maxUncontaminatedDist = 0
for LGdict in LGdicts:
if LGdict['contaminationDistance'] > maxUncontaminatedDist:
maxUncontaminatedDist = LGdict['contaminationDistance']
bestLG = LGdict
if bestLG['mass1'] < bestLG['mass2']:
MWindex = bestLG['ind1']
centre = bestLG['cop1']
else:
MWindex = bestLG['ind2']
centre = bestLG['cop2']
cop = bestLG['cops']
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
distmask = np.array(
[distance < maxdist and distance > mindist for distance in distances])
distances = distances[distmask]
relvel = (bestLG['vels'] - bestLG['vels'][MWindex])[distmask]
cop = cop[distmask]
directions = np.array(
[physUtils.sphericalCoordinates(pos - centre) for pos in cop])
radvel = np.array([
physUtils.velocityComponents(relvel[j], cop[j] - centre)[0]
for j in range(len(cop))
])
fitdata = clustering.precomputeDistances(directions)
db = DBSCAN(
mass,
cop,
quiet=True,
outputAll=True)
LG = LGs[LGfinder.chooseClosestToCentre(LGs, contaminationMask, fullCOP)]
if mass[LG[0]] > mass[LG[1]]:
centreIndex = LG[1]
else:
centreIndex = LG[0]
centre = cop[centreIndex]
centreVel = staticVel[centreIndex]
closestContDist = physUtils.findClosestDistance(centre,
contaminatedPositions)
distances = np.array([physUtils.distance(centre, c) for c in cop])
vel = physUtils.addExpansion(staticVel, cop, centre)
# LG mass from MW and andromeda
M_big2 = mass[LG[0]] + mass[LG[1]]
originalDistances = list(distances)
# contamination cut
distmask = distances < closestContDist
cop = cop[distmask]
vel = vel[distmask]
distances = distances[distmask]
# radial velocities
radvel = np.array([
def readAndSave(simulationfiles, datafile, mindist=1.5, maxdist=5.0, eps=1.6,
ms=4, scale_eps=False):
masses = []
H0s = []
inClusterH0s = []
outClusterH0s = []
zeropoints = []
inClusterZeros = []
outClusterZeros = []
allDispersions = []
clusterDispersions = []
unclusteredDispersions = []
radialVelocities = []
tangentialVelocities = []
LGdistances = []
timingArgumentMasses = []
f = open(simulationfiles, 'r')
for simdir in f.readlines():
dirname = simdir.strip()
staticVel = filereader.readAllFiles(dirname, "Subhalo/Velocity", 3)
mass = filereader.readAllFiles(dirname, "Subhalo/Mass", 1)
fullCOP = filereader.readAllFiles(dirname, "Subhalo/CentreOfPotential", 3)
FoFcontamination = filereader.readAllFiles(dirname, "FOF/ContaminationCount",
1)
groupNumbers = filereader.readAllFiles(dirname, "Subhalo/GroupNumber", 1)
fullCOP = fullCOP/h0 # to Mpc
# creation of mask with True if there is no contamination in Subhalo
contaminationMask = np.asarray([FoFcontamination[int(group)-1]<1 for group in
groupNumbers])
# save contaminated haloes for finding closest one
contaminatedPositions = fullCOP[contaminationMask==False, :]
# elimination of contaminated haloes
staticVel = staticVel[contaminationMask,:]
mass = mass[contaminationMask]
cop = fullCOP[contaminationMask, :]
groupNumbers = groupNumbers[contaminationMask]
# to physical units
mass = mass/h0*1e10 # to M_☉
LGs = LGfinder.findLocalGroup(staticVel, mass, cop, quiet=True,
outputAll=True)
unmaskedLGs = LGfinder.maskedToUnmasked(LGs, cop, fullCOP)
bestLGindex = LGfinder.chooseClosestToCentre(unmaskedLGs, contaminationMask, fullCOP)
LG = LGs[bestLGindex]
if mass[LG[0]] > mass[LG[1]]:
centreIndex = LG[1]
else:
centreIndex = LG[0]
massCentre = physUtils.massCentre(cop[LG[0]], cop[LG[1]], mass[LG[0]],
mass[LG[1]])
closestContDist = physUtils.findClosestDistance(massCentre,
contaminatedPositions)
if closestContDist < maxdist:
print("Warning: closest contaminated halo closer than the edge of\
Hubble flow fitting range in simulation " + dirname +
".\nExcluding simulation from analysis.")
