def test_cosmologies(self):
cosmolist = [
'WMAP1', 'WMAP3', 'WMAP5', 'WMAP7', 'WMAP9', 'Planck13', 'Planck15'
]
conclist = [
8.84952005507872, 6.570929526400634, 7.663081978810533,
7.906741464320479, 8.883912548303279, 9.250262507139139,
9.044999285760362
]
for ival, cosmo in enumerate(cosmolist):
output = commah.run(cosmo, Mi=[1e12], verbose=True)
assert (np.allclose(output['c'].flatten()[0],
conclist[ival],
rtol=1e-3))
def __init__(self, Mvir, zvir, c, cosm):
self.Mvir = Mvir # Msun
self.zvir = zvir
self.c = c
self.cosm = cosm
fb = 1. / 6
if c == None:
print "Automatically generating concentration from COMMAH"
output = commah.run("Planck13", zi=zvir, Mi=Mvir,
z=[zvir]) # TODO use cosm
c = np.ravel(output['c'])[0]
self.c = c
self.Msun = 2.e33 # g
self.yr = 3.156e7 # s
self.pc = 3.086e18 # cm
self.kpc = 3.086e21 # cm
self.kB = 1.38e-16 # erg/K
self.Gc = 6.67e-8 # (cm/s)^2 cm/g
self.mu = 0.59 # for ionized primordial gas
self.mp = 1.67e-24 # g
self.Delta_c = 200.
self.rhoc = cosm.rho_c(zvir) # g/cc
self.rhovir = self.Delta_c * self.rhoc # g/cc
self.Rvir = (3 * Mvir * self.Msun /
(4 * np.pi * self.rhovir))**(1. / 3) / self.kpc # kpc
self.rs = self.Rvir / c # kpc
self.rho0 = self.rhovir * c * c * c / (self.F(c) * 3.) # g/cc
self.Vvir = np.sqrt(self.Gc * Mvir * self.Msun /
(self.Rvir * self.kpc)) / 1.e5 #km/s
self.Vmax = self.Vvir * np.sqrt(0.2162 * c / (self.F(c))) # km/s
self.RVmax = 2.1626 * self.rs # kpc TODO check
self.Tvir = self.mp * (self.Vvir * 1e5)**2 / (2. * self.kB) * self.mu
# for isothermal density profile
self.Vesc0 = np.sqrt(2) * self.Vvir * np.sqrt(self.c / self.F(self.c))
self.mbar = self.mu * self.mp
A = 2 * c / self.F(c)
denom = quad(lambda t: (1 + t)**(A / t) * t * t, 0, c)[0]
self.rhoiso0 = cosm.rho_c(zvir) * (200. / 3) * (
c**3) * fb * cosm.Om * np.exp(A) / denom
def __init__(self, Mvir, zvir, c, cosm):
self.Mvir = Mvir # Msun
self.zvir = zvir
self.c = c
self.cosm = cosm
fb = 1.0 / 6
if c == None:
print "Automatically generating concentration from COMMAH"
output = commah.run("Planck13", zi=zvir, Mi=Mvir, z=[zvir]) # TODO use cosm
c = np.ravel(output["c"])[0]
self.c = c
self.Msun = 2.0e33 # g
self.yr = 3.156e7 # s
self.pc = 3.086e18 # cm
self.kpc = 3.086e21 # cm
self.kB = 1.38e-16 # erg/K
self.Gc = 6.67e-8 # (cm/s)^2 cm/g
self.mu = 0.59 # for ionized primordial gas
self.mp = 1.67e-24 # g
self.Delta_c = 200.0
self.rhoc = cosm.rho_c(zvir) # g/cc
self.rhovir = self.Delta_c * self.rhoc # g/cc
self.Rvir = (3 * Mvir * self.Msun / (4 * np.pi * self.rhovir)) ** (1.0 / 3) / self.kpc # kpc
self.rs = self.Rvir / c # kpc
self.rho0 = self.rhovir * c * c * c / (self.F(c) * 3.0) # g/cc
self.Vvir = np.sqrt(self.Gc * Mvir * self.Msun / (self.Rvir * self.kpc)) / 1.0e5 # km/s
self.Vmax = self.Vvir * np.sqrt(0.2162 * c / (self.F(c))) # km/s
self.RVmax = 2.1626 * self.rs # kpc TODO check
self.Tvir = self.mp * (self.Vvir * 1e5) ** 2 / (2.0 * self.kB) * self.mu
# for isothermal density profile
self.Vesc0 = np.sqrt(2) * self.Vvir * np.sqrt(self.c / self.F(self.c))
self.mbar = self.mu * self.mp
A = 2 * c / self.F(c)
denom = quad(lambda t: (1 + t) ** (A / t) * t * t, 0, c)[0]
self.rhoiso0 = cosm.rho_c(zvir) * (200.0 / 3) * (c ** 3) * fb * cosm.Om * np.exp(A) / denom
def plotcommand(cosmology='WMAP5', plotname=None):
""" Example ways to interrogate the dataset and plot the commah output """
# Plot the c-M relation as a functon of redshift
xarray = 10**(np.arange(1, 15, 0.2))
yval = 'c'
