Merge pull request #727 from aphearin/vdb_sv_testing

Add external code verification of biased NFW velocity dispersion profiles

docs/quickstart_and_tutorials/tutorials/model_building/preloaded_models/behroozi10_composite_model.rst

@@ -6,22 +6,22 @@ Behroozi et al. (2010) Composite Model
 
 .. currentmodule:: halotools.empirical_models
 
-This section of the documentation describes the basic behavior of 
-the ``behroozi10`` composite subhalo model. To see how this composite 
-model is built by the `~halotools.empirical_models.PrebuiltSubhaloModelFactory` class, 
-see `~halotools.empirical_models.behroozi10_model_dictionary`. 
+This section of the documentation describes the basic behavior of
+the ``behroozi10`` composite subhalo model. To see how this composite
+model is built by the `~halotools.empirical_models.PrebuiltSubhaloModelFactory` class,
+see `~halotools.empirical_models.behroozi10_model_dictionary`.
 
 Overview of the Behroozi et al. (2010) Model Features
 ========================================================
-This subhalo-based model is an implementation of 
-Behroozi et al. (2010), arXiv:1001.0015. 
-There is a one-to-one mapping between stellar mass and subhalo mass 
-governed by a parameterized form for the stellar-to-halo-mass relation (SMHM). 
-The class where the SMHM behavior is defined is `Behroozi10SmHm`. 
+This subhalo-based model is an implementation of
+Behroozi et al. (2010), arXiv:1001.0015.
+There is a one-to-one mapping between stellar mass and subhalo mass
+governed by a parameterized form for the stellar-to-halo-mass relation (SMHM).
+The class where the SMHM behavior is defined is `Behroozi10SmHm`.
 
-Building the Behroozi et al. (2010) Model 
+Building the Behroozi et al. (2010) Model
 ============================================
-You can build an instance of this model using the 
+You can build an instance of this model using the
 `~halotools.empirical_models.PrebuiltSubhaloModelFactory` class as follows:
 
 >>> from halotools.empirical_models import PrebuiltSubhaloModelFactory
@@ -31,75 +31,75 @@ You can build an instance of this model using the
 Customizing the Behroozi et al. (2010) Model
 =================================================
 
-There are several keyword arguments you can use to customize 
+There are several keyword arguments you can use to customize
 the instance returned by the factory:
 
-First, the ``redshift`` keyword argument must be set to the redshift of the 
-halo catalog you might populate with this model. 
+First, the ``redshift`` keyword argument must be set to the redshift of the
+halo catalog you might populate with this model.
 
 >>> model = PrebuiltSubhaloModelFactory('behroozi10', redshift = 2)
 
-It is not permissible to dynamically change the ``redshift`` 
-of the ``behroozi10`` composite model instance. 
-If you want to explore the model variations with redshift, 
-you should instantiate multiple models. 
-Or, alternatively, if you only want to study 
-the underlying analytical SMHM relation, and not populate mocks, 
-you can just build an instance of the `Behroozi10SmHm` component model 
-class without specifying a redshift, in which case you can 
-call the methods of the `Behroozi10SmHm` instance for any redshift. 
-
-Second, the ``prim_haloprop_key`` keyword argument allows you to choose 
-which subhalo property regulates mean stellar mass. 
-In principle, you can choose any column name in the halo catalog you will 
-be populating, but this key should be a mass-like variable in order to get 
-sensible results, e.g., ``halo_mpeak``, ``halo_macc``, etc. 
-It is not permissible to dynamically change the ``prim_haloprop_key`` 
-of the ``behroozi10`` composite model instance. 
-If you want to explore the effects of choosing different 
-halo properties, you should instantiate multiple models. 
-
-Finally, you can choose how stochasticity between halo and stellar mass is modeled 
-with the ``scatter_abscissa`` and ``scatter_ordinates`` keywords. 
-These arguments determine the level of scatter in stellar mass, given in dex. 
-The abscissa serve as control points and the ordinates the values of the scatter 
-at those control points. So, for example, if you wanted to have 0.3 dex of 
-scatter at :math:`M_{\rm halo} = 10^{12}M_{\odot}` and 0.1 dex of scatter 
+It is not permissible to dynamically change the ``redshift``
+of the ``behroozi10`` composite model instance.
+If you want to explore the model variations with redshift,
+you should instantiate multiple models.
+Or, alternatively, if you only want to study
+the underlying analytical SMHM relation, and not populate mocks,
+you can just build an instance of the `Behroozi10SmHm` component model
+class without specifying a redshift, in which case you can
+call the methods of the `Behroozi10SmHm` instance for any redshift.
+
+Second, the ``prim_haloprop_key`` keyword argument allows you to choose
+which subhalo property regulates mean stellar mass.
+In principle, you can choose any column name in the halo catalog you will
+be populating, but this key should be a mass-like variable in order to get
+sensible results, e.g., ``halo_mpeak``, ``halo_macc``, etc.
+It is not permissible to dynamically change the ``prim_haloprop_key``
+of the ``behroozi10`` composite model instance.
+If you want to explore the effects of choosing different
+halo properties, you should instantiate multiple models.
+
+Finally, you can choose how stochasticity between halo and stellar mass is modeled
+with the ``scatter_abscissa`` and ``scatter_ordinates`` keywords.
+These arguments determine the level of scatter in stellar mass, given in dex.
+The abscissa serve as control points and the ordinates the values of the scatter
+at those control points. So, for example, if you wanted to have 0.3 dex of
+scatter at :math:`M_{\rm halo} = 10^{12}M_{\odot}` and 0.1 dex of scatter
 at :math:`M_{\rm halo} = 10^{15}M_{\odot}`:
 
