Python:threading + curve_fit:内部例程的null参数 [英] Python: threading + curve_fit: null argument to internal routine

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问题描述

我有一些问题,使用下面的代码,它应该使用线程做高斯拟合:

  

来自PIL import Image
import numpy as np
来自scipy.optimize import curve_fit
import threading

class myThread(threading.Thread):
def __init __(self,index):
threading.Thread .__ init __(self)
self.index = index
def run(self):
for i in np .arange(n_Bild.shape [1]):
curve_fit(self.gauss,x_x,Intensitaet [self.index,...],p0 =(Intensitaet [self.index,i],i, 0))
def gauss(self,x,a,b,c,d):
return a * np.exp( - (xb)** 2 /(2 * c ** 2) )+ d

Bild = Image.open(test.bmp)
n_Bild = np.asarray(Bild)
Intensitaet = np.zeros((n_Bild.shape [ 0],n_Bild.shape [1]),dtype = np.uint32)
Intensitaet + = n_Bild [...,...,0]
Intensitaet + = n_Bild [..., ..,1]
Intensitaet + = n_Bild [...,...,2]
x_x = np.arange(n_Bild.shape [1])#Pixel aufx-Achse

threads = []
#创建新线程
thread0 = myThread(0)
thread1 = myThread(1)
#将线程添加到线程列表
threads.append(thread0)
threads.append(thread1)
#开始新线程
thread0.start()
thread1.start()

#等待所有线程在线程中完成
t:
t.join()
printfinished



如果我运行我的程序,我得到一个错误:

  
SystemError:内部例程的空参数
线程中的异常Thread-2:
回溯(最近调用最后一次):
文件C:\Anaconda\ lib \threading.py,行808,在__bootstrap_inner
self.run()
文件G:/DropBox/Daten/Dropbox/Uni/Bachelorarbeit/Python/ThreadTest.py,第12行,in run
curve_fit(self.gauss,x_x,Intensitaet [self.index,...],p0 =(Intensitaet [self.index,i],i,1,0))
文件func,p0,args = args,full_output =C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py,line 533 in curve_fit
res = 1,** kw)
文件C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py,第378行,至少为ss
gtol,maxfev ,epsfcn,factor,diag)
错误:构造参数列表的内部错误#

我只运行一个线程而不是两个,程序工作正常,但我不知道我做错了什么。
感谢您的帮助。

解决方案

我相信leastsq()不是线程安全的,一个threading.Lock()围绕你的调用curve_fit()(这可能会打败你的目的)或使用多处理。


I have got some problems using the following code, which is supposed to do gaussian fits using threads:



    from PIL import Image
    import numpy as np
    from scipy.optimize import curve_fit
    import threading

    class myThread (threading.Thread):
        def __init__(self, index):
            threading.Thread.__init__(self)
            self.index = index
        def run(self):
            for i in np.arange(n_Bild.shape[1]):
                curve_fit(self.gauss, x_x, Intensitaet[self.index, ...], p0=(Intensitaet[self.index, i], i, 1, 0))
        def gauss(self, x, a, b, c, d):
            return a * np.exp(-(x-b) ** 2 / (2 * c ** 2)) + d

    Bild = Image.open("test.bmp")
    n_Bild = np.asarray(Bild)
    Intensitaet = np.zeros((n_Bild.shape[0], n_Bild.shape[1]), dtype=np.uint32)
    Intensitaet += n_Bild[..., ..., 0]
    Intensitaet += n_Bild[..., ..., 1]
    Intensitaet += n_Bild[..., ..., 2]
    x_x = np.arange(n_Bild.shape[1]) #Pixel auf "x"-Achse

    threads = []
    # Create new threads
    thread0 = myThread(0)
    thread1 = myThread(1)
    # Add threads to thread list
    threads.append(thread0)
    threads.append(thread1)
    # Start new Threads
    thread0.start()
    thread1.start()

    # Wait for all threads to complete
    for t in threads:
        t.join()
    print "finished"

If I run my programm I get an error:


SystemError: null argument to internal routine
Exception in thread Thread-2:
Traceback (most recent call last):
  File "C:\Anaconda\lib\threading.py", line 808, in __bootstrap_inner
    self.run()
  File "G:/DropBox/Daten/Dropbox/Uni/Bachelorarbeit/Python/ThreadTest.py", line 12, in run
    curve_fit(self.gauss, x_x, Intensitaet[self.index, ...], p0=(Intensitaet[self.index, i], i, 1, 0))
  File "C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py", line 533, in curve_fit
    res = leastsq(func, p0, args=args, full_output=1, **kw)
  File "C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py", line 378, in leastsq
    gtol, maxfev, epsfcn, factor, diag)
error: Internal error constructing argument list.#

If I only run one thread instead of two, the programm works fine, but I have no idea what i'm doing wrong. Thanks for your help.

解决方案

I believe that leastsq() is not threadsafe, and you need to either use a threading.Lock() around your calls to curve_fit() (which might defeat your purpose) or use multiprocessing.

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