# Specify the redshift range
zarray = np.arange(0, 5, 0.5)
xtitle = r"Halo Mass (M$_{sol}$)"
ytitle = r"Concentration"
linelabel = "z="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
plt.ylim([2, 30])
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology, zi=zval, Mi=xarray)
# Access the column yval from the data file
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(xarray,
yarray,
label=linelabel + str(zval),
color=colors[zind])
# Overplot the D08 predictions in black
ax.plot(xarray, commah.commah.cduffy(zval, xarray), color="black")
ax.set_xscale('log')
ax.set_yscale('log')
leg = ax.legend(loc=1)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_CM_relation.png'" % (plotname))
fig.savefig(plotname + "_CM_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the c-z relation as a function of mass (so always Mz=M0)
xarray = 10**(np.arange(0, 1, 0.05)) - 1
yval = 'c'
# Specify the mass range
zarray = 10**np.arange(6, 14, 2)
xtitle = r"Redshift"
ytitle = r"NFW Concentration"
linelabel = r"log$_{10}$ M$_{z}$(M$_{sol}$)="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology, zi=xarray, Mi=zval)
# Access the column yval from the data file
yarray = output[yval].flatten()
# Plot each line in turn with different colours
ax.plot(
xarray,
yarray,
label=linelabel + "{0:.1f}".format(np.log10(zval)),
color=colors[zind],
)
leg = ax.legend(loc=1)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_Cz_relation.png'" % (plotname))
fig.savefig(plotname + "_Cz_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the zf-z relation for different masses (so always Mz=M0)
xarray = 10**(np.arange(0, 1, 0.05)) - 1
yval = 'zf'
# Specify the mass range
zarray = 10**np.arange(6, 14, 2)
xtitle = r"Redshift"
ytitle = r"Formation Redshift"
linelabel = r"log$_{10}$ M$_{z}$(M$_{sol}$)="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology, zi=xarray, Mi=zval)
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(
xarray,
yarray,
label=linelabel + "{0:.1f}".format(np.log10(zval)),
color=colors[zind],
)
leg = ax.legend(loc=2)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_zfz_relation.png'" % (plotname))
fig.savefig(plotname + "_zfz_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the dM/dt-z relation for different masses (so always Mz=M0)
xarray = 10**(np.arange(0, 1, 0.05)) - 1
yval = 'dMdt'
# Specify the mass range
zarray = 10**np.arange(10, 14, 0.5)
xtitle = r"log$_{10}$ (1+z)"
ytitle = r"log$_{10}$ Accretion Rate M$_{sol}$ yr$^{-1}$"
linelabel = r"log$_{10}$ M$_z$(M$_{sol}$)="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
cosmo = commah.getcosmo(cosmology)
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology,
zi=xarray,
Mi=zval,
com=False,
mah=True)
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(
np.log10(xarray + 1.),
np.log10(yarray),
label=linelabel + "{0:.1f}".format(np.log10(zval)),
color=colors[zind],
)
# Plot the semi-analytic approximate formula from Correa et al 2015b
semianalytic_approx = 71.6 * (zval / 1e12) * (cosmo['h'] / 0.7) *\
(-0.24 + 0.75 * (xarray + 1)) * np.sqrt(
cosmo['omega_M_0'] * (xarray + 1)**3 + cosmo['omega_lambda_0'])
ax.plot(np.log10(xarray + 1),
np.log10(semianalytic_approx),
color='black')
leg = ax.legend(loc=2)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_dMdtz_relation.png'" % (plotname))
fig.savefig(plotname + "_dMdtz_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the dMdt-M relation as a function of redshift
