# [SciPy-User] Speeding things up - how to use more than one computer core

Troels Emtekær Linnet tlinnet@gmail....
Sat Apr 6 10:40:25 CDT 2013

```Dear Scipy users.

I am doing analysis of some NMR data, where I repeatability are doing
leastsq fitting.
But I get a little impatient for the time-consumption. For a run of my
data, it takes
approx 3-5 min, but it in this testing phase, it is to slow.

A look in my  task manager, show that I only consume 25%=1 core on my
computer.
And I have access to a computer with 24 cores, so I would like to speed
things up.
------------------------------------------------
I have been looking at the descriptions of multithreading/Multiprocess
http://stackoverflow.com/questions/4598339/parallelism-with-scipy-optimize
http://www.scipy.org/ParallelProgramming

But I hope someone can guide me, which of these two methods I should go
for, and how to implement it?
I am little unsure about GIL, synchronisation?, and such things, which I

For the real data, I can see that I am always waiting for the call of the
leastsq fitting.
How can start a pool of cores when I go through fitting?

I have this test script, which exemplifies my problem:
*Fitting single peaks N=300 0:00:00.045000*
*Done with fitting single peaks 0:00:01.146000*
*
*
*Make a test on chisqr 0:00:01.147000*
*Done with test on chisqr 0:00:01.148000*
*
*
*Prepare for global fit 0:00:01.148000*
*Doing global fit 0:00:01.152000*
*Done with global fit 0:00:17.288000*
*global fit unpacked 0:00:17.301000 *
*
*
*Making figure 0:00:17.301000*
--------------------------------------
import pylab as pl
#import matplotlib.pyplot as pl
import numpy as np
import scipy.optimize
import lmfit
from datetime import datetime
startTime = datetime.now()
#
############# Fitting functions ################
def sim(pars,x,data=None,eps=None):
a = pars['a'].value
b = pars['b'].value
c = pars['c'].value
model = a*np.exp(-b*x)+c
if data is None:
return model
if eps is None:
return (model - data)
return (model-data)/eps
#
def err_global(pars,x_arr,y_arr,sel_p):
toterr = np.array([])
for i in range(len(sel_p)):
p = sel_p[i]
par = lmfit.Parameters()
x = x_arr[i]
y = y_arr[i]
Yfit = sim(par,x)
erri = Yfit - y
toterr = np.concatenate((toterr, erri))
#print len(toterr), type(toterr)
#
def unpack_global(dic, p_list):
for i in range(len(p_list)):
p = p_list[i]
par = lmfit.Parameters()
b = dic['gfit']['par']['b']
a = dic['gfit']['par']['a%s'%p]
c = dic['gfit']['par']['c%s'%p]
par['b'] = b; par['a'] = a; par['c'] = c
dic[str(p)]['gfit']['par'] = par
# Calc other parameters for the fit
Yfit = sim(par, dic[str(p)]['X'])
dic[str(p)]['gfit']['Yfit'] = Yfit
residual = Yfit - dic[str(p)]['Yran']
dic[str(p)]['gfit']['residual'] = residual
chisq = sum(residual**2)
dic[str(p)]['gfit']['chisq'] = chisq
NDF = len(residual)-len(par)
dic[str(p)]['gfit']['NDF'] = NDF
dic[str(p)]['gfit']['what_is_this_called'] = np.sqrt(chisq/NDF)
dic[str(p)]['gfit']['redchisq'] = chisq/NDF
return()
################ Random peak data generator ###########################
def gendat(nr):
pd = {}
for i in range(1,nr+1):
b = 0.15
a = np.random.random_integers(1, 80)/10.
c = np.random.random_integers(1, 80)/100.
par = lmfit.Parameters(); par.add('b', value=b, vary=True);
pd[str(i)] = par
return(pd)
#############################################################################
## Start
#############################################################################
limit = 0.6   # Limit set for chisq test, to select peaks
#############################################################################
# set up the data
data_x = np.linspace(0, 20, 50)
pd = {} # Parameter dictionary, the "true" values of the data sets
pd['1'] = par # parameters for the first trajectory
pd['2'] = par       # parameters for the second trajectory, same b
pd['3'] = par       # parameters for the third trajectory, same b
pd = gendat(300)  # You can generate a large number of peaks to test
#
#Start making a dictionary, which holds all data
dic = {}; dic['peaks']=range(1,len(pd)+1)
for p in dic['peaks']:
dic['%s'%p] = {}
dic[str(p)]['X'] = data_x
dic[str(p)]['Y'] = sim(pd[str(p)],data_x)
dic[str(p)]['Yran'] = dic[str(p)]['Y'] +
np.random.normal(size=len(dic[str(p)]['Y']), scale=0.12)
dic[str(p)]['fit'] = {}  # Make space for future fit results
dic[str(p)]['gfit'] = {}  # Male space for future global fit results
#print "keys for start dictionary:", dic.keys()
#
# independent fitting of the trajectories
print "Fitting single peaks N=%s %s"%(len(pd),(datetime.now()-startTime))
for p in dic['peaks']:
par = lmfit.Parameters(); par.add('b', value=2.0, vary=True, min=0.0);
lmf = lmfit.minimize(sim, par, args=(dic[str(p)]['X'],
dic[str(p)]['Yran']),method='leastsq')
dic[str(p)]['fit']['par']= par
dic[str(p)]['fit']['lmf']= lmf
Yfit = sim(par,dic[str(p)]['X'])
#Yfit2 = dic[str(p)]['Yran']+lmf.residual
#print sum(Yfit-Yfit2), "Test for difference in two ways to get the
fitted Y-values "
dic[str(p)]['fit']['Yfit'] = Yfit
#print "Best fit parameter for peak %s. %3.2f %3.2f
%3.2f."%(p,par['b'].value,par['a'].value,par['c'].value),
#print "Compare to real paramaters. %3.2f %3.2f
%3.2f."%(pd[str(p)]['b'].value,pd[str(p)]['a'].value,pd[str(p)]['c'].value)
print "Done with fitting single peaks %s\n"%(datetime.now()-startTime)
#
# Make a selection flag, based on some test. Now a chisq value, but could
be a Ftest between a simple and advanced model fit.
