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I'm looking into doing a delta_sigma emulator. This is testing if the cat side works. Then i'll make an emulator for it.





In [1]:



    


from pearce.mocks import cat_dict
import numpy as np
from os import path
from astropy.io import fits



    











In [2]:



    


import matplotlib
#matplotlib.use('Agg')
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()



    











In [3]:



    


z_bins = np.array([0.15, 0.3, 0.45, 0.6, 0.75, 0.9])
zbin=1



    











In [4]:



    


a = 0.81120
z = 1.0/a - 1.0

Load up a snapshot at a redshift near the center of this bin.





In [5]:



    


print z



    


















    






0.232741617357

















In [6]:



    


cosmo_params = {'simname':'chinchilla', 'Lbox':400.0, 'scale_factors':[a]}
cat = cat_dict[cosmo_params['simname']](**cosmo_params)#construct the specified catalog!

cat.load_catalog(a, particles=True, tol = 0.01, downsample_factor=1e-2)



    











In [7]:



    


cat.load_model(a, 'redMagic')



    











In [8]:



    


params = cat.model.param_dict.copy()
#params['mean_occupation_centrals_assembias_param1'] = 0.0
#params['mean_occupation_satellites_assembias_param1'] = 0.0
params['logMmin'] = 13.4
params['sigma_logM'] = 0.1
params['f_c'] = 1.0
params['alpha'] = 1.0
params['logM1'] = 14.0
params['logM0'] = 12.0

print params



    


















    






{'logM1': 14.0, 'logMmin': 13.4, 'f_c': 1.0, 'logM0': 12.0, 'sigma_logM': 0.1, 'alpha': 1.0}

















In [9]:



    


cat.populate(params)



    











In [10]:



    


nd_cat =  cat.calc_analytic_nd()
print nd_cat



    


















    






7.06666614097e-05

















In [47]:



    


rp_bins = np.logspace(-1.1, 1.5, 9) #binning used in buzzard mocks
rpoints = (rp_bins[1:]+rp_bins[:-1])/2



    











In [48]:



    


rpoints



    


















    
Out[48]:








array([  0.12365661,   0.2613469 ,   0.5523538 ,   1.1673937 ,
         2.46727379,   5.21455612,  11.0209072 ,  23.29256658])
n_cores = 1 from halotools.mock_observables import return_xyz_formatted_array x_g, y_g, z_g = [cat.model.mock.galaxy_table[c] for c in ['x', 'y', 'z']] pos_g = return_xyz_formatted_array(x_g, y_g, z_g, period=cat.Lbox) x_m, y_m, z_m = [cat.halocat.ptcl_table[c] for c in ['x', 'y', 'z']] pos_m = return_xyz_formatted_array(x_m, y_m, z_m, period=cat.Lbox)
#Halotools wnats downsampling factor defined oppositley #TODO verify little h! # TODO maybe split into a few lines for clarity from halotools.mock_observables import delta_sigma ds = cat.h*delta_sigma(pos_g / cat.h,pos_m/cat.h, cat.pmass*cat.h, downsampling_factor = 1./cat._downsample_factor, rp_bins = rp_bins, period=cat.Lbox / cat.h, num_threads=n_cores,cosmology = cat.cosmology)#[1]/(1e12)
ds = cat.calc_ds(rp_bins) 




In [12]:



    


ds = np.loadtxt('/u/ki/swmclau2/Git/pearce/bin/ds.npy')



    











In [13]:



    


plt.plot(rpoints, ds)
plt.loglog();
#plt.xscale('log')



    


















    
Out[13]:








[]










    
























In [14]:



    


from astropy import constants as c



    











In [15]:



    


rpoints.shape



    


















    
Out[15]:








(8,)

Use my code's wrapper for halotools' xi calculator. Full source code can be found here.





In [16]:



    


r_bins = np.logspace(-1.1, 1.6, 14)
rbc = (r_bins[1:] + r_bins[:-1])/2.0
xi = cat.calc_xi_gm(r_bins)



    


















    






/u/ki/swmclau2/.conda/envs/hodemulator/lib/python2.7/site-packages/halotools-0.6.dev4681-py2.7-linux-x86_64.egg/halotools/mock_observables/two_point_clustering/clustering_helpers.py:157: UserWarning: 
 `sample2` exceeds `max_sample_size` 
downsampling `sample2`...
  warn(msg)
xi = np.loadtxt('/u/ki/swmclau2/Git/pearce/bin/xi_gm.npy') 




In [17]:



    


xi



    


















    
Out[17]:








array([  9.64689465e+03,   4.16130871e+03,   1.88755923e+03,
         8.16293120e+02,   3.27757046e+02,   1.17700149e+02,
         3.45436956e+01,   9.56597686e+00,   3.44374136e+00,
         1.58129562e+00,   7.57737872e-01,   3.25998436e-01,
         1.28385455e-01])

















In [18]:



    


plt.plot(rbc, xi)
plt.loglog();



    


















    
























In [43]:



    


rbc



    


















    
Out[43]:








array([  0.10378753,   0.16743162,   0.27010324,   0.43573466,
         0.70293378,   1.13398346,   1.82935936,   2.95115035,
         4.76084062,   7.6802605 ,  12.38991304,  19.98759615,  32.2442941 ])

Interpolate with a Gaussian process. May want to do something else "at scale", but this is quick for now.





In [19]:



    


import george
from george.kernels import ExpSquaredKernel
kernel = ExpSquaredKernel(0.05)
gp = george.GP(kernel)
gp.compute(np.log10(rpoints))



    











In [20]:



    


print xi



    


















    






[  9.64689465e+03   4.16130871e+03   1.88755923e+03   8.16293120e+02
   3.27757046e+02   1.17700149e+02   3.45436956e+01   9.56597686e+00
   3.44374136e+00   1.58129562e+00   7.57737872e-01   3.25998436e-01
   1.28385455e-01]

















In [21]:



    


xi[xi<=0] = 1e-2 #ack
bias = cat.calc_bias(r_bins) print np.sqrt(bias)
# plt.plot(rpoints, bias) plt.xscale('log') plt.ylabel(r'$b^2$') plt.xlabel('r [Mpc]') plt.title('Bias, "Updated" Values') plt.ylim([0,8]);

This plot looks bad on large scales. I will need to implement a linear bias model for larger scales; however I believe this is not the cause of this issue. The overly large correlation function at large scales if anything should increase w(theta).

This plot shows the regimes of concern. The black lines show the value of r for u=0 in the below integral for each theta bin. The red lines show the maximum value of r for the integral I'm performing.

