notebook.community

Edit and run 





In [1]:



    


import pandas as pd
import numpy as np
import os
%matplotlib inline
import matplotlib.pyplot as plt
from haloPropertyAnalysis.comparisons import Sim, CompareSims



    











In [28]:



    


from lsscosmo import halomassfunction as hmf
from lsscosmo import psutils as psu
from lsscosmo import hacc
from utils import plotutils as pu

Define Models and Transfer functions at z=0





In [73]:



    


example_data = '/Users/rbiswas/data/datastar'
datadir = os.path.join(example_data,'simulations/')
M000simtransfer = os.path.join(datadir,  "MiraU/Grid/M000/CAMB/cmbM000.tf")

M000indatfile = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/indat.params")
M000n1indatfile = "/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/L2100/HACC000/run/indat.params"

M000simtransfer = os.path.join(datadir,  "MiraU/Grid/M000/CAMB/cmbM000.tf")
M000n1Simtransfer='/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/CAMB/cmbM001n.tf'

M000 = hacc.haccsim(M000indatfile, name = "M000")
M000n1 = hacc.haccsim(M000n1indatfile, name = "M000n1")

M011indatfile = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/indat.params")
M011Simtransfer = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/cmbM011.tf")
M011Simtransfer = '/Users/rbiswas/data/datastar/simulations/MiraU/Grid/MiraU/Grid/M011/L2100/HACC000/run/cmbM011.tf'



    


















    






printing omeganu **************
0.0
From indat  0.0
From cosmo  0.0
printing omeganu **************
0.01983733
From indat  0.01983733
From cosmo  0.01983733

















In [ ]:



    






    











In [74]:



    


M011 = hacc.haccsim(M011indatfile, name='M011')



    


















    






printing omeganu **************
0.0
From indat  0.0
From cosmo  0.0

















In [4]:



    


M000ps = psu.powerspectrum(koverh=None,
                           pstype="matter",
                           sigma8type="matter",
                           asciifile=M000simtransfer,
                           cosmo=M000.cosmo)
M000n1cbmps = psu.powerspectrum(koverh=None,
                               pstype="cbmatter",
                               sigma8type="matter",
                               asciifile=M000n1Simtransfer,
                               cosmo=M000n1.cosmo)



    


















    






/Users/rbiswas/.local/lib/python2.7/site-packages/lsscosmo/psutils.py:579: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  if koverh == None:

Define location of Halo Outputs from Simulation





In [5]:



    


datastar = '/Users/rbiswas/data/datastar'
M000n1dir = os.path.join(datastar, 'clustering')
M000dir = os.path.join(datastar, 'Testing_z2')
M000n1D

Describe Simulations





In [7]:



    


LCDM = Sim(simulationDir=M000dir, name='M000')
nLCDM = Sim(simulationDir=M000n1dir, name='M000n1')
# EnuLCDM = Sim(simulationDir=M011dir, name='M011')



    


















    






/Users/rbiswas/data/datastar/Testing_z2
/Users/rbiswas/data/datastar/clustering

Suppression From Simulation

Neutrino Mass Function / LCDM mass function





In [86]:



    


cmpLCDMnLCDM = CompareSims(LCDM, nLCDM)
df = cmpLCDMnLCDM.comparisons
df.to_csv('M000M000n1comparisons.csv', index=None)
fig = cmpLCDMnLCDM.plotSuppression()

Power Spectrum of cb part (smooth nu approximation)





In [75]:



    


M000ps = psu.powerspectrum(koverh=None,
                           pstype="matter",
                           sigma8type="matter",
                           asciifile=M000simtransfer,
                           cosmo=M000.cosmo)
M000n1cbmps = psu.powerspectrum(koverh=None,
                               pstype="cbmatter",
                               sigma8type="matter",
                               asciifile=M000n1Simtransfer,
                               cosmo=M000n1.cosmo)
M011cbmps = psu.powerspectrum(koverh=None,
                               pstype="cbmatter",
                               sigma8type="matter",
                               asciifile=M011Simtransfer,
                               cosmo=M011.cosmo)