print "Make a test on chisqr %s"%(datetime.now()-startTime)
sel_p = []
for p in dic['peaks']:
chisq = dic[str(p)]['fit']['lmf'].chisqr
#chisq2 = sum((dic[str(p)]['fit']['Yfit']-dic[str(p)]['Yran'])**2)
#print chisq - chisq2 "Test for difference in two ways to get chisqr"
if chisq < limit:
dic[str(p)]['Pval'] = 1.0
#print "Peak %s passed test"%p
sel_p.append(p)
else:
dic[str(p)]['Pval'] = False
print 'Done with test on chisqr %s\n'%(datetime.now()-startTime)
#print sel_p
#
# Global fitting
# Pick up x,y-values and parameters that passed the test
X_arr = []
Y_arr = []
P_arr = lmfit.Parameters(); P_arr.add('b', value=1.0, vary=True, min=0.0)
dic['gfit'] = {} # Make room for globat fit result
print "Prepare for global fit %s"%(datetime.now()-startTime)
for p in sel_p:
par = dic[str(p)]['fit']['par']
X_arr.append(dic[str(p)]['X'])
Y_arr.append(dic[str(p)]['Yran'])
print "Doing global fit %s"%(datetime.now()-startTime)
lmf = lmfit.minimize(err_global, P_arr, args=(X_arr, Y_arr,
sel_p),method='leastsq')
print "Done with global fit %s"%(datetime.now()-startTime)
dic['gfit']['par']= P_arr
dic['gfit']['lmf']= lmf
unpack_global(dic, sel_p) # Unpack the paramerts into the selected peaks
print "global fit unpacked %s \n"%(datetime.now()-startTime)
#
# Check result
#for p in sel_p:
#    ip= pd[str(p)]; sp = dic[str(p)]['fit']['par']; gp =
dic[str(p)]['gfit']['par']
#print p, "Single fit. %3.2f %3.2f
%3.2f"%(sp['b'].value,sp['a'].value,sp['c'].value),
#print "Global fit. %3.2f %3.2f
%3.2f"%(gp['b'].value,gp['a'].value,gp['c'].value)
#print p, "Single fit. %3.2f %3.2f
%3.2f"%(sp['b'].value-ip['b'].value,sp['a'].value-ip['a'].value,sp['c'].value-ip['c'].value),
#print "Global fit. %3.2f %3.2f
%3.2f"%(gp['b'].value-ip['b'].value,gp['a'].value-ip['a'].value,gp['c'].value-ip['c'].value)##
#
# Start plotting
print "Making figure %s"%(datetime.now()-startTime)
fig = pl.figure()
sel_p = sel_p[:9]
for i in range(len(sel_p)):
p = sel_p[i]
# Create figure
X = dic[str(p)]['X']
Y = dic[str(p)]['Y']
Ymeas = dic[str(p)]['Yran']
Yfit = dic[str(p)]['fit']['Yfit']
Yfit_global = dic[str(p)]['gfit']['Yfit']
rpar = pd[str(p)]
fpar = dic[str(p)]['fit']['par']
gpar = dic[str(p)]['gfit']['par']
fchisq = dic[str(p)]['fit']['lmf'].chisqr
gchisq = dic[str(p)]['gfit']['chisq']
# plot
ax.plot(X,Y,".-",label='real. Peak: %s'%p)
ax.plot(X,Ymeas,'o',label='Measured (real+noise)')
ax.plot(X,Yfit,'.-',label='leastsq fit. chisq:%3.3f'%fchisq)
ax.plot(X,Yfit_global,'.-',label='global fit. chisq:%3.3f'%gchisq)
# annotate
ax.annotate('p%s. real    par: %1.3f %1.3f %1.3f'%(p,
rpar['b'].value,rpar['a'].value,rpar['c'].value), xy=(1,1),
xycoords='data', xytext=(0.4, 0.8), textcoords='axes fraction')
ax.annotate('p%s. single  par: %1.3f %1.3f %1.3f'%(p,
fpar['b'].value,fpar['a'].value,fpar['c'].value), xy=(1,1),
xycoords='data', xytext=(0.4, 0.6), textcoords='axes fraction')
ax.annotate('p%s. global  par: %1.3f %1.3f %1.3f'%(p,
gpar['b'].value,gpar['a'].value,gpar['c'].value), xy=(1,1),
xycoords='data', xytext=(0.4, 0.4), textcoords='axes fraction')
# set title and axis name
#ax.set_title('Fitting for peak %s'%p)
ax.set_ylabel('Decay')
# Put legend to the right
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) # Shink
current axis by 20%
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),prop={'size':8}) #
Put a legend to the right of the current axis
ax.grid('on')
ax.set_xlabel('Time')
#
print "ready to show figure %s"%(datetime.now()-startTime)
pl.show()
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```