111)
X, Y = range(10), range(10)
line1,line2 = ax.plot(X,Y,'r-',X,Y,'r-')
for x in xrange(2, 6, 1):
    line2.set_ydata(range(0, 10*x, x))
    deconvFig.canvas.draw()
    time.sleep(2)

>>> import matplotlib
>>> matplotlib.__version__
'1.1.0'

而且它按预期工作.
也许是因为您生成了第二个数字.

and well it worked as expected.
maybe because your generating a second figure.

import time
from matplotlib import pyplot as pltlib

pltlib.ion()
pltlib.interactive(True)
pltlib.figure(1)
pltlib.plot(range(10),range(10), "r-")
pltlib.show()

deconvFig = pltlib.figure(2)
ax = deconvFig.add_subplot(111)
X, Y = range(10), range(10)
line1,line2 = ax.plot(X,Y,'r-',X,Y,'r-')
for x in xrange(2, 6, 1):
    line2.set_ydata(range(0, 10*x, x))
    deconvFig.canvas.draw()
    time.sleep(2)

仍然没有问题.
可能是我的设置.

nope still worked fine.
It could be my setup.

尽管也有可能以非常慢的速度将其最小化，所以当您绘制更新时，您无法分辨出差异，您可以计算出RMSE来查看差异有多大

Though its also possible that its minimizing at very slow rate, so when you plot the update you can't tell the difference, you can calculate the RMSE to see how big the difference is

print numpy.sqrt(numpy.sum((data - model)**2)/model.shape[0])/numpy.mean(data) * 100  

另外我通常使用scipy的最小化功能http:///docs.scipy.org/doc/scipy/reference/generation/scipy.optimize.minimize.html 可以最大限度地减少大多数功能，尽管它可以通过随机改变输入来工作，所以我不知道有多快可以，但是可以在许多情况下应用.

Also I usually use scipy's minimization function http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html being that it can minimize most functions, though it works by randomly mutating the input so I don't know how fast it can be, but it can be applied in many many situations.

我希望这会有所帮助.

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