xarray = 10**(np.arange(10, 14, 0.5))
yval = 'dMdt'
# Specify the redshift range
zarray = np.arange(0, 5, 0.5)
xtitle = r"Halo Mass M$_{sol}$"
ytitle = r"Accretion Rate M$_{sol}$ yr$^{-1}$"
linelabel = "z="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology,
zi=zval,
Mi=xarray,
com=False,
mah=True)
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(
xarray,
yarray,
label=linelabel + str(zval),
color=colors[zind],
)
ax.set_xscale('log')
ax.set_yscale('log')
leg = ax.legend(loc=2)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_MAH_M_relation.png'" % (plotname))
fig.savefig(plotname + "_MAH_M_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the (dM/M)dt-M relation as a function of redshift
xarray = 10**(np.arange(10, 14, 0.5))
yval = 'dMdt'
# Specify the redshift range
zarray = np.arange(0, 5, 0.5)
xtitle = r"Halo Mass M$_{sol}$"
ytitle = r"Specific Accretion Rate yr$^{-1}$"
linelabel = "z="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology,
zi=zval,
Mi=xarray,
mah=True,
com=False)
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(
xarray,
yarray / xarray,
label=linelabel + str(zval),
color=colors[zind],
)
ax.set_xscale('log')
ax.set_yscale('log')
leg = ax.legend(loc=1)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_specificMAH_M_relation.png'" % (plotname))
fig.savefig(plotname + "_specificMAH_M_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the Mz-z relation as a function of mass
# (so mass is decreasing to zero as z-> inf)
xarray = 10**(np.arange(0, 1, 0.05)) - 1
yval = 'Mz'
# Specify the mass range
zarray = 10**np.arange(10, 14, 0.5)
xtitle = r"Redshift"
ytitle = r"M(z) (M$_{sol}$)"
linelabel = r"log$_{10}$ M$_{0}$(M$_{sol}$)="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology, zi=0, Mi=zval, z=xarray)
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(
xarray,
yarray,
label=linelabel + "{0:.1f}".format(np.log10(zval)),
color=colors[zind],
)
ax.set_yscale('log')
leg = ax.legend(loc=1)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_Mzz_relation.png'" % (plotname))
fig.savefig(plotname + "_Mzz_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
# Plot the Mz/M0-z relation as a function of mass
xarray = 10**(np.arange(0, 1, 0.02)) - 1
yval = 'Mz'
# Specify the mass range
zarray = 10**np.arange(10, 14, 0.5)
xtitle = r"Redshift"
ytitle = r"log$_{10}$ M(z)/M$_{0}$"
linelabel = r"log$_{10}$ M$_{0}$(M$_{sol}$)="
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(xtitle)
ax.set_ylabel(ytitle)
colors = cm.rainbow(np.linspace(0, 1, len(zarray)))
for zind, zval in enumerate(zarray):
output = commah.run(cosmology=cosmology, zi=0, Mi=zval, z=xarray)
yarray = output[yval].flatten()
# Plot each line in turn with different colour
ax.plot(
xarray,
np.log10(yarray / zval),
label=linelabel + "{0:.1f}".format(np.log10(zval)),
color=colors[zind],
)
leg = ax.legend(loc=3)
# Make box totally transparent
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
for label in leg.get_texts():
label.set_fontsize('small') # the font size
for label in leg.get_lines():
label.set_linewidth(4) # the legend line width
if plotname:
fig.tight_layout(pad=0.2)
print("Plotting to '%s_MzM0z_relation.png'" % (plotname))
fig.savefig(plotname + "_MzM0z_relation.png", dpi=fig.dpi * 5)
else:
plt.show()
return ("Done")
def runcommand(cosmology='WMAP5'):
""" Example interface commands """
# Return the WMAP5 cosmology concentration predicted for
# z=0 range of masses
Mi = [1e8, 1e9, 1e10]
