from matplotlib import gridspec
import numpy as np
import pylab
import physUtils
from matplotlib import rc
from sklearn.cluster import DBSCAN
if __name__ == "__main__":
simpath = "/scratch/sawala/milkomedia_ii/milkomedia_26_DMO/groups_008_z000p000/"
saveloc = "../../kuvat/mollweide+hubble.pdf"
mindist = 1.5
maxdist = 5.0
eps = 0.16
ms = 4
LGdicts = LGfinder.readAndFind(simpath, output='haloes', expansion=True)
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']
# 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]
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)
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
rc('font', **{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
matplotlib.rcParams.update({'font.size': 13})
fig = plt.figure()
simlist_path = "../input/allButDuplicates-fullpath.txt"
simlist_file = open(simlist_path, 'r')
simlist = [line.strip() for line in simlist_file]
# simlist =["/scratch/sawala/milkomedia_ii/milkomedia_0_DMO/groups_008_z000p000/"]
for simdir in simlist:
print(simdir)
LGdicts = LGfinder.readAndFind(simdir, output='haloes', expansion=True)
# 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']
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]]: MWindex = LG[1] andromedaIndex = LG[0] else: MWindex = LG[0] andromedaIndex = LG[1] MWcop = cop[MWindex] # massCentre = physUtils.massCentre(cop[MWindex], cop[andromedaIndex], # mass[MWindex], mass[andromedaIndex]) closestContDist = physUtils.findClosestDistance(MWcop,
# 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]
centre = cop[centreIndex]
centreVel = staticVel[centreIndex]
import matplotlib.backends.backend_agg
import numpy as np
import os
import physUtils
import sys
from transitiondistance import findBestHubbleflow
if __name__ == "__main__":
dirname = "/scratch/sawala/milkomedia_ii/milkomedia_97_DMO/groups_008_z000p000/"
saveloc = ("/home/aajarven/Z-drive/duuni/extragal/gradu-yde/kuvat/"
"mollweide-anisotropy.pdf")
LGdicts = LGfinder.readAndFind(dirname, output='haloes')
# 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
if d['mass1'] < d['mass2']:
centre = d['cop1']
else:
centre = d['cop2']
cop = d['cops']
relvel = d['vels'] - d['centrevel']
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import matplotlib.backends.backend_agg
import numpy as np
import os
import physUtils
import sys
from transitiondistance import findBestHubbleflow
if __name__ == "__main__":
dirname = "/scratch/sawala/milkomedia_ii/milkomedia_97_DMO/groups_008_z000p000/"
saveloc = ("/home/aajarven/Z-drive/duuni/extragal/gradu-yde/kuvat/"
"mollweide-anisotropy.pdf")
LGdicts = LGfinder.readAndFind(dirname, output='haloes')
# 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
if d['mass1'] < d['mass2']:
centre = d['cop1']
else:
centre = d['cop2']
cop = d['cops']
relvel = d['vels'] - d['centrevel']
# 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
vel = vel[contaminationMask, :]
mass = mass[contaminationMask]
cop = fullCOP[contaminationMask, :]
groupNumbers = groupNumbers[contaminationMask]
LGs = LGfinder.findLocalGroup(vel,
mass,
cop,
quiet=True,
outputAll=True)
unmaskedLGs = LGfinder.maskedToUnmasked(LGs, cop, fullCOP)
bestLGindex = LGfinder.chooseClosestToCentre(unmaskedLGs,
contaminationMask,
fullCOP)
LG = LGs[bestLGindex]
(main1ind, main2ind) = LGfinder.orderIndices(LG, mass)
# center on MW
centre = cop[main1ind]
# distance range
distances = np.array([physUtils.distance(centre, pos) for pos in cop])
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] 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
edgecolors='k',
linewidth=0.9,
s=size)
ax.grid(b=True)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_title(r"$\varepsilon$={:.2f}".format(parameters[1]) +
", MinPts=" + str(parameters[0]))
if __name__ == "__main__":
infile = "/scratch/sawala/milkomedia_ii/milkomedia_97_DMO/groups_008_z000p000/"
saveloc = "../../kuvat/"
LGdicts = LGfinder.readAndFind(infile, output='haloes', expansion=True)
# 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']
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 vel = vel[contaminationMask,:] mass = mass[contaminationMask] cop = fullCOP[contaminationMask, :] groupNumbers = groupNumbers[contaminationMask] LGs = LGfinder.findLocalGroup(vel, mass, cop, quiet=True, outputAll=True) unmaskedLGs = LGfinder.maskedToUnmasked(LGs, cop, fullCOP) bestLGindex = LGfinder.chooseClosestToCentre(unmaskedLGs, contaminationMask, fullCOP) LG = LGs[bestLGindex] (main1ind, main2ind) = LGfinder.orderIndices(LG, mass) # center on MW massCentre = cop[main1ind] massCentreVel = vel[main1ind] # expansion vel = physUtils.addExpansion(vel, cop, massCentre) dist = np.zeros(len(vel)) speed = np.zeros(len(vel))
mask = (labels == l)
members = directions[mask]
ax.scatter(members[:, 0], members[:, 1], facecolors=col,
edgecolors='k', linewidth=0.9, s=size)
ax.grid(b=True)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_title(r"$\varepsilon$={:.2f}".format(parameters[1]) + ", MinPts=" +
str(parameters[0]))
if __name__ == "__main__":
infile = "/scratch/sawala/milkomedia_ii/milkomedia_97_DMO/groups_008_z000p000/"
saveloc = "../../kuvat/"
LGdicts = LGfinder.readAndFind(infile, output='haloes', expansion=True)
# 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']