    











In [80]:



    


M011cbmps = psu.powerspectrum(koverh=None,
                               pstype="cbmatter",
                               sigma8type="matter",
                               asciifile=M011Simtransfer,
                               cosmo=M011.cosmo)



    











In [79]:



    


from lsscosmo import psutils as psu

Calculate Halo Mass Functions at different time steps





In [91]:



    


massininvh = np.logspace(12, 15)
M000h = M000.cosmo.h
M000n1h = M000n1.cosmo.h
M011h = M011.cosmo.h
step=499
MF_M000_0 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,
                      z=M000.steptoredshift(step))/ M000h**3
MF_M000n1_0 = hmf.dndlnM0(massininvh/M000n1h, ps=M000n1cbmps, cosmo=M000n1.cosmo,
                      z=M000n1.steptoredshift(step))/ M000n1h**3
MF_M011_0 = hmf.dndlnM0(massininvh/M011h, ps=M000n1cbmps, cosmo=M011.cosmo,
                        z=M011.steptoredshift(step))/ M011h**3


step=247
MF_M000_1 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,
                      z=M000.steptoredshift(step))/ M000h**3
MF_M000n1_1 = hmf.dndlnM0(massininvh/M000n1h, ps=M000n1cbmps, cosmo=M000n1.cosmo,
                      z=M000n1.steptoredshift(step))/ M000n1h**3
MF_M011_1 = hmf.dndlnM0(massininvh/h, ps=M000n1cbmps, cosmo=M011.cosmo,
                        z=M011.steptoredshift(step))/ M011h**3

step=163
MF_M000_2 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,
                      z=M000.steptoredshift(step))/ M000h**3
MF_M000n1_2 = hmf.dndlnM0(massininvh/M000n1h, ps=M000n1cbmps, cosmo=M000n1.cosmo,
                      z=M000n1.steptoredshift(step))/ M000n1h**3
MF_M011_2 = hmf.dndlnM0(massininvh/M011h, ps=M000n1cbmps, cosmo=M011.cosmo,
                        z=M011.steptoredshift(step))/ M011h**3



    











In [20]:



    


plt.loglog(massininvh/M000h , MF_M000_0, '-k', label='LCDM')
plt.loglog(massininvh/M000h , MF_M000_1, '-k')
plt.loglog(massininvh/M000h , MF_M000_2, '-k')
plt.loglog(massininvh/M000n1h , MF_M000n1_0, '-r', label='nLCDM')
plt.loglog(massininvh/M000n1h , MF_M000n1_1, '-r')
plt.loglog(massininvh/M000n1h , MF_M000n1_2, '-r')
plt.grid(True)
plt.legend(loc='best')



    


















    
Out[20]:








<matplotlib.legend.Legend at 0x10c07a6d0>










    
























In [87]:



    


fig



    


















    
Out[87]:
























In [88]:



    


fig.axes[1].set_ylabel('ratio of mass function for neutrino to mass funciton for LCDM')



    


















    
Out[88]:








<matplotlib.text.Text at 0x10dce7290>

















In [89]:



    


example_data = '/Users/rbiswas/data/datastar'
datadir = os.path.join(example_data,'simulations/')
M000simtransfer = os.path.join(datadir,  "MiraU/Grid/M000/CAMB/cmbM000.tf")

M000indatfile = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/indat.params")
M000n1indatfile = "/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/L2100/HACC000/run/indat.params"

M000simtransfer = os.path.join(datadir,  "MiraU/Grid/M000/CAMB/cmbM000.tf")
M000n1Simtransfer='/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/CAMB/cmbM001n.tf'

M000 = hacc.haccsim(M000indatfile, name = "M000")
M000n1 = hacc.haccsim(M000n1indatfile, name = "M000n1")



    


















    






printing omeganu **************
0.0
From indat  0.0
From cosmo  0.0
printing omeganu **************
0.01983733
From indat  0.01983733
From cosmo  0.01983733

















In [92]:



    


fig.axes[0].plot(massininvh, MF_M000n1_0 / MF_M000_0, 'k')
fig.axes[1].plot(massininvh, MF_M000n1_1 / MF_M000_1, 'k')
fig.axes[2].plot(massininvh, MF_M000n1_2 / MF_M000_2, 'k')

fig.axes[0].plot(massininvh, MF_M011_0 / MF_M000_0, 'r')
fig.axes[1].plot(massininvh, MF_M011_1 / MF_M000_1, 'r')
fig.axes[2].plot(massininvh, MF_M011_2 / MF_M000_2, 'r')



    


















    
Out[92]:








[<matplotlib.lines.Line2D at 0x10cc442d0>]

















In [93]:



    


fig



    


















    
Out[93]:
























In [58]:



    


fig.savefig('suppression_M000n1.pdf')



    











In [38]:



    


from interfacecosmology import psutils as psu



    











In [54]:



    


M000n1cbmps = psu.powerspectrum(koverh=None,
                               pstype="cbmatter",
                               sigma8type="matter",
                               asciifile=M000n1Simtransfer,
                               cosmo=M000n1.cosmo)
M000n1cbps = psu.powerspectrum(koverh=M000n1cbmps[0],
                               pstype="cb",
                               sigma8type="matter",
                               asciifile=M000n1Simtransfer,
                               cosmo=M000n1.cosmo)
M000n1ps = psu.powerspectrum(koverh=M000n1cbmps[0],
                             pstype="matter",
                             sigma8type="matter",
                             asciifile=M000n1Simtransfer,
                             cosmo=M000n1.cosmo)
M000n1cbmps_1 = psu.powerspectrum(koverh=None,
                                  z=1.0,
                                  pstype="cbmatter",
                                  sigma8type="matter",
                                  asciifile=M000n1Simtransfer,
                                  cosmo=M000n1.cosmo)
M000n1cbps_1 = psu.powerspectrum(koverh=M000n1cbmps[0],
                                 pstype="cb",
                                 meth
                                 z=1.0,
                                 sigma8type="matter",
                                 asciifile=M000n1Simtransfer,
                                 cosmo=M000n1.cosmo)
M000n1ps_1 = psu.powerspectrum(koverh=M000n1cbmps[0],
                               pstype="matter",
                               z=1.0,
                               sigma8type="matter",
                               asciifile=M000n1Simtransfer,
                               cosmo=M000n1.cosmo)



    











In [51]:



    


fig, ax0, ax1 = pu.settwopanel(setdifflimits=[0.4, 1.4])
ax0.plot(M000n1cbmps[0], M000n1cbmps[1], 'k')
ax0.plot(M000n1cbmps[0], M000n1cbps[1], 'r')
ax1.plot(M000n1cbmps[0], M000n1cbmps[1]/M000n1cbps[1], '-k')
ax1.plot(M000n1cbmps[0], M000n1cbmps[1]/M000n1ps[1], '-r')
ax0.set_xscale('log')
ax0.set_yscale('log')
ax1.set_xscale('log')



    


















    
























In [57]:



    


plt.plot(M000n1cbmps[0], M000n1cbmps_1[1]/M000n1cbmps[1], '-k')
plt.ylim(0.5, 1.5)



    


















    
Out[57]:








(0.5, 1.5)










    
























In [46]:



    


delta_omegam = M000n1.cosmo.Om0 - (M000n1.cosmo.Ob0 + M000n1.cosmo.Oc0)



    











In [47]:



    


delta_omegam /



    


















    
Out[47]:








0.019837329999999986

Scratch





In [17]:



    


plt.errorbar(LCDM.mf_results(247).FOF_Mass,
             LCDM.mf_results(247).dndlnM, 
             yerr=LCDM.mf_results(247).frac_Err * LCDM.mf_results(499).dndlnM,
            fmt='o')
plt.xscale('log')
plt.yscale('log')



    


















    
























In [18]:



    


LCDM.mf_results(499)



    


















    
Out[18]:










  
    

