Did I find the right examples for you? yes no      Crawl my project      Python Jobs

All Samples(12)  |  Call(11)  |  Derive(0)  |  Import(1)

src/b/i/biskit-2.4/Biskit/EnsembleTraj.py   biskit(Download)
        slopes = [ M.linfit( range( l/n - last ), p )[0] for p in pm ]
 
        mean, sd = N.average( slopes ), M.SD( slopes )
 
        return [ r - mean < - z * sd for r in slopes ]

src/b/i/biskit-2.4/scripts/Biskit/averageASA.py   biskit(Download)
            msDic[n]    = N.average(msLst)[j]
            asDic[n]    = N.average(asLst)[j]
            msDic_sd[n] = MAU.SD( msLst )[j]
            asDic_sd[n] = MAU.SD( asLst )[j]
            j += 1

src/b/i/biskit-2.4/scripts/analysis/a_report_comEntropy.py   biskit(Download)
        -> float, standard dev of (v1 +/- v2)
        """
        sd1 = MU.SD( v1 )
        sd2 = MU.SD( v2 )
        return sqrt( sd1**2 + sd2**2 )

src/b/i/biskit-2.4/Biskit/FuzzyCluster.py   biskit(Download)
    def entropySD(self):
        centropy = N.sum(-N.log(self.msm)*\
                         self.msm)/float(self.n_cluster)
        return MU.SD(centropy)
 
 
    def standardDeviation(self):
        sd = MU.SD(self.msm)

src/b/i/biskit-2.4/Biskit/Mod/TemplateFilter.py   biskit(Download)
 
        avg = N.average( self.identities )
        sd  = M.SD( self.identities ) or 1e-10  ## replace 0 standard deviation
        z   = (self.identities - avg) / sd
 

src/b/i/biskit-2.4/Biskit/TrajFlexMaster.py   biskit(Download)
        """
        r = self.rmsList()
        return N.average(r), mathUtils.SD(r)
 
 

src/b/i/biskit-2.4/Biskit/rmsFit.py   biskit(Download)
        ## calculate rmsd and stdv
        rmsd = N.sqrt(N.average(N.compress(mask, d)**2))
        stdv = MU.SD(N.compress(mask, d))
 
        ## check conditions for convergence

src/b/i/biskit-2.4/Biskit/Hmmer.py   biskit(Download)
        p4 = []
        for i in range( len(p) ) :
            p_scale = (p[i] - N.average(p[i]) )/ math.SD(p[i])
            p4 += [ N.resize( p_scale[N.argmax( N.array(p_scale) )] ,
                              N.shape( p[i] ) ) ]

src/b/i/biskit-2.4/scripts/analysis/a_random_contacting.py   biskit(Download)
from Biskit import *
from Biskit.tools import *
from Biskit.mathUtils import SD
from Biskit.Statistics.lognormal import logConfidence