Temporary notebook to get the parameters for the MICE sims for the data ismael sent me.
In [2]:
import pandas as pd
import numpy as np
In [1]:
#fit given by Andres
#CENTRALS
def f_cen(logmhalo,logmmin,siglogm,fmaxcen,fmincen,k,logmdrop):
ncen = 0.5*( 1. + ss.erf((logmhalo[0,:] - logmmin)/siglogm) )
ncen = fmaxcen * ncen
ncen = ncen * (1.0 - (1.0-fmincen/fmaxcen)/(1.0 + 10**((2.0/k)*(logmhalo[0,:]-logmdrop))))
return log10(ncen)
#SATELLITES
def f_sat(logmhalo,logmmin,siglogm,logm1,alpha):
nsat = 0.5*( 1. + ss.erf((logmhalo[0,:] - logmmin)/siglogm) )
#nsat = 0.5*( 1. + ss.erf((logmhalo[0,:] - param_cen[0])/param_cen[1]) )
nsat = param_cen[2] * nsat
nsat = nsat * (10**logmhalo[0,:]/10**logm1)**alpha
return log10(nsat)
mean number of central/satellites per bin. read with pd.read_csv(‘hod_redmagicMICE.csv’, sep=' ’). First columm is the bins in Mhalo, and then columm are n_cen/n_sat + redshift bin + catalog. Where catalog are hd=high density , hl=high luminosity, rl=higher luminosity.
In [3]:
mean_gal_per_bin = pd.read_csv('hod_redmagicMICE.csv', sep=' ')
In [4]:
mean_gal_per_bin
Out[4]:
Unnamed: 0
xbin
n_cen_0.075-z-0.175_hd
n_sat_0.075-z-0.175_hd
n_cen_0.075-z-0.175_hl
n_sat_0.075-z-0.175_hl
n_cen_0.075-z-0.175_rl
n_sat_0.075-z-0.175_rl
n_cen_0.175-z-0.275_hd
n_sat_0.175-z-0.275_hd
...
n_cen_0.775-z-0.875_hd
n_sat_0.775-z-0.875_hd
n_cen_0.775-z-0.875_hl
n_sat_0.775-z-0.875_hl
n_cen_0.775-z-0.875_rl
n_sat_0.775-z-0.875_rl
n_cen_0.875-z-0.975_hl
n_sat_0.875-z-0.975_hl
n_cen_0.875-z-0.975_rl
n_sat_0.875-z-0.975_rl
0
0
10.1
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
1
1
10.4
0.000000e+00
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
2
2
10.7
0.000000e+00
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
3
3
11.0
8.372643e-07
0.000000
0.000000
0.000000
0.000000
0.000000
0.000004
0.000000
...
0.000000e+00
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
4
4
11.3
0.000000e+00
0.000000
0.000000
0.000000
0.000000
0.000000
0.001044
0.000141
...
0.000000e+00
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
5
5
11.6
3.007126e-04
0.000046
0.000000
0.000000
0.000000
0.000000
0.003087
0.001265
...
0.000000e+00
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
6
6
11.9
1.969040e-02
0.005405
0.000096
0.000012
0.000000
0.000000
0.031136
0.011201
...
3.062637e-07
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
7
7
12.2
5.141302e-02
0.027959
0.008022
0.001968
0.001183
0.000312
0.109684
0.057797
...
1.342383e-04
0.000039
0.003451
0.000333
0.000023
0.000002
0.000072
0.000002
0.000000
0.000000
8
8
12.5
9.019073e-02
0.071639
0.058483
0.018792
0.014801
0.003710
0.180140
0.145110
...
2.276298e-04
0.000147
0.060637
0.010363
0.005764
0.000561
0.005749
0.000575
0.000577
0.000057
9
9
12.8
1.358486e-01
0.134447
0.119885
0.055796
0.041039
0.015652
0.240573
0.262414
...
1.430216e-04
0.000241
0.150890
0.045228
0.054584
0.008078
0.026457
0.005199
0.011907
0.001414
10
10
13.1
1.709455e-01
0.223153
0.166875
0.102223
0.073262
0.028961
0.303412
0.442735
...
1.078929e-04
0.000378
0.200492
0.093236
0.144193
0.030129
0.040244
0.013605
0.045542
0.007774
11
11
13.4
1.880466e-01
0.373016
0.199708
0.162132
0.106252
0.046647
0.355834
0.763090
...
6.657878e-05
0.000692
0.225516
0.159962
0.224983
0.063170
0.044235
0.023729
0.086353
0.018575
12
12
13.7
1.985245e-01
0.635815
0.225687
0.267941
0.143863
0.084842
0.410480
1.311108
...