 >>> model = PrebuiltSubhaloModelFactory('behroozi10', scatter_abscissa = [1e12, 1e15], scatter_ordinates = [0.3, 0.1])
 
-For constant scatter, use a one-element python list. 
-It is not permissible to dynamically change the abscissa after instantiation, 
-though you can vary the ordinates by changing the appropriate values in the ``param_dict``. 
-For example, in the above model, the following line will change the 
+For constant scatter, use a one-element python list.
+It is not permissible to dynamically change the abscissa after instantiation,
+though you can vary the ordinates by changing the appropriate values in the ``param_dict``.
+For example, in the above model, the following line will change the
 scatter to 0.2 dex in :math:`M_{\rm halo} = 10^{12}M_{\odot}` halos:
 
 >>> model.param_dict['scatter_model_param1'] = 0.2
 
 Populating Mocks and Generating Behroozi et al. (2010) Model Predictions
 ===========================================================================
 
-As with any Halotools composite model, the model instance 
-can populate N-body simulations with mock galaxy catalogs. 
-In the following, we'll show how to do this 
-with fake simulation data via the ``halocat`` argument. 
+As with any Halotools composite model, the model instance
+can populate N-body simulations with mock galaxy catalogs.
+In the following, we'll show how to do this
+with fake simulation data via the ``halocat`` argument.
 
 >>> from halotools.sim_manager import FakeSim
 >>> halocat = FakeSim()
 >>> model = PrebuiltSubhaloModelFactory('behroozi10')
->>> model.populate_mock(halocat = halocat) 
+>>> model.populate_mock(halocat)  # doctest: +SKIP
 
-See `ModelFactory.populate_mock` for information about how to  
-populate your model into different simulations.  
-See :ref:`galaxy_catalog_analysis_tutorial` for a sequence of worked examples 
-on how to use the `~halotools.mock_observables` sub-package 
-to study a wide range of astronomical statistics predicted by your model. 
+See `ModelFactory.populate_mock` for information about how to
+populate your model into different simulations.
+See :ref:`galaxy_catalog_analysis_tutorial` for a sequence of worked examples
+on how to use the `~halotools.mock_observables` sub-package
+to study a wide range of astronomical statistics predicted by your model.
 
-Studying the Behroozi et al. (2010) Model Features 
+Studying the Behroozi et al. (2010) Model Features
 ======================================================
 
-In addition to populating mocks, the ``behroozi10`` model also gives you access to 
+In addition to populating mocks, the ``behroozi10`` model also gives you access to
 its underlying analytical relations. Here are a few examples:
 
 >>> import numpy as np
@@ -114,37 +114,37 @@ To compute the mean stellar mass as a function of halo mass:
 Parameters of the Behroozi et al. (2010) model
 =================================================
 
-The best way to learn what the parameters of a model do is to 
-just play with the code: change parameter values, make plots of how the 
-underying analytical relations vary, and also of how the 
-mock observables vary. Here we just give a simple description of the meaning 
-of each parameter. You can also refer to the original 
-Behroozi et al. (2010) publication, arXiv:1001.0015, 
-for further details. 
+The best way to learn what the parameters of a model do is to
+just play with the code: change parameter values, make plots of how the
+underying analytical relations vary, and also of how the
+mock observables vary. Here we just give a simple description of the meaning
+of each parameter. You can also refer to the original
+Behroozi et al. (2010) publication, arXiv:1001.0015,
+for further details.
 
