Question

基于https://stackoverflow.com/a/10552563/8235309，我试图并行执行scipy.optimize.leastsq。 xx，yy，zz是3D云点的坐标，我计算了一个线点距离。

 try{
                // add a row to the RawContacts table
         ContentValues values = new ContentValues();
         values.put(RawContacts.ACCOUNT_TYPE, "com.android.contacts.sim");
         values.put(RawContacts.ACCOUNT_NAME, "SIM");
         Uri rawContactUri = getContentResolver().insert(RawContacts.CONTENT_URI, values);

        // get the ID of the newly-added line
 long rawContactId = ContentUris.parseId(rawContactUri);

        // add a "name" line to the Data table, linking it to the new RawContact
        // with the CONTACT_ID column
 values.clear();
 values.put(Data.RAW_CONTACT_ID, rawContactId);
 values.put(Data.MIMETYPE, StructuredName.CONTENT_ITEM_TYPE);
 values.put(StructuredName.DISPLAY_NAME, "Name");
 cr.insert(Data.CONTENT_URI, values);
  getContentResolver().notifyChange(Uri_Here,null); 
        // add a "phone" line to the Data table, linking it to the new RawContact
        // with the CONTACT_ID column
 values.clear();
 values.put(Data.RAW_CONTACT_ID, rawContactId);
 values.put(Data.MIMETYPE, Phone.CONTENT_ITEM_TYPE);
 values.put(Phone.NUMBER, "+12345678901");
 values.put(Phone.TYPE, Phone.TYPE_MOBILE);
 cr.insert(Data.CONTENT_URI, values);
getContentResolver().notifyChange(Uri_Here,null); 
//New Edit
}
catch(Exception e){
    //exception handling
}

---我得到的错误是：

TypeError Traceback（最近一次调用最后一次） in（） 37返回RunDistance 38 ---＆GT; 39结果，cov，infodict，mesg，ier = leastsq（fun，p，args =（xx，yy，zz），Dfun = None，full_output = True，col_deriv = 0，ftol = 1.49012e-08，xtol = 1.49012e -08，gtol = 0.0，epsfcn =无，因子= 100，diag =无）

/usr/local/lib/python3.6/site-packages/scipy/optimize/minpack.py in leastsq（func，x0，args，Dfun，full_output，col_deriv，ftol，xtol，gtol，maxfev，epsfcn，因子，诊断） 378米=形状[0] 379如果n> L： - ＆GT; 380引发TypeError（＆＃39;输入错误：N =％s不得超过M =％s＆＃39;％（n，m）） 381如果epsfcn为None： 382 epsfcn = finfo（dtype）.eps

TypeError：输入不正确：N = 4不得超过M = 1

没有多重处理的有趣功能

import multiprocessing
import numpy as np
from scipy.optimize import leastsq

p = [55, 0, 55, 0]

xx=np.array([ 54.696,  54.272,  54.272,  53.424,  53.424,  53.424,  53.848,
53.424,  53.848,  53.424,  53.424,  53.424,  53.848,  53.848,
53.848,  53.424,  53.424,  53.424,  53.424,  53.424,  53.848,
53.848,  53.424,  53.424,  53.848,  53.848,  53.424,  53.848,
53.424])
yy=np.array([ 53.848,  53.424,  53.848,  53.424,  53.848,  53.848,  53.848,
53.848,  53.848,  52.576,  53.424,  53.848,  52.576,  53.424,
53.848,  52.576,  53.424,  53.848,  52.576,  53.848,  52.576,
53.848,  52.576,  53.848,  52.576,  53.848,  52.576,  52.576,
52.576])
zz=np.array([ 4.936 ,  5.4296,  5.4296,  5.9232,  5.9232,  6.4168,  6.4168,
6.9104,  6.9104,  7.404 ,  7.404 ,  7.404 ,  7.404 ,  7.404 ,
7.404 ,  7.8976,  7.8976,  7.8976,  8.3912,  8.3912,  8.3912,
8.3912,  8.8848,  8.8848,  8.8848,  8.8848,  9.3784,  9.3784,
9.872 ])


def fun(p,xx,yy,zz):
    distance=[]    
    v0 = np.array([p[0], p[2], 0]); v1 = np.array([p[1],p[3], 1])
    def funA(v0,v1,xx,yy,zz):        
        for point in range(len(xx)):
            pp = np.array([xx[point], yy[point], zz[point]])
            yield pp,v0,v1
    pool = multiprocessing.Pool()
    distance.append(pool.starmap(funB, funA(v0,v1,xx,yy,zz)))
    return distance

def funB(pp, v0,v1):
        RunDistance=(np.linalg.norm(np.cross(pp-v0,v1))/np.linalg.norm(v1))
        return RunDistance

result,cov,infodict,mesg,ier = leastsq(fun,p, args=(xx,yy,zz), Dfun=None, full_output=True, col_deriv=0, ftol=1.49012e-08, xtol=1.49012e-08, gtol=0.0, epsfcn=None, factor=100, diag=None)

Answer 1

要回答我自己的问题，问题是该示例创建了一个嵌套列表，所以len（距离）= 1.通过放置修复：

return distance[0]

现在的错误是OSError：[Errno 24]打开的文件太多

也可以从这里更改/Library/LaunchDaemons/limit.maxfiles.plist来解决：https://github.com/ansible/ansible/issues/12259

当前错误：

---> 67         self.pid = os.fork()
     68         if self.pid == 0:
     69             try:

BlockingIOError: [Errno 35] Resource temporarily unavailable

使用多处理并行化scipy.optimize.leastsq

1 个答案: