I have a universe machine catalog, gonna compute statistics on them now & make sure everything seems sensible.


In [1]:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
sns.set()

In [2]:
import numpy as np

In [3]:
from pearce.mocks.kittens import MDPL2, TrainingBox


/home/users/swmclau2/.local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [4]:
cat = TrainingBox(0,system = 'sherlock')

In [5]:
cat.load(1.0, particles = True)

In [6]:
cat.halocat.halo_table


Out[6]:
<Table length=10055159>
halo_upidhalo_vxhalo_yhalo_xhalo_zhalo_vyhalo_vzhalo_rshalo_rvirhalo_mvirhalo_idhalo_nfw_conchalo_hostidhalo_local_density_1halo_local_density_5halo_local_density_10halo_mvir_host_halo
int64float32float32float32float32float32float32float32float32float32int64float32int64float64float64float64float32
-1246.87485102.52383106.99912183.0585637.936943-535.235170.0198729990.235701011036800000000.0518709411.860365518709430.21457122760201517.7258817868598486.9997090010611331036800000000.0
5187174-974.71326104.59491110.74285177.8993572.3244719.5206810.0185650010.338480983070500000000.0518709518.232208518717470.5006661977380427.87797771933412212.53904705945483419540000000000.0
5187151-932.22003105.14327110.985855177.40489615.9403-1.1372150.0141150.3278972791400000000.0518709623.230394518715140.2860949701360231.90658721634772412.206686775951212838370000000000.0
5187102713.0239104.19983110.667179.15486772.3499269.755580.0209179990.451704987297500000000.0518709721.5940825187102423.003997186428228.522555238856311.5218231614589106310000000000.0
51871781496.8752104.97381110.18847179.91116-675.5206-17.73350.0435870030.57678315193000000000.0518709813.2329135187178382.717902216292227.87797771933412211.79375430250731973374000000000.0
51871511364.2133103.91988110.11275178.958631145.8024-661.193050.0530970.4433226898700000000.051870998.3492855187151120.8582849104080627.7974055293938511.541966208943968838370000000000.0
-1770.9919102.41203108.071335182.73191157.6224-103.783650.0289780010.3717394067400000000.0518710012.828318518710050.35761871267002519.9819031051874637.9766468040869314067400000000.0
51871781365.237104.34914110.74132180.617772048.1526550.42530.2147670.4363816579700000000.051871012.0318815187178161.1443798805440829.00598837849793211.00817545058966773374000000000.0
51871481100.5853104.63169111.21566179.271242912.1895245.314320.0358361.1032081106310000000000.0518710230.7849151871481107.867611678740629.97285465778119511.57218078017157150340000000000.0
-1-121.3899499.02711105.870544187.76155440.34564-1587.73520.0227940.339943110400000000.0518710314.913573518710320.1430474850680116.597871127696043.5753909285995713110400000000.0
...................................................
-1360.65402787.7205807.9131823.8222126.01348119.0622940.0121460.204576677910000000.0518708416.84307751870840.00.483433139641632150.7352212332049822677910000000.0
-1154.72903796.98584803.2846833.7656-94.21632186.451870.0172520.187076518400000000.0518708510.843728518708520.143047485068011.20858284910408040.8862940893429924518400000000.0
-1103.847626799.7953792.1817826.944-90.245514264.445220.0253330.212303757660000000.051870868.38049151870860.00.241716569820816070.7150781857199142757660000000.0
-1120.597015808.9011804.5984789.77905-345.85385287.06970.0169490.159123319020000000.051870879.388342518708720.143047485068010.96686627928326430.6949351382348462319020000000.0
-1351.06807.7606813.7482792.35474-460.61057294.907070.0161570.10024179754000000.051870886.20418451870880.00.88629408934299240.775507328175118279754000000.0
-1399.60214805.8018815.9428790.18463-455.32162367.836430.0125039990.212303757660000000.0518708916.978807518708910.0715237425340050.72514970946244831.1078676116787405757660000000.0
-1-3.825646802.99316794.1816796.77747-193.77182157.444290.0095440.208511717790000000.0518709021.84733851870900.00.402860949701360160.9970808505108664717790000000.0
-1-39.194416796.59906819.9801817.6079-740.96136503.271970.0344389980.196216598150000000.051870915.697494551870910.02.9005988378497933.504890262401833598150000000.0
-1334.18792.66815818.57416816.44604179.23204380.197020.0575490.19175501558280000000.051870923.332030351870920.01.93373255856652863.4545326436891632558280000000.0
-1245.128791.10986798.86536801.14343-208.66121193.092650.0359510.152196279140000000.051870934.23342951870930.00.402860949701360160.5136477108692342279140000000.0

In [10]:
from halotools.sim_manager import PtclTableCache

In [11]:
cache = PtclTableCache()

In [15]:
ptcl_vname = cat.version_name + '_particle_%.2f' % (-1 * np.log10(1e-2))

In [18]:
?? cache.matching_log_entry_generator

In [25]:
cat.simname


Out[25]:
'trainingbox'

In [26]:
cat.version_name


Out[26]:
'most_recent_00'

In [19]:
gen0 = cache.matching_log_entry_generator(simname = cat.simname,
        version_name = ptcl_vname, redshift = cat.z, dz_tol = 0.1)

In [21]:
entries = list(gen0)

In [24]:
entries[0].fname


Out[24]:
u'/scratch/users/swmclau2/halocats/ptcl_1.00.list.trainingbox_most_recent_00.hdf5'

In [13]:
cache.log


Out[13]:
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In [7]:
cat.halocat.ptcl_table



InvalidCacheLogEntryTraceback (most recent call last)
<ipython-input-7-bad5e4c16504> in <module>()
----> 1 cat.halocat.ptcl_table

/home/users/swmclau2/.local/lib/python2.7/site-packages/halotools-0.7.dev5048-py2.7-linux-x86_64.egg/halotools/sim_manager/cached_halo_catalog.pyc in ptcl_table(self)
    630                 return self._ptcl_table
    631             else:
--> 632                 raise InvalidCacheLogEntry(ptcl_log_entry._cache_safety_message)
    633 
    634     def _disallow_catalogs_with_known_bugs(self, simname=sim_defaults.default_simname,

InvalidCacheLogEntry: The particle catalog and/or its associated metadata fail the following tests:

1. The input filename does not exist.


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um_galcat = np.load('/home/users/swmclau2/scratch/UniverseMachine/cut_um_catalog.npy')

In [ ]:
pos = um_galcat['pos'][:, :3]

In [ ]:
rbins = np.logspace(-1.1, 1.6, 19)
rpoints = (rbins[1:] + rbins[:-1])/2.0

Note: make sure the luminosity cut i did makes snese with the min ptcl count I do on the emus


In [ ]:
cat.model.mock.galaxy_table.colnames

In [ ]:
plt.plot(rpoints, xi_gg)
plt.loglog();

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