1.479399e-04
0.001147
0.233671
0.250166
0.279643
0.102633
0.040499
0.034248
0.117020
0.032880
13
13
14.0
2.111853e-01
1.254591
0.260434
0.501669
0.169449
0.145242
0.438935
2.328837
...
0.000000e+00
0.001499
0.230885
0.398301
0.308096
0.162669
0.030360
0.052599
0.128686
0.050225
14
14
14.3
2.154812e-01
2.255230
0.274059
0.824268
0.205021
0.276151
0.459721
4.522019
...
0.000000e+00
0.004202
0.226891
0.690276
0.321128
0.225690
0.023409
0.082233
0.148860
0.072029
15
15
14.6
2.288136e-01
4.059322
0.271186
1.576271
0.186441
0.466102
0.433790
8.337900
...
0.000000e+00
0.004348
0.173913
1.152174
0.243478
0.413043
0.026087
0.095652
0.113043
0.100000
16
16
14.9
2.000000e-01
9.240000
0.280000
2.640000
0.160000
1.160000
0.489796
15.775510
...
0.000000e+00
0.000000
0.117647
1.705882
0.411765
0.764706
0.000000
0.117647
0.058824
0.176471
17
17
15.2
0.000000e+00
15.333333
0.000000
6.333333
0.000000
2.000000
0.000000
36.000000
...
0.000000e+00
0.000000
0.500000
2.000000
0.500000
0.500000
0.000000
0.000000
0.000000
0.000000
18
18
15.5
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
19 rows × 54 columns
parameters for function i sent before. read with pd.read_csv(‘hod_redmagicMICE_fit.csv’, sep=' ’).
In [5]:
hod_fit = pd.read_csv('hod_redmagicMICE_fit.csv', sep=' ')
In [6]:
hod_fit
Out[6]:
ft_bin
logmmin_cen_hd
siglogm_cen_hd
fmaxcen_cen_hd
fmincen_cen_hd
k_cen_hd
logmdrop_cen_hd
logmmin_sat_hd
siglogm_sat_hd
logm1_sat_hd
...
logmmin_cen_rl
siglogm_cen_rl
fmaxcen_cen_rl
fmincen_cen_rl
k_cen_rl
logmdrop_cen_rl
logmmin_sat_rl
siglogm_sat_rl
logm1_sat_rl
alpha_sat_rl
0
0.075-z-0.175
13.018649
0.831746
0.212444
0.800000
1.693541
12.465866
12.036011
0.307781
13.080950
...
13.221106
0.488229
0.172851
0.800000
0.446728
12.730779
12.504646
0.292284
14.029617
0.872154
1
0.175-z-0.275
12.029688
0.311107
0.476371
0.073332
2.191967
12.966289
12.018851
0.290070
13.142377
...
13.781139
0.843670
0.227788
0.800000
1.032275
13.720632
12.601565
0.736786
13.958900
0.873841
2
0.275-z-0.375
12.091316
0.451114
0.010000
0.144795
0.695210
14.680064
11.952711
0.292076
11.206747
...
13.167983
0.578004
0.073175
0.800000
2.082144
12.571158
12.580854
0.426130
13.182855
0.689937
3
0.375-z-0.475
12.082363
0.414470
0.010000
0.131122
0.477157
14.572139
12.019664
0.251791
11.324371
...
12.759979
0.268894
0.010000
0.071626
0.484546
14.631261
12.645472
0.235326
12.671399
0.789382
4
0.475-z-0.575
12.249344
0.584479
0.139899
0.207767
0.370076
14.390770
12.056892
0.248602
12.551026
...
12.931763
0.296952
0.193410
0.767027
0.073815
12.776305
12.700709
0.251201
13.850202
0.742017
5
0.575-z-0.675
11.987709
0.195099
0.010000
0.110854
0.695338
14.784718
12.109687
0.253232
11.287963
...
12.765567
0.277001
0.041667
0.150404
0.084717
14.657291
12.685256
0.249301
13.136547
0.760288
6
0.675-z-0.775
12.086976
0.216509
0.011996
0.044249
2.675639
14.130119
12.155971
0.225567
12.058973
...
12.853057
0.313932
0.040000
0.200961
0.090978
14.666739
12.764845
0.279843
13.056598
0.714394
7
0.775-z-0.875
15.000000
1.298594
0.010091
0.010000
0.120713
15.318451
14.278518
1.660251
15.000000
...
13.013770
0.287842
0.302326
0.800000
0.056201
12.777814
12.986777
0.354870
14.493610
0.571314
8
0.875-z-0.975
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
13.165759
0.365044
0.058824
0.125435
0.054657
14.617519
13.037216
0.326586
14.151735
0.608889
9 rows × 31 columns
In [ ]:
Content source: mclaughlin6464/pearce
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