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attempt to make enclosed dispersion calculation significantly faster #25

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Merged
bwkeller merged 1 commit into from
Jan 5, 2026
Merged

attempt to make enclosed dispersion calculation significantly faster #25

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52 changes: 46 additions & 6 deletions properties.py
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Original file line number Diff line number Diff line change
Expand Up @@ -705,15 +705,55 @@ def rstat(self, halo, maxrad):
proS = pynbody.analysis.profile.Profile(halo.s, type='lin', ndim=3, min=0, max=maxrad, nbins=nbins)
proDM = pynbody.analysis.profile.Profile(halo.dm, type='lin', ndim=3, min=0, max=maxrad, nbins=nbins)

halo['vr2'] = halo['vr']**2 #create new variable for the square of velocities
halo['vx2'] = halo['vx']**2
halo['vy2'] = halo['vy']**2
halo['vz2'] = halo['vz']**2
if len(halo.g)>5:
sigG = proG['vr_disp_encl']
sigG3d = proG['v_disp_tot_encl']
sqmean = np.nancumsum(proG['vr2']*proG['weight_fn'])/np.nancumsum(proG['weight_fn'])
meansq = (np.nancumsum(proG['vr']*proG['weight_fn'])/np.nancumsum(proG['weight_fn']))**2
sigG = np.sqrt(sqmean-meansq)

sqmeanx = np.nancumsum(proG['vx2']*proG['weight_fn'])/np.nancumsum(proG['weight_fn'])
meansqx = (np.nancumsum(proG['vx']*proG['weight_fn'])/np.nancumsum(proG['weight_fn']))**2
sqmeany = np.nancumsum(proG['vy2']*proG['weight_fn'])/np.nancumsum(proG['weight_fn'])
meansqy = (np.nancumsum(proG['vy']*proG['weight_fn'])/np.nancumsum(proG['weight_fn']))**2
sqmeanz = np.nancumsum(proG['vz2']*proG['weight_fn'])/np.nancumsum(proG['weight_fn'])
meansqz = (np.nancumsum(proG['vz']*proS['weight_fn'])/np.nancumsum(proG['weight_fn']))**2
sigGx2 = sqmeanx-meansqx
sigGy2 = sqmeany-meansqy
sigGz2 = sqmeanz-meansqz
sigG3d = np.sqrt(sigGx2+sigGy2+sigGz2)
if len(halo.s)>5:
sigS = proS['vr_disp_encl']
sigS3d = proS['v_disp_tot_encl']
sqmean = np.nancumsum(proS['vr2']*proS['weight_fn'])/np.nancumsum(proS['weight_fn'])
meansq = (np.nancumsum(proS['vr']*proS['weight_fn'])/np.nancumsum(proS['weight_fn']))**2
sigS = np.sqrt(sqmean-meansq)

sqmeanx = np.nancumsum(proS['vx2']*proS['weight_fn'])/np.nancumsum(proS['weight_fn'])
meansqx = (np.nancumsum(proS['vx']*proS['weight_fn'])/np.nancumsum(proS['weight_fn']))**2
sqmeany = np.nancumsum(proS['vy2']*proS['weight_fn'])/np.nancumsum(proS['weight_fn'])
meansqy = (np.nancumsum(proS['vy']*proS['weight_fn'])/np.nancumsum(proS['weight_fn']))**2
sqmeanz = np.nancumsum(proS['vz2']*proS['weight_fn'])/np.nancumsum(proS['weight_fn'])
meansqz = (np.nancumsum(proS['vz']*proS['weight_fn'])/np.nancumsum(proS['weight_fn']))**2
sigSx2 = sqmeanx-meansqx
sigSy2 = sqmeany-meansqy
sigSz2 = sqmeanz-meansqz
sigS3d = np.sqrt(sigSx2+sigSy2+sigSz2)
if len(halo.dm)>5:
sigDM = proDM['vr_disp_encl']
sigDM3d = proDM['v_disp_tot_encl']
sqmean = np.nancumsum(proDM['vr2']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn'])
meansq = (np.nancumsum(proDM['vr']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn']))**2
sigDM = np.sqrt(sqmean-meansq)

sqmeanx = np.nancumsum(proDM['vx2']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn'])
meansqx = (np.nancumsum(proDM['vx']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn']))**2
sqmeany = np.nancumsum(proDM['vy2']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn'])
meansqy = (np.nancumsum(proDM['vy']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn']))**2
sqmeanz = np.nancumsum(proDM['vz2']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn'])
meansqz = (np.nancumsum(proDM['vz']*proDM['weight_fn'])/np.nancumsum(proDM['weight_fn']))**2
sigDMx2 = sqmeanx-meansqx
sigDMy2 = sqmeany-meansqy
sigDMz2 = sqmeanz-meansqz
sigDM3d = np.sqrt(sigDMx2+sigDMy2+sigDMz2)

return sigS, sigG, sigDM, sigS3d, sigG3d, sigDM3d

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