      
      FOF_Mass
      numClusters
      dndlnM
      frac_Err
      oneoversigma
      f(sigma)
      fsigma_fit
      dn/dln_Mfit
    

  
  
    

      0
      3.484101e+12
      1082834
      0.001069
      0.0010
      0.6118
      0.3197
      0.3211
      0.001073
    

    

      1
      4.732952e+12
      819157
      0.000808
      0.0011
      0.6426
      0.3207
      0.3221
      0.000812
    

    

      2
      6.423003e+12
      620573
      0.000612
      0.0013
      0.6757
      0.3210
      0.3222
      0.000615
    

    

      3
      8.715419e+12
      466646
      0.000460
      0.0015
      0.7114
      0.3194
      0.3212
      0.000463
    

    

      4
      1.183044e+13
      352417
      0.000348
      0.0017
      0.7501
      0.3188
      0.3186
      0.000348
    

    

      5
      1.606262e+13
      262157
      0.000259
      0.0020
      0.7920
      0.3137
      0.3143
      0.000259
    

    

      6
      2.180363e+13
      193991
      0.000191
      0.0023
      0.8375
      0.3067
      0.3079
      0.000192
    

    

      7
      2.960028e+13
      141378
      0.000140
      0.0027
      0.8869
      0.2954
      0.2989
      0.000141
    

    

      8
      4.016602e+13
      102901
      0.000102
      0.0031
      0.9407
      0.2840
      0.2872
      0.000103
    

    

      9
      5.449870e+13
      73574
      0.000073
      0.0037
      0.9993
      0.2681
      0.2722
      0.000074
    

    

      10
      7.396668e+13
      51626
      0.000051
      0.0044
      1.0634
      0.2484
      0.2539
      0.000052
    

    

      11
      1.003738e+14
      35973
      0.000035
      0.0053
      1.1336
      0.2284
      0.2320
      0.000036
    

    

      12
      1.360173e+14
      23856
      0.000024
      0.0065
      1.2103
      0.1996
      0.2070
      0.000024
    

    

      13
      1.846259e+14
      15584
      0.000015
      0.0080
      1.2949
      0.1721
      0.1790
      0.000016
    

    

      14
      2.504609e+14
      9823
      0.000010
      0.0101
      1.3881
      0.1431
      0.1491
      0.000010
    

    

      15
      3.394858e+14
      5891
      0.000006
      0.0130
      1.4905
      0.1130
      0.1187
      0.000006
    

    

      16
      4.601758e+14
      3385
      0.000003
      0.0172
      1.6039
      0.0856
      0.0892
      0.000003
    

    

      17
      6.222863e+14
      1780
      0.000002
      0.0237
      1.7285
      0.0592
      0.0627
      0.000002
    

    

      18
      8.437435e+14
      814
      0.000001
      0.0350
      1.8680
      0.0356
      0.0402
      0.000001

For LCDM frac errors match calculation to rounding





In [19]:



    


xx = LCDM.mf_results(499).copy(deep=True)



    











In [20]:



    


xx['recalc'] = xx.numClusters**(-0.5)
xx[['recalc', 'frac_Err']]



    


















    
Out[20]:










  
    

      
      recalc
      frac_Err
    

  
  
    

      0
      0.000961
      0.0010
    

    

      1
      0.001105
      0.0011
    

    

      2
      0.001269
      0.0013
    

    

      3
      0.001464
      0.0015
    

    

      4
      0.001685
      0.0017
    

    

      5
      0.001953
      0.0020
    

    

      6
      0.002270
      0.0023
    

    

      7
      0.002660
      0.0027
    

    

      8
      0.003117
      0.0031
    

    

      9
      0.003687
      0.0037
    

    

      10
      0.004401
      0.0044
    

    

      11
      0.005272
      0.0053
    

    

      12
      0.006474
      0.0065
    

    

      13
      0.008011
      0.0080
    

    

      14
      0.010090
      0.0101
    

    

      15
      0.013029
      0.0130
    

    

      16
      0.017188
      0.0172
    

    