continue
centre = cop[centreIndex]
centreVel = staticVel[centreIndex]
closestContDist = physUtils.findClosestDistance(centre,
contaminatedPositions)
distances = np.array([physUtils.distance(centre, c) for c in
cop])
vel = physUtils.addExpansion(staticVel, cop, centre)
LGrelVel = vel[LG[0]] - vel[LG[1]]
LGrelVelComponents = physUtils.velocityComponents(LGrelVel, cop[LG[1]]-cop[LG[0]])
LGdistance = physUtils.distance(cop[LG[0]], cop[LG[1]])
closestContMask = distances < closestContDist
distRangeMask = np.array([d < maxdist and d > mindist for d in
distances])
# contamination and distance range cut
mask = np.logical_and(closestContMask, distRangeMask)
cop = cop[mask]
vel = vel[mask]
distances = distances[mask]
# radial velocities
radvel = np.array([physUtils.velocityComponents(vel[j] - centreVel,
cop[j] - centre)[0]
for j in range(len(vel))])
##### extracting clustering data #####
clusteringDB = clustering.runClustering(cop, centre, ms, eps,
meansep=scale_eps)
labels = clusteringDB.labels_
uniqueLabels = set(labels)
clusterMemberMask = labels != -1 # True for haloes in cluster
## all haloes ##
(H0, zero) = simpleFit(distances, radvel)
radvelResiduals = np.empty(radvel.shape)
for i in range(len(radvel)):
radvelResiduals[i] = radvel[i] - (distances[i] - zero) * H0
zeropoints.append(zero)
H0s.append(H0)
allDispersions.append(np.std(radvelResiduals, ddof=1))
## outside clusters ##
(H0, zero, dispersion) = outClusterFit(clusteringDB, radvel, distances,
minHaloes=10)
outClusterH0s.append(H0)
outClusterZeros.append(zero)
unclusteredDispersions.append(dispersion)
## inside clusters ##
(H0, zero, dispersion) = inClusterFit(clusteringDB, radvel, distances,
minSize=10)
inClusterH0s.append(H0)
inClusterZeros.append(zero)
clusterDispersions.append(dispersion)
# LG mass from MW and andromeda
M_big2 = mass[LG[0]] + mass[LG[1]]
masses.append(M_big2)
radialVelocities.append(LGrelVelComponents[0])
tangentialVelocities.append(LGrelVelComponents[1])
LGdistances.append(LGdistance)
timingArgumentMass = timingargument.timingArgumentMass(-1 *
LGrelVelComponents[0],
LGdistance*1000.0,
13.815, G)
timingArgumentMasses.append(timingArgumentMass)
##### finalizing data #####
masses = np.array(masses)
H0s = np.array(H0s)
inClusterH0s = np.array(inClusterH0s)
outClusterH0s = np.array(outClusterH0s)
zeropoints = np.array(zeropoints)
inClusterZeros = np.array(inClusterZeros)
outClusterZeros = np.array(outClusterZeros)
allDispersions = np.array(allDispersions)
clusterDispersions = np.array(clusterDispersions)
unclusteredDispersions = np.array(unclusteredDispersions)
radialVelocities = np.array(radialVelocities)
tangentialVelocities = np.array(tangentialVelocities)
LGdistances = np.array(LGdistances)
timingArgumentMasses = np.array(timingArgumentMasses)
data = np.array([masses, timingArgumentMasses, H0s, inClusterH0s,
outClusterH0s, zeropoints, inClusterZeros, outClusterZeros,
allDispersions, unclusteredDispersions, clusterDispersions,
radialVelocities, tangentialVelocities, LGdistances]).T
np.savetxt(datafile, data)
return data
def readAndSave(simulationfiles,
datafile,
mindist=1.5,
maxdist=5.0,
eps=1.6,
ms=4,
scale_eps=False):
masses = []
H0s = []
inClusterH0s = []
outClusterH0s = []
zeropoints = []
inClusterZeros = []
outClusterZeros = []
allDispersions = []
clusterDispersions = []
unclusteredDispersions = []
radialVelocities = []
