In both versions your lsPhasedValues contains the number of positions in the array that match a certain criteria. What I need in that step is the unique values and not their positions.
2010/2/24 Robert Kern <robert.k...@gmail.com> > On Wed, Feb 24, 2010 at 11:19, Bruno Santos <bacmsan...@gmail.com> wrote: > > It seems that the python 2.6.4 has a more efficient implementation of the > > lists. It runs faster on this version and slower on 2.5.4 on the same > > machine with debian. A lot faster in fact. > > I was trying to change my headche for the last couple of weeks. But you > > migth give me a lot more optimizations that I can pick. I am trying to > > optimize the following function > > def hypergeometric(self,lindex,rindex): > > """ > > loc.hypergeometric(lindex,rindex) > > Performs the hypergeometric test for the loci between lindex and > > rindex. > > Returns the minimum p-Value > > """ > > aASense = self.aASCounts[lindex*nSize:(rindex+1)*nSize] > > #Create the subarray to test > > aLoci = > > > numpy.hstack([self.aSCounts[lindex*nSize:(rindex+1)*nSize],aASense[::-1]]) > > #Get the values to test > > length = len(aLoci) > > lsPhasedValues = set([aLoci[i] for i in xrange(length) if > i%nSize==0 > > and aLoci[i]>0]) > > m = length/nSize > > n = (length-1)-(length/nSize-1) > > #Create an array to store the Pvalues > > lsPvalues = [] > > append = lsPvalues.append > > #Calculate matches in Phased and non Phased position > > for r in lsPhasedValues: > > #Initiate number of matches to 0 > > q = sum([1 for j in xrange(length) if j%nSize==0 and > > aLoci[j]>=r]) > > k = sum([1 for j in xrange(length) if aLoci[j]>=r]) > > key = '%i,%i,%i,%i'%(q-1,m,n,k) > > try:append(dtPhyper[key]) > > except KeyError: > > value = self.lphyper(q-1, m, n, k) > > append(value) > > dtPhyper[key]=value > > return min(lsPvalues) > > Is there any efficient way to test the array simultaneous for two > different > > conditions? > > j = np.arange(length) > j_nSize_mask = ((j % nSize) == 0) > lsPhasedValues = (j_nSize_mask & (aLoci >= 0)).sum() > ... > bigALoci = (aLoci >= r) > q = (j_nSize_mask & bigALoci).sum() > k = bigALoci.sum() > > > Another way to do it: > > j_nSize = np.arange(0, length, nSize) > lsPhasedValues = (aLoci[j_nSize] >= 0).sum() > ... > q = (aLoci[j_nSize] >= r).sum() > k = (aLoci >= r).sum() > > > -- > Robert Kern > > "I have come to believe that the whole world is an enigma, a harmless > enigma that is made terrible by our own mad attempt to interpret it as > though it had an underlying truth." > -- Umberto Eco > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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