Perform the below integral in each theta bin:

$$ \Delta \Sigma(\theta) = \int_0^\infty du \xi_{gm} \left(r = \sqrt{(u \theta)^2 + (u-D_A(z))^2} \right) $$

Where $\bar{x}$ is the median comoving distance to z.

x = cat.cosmology.comoving_distance(cat.z)#angular_diameter_distance(cat.z)/cat.h print x 




In [22]:



    


from scipy.interpolate import interp1d
import pyccl as ccl
from astropy import units

xi_interp = interp1d(np.log10(rbc), np.log10(xi))
'''
names, vals = cat._get_cosmo_param_names_vals()
param_dict = { n:v for n,v in zip(names, vals)}

if 'Omega_c' not in param_dict:
    param_dict['Omega_c'] = param_dict['Omega_m'] - param_dict['Omega_b']
    del param_dict['Omega_m']

cosmo = ccl.Cosmology(**param_dict)

big_rbins = np.logspace(1, 2.1, 21)
big_rbc = (big_rbins[1:] + big_rbins[:-1])/2.0
xi_mm = ccl.correlation_3d(cosmo, cat.a, big_rbc)

#bias2 = np.mean(xi[-3:]/xi_mm[-3:]) #estimate the large scale bias from the box
#note i don't use the bias builtin cuz i've already computed xi_gg. 

xi_mm_interp = interp1d(np.log10(big_rbc), np.log10(xi_mm))

bias2 = np.power(10, xi_interp(1.2)-xi_mm_interp(1.2))
'''

'''
theta_bins = np.logspace(np.log10(2.5), np.log10(250), 21)/60 #binning used in buzzard mocks
tpoints = (theta_bins[1:] + theta_bins[:-1])/2.0
ds = np.zeros_like(tpoints)
x = cat.cosmology.angular_diameter_distance(cat.z)/cat.h
print tpoints[0]*x.to("Mpc").value/cat.h, rp_bins[0]
assert tpoints[0]*x.to("Mpc").value/cat.h >= rp_bins[0]
#ubins = np.linspace(10**-6, 10**4.0, 1001)
ubins = np.logspace(-6, 2.0, 501)
ubc = (ubins[1:]+ubins[:-1])/2.0
'''



    


















    
Out[22]:








'\ntheta_bins = np.logspace(np.log10(2.5), np.log10(250), 21)/60 #binning used in buzzard mocks\ntpoints = (theta_bins[1:] + theta_bins[:-1])/2.0\nds = np.zeros_like(tpoints)\nx = cat.cosmology.angular_diameter_distance(cat.z)/cat.h\nprint tpoints[0]*x.to("Mpc").value/cat.h, rp_bins[0]\nassert tpoints[0]*x.to("Mpc").value/cat.h >= rp_bins[0]\n#ubins = np.linspace(10**-6, 10**4.0, 1001)\nubins = np.logspace(-6, 2.0, 501)\nubc = (ubins[1:]+ubins[:-1])/2.0\n'

















In [23]:



    


rhocrit = cat.cosmology.critical_density(0).to('Msun/(Mpc^3)').value
print rhocrit



    


















    






1.35992947349e+11

















In [24]:



    


def integrand_medium_scales(lRz, Rp, xi_interp):
    #integrand_params pars=*(integrand_params*)params;
    Rz = np.exp(lRz)
    #print Rz
    out= Rz * 10**xi_interp(np.log10(Rz*Rz + Rp*Rp)*0.5)
    print Rz, out
    return out



    











In [25]:



    


from scipy.integrate import quad



    











In [26]:



    


np.log10(np.exp(-10))



    


















    
Out[26]:








-4.3429448190325184

















In [44]:



    


def Sigma_at_R_arr(R, Rxi, xi, Om):
    rhom = Om*rhocrit*1e-12 #SM h^2/pc^2/Mpc; integral is over Mpc/h
    Rxi0, Rxi_max = Rxi[0], Rxi[-1]
    
    Sigma = np.zeros_like(R)
    for i, Rp in enumerate(R):
        print Rp
        ln_z_max = np.log(np.sqrt(Rxi_max*Rxi_max - Rp*Rp))#; //Max distance to integrate to
        result2 = quad(integrand_medium_scales, -10, ln_z_max, args=(Rp, xi))[0]
        print result2
        Sigma[i] = result2#(result1+result2)*rhom*2;

    return rhom*2*Sigma



    











In [45]:



    


print rpoints



    


















    






[  0.12365661   0.2613469    0.5523538    1.1673937    2.46727379
   5.21455612  11.0209072   23.29256658]

















In [46]:



    


sigma = Sigma_at_R_arr(rpoints, rbc, xi_interp, cat.cosmology.Om(0))



    


















    






0.123656612642
0.0382606576343 250.315669123
5.41248590908e-05 0.383739700059
27.0463137864 4.89076749409
0.000112677945028 0.798874625747
12.9916987947 9.05362387072
0.000393555263593 2.79024208134
3.71962481012 21.6769645298
0.00206428053745 14.6319552756
0.709146792818 220.889476889
0.0140343139142 98.3886307636
0.104307052811 461.094050358
4.67477816815e-05 0.331436991979
31.3143826286 4.25615361068
7.26763681824e-05 0.515267927244
20.1424198708 6.46817045734
0.000198754127083 1.40914341443
7.36527056871 12.4652719762
0.000863565789899 6.12233143335
1.69515506489 70.7045631574
0.00526560214457 37.273178118
0.278007696446 425.880235452
0.00131796478301 9.34330367835
4.95708059358e-05 0.351451939783
0.0350414147294 232.130663171
7.15232185382e-05 0.507092220483
0.0242862556351 166.552431029
0.000133668924305 0.94769815074
0.0129950261686 91.2481301627
0.000306134270232 2.17044925106
0.00567408270871 40.154358395
0.000798221063347 5.65909913441
0.00217612795381 15.4243274554
4.6068926674e-05 0.326623980574
0.0377050496869 247.228242684
5.74412918628e-05 0.407252859626
0.030240113217 203.726716618
9.49917017932e-05 0.673481187681
0.0182861359095 127.204699863
0.000198004611579 1.40382946155
0.00877268037048 61.9235199375
0.000488935545364 3.46645581528
0.00355267925545 25.1698730423
1.11071095486 134.876236322
0.0417756512949 269.340492607
29.5310494751 4.49957376903
0.0602759826218 354.321320368
20.4671706969 6.37078785632
0.112649093863 469.541826432
10.9515201849 10.032161037
0.257993758245 440.267061317
4.7818165588 16.3410465508
0.672698459689 232.293014039
1.83392544977 62.5459156484
0.0388244528191 253.426446109
31.7758200223 4.19750674388
0.0484084802231 302.778268906
25.4847667094 5.1745740258
0.0800539780442 417.241388918
15.4105873985 7.94699386171
0.166867805596 486.301818773
7.39315064905 12.4357759479
0.412048996646 343.969190775
2.99401002134 27.7213536204
0.206146868942 471.784675824
0.0399795434147 259.729470049
1.06295690108 141.644221483
0.0480228980244 300.924437832
0.884922262562 173.513537515
0.0656508066447 374.176861514
0.647311643931 239.952790776
0.0993529609826 454.638738233
0.427732914593 335.05795453
0.160430313399 487.299417904
0.264890909169 435.273699011
0.0385415243232 251.868166868
1.10261678335 135.980773748
0.0430364994907 275.947476984
0.987453256595 153.723876842
0.0553436341978 334.196948283
0.767866660558 202.655846539
0.079902890937 416.862082595
0.531852240592 282.256010916
0.125559808813 480.08883158
0.33845648521 385.460421129
5.98446550062 14.1980018687
1.16061039163 126.864851132
30.8577517369 4.31587111949
1.39410983025 95.6743332223
25.6893872714 5.1354718715
1.90584988979 58.6467876888
18.7915257755 6.83006891299
2.88422700978 29.2708325044
12.4171319409 9.37176638843
4.65730903757 16.7972264504
7.68981122773 12.134227009
1.11886454467 133.780300094
32.0090823316 4.16848703276
1.2493541512 113.284148625
28.6658729182 4.62828691637
1.60663157891 76.8301164187
22.2912507125 5.87525045089
2.31958941053 42.1965094556
15.4397270334 7.93553891099
3.64501458579 22.18078012
9.82542771371 10.6353229415
0.0888105555381 436.818914332
0.0391106586874 254.997023691
0.201666631033 473.951438248
0.0428647601174 275.054384326
0.184004640487 481.414802075
0.0501182904382 310.863428459
0.157373978761 487.576294688
0.061654761577 359.619288368
0.127927098788 481.463934142
0.0783464695767 412.869147054
0.100672242382 456.469996284
0.0384008341945 251.091191726
0.205394360316 472.154864517
0.040578378144 262.959678368
0.194372351379 477.265558447
0.0460161258721 291.095533133
0.171403277123 485.296443746
0.0552913840859 333.973902227
0.142649978932 486.77163787
0.0693108999916 386.488182998
0.113796167355 470.667796897
0.478507665188 307.152617167
0.210726640084 469.48197484
1.08657161502 138.220614326
0.230953586073 458.525425162
0.991409527504 153.043976959
0.270035312757 431.571124542
0.847924604032 181.849638643
0.332193350567 389.389764218
0.689266011053 227.554109006
0.422127595146 338.429951283
0.542417952004 277.783189687
0.206902134553 471.410721363
1.10665646895 135.427426213
0.218634652887 465.330277934
1.04727033258 144.006486934
0.247933016731 447.588450241
0.923513893644 165.539287117
0.297907731166 411.9924741
0.768592291135 202.447800017
0.373444313304 364.675551059
0.613129126584 251.103643111
2.57817986 35.3386189175
1.13538657571 131.214006364
5.85440371827 14.3948921625
1.24436853895 113.983182575
5.34167425682 15.2459389408
1.45493929458 89.5679576385
4.56858331842 17.1678962799
1.78984427704 64.9897082873
3.71373726517 21.715937851
2.27440633313 43.6118691696
2.92252588892 28.6292573001
1.11478029529 134.327196728
5.96262009528 14.2305839446
1.17799462742 124.002214635
5.64265000517 14.7311261828
1.33585302152 102.18613844
4.97585533993 15.9388916055
1.60511475261 76.9425611678
4.14114403951 19.1920105461
2.01210278839 53.554522351
3.30351482482 24.7975177614
13.8911283436 8.60251247642
6.11741674329 14.0037952089
31.5432893908 4.22685152515
6.70460713369 13.2218572009
28.7807238829 4.61076653987
7.83915381027 12.0093262032
24.6153413147 5.34777530238
9.64360824983 10.7427469463
20.0094655091 6.50892749693
12.254408922 9.44330736547
15.7464507579 7.81723519844
6.00639094153 14.165494129
32.1263548337 4.15405249532
6.3469871949 13.6841056989
30.4023689874 4.37716520527
7.19752180911 12.646651877
26.8097064206 4.93169903718
8.64829307708 11.3911803435
22.3123158452 5.86998917398
10.8411280794 10.0870024927
17.7992036663 7.11896224943
0.314074604104 401.109698622
0.208424175795 470.650000349
0.473279342797 309.821224022
0.218197980375 465.564613315
0.452079605749 321.107691049
0.23593841194 455.686662069
0.41808731411 340.89935729
0.261688018646 437.588874291
0.376948312168 362.701798224
0.294992003338 413.995304885
0.334391630373 388.003406523
0.206524156494 471.598167855
0.477633506016 307.595744942
0.212298961691 468.673064244
0.464641259466 314.328091181
0.226076569123 461.260059072
0.436324990803 330.00807117
0.247815951895 447.673763416
0.3980488592 351.218471956
0.277460584429 426.268732602
0.355520252169 375.077136459
0.729029290021 214.390578908
0.483793744918 304.498765085
1.09857498426 136.538622081
0.506480678916 293.591574372
1.04936620059 143.686654506
0.547659730195 275.61526926
0.97046336695 156.708232903
0.607429661448 253.064331689
0.874971604847 175.685459154
0.6847347985 228.83072123
0.776189127342 200.292956195
0.479383424236 306.709898125
1.10868185848 135.151562022
0.492787889536 300.082816461
1.07852428396 139.369919653
0.524768442033 285.334360762
1.01279662254 149.462825561
0.575229850185 264.739078009
0.923950148863 165.45301383
0.644040906943 240.97669229
0.82523283844 187.335758768
0.058291939921 346.447244923
0.0386833235632 252.649857109
0.0878401840061 434.874628289
0.040497332152 262.524009261
0.0839055335053 426.425455912
0.0437899389321 279.839950601
0.0775965973606 410.889209094
0.0485690407999 303.546915274
0.0699612387602 388.575340553
0.0547502278473 331.651606616
0.062062760163 361.15980658
0.0383306818357 250.703248416
0.0886483124491 436.497594401
0.0394024800428 256.592441005
0.0862369641725 431.542718722
0.0419595905325 270.311582058
0.080981492351 419.53806235
0.0459943987529 290.987464164
0.0738774796908 400.517601933
0.0514964136121 317.218869463
0.0659842117425 375.338418165
0.135307124547 484.777947034
0.0897916467756 438.730089128
0.203894444646 472.885364987
0.0940023195786 446.327424609
0.194761342435 477.096255915
0.101645111297 457.767445452
0.180117053536 482.765087753
0.11273835219 469.617064648
0.162393875709 487.05239721
0.127086110161 480.993741261
0.144059944316 487.021546166
0.0889730955624 437.139317054
0.205770270636 471.970242351
0.0914609512367 441.857923024
0.200173054245 474.652822666
0.0973965105606 451.767186384
0.187974064448 479.914023625
0.106762098653 463.88202375
0.171484245666 485.276416533
0.119533363614 475.829749106
0.153162409219 487.727399144
1.6922212072 70.8946391696
1.12298099167 133.233257486
2.55000987136 35.996414144
1.17564185367 124.38345907