tangentialVelocities = []
LGdistances = []
timingArgumentMasses = []
f = open(simulationfiles, 'r')
for simdir in f.readlines():
dirname = simdir.strip()
staticVel = filereader.readAllFiles(dirname, "Subhalo/Velocity", 3)
mass = filereader.readAllFiles(dirname, "Subhalo/Mass", 1)
fullCOP = filereader.readAllFiles(dirname, "Subhalo/CentreOfPotential",
3)
FoFcontamination = filereader.readAllFiles(dirname,
"FOF/ContaminationCount", 1)
groupNumbers = filereader.readAllFiles(dirname, "Subhalo/GroupNumber",
1)
fullCOP = fullCOP / h0 # to Mpc
# creation of mask with True if there is no contamination in Subhalo
contaminationMask = np.asarray(
[FoFcontamination[int(group) - 1] < 1 for group in groupNumbers])
# save contaminated haloes for finding closest one
contaminatedPositions = fullCOP[contaminationMask == False, :]
# elimination of contaminated haloes
staticVel = staticVel[contaminationMask, :]
mass = mass[contaminationMask]
cop = fullCOP[contaminationMask, :]
groupNumbers = groupNumbers[contaminationMask]
# to physical units
mass = mass / h0 * 1e10 # to M_☉
LGs = LGfinder.findLocalGroup(staticVel,
mass,
cop,
quiet=True,
outputAll=True)
unmaskedLGs = LGfinder.maskedToUnmasked(LGs, cop, fullCOP)
bestLGindex = LGfinder.chooseClosestToCentre(unmaskedLGs,
contaminationMask,
fullCOP)
LG = LGs[bestLGindex]
if mass[LG[0]] > mass[LG[1]]:
centreIndex = LG[1]
else:
centreIndex = LG[0]
massCentre = physUtils.massCentre(cop[LG[0]], cop[LG[1]], mass[LG[0]],
mass[LG[1]])
closestContDist = physUtils.findClosestDistance(
massCentre, contaminatedPositions)
if closestContDist < maxdist:
print("Warning: closest contaminated halo closer than the edge of\
Hubble flow fitting range in simulation " + dirname +
".\nExcluding simulation from analysis.")
continue
centre = cop[centreIndex]
centreVel = staticVel[centreIndex]
closestContDist = physUtils.findClosestDistance(
centre, contaminatedPositions)
distances = np.array([physUtils.distance(centre, c) for c in cop])
vel = physUtils.addExpansion(staticVel, cop, centre)
LGrelVel = vel[LG[0]] - vel[LG[1]]
LGrelVelComponents = physUtils.velocityComponents(
LGrelVel, cop[LG[1]] - cop[LG[0]])
LGdistance = physUtils.distance(cop[LG[0]], cop[LG[1]])
closestContMask = distances < closestContDist
distRangeMask = np.array(
[d < maxdist and d > mindist for d in distances])
# contamination and distance range cut
mask = np.logical_and(closestContMask, distRangeMask)
cop = cop[mask]
vel = vel[mask]
distances = distances[mask]
# radial velocities
radvel = np.array([
physUtils.velocityComponents(vel[j] - centreVel,
cop[j] - centre)[0]
for j in range(len(vel))
])
##### extracting clustering data #####
clusteringDB = clustering.runClustering(cop,
centre,
ms,
eps,
meansep=scale_eps)
labels = clusteringDB.labels_
uniqueLabels = set(labels)
clusterMemberMask = labels != -1 # True for haloes in cluster
## all haloes ##
(H0, zero) = simpleFit(distances, radvel)
radvelResiduals = np.empty(radvel.shape)
for i in range(len(radvel)):
radvelResiduals[i] = radvel[i] - (distances[i] - zero) * H0
zeropoints.append(zero)
H0s.append(H0)
allDispersions.append(np.std(radvelResiduals, ddof=1))
## outside clusters ##
(H0, zero, dispersion) = outClusterFit(clusteringDB,
radvel,
distances,
minHaloes=10)
outClusterH0s.append(H0)
outClusterZeros.append(zero)
unclusteredDispersions.append(dispersion)