zi = 0
print("Concentrations for haloes of mass %s at z=%s" % (Mi, zi))
output = commah.run(cosmology=cosmology, zi=zi, Mi=Mi)
print(output['c'].flatten())
# Return the WMAP5 cosmology concentration predicted for
# z=0 range of masses AND cosmological parameters
Mi = [1e8, 1e9, 1e10]
zi = 0
print("Concentrations for haloes of mass %s at z=%s" % (Mi, zi))
output, cosmo = commah.run(cosmology=cosmology,
zi=zi,
Mi=Mi,
retcosmo=True)
print(output['c'].flatten())
print(cosmo)
# Return the WMAP5 cosmology concentration predicted for MW
# mass (2e12 Msol) across redshift
Mi = 2e12
z = [0, 0.5, 1, 1.5, 2, 2.5]
output = commah.run(cosmology=cosmology, zi=0, Mi=Mi, z=z)
for zval in z:
print("M(z=0)=%s has c(z=%s)=%s" %
(Mi, zval, output[output['z'] == zval]['c'].flatten()))
# Return the WMAP5 cosmology concentration predicted for MW
# mass (2e12 Msol) across redshift
Mi = 2e12
zi = [0, 0.5, 1, 1.5, 2, 2.5]
output = commah.run(cosmology=cosmology, zi=zi, Mi=Mi)
for zval in zi:
print("M(z=%s)=%s has concentration %s" %
(zval, Mi, output[(output['zi'] == zval)
& (output['z'] == zval)]['c'].flatten()))
# Return the WMAP5 cosmology concentration and
# rarity of high-z cluster
Mi = 2e14
zi = 6
output = commah.run(cosmology=cosmology, zi=zi, Mi=Mi)
print("Concentrations for haloes of mass %s at z=%s" % (Mi, zi))
print(output['c'].flatten())
print("Mass variance sigma of haloes of mass %s at z=%s" % (Mi, zi))
print(output['sig'].flatten())
print("Fluctuation for haloes of mass %s at z=%s" % (Mi, zi))
print(output['nu'].flatten())
# Return the WMAP5 cosmology accretion rate prediction
# for haloes at range of redshift and mass
Mi = [1e8, 1e9, 1e10]
zi = [0]
z = [0, 0.5, 1, 1.5, 2, 2.5]
output = commah.run(cosmology=cosmology, zi=zi, Mi=Mi, z=z)
for Mval in Mi:
print("dM/dt for halo of mass %s at z=%s across redshift %s is: " %
(Mval, zi, z))
print(output[output['Mi'] == Mval]['dMdt'].flatten())
# Return the WMAP5 cosmology Halo Mass History for haloes with M(z=0) = 1e8
M = [1e8]
z = [0, 0.5, 1, 1.5, 2, 2.5]
print("Halo Mass History for z=0 mass of %s across z=%s" % (M, z))
output = commah.run(cosmology=cosmology, zi=0, Mi=M, z=z)
print(output['Mz'].flatten())
# Return the WMAP5 cosmology formation redshifts for haloes at
# range of redshift and mass
M = [1e8, 1e9, 1e10]
z = [0]
print("Formation Redshifts for haloes of mass %s at z=%s" % (M, z))
output = commah.run(cosmology=cosmology, zi=0, Mi=M, z=z)
for Mval in M:
print(output[output['Mi'] == Mval]['zf'].flatten())
return ("Done")
def test_startingz(self):
zlist = np.array([0, 1, 2])
conclist = np.array([4.55295, 4.43175, 4.26342])
output = commah.run('WMAP5', zi=zlist, Mi=[1e12], z=2)
assert (np.allclose(output['c'].flatten(), conclist, rtol=1e-3))
def test_evolution(self):
zlist = np.array([0, 1, 2])
conclist = np.array([7.66308, 5.70009, 4.55295])
output = commah.run('WMAP5', zi=[0], Mi=[1e12], z=zlist)
assert (np.allclose(output['c'].flatten(), conclist, rtol=1e-3))
#!/usr/bin/env python
from __future__ import absolute_import, division, print_function
import commah
# Here's an example run script, modify as preferred
commah.run('WMAP5',
zi=0,
Mi=[1e8, 1e9, 1e10, 1e11, 1e12, 1e13, 1e14],
z=[0, 0.5, 1, 1.5, 2],
filename='WMAP5_Test.txt')
def main(argv):
z_list_lgal = loadtxt(snapfile)
lastsnap = len(z_list_lgal)-1
gadget_m_conv = 1.e10
hubble_h = 0.7
#m6 = [9.,10.0,10.5,11.0,11.5]
color = 'r'#['r''r','r','r','r','r']
minz = z_start
maxz = 30.