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16.592314952 5.44198005478
16.8053080261 5.42067992
16.604145407 5.44111111907
16.7933342427 5.42207974791
16.624344084 5.43962452086
16.7729302417 5.42446305373
16.6511476797 5.43764595863
16.7459306108 5.42761269689
16.682200551 5.43502838786
16.71475911 5.43124316483
16.589951828 5.44215346657
16.8077018262 5.42039995863
16.5970691387 5.44163102205
16.80049419 5.42124280267
16.6133080824 5.44043722884
16.7840722782 5.42316192522
16.6370481502 5.43868757283
16.7601224158 5.42595773261
16.6663113066 5.43652369066
16.7306945433 5.4293879924
16.9186085285 5.40739064263
16.811052114 5.42000807339
17.0268530845 5.3946237282
16.8230385304 5.41860544312
17.0147214502 5.39605790364
16.8435034874 5.41620861559
16.9940484624 5.3984999212
16.8706604359 5.41302407512
16.9666928943 5.40172760755
16.9021226785 5.40932911322
16.9351104583 5.40544867902
16.8086578368 5.4202881406
17.0292784421 5.39433690951
16.8158689753 5.41944451964
17.0219757873 5.3952004102
16.8323219977 5.4175184959
17.0053373849 5.3971667035
16.8563750317 5.4146998022
16.9810717905 5.40003157129
16.8860239656 5.41122050518
16.9512559691 5.40354712117
16.6438867495 5.43818259493
16.5908974332 5.44208408206
16.6970453072 5.43330342037
16.5968111014 5.44164997152
16.6910959241 5.43399491678
16.6069029107 5.4409083946
16.6809529518 5.43517329281
16.6202852563 5.43992355095
16.6675217577 5.43643401362
16.6357756954 5.43878148609
16.6520017582 5.437582804
16.5897159301 5.44217077445
16.6982344542 5.43316517784
16.5932741532 5.44190965128
16.6946537236 5.43358142211
16.6013897839 5.4413136342
16.6864925006 5.43452981154
16.6132470678 5.44044171889
16.6745829396 5.43591061427
16.6278512949 5.43936600728
16.6599376682 5.4369956642
16.7532360579 5.42676094036
16.6998986051 5.43297169875
16.8067438641 5.42051199917
16.7058511258 5.4322794918
16.800755394 5.42121226377
16.7160092376 5.43109768729
16.7905457829 5.42240561123
16.7294795044 5.42952950313
16.7770263467 5.42398481618
16.7450717148 5.42771281472
16.7614043817 5.42580817377
16.6987093396 5.43310996813
16.8079408237 5.42037200525
16.70229094 5.43269352686
16.8043365679 5.42079352444
16.7104598899 5.43174338762
16.7961217262 5.42175394981
16.7223950755 5.43035440703
16.7841339201 5.4231547247
16.7370952514 5.42864236945
16.7693924302 5.42487602236
16.616661125 5.44019042565
16.5901887192 5.44213608528
16.6431757719 5.43823511527
16.5931451636 5.44191911937
16.6402104135 5.43845411776
16.598189187 5.44154876185
16.6351536202 5.43882739278
16.6048755022 5.44105745131
16.6284551129 5.43932148884
16.6126117385 5.4404884706
16.6207114985 5.43989215518
16.5895979824 5.4421794281
16.6437684166 5.43819133657
16.5913769859 5.4420488913
16.6419837954 5.43832315682
16.5954338413 5.44175110276
16.6379155606 5.43862354519
16.6013592985 5.44131587422
16.6319770556 5.43906175475
16.608654584 5.44077957922
16.6246715257 5.43960039025
16.6711569818 5.43616461511
16.6445977574 5.43813006767
16.6977585859 5.43322050032
16.6475638977 5.4379108873
16.6947835023 5.43356633738
16.6526244634 5.43753675397
16.6897101248 5.4341559545
16.659332707 5.43704044274
16.6829896492 5.43493673109
16.667094315 5.43646568251
16.6752206389 5.43583895071
16.6440050831 5.43817385309
16.6983531743 5.4331513757
16.6457899211 5.43804198352
16.6965627003 5.43335952246
16.6498600813 5.43774115709
16.6924811233 5.43383393608
16.6558049715 5.43730149478
16.6865231424 5.43452625158
16.6631241826 5.43675974656
16.6791936534 5.43537761251
16.6575162851 5.43717487205
16.6442422496 5.43815633216
16.6708009068 5.43619100839
16.645725222 5.43804676421
16.6693157004 5.43630108356
16.6482550332 5.43785980478
16.6667826889 5.4364887696
16.6516079373 5.43761192583
16.6634267293 5.43673734229
16.6554864901 5.43732505675
16.6595463275 5.43702463118
16.6439459162 5.43817822403
16.6710977184 5.43616900797
16.6448383081 5.43811229541
16.6702039186 5.43623525623
16.6468731475 5.43796193694
16.6681662274 5.4363862622
16.6498447941 5.43774228726
16.6651913108 5.43660665431
16.6535026883 5.43747180181
16.6615308492 5.43687772266
16.6848088488 5.43472540832
16.6715130645 5.43613822011
16.6981152368 5.4331790377
16.6729984666 5.43602810058
16.6966275969 5.43335197845
16.6755324228 5.43580275082
16.6940904352 5.43364689421
16.6788908204 5.43541278055
16.690728977 5.43403755935
16.6827757281 5.43496157925
16.6868422173 5.43448918114
16.6712162455 5.43616022221
16.6984125346 5.43314447455
16.6721100995 5.43609396164
16.6975172704 5.43324855409
16.6741482729 5.43594284644
16.6954762405 5.43348581561
16.6771247884 5.43561785796
16.6924964497 5.43383215486
16.6807886759 5.4351923722
16.6888299906 5.43425822447
13.3089963143
23.2925665813
0.0318162972727 0.00769785514819
5.38649943622e-05 1.30324932014e-05
18.7928502384 2.78983961618
0.000109908205344 2.65920001637e-05
9.21020199514 1.93429552696
0.000370958678432 8.97524730251e-05
2.72881275192 0.651518490219
0.00185943972729 0.000449886530964
0.544398808568 0.131645792147
0.0119953698313 0.00290224731724
0.0843889589385 0.0204174247504
4.67103513247e-05 1.13014462042e-05
21.6713585626 2.85591683169
7.17461557989e-05 1.7358793011e-05
14.1091430038 2.51751112069
0.000190879391912 4.61827650161e-05
5.30322714257 1.22145358592
0.000796656408323 0.000192748914884
1.27065666147 0.306543275605
0.00462303241057 0.00111853044564
0.218963806057 0.0529731540177
0.00120185592376 0.000290785867526
4.94516628709e-05 1.19646992967e-05
0.0292094861451 0.00706714724234
7.0638691968e-05 1.70908450604e-05
0.0204485335337 0.00494746499663
0.000129774797036 3.13986695823e-05
0.0111304944756 0.00269299315871
0.000290548503725 7.02974435205e-05
0.00497148545927 0.00120283773053
0.000737962700829 0.000178548127331
0.00195735863054 0.000473577750667
4.60504795776e-05 1.11417919768e-05
0.0313668321097 0.00758910882314
5.70725015572e-05 1.38085411005e-05
0.0253091703022 0.00612347990365
9.30908748799e-05 2.25230914499e-05
0.0155166407378 0.00375420864493
0.000190179244353 4.60133661625e-05
0.0075952434578 0.0018376489155
0.000458132455442 0.000110843938245
0.00315292584999 0.000762842073179
0.842261332768 0.203523638109
0.0346557541996 0.00838485016573
20.470024937 2.83588318414
0.0495036365555 0.0119772214844
14.330344234 2.53602369437
0.0909462534983 0.0220038791421
7.80025702422 1.70159217625
0.203616560973 0.049260821909
3.48401991119 0.82497212922
0.517164691411 0.125066563845
1.37171807058 0.330765985388
0.0322722029627 0.00780815971627
21.981894248 2.85953500144
0.0399964423985 0.00967700586273
17.736681318 2.74845352639
0.0652381394431 0.0157840749986
10.8740708845 2.1711383329
0.13327772543 0.0322451653073
5.32275104776 1.22551632249
0.321059492137 0.0776651204508
2.2095722757 0.529954463808
0.163699837949 0.0396048378168
0.0332056889377 0.0080340128796
0.807019453653 0.195028136511
0.0396865520892 0.00960202936156
0.675232176492 0.163236993048
0.0537919421209 0.0130147507778
0.498171954536 0.120477697298
0.0804880427986 0.0194736429486
0.332939353634 0.0805377661843
0.128274181194 0.0310346825328
0.208909046975 0.0505410314599
0.0320434393146 0.00775281126381
0.836290907526 0.202084579386
0.0356726604166 0.00863088630157
0.751209375238 0.181569130431
0.0455591487853 0.0110228747026
0.58819441669 0.142223726015
0.0651183824439 0.0157551007719
0.411521845889 0.0995363542624
0.101068908394 0.0244529070474
0.265142241767 0.0641423680339
4.33356661536 1.01430370445
0.879042195904 0.212387233823
21.3639341743 2.85172502812
1.0506077426 0.253688942442
17.875177241 2.7544737934
1.42401463233 0.343286573824
13.187926011 2.43356743389
2.13073085213 0.511356185527
8.81378311623 1.87181349674
3.39575601415 0.804930887507
5.53037365802 1.26844184522
0.848274381908 0.204972891752
22.138826788 2.86113496801
0.944349439798 0.228117543222
19.8864941496 2.82240747709
1.20607087138 0.29104544376
15.571058099 2.62863386244
1.72385539131 0.414868507517
10.8940690179 2.17373600896
2.67556066491 0.639130110588
7.01901468954 1.56034362528
4.32613711306

















In [29]:



    


rhom = cat.cosmology.Om(0)*rhocrit*1e-12 #SM h^2/pc^2/Mpc; integral is over Mpc/h
print rhom



    


















    






0.0388939829419

















In [30]:



    


plt.plot(rpoints, sigma)
#plt.xscale('log')
plt.loglog();



    


















    
























In [42]:



    


print rpoints



    


















    






[  0.12365661   0.2613469    0.5523538    1.1673937    2.46727379
   5.21455612  11.0209072   23.29256658]

















In [31]:



    


sigma



    


















    
Out[31]:








array([ 121.95145996,   65.90218232,   31.46144416,   12.26835331,
          4.28237926,    2.03088395,    1.03527975,    0.33652141])

















In [32]:



    


def DS_integrand_medium_scales(lR, sigma_interp):
    R = np.exp(lR);
    return R * R * sigma_interp(np.log10(R))



    











In [33]:



    


small_scales = r_bins<1.
np.sum(~small_scales)



    


















    
Out[33]:








8

















In [50]:



    


def DeltaSigma_at_R_arr(R, Rs, Sigma):
    lrmin = np.log(Rs[0]);
    DeltaSigma = np.zeros_like(R)
    sigma_interp = interp1d(np.log10(Rs), Sigma)
    print R
    for i,r in enumerate(R):
        result2 = quad(DS_integrand_medium_scales, lrmin, np.log(r), args= (sigma_interp,))[0]
        #print result2*2/(r**2), sigma_interp(np.log10(r));
        #print result2, r, sigma_interp(np.log10(r))
        DeltaSigma[i] = result2*2/(r**2) - sigma_interp(np.log10(r));

    return cat.h*DeltaSigma



    











In [51]:



    


rpoints



    


















    
Out[51]:








array([  0.12365661,   0.2613469 ,   0.5523538 ,   1.1673937 ,
         2.46727379,   5.21455612,  11.0209072 ,  23.29256658])

















In [52]:



    


rpoints2 = np.logspace(np.log10(rpoints[0]), np.log10(rpoints[-1]), 500)
ds2 = DeltaSigma_at_R_arr(rpoints, rpoints, sigma)



    


















    






[  0.12365661   0.2613469    0.5523538    1.1673937    2.46727379
   5.21455612  11.0209072   23.29256658]

















In [53]:



    


print ds2



    


















    






[-85.36602197   1.23461256  12.77825827   9.82771801   5.0990316
   1.95881561   0.7999249    0.43272762]

















In [33]:



    


plt.plot(rpoints, ds, label = 'Halotools')
plt.plot(rpoints2, ds2, label = 'Integral')

plt.loglog();
plt.legend(loc = 'best')



    


















    
Out[33]:








<matplotlib.legend.Legend at 0x7fef62ad0a90>










    
























In [70]:



    


ds2



    


















    
Out[70]:








array([-85.80480241, -83.47114606, -81.18597525, -78.94828262,
       -76.75708174, -74.61140665, -72.5103115 , -70.45287005,
       -68.43817533, -66.4653392 , -64.53349199, -62.64178207,
       -60.78937555, -58.97545581, -57.19922324, -55.45989482,
       -53.7567038 , -52.08889938, -50.45574634, -48.85652474,
       -47.2905296 , -45.75707058, -44.25547169, -42.78507098,
       -41.34522027, -39.93528483, -38.55464311, -37.20268649,
       -35.878819  , -34.58245703, -33.3130291 , -32.06997564,
       -30.85274865, -29.66081155, -28.4936389 , -27.35071619,
       -26.23153957, -25.13561569, -24.06246143, -23.01160371,
       -21.98257929, -20.97493455, -19.98822528, -19.02201651,
       -18.07588232, -17.14940563, -16.24217801, -15.35379954,
       -14.48387859, -13.63203168, -12.79788328, -11.9810657 ,
       -11.18121884, -10.39799011,  -9.63103425,  -8.88001316,
        -8.14459576,  -7.42445787,  -6.71928203,  -6.02875738,
        -5.35257951,  -4.69045035,  -4.04207801,  -3.40717667,
        -2.78546645,  -2.17667328,  -1.58052879,  -0.99677019,
        -0.42514013,   0.13461338,   0.68273709,   1.21947263,
         1.58978434,   1.89094238,   2.18584336,   2.47461729,
         2.75739152,   3.03429057,   3.30543664,   3.57094916,
         3.83094525,   4.08553949,   4.33484409,   4.57896907,
         4.81802184,   5.05210797,   5.28133053,   5.50579062,
         5.72558721,   5.94081715,   6.15157534,   6.35795466,
         6.56004617,   6.7579388 ,   6.95171994,   7.14147317,
         7.32728756,   7.50923948,   7.68741111,   7.86188135,
         8.03272585,   8.20002127,   8.3638407 ,   8.52425654,
         8.68133956,   8.83515891,   8.98578235,   9.13327638,
         9.27770598,   9.41913481,   9.55762523,   9.69323829,
         9.82603374,   9.95607016,  10.08340489,  10.20809398,
        10.33019244,  10.44975416,  10.56683173,  10.68147688,
        10.79374015,  10.90367082,  11.01131752,  11.11672774,
        11.21994787,  11.32102343,  11.41999898,  11.51691812,
        11.61182365,  11.70475737,  11.79576023,  11.88487235,
        11.972133  ,  12.05758068,  12.14125309,  12.22318698,
        12.30341864,  12.38198328,  12.45891564,  12.53424966,
        12.6080184 ,  12.68025451,  12.7509898 ,  12.75614271,
        12.67659736,  12.59870466,  12.52243038,  12.44774079,
        12.37460301,  12.3029848 ,  12.23285449,  12.16418134,
        12.09693499,  12.03108574,  11.96660464,  11.90346323,
        11.84163376,  11.78108879,  11.72180179,  11.66374658,
        11.60689752,  11.55122966,  11.49671836,  11.44333971,
        11.39106992,  11.3398862 ,  11.289766  ,  11.24068695,
        11.19262769,  11.14556687,  11.09948392,  11.0543584 ,
        11.01017051,  10.96690272,  10.92452961,  10.88303905,
        10.84241053,  10.8026261 ,  10.76366828,  10.72551986,
        10.68816406,  10.6515842 ,  10.61576465,  10.58068915,
        10.54634244,  10.51270932,  10.47977499,  10.44752489,
        10.4159449 ,  10.38502101,  10.3547364 ,  10.32508734,
        10.29605119,  10.26761828,  10.23977612,  10.21251243,
        10.18581518,  10.15967261,  10.13407389,  10.10900566,
        10.08445893,  10.0604222 ,  10.036885  ,  10.01383658,
         9.99126714,   9.96916665,   9.9475253 ,   9.9263336 ,
         9.90558218,   9.88526195,   9.86536389,   9.84587917,
         9.82679933,   9.8081159 ,   9.77431962,   9.64918933,
         9.52665884,   9.40667412,   9.28918227,   9.17413151,
         9.06147114,   8.95115143,   8.84312383,   8.73734066,
         8.63375531,   8.53232201,   8.43299638,   8.33573429,
         8.24049299,   8.14723045,   8.05590565,   7.96647822,
         7.87890881,   7.79315881,   7.70919039,   7.62696658,
         7.54645107,   7.46760841,   7.39040385,   7.31480333,
         7.24077355,   7.16828184,   7.09729628,   7.02778555,
         6.95971902,   6.89306669,   6.82779917,   6.7638877 ,
         6.70130409,   6.64002086,   6.58001067,   6.5212474 ,
         6.46370505,   6.40735836,   6.35218211,   6.29815237,
         6.24524516,   6.19343719,   6.14270562,   6.0930281 ,
         6.04438269,   5.99674805,   5.95010296,   5.90442707,
         5.85970015,   5.81590252,   5.77301486,   5.73101828,
         5.68989422,   5.64962457,   5.6101916 ,   5.57157792,
         5.53376651,   5.49674069,   5.46048413,   5.42498087,
         5.39021525,   5.35617194,   5.32283593,   5.29019254,
         5.25822737,   5.22692632,   5.19627561,   5.16626171,
         5.13687141,   5.10808836,   5.03236144,   4.95036265,
         4.87006752,   4.79144066,   4.7144474 ,   4.63905381,
         4.56522663,   4.49293334,   4.42214206,   4.35282159,
         4.28494136,   4.21847146,   4.15338258,   4.08964603,
         4.0272337 ,   3.9661181 ,   3.90627228,   3.84766984,
         3.79028497,   3.73409238,   3.67906726,   3.62518539,
         3.572423  ,   3.52075683,   3.47016412,   3.42062255,
         3.37211029,   3.32460596,   3.27808859,   3.23253772,
         3.18793324,   3.14425548,   3.1014852 ,   3.05960354,
         3.01859205,   2.97843263,   2.9391076 ,   2.90059959,
         2.86289167,   2.82596722,   2.78980987,   2.75440383,
         2.71973331,   2.68578318,   2.65253842,   2.61998437,
         2.58810676,   2.55689131,   2.52632449,   2.49639274,
         2.46708288,   2.43838197,   2.41027738,   2.38275681,
         2.35580781,   2.32941883,   2.30357811,   2.27827429,
         2.2534962 ,   2.22923291,   2.2054745 ,   2.18220818,
         2.15942602,   2.13711719,   2.11527186,   2.0938801 ,
         2.0729334 ,   2.0524216 ,   2.03233596,   2.01266766,
         1.99340796,   1.96726088,   1.9362485 ,   1.90588044,
         1.87614333,   1.84702406,   1.81850979,   1.79058795,
         1.76324623,   1.73647256,   1.71025519,   1.68458252,
         1.65944324,   1.63482627,   1.61072076,   1.58711607,
         1.56400182,   1.54136781,   1.51920404,   1.49750077,
         1.47624842,   1.45543762,   1.4350592 ,   1.41510419,
         1.39556371,   1.37642932,   1.35769242,   1.33934481,
         1.32137841,   1.30378529,   1.28655777,   1.26968804,
         1.25316888,   1.23699292,   1.22115305,   1.20564228,
         1.19045376,   1.17558082,   1.16101689,   1.14675554,
         1.13279051,   1.11911561,   1.10572483,   1.09261227,
         1.07977214,   1.06719879,   1.05488667,   1.04283035,
         1.03102453,   1.01946399,   1.00814364,   0.99705849,
         0.98620365,   0.97557434,   0.96516587,   0.95497365,
         0.94499319,   0.9352201 ,   0.92565005,   0.91627886,
         0.90710233,   0.89811647,   0.88931732,   0.88070098,
         0.87226366,   0.86400164,   0.85591127,   0.847989  ,
         0.84023132,   0.83263482,   0.82519616,   0.81791204,
         0.80990968,   0.79992233,   0.79014243,   0.78056576,
         0.77118805,   0.76200519,   0.7530131 ,   0.74420784,
         0.73558556,   0.72714237,   0.71887462,   0.71077865,
         0.7028509 ,   0.69508783,   0.68748608,   0.68004226,
         0.67275309,   0.66561538,   0.65862594,   0.65178174,
         0.64507973,   0.63851697,   0.63209056,   0.62579767,
         0.61963553,   0.61360146,   0.60769265,   0.60190668,
         0.59624091,   0.59069286,   0.58526007,   0.57994016,
         0.57473078,   0.56962964,   0.56463448,   0.55974319,
         0.55495336,   0.55026311,   0.5456703 ,   0.54117296,
         0.53676903,   0.53245659,   0.52823377,   0.52409868,
         0.5200495 ,   0.51608443,   0.51220177,   0.50839977,
         0.50467675,   0.50103114,   0.49746117,   0.49396544,
         0.49054233,   0.48719033,   0.48390799,   0.48069383,
         0.47754646,   0.47446448,   0.47144654,   0.46849128,
         0.46559744,   0.46276372,   0.45998887,   0.45727167,
         0.45461094,   0.45200548,   0.44945415,   0.44695583,
         0.44450942,   0.44211384,   0.43976803,   0.43747096])

















