Question

定义了一个函数和一个列表，如何让python多线程执行呢，python高手在吗？ 告诉我失败原因，如何修改。

# -*- coding: utf-8 -*-

def PointsTuple2(R,L,ALPHA,M,ANGLE):

lst=[]

pitch=2.0*np.pi*R/np.tan(np.radians(ALPHA))

pitch1=2.0*np.pi*11.5/np.tan(np.radians(7.16))

newM=int(np.ceil(M*L/pitch))

DtaPitch=0

if pitch > pitch1 :

DtaPitch= pitch-pitch1

else:

DtaPitch=0

for i in range(newM):

ii=float(i)

x=R*np.cos(2.0*np.pi*ii/M+np.radians(ANGLE))

y=R*np.sin(2.0*np.pi*ii/M+np.radians(ANGLE))

z=ii*(pitch-DtaPitch)/M

pointCoordinates=(x,y,z)

lst.append(pointCoordinates)

Points=tuple(lst)

print 'PointsNum:',len(Points),'DtaPitch:',DtaPitch,'newM:',newM,'R=%f,pitch=%f'%(R,pitch)

return Points

#

import numpy as np

from multiprocessing.dummy import Pool as ThreadPool

pool = ThreadPool(4)

#

global newM

L1,L2,L3,ALPHA1,M1,newM=912.0,938.0,991.0,7.16,102.0,0.0

dAngle=12.629916088

ddAngle=(36.0-dAngle)/8.0 #意欲将阴线分为8个网格

Points1=PointsTuple2(R=11.5,L=L1,ALPHA=ALPHA1,M=M1,ANGLE=11.685041956)

Points2=PointsTuple2(R=11.5,L=L1,ALPHA=ALPHA1,M=M1,ANGLE=23.954074918)

#对比多线程

P1=[11.5,912.0,7.16,102.0,11.685041956]

P2=[11.5,L1,ALPHA1,M1,23.954074918]

P3=[11.85,L2,ALPHA1,M1,11.685041956]

P4=[11.85,L2,ALPHA1,M1,23.954074918]

list1=[P1,P2,P3,P4]

results9=pool.map(PointsTuple2,list1)

pool.close()

pool.join()

Answer 1

提示错误时因为你通过map的话list取的话是一个P1对象，是一个列表，传到P ointsTuple2的话是一整个列表，但是你定义的函数需要5个参数，才会报那样的错，改的方法有两个，下面这个改参数为tuple，在把参数赋值，或者加个中介，希望有帮助
修改成一个参数：
# -*- coding: utf-8 -*-
def PointsTuple2(tuple1):
R,L,ALPHA,M,ANGLE=tuple1
lst=[]
pitch=2.0*np.pi*R/np.tan(np.radians(ALPHA))
pitch1=2.0*np.pi*11.5/np.tan(np.radians(7.16))
newM=int(np.ceil(M*L/pitch))
DtaPitch=0
if pitch > pitch1 :
DtaPitch= pitch-pitch1
else:
DtaPitch=0
for i in range(newM):
ii=float(i)
x=R*np.cos(2.0*np.pi*ii/M+np.radians(ANGLE))
y=R*np.sin(2.0*np.pi*ii/M+np.radians(ANGLE))
z=ii*(pitch-DtaPitch)/M
pointCoordinates=(x,y,z)
lst.append(pointCoordinates)
Points=tuple(lst)
print 'PointsNum:',len(Points),'DtaPitch:',DtaPitch,'newM:',newM,'R=%f,pitch=%f'%(R,pitch)
return Points
#
import numpy as np
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
#
global newM,L1,L2,L3,ALPHA1,M1
L1,L2,L3,ALPHA1,M1,newM=912.0,938.0,991.0,7.16,102.0,0.0
dAngle=12.629916088
ddAngle=(36.0-dAngle)/8.0 #意欲将阴线分为8个网格
tuple1=(11.5,L1,ALPHA1,M1,11.685041956)
Points1=PointsTuple2(tuple1)
tuple2=(11.5,L1,ALPHA1,M1,11.685041956)
Points2=PointsTuple2(tuple2)
#对比多线程
P1=(11.5,912.0,7.16,102.0,11.685041956)
P2=(11.5,L1,ALPHA1,M1,23.954074918)
P3=(11.85,L2,ALPHA1,M1,11.685041956)
P4=(11.85,L2,ALPHA1,M1,23.954074918)
list1=[P1,P2,P3,P4]
results9=pool.map(PointsTuple2,list1)
pool.close()
pool.join()
另外这个运行可能有点问题（google的样本，可以借鉴一下他的写法）：
from multiprocessing import Pool
import numpy as np
pool = Pool(processes=2) # I like to calculate only 2 FFTs parallel
# in every time step, therefor 2 processes
def Splitter(args):
'''I have to pass 2 arguments'''
return makeSomething(*args)
def makeSomething(a,b):
'''dummy function instead of the one with the FFT'''
return a*b
def RungeK():
# ...
# a lot of code which create the vectors A and B and calculates
# one Kunge-Kutta step for them
# ...
pool = Pool(processes=2)
n = 20 # Just something for the example
A = np.arange(50000)
B = np.ones_like(A)
for i in xrange(n): # loop over the time steps
A *= np.mean(B)*B - A
B *= np.sqrt(A)
results = pool.map(Splitter, [(A, 3), (B, 2)])
A = results[0]
B = results[1]
pool.close()
print np.mean(A) # Some output
print np.max(B)
if __name__== '__main__':
RungeK()

Answer 2

这个很简单。你把map的用法搞混了，再定义一个函数

def PointsTuple1(L):
    R,L,ALPHA,M,ANGLE=L
    return PointsTuple2(R,L,ALPHA,M,ANGLE)

然后把results9=pool.map(PointsTuple2,list1)的函数PointsTuple2换成上面的PointsTuple1
就可以了。

推荐你看看云课堂的用Python做些事，里面第四章里map,reduce讲过。