## inside clusters ##
(H0, zero, dispersion) = inClusterFit(clusteringDB,
radvel,
distances,
minSize=10)
inClusterH0s.append(H0)
inClusterZeros.append(zero)
clusterDispersions.append(dispersion)
# LG mass from MW and andromeda
M_big2 = mass[LG[0]] + mass[LG[1]]
masses.append(M_big2)
radialVelocities.append(LGrelVelComponents[0])
tangentialVelocities.append(LGrelVelComponents[1])
LGdistances.append(LGdistance)
timingArgumentMass = timingargument.timingArgumentMass(
-1 * LGrelVelComponents[0], LGdistance * 1000.0, 13.815, G)
timingArgumentMasses.append(timingArgumentMass)
##### finalizing data #####
masses = np.array(masses)
H0s = np.array(H0s)
inClusterH0s = np.array(inClusterH0s)
outClusterH0s = np.array(outClusterH0s)
zeropoints = np.array(zeropoints)
inClusterZeros = np.array(inClusterZeros)
outClusterZeros = np.array(outClusterZeros)
allDispersions = np.array(allDispersions)
clusterDispersions = np.array(clusterDispersions)
unclusteredDispersions = np.array(unclusteredDispersions)
radialVelocities = np.array(radialVelocities)
tangentialVelocities = np.array(tangentialVelocities)
LGdistances = np.array(LGdistances)
timingArgumentMasses = np.array(timingArgumentMasses)
data = np.array([
masses, timingArgumentMasses, H0s, inClusterH0s, outClusterH0s,
zeropoints, inClusterZeros, outClusterZeros, allDispersions,
unclusteredDispersions, clusterDispersions, radialVelocities,
tangentialVelocities, LGdistances
]).T
np.savetxt(datafile, data)
return data
# find LG analogue that is closest to the centre of the high res area
maxUncontaminatedDist = 0
for LGdict in LGdicts:
if LGdict['contaminationDistance'] > maxUncontaminatedDist:
maxUncontaminatedDist = LGdict['contaminationDistance']
d = LGdict
centre = d['centre']
if d['mass1'] < d['mass2']:
centre = d['cop1']
else:
centre = d['cop2']
cop = d['cops']
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
if max(distances) < 5.0:
print("distance criteria failed")
continue
distmask = np.array([distance < 5.0 and distance > 1.5 for distance in
distances])
distances = distances[distmask]
cop = cop[distmask]
directions = np.array([physUtils.sphericalCoordinates(pos - centre) for
pos in cop])
fitdata = clustering.precomputeDistances(directions)
meansep = np.mean([min(x[x>0]) for x in fitdata])
if mass[LG[0]] > mass[LG[1]]:
centreIndex = LG[1]
else:
centreIndex = LG[0]
centre = cop[centreIndex]
centreVel = staticVel[centreIndex]
massCentre = physUtils.massCentre(cop[LG[0]], cop[LG[1]], mass[LG[0]],
mass[LG[1]])
closestContDist = physUtils.findClosestDistance(
massCentre, contaminatedPositions)
if closestContDist < maxdist:
continue
distances = np.array([physUtils.distance(centre, c) for c in cop])
vel = physUtils.addExpansion(staticVel, cop, centre)
LGrelVel = vel[LG[0]] - vel[LG[1]]
LGrelVelComponents = physUtils.velocityComponents(
LGrelVel, cop[LG[1]] - cop[LG[0]])
LGdistance = physUtils.distance(cop[LG[0]], cop[LG[1]])
mask = np.array([d < maxdist and d > mindist for d in distances])
cop = cop[mask]
vel = vel[mask]
distances = distances[mask]
mass = mass[mask]
# radial velocities
radvel = np.array([
andromedaIndex = LG[1]
MWcop = cop[MWindex]
# massCentre = physUtils.massCentre(cop[MWindex], cop[andromedaIndex],
# mass[MWindex], mass[andromedaIndex])
closestContDist = physUtils.findClosestDistance(MWcop,
contaminatedPositions)
if closestContDist < maxdist:
excludedSims += 1
print("excluding " + simdir)