In [37]:



    


# TODO this is like this cause of a half-assed attempt at parraleization
# this is unesscary now, and could be discarded for a simpler for loop
def integrate_xi(bin_no):#, w_theta, bin_no, ubc, ubins)
    int_xi1, int_xi2 = 0, 0 
    #t_med = np.radians(tpoints[bin_no])
    rp = rpoints[bin_no]
    dr = ((rp-rpoints[0])/500)*units.Mpc/cat.h
    for _r in np.linspace(rbc[0]+1e-5, rp, 500):
        if _r < rp:
            continue
        print _r
        r = _r*units.Mpc/cat.h
        for ubin_no, _u in enumerate(ubc):
            _du = ubins[ubin_no+1]-ubins[ubin_no]
            u = _u*units.Mpc/cat.h
            du = _du*units.Mpc/cat.h

            rad = np.sqrt(r**2 + u**2 )#*cat.h#not sure about the h
            
            if rad >= (units.Mpc)*rpoints[-1]:
                try:
                    int_xi1+=du*r*dr*bias2*(np.power(10, \
                            xi_mm_interp(np.log10(rad.value))))
                except ValueError:
                    #interpolation failed
                    int_xi1+=du*0*dr*r
            else:
                int_xi1+=du*r*dr*(np.power(10, \
                            xi_interp(np.log10(rad.value))))
                
    for ubin_no, _u in enumerate(ubc):
        _du = ubins[ubin_no+1]-ubins[ubin_no]
        u = _u*units.Mpc/cat.h
        du = _du*units.Mpc/cat.h

        rad = np.sqrt((rp*units.Mpc)**2 + u**2 )#*cat.h#not sure about the h

        if rad >= (units.Mpc)*rpoints[-1]:
            try:
                int_xi2+=du*bias2*(np.power(10, \
                        xi_mm_interp(np.log10(rad.value))))
            except ValueError:
                #interpolation failed
                int_xi2+=du*0
        else:
            int_xi2+=du*(np.power(10, \
                        xi_interp(np.log10(rad.value))))
            
    print int_xi1, int_xi2
    ds[bin_no] = ( (4/( (rp*units.Mpc)**2))*int_xi1 - 2*int_xi2).value

#Currently this doesn't work cuz you can't pickle the integrate_xi function.
#I'll just ignore for now. This is why i'm making an emulator anyway
#p = Pool(n_cores)  
map(integrate_xi, range(rpoints.shape[0]))
#p.map(integrate_xi, range(tpoints.shape[0]))
#p.terminate()

print ds



    


















    






0.123656612642










    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-37-bc177fb03518> in <module>()
     49 #I'll just ignore for now. This is why i'm making an emulator anyway
     50 #p = Pool(n_cores)
---> 51 map(integrate_xi, range(rpoints.shape[0]))
     52 #p.map(integrate_xi, range(tpoints.shape[0]))
     53 #p.terminate()

<ipython-input-37-bc177fb03518> in integrate_xi(bin_no)
     11         print _r
     12         r = _r*units.Mpc/cat.h
---> 13         for ubin_no, _u in enumerate(ubc):
     14             _du = ubins[ubin_no+1]-ubins[ubin_no]
     15             u = _u*units.Mpc/cat.h

NameError: global name 'ubc' is not defined
#a subset of the data from above. I've verified it's correct, but we can look again. wt_redmagic = np.loadtxt('/u/ki/swmclau2/Git/pearce/bin/mcmc/buzzard2_wt_%d%d.npy'%(zbin,zbin))

The below plot shows the problem. There appears to be a constant multiplicative offset between the redmagic calculation and the one we just performed. The plot below it shows their ratio. It is near-constant, but there is some small radial trend. Whether or not it is significant is tough to say.

from scipy.special import gamma def wt_analytic(m,b,t,x): return W*b*np.sqrt(np.pi)*(t*x)**(1 + m)*(gamma(-(1./2) - m/2.)/(2*gamma(-(m/2.))) ) 




In [ ]:



    


plt.plot(tpoints, ds, label = 'My Calculation')
plt.plot(tpoints_rm, ds, label = 'Halotools')
#plt.plot(tpoints_rm, W.to("1/Mpc").value*mathematica_calc, label = 'Mathematica Calc')
#plt.plot(tpoints, wt_analytic(m,10**b, np.radians(tpoints), x),label = 'Mathematica Calc' )

plt.ylabel(r'$w(\theta)$')
plt.xlabel(r'$\theta \mathrm{[degrees]}$')
plt.loglog();
plt.legend(loc='best')



    











In [38]:



    


wt_redmagic/(W.to("1/Mpc").value*mathematica_calc)



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-38-9c8478bd3deb> in <module>()
----> 1 wt_redmagic/(W.to("1/Mpc").value*mathematica_calc)

NameError: name 'wt_redmagic' is not defined
















In [ ]:



    


import cPickle as pickle
with open('/u/ki/jderose/ki23/bigbrother-addgals/bbout/buzzard-flock/buzzard-0/buzzard0_lb1050_xigg_ministry.pkl') as f:
    xi_rm = pickle.load(f)



    











In [ ]:



    


xi_rm.metrics[0].xi.shape



    











In [ ]:



    


xi_rm.metrics[0].mbins



    











In [ ]:



    


xi_rm.metrics[0].cbins



    











In [ ]:



    


#plt.plot(np.log10(rpoints), b2+(np.log10(rpoints)*m2))
#plt.plot(np.log10(rpoints), 90+(np.log10(rpoints)*(-2)))

plt.scatter(rpoints, xi)
for i in xrange(3):
    for j in xrange(3):
        plt.plot(xi_rm.metrics[0].rbins[:-1], xi_rm.metrics[0].xi[:,i,j,0])
plt.loglog();



    











In [ ]:



    


plt.subplot(211)
plt.plot(tpoints_rm, wt_redmagic/wt)
plt.xscale('log')
#plt.ylim([0,10])
plt.subplot(212)
plt.plot(tpoints_rm, wt_redmagic/wt)
plt.xscale('log')
plt.ylim([2.0,4])



    











In [ ]:



    


xi_rm.metrics[0].xi.shape



    











In [ ]:



    


xi_rm.metrics[0].rbins #Mpc/h

The below cell calculates the integrals jointly instead of separately. It doesn't change the results significantly, but is quite slow. I've disabled it for that reason.