dz = 0.25
#limit = 300
zlist = arange(minz,maxz,dz)
#rc('text', usetex=True)
(nTrees,nHalos,nTreeHalos,output_Halos,output_HaloIDs) = read_lgal_input_fulltrees_withids(folder,lastsnap,firstfile,lastfile,verbose=True)
rootindex = numpy.cumsum(nTreeHalos)-nTreeHalos
lastleafindex = numpy.cumsum(nTreeHalos)
for j in range(len(m6)):
fig = figure()
ax = fig.add_subplot(111)
t_m6 = m6[j]
maxmass = 15.0
minmass = 9.0
mbin = len(zlist)
dm = (maxmass-minmass)/mbin
mz = commah.run('planck15',zi=z_start,Mi=10.**t_m6,z=zlist)['Mz'][0]
mass = zeros(len(z_list_lgal),dtype=float64)
count = zeros(len(z_list_lgal),dtype=int64)
mask = ones(len(z_list_lgal),dtype=int32)
count_2d = zeros((mbin,len(zlist)),dtype=int64)
l_m = t_m6-0.1
r_m = t_m6+0.1
stsn = 1000
for ii in range(len(nTreeHalos)):
listrange = numpy.arange(rootindex[ii],lastleafindex[ii])
r_list = numpy.where((log10(output_Halos[listrange]['M_Crit200']*gadget_m_conv) <=r_m) & (log10(output_Halos[listrange]['M_Crit200']*gadget_m_conv) >=l_m) & (output_Halos[listrange]['SnapNum']==lastsnap) & (output_HaloIDs[listrange]["HaloID"] == output_HaloIDs[listrange]["FirstHaloInFOFgroup"]))[0]
#print "r_list",listrange[r_list]
for i in r_list:
root = listrange[i]
M0 = output_Halos[root]["M_Crit200"]*Gadget2Msun
nexthaloid = output_Halos[root]['FirstProgenitor']
if (nexthaloid > -1) & (output_Halos[root]['Len'] >= limit):
bin_m = (log10(M0) - minmass)/dm
mass[output_Halos[root]["SnapNum"]] += M0
count[output_Halos[root]["SnapNum"]] += 1
if (bin_m < mbin) & (bin_m >= 0):
z = z_list_lgal[output_Halos[root]["SnapNum"]]
bin_z = (z-minz)/dz
if (z >= minz) & (z < maxz):
count_2d[bin_m,bin_z] += 1
while nexthaloid > -1:
nexthalo = output_Halos[rootindex[ii]+nexthaloid]
if nexthalo['Len'] >= limit:
mass[nexthalo["SnapNum"]] += nexthalo["M_Crit200"]*Gadget2Msun
bin_m = (log10(nexthalo["M_Crit200"]*Gadget2Msun) - minmass)/dm
count[nexthalo["SnapNum"]] += 1
if (bin_m < mbin) & (bin_m >= 0):
z = z_list_lgal[nexthalo["SnapNum"]]
bin_z = (z-minz)/dz
if (z >= minz) & (z < maxz):
count_2d[bin_m,bin_z] += 1
nexthaloid = nexthalo['FirstProgenitor']
stsn = min(stsn,nexthalo["SnapNum"])
else:
nexthaloid = -1
#mask = count > count[len(z_list_lgal)-1]/2
#print count_2d
numpy.savetxt("count_2D_"+str(t_m6)+"_"+str(limit)+"p.txt",count_2d)
numpy.savetxt("summass_"+str(t_m6)+"_"+str(limit)+"p.txt",mass)
numpy.savetxt("countcol_"+str(t_m6)+"_"+str(limit)+"p.txt",count)
print "min snap",stsn
#mass = mass*mask
if numpy.sum(count) > 0:
ax.plot(z_list_lgal,log10(mass/count), color='r',linestyle='--',label="Average ("+str(limit)+"+ particles)")
mask = count > count[len(z_list_lgal)-1]/2
mass = mass*mask
ax.plot(z_list_lgal,log10(mass/count), color='k',linestyle='-',label="Average ("+str(limit)+"+ particles,50\% limit)")
extent = [minz,maxz,minmass,maxmass]
#print extent
im = ax.imshow(count_2d,origin='lower',aspect='auto',norm=LogNorm(),extent=extent,interpolation='none')
fig.colorbar(im,ax=ax)
ax.plot(zlist,log10(mz),color='r',linestyle = '-',label="Correa et al. (2015)")
ax.set_xlim([minz,maxz])
ax.set_ylim([minmass,maxmass])
ax.set_xlabel(r"$z$")
ax.set_ylabel(r"$\log(h M_{200c}/M_\odot)$")
leg = ax.legend(loc='best', handlelength = 10,ncol=1, fancybox=True, prop={'size':10})
leg.get_frame().set_linewidth(0)
#fig.savefig(str(t_m6)+"_"+str(limit)+"p.pgf")
fig.savefig(str(t_m6)+"_"+str(limit)+"p.pdf")
close(fig)
#ax.imshow(count_2d,origin='lower')
#fig.show()
return 0