 To see how the following parameters are implemented, see `Behroozi10SmHm.mean_stellar_mass`.
 
-* param_dict['smhm_m0_0'] - Characteristic stellar mass at redshift-zero in the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map.  
+* param_dict['smhm_m0_0'] - Characteristic stellar mass at redshift-zero in the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map.
 
-* param_dict['smhm_m0_a'] - Redshift evolution of the characteristic stellar mass. 
+* param_dict['smhm_m0_a'] - Redshift evolution of the characteristic stellar mass.
 
-* param_dict['smhm_m1_0'] - Characteristic halo mass at redshift-zero in the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map. 
+* param_dict['smhm_m1_0'] - Characteristic halo mass at redshift-zero in the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map.
 
-* param_dict['smhm_m1_a'] - Redshift evolution of the characteristic halo mass. 
+* param_dict['smhm_m1_a'] - Redshift evolution of the characteristic halo mass.
 
-* param_dict['smhm_beta_0'] - Low-mass slope at redshift-zero of the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map. 
+* param_dict['smhm_beta_0'] - Low-mass slope at redshift-zero of the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map.
 
-* param_dict['smhm_beta_a'] - Redshift evolution of the low-mass slope. 
+* param_dict['smhm_beta_a'] - Redshift evolution of the low-mass slope.
 
-* param_dict['smhm_delta_0'] - High-mass slope at redshift-zero of the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map. 
+* param_dict['smhm_delta_0'] - High-mass slope at redshift-zero of the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map.
 
-* param_dict['smhm_delta_a'] - Redshift evolution of the high-mass slope. 
+* param_dict['smhm_delta_a'] - Redshift evolution of the high-mass slope.
 
-* param_dict['smhm_gamma_0'] - Transition between low- and high-mass behavior at redshift-zero of the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map. 
+* param_dict['smhm_gamma_0'] - Transition between low- and high-mass behavior at redshift-zero of the :math:`\langle M_{\ast} \rangle(M_{\rm halo})` map.
 
-* param_dict['smhm_gamma_a'] - Redshift evolution of the transition. 
+* param_dict['smhm_gamma_a'] - Redshift evolution of the transition.
 
-* param_dict['u'scatter_model_param1'] - Log-normal scatter in the stellar-to-halo mass relation. 
+* param_dict['u'scatter_model_param1'] - Log-normal scatter in the stellar-to-halo mass relation.
 
 
 

docs/quickstart_and_tutorials/tutorials/model_building/preloaded_models/cacciato09_composite_model.rst

@@ -77,7 +77,7 @@ with fake simulation data via the ``halocat`` argument.
 >>> from halotools.sim_manager import FakeSim
 >>> halocat = FakeSim()
 >>> model = PrebuiltHodModelFactory('cacciato09')
->>> model.populate_mock(halocat = halocat)
+>>> model.populate_mock(halocat)  # doctest: +SKIP
 
 See `ModelFactory.populate_mock` for information about how to
 populate your model into different simulations.
@@ -100,7 +100,7 @@ To compute the median luminosity of central galaxies as a function of halo mass:
 
 To compute the average number of satellites per halo as a function of halo mass:
 
->>> mean_nsat = model.mean_occupation_satellites(prim_haloprop = halo_mass)
+>>> mean_nsat = model.mean_occupation_satellites(prim_haloprop=halo_mass)
 
 By modifying the parameters stored in the ``param_dict``, the underlying analytical
 relations such as those above allow you to study how the model behaves without the
@@ -126,7 +126,7 @@ Briefly, changing the delta parameters should only affect the abundance of
 satellites that have luminosities similar to the central luminosity. On the other
 hand, faint satellites should be unaffected. The details of the 2 delta parameters
 are described in Lange et al. (in prep.). Setting both to 0, as done by default, is
-equivalent to the model of Cacciato et al. (2009). 
+equivalent to the model of Cacciato et al. (2009).
 
 * param_dict['log_L_0'] -  Normalization of central mass-to-light ratio.