In [ ]:



    


x = cat.cosmology.comoving_distance(z)*a
#ubins = np.linspace(10**-6, 10**2.0, 1001)
ubins = np.logspace(-6, 2.0, 51)
ubc = (ubins[1:]+ubins[:-1])/2.0

#NLL
def liklihood(params, wt_redmagic,x, tpoints):
    #print _params
    #prior  = np.array([ PRIORS[pname][0] < v < PRIORS[pname][1] for v,pname in zip(_params, param_names)])
    #print param_names
    #print prior
    #if not np.all(prior):
    #    return 1e9
    #params = {p:v for p,v in zip(param_names, _params)}
    #cat.populate(params)
    #nd_cat =  cat.calc_analytic_nd(parmas)
    #wt = np.zeros_like(tpoints_rm[:-5])
    
    #xi = cat.calc_xi(r_bins, do_jackknife=False)
    #m,b,_,_,_ = linregress(np.log10(rpoints), np.log10(xi))
    
    #if np.any(xi < 0):
    #    return 1e9
    #kernel = ExpSquaredKernel(0.05)
    #gp = george.GP(kernel)
    #gp.compute(np.log10(rpoints))
    
    #for bin_no, t_med in enumerate(np.radians(tpoints_rm[:-5])):
    #    int_xi = 0
    #    for ubin_no, _u in enumerate(ubc):
    #        _du = ubins[ubin_no+1]-ubins[ubin_no]
    #        u = _u*unit.Mpc*a
    #        du = _du*unit.Mpc*a
            #print np.sqrt(u**2+(x*t_med)**2)
    #        r = np.sqrt((u**2+(x*t_med)**2))#*cat.h#not sure about the h
            #if r > unit.Mpc*10**1.7: #ignore large scales. In the full implementation this will be a transition to a bias model. 
            #    int_xi+=du*0
            #else:
                # the GP predicts in log, so i predict in log and re-exponate
            #    int_xi+=du*(np.power(10, \
            #            gp.predict(np.log10(xi), np.log10(r.value), mean_only=True)[0]))
    #        int_xi+=du*(10**b)*(r.to("Mpc").value**m)

        #print (((int_xi*W))/wt_redmagic[0]).to("m/m")
        #break
    #    wt[bin_no] = int_xi*W.to("1/Mpc")
        
    wt = wt_analytic(params[0],params[1], tpoints, x.to("Mpc").value)   
    chi2 = np.sum(((wt - wt_redmagic[:-5])**2)/(1e-3*wt_redmagic[:-5]) )
    
    #chi2=0
    #print nd_cat
    #print wt
    #chi2+= ((nd_cat-nd_mock.value)**2)/(1e-6)
    
    #mf = cat.calc_mf()
    #HOD = cat.calc_hod()
    #mass_bin_range = (9,16)
    #mass_bin_size = 0.01
    #mass_bins = np.logspace(mass_bin_range[0], mass_bin_range[1], int( (mass_bin_range[1]-mass_bin_range[0])/mass_bin_size )+1 )

    #mean_host_mass = np.sum([mass_bin_size*mf[i]*HOD[i]*(mass_bins[i]+mass_bins[i+1])/2 for i in xrange(len(mass_bins)-1)])/\
    #                    np.sum([mass_bin_size*mf[i]*HOD[i] for i in xrange(len(mass_bins)-1)])
        
    #chi2+=((13.35-np.log10(mean_host_mass))**2)/(0.2)
    print chi2
    return chi2 #nll



    











In [ ]:



    


print nd_mock
print wt_redmagic[:-5]



    











In [ ]:



    


import scipy.optimize as op
args = ([p for p in params],wt_redmagic, nd_mock) PRIORS = {'f_c': (0, 0.45), 'alpha': (0.6, 1.4), 'logMmin':(10.9,13.6), 'logM1': (13., 14.1), 'logM0': (9,16), 'sigma_logM': (0.01, 0.5)}
results = op.minimize(liklihood, np.array([v for v in params.values()]) ,args,method = 'L-BFGS-B', bounds = [PRIORS[p] for p in params]) 




In [ ]:



    


results = op.minimize(liklihood, np.array([-2.2, 10**1.7]),(wt_redmagic,x, tpoints_rm[:-5]))



    











In [ ]:



    


results



    











In [ ]:



    


#plt.plot(tpoints_rm, wt, label = 'My Calculation')
plt.plot(tpoints_rm, wt_redmagic, label = 'Buzzard Mock')
plt.plot(tpoints_rm, wt_analytic(-1.88359, 2.22353827e+03,tpoints_rm, x.to("Mpc").value), label = 'Mathematica Calc')

plt.ylabel(r'$w(\theta)$')
plt.xlabel(r'$\theta \mathrm{[degrees]}$')
plt.loglog();
plt.legend(loc='best')



    











In [ ]:



    


plt.plot(np.log10(rpoints), np.log10(2.22353827e+03)+(np.log10(rpoints)*(-1.88)))
plt.scatter(np.log10(rpoints), np.log10(xi) )



    











In [ ]:



    


np.array([v for v in params.values()])
#Try integrating over z and u jointly, explicitly nz_zspec = hdulist[8] #N = 0#np.zeros((5,)) N_total = np.sum([row[2+zbin] for row in nz_zspec.data]) dNdzs = [] zs = [] W = 0 wt2 = np.zeros_like(tpoints_rm) ubins = np.linspace(10**-6, 10**2.0, 1001) for bin_no, t_med in enumerate(np.radians(tpoints_rm)): print bin_no int_xi = 0 for row in nz_zspec.data: N = row[2+zbin] dN = N*1.0/N_total dz = row[2] - row[0] dNdz = dN/dz H = cat.cosmology.H(row[1]) x = cat.cosmology.comoving_distance(row[1]) for ubin_no, _u in enumerate(ubins[:-1]): _du = ubins[ubin_no+1]-ubins[ubin_no] u = _u*unit.Mpc du = _du*unit.Mpc r = a*np.sqrt((u**2+(x*t_med)**2).value)#*cat.h#not sure about the h #print r if r <= 10**1.7: int_xi+=du*(np.power(10, \ gp.predict(np.log10(xi), np.log10(r), mean_only=True)[0]))*dNdz*dN*H*2.0/const.c wt2[bin_no] = int_xi
plt.plot(tpoints_rm, wt2, label = 'My Calculation') plt.plot(tpoints_rm, wt_redmagic, label = 'Buzzard Mock') plt.ylabel(r'$w(\theta)$') plt.xlabel(r'$\theta \mathrm{[degrees]}$') plt.loglog(); plt.legend(loc='best')
wt_redmagic/wt2 




In [ ]:

Content source: mclaughlin6464/pearce

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