      17
      0.023702
      0.0237
    

    

      18
      0.035050
      0.0350

Roughly matches expectation





In [21]:



    


df.M000_MF * df.numClusters1**(-0.5) / df.ApproxErrorHigh



    


















    
Out[21]:








0     8.155155e-04
1     6.174969e-04
2     4.708310e-04
3     3.545583e-04
4     2.705113e-04
5     2.025196e-04
6     1.505353e-04
7     1.101655e-04
8     8.128796e-05
9     5.825866e-05
10    4.102348e-05
11    2.925124e-05
12    1.921025e-05
13    1.266308e-05
14    8.149507e-06
15    4.854172e-06
16    2.818878e-06
17    1.454904e-06
18    6.561413e-07
0     6.759506e-04
1     4.834035e-04
2     3.470978e-04
3     2.453769e-04
4     1.710966e-04
5     1.161154e-04
6     7.721547e-05
7     5.001131e-05
8     3.143715e-05
9     1.906971e-05
10    1.100809e-05
11    5.755816e-06
12    2.933857e-06
13    1.433011e-06
14    5.435414e-07
15    1.767254e-07
16    4.643254e-08
17    1.734052e-08
18             NaN
0     3.485959e-04
1     2.235978e-04
2     1.417031e-04
3     8.706170e-05
4     5.152754e-05
5     2.914194e-05
6     1.545438e-05
7     7.808857e-06
8     3.897166e-06
9     1.582751e-06
10    5.193547e-07
11    1.877631e-07
12    4.663675e-08
13    9.703840e-09
14    5.334509e-09
15             NaN
dtype: float64

















In [22]:



    


df.M000_MF * df.numClusters1**(-0.5) / df.ApproxErrorLow



    


















    
Out[22]:








0     8.163213e-04
1     6.181987e-04
2     4.714475e-04
3     3.550941e-04
4     2.709837e-04
5     2.029307e-04
6     1.508913e-04
7     1.104712e-04
8     8.155395e-05
9     5.848444e-05
10    4.121374e-05
11    2.941539e-05
12    1.934213e-05
13    1.277129e-05
14    8.238142e-06
15    4.922347e-06
16    2.871506e-06
17    1.492376e-06
18    6.811491e-07
0     6.767114e-04
1     4.840471e-04
2     3.476475e-04
3     2.458431e-04
4     1.714884e-04
5     1.164395e-04
6     7.748191e-05
7     5.022741e-05
8     3.161055e-05
9     1.920608e-05
10    1.111333e-05
11    5.830527e-06
12    2.988619e-06
13    1.472113e-06
14    5.667058e-07
15    1.900352e-07
16    5.237010e-08
17    2.177197e-08
18             NaN
0     3.491648e-04
1     2.240540e-04
2     1.420678e-04
3     8.734961e-05
4     5.175043e-05
5     2.931129e-05
6     1.557799e-05
7     7.897965e-06
8     3.962879e-06
9     1.623380e-06
10    5.406050e-07
11    2.016607e-07
12    5.318188e-08
13    1.290975e-08
14    8.169807e-09
15             NaN
dtype: float64

















In [23]:



    


from astropy.cosmology import Planck13 as cosmo



    











In [24]:



    


M000indatfile = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/indat.params")



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-24-0aef06cde73c> in <module>()
----> 1 M000indatfile = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/indat.params")

NameError: name 'datadir' is not defined
















In [33]:



    


from lsscosmo import halomassfunction as hmf
from lsscosmo import psutils as psu
from lsscosmo import hacc

example_data = '/Users/rbiswas/data/datastar'
datadir = os.path.join(example_data,'simulations/')
M000simtransfer = os.path.join(datadir,  "MiraU/Grid/M000/CAMB/cmbM000.tf")

M000indatfile = os.path.join(datadir, "MiraU/Grid/M000/L2100/HACC001/run/indat.params")
M000n1indatfile = "/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/L2100/HACC000/run/indat.params"