continue
LGvector = cop[andromedaIndex] - cop[MWindex]
centreVel = staticVel[MWindex]
distances = np.array([physUtils.distance(MWcop, c) for c in
cop])
vel = physUtils.addExpansion(staticVel, cop, MWcop)
LGrelVel = vel[LG[0]] - vel[LG[1]]
LGrelVelComponents = physUtils.velocityComponents(LGrelVel, cop[LG[1]]-cop[LG[0]])
LGdistance = physUtils.distance(cop[LG[0]], cop[LG[1]])
mask = np.array([d < maxdist and d >= mindist for d in
distances])
cop = cop[mask]
vel = vel[mask]
distances = distances[mask]
radvel = np.array([physUtils.velocityComponents(vel[j] - centreVel,
cop[j] - MWcop)[0]
groupNumbers = groupNumbers[contaminationMask] LGs = LGfinder.findLocalGroup(staticVel, mass, cop, quiet=True, outputAll=True) LG = LGs[LGfinder.chooseClosestToCentre(LGs, contaminationMask, fullCOP)] if mass[LG[0]] > mass[LG[1]]: centreIndex = LG[1] else: centreIndex = LG[0] centre = cop[centreIndex] centreVel = staticVel[centreIndex] closestContDist = physUtils.findClosestDistance(centre, contaminatedPositions) distances = np.array([physUtils.distance(centre, c) for c in cop]) vel = physUtils.addExpansion(staticVel, cop, centre) # LG mass from MW and andromeda M_big2 = mass[LG[0]] + mass[LG[1]] originalDistances = list(distances) # contamination cut distmask = distances < closestContDist cop = cop[distmask] vel = vel[distmask] distances = distances[distmask] # radial velocities radvel = np.array([physUtils.velocityComponents(vel[j] - centreVel,
massCentre = cop[main1ind] massCentreVel = vel[main1ind] # expansion vel = physUtils.addExpansion(vel, cop, massCentre) dist = np.zeros(len(vel)) speed = np.zeros(len(vel)) # find most distant contaminated closestContDistance = physUtils.findClosestDistance(massCentre, contaminatedPositions) # distances and velocities relative to centre for i in range(len(vel)): dist[i] = physUtils.distance(cop[i], massCentre) relVel = vel[i]-massCentreVel # velocity relative to movement of the mass CentreOfPotential diffInLocation = cop[i]-massCentre # vector defining difference in position speed[i] = physUtils.velocityComponents(relVel, diffInLocation)[0] # radial velocity colours = np.zeros((len(vel), 4)) colours[:, 3] = 1 ax.scatter(dist[dist<closestContDistance], speed[dist<closestContDistance], color=colours[dist<closestContDistance], s=1) #ax.set_xlim([0,closestContDistance]) ax.set_xlim([0, 6.1]) ax.set_ylim([-700, 900])
centreIndex = LG[1]
else:
centreIndex = LG[0]
centre = cop[centreIndex]
centreVel = staticVel[centreIndex]
vel = physUtils.addExpansion(staticVel, cop, centre)
LGrelVel = vel[LG[0]] - vel[LG[1]]
(rad, tan) = physUtils.velocityComponents(LGrelVel,
cop[LG[1]] - cop[LG[0]])
radvel[sim] = rad
tanvel[sim] = tan
distance[sim] = physUtils.distance(cop[LG[0]], cop[LG[1]])
totalmass[sim] = masses[LG[0]] + masses[LG[1]]
massdifference[sim] = math.fabs(masses[LG[0]] - masses[LG[1]])
bighalomasses[sim] = max(masses[LG[0]], masses[LG[1]])
smallhalomasses[sim] = min(masses[LG[0]], masses[LG[1]])
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
mass2mpc = sum(masses[np.where(distances <= 2)])
overdensity2mpc[sim] = (mass2mpc / (4.0 / 3.0 * math.pi *
(2**3))) / critical_density
#### plotting #####
rc('font', **{'family': 'serif', 'serif': ['Palatino']})
rc('text', usetex=True)
rcParams['text.latex.preamble'] = [r'\usepackage{wasysym}']
# rcParams.update({'font.size': 13})