M000simtransfer = os.path.join(datadir,  "MiraU/Grid/M000/CAMB/cmbM000.tf")
M000n1Simtransfer='/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/CAMB/cmbM001n.tf'

M000 = hacc.haccsim(M000indatfile, name = "M000")
M000n1 = hacc.haccsim(M000n1indatfile, name = "M000n1")



    











In [42]:



    


M000ps = psu.powerspectrum(koverh=None,
                           pstype="matter",
                           sigma8type="matter",
                           asciifile=M000simtransfer,
                           cosmo=M000.cosmo)
M000n1cbmps = psu.powerspectrum(koverh=None,
                               pstype="cbmatter",
                               sigma8type="matter",
                               asciifile=M000n1Simtransfer,
                               cosmo=M000n1.cosmo)



    











In [52]:



    


massininvh = np.logspace(12, 15)
M000h = M000.cosmo.h
step=499
MF_M000_0 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,
                      z=M000.steptoredshift(step))/ M000h**3
h = M000n1.cosmo.h
MF_M000n1_0 = hmf.dndlnM0(massininvh/h, ps=M000n1cbmps, cosmo=M000n1.cosmo,
                      z=M000n1.steptoredshift(step))/ h**3
step=247
MF_M000_1 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,
                      z=M000.steptoredshift(step))/ M000h**3
h = M000n1.cosmo.h
MF_M000n1_1 = hmf.dndlnM0(massininvh/h, ps=M000n1cbmps, cosmo=M000n1.cosmo,
                      z=M000n1.steptoredshift(step))/ h**3
step=163
MF_M000_2 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,
                      z=M000.steptoredshift(step))/ M000h**3
h = M000n1.cosmo.h
MF_M000n1_2 = hmf.dndlnM0(massininvh/h, ps=M000n1cbmps, cosmo=M000n1.cosmo,
                      z=M000n1.steptoredshift(step))/ h**3



plt.plot(massininvh, MF_M000n1 / MF_M000)



    


















    
Out[52]:








[<matplotlib.lines.Line2D at 0x1096a0c50>]










    
























In [31]:



    


dir(LCDM)



    


















    
Out[31]:








['__class__',
 '__delattr__',
 '__dict__',
 '__doc__',
 '__format__',
 '__getattribute__',
 '__hash__',
 '__init__',
 '__module__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__setattr__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 '__weakref__',
 'filelist',
 'interpolated_MF',
 'massrange',
 'mf_fname',
 'mf_results',
 'name',
 'simDir']

















In [ ]:



    






    











In [44]:



    


massininvh = np.logspace(12,15)



    











In [45]:



    


massininvh



    


















    
Out[45]:








array([  1.00000000e+12,   1.15139540e+12,   1.32571137e+12,
         1.52641797e+12,   1.75751062e+12,   2.02358965e+12,
         2.32995181e+12,   2.68269580e+12,   3.08884360e+12,
         3.55648031e+12,   4.09491506e+12,   4.71486636e+12,
         5.42867544e+12,   6.25055193e+12,   7.19685673e+12,
         8.28642773e+12,   9.54095476e+12,   1.09854114e+13,
         1.26485522e+13,   1.45634848e+13,   1.67683294e+13,
         1.93069773e+13,   2.22299648e+13,   2.55954792e+13,
         2.94705170e+13,   3.39322177e+13,   3.90693994e+13,
         4.49843267e+13,   5.17947468e+13,   5.96362332e+13,
         6.86648845e+13,   7.90604321e+13,   9.10298178e+13,
         1.04811313e+14,   1.20679264e+14,   1.38949549e+14,
         1.59985872e+14,   1.84206997e+14,   2.12095089e+14,
         2.44205309e+14,   2.81176870e+14,   3.23745754e+14,
         3.72759372e+14,   4.29193426e+14,   4.94171336e+14,
         5.68986603e+14,   6.55128557e+14,   7.54312006e+14,
         8.68511374e+14,   1.00000000e+15])

















In [ ]:

Content source: rbiswas4/HaloComparisons

Similar notebooks:

notebook.community | gallery | about